From c8495f8c4df9fa397fff192c13a687593c9b77a8 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Thu, 8 Jun 2017 11:57:41 +0800
Subject: [PATCH 001/314] update ctc_beam_search_decoder design doc

---
 doc/design/speech/README.MD             |  13 ++++++++++++-
 doc/design/speech/image/beam_search.png | Bin 0 -> 474749 bytes
 2 files changed, 12 insertions(+), 1 deletion(-)
 create mode 100644 doc/design/speech/image/beam_search.png

diff --git a/doc/design/speech/README.MD b/doc/design/speech/README.MD
index 7304650e62..cc03aac7b4 100644
--- a/doc/design/speech/README.MD
+++ b/doc/design/speech/README.MD
@@ -140,7 +140,17 @@ TODO by Assignees
 
 ### Beam Search with CTC and LM
 
-TODO by Assignees
+<div align="center">
+<img src="image/beam_search.png" width=400><br/>
+Figure 2. Algorithm for Beam Search Decoder.
+</div>
+
+- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] with a modification for the ambiguous part, as shown in Figure 2.
+- An **external defined scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a space character appended.
+- Such scorer is a unified class, may consisting of language model, word count or any customed evaluators.
+- The **language model** is built from Task 5, with a parameter should be carefully tuned to achieve minimum WER/CER (c.f. Task 7)
+- This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. 
+ 
 
 ## Future Work
 
@@ -153,3 +163,4 @@ TODO by Assignees
 
 1. Dario Amodei, etc., [Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin](http://proceedings.mlr.press/v48/amodei16.pdf). ICML 2016.
 2. Dario Amodei, etc., [Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595). 	arXiv:1512.02595.
+3. Awni Y. Hannun, etc. [First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs](https://arxiv.org/abs/1408.2873). arXiv:1408.2873
diff --git a/doc/design/speech/image/beam_search.png b/doc/design/speech/image/beam_search.png
new file mode 100644
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HcmV?d00001


From 2b202f754bcb4f035eb22b316aea6f9d96c7386b Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Wed, 10 Jan 2018 21:40:46 +0800
Subject: [PATCH 002/314] Optimize maxPoolForward.

---
 paddle/math/Matrix.cpp | 24 +++++++++++-------------
 1 file changed, 11 insertions(+), 13 deletions(-)

diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp
index 1ec4336cab..cc86b12be0 100644
--- a/paddle/math/Matrix.cpp
+++ b/paddle/math/Matrix.cpp
@@ -2015,13 +2015,6 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
     CHECK_EQ(channels * outLength, maskMatP->getWidth());
   }
 
-  /* initialize the data_ */
-  for (size_t i = 0; i < height_; i++) {
-    for (size_t j = 0; j < width_; j++) {
-      outData[i * outStride + j] = -(real)FLT_MAX;
-    }
-  }
-
   /* pool max one by one */
   for (size_t n = 0; n < num; ++n) {  // frame by frame
     if (!isContiguous()) {
@@ -2030,19 +2023,24 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
     for (size_t c = 0; c < channels; ++c) {  // channel by channel
       for (size_t ph = 0; ph < outputH; ++ph) {
         int hstart = ph * strideH - paddingH;
-        int hend = std::min(hstart + sizeY, imgSizeH);
-        hstart = std::max(hstart, 0);
+        int hend = hstart + sizeY;
+        hstart = hstart < 0 ? 0 : hstart;
+        hend = hend < (int)imgSizeH ? hend : (int)imgSizeH;
         for (size_t pw = 0; pw < outputW; ++pw) {
           int wstart = pw * strideW - paddingW;
-          int wend = std::min(wstart + sizeX, imgSizeW);
-          wstart = std::max(wstart, 0);
+          int wend = wstart + sizeX;
+          wstart = wstart < 0 ? 0 : wstart;
+          wend = wend < (int)imgSizeW ? wend : (int)imgSizeW;
           if (maskData == NULL) {
+            real tmp = -(real)FLT_MAX;
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {
-                outData[ph * outputW + pw] = std::max(
-                    outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
+                tmp = tmp < inputData[h * imgSizeW + w]
+                          ? inputData[h * imgSizeW + w]
+                          : tmp;
               }
             }
+            outData[ph * outputW + pw] = tmp;
           } else {
             for (int h = hstart; h < hend; ++h) {
               for (int w = wstart; w < wend; ++w) {

From ed0a564c909353c862bcb1533e41420fdd87eb9e Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Thu, 11 Jan 2018 13:42:40 +0800
Subject: [PATCH 003/314] Optimize GemmConvMobileFunction.

---
 paddle/function/GemmConvOp.cpp | 63 +++++++++++++++++++---------------
 paddle/function/Im2Col.h       | 53 ++++++++++++++++++++++++++++
 2 files changed, 88 insertions(+), 28 deletions(-)

diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp
index cbdbf5335d..a9876cec2a 100644
--- a/paddle/function/GemmConvOp.cpp
+++ b/paddle/function/GemmConvOp.cpp
@@ -178,19 +178,22 @@ public:
     real* inputData = inputs[0].data<real>();
     real* filterData = inputs[1].data<real>();
     real* outputData = outputs[0].data<real>();
+    real* colData = NULL;
     bool needIm2col = isNeedIm2col(filter);
 
     TensorShape imShape =
         TensorShape({inputChannels / groups_, inputHeight, inputWidth});
-
     TensorShape colShape;
-    real* colData = NULL;
 
-    size_t colHeight = inputChannels / groups_ * filterHeight * filterWidth;
-    size_t colWidth = outputHeight * outputWidth;
-    // Max col matrix height 256, Max col matrix width 1024
-    size_t stepColHeight = std::min(colHeight, static_cast<size_t>(256));
-    size_t stepColWidth = std::min(colWidth, static_cast<size_t>(2048));
+    // Max col matrix width 4096, Max col matrix size 4M.
+    size_t outputHeightSteps =
+        std::min(std::max(4096 / outputWidth, (size_t)1), outputHeight);
+    size_t maxColWidth = outputHeightSteps * outputWidth;
+    size_t channelSteps =
+        std::min(std::max((1048576 / maxColWidth) / filterHeight * filterWidth,
+                          (size_t)1),
+                 inputChannels / groups_);
+    size_t maxColHeight = channelSteps * filterHeight * filterWidth;
 
     if (needIm2col) {
       colShape = TensorShape({inputChannels / groups_,
@@ -199,7 +202,7 @@ public:
                               outputHeight,
                               outputWidth});
 
-      resizeBuffer<Device>(stepColHeight * stepColWidth * sizeof(real));
+      resizeBuffer<Device>(maxColHeight * maxColWidth * sizeof(real));
       colData = reinterpret_cast<real*>(memory_->getBuf());
     }
 
@@ -209,20 +212,24 @@ public:
         (outputChannels / groups_) * outputHeight * outputWidth;
     size_t filterOffset = filter.getElements() / groups_;
 
-    int nStride = colWidth;
-    int kStride = colHeight;
+    int nStride = outputHeight * outputWidth;
+    int kStride = inputChannels / groups_ * filterHeight * filterWidth;
     for (size_t i = 0; i < batchSize; i++) {
+      filterData = inputs[1].data<real>();
       for (size_t g = 0; g < groups_; g++) {
         if (needIm2col) {
           real beta_ = beta;
-          for (size_t colHeightStart = 0; colHeightStart < colHeight;
-               colHeightStart += stepColHeight) {
-            for (size_t colWidthStart = 0; colWidthStart < colWidth;
-                 colWidthStart += stepColWidth) {
-              int N = std::min(colWidth - colWidthStart, stepColWidth);
-              int K = std::min(colHeight - colHeightStart, stepColHeight);
+          for (size_t ic = 0; ic < inputChannels / groups_;
+               ic += channelSteps) {
+            int channels = std::min(inputChannels / groups_ - ic, channelSteps);
+            for (size_t oh = 0; oh < outputHeight; oh += outputHeightSteps) {
+              int height = std::min(outputHeight - oh, outputHeightSteps);
+
+              int M = outputChannels / groups_;
+              int N = height * outputWidth;
+              int K = channels * filterHeight * filterWidth;
               // im2col
-              im2col(inputData + g * inputOffset,
+              im2col(inputData,
                      imShape,
                      colData,
                      colShape,
@@ -232,13 +239,12 @@ public:
                      paddingW(),
                      dilationH(),
                      dilationW(),
-                     colHeightStart,
-                     K,
-                     colWidthStart,
+                     channels,
+                     oh,
+                     height,
                      N);
 
               // gemm
-              int M = outputChannels / groups_;
               BlasGemm<Device, real>::compute(
                   false,
                   false,
@@ -246,12 +252,12 @@ public:
                   N,
                   K,
                   1.0f,
-                  filterData + g * filterOffset + colHeightStart,
+                  filterData + ic * filterHeight * filterWidth,
                   kStride,
                   colData,
                   N,
                   beta_,
-                  outputData + g * outputOffset + colWidthStart,
+                  outputData + oh * outputWidth,
                   nStride);
             }
             beta_ = 1.0;
@@ -266,17 +272,18 @@ public:
                                           N,
                                           K,
                                           1.0f,
-                                          filterData + g * filterOffset,
+                                          filterData,
                                           K,
-                                          inputData + g * inputOffset,
+                                          inputData,
                                           N,
                                           beta,
-                                          outputData + g * outputOffset,
+                                          outputData,
                                           N);
         }
+        inputData += inputOffset;
+        outputData += outputOffset;
+        filterData += filterOffset;
       }
-      inputData += inputChannels * inputHeight * inputWidth;
-      outputData += outputChannels * outputHeight * outputWidth;
     }
 
     memory_.reset();
diff --git a/paddle/function/Im2Col.h b/paddle/function/Im2Col.h
index 36a9bcf84e..361ba4c18a 100644
--- a/paddle/function/Im2Col.h
+++ b/paddle/function/Im2Col.h
@@ -98,6 +98,7 @@ public:
                   int dilationWidth = 1);
 };
 
+#if 0
 template <class T>
 class Im2ColMobileFunctor {
 public:
@@ -147,5 +148,57 @@ public:
     }
   }
 };
+#endif
+
+template <class T>
+class Im2ColMobileFunctor {
+public:
+  void operator()(const T* imData,
+                  const TensorShape& imShape,
+                  T* colData,
+                  const TensorShape& colShape,
+                  int strideHeight,
+                  int strideWidth,
+                  int paddingHeight,
+                  int paddingWidth,
+                  int dilationHeight,
+                  int dilationWidth,
+                  int inputChannels,
+                  int colOffset,
+                  int colOutputHeight,
+                  int colWidth) {
+    int inputHeight = imShape[1];
+    int inputWidth = imShape[2];
+    int filterHeight = colShape[1];
+    int filterWidth = colShape[2];
+    int outputWidth = colShape[4];
+
+    for (int ic = 0; ic < inputChannels; ic++) {
+      for (int oh = 0; oh < colOutputHeight; oh++) {
+        T* dstData = colData + oh * outputWidth;
+        for (int fh = 0; fh < filterHeight; fh++) {
+          for (int fw = 0; fw < filterWidth; fw++) {
+            int imRowIdx = (oh + colOffset) * strideHeight + fh - paddingHeight;
+            if (imRowIdx < 0 || imRowIdx >= inputHeight) {
+              memset(dstData, 0, outputWidth * sizeof(T));
+            } else {
+              for (int ow = 0; ow < outputWidth; ow++) {
+                int imColIdx = ow * strideWidth + fw - paddingWidth;
+                if (imColIdx < 0 || imColIdx >= inputWidth) {
+                  dstData[ow] = T(0);
+                } else {
+                  dstData[ow] = imData[imRowIdx * inputWidth + imColIdx];
+                }
+              }
+            }
+            dstData += colWidth;
+          }
+        }
+      }
+      colData += filterHeight * filterWidth * colWidth;
+      imData += inputHeight * inputWidth;
+    }
+  }
+};
 
 }  // namespace paddle

From 784e59406c541def813e75db5fd11bb0361eccef Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Thu, 11 Jan 2018 14:02:57 +0800
Subject: [PATCH 004/314] Bug fix of Im2ColMobileFunctor.

---
 paddle/function/Im2Col.h       | 58 +++-------------------------------
 paddle/function/Im2ColTest.cpp |  6 ++--
 2 files changed, 7 insertions(+), 57 deletions(-)

diff --git a/paddle/function/Im2Col.h b/paddle/function/Im2Col.h
index 361ba4c18a..915119e291 100644
--- a/paddle/function/Im2Col.h
+++ b/paddle/function/Im2Col.h
@@ -98,58 +98,6 @@ public:
                   int dilationWidth = 1);
 };
 
-#if 0
-template <class T>
-class Im2ColMobileFunctor {
-public:
-  void operator()(const T* imData,
-                  const TensorShape& imShape,
-                  T* colData,
-                  const TensorShape& colShape,
-                  int strideHeight,
-                  int strideWidth,
-                  int paddingHeight,
-                  int paddingWidth,
-                  int dilationHeight,
-                  int dilationWidth,
-                  int colHeightStart,
-                  int colHeightSize,
-                  int colWidthStart,
-                  int colWidthSize) {
-    int inputHeight = imShape[1];
-    int inputWidth = imShape[2];
-    int filterHeight = colShape[1];
-    int filterWidth = colShape[2];
-    int outputWidth = colShape[4];
-
-    for (int colh = 0; colh < colHeightSize; colh++) {
-      int wOffset = (colHeightStart + colh) % filterWidth;
-      int hOffset = ((colHeightStart + colh) / filterWidth) % filterHeight;
-      int c_im = (colHeightStart + colh) / filterWidth / filterHeight;
-
-      for (int colw = 0; colw < colWidthSize; colw++) {
-        int h = (colWidthStart + colw) / outputWidth;
-        int w = (colWidthStart + colw) % outputWidth;
-
-        int imRowIdx = h * strideHeight + hOffset * dilationHeight;
-        int imColIdx = w * strideWidth + wOffset * dilationWidth;
-        if ((imRowIdx - paddingHeight) < 0 ||
-            (imRowIdx - paddingHeight) >= inputHeight ||
-            (imColIdx - paddingWidth) < 0 ||
-            (imColIdx - paddingWidth) >= inputWidth) {
-          colData[colh * colWidthSize + colw] = static_cast<T>(0);
-        } else {
-          imRowIdx += c_im * inputHeight - paddingHeight;
-          imColIdx -= paddingWidth;
-          colData[colh * colWidthSize + colw] =
-              imData[imRowIdx * inputWidth + imColIdx];
-        }
-      }
-    }
-  }
-};
-#endif
-
 template <class T>
 class Im2ColMobileFunctor {
 public:
@@ -178,12 +126,14 @@ public:
         T* dstData = colData + oh * outputWidth;
         for (int fh = 0; fh < filterHeight; fh++) {
           for (int fw = 0; fw < filterWidth; fw++) {
-            int imRowIdx = (oh + colOffset) * strideHeight + fh - paddingHeight;
+            int imRowIdx = (oh + colOffset) * strideHeight +
+                           fh * dilationHeight - paddingHeight;
             if (imRowIdx < 0 || imRowIdx >= inputHeight) {
               memset(dstData, 0, outputWidth * sizeof(T));
             } else {
               for (int ow = 0; ow < outputWidth; ow++) {
-                int imColIdx = ow * strideWidth + fw - paddingWidth;
+                int imColIdx =
+                    ow * strideWidth + fw * dilationWidth - paddingWidth;
                 if (imColIdx < 0 || imColIdx >= inputWidth) {
                   dstData[ow] = T(0);
                 } else {
diff --git a/paddle/function/Im2ColTest.cpp b/paddle/function/Im2ColTest.cpp
index 3ba866dcdd..fe44a8bf79 100644
--- a/paddle/function/Im2ColTest.cpp
+++ b/paddle/function/Im2ColTest.cpp
@@ -202,10 +202,10 @@ void TestIm2ColMobileFunctor() {
                           padding,
                           dilation,
                           dilation,
+                          channels,
                           0,
-                          height,
-                          0,
-                          width);
+                          outputHeight,
+                          outputHeight * outputWidth);
 
                   autotest::TensorCheckEqual(*output1, *output2);
                 }

From 373f8ba036ff60d6781c9ec2717102336de89b0f Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 15 Jan 2018 21:03:13 +0800
Subject: [PATCH 005/314] add v2 dist benchmark vgg

---
 benchmark/cluster/v2/Dockerfile   |   4 +
 benchmark/cluster/v2/pserver.yaml |  64 +++++++++++++++
 benchmark/cluster/v2/reader.py    |  56 +++++++++++++
 benchmark/cluster/v2/trainer.yaml |  63 +++++++++++++++
 benchmark/cluster/v2/vgg16.py     | 125 ++++++++++++++++++++++++++++++
 5 files changed, 312 insertions(+)
 create mode 100644 benchmark/cluster/v2/Dockerfile
 create mode 100644 benchmark/cluster/v2/pserver.yaml
 create mode 100644 benchmark/cluster/v2/reader.py
 create mode 100644 benchmark/cluster/v2/trainer.yaml
 create mode 100644 benchmark/cluster/v2/vgg16.py

diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/v2/Dockerfile
new file mode 100644
index 0000000000..c52acd51a2
--- /dev/null
+++ b/benchmark/cluster/v2/Dockerfile
@@ -0,0 +1,4 @@
+FROM registry.baidu.com/paddlepaddle/rawjob
+RUN mkdir -p /workspace && mkdir -p /root/.cache/paddle/dataset/flowers/
+ADD vgg16.py reader.py /workspace/
+ADD 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/
diff --git a/benchmark/cluster/v2/pserver.yaml b/benchmark/cluster/v2/pserver.yaml
new file mode 100644
index 0000000000..ed1671bbbd
--- /dev/null
+++ b/benchmark/cluster/v2/pserver.yaml
@@ -0,0 +1,64 @@
+apiVersion: extensions/v1beta1
+kind: ReplicaSet
+metadata:
+  name: vgg16job-pserver
+spec:
+  replicas: 10
+  template:
+    metadata:
+      labels:
+        paddle-job-pserver: vgg16job
+    spec:
+      hostNetwork: true
+      imagePullSecrets:
+      - name: job-registry-secret
+      containers:
+      - name: pserver
+        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16"
+        imagePullPolicy: Always
+        ports:
+        - name: jobport-30236
+          containerPort: 30236
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "python train.py"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        command: ["paddle_k8s", "start_pserver"]
+        resources:
+          requests:
+            memory: 10Gi
+            cpu: 4
+          limits:
+            memory: 10Gi
+            cpu: 4
diff --git a/benchmark/cluster/v2/reader.py b/benchmark/cluster/v2/reader.py
new file mode 100644
index 0000000000..a5a2d54841
--- /dev/null
+++ b/benchmark/cluster/v2/reader.py
@@ -0,0 +1,56 @@
+import random
+from paddle.v2.image import load_and_transform
+import paddle.v2 as paddle
+from multiprocessing import cpu_count
+
+
+def train_mapper(sample):
+    '''
+    map image path to type needed by model input layer for the training set
+    '''
+    img, label = sample
+    img = paddle.image.load_image(img)
+    img = paddle.image.simple_transform(img, 256, 224, True)
+    return img.flatten().astype('float32'), label
+
+
+def test_mapper(sample):
+    '''
+    map image path to type needed by model input layer for the test set
+    '''
+    img, label = sample
+    img = paddle.image.load_image(img)
+    img = paddle.image.simple_transform(img, 256, 224, True)
+    return img.flatten().astype('float32'), label
+
+
+def train_reader(train_list, buffered_size=1024):
+    def reader():
+        with open(train_list, 'r') as f:
+            lines = [line.strip() for line in f]
+            for line in lines:
+                img_path, lab = line.strip().split('\t')
+                yield img_path, int(lab)
+
+    return paddle.reader.xmap_readers(train_mapper, reader,
+                                      cpu_count(), buffered_size)
+
+
+def test_reader(test_list, buffered_size=1024):
+    def reader():
+        with open(test_list, 'r') as f:
+            lines = [line.strip() for line in f]
+            for line in lines:
+                img_path, lab = line.strip().split('\t')
+                yield img_path, int(lab)
+
+    return paddle.reader.xmap_readers(test_mapper, reader,
+                                      cpu_count(), buffered_size)
+
+
+if __name__ == '__main__':
+    #for im in train_reader('train.list'):
+    #    print len(im[0])
+    #for im in train_reader('test.list'):
+    #    print len(im[0])
+    paddle.dataset.flowers.train()
diff --git a/benchmark/cluster/v2/trainer.yaml b/benchmark/cluster/v2/trainer.yaml
new file mode 100644
index 0000000000..33c95df365
--- /dev/null
+++ b/benchmark/cluster/v2/trainer.yaml
@@ -0,0 +1,63 @@
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: vgg16job-trainer
+spec:
+  parallelism: 20
+  completions: 20
+  template:
+    metadata:
+      labels:
+        paddle-job: vgg16job
+    spec:
+      imagePullSecrets:
+        - name: job-registry-secret
+      hostNetwork: true
+      containers:
+      - name: trainer
+        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16"
+        imagePullPolicy: Always
+        command: ["paddle_k8s", "start_trainer", "v2"]
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "cd /workspace && python /workspace/vgg16.py"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        resources:
+          requests:
+            memory: 40Gi
+            cpu: 2
+          limits:
+            memory: 40Gi
+            cpu: 2
+      restartPolicy: Never
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
new file mode 100644
index 0000000000..699fc07628
--- /dev/null
+++ b/benchmark/cluster/v2/vgg16.py
@@ -0,0 +1,125 @@
+import gzip
+
+import paddle.v2.dataset.flowers as flowers
+import paddle.v2 as paddle
+import reader
+
+DATA_DIM = 3 * 224 * 224  # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
+CLASS_DIM = 102
+BATCH_SIZE = 128
+
+
+def vgg(input, nums, class_dim):
+    def conv_block(input, num_filter, groups, num_channels=None):
+        return paddle.networks.img_conv_group(
+            input=input,
+            num_channels=num_channels,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act=paddle.activation.Relu(),
+            pool_type=paddle.pooling.Max())
+
+    assert len(nums) == 5
+    # the channel of input feature is 3
+    conv1 = conv_block(input, 64, nums[0], 3)
+    conv2 = conv_block(conv1, 128, nums[1])
+    conv3 = conv_block(conv2, 256, nums[2])
+    conv4 = conv_block(conv3, 512, nums[3])
+    conv5 = conv_block(conv4, 512, nums[4])
+
+    fc_dim = 4096
+    fc1 = paddle.layer.fc(input=conv5,
+                          size=fc_dim,
+                          act=paddle.activation.Relu(),
+                          layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    fc2 = paddle.layer.fc(input=fc1,
+                          size=fc_dim,
+                          act=paddle.activation.Relu(),
+                          layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    out = paddle.layer.fc(input=fc2,
+                          size=class_dim,
+                          act=paddle.activation.Softmax())
+    return out
+
+
+def vgg13(input, class_dim):
+    nums = [2, 2, 2, 2, 2]
+    return vgg(input, nums, class_dim)
+
+
+def vgg16(input, class_dim):
+    nums = [2, 2, 3, 3, 3]
+    return vgg(input, nums, class_dim)
+
+
+def vgg19(input, class_dim):
+    nums = [2, 2, 4, 4, 4]
+    return vgg(input, nums, class_dim)
+
+
+def main():
+    paddle.init(use_gpu=True, trainer_count=1)
+    image = paddle.layer.data(
+        name="image", type=paddle.data_type.dense_vector(DATA_DIM))
+    lbl = paddle.layer.data(
+        name="label", type=paddle.data_type.integer_value(CLASS_DIM))
+
+    extra_layers = None
+    learning_rate = 0.01
+    out = vgg16(image, class_dim=CLASS_DIM)
+    cost = paddle.layer.classification_cost(input=out, label=lbl)
+
+    # Create parameters
+    parameters = paddle.parameters.create(cost)
+
+    # Create optimizer
+    optimizer = paddle.optimizer.Momentum(
+        momentum=0.9,
+        regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
+                                                         BATCH_SIZE),
+        learning_rate=learning_rate / BATCH_SIZE,
+        learning_rate_decay_a=0.1,
+        learning_rate_decay_b=128000 * 35,
+        learning_rate_schedule="discexp", )
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            flowers.train(),
+            # To use other data, replace the above line with:
+            # reader.train_reader('train.list'),
+            buf_size=1000),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        flowers.valid(),
+        # To use other data, replace the above line with:
+        # reader.test_reader('val.list'),
+        batch_size=BATCH_SIZE)
+
+    # Create trainer
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer,
+                                 extra_layers=extra_layers,
+                                 is_local=False)
+
+    # End batch and end pass event handler
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 1 == 0:
+                print "\nPass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+        if isinstance(event, paddle.event.EndPass):
+            with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+                trainer.save_parameter_to_tar(f)
+
+            result = trainer.test(reader=test_reader)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
+    trainer.train(
+        reader=train_reader, num_passes=200, event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

From bbff57e085675edefc27f6bdc34e8baac5b59a05 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 16 Jan 2018 08:52:06 +0800
Subject: [PATCH 006/314] update docker file

---
 benchmark/cluster/v2/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/v2/Dockerfile
index c52acd51a2..3377cf0100 100644
--- a/benchmark/cluster/v2/Dockerfile
+++ b/benchmark/cluster/v2/Dockerfile
@@ -1,4 +1,4 @@
-FROM registry.baidu.com/paddlepaddle/rawjob
+FROM paddlepaddle/paddlecloud-job
 RUN mkdir -p /workspace && mkdir -p /root/.cache/paddle/dataset/flowers/
 ADD vgg16.py reader.py /workspace/
 ADD 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/

From 9ad149a928e1c9916ffd421bf9e365045108c482 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 16 Jan 2018 08:54:21 +0800
Subject: [PATCH 007/314] fix copyright check

---
 benchmark/cluster/v2/reader.py | 14 ++++++++++++++
 benchmark/cluster/v2/vgg16.py  | 14 ++++++++++++++
 2 files changed, 28 insertions(+)

diff --git a/benchmark/cluster/v2/reader.py b/benchmark/cluster/v2/reader.py
index a5a2d54841..060bf2bda2 100644
--- a/benchmark/cluster/v2/reader.py
+++ b/benchmark/cluster/v2/reader.py
@@ -1,3 +1,17 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
 import random
 from paddle.v2.image import load_and_transform
 import paddle.v2 as paddle
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 699fc07628..dc9573bd79 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -1,3 +1,17 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+
 import gzip
 
 import paddle.v2.dataset.flowers as flowers

From 311d159e11a004c11676a47a5e7945dfadc718b5 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 16 Jan 2018 11:02:14 +0800
Subject: [PATCH 008/314] add copyright for newly merged files

---
 benchmark/cluster/v2/Dockerfile                     |  3 ++-
 benchmark/cluster/v2/vgg16.py                       |  2 +-
 benchmark/tensorflow/image/googlenet_multi_gpu.py   | 13 +++++++++++++
 doc/getstarted/concepts/src/infer.py                | 13 +++++++++++++
 paddle/gserver/layers/MultiBoxLossLayer.h           | 13 +++++++++++++
 .../v2/fluid/tests/test_dynrnn_static_input.py      | 13 +++++++++++++
 6 files changed, 55 insertions(+), 2 deletions(-)

diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/v2/Dockerfile
index 3377cf0100..32e68b6150 100644
--- a/benchmark/cluster/v2/Dockerfile
+++ b/benchmark/cluster/v2/Dockerfile
@@ -1,4 +1,5 @@
 FROM paddlepaddle/paddlecloud-job
 RUN mkdir -p /workspace && mkdir -p /root/.cache/paddle/dataset/flowers/
 ADD vgg16.py reader.py /workspace/
-ADD 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/
+COPY 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/
+
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index dc9573bd79..8644a547b3 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -74,7 +74,7 @@ def vgg19(input, class_dim):
 
 
 def main():
-    paddle.init(use_gpu=True, trainer_count=1)
+    paddle.init(use_gpu=False, trainer_count=1)
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(DATA_DIM))
     lbl = paddle.layer.data(
diff --git a/benchmark/tensorflow/image/googlenet_multi_gpu.py b/benchmark/tensorflow/image/googlenet_multi_gpu.py
index 31466faa37..44de3800a8 100644
--- a/benchmark/tensorflow/image/googlenet_multi_gpu.py
+++ b/benchmark/tensorflow/image/googlenet_multi_gpu.py
@@ -1,3 +1,16 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 from six.moves import xrange  # pylint: disable=redefined-builtin
 from datetime import datetime
 import math
diff --git a/doc/getstarted/concepts/src/infer.py b/doc/getstarted/concepts/src/infer.py
index 4cc58dfee0..ee71cd7a9a 100644
--- a/doc/getstarted/concepts/src/infer.py
+++ b/doc/getstarted/concepts/src/infer.py
@@ -1,3 +1,16 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import paddle.v2 as paddle
 import numpy as np
 
diff --git a/paddle/gserver/layers/MultiBoxLossLayer.h b/paddle/gserver/layers/MultiBoxLossLayer.h
index 9935da5644..40df312a25 100644
--- a/paddle/gserver/layers/MultiBoxLossLayer.h
+++ b/paddle/gserver/layers/MultiBoxLossLayer.h
@@ -1,3 +1,16 @@
+//  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//    http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
 /* copyright (c) 2016 paddlepaddle authors. all rights reserve.
 
 licensed under the apache license, version 2.0 (the "license");
diff --git a/python/paddle/v2/fluid/tests/test_dynrnn_static_input.py b/python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
index 9b138a6207..d6878f0b6d 100644
--- a/python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
+++ b/python/paddle/v2/fluid/tests/test_dynrnn_static_input.py
@@ -1,3 +1,16 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
 import unittest
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core

From a0ac133987a925df1907f3804ccb3cbc32b763b7 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 16 Jan 2018 14:58:22 +0800
Subject: [PATCH 009/314] update job

---
 benchmark/cluster/v2/trainer.yaml | 6 ++++--
 benchmark/cluster/v2/vgg16.py     | 4 ++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/benchmark/cluster/v2/trainer.yaml b/benchmark/cluster/v2/trainer.yaml
index 33c95df365..a4958b2278 100644
--- a/benchmark/cluster/v2/trainer.yaml
+++ b/benchmark/cluster/v2/trainer.yaml
@@ -21,6 +21,8 @@ spec:
         env:
         - name: PADDLE_JOB_NAME
           value: vgg16job
+        - name: OMP_NUM_THREADS
+          value: "1"
         - name: TRAINERS
           value: "20"
         - name: PSERVERS
@@ -36,7 +38,7 @@ spec:
         - name: PADDLE_INIT_NICS
           value: "xgbe0"
         - name: PADDLE_INIT_TRAINER_COUNT
-          value: "1"
+          value: "2"
         - name: PADDLE_INIT_PORTS_NUM
           value: "1"
         - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
@@ -44,7 +46,7 @@ spec:
         - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
           value: "20"
         - name: PADDLE_INIT_NUM_PASSES
-          value: "1"
+          value: "2"
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 8644a547b3..85502c38e4 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -74,14 +74,14 @@ def vgg19(input, class_dim):
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=False)
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(DATA_DIM))
     lbl = paddle.layer.data(
         name="label", type=paddle.data_type.integer_value(CLASS_DIM))
 
     extra_layers = None
-    learning_rate = 0.01
+    learning_rate = 1e-3
     out = vgg16(image, class_dim=CLASS_DIM)
     cost = paddle.layer.classification_cost(input=out, label=lbl)
 

From b315a408e915e49fe8ffe4cf66bddfc512348e9d Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 16 Jan 2018 15:42:49 +0800
Subject: [PATCH 010/314] update

---
 benchmark/cluster/v2/vgg16.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 85502c38e4..400dcf1b41 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -81,7 +81,7 @@ def main():
         name="label", type=paddle.data_type.integer_value(CLASS_DIM))
 
     extra_layers = None
-    learning_rate = 1e-3
+    learning_rate = 1e-3 / 20
     out = vgg16(image, class_dim=CLASS_DIM)
     cost = paddle.layer.classification_cost(input=out, label=lbl)
 

From 9f50195b9d6346e08a59e6656f55b0d22efb3d81 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 19 Jan 2018 11:15:55 +0800
Subject: [PATCH 011/314] update using cifar10

---
 benchmark/cluster/v2/Dockerfile |  8 ++++----
 benchmark/cluster/v2/reader.py  |  2 +-
 benchmark/cluster/v2/vgg16.py   | 22 +++++++++++++++-------
 3 files changed, 20 insertions(+), 12 deletions(-)

diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/v2/Dockerfile
index 32e68b6150..ac85b1a7d0 100644
--- a/benchmark/cluster/v2/Dockerfile
+++ b/benchmark/cluster/v2/Dockerfile
@@ -1,5 +1,5 @@
 FROM paddlepaddle/paddlecloud-job
-RUN mkdir -p /workspace && mkdir -p /root/.cache/paddle/dataset/flowers/
-ADD vgg16.py reader.py /workspace/
-COPY 102flowers.tgz imagelabels.mat setid.mat /root/.cache/paddle/dataset/flowers/
-
+RUN mkdir -p /workspace
+ADD reader.py /workspace/
+RUN python /workspace/reader.py
+ADD vgg16.py /workspace/
diff --git a/benchmark/cluster/v2/reader.py b/benchmark/cluster/v2/reader.py
index 060bf2bda2..16ac2dbcef 100644
--- a/benchmark/cluster/v2/reader.py
+++ b/benchmark/cluster/v2/reader.py
@@ -67,4 +67,4 @@ if __name__ == '__main__':
     #    print len(im[0])
     #for im in train_reader('test.list'):
     #    print len(im[0])
-    paddle.dataset.flowers.train()
+    paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 8644a547b3..9189493276 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -14,13 +14,15 @@
 
 import gzip
 
-import paddle.v2.dataset.flowers as flowers
+import paddle.v2.dataset.cifar as cifar
 import paddle.v2 as paddle
 import reader
+import time
 
-DATA_DIM = 3 * 224 * 224  # Use 3 * 331 * 331 or 3 * 299 * 299 for Inception-ResNet-v2.
-CLASS_DIM = 102
+DATA_DIM = 3 * 32 * 32
+CLASS_DIM = 10
 BATCH_SIZE = 128
+ts = 0
 
 
 def vgg(input, nums, class_dim):
@@ -74,6 +76,7 @@ def vgg19(input, class_dim):
 
 
 def main():
+    global ts
     paddle.init(use_gpu=False, trainer_count=1)
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(DATA_DIM))
@@ -100,13 +103,13 @@ def main():
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            flowers.train(),
+            cifar.train10(),
             # To use other data, replace the above line with:
             # reader.train_reader('train.list'),
             buf_size=1000),
         batch_size=BATCH_SIZE)
     test_reader = paddle.batch(
-        flowers.valid(),
+        cifar.test10(),
         # To use other data, replace the above line with:
         # reader.test_reader('val.list'),
         batch_size=BATCH_SIZE)
@@ -120,10 +123,14 @@ def main():
 
     # End batch and end pass event handler
     def event_handler(event):
+        global ts
+        if isinstance(event, paddle.event.BeginIteration):
+            ts = time.time()
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 1 == 0:
-                print "\nPass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
+                print "\nPass %d, Batch %d, Cost %f, %s, spent: %f" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    time.time() - ts)
         if isinstance(event, paddle.event.EndPass):
             with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
                 trainer.save_parameter_to_tar(f)
@@ -137,3 +144,4 @@ def main():
 
 if __name__ == '__main__':
     main()
+

From c5a14ed4cdbaebb68490a28a914a094b62c35bcc Mon Sep 17 00:00:00 2001
From: wanghaox <wanghaox@gmail.com>
Date: Fri, 19 Jan 2018 11:31:58 +0800
Subject: [PATCH 012/314] add mine_hard_examples operator

---
 paddle/operators/mine_hard_examples_op.cc     | 184 ++++++++++++++++++
 paddle/operators/mine_hard_examples_op.h      | 148 ++++++++++++++
 .../fluid/tests/test_mine_hard_examples_op.py |  99 ++++++++++
 3 files changed, 431 insertions(+)
 create mode 100644 paddle/operators/mine_hard_examples_op.cc
 create mode 100755 paddle/operators/mine_hard_examples_op.h
 create mode 100755 python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py

diff --git a/paddle/operators/mine_hard_examples_op.cc b/paddle/operators/mine_hard_examples_op.cc
new file mode 100644
index 0000000000..75098d0bcd
--- /dev/null
+++ b/paddle/operators/mine_hard_examples_op.cc
@@ -0,0 +1,184 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/mine_hard_examples_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MineHardExamplesOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("ClsLoss"),
+                   "Input(ClsLoss) of MineHardExamplesOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasInput("MatchIndics"),
+        "Input(MatchIndics) of MineHardExamplesOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("MatchDis"),
+                   "Input(MatchDis) of MineHardExamplesOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("NegIndics"),
+        "Output(NegIndics) of MineHardExamplesOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("UpdatedMatchIndics"),
+        "Output(UpdatedMatchIndics) of MineHardExamplesOp should not be null.");
+
+    auto cls_loss_dims = ctx->GetInputDim("ClsLoss");
+    auto idx_dims = ctx->GetInputDim("MatchIndics");
+    auto dis_dims = ctx->GetInputDim("MatchDis");
+
+    PADDLE_ENFORCE_EQ(cls_loss_dims.size(), 2UL,
+                      "The shape of ClsLoss is [N, Np].");
+    PADDLE_ENFORCE_EQ(idx_dims.size(), 2UL,
+                      "The shape of MatchIndics is [N, Np].");
+    PADDLE_ENFORCE_EQ(dis_dims.size(), 2UL,
+                      "The shape of MatchDis is [N, Np].");
+
+    if (ctx->HasInput("LocLoss")) {
+      auto loc_loss_dims = ctx->GetInputDim("LocLoss");
+      PADDLE_ENFORCE_EQ(loc_loss_dims.size(), 2UL,
+                        "The shape of LocLoss is [N, Np].");
+      PADDLE_ENFORCE_EQ(cls_loss_dims[0], loc_loss_dims[0],
+                        "Batch size of ClsLoss and LocLoss must be the same.");
+      PADDLE_ENFORCE_EQ(
+          cls_loss_dims[1], loc_loss_dims[1],
+          "Prior box number of ClsLoss and LocLoss must be the same.");
+    }
+
+    PADDLE_ENFORCE_EQ(
+        cls_loss_dims[0], idx_dims[0],
+        "Batch size of ClsLoss and MatchIndics must be the same.");
+    PADDLE_ENFORCE_EQ(
+        cls_loss_dims[1], idx_dims[1],
+        "Prior box number of ClsLoss and MatchIndics must be the same.");
+
+    PADDLE_ENFORCE_EQ(cls_loss_dims[0], dis_dims[0],
+                      "Batch size of ClsLoss and MatchDis must be the same.");
+    PADDLE_ENFORCE_EQ(
+        cls_loss_dims[1], idx_dims[1],
+        "Prior box number of ClsLoss and MatchDis must be the same.");
+
+    auto mining_type =
+        GetMiningType(ctx->Attrs().Get<std::string>("mining_type"));
+
+    PADDLE_ENFORCE_NE(mining_type, MiningType::kNone,
+                      "mining_type must be hard_example or max_negative");
+
+    if (mining_type == MiningType::kMaxNegative) {
+      auto neg_pos_ratio = ctx->Attrs().Get<float>("neg_pos_ratio");
+      auto neg_dis_threshold = ctx->Attrs().Get<float>("neg_dis_threshold");
+      PADDLE_ENFORCE_GT(
+          neg_pos_ratio, 0.0f,
+          "neg_pos_ratio must greater than zero in max_negative mode");
+      PADDLE_ENFORCE_GT(
+          neg_dis_threshold, 0.0f,
+          "neg_dis_threshold must greater than zero in max_negative mode");
+    } else if (mining_type == MiningType::kHardExample) {
+      auto sample_size = ctx->Attrs().Get<int>("sample_size");
+      PADDLE_ENFORCE_GT(
+          sample_size, 0,
+          "sample_size must greater than zero in hard_example mode");
+    }
+
+    ctx->SetOutputDim("UpdatedMatchIndics", idx_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("ClsLoss")->type()),
+        ctx.device_context());
+  }
+};
+
+class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MineHardExamplesOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "ClsLoss",
+        "(Tensor, default Tensor<float>), The classification loss wit shape "
+        "[N, Np], N is the batch size and Np is the number of prior box.");
+    AddInput("LocLoss",
+             "(Tensor, optional, default Tensor<float>), The localization loss "
+             "wit shape [N, Np], N is the batch size and Np is the number of "
+             "prior box.")
+        .AsDispensable();
+    AddInput("MatchIndics",
+             "(Tensor, Tensor<int>), Matched indices with shape [N, Np], N is "
+             "the batch size and Np is the number of prior box. "
+             "MatchIndics[i][j] equal -1 means box[j] does not match any "
+             "entity, otherwise means Box[j] is matched to row.");
+    AddInput("MatchDis",
+             "(Tensor, default Tensor<float>) Matched indices with shape [N, "
+             "Np], N is the batch size and Np is the number of prior box.");
+    AddAttr<float>("neg_pos_ratio",
+                   "(float) The ratio of the negative box to the positive "
+                   "box. Use only when mining_type is equal to max_negative.")
+        .SetDefault(1.0);
+    AddAttr<float>("neg_dis_threshold",
+                   "(float) The negative box dis value threshold. "
+                   "Use only when mining_type is equal to max_negative.")
+        .SetDefault(0.5);
+    AddAttr<int>("sample_size",
+                 "(float) The max sample size of negative box. Use only when "
+                 "mining_type is equal to hard_example.")
+        .SetDefault(0);
+    AddAttr<std::string>("mining_type",
+                         "(float) The mining algorithm name, the value is "
+                         "hard_example or max_negative.")
+        .SetDefault("max_negative")
+        .InEnum({"hard_example", "max_negative"});
+
+    AddOutput("NegIndics",
+              "(LoDTensor) The output of negative example indics.a lod tensor "
+              "with shape [Neg, 1]. The size of lod[0] is batch size, "
+              "and each element is the box index. "
+              "For example, the batch size is 2, the lod is [[0, 1, 2]], "
+              "the sample 0's box 1(MatchIndics[0][1]) is selected, "
+              "and sample 1's box 0 is selected. The output NegIndics is "
+              "[[1], [0]].");
+
+    AddOutput("UpdatedMatchIndics",
+              "(Tensor) The output of updated MatchIndics, a tensor with "
+              "shape [N, M]. Only update when mining_type is equal to "
+              "hard_example. The input MatchIndics elements will be update to "
+              "-1 when it not in the highest loss list");
+
+    AddComment(R"DOC(
+Mine hard examples Operator.
+This operator implements hard example mining to select a subset of negative box indics.
+For each image, selects the box with highest losses. subject to the condition that the box cannot have
+an MatchDis > neg_dis_threshold when mining_type is equals max_negative. The selected number is 
+min(sample_size, max_negative_box_number) when mining_type is equals hard_example,
+or min(neg_pos_ratio * positive_box_number, max_negative_box_number) when mining_type is 
+equals max_negative, where the max_negative_box_number is the count of MatchIndics elements with value -1.
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(mine_hard_examples, ops::MineHardExamplesOp,
+                             ops::MineHardExamplesOpMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    mine_hard_examples,
+    ops::MineHardExamplesKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MineHardExamplesKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/mine_hard_examples_op.h b/paddle/operators/mine_hard_examples_op.h
new file mode 100755
index 0000000000..0a652a60c5
--- /dev/null
+++ b/paddle/operators/mine_hard_examples_op.h
@@ -0,0 +1,148 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+enum MiningType { kNone = 0, kMaxNegative, kHardExample };
+
+template <typename T>
+bool SortScoreDescend(const std::pair<float, T>& pair1,
+                      const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+inline bool IsEligibleMining(const MiningType mining_type, const int match_idx,
+                             const float match_dis,
+                             const float neg_dis_threshold) {
+  if (mining_type == MiningType::kMaxNegative) {
+    return match_idx == -1 && match_dis < neg_dis_threshold;
+  } else if (mining_type == MiningType::kHardExample) {
+    return true;
+  } else {
+    return false;
+  }
+}
+
+MiningType GetMiningType(std::string str) {
+  if (str == "max_negative") {
+    return MiningType::kMaxNegative;
+  } else if (str == "hard_example") {
+    return MiningType::kHardExample;
+  } else {
+    return MiningType::kNone;
+  }
+}
+
+template <typename DeviceContext, typename T>
+class MineHardExamplesKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in_cls_loss = ctx.Input<framework::Tensor>("ClsLoss");
+    auto* in_loc_loss = ctx.Input<framework::Tensor>("LocLoss");
+    auto* in_matched_indics = ctx.Input<framework::Tensor>("MatchIndics");
+    auto* in_match_dis = ctx.Input<framework::Tensor>("MatchDis");
+    float neg_pos_ratio = ctx.Attr<float>("neg_pos_ratio");
+    T neg_dis_threshold = static_cast<T>(ctx.Attr<float>("neg_dis_threshold"));
+    int sample_size = ctx.Attr<int>("sample_size");
+    MiningType mining_type =
+        GetMiningType(ctx.Attr<std::string>("mining_type"));
+
+    auto out_neg_indics = ctx.Output<framework::LoDTensor>("NegIndics");
+    auto out_match_indics = ctx.Output<framework::Tensor>("UpdatedMatchIndics");
+
+    framework::Copy(*in_matched_indics, ctx.GetPlace(), out_match_indics);
+
+    int batch_size = in_matched_indics->dims()[0];
+    int prior_num = in_matched_indics->dims()[1];
+
+    auto match_indices = framework::EigenMatrix<int>::From(*in_matched_indics);
+
+    auto match_indices_et =
+        framework::EigenMatrix<int>::From(*out_match_indics);
+
+    auto match_dis = framework::EigenMatrix<float>::From(*in_match_dis);
+    auto cls_loss = framework::EigenMatrix<float>::From(*in_cls_loss);
+    auto loc_loss = framework::EigenMatrix<float>::From(*in_loc_loss);
+
+    std::vector<std::vector<int>> all_neg_indices;
+    int all_neg_num = 0;
+    for (int n = 0; n < batch_size; ++n) {
+      std::vector<std::pair<float, size_t>> loss_idx;
+      int neg_sel = 0;
+      for (int m = 0; m < prior_num; ++m) {
+        if (IsEligibleMining(mining_type, match_indices(n, m), match_dis(n, m),
+                             neg_dis_threshold)) {
+          T loss = cls_loss(n, m);
+          if (mining_type == MiningType::kHardExample) {
+            loss = cls_loss(n, m) + loc_loss(n, m);
+          }
+          loss_idx.push_back(std::make_pair(loss, m));
+          ++neg_sel;
+        }
+      }
+      if (mining_type == MiningType::kMaxNegative) {
+        int num_pos = 0;
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) != -1) ++num_pos;
+        }
+        neg_sel = std::min(static_cast<int>(num_pos * neg_pos_ratio), neg_sel);
+      } else if (mining_type == MiningType::kHardExample) {
+        neg_sel = std::min(sample_size, neg_sel);
+      }
+      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<int>);
+      std::set<int> sel_indices;
+      std::vector<int> neg_indices;
+      for (int n = 0; n < neg_sel; ++n) {
+        sel_indices.insert(loss_idx[n].second);
+      }
+
+      for (int m = 0; m < prior_num; ++m) {
+        if (match_indices(n, m) > -1) {
+          if (mining_type == MiningType::kHardExample &&
+              sel_indices.find(m) == sel_indices.end()) {
+            match_indices_et(n, m) = -1;
+          }
+        } else {
+          if (sel_indices.find(m) != sel_indices.end()) {
+            neg_indices.push_back(m);
+          }
+        }
+      }
+      all_neg_indices.push_back(neg_indices);
+      all_neg_num += neg_indices.size();
+    }
+
+    framework::LoD out_neg_indics_lod;
+    out_neg_indics_lod.resize(1);
+    int neg_offset = 0;
+    auto neg_data = out_neg_indics->mutable_data<int>(
+        framework::make_ddim({all_neg_num, 1}), ctx.GetPlace());
+    out_neg_indics_lod[0].push_back(neg_offset);
+    for (auto neg_indices : all_neg_indices) {
+      for (auto neg_idx : neg_indices) {
+        neg_data[neg_offset++] = neg_idx;
+      }
+      out_neg_indics_lod[0].push_back(neg_offset);
+    }
+    out_neg_indics->set_lod(out_neg_indics_lod);
+    return;
+  }
+};
+}  // namespace operators
+
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py b/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py
new file mode 100755
index 0000000000..e7dd04740a
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py
@@ -0,0 +1,99 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+class TestMineHardExamplesOp(OpTest):
+    def set_data(self):
+        self.init_test_data()
+        self.inputs = {
+            'ClsLoss': self.cls_loss,
+            'LocLoss': self.loc_loss,
+            'MatchIndics': self.match_indices,
+            'MatchDis': self.match_dis
+        }
+
+        self.attrs = {
+            'neg_pos_ratio': self.neg_pos_ratio,
+            'neg_overlap': self.neg_overlap,
+            'sample_size': self.sample_size,
+            'mining_type': self.mining_type
+        }
+
+        self.outputs = {
+            'NegIndics': (self.neg_indices, self.neg_indices_lod),
+            'UpdatedMatchIndics': self.updated_match_indices
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        return
+
+    def setUp(self):
+        self.op_type = "mine_hard_examples"
+        self.set_data()
+
+    def init_test_data(self):
+        self.neg_pos_ratio = 1.0
+        self.neg_overlap = 0.5
+        self.sample_size = 0
+        self.mining_type = "max_negative"
+        self.cls_loss = np.array([[0.1, 0.1, 0.3],
+                                  [0.3, 0.1, 0.1]]).astype('float32')
+
+        self.loc_loss = np.array([[0.1, 0.2, 0.3],
+                                  [0.3, 0.4, 0.1]]).astype('float32')
+
+        self.match_dis = np.array([[0.2, 0.4, 0.8],
+                                   [0.1, 0.9, 0.3]]).astype('float32')
+
+        self.match_indices = np.array([[0, -1, -1],
+                                       [-1, 0, -1]]).astype('int32')
+
+        self.updated_match_indices = self.match_indices
+
+        self.neg_indices_lod = [[0, 1, 2]]
+        self.neg_indices = np.array([[1], [0]]).astype('int32')
+
+
+class TestMineHardExamplesOpHardExample(TestMineHardExamplesOp):
+    def init_test_data(self):
+        super(TestMineHardExamplesOpHardExample, self).init_test_data()
+        self.mining_type = "hard_example"
+        self.sample_size = 2
+
+        self.cls_loss = np.array([[0.5, 0.1, 0.3],
+                                  [0.3, 0.1, 0.1]]).astype('float32')
+
+        self.loc_loss = np.array([[0.2, 0.2, 0.3],
+                                  [0.3, 0.1, 0.2]]).astype('float32')
+
+        self.match_indices = np.array([[0, -1, -1],
+                                       [-1, 0, -1]]).astype('int32')
+
+        self.updated_match_indices = np.array([[0, -1, -1],
+                                               [-1, -1, -1]]).astype('int32')
+
+        self.neg_indices_lod = [[0, 1, 3]]
+        self.neg_indices = np.array([[2], [0], [2]]).astype('int32')
+
+
+if __name__ == '__main__':
+    unittest.main()

From 541b42e6fb7a5f4adaaad96251659e3bc9591b9d Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 19 Jan 2018 13:56:42 +0800
Subject: [PATCH 013/314] fix style

---
 benchmark/cluster/v2/trainer.yaml | 2 +-
 benchmark/cluster/v2/vgg16.py     | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/benchmark/cluster/v2/trainer.yaml b/benchmark/cluster/v2/trainer.yaml
index 33c95df365..75fffc64b0 100644
--- a/benchmark/cluster/v2/trainer.yaml
+++ b/benchmark/cluster/v2/trainer.yaml
@@ -28,7 +28,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && python /workspace/vgg16.py"
+          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/v2/vgg16.py
index 9189493276..59e3997d78 100644
--- a/benchmark/cluster/v2/vgg16.py
+++ b/benchmark/cluster/v2/vgg16.py
@@ -144,4 +144,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-

From d3905fbc1e53dcb8ef5481860e44e9ab4a704e5d Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 19 Jan 2018 19:02:02 +0800
Subject: [PATCH 014/314] add fluid vgg16 dist test

---
 benchmark/cluster/vgg16/fluid/Dockerfile      |  12 +
 benchmark/cluster/vgg16/fluid/k8s_tools.py    |  78 ++++++
 benchmark/cluster/vgg16/fluid/paddle_k8s      | 200 ++++++++++++++
 benchmark/cluster/vgg16/fluid/pserver.yaml    |  72 +++++
 benchmark/cluster/vgg16/fluid/reader.py       |   2 +
 benchmark/cluster/vgg16/fluid/trainer.yaml    |  69 +++++
 benchmark/cluster/vgg16/fluid/vgg16.py        | 248 ++++++++++++++++++
 benchmark/cluster/{ => vgg16}/v2/Dockerfile   |   2 +
 benchmark/cluster/{ => vgg16}/v2/pserver.yaml |   0
 benchmark/cluster/{ => vgg16}/v2/reader.py    |   0
 benchmark/cluster/{ => vgg16}/v2/trainer.yaml |   0
 benchmark/cluster/{ => vgg16}/v2/vgg16.py     |   0
 12 files changed, 683 insertions(+)
 create mode 100644 benchmark/cluster/vgg16/fluid/Dockerfile
 create mode 100644 benchmark/cluster/vgg16/fluid/k8s_tools.py
 create mode 100755 benchmark/cluster/vgg16/fluid/paddle_k8s
 create mode 100644 benchmark/cluster/vgg16/fluid/pserver.yaml
 create mode 100644 benchmark/cluster/vgg16/fluid/reader.py
 create mode 100644 benchmark/cluster/vgg16/fluid/trainer.yaml
 create mode 100644 benchmark/cluster/vgg16/fluid/vgg16.py
 rename benchmark/cluster/{ => vgg16}/v2/Dockerfile (81%)
 rename benchmark/cluster/{ => vgg16}/v2/pserver.yaml (100%)
 rename benchmark/cluster/{ => vgg16}/v2/reader.py (100%)
 rename benchmark/cluster/{ => vgg16}/v2/trainer.yaml (100%)
 rename benchmark/cluster/{ => vgg16}/v2/vgg16.py (100%)

diff --git a/benchmark/cluster/vgg16/fluid/Dockerfile b/benchmark/cluster/vgg16/fluid/Dockerfile
new file mode 100644
index 0000000000..77cd17f2b9
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/Dockerfile
@@ -0,0 +1,12 @@
+#FROM paddlepaddle/paddlecloud-job
+#RUN mkdir -p /workspace
+#ADD reader.py /workspace/
+#RUN python /workspace/reader.py
+FROM python:2.7.14
+ADD *.whl /
+RUN pip install /*.whl && rm -f /*.whl
+ADD paddle_k8s /usr/bin
+ADD k8s_tools.py /root
+RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev 
+
+ADD vgg16.py /workspace/
diff --git a/benchmark/cluster/vgg16/fluid/k8s_tools.py b/benchmark/cluster/vgg16/fluid/k8s_tools.py
new file mode 100644
index 0000000000..8a64dbd361
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/k8s_tools.py
@@ -0,0 +1,78 @@
+#!/bin/env python
+import os
+import sys
+import time
+import socket
+from kubernetes import client, config
+PADDLE_JOB_NAME = os.getenv("PADDLE_JOB_NAME")
+NAMESPACE = os.getenv("NAMESPACE")
+PORT = os.getenv("PSERVER_PORT")
+if os.getenv("KUBERNETES_SERVICE_HOST", None):
+    config.load_incluster_config()
+else:
+    config.load_kube_config()
+v1 = client.CoreV1Api()
+
+
+def fetch_pods_info(label_selector):
+    api_response = v1.list_namespaced_pod(
+        namespace=NAMESPACE, pretty=True, label_selector=label_selector)
+    pod_list = []
+    for item in api_response.items:
+        pod_list.append((item.status.phase, item.status.pod_ip))
+    return pod_list
+
+
+def wait_pods_running(label_selector, desired):
+    print "label selector: %s, desired: %s" % (label_selector, desired)
+    while True:
+        count = count_pods_by_phase(label_selector, 'Running')
+        # NOTE: pods may be scaled.
+        if count >= int(desired):
+            break
+        print 'current cnt: %d sleep for 5 seconds...' % count
+        time.sleep(5)
+
+def count_pods_by_phase(label_selector, phase):
+    pod_list = fetch_pods_info(label_selector)
+    filtered_pod_list = filter(lambda x: x[0] == phase, pod_list)
+    return len(filtered_pod_list)
+
+
+def fetch_pserver_ips():
+    label_selector = "paddle-job-pserver=%s" % PADDLE_JOB_NAME
+    pod_list = fetch_pods_info(label_selector)
+    pserver_ips = [item[1] for item in pod_list]
+    return ",".join(pserver_ips)
+
+def fetch_master_ip():
+    label_selector = "paddle-job-master=%s" % PADDLE_JOB_NAME
+    pod_list = fetch_pods_info(label_selector)
+    master_ips = [item[1] for item in pod_list]
+    return master_ips[0]
+
+def fetch_trainer_id():
+    label_selector = "paddle-job=%s" % PADDLE_JOB_NAME
+    pod_list = fetch_pods_info(label_selector)
+    trainer_ips = [item[1] for item in pod_list]
+    trainer_ips.sort()
+    local_ip = socket.gethostbyname(socket.gethostname())
+    for i in xrange(len(trainer_ips)):
+        if trainer_ips[i] == local_ip:
+            return i
+    return None
+
+
+if __name__ == "__main__":
+    command = sys.argv[1]
+    if command == "fetch_pserver_ips":
+        print fetch_pserver_ips()
+    elif command == "fetch_trainer_id":
+        print fetch_trainer_id()
+    elif command == "fetch_master_ip":
+        print fetch_master_ip()
+    elif command == "count_pods_by_phase":
+        print count_pods_by_phase(sys.argv[2], sys.argv[3])
+    elif command == "wait_pods_running":
+        wait_pods_running(sys.argv[2], sys.argv[3])
+
diff --git a/benchmark/cluster/vgg16/fluid/paddle_k8s b/benchmark/cluster/vgg16/fluid/paddle_k8s
new file mode 100755
index 0000000000..8f1c5db717
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/paddle_k8s
@@ -0,0 +1,200 @@
+#!/bin/bash
+start_pserver() {
+    stdbuf -oL paddle pserver \
+      --use_gpu=0 \
+      --port=$PADDLE_INIT_PORT \
+      --ports_num=$PADDLE_INIT_PORTS_NUM \
+      --ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
+      --nics=$PADDLE_INIT_NICS \
+      --comment=paddle_process_k8s \
+      --num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
+}
+
+start_new_pserver() {
+  stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
+  export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
+  stdbuf -oL /usr/bin/pserver \
+    -port=$PADDLE_INIT_PORT \
+    -num-pservers=$PSERVERS \
+    -log-level=debug \
+    -etcd-endpoint=http://$MASTER_IP:2379
+}
+
+start_master() {
+  stdbuf -oL /usr/bin/master \
+  -port=8080 \
+  -chunk-per-task=1\
+  -task-timout-dur=16s\
+  -endpoints=http://127.0.0.1:2379
+}
+
+check_failed_cnt() {
+  max_failed=$1
+  failed_count=$(python /root/k8s_tools.py count_pods_by_phase paddle-job=${PADDLE_JOB_NAME} Failed) 
+  if [ $failed_count -gt $max_failed ]; then
+    stdbuf -oL echo "Failed trainer count beyond the threadhold: "$max_failed
+    echo "Failed trainer count beyond the threshold: " $max_failed > /dev/termination-log 
+    exit 0
+  fi
+}
+
+check_trainer_ret() {
+  ret=$1
+  stdbuf -oL echo "job returned $ret...setting pod return message..."
+  stdbuf -oL echo "==============================="
+
+  if [ $ret -eq 136 ] ; then
+    echo "Error Arithmetic Operation(Floating Point Exception)" > /dev/termination-log
+  elif [ $ret -eq 139 ] ; then
+    echo "Segmentation Fault" > /dev/termination-log
+  elif [ $ret -eq 1 ] ; then
+    echo "General Error" > /dev/termination-log
+  elif [ $ret -eq 134 ] ; then
+    echo "Program Abort" > /dev/termination-log
+  fi
+  stdbuf -oL echo "termination log wroted..."
+  exit $ret
+}
+
+start_fluid_process() {
+  stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
+  if [ "${TRAINING_ROLE}" == "TRAINER" ]; then
+    check_failed_cnt ${TRAINERS}
+    sleep 5
+    stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
+    export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
+  fi
+  export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
+  stdbuf -oL sh -c "${ENTRY}"
+  check_trainer_ret $?
+}
+
+start_new_trainer() {
+  # FIXME(Yancey1989): use command-line interface to configure the max failed count
+  check_failed_cnt ${TRAINERS}
+  stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
+  sleep 5
+  stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
+  export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
+  export ETCD_IP="$MASTER_IP"
+
+  # NOTE: $TRAINER_PACKAGE may be large, do not copy
+  export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
+  cd $TRAINER_PACKAGE
+
+  stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
+  $PADDLE_INIT_NUM_GRADIENT_SERVERS, "version: " $1 
+
+  stdbuf -oL sh -c "${ENTRY}"
+  check_trainer_ret $?
+}
+
+start_trainer() {
+    # paddle v1 and V2 distributed training does not allow any trainer failed. 
+    check_failed_cnt 0
+    stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
+    stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job=${PADDLE_JOB_NAME} ${TRAINERS}
+
+    export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
+    export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
+    stdbuf -oL echo $PADDLE_INIT_TRAINER_ID > /trainer_id
+    # FIXME: /trainer_count = PADDLE_INIT_NUM_GRADIENT_SERVERS
+    stdbuf -oL echo $PADDLE_INIT_NUM_GRADIENT_SERVERS > /trainer_count
+
+    # NOTE: $TRAINER_PACKAGE may be large, do not copy
+    export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
+    cd $TRAINER_PACKAGE
+
+    stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
+    $PADDLE_INIT_NUM_GRADIENT_SERVERS, "trainer_id: " $PADDLE_INIT_TRAINER_ID, \
+    "version: " $1
+
+    # FIXME: If we use the new PServer by Golang, add Kubernetes healthz
+    # to wait PServer process get ready.Now only sleep 20 seconds.
+    sleep 20
+
+    case "$1" in
+      "v1")
+        FILE_COUNT=$(wc -l $TRAIN_LIST | awk '{print $1}')
+        if [ $FILE_COUNT -le $PADDLE_INIT_NUM_GRADIENT_SERVERS ]; then
+          echo "file count less than trainers"
+          check_trainer_ret 0
+        fi
+        let lines_per_node="$FILE_COUNT / ($PADDLE_INIT_NUM_GRADIENT_SERVERS + 1)"
+        echo "spliting file to" $lines_per_node
+        cp $TRAIN_LIST /
+        cd /
+        split -l $lines_per_node -d -a 3 $TRAIN_LIST train.list
+        CURRENT_LIST=$(printf "train.list%03d" $PADDLE_INIT_TRAINER_ID)
+        # always use /train.list for paddle v1 for each node.
+        echo "File for current node ${CURRENT_LIST}"
+        sleep 10
+        cp $CURRENT_LIST train.list
+
+        cd $TRAINER_PACKAGE
+
+        stdbuf -oL  paddle train \
+          --port=$PADDLE_INIT_PORT \
+          --nics=$PADDLE_INIT_NICS \
+          --ports_num=$PADDLE_INIT_PORTS_NUM \
+          --ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
+          --num_passes=$PADDLE_INIT_NUM_PASSES \
+          --trainer_count=$PADDLE_INIT_TRAINER_COUNT \
+          --saving_period=1 \
+          --log_period=20 \
+          --local=0 \
+          --rdma_tcp=tcp \
+          --config=$TOPOLOGY \
+          --use_gpu=$PADDLE_INIT_USE_GPU \
+          --trainer_id=$PADDLE_INIT_TRAINER_ID \
+          --save_dir=$OUTPUT \
+          --pservers=$PADDLE_INIT_PSERVERS \
+          --num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
+        # paddle v1 API does not allow any trainer failed.
+        check_trainer_ret $? 
+        ;;
+      "v2")
+        stdbuf -oL sh -c "${ENTRY}"
+        # paddle v2 API does not allow any trainer failed.
+        check_trainer_ret $? 
+        ;;
+      *)
+        ;;
+    esac
+}
+
+usage() {
+    echo "usage: paddle_k8s [<args>]:"
+    echo "  start_trainer  [v1|v2]    Start a trainer process with v1 or v2 API"
+    echo "  start_pserver             Start a pserver process"
+    echo "  start_new_pserver         Start a new pserver process"
+    echo "  start_new_trainer         Start a new triner process"
+}
+
+case "$1" in
+    start_pserver)
+        start_pserver
+        ;;
+    start_trainer)
+        start_trainer $2
+        ;;
+    start_new_trainer)
+        start_new_trainer
+        ;;
+    start_new_pserver)
+        start_new_pserver
+        ;;
+    start_master)
+        start_master
+        ;;
+    start_fluid)
+        start_fluid_process
+        ;;
+    --help)
+        usage
+        ;;
+    *)
+        usage
+        ;;
+esac
+
diff --git a/benchmark/cluster/vgg16/fluid/pserver.yaml b/benchmark/cluster/vgg16/fluid/pserver.yaml
new file mode 100644
index 0000000000..47d2380d2e
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/pserver.yaml
@@ -0,0 +1,72 @@
+apiVersion: extensions/v1beta1
+kind: ReplicaSet
+metadata:
+  name: vgg16job-pserver
+spec:
+  replicas: 10
+  template:
+    metadata:
+      labels:
+        paddle-job-pserver: vgg16job
+    spec:
+      hostNetwork: true
+      imagePullSecrets:
+      - name: job-registry-secret
+      containers:
+      - name: pserver
+        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16_fluid"
+        imagePullPolicy: Always
+        ports:
+        - name: jobport-30236
+          containerPort: 30236
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: MKL_NUM_THREADS
+          value: "1"
+        - name: TRAINING_ROLE
+          value: "PSERVER"
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        - name: POD_IP
+          valueFrom:
+            fieldRef:
+              fieldPath: "status.podIP"
+        command: ["paddle_k8s", "start_fluid"]
+        resources:
+          requests:
+            memory: 10Gi
+            cpu: 4
+          limits:
+            memory: 10Gi
+            cpu: 4
diff --git a/benchmark/cluster/vgg16/fluid/reader.py b/benchmark/cluster/vgg16/fluid/reader.py
new file mode 100644
index 0000000000..c5161ddea2
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/reader.py
@@ -0,0 +1,2 @@
+import paddle.v2 as paddle
+paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/vgg16/fluid/trainer.yaml b/benchmark/cluster/vgg16/fluid/trainer.yaml
new file mode 100644
index 0000000000..bada190764
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/trainer.yaml
@@ -0,0 +1,69 @@
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: vgg16job-trainer
+spec:
+  parallelism: 20
+  completions: 20
+  template:
+    metadata:
+      labels:
+        paddle-job: vgg16job
+    spec:
+      imagePullSecrets:
+        - name: job-registry-secret
+      hostNetwork: true
+      containers:
+      - name: trainer
+        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16_fluid"
+        imagePullPolicy: Always
+        command: ["paddle_k8s", "start_trainer", "v2"]
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: TRAINING_ROLE
+          value: "TRAINER"
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16.py"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        - name: POD_IP
+          valueFrom:
+            fieldRef:
+              fieldPath: "status.podIP"
+        resources:
+          requests:
+            memory: 40Gi
+            cpu: 2
+          limits:
+            memory: 40Gi
+            cpu: 2
+      restartPolicy: Never
diff --git a/benchmark/cluster/vgg16/fluid/vgg16.py b/benchmark/cluster/vgg16/fluid/vgg16.py
new file mode 100644
index 0000000000..0595a28784
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/vgg16.py
@@ -0,0 +1,248 @@
+"""VGG16 benchmark in Fluid"""
+from __future__ import print_function
+
+import sys
+import time
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import argparse
+import functools
+import os
+
+def str2bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    '--batch_size', type=int, default=128, help="Batch size for training.")
+parser.add_argument(
+    '--learning_rate',
+    type=float,
+    default=1e-3,
+    help="Learning rate for training.")
+parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
+parser.add_argument(
+    '--device',
+    type=str,
+    default='CPU',
+    choices=['CPU', 'GPU'],
+    help="The device type.")
+parser.add_argument(
+    '--data_format',
+    type=str,
+    default='NCHW',
+    choices=['NCHW', 'NHWC'],
+    help='The data order, now only support NCHW.')
+parser.add_argument(
+    '--data_set',
+    type=str,
+    default='cifar10',
+    choices=['cifar10', 'flowers'],
+    help='Optional dataset for benchmark.')
+parser.add_argument(
+    '--local',
+    type=str2bool,
+    default=True,
+    help='Whether to run as local mode.')
+args = parser.parse_args()
+
+
+def vgg16_bn_drop(input):
+    def conv_block(input, num_filter, groups, dropouts):
+        return fluid.nets.img_conv_group(
+            input=input,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type='max')
+
+    conv1 = conv_block(input, 64, 2, [0.3, 0])
+    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
+    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
+    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
+    return fc2
+
+
+def main():
+    if args.data_set == "cifar10":
+        classdim = 10
+        if args.data_format == 'NCHW':
+            data_shape = [3, 32, 32]
+        else:
+            data_shape = [32, 32, 3]
+    else:
+        classdim = 102
+        if args.data_format == 'NCHW':
+            data_shape = [3, 224, 224]
+        else:
+            data_shape = [224, 224, 3]
+
+    # Input data
+    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    # Train program
+    net = vgg16_bn_drop(images)
+    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    # Evaluator
+    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+    # inference program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        test_target = accuracy.metrics + accuracy.states
+        inference_program = fluid.io.get_inference_program(test_target)
+
+    # Optimization
+    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+    optimize_ops, params_grads = optimizer.minimize(avg_cost)
+
+    # Initialize executor
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
+    exe = fluid.Executor(place)
+
+
+    # test
+    def test(exe):
+        accuracy.reset(exe)
+        for batch_id, data in enumerate(test_reader()):
+            img_data = np.array(map(lambda x: x[0].reshape(data_shape),
+                                    data)).astype("float32")
+            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+            y_data = y_data.reshape([-1, 1])
+
+            exe.run(inference_program,
+                    feed={"pixel": img_data,
+                          "label": y_data})
+
+        return accuracy.eval(exe)
+
+    def train_loop(exe, trainer_prog):
+        iters = 0
+        for pass_id in range(args.num_passes):
+            # train
+            start_time = time.time()
+            num_samples = 0
+            accuracy.reset(exe)
+            for batch_id, data in enumerate(train_reader()):
+                img_data = np.array(map(lambda x: x[0].reshape(data_shape),
+                                        data)).astype("float32")
+                y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+                y_data = y_data.reshape([-1, 1])
+
+                loss, acc = exe.run(trainer_prog,
+                                    feed={"pixel": img_data,
+                                        "label": y_data},
+                                    fetch_list=[avg_cost] + accuracy.metrics)
+                iters += 1
+                num_samples += len(data)
+                print(
+                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f" %
+                    (pass_id, iters, loss, acc)
+                )  # The accuracy is the accumulation of batches, but not the current batch.
+
+            pass_elapsed = time.time() - start_time
+            pass_train_acc = accuracy.eval(exe)
+            pass_test_acc = test(exe)
+            print(
+                "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
+                % (pass_id, num_samples / pass_elapsed, pass_train_acc,
+                pass_test_acc))
+
+    if args.local:
+        # Parameter initialization
+        exe.run(fluid.default_startup_program())
+
+        # data reader
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.cifar.train10()
+                if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                buf_size=5120),
+            batch_size=args.batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.cifar.test10()
+            if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+            batch_size=args.batch_size)
+        train_loop(exe, fluid.default_main_program())
+    else:
+        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # all pserver endpoints
+        eplist = []
+        for ip in pserver_ips.split(","):
+            eplist.append(':'.join([ip, "6174"]))
+        pserver_endpoints = ",".join(eplist)
+        print("pserver endpoints: ", pserver_endpoints)
+        trainers = int(os.getenv("TRAINERS"))  # total trainer count
+        current_endpoint = os.getenv("POD_IP") + ":6174"  # current pserver endpoint
+        training_role = os.getenv("TRAINING_ROLE",
+                                "TRAINER")  # get the training role: trainer/pserver
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            optimize_ops, params_grads, pservers=pserver_endpoints, trainers=trainers)
+
+        if training_role == "PSERVER":
+            if not current_endpoint:
+                print("need env SERVER_ENDPOINT")
+                exit(1)
+            pserver_prog = t.get_pserver_program(current_endpoint)
+            pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+            print("starting server side startup")
+            exe.run(pserver_startup)
+            print("starting parameter server...")
+            exe.run(pserver_prog)
+        elif training_role == "TRAINER":
+            # Parameter initialization
+            exe.run(fluid.default_startup_program())
+
+            # data reader
+            train_reader = paddle.batch(
+                paddle.reader.shuffle(
+                    paddle.dataset.cifar.train10()
+                    if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                    buf_size=5120),
+                batch_size=args.batch_size)
+            test_reader = paddle.batch(
+                paddle.dataset.cifar.test10()
+                if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+                batch_size=args.batch_size)
+
+            trainer_prog = t.get_trainer_program()
+            feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
+            # TODO(typhoonzero): change trainer startup program to fetch parameters from pserver
+            exe.run(fluid.default_startup_program())
+            train_loop(exe, trainer_prog)
+        else:
+            print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+def print_arguments():
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+if __name__ == "__main__":
+    print_arguments()
+    main()
diff --git a/benchmark/cluster/v2/Dockerfile b/benchmark/cluster/vgg16/v2/Dockerfile
similarity index 81%
rename from benchmark/cluster/v2/Dockerfile
rename to benchmark/cluster/vgg16/v2/Dockerfile
index ac85b1a7d0..5f129a8e32 100644
--- a/benchmark/cluster/v2/Dockerfile
+++ b/benchmark/cluster/vgg16/v2/Dockerfile
@@ -3,3 +3,5 @@ RUN mkdir -p /workspace
 ADD reader.py /workspace/
 RUN python /workspace/reader.py
 ADD vgg16.py /workspace/
+
+ADD vgg16_fluid.py /workspace
diff --git a/benchmark/cluster/v2/pserver.yaml b/benchmark/cluster/vgg16/v2/pserver.yaml
similarity index 100%
rename from benchmark/cluster/v2/pserver.yaml
rename to benchmark/cluster/vgg16/v2/pserver.yaml
diff --git a/benchmark/cluster/v2/reader.py b/benchmark/cluster/vgg16/v2/reader.py
similarity index 100%
rename from benchmark/cluster/v2/reader.py
rename to benchmark/cluster/vgg16/v2/reader.py
diff --git a/benchmark/cluster/v2/trainer.yaml b/benchmark/cluster/vgg16/v2/trainer.yaml
similarity index 100%
rename from benchmark/cluster/v2/trainer.yaml
rename to benchmark/cluster/vgg16/v2/trainer.yaml
diff --git a/benchmark/cluster/v2/vgg16.py b/benchmark/cluster/vgg16/v2/vgg16.py
similarity index 100%
rename from benchmark/cluster/v2/vgg16.py
rename to benchmark/cluster/vgg16/v2/vgg16.py

From f2c4bb679bb3f247c12888b878ec4dddff358e49 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Sun, 21 Jan 2018 09:24:40 -0800
Subject: [PATCH 015/314] Add lstm with recurrent projection operator

---
 paddle/operators/lstmp_op.cc                  | 296 ++++++++++++++
 paddle/operators/lstmp_op.cu.cc               |  24 ++
 paddle/operators/lstmp_op.h                   | 384 ++++++++++++++++++
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 314 ++++++++++++++
 4 files changed, 1018 insertions(+)
 create mode 100644 paddle/operators/lstmp_op.cc
 create mode 100644 paddle/operators/lstmp_op.cu.cc
 create mode 100644 paddle/operators/lstmp_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_lstmp_op.py

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
new file mode 100644
index 0000000000..4c7f7713ee
--- /dev/null
+++ b/paddle/operators/lstmp_op.cc
@@ -0,0 +1,296 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/lstmp_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LSTMPOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(Weight) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
+                   "Input(ProjWeight) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Bias"),
+                   "Input(Bias) of LSTMP should not be null.");
+
+    PADDLE_ENFORCE(ctx->HasOutput("Projection"),
+                   "Output(Projection) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Cell"),
+                   "Output(Cell) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
+                   "Output(BatchGate) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
+                   "Output(BatchGate) of LSTMP should not be null.");
+
+    auto in_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE_EQ(in_dims.size(), 2, "Input(X)'s rank must be 2.");
+
+    if (ctx->HasInput("H0")) {
+      PADDLE_ENFORCE(ctx->HasInput("C0"),
+                     "Input(C0) and Input(H0) of LSTMP should not "
+                     "be null at the same time.");
+      auto h_dims = ctx->GetInputDim("H0");
+      auto c_dims = ctx->GetInputDim("C0");
+      PADDLE_ENFORCE(h_dims == c_dims,
+                     "The dimension of Input(H0) and Input(C0) "
+                     "should be the same.");
+    }
+
+    int frame_size = in_dims[1] / 4;
+    auto w_dims = ctx->GetInputDim("Weight");
+    auto proj_dims = ctx->GetInputDim("ProjWeight");
+    PADDLE_ENFORCE_EQ(w_dims.size(), 2,
+                      "The rank of Input(Weight) should be 2.");
+    PADDLE_ENFORCE_EQ(w_dims[0], proj_dims[1],
+                      "The first dimension of Input(Weight) "
+                      "should be %d.",
+                      proj_dims[1]);
+    PADDLE_ENFORCE_EQ(w_dims[1], 4 * frame_size,
+                      "The second dimension of Input(Weight) "
+                      "should be 4 * %d.",
+                      frame_size);
+
+    PADDLE_ENFORCE_EQ(proj_dims.size(), 2,
+                      "The rank of Input(ProjWeight) should be 2.");
+    PADDLE_ENFORCE_EQ(proj_dims[0], frame_size,
+                      "The first dimension of Input(ProjWeight) "
+                      "should be %d.",
+                      frame_size);
+
+    auto b_dims = ctx->GetInputDim("Bias");
+    PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
+    PADDLE_ENFORCE_EQ(b_dims[0], 1,
+                      "The first dimension of Input(Bias) should be 1.");
+
+    if (ctx->Attrs().Get<bool>("use_peepholes")) {
+      PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
+                        "The second dimension of Input(Bias) should be "
+                        "7 * %d if enable peepholes connection",
+                        frame_size);
+    } else {
+      PADDLE_ENFORCE_EQ(b_dims[1], 4 * frame_size,
+                        "The second dimension of Input(Bias) should be "
+                        "4 * %d if disable peepholes connection",
+                        frame_size);
+    }
+
+    framework::DDim out_dims({in_dims[0], frame_size});
+    framework::DDim proj_out_dims({in_dims[0], proj_dims[1]});
+    ctx->SetOutputDim("Projection", proj_out_dims);
+    ctx->SetOutputDim("Cell", out_dims);
+    ctx->SetOutputDim("BatchGate", in_dims);
+    ctx->SetOutputDim("BatchCellPreAct", out_dims);
+    ctx->ShareLoD("Input", "Projection");
+    ctx->ShareLoD("Input", "Cell");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
+        ctx.device_context());
+  }
+};
+
+class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LSTMPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input",
+             "(LoDTensor) the first input is a LodTensor, which support "
+             "variable-time length input sequence. The underlying tensor in "
+             "this LoDTensor is a matrix with shape (T X 4D), where T is the "
+             "total time steps in this mini-batch, D is the hidden size.");
+    AddInput("H0",
+             "(Tensor, optional) the initial hidden state is an optional "
+             "input. This is a tensor with shape (N x D), where N is the "
+             "batch size and D is the hidden size.")
+        .AsDispensable();
+    AddInput("C0",
+             "(Tensor, optional) the initial cell state is an optional "
+             "input. This is a tensor with shape (N x D), where N is the "
+             "batch size. `H0` and `C0` can be NULL but only at the same time")
+        .AsDispensable();
+    AddInput("Weight",
+             "(Tensor) the learnable hidden-hidden weights."
+             " - The shape is (P x 4D), where P is the recurrent projection "
+             "layer size and  D is the hidden size. "
+             " - Weight = {W_cr, W_ir, W_fr, W_or}");
+    AddInput("ProjWeight",
+             "(Tensor) the learnable weight `W_rh` of the projection layer."
+             " - The shape is (D x P), where P is the recurrent projection "
+             "layer size and  D is the hidden size.");
+    AddInput("Bias",
+             "(Tensor) the learnable weights, which contains two parts: "
+             "input-hidden bias weight and peephole connections weight if "
+             "setting `use_peepholes` True. "
+             "1. `use_peepholes = False` "
+             " - The shape is (1 x 4D). "
+             " - Bias = {b_c, b_i, b_f, b_o}."
+             "2. `use_peepholes = True` "
+             " - The shape is (1 x 7D). "
+             " - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
+    AddOutput("Projection",
+              "(LoDTensor) the projection of the hidden state of LSTMP "
+              "operator. The shape is (T x P), and lod is the same with the "
+              "`Input`.");
+    AddOutput("Cell",
+              "(LoDTensor) the cell state of LSTMP operator. "
+              "The shape is (T x D), and lod is the same with the `Input`.");
+    AddOutput("BatchGate",
+              "(LoDTensor) This LoDTensor contains input gate, forget gate "
+              "and output gate after the nonlinear computation. This "
+              "LoDTensor has the same shape as the reorganized input, which "
+              "is also be called batch input. The LoD size is 2. The first "
+              "LoD is the batch offsets and the second LoD contains the "
+              "indexes, which denote the position of reorganized sequence "
+              "in the raw input.")
+        .AsIntermediate();
+    AddOutput("BatchCellPreAct",
+              "(LoDTensor) This LoDTensor is obtained in the forward and used "
+              "in the backward.")
+        .AsIntermediate();
+    AddAttr<bool>("use_peepholes",
+                  "(bool, defalut: True) "
+                  "whether to enable diagonal/peephole connections.")
+        .SetDefault(true);
+    AddAttr<bool>("is_reverse",
+                  "(bool, defalut: False) "
+                  "whether to compute reversed LSTMP.")
+        .SetDefault(false);
+    AddAttr<std::string>(
+        "gate_activation",
+        "(string, default: sigmoid)"
+        "The activation for input gate, forget gate and output "
+        "gate, `sigmoid` by default.")
+        .SetDefault("sigmoid")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
+    AddAttr<std::string>("cell_activation",
+                         "(string, default: tanh)"
+                         "The activation for cell output, `tanh` by defalut.")
+        .SetDefault("tanh")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
+    AddAttr<std::string>("candidate_activation",
+                         "(string, default: tanh)"
+                         "The activation for candidate hidden state, "
+                         "`tanh` by default.")
+        .SetDefault("tanh")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
+    AddComment(R"DOC(
+Long-Short Term Memory with Recurrent Projection (LSTMP) Operator.
+
+LATMP is stand LSTM appended by a recurrent projection layer to reduce the
+number of parameters, espeacially when the output size is relative large. 
+The formula is as follows:
+
+$$
+i_t = \sigma(W_{ix}x_{t} + W_{ih}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
+
+f_t = \sigma(W_{fx}x_{t} + W_{fh}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
+
+c_t = f_t \odot c_{t-1} + i_t \odot act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
+
+o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
+
+h_t = o_t \odot act_h(c_t)
+
+r_t = W_{rh}h_t
+$$
+
+where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
+of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
+are diagonal weight matrices for peephole connections. In our implementation,
+we use vectors to reprenset these diagonal weight matrices. The b terms
+denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
+is the non-line activations, such as logistic sigmoid function, and
+$i, f, o$ and $c$ are the input gate, forget gate, output gate,
+and cell activation vectors, respectively, all of which have the same size as
+the cell output activation vector $h$. $r$ denotes the recurrent projection 
+layer.
+
+The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
+are the cell input and cell output activation functions and `tanh` is usually
+used for them.
+
+Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
+operations on the input $x_{t}$ are NOT included in this operator.
+Users can choose to use fully-connect operator before LSTMP operator.
+
+)DOC");
+  }
+};
+
+class LSTMPGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
+                   "Input(Hidden) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Cell"),
+                   "Input(Cell) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Weight"),
+                   "Input(Weight) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Bias"),
+                   "Input(Bias) of LSTMP should not be null.");
+
+    PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
+                   "Input(BatchGate) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
+                   "Input(BatchGate) of LSTMP should not be null.");
+
+    auto SetOutGradDim = [&ctx](const std::string& name) {
+      auto g_name = framework::GradVarName(name);
+      if (ctx->HasOutput(g_name))
+        ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
+    };
+
+    SetOutGradDim("Input");
+    SetOutGradDim("Weight");
+    SetOutGradDim("Bias");
+    SetOutGradDim("H0");
+    SetOutGradDim("C0");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("Input")->type()),
+        ctx.device_context());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(lstmp, ops::LSTMPOp, ops::LSTMPOpMaker, lstmp_grad,
+            ops::LSTMPGradOp);
+REGISTER_OP_CPU_KERNEL(
+    lstmp, ops::LSTMPKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LSTMPKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    lstmp_grad, ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LSTMPGradKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/lstmp_op.cu.cc b/paddle/operators/lstmp_op.cu.cc
new file mode 100644
index 0000000000..7fcbcfecc8
--- /dev/null
+++ b/paddle/operators/lstmp_op.cu.cc
@@ -0,0 +1,24 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/lstmp_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    lstmp, ops::LSTMPKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LSTMPKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    lstmp_grad,
+    ops::LSTMPGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LSTMPGradKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
new file mode 100644
index 0000000000..f5a38b2ff5
--- /dev/null
+++ b/paddle/operators/lstmp_op.h
@@ -0,0 +1,384 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/detail/activation_functions.h"
+#include "paddle/operators/math/lstm_compute.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/sequence2batch.h"
+
+namespace paddle {
+namespace operators {
+
+using LoDTensor = framework::LoDTensor;
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+inline void ReorderInitState(const DeviceContext& ctx,
+                             const framework::Tensor& src, const size_t* index,
+                             framework::Tensor* dst, bool indexed_src) {
+  math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
+  dst->mutable_data<T>(src.dims(), ctx.GetPlace());
+  row_shuffle(ctx, src, index, *dst, indexed_src);
+}
+
+template <typename DeviceContext, typename T>
+class LSTMPKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<LoDTensor>("Input");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* proj_weight = ctx.Input<Tensor>("ProjWeight");
+    auto* bias = ctx.Input<Tensor>("Bias");
+
+    auto* hidden_t0 = ctx.Input<Tensor>("H0");
+    auto* cell_t0 = ctx.Input<Tensor>("C0");
+
+    auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
+    batch_gate->mutable_data<T>(ctx.GetPlace());
+    auto* proj_out = ctx.Output<LoDTensor>("Projection");
+    proj_out->mutable_data<T>(ctx.GetPlace());
+    auto* cell_out = ctx.Output<LoDTensor>("Cell");
+    cell_out->mutable_data<T>(ctx.GetPlace());
+
+    bool is_reverse = ctx.Attr<bool>("is_reverse");
+    math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
+    auto& device_ctx = ctx.template device_context<DeviceContext>();
+    to_batch(device_ctx, *input, *batch_gate, true, is_reverse);
+
+    auto in_dims = input->dims();
+    int frame_size = static_cast<int>(in_dims[1] / 4);
+    framework::DDim dims({in_dims[0], frame_size});
+    framework::DDim proj_dims({in_dims[0], proj_weight->dims()[1]});
+
+    if (bias) {
+      Tensor b = *bias;
+      b.Resize({bias->numel(), 1});
+      Tensor gate_bias = b.Slice(0, 4 * frame_size);
+      math::RowwiseAdd<DeviceContext, T> add_bias;
+      add_bias(device_ctx, *batch_gate, gate_bias, batch_gate);
+    }
+
+    math::LstmMetaValue<T> lstmp_value;
+    if (bias && ctx.Attr<bool>("use_peepholes")) {
+      T* bias_data = const_cast<T*>(bias->data<T>());
+      // the code style in LstmpMetaValue will be updated later.
+
+      lstmp_value.check_ig = bias_data + 4 * frame_size;
+      lstmp_value.check_fg = lstmp_value.check_ig + frame_size;
+      lstmp_value.check_og = lstmp_value.check_fg + frame_size;
+    } else {
+      lstmp_value.check_ig = nullptr;
+      lstmp_value.check_fg = nullptr;
+      lstmp_value.check_og = nullptr;
+    }
+    lstmp_value.prev_state_value = nullptr;
+    Tensor ordered_c0;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (cell_t0) {
+      // Since the batch computing for LSTMP reorders the input sequence
+      // according to their length. The initialized cell state also needs
+      // to reorder.
+      ReorderInitState<DeviceContext, T>(device_ctx, *cell_t0, order,
+                                         &ordered_c0, true);
+      lstmp_value.prev_state_value = ordered_c0.data<T>();
+    }
+
+    // Use the local variable as here.
+    LoDTensor batch_hidden, batch_proj, batch_cell;
+    auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
+    batch_hidden.mutable_data<T>(dims, ctx.GetPlace());     // T x D
+    batch_proj.mutable_data<T>(proj_dims, ctx.GetPlace());  // T x P
+    batch_cell.mutable_data<T>(dims, ctx.GetPlace());       // T x D
+    batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());
+
+    auto batch_starts = batch_gate->lod()[0];
+    size_t num_batch = batch_starts.size() - 1;
+    auto gate_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("gate_activation"));
+    auto cell_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("cell_activation"));
+    auto cand_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("candidate_activation"));
+
+    for (size_t n = 0; n < num_batch; n++) {
+      int bstart = static_cast<int>(batch_starts[n]);
+      int bend = static_cast<int>(batch_starts[n + 1]);
+
+      Tensor gate_t = batch_gate->Slice(bstart, bend);
+      Tensor hidden_t = batch_hidden.Slice(bstart, bend);
+      Tensor proj_t = batch_proj.Slice(bstart, bend);
+      Tensor cell_t = batch_cell.Slice(bstart, bend);
+      Tensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);
+
+      int cur_batch_size = bend - bstart;
+
+      if (n > 0) {
+        int pre_h_start = static_cast<int>(batch_starts[n - 1]);
+        int pre_h_end = pre_h_start + cur_batch_size;
+        auto pre_proj_t = batch_proj.Slice(pre_h_start, pre_h_end);
+        math::matmul<DeviceContext, T>(device_ctx, pre_proj_t, false, *weight,
+                                       false, static_cast<T>(1.0), &gate_t,
+                                       static_cast<T>(1.0));
+      } else if (hidden_t0) {
+        // If n == 0 and there is no initialized hidden state, that is to say
+        // the H0 is zeros, the calculation W_h * H0 will be skiped.
+        // If n == 0 and there is initialized hidden state, calculate W_h * H0.
+
+        // Since the batch computing for LSTMP reorders the input sequence
+        // according to their length. The initialized hidden state also needs
+        // to reorder.
+        Tensor ordered_h0, ordered_proj0;
+        ordered_proj0.Resize({1, proj_weight->dims()[1]});
+        ordered_proj0.mutable_data<T>(ctx.GetPlace());
+        ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
+                                           &ordered_h0, true);
+        math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false,
+                                       *proj_weight, false, static_cast<T>(1.0),
+                                       &ordered_proj0, static_cast<T>(0.0));
+        math::matmul<DeviceContext, T>(device_ctx, ordered_proj0, false,
+                                       *weight, false, static_cast<T>(1.0),
+                                       &gate_t, static_cast<T>(1.0));
+      }
+
+      lstmp_value.gate_value = gate_t.data<T>();
+      lstmp_value.output_value = hidden_t.data<T>();
+      lstmp_value.state_value = cell_t.data<T>();
+      lstmp_value.state_active_value = cell_pre_act_t.data<T>();
+      math::LstmUnitFunctor<DeviceContext, T>::compute(
+          device_ctx, lstmp_value, frame_size, cur_batch_size, gate_act,
+          cell_act, cand_act);
+      lstmp_value.prev_state_value = lstmp_value.state_value;
+      math::matmul<DeviceContext, T>(device_ctx, hidden_t, false, *proj_weight,
+                                     false, static_cast<T>(1.0), &proj_t,
+                                     static_cast<T>(0.0));
+    }
+
+    math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
+    batch_proj.set_lod(batch_gate->lod());
+    // restore the output hidden in LoDTensor from the batch hidden
+    to_seq(device_ctx, batch_proj, *proj_out);
+
+    batch_cell.set_lod(batch_gate->lod());
+    // restore the output cell state in LoDTensor from the batch cell
+    to_seq(device_ctx, batch_cell, *cell_out);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class LSTMPGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<LoDTensor>("Input");
+    auto* weight = ctx.Input<Tensor>("Weight");
+    auto* bias = ctx.Input<Tensor>("Bias");
+
+    auto* proj_out = ctx.Input<LoDTensor>("Projection");
+    auto* cell_out = ctx.Input<LoDTensor>("Cell");
+
+    auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
+    auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
+
+    auto* hidden_g = ctx.Input<LoDTensor>(framework::GradVarName("Projection"));
+
+    auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
+    auto* weight_g = ctx.Output<Tensor>(framework::GradVarName("Weight"));
+    auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    auto* h0 = ctx.Input<Tensor>("H0");
+    auto* c0 = ctx.Input<Tensor>("C0");
+
+    auto* h0_g = ctx.Output<Tensor>(framework::GradVarName("H0"));
+    auto* c0_g = ctx.Output<Tensor>(framework::GradVarName("C0"));
+
+    auto& device_ctx = ctx.template device_context<DeviceContext>();
+    math::SetConstant<DeviceContext, T> zero;
+    if (weight_g) {
+      weight_g->mutable_data<T>(ctx.GetPlace());
+      zero(device_ctx, weight_g, static_cast<T>(0.0));
+    }
+
+    // ordered_h0/c0 is the reordered hidden/cell initialization.
+    // ordered_h0_g/c0_g is the reordered gradient of hidden/cell
+    // initialization.
+    Tensor ordered_h0, ordered_c0, ordered_h0_g, ordered_c0_g;
+    const size_t* order = batch_gate->lod()[2].data();
+    if (c0) {
+      ReorderInitState<DeviceContext, T>(device_ctx, *c0, order, &ordered_c0,
+                                         true);
+    }
+    if (c0 && c0_g) {
+      ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
+    }
+
+    auto in_dims = input->dims();
+    auto out_dims = hidden_g->dims();
+    int frame_size = static_cast<int>(in_dims[1] / 4);
+    PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
+
+    math::LstmMetaValue<T> lstmp_value;
+    if (bias && ctx.Attr<bool>("use_peepholes")) {
+      T* bias_data = const_cast<T*>(bias->data<T>());
+      lstmp_value.check_ig = bias_data + 4 * frame_size;
+      lstmp_value.check_fg = lstmp_value.check_ig + frame_size;
+      lstmp_value.check_og = lstmp_value.check_fg + frame_size;
+    } else {
+      lstmp_value.check_ig = nullptr;
+      lstmp_value.check_fg = nullptr;
+      lstmp_value.check_og = nullptr;
+    }
+
+    math::LstmMetaGrad<T> lstmp_grad;
+
+    if (bias && bias_g) {
+      bias_g->mutable_data<T>(ctx.GetPlace());
+      zero(device_ctx, bias_g, static_cast<T>(0.0));
+    }
+    if (bias && bias_g && ctx.Attr<bool>("use_peepholes")) {
+      T* bias_g_data = bias_g->data<T>();
+      lstmp_grad.check_ig_grad = bias_g_data + 4 * frame_size;
+      lstmp_grad.check_fg_grad = lstmp_grad.check_ig_grad + frame_size;
+      lstmp_grad.check_og_grad = lstmp_grad.check_fg_grad + frame_size;
+    } else {
+      lstmp_grad.check_ig_grad = nullptr;
+      lstmp_grad.check_fg_grad = nullptr;
+      lstmp_grad.check_og_grad = nullptr;
+    }
+
+    math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
+
+    auto ToBatch = [&batch_gate, &to_batch](
+        const DeviceContext& ctx, const framework::LoDTensor& src,
+        const framework::DDim& dims, framework::LoDTensor& dst) {
+      dst.mutable_data<T>(dims, ctx.GetPlace());
+      dst.set_lod(batch_gate->lod());
+      to_batch(ctx, src, dst, false);
+    };
+
+    LoDTensor batch_proj, batch_proj_g, batch_cell;
+    ToBatch(device_ctx, *proj_out, out_dims, batch_proj);
+    ToBatch(device_ctx, *hidden_g, out_dims, batch_proj_g);
+    ToBatch(device_ctx, *cell_out, out_dims, batch_cell);
+
+    LoDTensor batch_cell_g, batch_gate_g;
+    batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
+    // TODO(qingqing) support the case output cell has gradient.
+    // to_batch(device_ctx, *cell_g, batch_cell_g, false);
+    zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
+    batch_gate_g.mutable_data<T>(batch_gate->dims(), ctx.GetPlace());
+    batch_gate_g.set_lod(batch_gate->lod());
+
+    auto gate_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("gate_activation"));
+    auto cell_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("cell_activation"));
+    auto cand_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("candidate_activation"));
+
+    auto batch_starts = batch_gate->lod()[0];
+    size_t num_batch = batch_starts.size() - 1;
+    for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
+      int bstart = static_cast<int>(batch_starts[n]);
+      int bend = static_cast<int>(batch_starts[n + 1]);
+
+      Tensor gate = batch_gate->Slice(bstart, bend);
+      Tensor cell = batch_cell.Slice(bstart, bend);
+      Tensor cell_pre_act = batch_cell_pre_act->Slice(bstart, bend);
+      lstmp_value.gate_value = gate.data<T>();
+      lstmp_value.state_value = cell.data<T>();
+      lstmp_value.state_active_value = cell_pre_act.data<T>();
+
+      Tensor out_g = batch_proj_g.Slice(bstart, bend);
+      Tensor gate_g = batch_gate_g.Slice(bstart, bend);
+      Tensor cell_g = batch_cell_g.Slice(bstart, bend);
+      lstmp_grad.state_grad = cell_g.data<T>();
+      lstmp_grad.gate_grad = gate_g.data<T>();
+      lstmp_grad.output_grad = out_g.data<T>();
+
+      if (n > 0) {
+        int bstart_pre = static_cast<int>(batch_starts[n - 1]);
+        Tensor cell_pre = batch_cell.Slice(bstart_pre, bstart);
+        Tensor cell_pre_g = batch_cell_g.Slice(bstart_pre, bstart);
+        lstmp_value.prev_state_value = cell_pre.data<T>();
+        lstmp_grad.prev_state_grad = cell_pre_g.data<T>();
+      } else {
+        lstmp_value.prev_state_value = c0 ? ordered_c0.data<T>() : nullptr;
+        lstmp_grad.prev_state_grad = c0_g ? ordered_c0_g.data<T>() : nullptr;
+      }
+
+      int cur_batch_size = bend - bstart;
+      math::LstmUnitGradFunctor<DeviceContext, T>::compute(
+          device_ctx, lstmp_value, lstmp_grad, frame_size, cur_batch_size,
+          gate_act, cell_act, cand_act);
+
+      if (n > 0) {
+        int pre_h_start = static_cast<int>(batch_starts[n - 1]);
+        int pre_h_end = pre_h_start + cur_batch_size;
+        auto pre_proj_g = batch_proj_g.Slice(pre_h_start, pre_h_end);
+        math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight, true,
+                                       static_cast<T>(1.0), &pre_proj_g,
+                                       static_cast<T>(1.0));
+        if (weight_g) {
+          /* backward weight */
+          auto pre_proj = batch_proj.Slice(pre_h_start, pre_h_end);
+          math::matmul<DeviceContext, T>(device_ctx, pre_proj, true, gate_g,
+                                         false, static_cast<T>(1.0), weight_g,
+                                         static_cast<T>(1.0));
+        }
+      } else {
+        if (h0 && weight_g) {
+          ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
+                                             &ordered_h0, true);
+          math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true, gate_g,
+                                         false, static_cast<T>(1.0), weight_g,
+                                         static_cast<T>(1.0));
+        }
+        if (h0 && h0_g) {
+          ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
+          math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
+                                         true, static_cast<T>(1.0),
+                                         &ordered_h0_g, static_cast<T>(0.0));
+        }
+      }
+    }
+
+    math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
+    if (in_g) {
+      /* backward data */
+      in_g->mutable_data<T>(ctx.GetPlace());
+      to_seq(device_ctx, batch_gate_g, *in_g);
+    }
+    if (bias && bias_g) {
+      /* backward bias */
+      Tensor b_g = *bias_g;
+      b_g.Resize({bias_g->numel(), 1});
+      Tensor gate_bias_g = b_g.Slice(0, 4 * frame_size);
+      math::ColwiseSum<DeviceContext, T> col_sum;
+      col_sum(device_ctx, batch_gate_g, &gate_bias_g);
+    }
+
+    if (h0 && h0_g) {
+      ReorderInitState<DeviceContext, T>(device_ctx, ordered_h0_g, order, h0_g,
+                                         false);
+    }
+    if (c0 && c0_g) {
+      ReorderInitState<DeviceContext, T>(device_ctx, ordered_c0_g, order, c0_g,
+                                         false);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
new file mode 100644
index 0000000000..e35712ec06
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -0,0 +1,314 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+import unittest
+import numpy as np
+from op_test import OpTest
+
+SIGMOID_THRESHOLD_MIN = -40.0
+SIGMOID_THRESHOLD_MAX = 13.0
+EXP_MAX_INPUT = 40.0
+
+
+def identity(x):
+    return x
+
+
+def sigmoid(x):
+    y = np.copy(x)
+    y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN
+    y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX
+    return 1. / (1. + np.exp(-y))
+
+
+def tanh(x):
+    y = -2. * x
+    y[y > EXP_MAX_INPUT] = EXP_MAX_INPUT
+    return (2. / (1. + np.exp(y))) - 1.
+
+
+def relu(x):
+    return np.maximum(x, 0)
+
+
+ACTVATION = {
+    'identity': identity,
+    'sigmoid': sigmoid,
+    'tanh': tanh,
+    'relu': relu
+}
+
+
+# LSTM with recurrent projection Layer
+def lstmp(
+        input,  # T x 4D
+        lod,  # 1 x N
+        h0=None,  # N x D
+        c0=None,  # N x D
+        w_r=None,  # P x 5D
+        w_rh=None,  # D x P
+        w_b=None,  # 1 x 4D
+        w_c=None,  # 1 x 3D
+        is_reverse=False,
+        act_gate=None,
+        act_cell=None,
+        act_cand=None):
+    def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand):
+        g = np.dot(r_pre, w_r)  # 1 x 4D
+        g = g + x
+        g = np.reshape(g, (1, g.size))
+        c, g_i, g_f, g_o = np.split(g, 4, axis=1)
+        if w_c is None:
+            g_i = act_gate(g_i)  # 1 x D
+            g_f = act_gate(g_f)  # 1 x D
+        else:
+            w_ic, w_fc, _ = np.split(w_c, 3, axis=1)
+            g_i = act_gate(g_i + w_ic * c_pre)  # 1 x D
+            g_f = act_gate(g_f + w_fc * c_pre)  # 1 x D
+        c = g_f * c_pre + g_i * act_cand(c)  # 1 x D
+
+        if w_c is None:
+            g_o = act_gate(g_o)  # 1 x D
+        else:
+            _, _, w_oc = np.split(w_c, 3, axis=1)
+            g_o = act_gate(g_o + w_oc * c)  # 1 x D
+        h = g_o * act_cell(c)
+        # projection
+        r = np.dot(h, w_rh)
+        return r, c
+
+    def _reverse(x, lod):
+        y = np.zeros_like(x)
+        for i in range(len(lod) - 1):
+            b, e = lod[i], lod[i + 1]
+            y[b:e, :] = np.flip(x[b:e, :], 0)
+        return y
+
+    offset = lod[0]
+    batch_size = len(offset) - 1
+    # recurrent projection state
+    projection = []
+    cell = []
+    input = _reverse(input, offset) if is_reverse else input
+    if w_b is not None:
+        input = input + np.tile(w_b, (offset[-1], 1))
+    for i in range(batch_size):
+        # compute one sequence
+        seq_len = offset[i + 1] - offset[i]
+        x = input[offset[i]:offset[i + 1], :]
+        r_pre = np.dot(h0[i], w_rh)  # 1 x P
+        c_pre = c0[i]  # 1 x D
+        for j in range(seq_len):
+            # compute one step
+            r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, act_gate,
+                                 act_cell, act_cand)
+            projection.append(r_pre.flatten())
+            cell.append(c_pre.flatten())
+
+    projection = np.array(projection).astype('float64')
+    cell = np.array(cell).astype('float64')
+
+    projection = _reverse(projection, offset) if is_reverse else projection
+    cell = _reverse(cell, offset) if is_reverse else cell
+
+    assert projection.shape == (input.shape[0], w_r.shape[0])  # T x P
+    assert cell.shape == (input.shape[0], input.shape[1] / 4)  # T x D
+    return projection, cell
+
+
+class TestLstmOp(OpTest):
+    def set_argument(self):
+        self.lod = [[0, 2, 5, 7]]
+        # hidden size
+        self.D = 16
+        # projection size
+        self.P = 10
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = False
+        self.is_reverse = False
+        self.use_peepholes = True
+
+    def setUp(self):
+        self.set_argument()
+        self.op_type = 'lstmp'
+
+        T = self.lod[0][-1]
+        N = len(self.lod[0]) - 1
+
+        x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
+        if self.has_initial_state:
+            h0 = np.random.normal(size=(N, self.D)).astype('float64')
+            c0 = np.random.normal(size=(N, self.D)).astype('float64')
+        else:
+            h0 = np.zeros((N, self.D)).astype('float64')
+            c0 = np.zeros((N, self.D)).astype('float64')
+        w = np.random.normal(size=(self.P, 4 * self.D)).astype('float64')
+        if self.use_peepholes:
+            b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
+        else:
+            b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
+
+        w_b = b[:, 0:4 * self.D]
+        w_c = b[:, 4 * self.D:] if self.use_peepholes else None
+        w_rh = np.random.normal(size=(self.D, self.P)).astype('float64')
+        r, c = lstmp(x, self.lod, h0, c0, w, w_rh, w_b, w_c, self.is_reverse,
+                     ACTVATION[self.act_gate], ACTVATION[self.act_cell],
+                     ACTVATION[self.act_cand])
+
+        self.inputs = {'Input': (x, self.lod), 'Weight': w, 'ProjWeight': w_rh}
+
+        self.inputs['Bias'] = b
+
+        if self.has_initial_state:
+            self.inputs['H0'] = h0
+            self.inputs['C0'] = c0
+
+        self.outputs = {
+            'Projection': (r, self.lod),
+            'Cell': (c, self.lod),
+        }
+        self.attrs = {
+            'use_peepholes': self.use_peepholes,
+            'is_reverse': self.is_reverse,
+            'gate_activation': self.act_gate,
+            'cell_activation': self.act_cell,
+            'candidate_activation': self.act_cand
+        }
+
+    def test_check_output(self):
+        self.check_output(atol=1e-8)
+
+    """
+    def test_check_grad(self):
+        # TODO(qingqing) remove folowing lines after the check_grad is refined.
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
+    """
+
+
+"""
+class TestLstmOpHasInitial(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 5, 7]]
+        self.D = 16
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = True
+        self.is_reverse = True
+        self.use_peepholes = True
+
+    def test_check_grad(self):
+        # TODO(qingqing) remove folowing lines after the check_grad is refined.
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Hidden'],
+            max_relative_error=5e-4)
+
+    def test_check_grad_ingore_bias(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Bias'))
+
+    def test_check_grad_ingore_weight(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Bias'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Weight'))
+
+    def test_check_grad_ingore_input(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Weight', 'Bias'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('Input'))
+
+    def test_check_grad_ingore_h0(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'C0'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('H0'))
+
+    def test_check_grad_ingore_c0(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias', 'H0'], ['Hidden'],
+            max_relative_error=5e-4,
+            no_grad_set=set('C0'))
+"""
+
+
+class TestLstmOpRerverse(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 5, 7]]
+        self.D = 16
+        self.P = 10
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = False
+        self.is_reverse = True
+        self.use_peepholes = True
+
+
+class TestLstmOpNotUsePeepholes(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 5, 7]]
+        self.D = 16
+        self.P = 10
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.has_initial_state = False
+        self.is_reverse = True
+        self.use_peepholes = False
+
+
+if __name__ == '__main__':
+    unittest.main()

From cb34f6a230bf51cc6cb0b8b2ef93b3e13ed3f516 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 22 Jan 2018 14:45:06 +0800
Subject: [PATCH 016/314] update fluid vgg16 and add readme

---
 benchmark/cluster/vgg16/fluid/Dockerfile   |  7 +++++--
 benchmark/cluster/vgg16/fluid/README.md    | 15 +++++++++++++++
 benchmark/cluster/vgg16/fluid/paddle_k8s   |  1 -
 benchmark/cluster/vgg16/fluid/pserver.yaml |  2 +-
 benchmark/cluster/vgg16/fluid/trainer.yaml |  4 ++--
 benchmark/cluster/vgg16/fluid/vgg16.py     |  6 ++++--
 6 files changed, 27 insertions(+), 8 deletions(-)
 create mode 100644 benchmark/cluster/vgg16/fluid/README.md

diff --git a/benchmark/cluster/vgg16/fluid/Dockerfile b/benchmark/cluster/vgg16/fluid/Dockerfile
index 77cd17f2b9..711076b09e 100644
--- a/benchmark/cluster/vgg16/fluid/Dockerfile
+++ b/benchmark/cluster/vgg16/fluid/Dockerfile
@@ -3,10 +3,13 @@
 #ADD reader.py /workspace/
 #RUN python /workspace/reader.py
 FROM python:2.7.14
-ADD *.whl /
-RUN pip install /*.whl && rm -f /*.whl
 ADD paddle_k8s /usr/bin
 ADD k8s_tools.py /root
 RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev 
+ADD *.whl /
+RUN pip install /*.whl && rm -f /*.whl
+ENV LD_LIBRARY_PATH=/usr/local/lib
+ADD reader.py /workspace/
+RUN python /workspace/reader.py
 
 ADD vgg16.py /workspace/
diff --git a/benchmark/cluster/vgg16/fluid/README.md b/benchmark/cluster/vgg16/fluid/README.md
new file mode 100644
index 0000000000..63a460f7a6
--- /dev/null
+++ b/benchmark/cluster/vgg16/fluid/README.md
@@ -0,0 +1,15 @@
+# Fluid distributed training perf test
+
+## Steps to get started
+
+1. You must re-compile PaddlePaddle and enable `-DWITH_DISTRIBUTE` to build PaddlePaddle with distributed support.
+1. When the build finishes, copy the output `whl` package located under `build/python/dist` to current directory.
+1. Run `docker build -t [image:tag] .` to build the docker image and run `docker push [image:tag]` to push the image to reponsitory so kubernetes can find it.
+1. Run `kubectl create -f pserver.yaml && kubectl create -f trainer.yaml` to start the job on your kubernetes cluster (you must configure the `kubectl` client before this step).
+1. Run `kubectl get po` to get running pods, and run `kubectl logs [podID]` to fetch the pod log of pservers and trainers.
+
+Check the logs for the distributed training progress and analyze the performance.
+
+## Enable verbos logs
+
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
\ No newline at end of file
diff --git a/benchmark/cluster/vgg16/fluid/paddle_k8s b/benchmark/cluster/vgg16/fluid/paddle_k8s
index 8f1c5db717..af5f35b3ec 100755
--- a/benchmark/cluster/vgg16/fluid/paddle_k8s
+++ b/benchmark/cluster/vgg16/fluid/paddle_k8s
@@ -61,7 +61,6 @@ start_fluid_process() {
   if [ "${TRAINING_ROLE}" == "TRAINER" ]; then
     check_failed_cnt ${TRAINERS}
     sleep 5
-    stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
     export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
   fi
   export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
diff --git a/benchmark/cluster/vgg16/fluid/pserver.yaml b/benchmark/cluster/vgg16/fluid/pserver.yaml
index 47d2380d2e..e1a58260af 100644
--- a/benchmark/cluster/vgg16/fluid/pserver.yaml
+++ b/benchmark/cluster/vgg16/fluid/pserver.yaml
@@ -33,7 +33,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
+          value: "LD_LIBRARY_PATH=/usr/local/lib MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/fluid/trainer.yaml b/benchmark/cluster/vgg16/fluid/trainer.yaml
index bada190764..c8e26d4b51 100644
--- a/benchmark/cluster/vgg16/fluid/trainer.yaml
+++ b/benchmark/cluster/vgg16/fluid/trainer.yaml
@@ -17,7 +17,7 @@ spec:
       - name: trainer
         image: "registry.baidu.com/paddlepaddle/rawjob:vgg16_fluid"
         imagePullPolicy: Always
-        command: ["paddle_k8s", "start_trainer", "v2"]
+        command: ["paddle_k8s", "start_fluid"]
         env:
         - name: PADDLE_JOB_NAME
           value: vgg16job
@@ -30,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16.py"
+          value: "cd /workspace && LD_LIBRARY_PATH=/usr/local/lib MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/fluid/vgg16.py b/benchmark/cluster/vgg16/fluid/vgg16.py
index 0595a28784..a973f9d2a6 100644
--- a/benchmark/cluster/vgg16/fluid/vgg16.py
+++ b/benchmark/cluster/vgg16/fluid/vgg16.py
@@ -140,12 +140,14 @@ def main():
 
     def train_loop(exe, trainer_prog):
         iters = 0
+        ts = time.time()
         for pass_id in range(args.num_passes):
             # train
             start_time = time.time()
             num_samples = 0
             accuracy.reset(exe)
             for batch_id, data in enumerate(train_reader()):
+                ts = time.time()
                 img_data = np.array(map(lambda x: x[0].reshape(data_shape),
                                         data)).astype("float32")
                 y_data = np.array(map(lambda x: x[1], data)).astype("int64")
@@ -158,8 +160,8 @@ def main():
                 iters += 1
                 num_samples += len(data)
                 print(
-                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f" %
-                    (pass_id, iters, loss, acc)
+                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f" %
+                    (pass_id, iters, loss, acc, time.time() - ts)
                 )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time

From b38452dffaa766311450fd79fb0432b63899545d Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 22 Jan 2018 17:01:01 +0800
Subject: [PATCH 017/314] fix styles

---
 benchmark/cluster/vgg16/fluid/README.md    |  3 +-
 benchmark/cluster/vgg16/fluid/k8s_tools.py | 18 ++++++-
 benchmark/cluster/vgg16/fluid/reader.py    | 14 ++++++
 benchmark/cluster/vgg16/fluid/vgg16.py     | 58 +++++++++++++++-------
 4 files changed, 73 insertions(+), 20 deletions(-)

diff --git a/benchmark/cluster/vgg16/fluid/README.md b/benchmark/cluster/vgg16/fluid/README.md
index 63a460f7a6..02b17dceb9 100644
--- a/benchmark/cluster/vgg16/fluid/README.md
+++ b/benchmark/cluster/vgg16/fluid/README.md
@@ -12,4 +12,5 @@ Check the logs for the distributed training progress and analyze the performance
 
 ## Enable verbos logs
 
-Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
\ No newline at end of file
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
+
diff --git a/benchmark/cluster/vgg16/fluid/k8s_tools.py b/benchmark/cluster/vgg16/fluid/k8s_tools.py
index 8a64dbd361..4bee96a7a8 100644
--- a/benchmark/cluster/vgg16/fluid/k8s_tools.py
+++ b/benchmark/cluster/vgg16/fluid/k8s_tools.py
@@ -1,3 +1,17 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 #!/bin/env python
 import os
 import sys
@@ -33,6 +47,7 @@ def wait_pods_running(label_selector, desired):
         print 'current cnt: %d sleep for 5 seconds...' % count
         time.sleep(5)
 
+
 def count_pods_by_phase(label_selector, phase):
     pod_list = fetch_pods_info(label_selector)
     filtered_pod_list = filter(lambda x: x[0] == phase, pod_list)
@@ -45,12 +60,14 @@ def fetch_pserver_ips():
     pserver_ips = [item[1] for item in pod_list]
     return ",".join(pserver_ips)
 
+
 def fetch_master_ip():
     label_selector = "paddle-job-master=%s" % PADDLE_JOB_NAME
     pod_list = fetch_pods_info(label_selector)
     master_ips = [item[1] for item in pod_list]
     return master_ips[0]
 
+
 def fetch_trainer_id():
     label_selector = "paddle-job=%s" % PADDLE_JOB_NAME
     pod_list = fetch_pods_info(label_selector)
@@ -75,4 +92,3 @@ if __name__ == "__main__":
         print count_pods_by_phase(sys.argv[2], sys.argv[3])
     elif command == "wait_pods_running":
         wait_pods_running(sys.argv[2], sys.argv[3])
-
diff --git a/benchmark/cluster/vgg16/fluid/reader.py b/benchmark/cluster/vgg16/fluid/reader.py
index c5161ddea2..3e20f830fc 100644
--- a/benchmark/cluster/vgg16/fluid/reader.py
+++ b/benchmark/cluster/vgg16/fluid/reader.py
@@ -1,2 +1,16 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.v2 as paddle
 paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/vgg16/fluid/vgg16.py b/benchmark/cluster/vgg16/fluid/vgg16.py
index a973f9d2a6..3c7b5bf2f1 100644
--- a/benchmark/cluster/vgg16/fluid/vgg16.py
+++ b/benchmark/cluster/vgg16/fluid/vgg16.py
@@ -1,3 +1,17 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 """VGG16 benchmark in Fluid"""
 from __future__ import print_function
 
@@ -11,6 +25,7 @@ import argparse
 import functools
 import os
 
+
 def str2bool(v):
     if v.lower() in ('yes', 'true', 't', 'y', '1'):
         return True
@@ -19,6 +34,7 @@ def str2bool(v):
     else:
         raise argparse.ArgumentTypeError('Boolean value expected.')
 
+
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument(
     '--batch_size', type=int, default=128, help="Batch size for training.")
@@ -122,7 +138,6 @@ def main():
     place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
     exe = fluid.Executor(place)
 
-
     # test
     def test(exe):
         accuracy.reset(exe)
@@ -148,20 +163,21 @@ def main():
             accuracy.reset(exe)
             for batch_id, data in enumerate(train_reader()):
                 ts = time.time()
-                img_data = np.array(map(lambda x: x[0].reshape(data_shape),
-                                        data)).astype("float32")
+                img_data = np.array(
+                    map(lambda x: x[0].reshape(data_shape), data)).astype(
+                        "float32")
                 y_data = np.array(map(lambda x: x[1], data)).astype("int64")
                 y_data = y_data.reshape([-1, 1])
 
                 loss, acc = exe.run(trainer_prog,
                                     feed={"pixel": img_data,
-                                        "label": y_data},
+                                          "label": y_data},
                                     fetch_list=[avg_cost] + accuracy.metrics)
                 iters += 1
                 num_samples += len(data)
                 print(
-                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f" %
-                    (pass_id, iters, loss, acc, time.time() - ts)
+                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
+                    % (pass_id, iters, loss, acc, time.time() - ts)
                 )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time
@@ -170,7 +186,7 @@ def main():
             print(
                 "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
                 % (pass_id, num_samples / pass_elapsed, pass_train_acc,
-                pass_test_acc))
+                   pass_test_acc))
 
     if args.local:
         # Parameter initialization
@@ -179,8 +195,8 @@ def main():
         # data reader
         train_reader = paddle.batch(
             paddle.reader.shuffle(
-                paddle.dataset.cifar.train10()
-                if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                else paddle.dataset.flowers.train(),
                 buf_size=5120),
             batch_size=args.batch_size)
         test_reader = paddle.batch(
@@ -196,19 +212,25 @@ def main():
         pserver_endpoints = ",".join(eplist)
         print("pserver endpoints: ", pserver_endpoints)
         trainers = int(os.getenv("TRAINERS"))  # total trainer count
-        current_endpoint = os.getenv("POD_IP") + ":6174"  # current pserver endpoint
-        training_role = os.getenv("TRAINING_ROLE",
-                                "TRAINER")  # get the training role: trainer/pserver
+        current_endpoint = os.getenv(
+            "POD_IP") + ":6174"  # current pserver endpoint
+        training_role = os.getenv(
+            "TRAINING_ROLE",
+            "TRAINER")  # get the training role: trainer/pserver
         t = fluid.DistributeTranspiler()
         t.transpile(
-            optimize_ops, params_grads, pservers=pserver_endpoints, trainers=trainers)
+            optimize_ops,
+            params_grads,
+            pservers=pserver_endpoints,
+            trainers=trainers)
 
         if training_role == "PSERVER":
             if not current_endpoint:
                 print("need env SERVER_ENDPOINT")
                 exit(1)
             pserver_prog = t.get_pserver_program(current_endpoint)
-            pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+            pserver_startup = t.get_startup_program(current_endpoint,
+                                                    pserver_prog)
             print("starting server side startup")
             exe.run(pserver_startup)
             print("starting parameter server...")
@@ -220,13 +242,13 @@ def main():
             # data reader
             train_reader = paddle.batch(
                 paddle.reader.shuffle(
-                    paddle.dataset.cifar.train10()
-                    if args.data_set == 'cifar10' else paddle.dataset.flowers.train(),
+                    paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                    else paddle.dataset.flowers.train(),
                     buf_size=5120),
                 batch_size=args.batch_size)
             test_reader = paddle.batch(
-                paddle.dataset.cifar.test10()
-                if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+                paddle.dataset.cifar.test10() if args.data_set == 'cifar10' else
+                paddle.dataset.flowers.test(),
                 batch_size=args.batch_size)
 
             trainer_prog = t.get_trainer_program()

From 900e911f4223e654c20a68a2db1404dadccfb953 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 22 Jan 2018 18:54:54 +0800
Subject: [PATCH 018/314] fix style check

---
 benchmark/cluster/vgg16/fluid/README.md | 1 -
 benchmark/cluster/vgg16/fluid/vgg16.py  | 1 -
 benchmark/cluster/vgg16/v2/pserver.yaml | 6 +++---
 benchmark/cluster/vgg16/v2/trainer.yaml | 6 +++---
 4 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/benchmark/cluster/vgg16/fluid/README.md b/benchmark/cluster/vgg16/fluid/README.md
index 02b17dceb9..71a3a934d2 100644
--- a/benchmark/cluster/vgg16/fluid/README.md
+++ b/benchmark/cluster/vgg16/fluid/README.md
@@ -13,4 +13,3 @@ Check the logs for the distributed training progress and analyze the performance
 ## Enable verbos logs
 
 Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
-
diff --git a/benchmark/cluster/vgg16/fluid/vgg16.py b/benchmark/cluster/vgg16/fluid/vgg16.py
index 3c7b5bf2f1..88d6d79cc0 100644
--- a/benchmark/cluster/vgg16/fluid/vgg16.py
+++ b/benchmark/cluster/vgg16/fluid/vgg16.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """VGG16 benchmark in Fluid"""
 from __future__ import print_function
 
diff --git a/benchmark/cluster/vgg16/v2/pserver.yaml b/benchmark/cluster/vgg16/v2/pserver.yaml
index ed1671bbbd..943675e147 100644
--- a/benchmark/cluster/vgg16/v2/pserver.yaml
+++ b/benchmark/cluster/vgg16/v2/pserver.yaml
@@ -1,13 +1,13 @@
 apiVersion: extensions/v1beta1
 kind: ReplicaSet
 metadata:
-  name: vgg16job-pserver
+  name: vgg16v2job-pserver
 spec:
   replicas: 10
   template:
     metadata:
       labels:
-        paddle-job-pserver: vgg16job
+        paddle-job-pserver: vgg16v2job
     spec:
       hostNetwork: true
       imagePullSecrets:
@@ -21,7 +21,7 @@ spec:
           containerPort: 30236
         env:
         - name: PADDLE_JOB_NAME
-          value: vgg16job
+          value: vgg16v2job
         - name: TRAINERS
           value: "20"
         - name: PSERVERS
diff --git a/benchmark/cluster/vgg16/v2/trainer.yaml b/benchmark/cluster/vgg16/v2/trainer.yaml
index 75fffc64b0..200b6dc304 100644
--- a/benchmark/cluster/vgg16/v2/trainer.yaml
+++ b/benchmark/cluster/vgg16/v2/trainer.yaml
@@ -1,14 +1,14 @@
 apiVersion: batch/v1
 kind: Job
 metadata:
-  name: vgg16job-trainer
+  name: vgg16v2job-trainer
 spec:
   parallelism: 20
   completions: 20
   template:
     metadata:
       labels:
-        paddle-job: vgg16job
+        paddle-job: vgg16v2job
     spec:
       imagePullSecrets:
         - name: job-registry-secret
@@ -20,7 +20,7 @@ spec:
         command: ["paddle_k8s", "start_trainer", "v2"]
         env:
         - name: PADDLE_JOB_NAME
-          value: vgg16job
+          value: vgg16v2job
         - name: TRAINERS
           value: "20"
         - name: PSERVERS

From 3772d27dfbf83b22333b7cc0eacfb3acd805c036 Mon Sep 17 00:00:00 2001
From: zlx <zlx_hg@163.com>
Date: Mon, 22 Jan 2018 21:09:41 +0800
Subject: [PATCH 019/314] add depthwise conv forward

---
 paddle/operators/conv_op.cc             |   7 +
 paddle/operators/conv_op.cu.cc          |   5 +
 paddle/operators/conv_op.h              |  30 ++
 paddle/operators/math/depthwise_conv.cu | 347 ++++++++++++++++++++++++
 paddle/operators/math/depthwise_conv.h  |  57 ++++
 5 files changed, 446 insertions(+)
 create mode 100644 paddle/operators/math/depthwise_conv.cu
 create mode 100644 paddle/operators/math/depthwise_conv.h

diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index d6882b275b..55a78efea1 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -318,9 +318,16 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
 namespace ops = paddle::operators;
 REGISTER_OP(conv2d, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
             ops::ConvOpGrad);
+REGISTER_OP(depthwiseConv, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
+            ops::ConvOpGrad);
 REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
             ops::ConvOpGrad);
 
+REGISTER_OP_CPU_KERNEL(
+    depthwiseConv,
+    ops::DepthwiseConvKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::DepthwiseConvKernel<paddle::platform::CPUDeviceContext, double>);
+
 REGISTER_OP_CPU_KERNEL(
     conv2d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
     ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/conv_op.cu.cc b/paddle/operators/conv_op.cu.cc
index 4f942444f3..4c7a345784 100644
--- a/paddle/operators/conv_op.cu.cc
+++ b/paddle/operators/conv_op.cu.cc
@@ -16,6 +16,11 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 
+REGISTER_OP_CUDA_KERNEL(
+    depthwiseConv,
+    ops::DepthwiseConvKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::DepthwiseConvKernel<paddle::platform::CUDADeviceContext, double>);
+
 REGISTER_OP_CUDA_KERNEL(
     conv2d, ops::GemmConvKernel<paddle::platform::CUDADeviceContext, float>,
     ops::GemmConvKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/conv_op.h b/paddle/operators/conv_op.h
index 5a8933e791..ca61f1c6e6 100644
--- a/paddle/operators/conv_op.h
+++ b/paddle/operators/conv_op.h
@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/depthwise_conv.h"
 #include "paddle/operators/math/im2col.h"
 #include "paddle/operators/math/math_function.h"
 #include "paddle/operators/math/vol2col.h"
@@ -350,5 +351,34 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
     }
   }
 };
+
+template <typename DeviceContext, typename T>
+class DepthwiseConvKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* input = context.Input<Tensor>("Input");
+    // The filter will be reshaped in the calculations,
+    // so here use an assignment operation,
+    // that avoids modifying the variable in the Scope.
+    Tensor filter = *context.Input<Tensor>("Filter");
+    Tensor* output = context.Output<Tensor>("Output");
+    output->mutable_data<T>(context.GetPlace());
+
+    std::vector<int> strides = context.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
+
+    framework::DDim filter_matrix_shape = {filter.dims()[0],
+                                           filter.numel() / filter.dims()[0]};
+    filter.Resize(filter_matrix_shape);
+
+    math::DepthwiseConvFunctor<DeviceContext, T> depthwiseConv;
+
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    depthwiseConv(dev_ctx, input, filter, filter_shape_vec, strides, paddings,
+                  output);
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/depthwise_conv.cu b/paddle/operators/math/depthwise_conv.cu
new file mode 100644
index 0000000000..16a0037ab1
--- /dev/null
+++ b/paddle/operators/math/depthwise_conv.cu
@@ -0,0 +1,347 @@
+/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/math/pooling.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// CUDA kernel to compute the depthwise convolution forward pass
+template <typename T>
+__global__ void KernelDepthwiseConv(
+    const int nthreads, const T* const input_data, const T* const filter_data,
+    const int batch_size, const int output_channels, const int output_height,
+    const int output_width, const int input_channels, const int input_height,
+    const int input_width, const int filter_multiplier, const int filter_height,
+    const int filter_width, const int stride_height, const int stride_width,
+    const int padding_height, const int padding_width, T* const output_data) {
+  int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
+
+  if (index < nthreads) {
+    const int batch = index / output_channels / output_height / output_width;
+    const int c_out = (index / output_height / output_width) % output_channels;
+    const int h_out = (index / output_width) % output_height;
+    const int w_out = index % output_width;
+
+    const int c_in = c_out / filter_multiplier;
+    const T* weight = filter_data + c_out * filter_height * filter_width;
+    T value = 0;
+    const int h_in_start = -padding_height + h_out * stride_height;
+    const int w_in_start = -padding_width + w_out * stride_width;
+    const int h_in_end =
+        -padding_height + h_out * stride_height + filter_height - 1;
+    const int w_in_end =
+        -padding_width + w_out * stride_width + filter_width - 1;
+    if ((h_in_start >= 0) && (h_in_end < input_height) && (w_in_start >= 0) &&
+        (w_in_end < input_width)) {
+      for (int kh = 0; kh < filter_height; ++kh) {
+        for (int kw = 0; kw < filter_width; ++kw) {
+          const int h_in = -padding_height + h_out * stride_height + kh;
+          const int w_in = -padding_width + w_out * stride_width + kw;
+          const int offset =
+              ((batch * input_channels + c_in) * input_height + h_in) *
+                  input_width +
+              w_in;
+          value += (*weight) * input_data[offset];
+          ++weight;
+        }
+      }
+    } else {
+      for (int kh = 0; kh < filter_height; ++kh) {
+        for (int kw = 0; kw < filter_width; ++kw) {
+          const int h_in = -padding_height + h_out * stride_height + kh;
+          const int w_in = -padding_width + w_out * stride_width + kw;
+          if ((h_in >= 0) && (h_in < input_height) && (w_in >= 0) &&
+              (w_in < input_width)) {
+            const int offset =
+                ((batch * input_channels + c_in) * input_height + h_in) *
+                    input_width +
+                w_in;
+            value += (*weight) * input_data[offset];
+          }
+          ++weight;
+        }
+      }
+    }
+    output_data[index] = value;
+  }
+}
+/*
+// CUDA kernel to compute the depthwise convolution backprop w.r.t input.
+template <typename T>
+__global__ void KernelDepthwiseConvInputGrad(const int nthreads,
+                                      const T* const top_diff,
+                                      const T* const weight_data,
+                                      const int num,
+                                      const int outputChannels,
+                                      const int outputHeight,
+                                      const int outputWidth,
+                                      const int inputChannels,
+                                      const int inputHeight,
+                                      const int inputWidth,
+                                      const int filterMultiplier,
+                                      const int filterHeight,
+                                      const int filterWidth,
+                                      const int strideH,
+                                      const int strideW,
+                                      const int paddingH,
+                                      const int paddingW,
+                                      T* const bottom_diff) {
+  int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
+  if (index < nthreads) {
+    const int batch = index / inputChannels / inputHeight / inputWidth;
+    const int c_in = (index / inputHeight / inputWidth) % inputChannels;
+    const int h_in = (index / inputWidth) % inputHeight;
+    const int w_in = index % inputWidth;
+
+    const int c_out_start = c_in * filterMultiplier;
+
+    int h_out_start = (h_in - filterHeight + paddingH + strideH) / strideH;
+    h_out_start = 0 > h_out_start ? 0 : h_out_start;
+    int h_out_end = (h_in + paddingH) / strideH;
+    h_out_end = outputHeight - 1 < h_out_end ? outputHeight - 1 : h_out_end;
+    int w_out_start = (w_in - filterWidth + paddingW + strideW) / strideW;
+    w_out_start = 0 > w_out_start ? 0 : w_out_start;
+    int w_out_end = (w_in + paddingW) / strideW;
+    w_out_end = outputWidth - 1 < w_out_end ? outputWidth - 1 : w_out_end;
+
+    T value = 0;
+
+    for (int c_out = c_out_start; c_out < c_out_start + filterMultiplier;
+         c_out++) {
+      for (int h_out = h_out_start; h_out <= h_out_end; ++h_out) {
+        const int filter_h = h_in + paddingH - h_out * strideH;
+        for (int w_out = w_out_start; w_out <= w_out_end; ++w_out) {
+          const int filter_w = w_in + paddingW - w_out * strideW;
+          const int filter_offset = c_out * filterHeight * filterWidth +
+                                    filter_h * filterWidth + filter_w;
+          const int top_diff_offset =
+              ((batch * outputChannels + c_out) * outputHeight + h_out) *
+                  outputWidth +
+              w_out;
+          value += top_diff[top_diff_offset] * weight_data[filter_offset];
+        }
+      }
+    }
+    bottom_diff[index] += value;
+  }
+}
+
+// CUDA kernel to compute the depthwise convolution backprop w.r.t filter.
+template <typename T>
+__global__ void KernelDepthwiseConvFilterGrad(const int num_i,
+                                       const int nthreads,
+                                       const T* const top_diff,
+                                       const T* const inputData,
+                                       const int num,
+                                       const int outputChannels,
+                                       const int outputHeight,
+                                       const int outputWidth,
+                                       const int inputChannels,
+                                       const int inputHeight,
+                                       const int inputWidth,
+                                       const int filterMultiplier,
+                                       const int filterHeight,
+                                       const int filterWidth,
+                                       const int strideH,
+                                       const int strideW,
+                                       const int paddingH,
+                                       const int paddingW,
+                                       T* const buffer_data) {
+  int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
+  if (index < nthreads) {
+    const int h_out = (index / outputWidth) % outputHeight;
+    const int w_out = index % outputWidth;
+    const int kh =
+        (index / filterWidth / outputHeight / outputWidth) % filterHeight;
+    const int kw = (index / outputHeight / outputWidth) % filterWidth;
+    const int h_in = -paddingH + h_out * strideH + kh;
+    const int w_in = -paddingW + w_out * strideW + kw;
+    if ((h_in >= 0) && (h_in < inputHeight) && (w_in >= 0) &&
+        (w_in < inputWidth)) {
+      const int c_out =
+          index / (filterHeight * filterWidth * outputHeight * outputWidth);
+      const int c_in = c_out / filterMultiplier;
+      const int batch = num_i;
+      const int top_offset =
+          ((batch * outputChannels + c_out) * outputHeight + h_out) *
+              outputWidth + w_out;
+      const int bottom_offset =
+          ((batch * inputChannels + c_in) * inputHeight + h_in) * inputWidth +
+          w_in;
+      buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset];
+    } else {
+      buffer_data[index] = 0;
+    }
+  }
+}
+*/
+
+/*
+ * All tensors are in NCHW format.
+ * Ksize, strides, paddings are two elements. These two elements represent
+ * height and width, respectively.
+ */
+template <typename T>
+class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
+ public:
+  void operator()(const platform::CUDADeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& filter, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  framework::Tensor* output) {
+    const int batch_size = input.dims()[0];
+    const int input_channels = input.dims()[1];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    const int output_height = output->dims()[2];
+    const int output_width = output->dims()[3];
+    const int ksize_height = ksize[0];
+    const int ksize_width = ksize[1];
+    const int stride_height = strides[0];
+    const int stride_width = strides[1];
+    const int padding_height = paddings[0];
+    const int padding_width = paddings[1];
+
+    const T* input_data = input.data<T>();
+    const T* filter_data = filter.data<T>();
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+
+    int nthreads = batch_size * output_channels * output_height * output_width;
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelDepthwiseConv<T><<<grid, threads, 0, STREAM_DEFAULT>>>(
+        nthreads, input_data, filter_data, batch_size, output_channels,
+        output_height, output_width, input_channels, input_height, input_width,
+        output_channels / input_channels, ksize_height, ksize_width,
+        stride_height, stride_width, padding_height, padding_width,
+        output_data);
+  }
+};
+
+/*
+
+template <typename T>
+class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, PoolProcess, T>
+{
+ public:
+  void operator()(const platform::CUDADeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  PoolProcess pool_process, framework::Tensor* input_grad) {
+    const int batch_size = input.dims()[0];
+    const int input_channels = input.dims()[1];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_height = output.dims()[2];
+    const int output_width = output.dims()[3];
+    const int ksize_height = ksize[0];
+    const int ksize_width = ksize[1];
+    const int stride_height = strides[0];
+    const int stride_width = strides[1];
+    const int padding_height = paddings[0];
+    const int padding_width = paddings[1];
+
+    const T* input_data = input.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+
+    int nthreads = batch_size * input_channels * input_height * input_width;
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelPool2DGrad<PoolProcess, T><<<grid, threads, 0, context.stream()>>>(
+        nthreads, input_data, output_data, output_grad_data, input_channels,
+        input_height, input_width, output_height, output_width, ksize_height,
+        ksize_width, stride_height, stride_width, padding_height, padding_width,
+        pool_process, input_grad_data);
+  }
+};
+
+template <typename T>
+class DepthwiseConvdFilterGradFunctor<platform::CUDADeviceContext, T> {
+ public:
+  void operator()(const platform::CUDADeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  framework::Tensor* input_grad) {
+    const int batch_size = input.dims()[0];
+    const int input_channels = input.dims()[1];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output.dims()[1];
+    const int output_height = output.dims()[2];
+    const int output_width = output.dims()[3];
+    const int ksize_height = ksize[0];
+    const int ksize_width = ksize[1];
+    const int stride_height = strides[0];
+    const int stride_width = strides[1];
+    const int padding_height = paddings[0];
+    const int padding_width = paddings[1];
+
+    const T* input_data = input.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+
+    int nthreads = batch_size * output_channels * output_height * output_width;
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelMaxPool2DGrad<T><<<grid, threads, 0, context.stream()>>>(
+        nthreads, input_data, output_data, output_grad_data, input_channels,
+        input_height, input_width, output_height, output_width, ksize_height,
+        ksize_width, stride_height, stride_width, padding_height, padding_width,
+        input_grad_data);
+  }
+};
+*/
+
+template class DepthwiseConvFunctor<platform::CUDADeviceContext,
+                                    paddle::operators::math::MaxPool<float>,
+                                    float>;
+
+/*
+template class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext,
+                                 paddle::operators::math::MaxPoolGrad<float>,
+                                 float>;
+template class DepthwiseConvFilterGradFunctor<platform::CUDADeviceContext,
+                                 paddle::operators::math::MaxPoolGrad<float>,
+                                 float>;
+
+template class DepthwiseConvFunctor<platform::CUDADeviceContext,
+                             paddle::operators::math::MaxPool<double>, double>;
+template class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext,
+                                 paddle::operators::math::MaxPoolGrad<double>,
+                                 double>;
+template class DepthwiseConvFilterGradFunctor<platform::CUDADeviceContext,
+                                 paddle::operators::math::MaxPoolGrad<double>,
+                                 double>;
+*/
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/depthwise_conv.h b/paddle/operators/math/depthwise_conv.h
new file mode 100644
index 0000000000..2e48fe5912
--- /dev/null
+++ b/paddle/operators/math/depthwise_conv.h
@@ -0,0 +1,57 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/device_context.h"
+#include "paddle/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename DeviceContext, typename T>
+class DepthwiseConvFunctor {
+ public:
+  void operator()(const DeviceContext& context, const framework::Tensor& input,
+                  const framework::Tensor& filter, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  framework::Tensor* output);
+};
+
+/*
+template <typename DeviceContext, typename T>
+class DepthwiseConvInputGradFunctor {
+public:
+  void operator()(const DeviceContext& context,
+                  const framework::Tensor& filter,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  framework::Tensor* input_grad);
+};
+
+template <typename DeviceContext, typename T>
+class DepthwiseConvFilterGradFunctor {
+public:
+  void operator()(const DeviceContext& context,
+                  const framework::Tensor& input,
+                  const framework::Tensor& output_grad, std::vector<int>& ksize,
+                  std::vector<int>& strides, std::vector<int>& paddings,
+                  framework::Tensor* filter_grad);
+};
+*/
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

From 22032e49cb88bf8942f96cbbcd28fcdcb553cb50 Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Mon, 22 Jan 2018 21:12:39 +0800
Subject: [PATCH 020/314] Add python wrapper for multiplex operator.

---
 doc/api/v2/fluid/layers.rst                 |  5 ++
 python/paddle/v2/fluid/layers/nn.py         | 55 +++++++++++++++++++--
 python/paddle/v2/fluid/tests/test_layers.py | 10 ++++
 3 files changed, 66 insertions(+), 4 deletions(-)

diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index 986026e0b9..4c4713b17c 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -509,3 +509,8 @@ sequence_reshape
 ----------------
 ..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
     :noindex:
+
+multiplex
+---------
+..  autofunction:: paddle.v2.fluid.layers.multiplex
+    :noindex:
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index b1db16a83e..26e36ec0bb 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -28,7 +28,7 @@ __all__ = [
     'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
     'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
     'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
-    'l2_normalize', 'matmul', 'warpctc', 'sequence_reshape'
+    'l2_normalize', 'matmul', 'warpctc', 'sequence_reshape', 'multiplex'
 ]
 
 
@@ -1813,11 +1813,11 @@ def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
 
       - If both are 2-D, they are multiplied like conventional matrices.
       - If either is n-D, it is treated as a stack of matrices residing in the
-        last two dimensions and a batched matrix multiply supporting broadcast 
+        last two dimensions and a batched matrix multiply supporting broadcast
         applies on the two tensors.
 
-    Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and 
-    nontransposed, the prepended or appended dimension :math:`1` will be 
+    Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
+    nontransposed, the prepended or appended dimension :math:`1` will be
     removed after matrix multiplication.
 
     Args:
@@ -1971,3 +1971,50 @@ def sequence_reshape(input, new_dim):
         outputs={'Out': [out]},
         attrs={'new_dim': new_dim})
     return out
+
+
+def multiplex(inputs, index):
+    """
+    **Multiplex Layer**
+
+    Referring to the given index variable, this layer gathers from the input
+    variables to output a multiplex variable. Assuming that there are :math:`m`
+    input variables and let :math:`I_i` represents the i-th input variable and i
+    is in [0, :math:`m`). All input variables are tensors with same shape
+    [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. Please note that rank of the
+    input tensor should be at least 2. Each input variable will be viewed as a
+    2-D matrix with shape [:math:`M`, :math:`N`] where :math:`M` for :math:`d_0`
+    and :math:`N` for :math:`d_1` * :math:`d_2` * ... * :math:`d_R`. Let
+    :math:`I_i[j]` be the j-th row of the i-th input variable. The given index
+    variable should be a 2-D tensor with shape [:math:`M`, 1]. Let `ID[i]` be
+    the i-th index value of index variable. Then the output variable will be a
+    tensor with shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. If we view
+    the output tensor as a 2-D matrix with shape [:math:`M`, :math:`N`] and let
+    :math:`O[i]` be the i-th row of the matrix, then values of `O[i]` come from
+    :math:`I_{ID[i]}[i]`.
+
+    Args:
+       inputs (list): Input variables which are tensors with same shape and the
+                rank is at least 2.
+       index (Variable): Tensor<int>, index variable which is a 2-D tensor with
+                shape [M, 1] where M for batch size.
+
+    Returns:
+        Variable: Multiplex variable gathered from input variables.
+
+    Examples:
+        .. code-block:: python
+
+            x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
+            index = fluid.layers.data(name='index', shape=[1], dtype='int32')
+            out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
+    """
+    helper = LayerHelper('multiplex', **locals())
+    out = helper.create_tmp_variable(helper.input_dtype())
+    helper.append_op(
+        type='multiplex',
+        inputs={'X': inputs,
+                'Ids': index},
+        outputs={'Out': [out]})
+    return out
diff --git a/python/paddle/v2/fluid/tests/test_layers.py b/python/paddle/v2/fluid/tests/test_layers.py
index 709abd6c6a..dc143f6b9f 100644
--- a/python/paddle/v2/fluid/tests/test_layers.py
+++ b/python/paddle/v2/fluid/tests/test_layers.py
@@ -225,6 +225,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_multiplex(self):
+        program = Program()
+        with program_guard(program):
+            x1 = layers.data(name='x1', shape=[4], dtype='float32')
+            x2 = layers.data(name='x2', shape=[4], dtype='float32')
+            index = layers.data(name='index', shape=[1], dtype='int32')
+            out = layers.multiplex(inputs=[x1, x2], index=index)
+            self.assertIsNotNone(out)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

From ca636eedb9813cbd99400d5be60e86bae709dc56 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 23 Jan 2018 11:45:42 +0800
Subject: [PATCH 021/314] remove libwarpctc.so in core.so and
 libpaddle_fluid.so

---
 cmake/generic.cmake | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/cmake/generic.cmake b/cmake/generic.cmake
index 585db019d5..147de8b242 100644
--- a/cmake/generic.cmake
+++ b/cmake/generic.cmake
@@ -186,6 +186,11 @@ function(cc_library TARGET_NAME)
       add_library(${TARGET_NAME} STATIC ${cc_library_SRCS})
     endif()
     if (cc_library_DEPS)
+      # Don't need link libwarpctc.so
+      if ("${cc_library_DEPS};" MATCHES "warpctc;")
+        list(REMOVE_ITEM cc_library_DEPS warpctc)
+        add_dependencies(${TARGET_NAME} warpctc)
+      endif()
       add_dependencies(${TARGET_NAME} ${cc_library_DEPS})
       target_link_libraries(${TARGET_NAME} ${cc_library_DEPS})
     endif()

From 06db70384397a4d5b61cd7493ebab9b06faf3244 Mon Sep 17 00:00:00 2001
From: xzl <zlx_hg@163.com>
Date: Tue, 23 Jan 2018 14:22:01 +0800
Subject: [PATCH 022/314] ../../../../../paddle/api

---
 paddle/operators/CMakeLists.txt         |  3 ++-
 paddle/operators/conv_op.cc             | 11 ++++++++---
 paddle/operators/conv_op.h              |  7 ++-----
 paddle/operators/math/CMakeLists.txt    |  1 +
 paddle/operators/math/depthwise_conv.cu | 18 ++++++------------
 5 files changed, 19 insertions(+), 21 deletions(-)

diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 6745a8da17..fa2f8caacf 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -155,7 +155,8 @@ op_library(parallel_do_op DEPS executor)
 
 # Regist multiple Kernel to pybind
 if (WITH_GPU)
-op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS vol2col)
+op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS
+    vol2col depthwise_conv)
 op_library(pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling)
 op_library(conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
   conv_transpose_cudnn_op.cu.cc DEPS vol2col)
diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index 55a78efea1..a53b11615c 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -318,15 +318,20 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
 namespace ops = paddle::operators;
 REGISTER_OP(conv2d, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
             ops::ConvOpGrad);
-REGISTER_OP(depthwiseConv, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
+REGISTER_OP(depthwiseConv, ops::ConvOp, ops::Conv2DOpMaker, depthwiseConv_grad,
             ops::ConvOpGrad);
 REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
             ops::ConvOpGrad);
 
 REGISTER_OP_CPU_KERNEL(
     depthwiseConv,
-    ops::DepthwiseConvKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::DepthwiseConvKernel<paddle::platform::CPUDeviceContext, double>);
+    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
+
+REGISTER_OP_CPU_KERNEL(
+    depthwiseConv_grad,
+    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
 
 REGISTER_OP_CPU_KERNEL(
     conv2d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
diff --git a/paddle/operators/conv_op.h b/paddle/operators/conv_op.h
index ca61f1c6e6..a9138dbf93 100644
--- a/paddle/operators/conv_op.h
+++ b/paddle/operators/conv_op.h
@@ -364,18 +364,15 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {
     Tensor* output = context.Output<Tensor>("Output");
     output->mutable_data<T>(context.GetPlace());
 
+    std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
 
-    framework::DDim filter_matrix_shape = {filter.dims()[0],
-                                           filter.numel() / filter.dims()[0]};
-    filter.Resize(filter_matrix_shape);
-
     math::DepthwiseConvFunctor<DeviceContext, T> depthwiseConv;
 
     auto& dev_ctx = context.template device_context<DeviceContext>();
-    depthwiseConv(dev_ctx, input, filter, filter_shape_vec, strides, paddings,
+    depthwiseConv(dev_ctx, *input, filter, ksize, strides, paddings,
                   output);
   }
 };
diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
index c607704efa..6fb1531236 100644
--- a/paddle/operators/math/CMakeLists.txt
+++ b/paddle/operators/math/CMakeLists.txt
@@ -8,6 +8,7 @@ if(WITH_GPU)
     nv_library(softmax SRCS softmax.cc softmax.cu DEPS device_context)
     nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS device_context)
     nv_library(pooling SRCS pooling.cc pooling.cu DEPS device_context)
+    nv_library(depthwise_conv SRCS depthwise_conv.cu DEPS device_context)
     nv_library(sequence_pooling SRCS sequence_pooling.cc sequence_pooling.cu DEPS device_context math_function)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context tensor)
     nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context math_function)
diff --git a/paddle/operators/math/depthwise_conv.cu b/paddle/operators/math/depthwise_conv.cu
index 16a0037ab1..aee052d379 100644
--- a/paddle/operators/math/depthwise_conv.cu
+++ b/paddle/operators/math/depthwise_conv.cu
@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/operators/math/pooling.h"
+#include "paddle/operators/math/depthwise_conv.h"
 #include "paddle/platform/cuda_helper.h"
 
 namespace paddle {
@@ -195,7 +195,7 @@ __global__ void KernelDepthwiseConvFilterGrad(const int num_i,
  * Ksize, strides, paddings are two elements. These two elements represent
  * height and width, respectively.
  */
-template <typename T>
+template <class T>
 class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
@@ -226,7 +226,7 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
     dim3 threads(1024, 1);
     dim3 grid(blocks, 1);
 
-    KernelDepthwiseConv<T><<<grid, threads, 0, STREAM_DEFAULT>>>(
+    KernelDepthwiseConv<T><<<grid, threads, 0, context.stream()>>>(
         nthreads, input_data, filter_data, batch_size, output_channels,
         output_height, output_width, input_channels, input_height, input_width,
         output_channels / input_channels, ksize_height, ksize_width,
@@ -236,7 +236,6 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
 };
 
 /*
-
 template <typename T>
 class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, PoolProcess, T>
 {
@@ -254,8 +253,7 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, PoolProcess, T>
     const int output_height = output.dims()[2];
     const int output_width = output.dims()[3];
     const int ksize_height = ksize[0];
-    const int ksize_width = ksize[1];
-    const int stride_height = strides[0];
+    const int ksize_width = ksize[1]; const int stride_height = strides[0];
     const int stride_width = strides[1];
     const int padding_height = paddings[0];
     const int padding_width = paddings[1];
@@ -321,24 +319,20 @@ class DepthwiseConvdFilterGradFunctor<platform::CUDADeviceContext, T> {
 */
 
 template class DepthwiseConvFunctor<platform::CUDADeviceContext,
-                                    paddle::operators::math::MaxPool<float>,
                                     float>;
+template class DepthwiseConvFunctor<platform::CUDADeviceContext,
+                                    double>;
 
 /*
 template class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext,
-                                 paddle::operators::math::MaxPoolGrad<float>,
                                  float>;
 template class DepthwiseConvFilterGradFunctor<platform::CUDADeviceContext,
-                                 paddle::operators::math::MaxPoolGrad<float>,
                                  float>;
 
 template class DepthwiseConvFunctor<platform::CUDADeviceContext,
-                             paddle::operators::math::MaxPool<double>, double>;
 template class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext,
-                                 paddle::operators::math::MaxPoolGrad<double>,
                                  double>;
 template class DepthwiseConvFilterGradFunctor<platform::CUDADeviceContext,
-                                 paddle::operators::math::MaxPoolGrad<double>,
                                  double>;
 */
 

From 70142ae65eb234b804fd7b96a953f1f6ea2aff90 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 23 Jan 2018 18:33:08 +0800
Subject: [PATCH 023/314] update dist benchmark to one image

---
 .../cluster/vgg16/{fluid => }/Dockerfile      |  2 +-
 benchmark/cluster/vgg16/README.md             | 58 +++++++++++++++
 benchmark/cluster/vgg16/fluid/README.md       | 15 ----
 .../pserver.yaml => fluid_pserver.yaml}       |  6 +-
 .../trainer.yaml => fluid_trainer.yaml}       |  6 +-
 .../cluster/vgg16/{fluid => }/k8s_tools.py    |  0
 .../cluster/vgg16/{fluid => }/paddle_k8s      |  0
 benchmark/cluster/vgg16/{fluid => }/reader.py |  0
 benchmark/cluster/vgg16/v2/Dockerfile         |  7 --
 benchmark/cluster/vgg16/v2/reader.py          | 70 -------------------
 .../{v2/pserver.yaml => v2_pserver.yaml}      |  4 +-
 .../{v2/trainer.yaml => v2_trainer.yaml}      |  8 ++-
 .../vgg16/{fluid/vgg16.py => vgg16_fluid.py}  |  0
 .../vgg16/{v2/vgg16.py => vgg16_v2.py}        | 21 ++++--
 14 files changed, 86 insertions(+), 111 deletions(-)
 rename benchmark/cluster/vgg16/{fluid => }/Dockerfile (91%)
 create mode 100644 benchmark/cluster/vgg16/README.md
 delete mode 100644 benchmark/cluster/vgg16/fluid/README.md
 rename benchmark/cluster/vgg16/{fluid/pserver.yaml => fluid_pserver.yaml} (89%)
 rename benchmark/cluster/vgg16/{fluid/trainer.yaml => fluid_trainer.yaml} (87%)
 rename benchmark/cluster/vgg16/{fluid => }/k8s_tools.py (100%)
 rename benchmark/cluster/vgg16/{fluid => }/paddle_k8s (100%)
 rename benchmark/cluster/vgg16/{fluid => }/reader.py (100%)
 delete mode 100644 benchmark/cluster/vgg16/v2/Dockerfile
 delete mode 100644 benchmark/cluster/vgg16/v2/reader.py
 rename benchmark/cluster/vgg16/{v2/pserver.yaml => v2_pserver.yaml} (92%)
 rename benchmark/cluster/vgg16/{v2/trainer.yaml => v2_trainer.yaml} (88%)
 rename benchmark/cluster/vgg16/{fluid/vgg16.py => vgg16_fluid.py} (100%)
 rename benchmark/cluster/vgg16/{v2/vgg16.py => vgg16_v2.py} (90%)

diff --git a/benchmark/cluster/vgg16/fluid/Dockerfile b/benchmark/cluster/vgg16/Dockerfile
similarity index 91%
rename from benchmark/cluster/vgg16/fluid/Dockerfile
rename to benchmark/cluster/vgg16/Dockerfile
index 711076b09e..dfaffb8c21 100644
--- a/benchmark/cluster/vgg16/fluid/Dockerfile
+++ b/benchmark/cluster/vgg16/Dockerfile
@@ -12,4 +12,4 @@ ENV LD_LIBRARY_PATH=/usr/local/lib
 ADD reader.py /workspace/
 RUN python /workspace/reader.py
 
-ADD vgg16.py /workspace/
+ADD vgg16_fluid.py vgg16_v2.py /workspace/
diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
new file mode 100644
index 0000000000..18128e5276
--- /dev/null
+++ b/benchmark/cluster/vgg16/README.md
@@ -0,0 +1,58 @@
+# Performance for distributed vgg16
+
+## Test Result
+
+### Single node single thread
+
+| Batch Size | 32 | 64 | 128 | 256 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | 16.74 | - |
+| PaddlePaddle v2 | - | - | 17.60 | - |
+| TensorFlow | - | - | - | - |
+
+### different batch size
+
+- PServer Count: 10
+- Trainer Count: 20
+- Metrics: samples / sec
+
+| Batch Size | 32 | 64 | 128 | 256 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | 247.40 | - | - |
+| PaddlePaddle v2 | - | - | 256.14 | - |
+| TensorFlow | - | - | - | - |
+
+### different pserver number
+
+- Trainer Count: 100
+- Batch Size: 64
+- Metrics: mini-batch / sec
+
+| PServer Count | 10 | 20 | 40 | 60 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - |
+| TensorFlow | - | - | - | - |
+
+### Accelerate rate
+
+| Trainer Counter | 20 | 40 | 80 | 100 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - |
+| TensorFlow | - | - | - | - |
+
+
+## Steps to run the performance test
+
+1. You must re-compile PaddlePaddle and enable `-DWITH_DISTRIBUTE` to build PaddlePaddle with distributed support.
+1. When the build finishes, copy the output `whl` package located under `build/python/dist` to current directory.
+1. Run `docker build -t [image:tag] .` to build the docker image and run `docker push [image:tag]` to push the image to reponsitory so kubernetes can find it.
+1. Run `kubectl create -f pserver.yaml && kubectl create -f trainer.yaml` to start the job on your kubernetes cluster (you must configure the `kubectl` client before this step).
+1. Run `kubectl get po` to get running pods, and run `kubectl logs [podID]` to fetch the pod log of pservers and trainers.
+
+Check the logs for the distributed training progress and analyze the performance.
+
+## Enable verbos logs
+
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
diff --git a/benchmark/cluster/vgg16/fluid/README.md b/benchmark/cluster/vgg16/fluid/README.md
deleted file mode 100644
index 71a3a934d2..0000000000
--- a/benchmark/cluster/vgg16/fluid/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# Fluid distributed training perf test
-
-## Steps to get started
-
-1. You must re-compile PaddlePaddle and enable `-DWITH_DISTRIBUTE` to build PaddlePaddle with distributed support.
-1. When the build finishes, copy the output `whl` package located under `build/python/dist` to current directory.
-1. Run `docker build -t [image:tag] .` to build the docker image and run `docker push [image:tag]` to push the image to reponsitory so kubernetes can find it.
-1. Run `kubectl create -f pserver.yaml && kubectl create -f trainer.yaml` to start the job on your kubernetes cluster (you must configure the `kubectl` client before this step).
-1. Run `kubectl get po` to get running pods, and run `kubectl logs [podID]` to fetch the pod log of pservers and trainers.
-
-Check the logs for the distributed training progress and analyze the performance.
-
-## Enable verbos logs
-
-Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
diff --git a/benchmark/cluster/vgg16/fluid/pserver.yaml b/benchmark/cluster/vgg16/fluid_pserver.yaml
similarity index 89%
rename from benchmark/cluster/vgg16/fluid/pserver.yaml
rename to benchmark/cluster/vgg16/fluid_pserver.yaml
index e1a58260af..ee8b0763b6 100644
--- a/benchmark/cluster/vgg16/fluid/pserver.yaml
+++ b/benchmark/cluster/vgg16/fluid_pserver.yaml
@@ -14,7 +14,7 @@ spec:
       - name: job-registry-secret
       containers:
       - name: pserver
-        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16_fluid"
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
         imagePullPolicy: Always
         ports:
         - name: jobport-30236
@@ -33,7 +33,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "LD_LIBRARY_PATH=/usr/local/lib MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
@@ -53,7 +53,7 @@ spec:
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH
-          value: "/usr/local/nvidia/lib64"
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
         - name: NAMESPACE
           valueFrom:
             fieldRef:
diff --git a/benchmark/cluster/vgg16/fluid/trainer.yaml b/benchmark/cluster/vgg16/fluid_trainer.yaml
similarity index 87%
rename from benchmark/cluster/vgg16/fluid/trainer.yaml
rename to benchmark/cluster/vgg16/fluid_trainer.yaml
index c8e26d4b51..0a0ed25ebe 100644
--- a/benchmark/cluster/vgg16/fluid/trainer.yaml
+++ b/benchmark/cluster/vgg16/fluid_trainer.yaml
@@ -15,7 +15,7 @@ spec:
       hostNetwork: true
       containers:
       - name: trainer
-        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16_fluid"
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
         imagePullPolicy: Always
         command: ["paddle_k8s", "start_fluid"]
         env:
@@ -30,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && LD_LIBRARY_PATH=/usr/local/lib MKL_NUM_THREADS=1 python /workspace/vgg16.py --local 0"
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 128"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
@@ -50,7 +50,7 @@ spec:
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH
-          value: "/usr/local/nvidia/lib64"
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
         - name: NAMESPACE
           valueFrom:
             fieldRef:
diff --git a/benchmark/cluster/vgg16/fluid/k8s_tools.py b/benchmark/cluster/vgg16/k8s_tools.py
similarity index 100%
rename from benchmark/cluster/vgg16/fluid/k8s_tools.py
rename to benchmark/cluster/vgg16/k8s_tools.py
diff --git a/benchmark/cluster/vgg16/fluid/paddle_k8s b/benchmark/cluster/vgg16/paddle_k8s
similarity index 100%
rename from benchmark/cluster/vgg16/fluid/paddle_k8s
rename to benchmark/cluster/vgg16/paddle_k8s
diff --git a/benchmark/cluster/vgg16/fluid/reader.py b/benchmark/cluster/vgg16/reader.py
similarity index 100%
rename from benchmark/cluster/vgg16/fluid/reader.py
rename to benchmark/cluster/vgg16/reader.py
diff --git a/benchmark/cluster/vgg16/v2/Dockerfile b/benchmark/cluster/vgg16/v2/Dockerfile
deleted file mode 100644
index 5f129a8e32..0000000000
--- a/benchmark/cluster/vgg16/v2/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-FROM paddlepaddle/paddlecloud-job
-RUN mkdir -p /workspace
-ADD reader.py /workspace/
-RUN python /workspace/reader.py
-ADD vgg16.py /workspace/
-
-ADD vgg16_fluid.py /workspace
diff --git a/benchmark/cluster/vgg16/v2/reader.py b/benchmark/cluster/vgg16/v2/reader.py
deleted file mode 100644
index 16ac2dbcef..0000000000
--- a/benchmark/cluster/vgg16/v2/reader.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
-
-import random
-from paddle.v2.image import load_and_transform
-import paddle.v2 as paddle
-from multiprocessing import cpu_count
-
-
-def train_mapper(sample):
-    '''
-    map image path to type needed by model input layer for the training set
-    '''
-    img, label = sample
-    img = paddle.image.load_image(img)
-    img = paddle.image.simple_transform(img, 256, 224, True)
-    return img.flatten().astype('float32'), label
-
-
-def test_mapper(sample):
-    '''
-    map image path to type needed by model input layer for the test set
-    '''
-    img, label = sample
-    img = paddle.image.load_image(img)
-    img = paddle.image.simple_transform(img, 256, 224, True)
-    return img.flatten().astype('float32'), label
-
-
-def train_reader(train_list, buffered_size=1024):
-    def reader():
-        with open(train_list, 'r') as f:
-            lines = [line.strip() for line in f]
-            for line in lines:
-                img_path, lab = line.strip().split('\t')
-                yield img_path, int(lab)
-
-    return paddle.reader.xmap_readers(train_mapper, reader,
-                                      cpu_count(), buffered_size)
-
-
-def test_reader(test_list, buffered_size=1024):
-    def reader():
-        with open(test_list, 'r') as f:
-            lines = [line.strip() for line in f]
-            for line in lines:
-                img_path, lab = line.strip().split('\t')
-                yield img_path, int(lab)
-
-    return paddle.reader.xmap_readers(test_mapper, reader,
-                                      cpu_count(), buffered_size)
-
-
-if __name__ == '__main__':
-    #for im in train_reader('train.list'):
-    #    print len(im[0])
-    #for im in train_reader('test.list'):
-    #    print len(im[0])
-    paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/vgg16/v2/pserver.yaml b/benchmark/cluster/vgg16/v2_pserver.yaml
similarity index 92%
rename from benchmark/cluster/vgg16/v2/pserver.yaml
rename to benchmark/cluster/vgg16/v2_pserver.yaml
index 943675e147..dd1271e0cf 100644
--- a/benchmark/cluster/vgg16/v2/pserver.yaml
+++ b/benchmark/cluster/vgg16/v2_pserver.yaml
@@ -14,7 +14,7 @@ spec:
       - name: job-registry-secret
       containers:
       - name: pserver
-        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16"
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
         imagePullPolicy: Always
         ports:
         - name: jobport-30236
@@ -49,7 +49,7 @@ spec:
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH
-          value: "/usr/local/nvidia/lib64"
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
         - name: NAMESPACE
           valueFrom:
             fieldRef:
diff --git a/benchmark/cluster/vgg16/v2/trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
similarity index 88%
rename from benchmark/cluster/vgg16/v2/trainer.yaml
rename to benchmark/cluster/vgg16/v2_trainer.yaml
index 3288fbae26..9d52e231f0 100644
--- a/benchmark/cluster/vgg16/v2/trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -15,12 +15,14 @@ spec:
       hostNetwork: true
       containers:
       - name: trainer
-        image: "registry.baidu.com/paddlepaddle/rawjob:vgg16"
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
         imagePullPolicy: Always
         command: ["paddle_k8s", "start_trainer", "v2"]
         env:
         - name: PADDLE_JOB_NAME
           value: vgg16v2job
+        - name: BATCH_SIZE
+          value: "128"
         - name: TRAINERS
           value: "20"
         - name: PSERVERS
@@ -28,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16.py"
+          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16_v2.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
@@ -48,7 +50,7 @@ spec:
         - name: PADDLE_INIT_USE_GPU
           value: "0"
         - name: LD_LIBRARY_PATH
-          value: "/usr/local/nvidia/lib64"
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
         - name: NAMESPACE
           valueFrom:
             fieldRef:
diff --git a/benchmark/cluster/vgg16/fluid/vgg16.py b/benchmark/cluster/vgg16/vgg16_fluid.py
similarity index 100%
rename from benchmark/cluster/vgg16/fluid/vgg16.py
rename to benchmark/cluster/vgg16/vgg16_fluid.py
diff --git a/benchmark/cluster/vgg16/v2/vgg16.py b/benchmark/cluster/vgg16/vgg16_v2.py
similarity index 90%
rename from benchmark/cluster/vgg16/v2/vgg16.py
rename to benchmark/cluster/vgg16/vgg16_v2.py
index 0ffa9703b7..284dbec48d 100644
--- a/benchmark/cluster/vgg16/v2/vgg16.py
+++ b/benchmark/cluster/vgg16/vgg16_v2.py
@@ -16,12 +16,17 @@ import gzip
 
 import paddle.v2.dataset.cifar as cifar
 import paddle.v2 as paddle
-import reader
 import time
+import os
 
 DATA_DIM = 3 * 32 * 32
 CLASS_DIM = 10
-BATCH_SIZE = 128
+BATCH_SIZE = os.getenv("BATCH_SIZE")
+if BATCH_SIZE:
+    BATCH_SIZE = int(BATCH_SIZE)
+else:
+    BATCH_SIZE = 128
+NODE_COUNT = int(os.getenv("TRAINERS"))
 ts = 0
 
 
@@ -84,7 +89,8 @@ def main():
         name="label", type=paddle.data_type.integer_value(CLASS_DIM))
 
     extra_layers = None
-    learning_rate = 1e-3 / 20
+    # NOTE: for v2 distributed training need averaging updates.
+    learning_rate = 1e-3 / NODE_COUNT
     out = vgg16(image, class_dim=CLASS_DIM)
     cost = paddle.layer.classification_cost(input=out, label=lbl)
 
@@ -123,7 +129,9 @@ def main():
 
     # End batch and end pass event handler
     def event_handler(event):
-        global ts
+        global ts, ts_pass
+        if isinstance(event, paddle.event.BeginPass):
+            ts_pass = time.time()
         if isinstance(event, paddle.event.BeginIteration):
             ts = time.time()
         if isinstance(event, paddle.event.EndIteration):
@@ -132,9 +140,8 @@ def main():
                     event.pass_id, event.batch_id, event.cost, event.metrics,
                     time.time() - ts)
         if isinstance(event, paddle.event.EndPass):
-            with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-                trainer.save_parameter_to_tar(f)
-
+            print "Pass %d end, spent: %f" % (event.pass_id,
+                                              time.time() - ts_pass)
             result = trainer.test(reader=test_reader)
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 

From bcc67401113f04cbd52438d9a861f03725ad9a1a Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 23 Jan 2018 19:32:44 +0800
Subject: [PATCH 024/314] WIP python binding of send recv

---
 paddle/operators/recv_op.cc                  |  18 ++--
 python/paddle/v2/fluid/layers/io.py          | 101 +++++++++++++++++++
 python/paddle/v2/fluid/tests/test_recv_op.py |  45 +++++++++
 3 files changed, 155 insertions(+), 9 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_recv_op.py

diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 593c35879a..e3c86966b8 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -49,7 +49,7 @@ static void CreateTensorFromMessageType(framework::Variable *var,
     var->GetMutable<framework::SelectedRows>();
   } else {
     PADDLE_THROW(
-        "VariableMessage type %d is not in "
+        "VraibleMessage type %d is not in "
         "[LoDTensor, SelectedRows]",
         var_type);
   }
@@ -121,17 +121,17 @@ class RecvOp : public framework::OperatorBase {
         if (it != grad_list.end()) {
           param_var_name = param_list[it - grad_list.begin()];
         } else {
-          LOG(ERROR) << "grad has no paired param:" << grad_var_name;
+          LOG(ERROR) << "grad have no paired param:" << grad_var_name;
         }
-        VLOG(3) << "received grad: " << grad_var_name
+        VLOG(3) << "recved grad: " << grad_var_name
                 << " updating param: " << param_var_name;
         if (fan_in > 1) {
           grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
         }
         auto *var = recv_scope.FindVar(grad_var_name);
         if (var == nullptr) {
-          LOG(ERROR) << "Can not find server side var: " << grad_var_name;
-          PADDLE_THROW("Can not find server side var");
+          LOG(ERROR) << "can not find server side var: " << grad_var_name;
+          PADDLE_THROW("can not find server side var");
         }
         detail::DeserializeFromMessage(v.second, dev_ctx, var);
       }
@@ -161,11 +161,11 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   RecvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("RX", "(Tensor) Input tensor to be optimized").AsDuplicable();
+    // AddInput("RX", "(Tensor) Input tensor to be optimized").AsDuplicable();
     AddComment(R"DOC(
 Recv operator
 
-This operator will recieve tensor from send_op
+This operator will recv tensor from send_op
 )DOC");
     AddAttr<std::string>("endpoint",
                          "(string, default 127.0.0.1:6164)"
@@ -176,11 +176,11 @@ This operator will recieve tensor from send_op
         kOptimizeBlock, "Serialized ProgramDesc string for recv to run.");
     AddAttr<std::vector<std::string>>(
         "ParamList", "type list of string",
-        "grad->param name mapping to find which parameters to optimize.")
+        "grad->param name mapping to find which param to optimize.")
         .SetDefault({});
     AddAttr<std::vector<std::string>>(
         "GradList", "type list of string",
-        "grad->param name mapping to find which parameters to optimize.")
+        "grad->param name mapping to find which param to optimize.")
         .SetDefault({});
     AddAttr<int>("Fanin", "type int",
                  "Number of trainers in the current cluster job")
diff --git a/python/paddle/v2/fluid/layers/io.py b/python/paddle/v2/fluid/layers/io.py
index 9af00e7de5..6a6c561641 100644
--- a/python/paddle/v2/fluid/layers/io.py
+++ b/python/paddle/v2/fluid/layers/io.py
@@ -74,3 +74,104 @@ def data(name,
         type=type,
         stop_gradient=stop_gradient,
         lod_level=lod_level)
+
+
+class BlockGuardServ(BlockGuard):
+    """
+    BlockGuardServ class.
+
+    BlockGuardServ class is used to create an op with a block in a program.
+    """
+
+    def __init__(self, server):
+        if not (isinstance(server, ListenAndServ)):
+            raise TypeError("BlockGuardServ takes a ListenAndServ")
+        super(BlockGuardServ, self).__init__(server.helper.main_program)
+        self.server = server
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if exc_type is not None:
+            return False
+
+        self.server.complete_op()
+        return super(BlockGuardServ, self).__exit__(exc_type, exc_val, exc_tb)
+
+
+class ListenAndServ(object):
+    """
+    ListenAndServ class.
+
+    ListenAndServ class is used to wrap listen_and_serv op to create a server
+    which can receive variables from clients and run a block.
+    """
+
+    def __init__(self, endpoint, fan_in=1):
+        self.helper = LayerHelper("recv", name=name)
+        self.inputs = []
+        self.outputs = []
+        self.endpoint = endpoint
+        self.fan_in = fan_in
+
+    def do(self):
+        return BlockGuardServ(self)
+
+    def get_params_and_grads(self):
+        main_program = self.helper.main_program
+        current_block = main_program.current_block()
+        parent_block = self.parent_block()
+        # params and grads in the same order.
+        params = list()
+        grads = list()
+        for op in current_block.ops:
+            # FIXME(typhoonzero): op.inputs is None if it's cloned.
+            if "Grad" in op.inputs and "Param" in op.inputs:
+                params.append(op.inputs["Param"].name)
+                grads.append(op.inputs["Grad"].name)
+
+        return params, grads
+
+    def complete_op(self):
+        main_program = self.helper.main_program
+        current_block = main_program.current_block()
+        parent_block = self.parent_block()
+
+        params, grads = self.get_params_and_grads()
+        parent_block.append_op(
+            type='recv',
+            inputs={},
+            outputs={},
+            attrs={
+                'endpoint': self.endpoint,
+                'Fanin': self.fan_in,
+                'ParamList': params,
+                'GradList': grads,
+                'OptimizeBlock': current_block
+            })
+
+
+def Send(endpoints, send_vars, get_vars):
+    """
+    Send layer
+
+    Args:
+        endpoints: comma seperated IP:PORT pairs in the order
+                   of send_vars to send
+        send_vars: vars to send
+        get_vars: vars to get from server after send completes.
+
+    Send variables to the server side, and get vars from server
+    side when server have finished running server side program.
+    """
+    assert (type(send_vars) == list)
+    assert (type(get_vars) == list)
+
+    epmap = endpoints.split(",")
+    endpoints = set(epmap)
+
+    helper = LayerHelper("Send", **locals())
+    helper.append_op(
+        type="send",
+        inputs={"X": send_vars},
+        outputs={"Out": get_vars},
+        attrs={"endpoints": endpoints,
+               "epmap": epmap})
diff --git a/python/paddle/v2/fluid/tests/test_recv_op.py b/python/paddle/v2/fluid/tests/test_recv_op.py
new file mode 100644
index 0000000000..fbd182a716
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_recv_op.py
@@ -0,0 +1,45 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import numpy
+
+
+class TestRecvOp(unittest.TestCase):
+    def run_test(self):
+        # Run init_serv in a thread
+        pass
+
+    def init_serv(self, place):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            x = layers.data(shape=[32, 32], dtype='float32', name='X')
+            serv = fluid.ListenAndServ("127.0.0.1:6174")
+            with serv.do():
+                layers.scale(input=x, scale=10)
+        exe = fluid.Executor(place)
+        exe.run(main)
+
+    def init_client(self, place):
+        main = fluid.Program()
+        with fluid.program_guard(main):
+            x = layers.data(shape=[32, 32], dtype='float32', name='X')
+            i = fluid.initializer.Constant(x=1.0)
+            i(x, main.global_block())
+            layers.Send("127.0.0.1:6174", [x], [x])
+        exe = fluid.Executor(place)
+        exe.run(main)

From 552c901204744003f0653dbcc9afe615d5a66334 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 23 Jan 2018 04:04:06 -0800
Subject: [PATCH 025/314] Enable backward computation in lstmp_op

---
 paddle/operators/lstmp_op.cc                  |  53 ++++--
 .../operators/{lstmp_op.cu.cc => lstmp_op.cu} |   0
 paddle/operators/lstmp_op.h                   | 151 +++++++++++++++---
 python/paddle/v2/fluid/tests/test_lstmp_op.py |  59 ++++---
 4 files changed, 206 insertions(+), 57 deletions(-)
 rename paddle/operators/{lstmp_op.cu.cc => lstmp_op.cu} (100%)

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
index 4c7f7713ee..266612294c 100644
--- a/paddle/operators/lstmp_op.cc
+++ b/paddle/operators/lstmp_op.cc
@@ -39,21 +39,12 @@ class LSTMPOp : public framework::OperatorWithKernel {
                    "Output(BatchGate) of LSTMP should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
                    "Output(BatchGate) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"),
+                   "Output(BatchHidden) of LSTMP should not be null.");
 
     auto in_dims = ctx->GetInputDim("Input");
     PADDLE_ENFORCE_EQ(in_dims.size(), 2, "Input(X)'s rank must be 2.");
 
-    if (ctx->HasInput("H0")) {
-      PADDLE_ENFORCE(ctx->HasInput("C0"),
-                     "Input(C0) and Input(H0) of LSTMP should not "
-                     "be null at the same time.");
-      auto h_dims = ctx->GetInputDim("H0");
-      auto c_dims = ctx->GetInputDim("C0");
-      PADDLE_ENFORCE(h_dims == c_dims,
-                     "The dimension of Input(H0) and Input(C0) "
-                     "should be the same.");
-    }
-
     int frame_size = in_dims[1] / 4;
     auto w_dims = ctx->GetInputDim("Weight");
     auto proj_dims = ctx->GetInputDim("ProjWeight");
@@ -75,6 +66,18 @@ class LSTMPOp : public framework::OperatorWithKernel {
                       "should be %d.",
                       frame_size);
 
+    if (ctx->HasInput("H0")) {
+      PADDLE_ENFORCE(ctx->HasInput("C0"),
+                     "Input(C0) and Input(H0) of LSTMP should not "
+                     "be null at the same time.");
+      auto h_dims = ctx->GetInputDim("H0");
+      auto c_dims = ctx->GetInputDim("C0");
+      PADDLE_ENFORCE(h_dims == c_dims,
+                     "The dimension of Input(H0) and Input(C0) "
+                     "should be the same.");
+      ctx->SetOutputDim("OrderedP0", {h_dims[0], proj_dims[1]});
+    }
+
     auto b_dims = ctx->GetInputDim("Bias");
     PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
     PADDLE_ENFORCE_EQ(b_dims[0], 1,
@@ -98,6 +101,7 @@ class LSTMPOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Cell", out_dims);
     ctx->SetOutputDim("BatchGate", in_dims);
     ctx->SetOutputDim("BatchCellPreAct", out_dims);
+    ctx->SetOutputDim("BatchHidden", out_dims);
     ctx->ShareLoD("Input", "Projection");
     ctx->ShareLoD("Input", "Cell");
   }
@@ -169,6 +173,15 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
               "(LoDTensor) This LoDTensor is obtained in the forward and used "
               "in the backward.")
         .AsIntermediate();
+    AddOutput("BatchHidden",
+              "(LoDTensor) This LoDTensor is obtained in the forward and used "
+              "in the backward.")
+        .AsIntermediate();
+    AddOutput("OrderedP0",
+              "(Tensor) the projection of the initial hidden state "
+              "H0. This is a tensor with shape (N x P), where N is the "
+              "batch size and P is the hidden size.")
+        .AsIntermediate();
     AddAttr<bool>("use_peepholes",
                   "(bool, defalut: True) "
                   "whether to enable diagonal/peephole connections.")
@@ -177,6 +190,12 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, defalut: False) "
                   "whether to compute reversed LSTMP.")
         .SetDefault(false);
+    AddAttr<bool>("share_cell_act",
+                  "(bool, defalut: True) "
+                  "whether to share activation with cell output. "
+                  "If false, the projection would be linear, else "
+                  "through an activation same with the cell output.")
+        .SetDefault(true);
     AddAttr<std::string>(
         "gate_activation",
         "(string, default: sigmoid)"
@@ -213,7 +232,7 @@ o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
 
 h_t = o_t \odot act_h(c_t)
 
-r_t = W_{rh}h_t
+r_t = act_h'(W_{rh}h_t)
 $$
 
 where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
@@ -229,7 +248,8 @@ layer.
 
 The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
 are the cell input and cell output activation functions and `tanh` is usually
-used for them.
+used for them. If `share_cell_act` setted to `False`, $act_h'$ will be linear
+else will be same with $act_h$.
 
 Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
 operations on the input $x_{t}$ are NOT included in this operator.
@@ -246,12 +266,14 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Input"),
                    "Input(Input) of LSTMP should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Hidden"),
-                   "Input(Hidden) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Projection"),
+                   "Input(Projection) of LSTMP should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Cell"),
                    "Input(Cell) of LSTMP should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Weight"),
                    "Input(Weight) of LSTMP should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
+                   "Input(ProjWeight) of LSTMP should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Bias"),
                    "Input(Bias) of LSTMP should not be null.");
 
@@ -268,6 +290,7 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
 
     SetOutGradDim("Input");
     SetOutGradDim("Weight");
+    SetOutGradDim("ProjWeight");
     SetOutGradDim("Bias");
     SetOutGradDim("H0");
     SetOutGradDim("C0");
diff --git a/paddle/operators/lstmp_op.cu.cc b/paddle/operators/lstmp_op.cu
similarity index 100%
rename from paddle/operators/lstmp_op.cu.cc
rename to paddle/operators/lstmp_op.cu
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
index f5a38b2ff5..9467ccdb5a 100644
--- a/paddle/operators/lstmp_op.h
+++ b/paddle/operators/lstmp_op.h
@@ -13,18 +13,25 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/framework/op_registry.h"
+#include "paddle/operators/activation_op.h"
 #include "paddle/operators/math/detail/activation_functions.h"
 #include "paddle/operators/math/lstm_compute.h"
 #include "paddle/operators/math/math_function.h"
 #include "paddle/operators/math/sequence2batch.h"
 
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
 namespace paddle {
 namespace operators {
 
 using LoDTensor = framework::LoDTensor;
 using Tensor = framework::Tensor;
 
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
 template <typename DeviceContext, typename T>
 inline void ReorderInitState(const DeviceContext& ctx,
                              const framework::Tensor& src, const size_t* index,
@@ -37,6 +44,21 @@ inline void ReorderInitState(const DeviceContext& ctx,
 template <typename DeviceContext, typename T>
 class LSTMPKernel : public framework::OpKernel<T> {
  public:
+  template <typename Device, typename X, typename Y>
+  void ActCompute(const math::detail::ActivationType act_type, const Device& d,
+                  X x, Y y) const {
+    if (act_type == math::detail::ActivationType::kIdentity)
+      y.device(d) = x;
+    else if (act_type == math::detail::ActivationType::kSigmoid)
+      SigmoidFunctor<T>()(d, x, y);
+    else if (act_type == math::detail::ActivationType::kTanh)
+      TanhFunctor<T>()(d, x, y);
+    else if (act_type == math::detail::ActivationType::kReLU)
+      ReluFunctor<T>()(d, x, y);
+    else
+      PADDLE_THROW("unsupported activation type");
+  }
+
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input = ctx.Input<LoDTensor>("Input");
     auto* weight = ctx.Input<Tensor>("Weight");
@@ -44,6 +66,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
     auto* bias = ctx.Input<Tensor>("Bias");
 
     auto* hidden_t0 = ctx.Input<Tensor>("H0");
+    auto* ordered_proj0 = ctx.Output<Tensor>("OrderedP0");
     auto* cell_t0 = ctx.Input<Tensor>("C0");
 
     auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
@@ -97,12 +120,13 @@ class LSTMPKernel : public framework::OpKernel<T> {
     }
 
     // Use the local variable as here.
-    LoDTensor batch_hidden, batch_proj, batch_cell;
+    LoDTensor batch_proj, batch_cell;
     auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
-    batch_hidden.mutable_data<T>(dims, ctx.GetPlace());     // T x D
+    batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());
+    auto* batch_hidden = ctx.Output<LoDTensor>("BatchHidden");
+    batch_hidden->mutable_data<T>(dims, ctx.GetPlace());    // T x D
     batch_proj.mutable_data<T>(proj_dims, ctx.GetPlace());  // T x P
     batch_cell.mutable_data<T>(dims, ctx.GetPlace());       // T x D
-    batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());
 
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
@@ -112,13 +136,15 @@ class LSTMPKernel : public framework::OpKernel<T> {
         ctx.Attr<std::string>("cell_activation"));
     auto cand_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("candidate_activation"));
+    auto share_cell_act = ctx.Attr<bool>("share_cell_act");
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
     for (size_t n = 0; n < num_batch; n++) {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
 
       Tensor gate_t = batch_gate->Slice(bstart, bend);
-      Tensor hidden_t = batch_hidden.Slice(bstart, bend);
+      Tensor hidden_t = batch_hidden->Slice(bstart, bend);
       Tensor proj_t = batch_proj.Slice(bstart, bend);
       Tensor cell_t = batch_cell.Slice(bstart, bend);
       Tensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);
@@ -140,15 +166,19 @@ class LSTMPKernel : public framework::OpKernel<T> {
         // Since the batch computing for LSTMP reorders the input sequence
         // according to their length. The initialized hidden state also needs
         // to reorder.
-        Tensor ordered_h0, ordered_proj0;
-        ordered_proj0.Resize({1, proj_weight->dims()[1]});
-        ordered_proj0.mutable_data<T>(ctx.GetPlace());
+
+        Tensor ordered_h0;
+        ordered_proj0->mutable_data<T>(ctx.GetPlace());
         ReorderInitState<DeviceContext, T>(device_ctx, *hidden_t0, order,
                                            &ordered_h0, true);
         math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false,
                                        *proj_weight, false, static_cast<T>(1.0),
-                                       &ordered_proj0, static_cast<T>(0.0));
-        math::matmul<DeviceContext, T>(device_ctx, ordered_proj0, false,
+                                       ordered_proj0, static_cast<T>(0.0));
+        if (share_cell_act) {
+          auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
+          ActCompute(cell_act, place, proj0_dev, proj0_dev);
+        }
+        math::matmul<DeviceContext, T>(device_ctx, *ordered_proj0, false,
                                        *weight, false, static_cast<T>(1.0),
                                        &gate_t, static_cast<T>(1.0));
       }
@@ -164,6 +194,10 @@ class LSTMPKernel : public framework::OpKernel<T> {
       math::matmul<DeviceContext, T>(device_ctx, hidden_t, false, *proj_weight,
                                      false, static_cast<T>(1.0), &proj_t,
                                      static_cast<T>(0.0));
+      if (share_cell_act) {
+        auto proj_t_dev = EigenMatrix<T>::From(proj_t);
+        ActCompute(cell_act, place, proj_t_dev, proj_t_dev);
+      }
     }
 
     math::Batch2LoDTensorFunctor<DeviceContext, T> to_seq;
@@ -180,9 +214,26 @@ class LSTMPKernel : public framework::OpKernel<T> {
 template <typename DeviceContext, typename T>
 class LSTMPGradKernel : public framework::OpKernel<T> {
  public:
+  template <typename Device, typename X, typename Y, typename DX, typename DY>
+  void ActGradCompute(const math::detail::ActivationType act_type,
+                      const Device& d, X x, Y y, DX dx, DY dy) const {
+    // x is dummy and won't be used even in Relu(use y instead)
+    if (act_type == math::detail::ActivationType::kIdentity)
+      dx.device(d) = dy;
+    else if (act_type == math::detail::ActivationType::kSigmoid)
+      SigmoidGradFunctor<T>()(d, x, y, dy, dx);
+    else if (act_type == math::detail::ActivationType::kTanh)
+      TanhGradFunctor<T>()(d, x, y, dy, dx);
+    else if (act_type == math::detail::ActivationType::kReLU)
+      ReluGradFunctor<T>()(d, x, y, dy, dx);
+    else
+      PADDLE_THROW("unsupported activation type");
+  }
+
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input = ctx.Input<LoDTensor>("Input");
     auto* weight = ctx.Input<Tensor>("Weight");
+    auto* proj_weight = ctx.Input<Tensor>("ProjWeight");
     auto* bias = ctx.Input<Tensor>("Bias");
 
     auto* proj_out = ctx.Input<LoDTensor>("Projection");
@@ -190,14 +241,19 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
 
     auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
     auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
+    auto* batch_hidden = ctx.Input<LoDTensor>("BatchHidden");
 
-    auto* hidden_g = ctx.Input<LoDTensor>(framework::GradVarName("Projection"));
+    auto* projection_g =
+        ctx.Input<LoDTensor>(framework::GradVarName("Projection"));
 
     auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
     auto* weight_g = ctx.Output<Tensor>(framework::GradVarName("Weight"));
+    auto* proj_weight_g =
+        ctx.Output<Tensor>(framework::GradVarName("ProjWeight"));
     auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
     auto* h0 = ctx.Input<Tensor>("H0");
+    auto* ordered_proj0 = ctx.Input<Tensor>("OrderedP0");
     auto* c0 = ctx.Input<Tensor>("C0");
 
     auto* h0_g = ctx.Output<Tensor>(framework::GradVarName("H0"));
@@ -209,6 +265,10 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       weight_g->mutable_data<T>(ctx.GetPlace());
       zero(device_ctx, weight_g, static_cast<T>(0.0));
     }
+    if (proj_weight_g) {
+      proj_weight_g->mutable_data<T>(ctx.GetPlace());
+      zero(device_ctx, proj_weight_g, static_cast<T>(0.0));
+    }
 
     // ordered_h0/c0 is the reordered hidden/cell initialization.
     // ordered_h0_g/c0_g is the reordered gradient of hidden/cell
@@ -224,7 +284,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
     }
 
     auto in_dims = input->dims();
-    auto out_dims = hidden_g->dims();
+    auto out_dims = cell_out->dims();
+    framework::DDim proj_dims({in_dims[0], proj_weight->dims()[1]});
     int frame_size = static_cast<int>(in_dims[1] / 4);
     PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
 
@@ -267,10 +328,11 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       to_batch(ctx, src, dst, false);
     };
 
-    LoDTensor batch_proj, batch_proj_g, batch_cell;
-    ToBatch(device_ctx, *proj_out, out_dims, batch_proj);
-    ToBatch(device_ctx, *hidden_g, out_dims, batch_proj_g);
-    ToBatch(device_ctx, *cell_out, out_dims, batch_cell);
+    LoDTensor batch_hidden_g, batch_proj, batch_proj_g, batch_cell;
+    batch_hidden_g.mutable_data<T>(out_dims, ctx.GetPlace());
+    ToBatch(device_ctx, *proj_out, proj_dims, batch_proj);        // T x P
+    ToBatch(device_ctx, *projection_g, proj_dims, batch_proj_g);  // T x P
+    ToBatch(device_ctx, *cell_out, out_dims, batch_cell);         // T x D
 
     LoDTensor batch_cell_g, batch_gate_g;
     batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
@@ -286,6 +348,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
         ctx.Attr<std::string>("cell_activation"));
     auto cand_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("candidate_activation"));
+    auto share_cell_act = ctx.Attr<bool>("share_cell_act");
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
     auto batch_starts = batch_gate->lod()[0];
     size_t num_batch = batch_starts.size() - 1;
@@ -293,6 +357,19 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       int bstart = static_cast<int>(batch_starts[n]);
       int bend = static_cast<int>(batch_starts[n + 1]);
 
+      Tensor cur_proj = batch_proj.Slice(bstart, bend);
+      Tensor proj_g = batch_proj_g.Slice(bstart, bend);
+      if (share_cell_act) {
+        auto cur_proj_dev = EigenMatrix<T>::From(cur_proj);
+        auto proj_g_dev = EigenMatrix<T>::From(proj_g);
+        ActGradCompute(cell_act, place, cur_proj_dev, cur_proj_dev, proj_g_dev,
+                       proj_g_dev);
+      }
+      Tensor out_g = batch_hidden_g.Slice(bstart, bend);
+      math::matmul<DeviceContext, T>(device_ctx, proj_g, false, *proj_weight,
+                                     true, static_cast<T>(1.0), &out_g,
+                                     static_cast<T>(0.0));
+
       Tensor gate = batch_gate->Slice(bstart, bend);
       Tensor cell = batch_cell.Slice(bstart, bend);
       Tensor cell_pre_act = batch_cell_pre_act->Slice(bstart, bend);
@@ -300,7 +377,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
       lstmp_value.state_value = cell.data<T>();
       lstmp_value.state_active_value = cell_pre_act.data<T>();
 
-      Tensor out_g = batch_proj_g.Slice(bstart, bend);
       Tensor gate_g = batch_gate_g.Slice(bstart, bend);
       Tensor cell_g = batch_cell_g.Slice(bstart, bend);
       lstmp_grad.state_grad = cell_g.data<T>();
@@ -337,19 +413,48 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
                                          false, static_cast<T>(1.0), weight_g,
                                          static_cast<T>(1.0));
         }
+        if (proj_weight_g) {
+          /* backward proj weigh */
+          Tensor hidden_t = batch_hidden->Slice(bstart, bend);
+          math::matmul<DeviceContext, T>(device_ctx, hidden_t, true, proj_g,
+                                         false, static_cast<T>(1.0),
+                                         proj_weight_g, static_cast<T>(1.0));
+        }
       } else {
         if (h0 && weight_g) {
           ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
                                              &ordered_h0, true);
-          math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true, gate_g,
-                                         false, static_cast<T>(1.0), weight_g,
-                                         static_cast<T>(1.0));
+          if (weight_g) {
+            math::matmul<DeviceContext, T>(device_ctx, *ordered_proj0, true,
+                                           gate_g, false, static_cast<T>(1.0),
+                                           weight_g, static_cast<T>(1.0));
+          }
         }
-        if (h0 && h0_g) {
+        if (h0 && (h0_g || proj_weight_g)) {
           ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
+          Tensor proj0_g;
+          proj0_g.Resize({in_dims[0], proj_weight->dims()[1]});
+          proj0_g.mutable_data<T>(ctx.GetPlace());
           math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
-                                         true, static_cast<T>(1.0),
-                                         &ordered_h0_g, static_cast<T>(0.0));
+                                         true, static_cast<T>(1.0), &proj0_g,
+                                         static_cast<T>(0.0));
+          if (share_cell_act) {
+            auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
+            auto proj0_g_dev = EigenMatrix<T>::From(proj0_g);
+            ActGradCompute(cell_act, place, proj0_dev, proj0_dev, proj0_g_dev,
+                           proj0_g_dev);
+          }
+          // Tensor proj0_g = proj_g.Slice(bstart, bend);
+          if (h0_g) {
+            math::matmul<DeviceContext, T>(
+                device_ctx, proj0_g, false, *proj_weight, true,
+                static_cast<T>(1.0), &ordered_h0_g, static_cast<T>(0.0));
+          }
+          if (proj_weight_g) {
+            math::matmul<DeviceContext, T>(device_ctx, ordered_h0, true,
+                                           proj0_g, false, static_cast<T>(1.0),
+                                           proj_weight_g, static_cast<T>(1.0));
+          }
         }
       }
     }
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index e35712ec06..81e06063fc 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -62,7 +62,8 @@ def lstmp(
         is_reverse=False,
         act_gate=None,
         act_cell=None,
-        act_cand=None):
+        act_cand=None,
+        share_cell_act=True):
     def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand):
         g = np.dot(r_pre, w_r)  # 1 x 4D
         g = g + x
@@ -85,6 +86,8 @@ def lstmp(
         h = g_o * act_cell(c)
         # projection
         r = np.dot(h, w_rh)
+        if share_cell_act:
+            r = act_cell(r)
         return r, c
 
     def _reverse(x, lod):
@@ -107,6 +110,8 @@ def lstmp(
         seq_len = offset[i + 1] - offset[i]
         x = input[offset[i]:offset[i + 1], :]
         r_pre = np.dot(h0[i], w_rh)  # 1 x P
+        if share_cell_act:
+            r_pre = act_cell(r_pre)
         c_pre = c0[i]  # 1 x D
         for j in range(seq_len):
             # compute one step
@@ -138,6 +143,7 @@ class TestLstmOp(OpTest):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
+        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = False
         self.use_peepholes = True
@@ -167,7 +173,7 @@ class TestLstmOp(OpTest):
         w_rh = np.random.normal(size=(self.D, self.P)).astype('float64')
         r, c = lstmp(x, self.lod, h0, c0, w, w_rh, w_b, w_c, self.is_reverse,
                      ACTVATION[self.act_gate], ACTVATION[self.act_cell],
-                     ACTVATION[self.act_cand])
+                     ACTVATION[self.act_cand], self.share_cell_act)
 
         self.inputs = {'Input': (x, self.lod), 'Weight': w, 'ProjWeight': w_rh}
 
@@ -192,28 +198,30 @@ class TestLstmOp(OpTest):
     def test_check_output(self):
         self.check_output(atol=1e-8)
 
-    """
     def test_check_grad(self):
         # TODO(qingqing) remove folowing lines after the check_grad is refined.
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
-    """
+            ['Input', 'Weight', 'Bias'], ['Projection'],
+            max_relative_error=5e-3)
 
 
-"""
 class TestLstmOpHasInitial(TestLstmOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
+        self.P = 5
 
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
+        self.share_cell_act = True
         self.has_initial_state = True
         self.is_reverse = True
         self.use_peepholes = True
@@ -221,63 +229,74 @@ class TestLstmOpHasInitial(TestLstmOp):
     def test_check_grad(self):
         # TODO(qingqing) remove folowing lines after the check_grad is refined.
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Hidden'],
-            max_relative_error=5e-4)
+            ['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Projection'],
+            max_relative_error=5e-3)
 
     def test_check_grad_ingore_bias(self):
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight'], ['Hidden'],
-            max_relative_error=5e-4,
+            ['Input', 'Weight'], ['Projection'],
+            max_relative_error=5e-3,
             no_grad_set=set('Bias'))
 
     def test_check_grad_ingore_weight(self):
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Bias'], ['Hidden'],
-            max_relative_error=5e-4,
+            ['Input', 'Bias'], ['Projection'],
+            max_relative_error=5e-3,
             no_grad_set=set('Weight'))
 
     def test_check_grad_ingore_input(self):
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Weight', 'Bias'], ['Hidden'],
-            max_relative_error=5e-4,
+            ['Weight', 'Bias'], ['Projection'],
+            max_relative_error=5e-3,
             no_grad_set=set('Input'))
 
     def test_check_grad_ingore_h0(self):
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'C0'], ['Hidden'],
-            max_relative_error=5e-4,
+            ['Input', 'Weight', 'Bias', 'C0'], ['Projection'],
+            max_relative_error=5e-3,
             no_grad_set=set('H0'))
 
     def test_check_grad_ingore_c0(self):
         N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
         self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'H0'], ['Hidden'],
-            max_relative_error=5e-4,
+            ['Input', 'Weight', 'Bias', 'H0'], ['Projection'],
+            max_relative_error=5e-3,
             no_grad_set=set('C0'))
-"""
 
 
 class TestLstmOpRerverse(TestLstmOp):
@@ -290,6 +309,7 @@ class TestLstmOpRerverse(TestLstmOp):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
+        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = True
         self.use_peepholes = True
@@ -305,6 +325,7 @@ class TestLstmOpNotUsePeepholes(TestLstmOp):
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
 
+        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = True
         self.use_peepholes = False

From 0e850c7417084675dcc997768c7f854333625bfe Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 23 Jan 2018 20:26:00 +0800
Subject: [PATCH 026/314] WIP

---
 python/paddle/v2/fluid/layers/io.py          | 23 +++++++++++++++-----
 python/paddle/v2/fluid/tests/test_recv_op.py | 21 +++++++++++++-----
 2 files changed, 33 insertions(+), 11 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/io.py b/python/paddle/v2/fluid/layers/io.py
index 6a6c561641..be581531d1 100644
--- a/python/paddle/v2/fluid/layers/io.py
+++ b/python/paddle/v2/fluid/layers/io.py
@@ -14,8 +14,10 @@
 
 from .. import core
 from ..layer_helper import LayerHelper
+from control_flow import BlockGuard
+from ..layer_helper import LayerHelper
 
-__all__ = ['data']
+__all__ = ['data', 'BlockGuardServ', 'ListenAndServ', 'Send']
 
 
 def data(name,
@@ -105,12 +107,14 @@ class ListenAndServ(object):
     which can receive variables from clients and run a block.
     """
 
-    def __init__(self, endpoint, fan_in=1):
-        self.helper = LayerHelper("recv", name=name)
+    def __init__(self, endpoint, fan_in=1, optimizer_mode=True):
+        self.helper = LayerHelper("recv")
         self.inputs = []
         self.outputs = []
         self.endpoint = endpoint
         self.fan_in = fan_in
+        # FIXME(typhoonzero): Add this switch is stupid
+        self.optimizer_mode = optimizer_mode
 
     def do(self):
         return BlockGuardServ(self)
@@ -124,9 +128,16 @@ class ListenAndServ(object):
         grads = list()
         for op in current_block.ops:
             # FIXME(typhoonzero): op.inputs is None if it's cloned.
-            if "Grad" in op.inputs and "Param" in op.inputs:
-                params.append(op.inputs["Param"].name)
-                grads.append(op.inputs["Grad"].name)
+            if self.optimizer_mode:
+                if "Grad" in op.inputs and "Param" in op.inputs:
+                    params.append(op.inputs["Param"].name)
+                    grads.append(op.inputs["Grad"].name)
+            else:
+                # simple recv mode, recv operators inputs.
+                for iname in op.input_names:
+                    for in_var_name in op.input(iname):
+                        params.append(parent_block.var(name))
+                        grads.append(parent_block.var(name))
 
         return params, grads
 
diff --git a/python/paddle/v2/fluid/tests/test_recv_op.py b/python/paddle/v2/fluid/tests/test_recv_op.py
index fbd182a716..e06f468648 100644
--- a/python/paddle/v2/fluid/tests/test_recv_op.py
+++ b/python/paddle/v2/fluid/tests/test_recv_op.py
@@ -17,20 +17,27 @@ import unittest
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.layers as layers
 import numpy
+import threading
 
 
 class TestRecvOp(unittest.TestCase):
-    def run_test(self):
+    def test_send(self):
         # Run init_serv in a thread
-        pass
+        place = fluid.CPUPlace()
+        t = threading.Thread(target=self.init_serv, args=(place, ))
+        t.start()
+        self.init_client(place)
+        t.join()
 
     def init_serv(self, place):
         main = fluid.Program()
         with fluid.program_guard(main):
             x = layers.data(shape=[32, 32], dtype='float32', name='X')
-            serv = fluid.ListenAndServ("127.0.0.1:6174")
+            i = fluid.initializer.Constant(value=1.0)
+            y = i(x, main.global_block())
+            serv = layers.ListenAndServ("127.0.0.1:6174")
             with serv.do():
-                layers.scale(input=x, scale=10)
+                layers.scale(input=y, scale=10.0)
         exe = fluid.Executor(place)
         exe.run(main)
 
@@ -38,8 +45,12 @@ class TestRecvOp(unittest.TestCase):
         main = fluid.Program()
         with fluid.program_guard(main):
             x = layers.data(shape=[32, 32], dtype='float32', name='X')
-            i = fluid.initializer.Constant(x=1.0)
+            i = fluid.initializer.Constant(value=1.0)
             i(x, main.global_block())
             layers.Send("127.0.0.1:6174", [x], [x])
         exe = fluid.Executor(place)
         exe.run(main)
+
+
+if __name__ == "__main__":
+    unittest.main()

From 7a5b8ffacb2aa64981d6262790501b10257b6321 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 23 Jan 2018 09:51:15 -0800
Subject: [PATCH 027/314] Pass grad checking for projection weight

---
 paddle/operators/lstmp_op.cc                  |  4 +-
 paddle/operators/lstmp_op.h                   | 18 ++++----
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 41 ++++++++++++-------
 3 files changed, 38 insertions(+), 25 deletions(-)

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
index 266612294c..932e76e913 100644
--- a/paddle/operators/lstmp_op.cc
+++ b/paddle/operators/lstmp_op.cc
@@ -217,7 +217,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Long-Short Term Memory with Recurrent Projection (LSTMP) Operator.
 
-LATMP is stand LSTM appended by a recurrent projection layer to reduce the
+LSTMP is stand LSTM appended by a recurrent projection layer to reduce the
 number of parameters, espeacially when the output size is relative large. 
 The formula is as follows:
 
@@ -232,7 +232,7 @@ o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
 
 h_t = o_t \odot act_h(c_t)
 
-r_t = act_h'(W_{rh}h_t)
+r_t = act_{h'}(W_{rh}h_t)
 $$
 
 where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
index 9467ccdb5a..0048f7e1c6 100644
--- a/paddle/operators/lstmp_op.h
+++ b/paddle/operators/lstmp_op.h
@@ -365,10 +365,18 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
         ActGradCompute(cell_act, place, cur_proj_dev, cur_proj_dev, proj_g_dev,
                        proj_g_dev);
       }
+      /* hidden state backwarad */
       Tensor out_g = batch_hidden_g.Slice(bstart, bend);
       math::matmul<DeviceContext, T>(device_ctx, proj_g, false, *proj_weight,
                                      true, static_cast<T>(1.0), &out_g,
                                      static_cast<T>(0.0));
+      /* projection weight backward*/
+      if (proj_weight_g) {
+        Tensor hidden_t = batch_hidden->Slice(bstart, bend);
+        math::matmul<DeviceContext, T>(device_ctx, hidden_t, true, proj_g,
+                                       false, static_cast<T>(1.0),
+                                       proj_weight_g, static_cast<T>(1.0));
+      }
 
       Tensor gate = batch_gate->Slice(bstart, bend);
       Tensor cell = batch_cell.Slice(bstart, bend);
@@ -407,19 +415,12 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
                                        static_cast<T>(1.0), &pre_proj_g,
                                        static_cast<T>(1.0));
         if (weight_g) {
-          /* backward weight */
+          /* weight backward*/
           auto pre_proj = batch_proj.Slice(pre_h_start, pre_h_end);
           math::matmul<DeviceContext, T>(device_ctx, pre_proj, true, gate_g,
                                          false, static_cast<T>(1.0), weight_g,
                                          static_cast<T>(1.0));
         }
-        if (proj_weight_g) {
-          /* backward proj weigh */
-          Tensor hidden_t = batch_hidden->Slice(bstart, bend);
-          math::matmul<DeviceContext, T>(device_ctx, hidden_t, true, proj_g,
-                                         false, static_cast<T>(1.0),
-                                         proj_weight_g, static_cast<T>(1.0));
-        }
       } else {
         if (h0 && weight_g) {
           ReorderInitState<DeviceContext, T>(device_ctx, *h0, order,
@@ -444,7 +445,6 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
             ActGradCompute(cell_act, place, proj0_dev, proj0_dev, proj0_g_dev,
                            proj0_g_dev);
           }
-          // Tensor proj0_g = proj_g.Slice(bstart, bend);
           if (h0_g) {
             math::matmul<DeviceContext, T>(
                 device_ctx, proj0_g, false, *proj_weight, true,
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index 81e06063fc..a0f6955d77 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -207,8 +207,8 @@ class TestLstmOp(OpTest):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias'], ['Projection'],
-            max_relative_error=5e-3)
+            ['Input', 'Weight', 'ProjWeight', 'Bias'], ['Projection'],
+            max_relative_error=1e-2)
 
 
 class TestLstmOpHasInitial(TestLstmOp):
@@ -235,8 +235,9 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Projection'],
-            max_relative_error=5e-3)
+            ['Input', 'Weight', 'ProjWeight', 'Bias', 'H0', 'C0'],
+            ['Projection'],
+            max_relative_error=1e-2)
 
     def test_check_grad_ingore_bias(self):
         N = len(self.lod[0]) - 1
@@ -246,8 +247,8 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight'], ['Projection'],
-            max_relative_error=5e-3,
+            ['Input', 'ProjWeight', 'Weight'], ['Projection'],
+            max_relative_error=1e-2,
             no_grad_set=set('Bias'))
 
     def test_check_grad_ingore_weight(self):
@@ -258,10 +259,22 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Bias'], ['Projection'],
-            max_relative_error=5e-3,
+            ['Input', 'ProjWeight', 'Bias'], ['Projection'],
+            max_relative_error=1e-2,
             no_grad_set=set('Weight'))
 
+    def test_check_grad_ingore_proj_weight(self):
+        N = len(self.lod[0]) - 1
+        self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
+        self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
+        self.outputs['BatchHidden'] = np.zeros((N, self.D)).astype('float64')
+        self.outputs['BatchCellPreAct'] = np.zeros(
+            (N, self.D)).astype('float64')
+        self.check_grad(
+            ['Input', 'Weight', 'Bias'], ['Projection'],
+            max_relative_error=1e-2,
+            no_grad_set=set('ProjWeight'))
+
     def test_check_grad_ingore_input(self):
         N = len(self.lod[0]) - 1
         self.outputs['OrderedP0'] = np.zeros((N, self.P)).astype('float64')
@@ -270,8 +283,8 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Weight', 'Bias'], ['Projection'],
-            max_relative_error=5e-3,
+            ['Weight', 'ProjWeight', 'Bias'], ['Projection'],
+            max_relative_error=1e-2,
             no_grad_set=set('Input'))
 
     def test_check_grad_ingore_h0(self):
@@ -282,8 +295,8 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'C0'], ['Projection'],
-            max_relative_error=5e-3,
+            ['Input', 'Weight', 'ProjWeight', 'Bias', 'C0'], ['Projection'],
+            max_relative_error=1e-2,
             no_grad_set=set('H0'))
 
     def test_check_grad_ingore_c0(self):
@@ -294,8 +307,8 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.outputs['BatchCellPreAct'] = np.zeros(
             (N, self.D)).astype('float64')
         self.check_grad(
-            ['Input', 'Weight', 'Bias', 'H0'], ['Projection'],
-            max_relative_error=5e-3,
+            ['Input', 'Weight', 'ProjWeight', 'Bias', 'H0'], ['Projection'],
+            max_relative_error=1e-2,
             no_grad_set=set('C0'))
 
 

From a3d1f86947dc46dfbff734cf0b5b529eaff4703e Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 23 Jan 2018 10:27:44 -0800
Subject: [PATCH 028/314] Add unit test for linear projection

---
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 21 +++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index a0f6955d77..8835cae504 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -192,7 +192,8 @@ class TestLstmOp(OpTest):
             'is_reverse': self.is_reverse,
             'gate_activation': self.act_gate,
             'cell_activation': self.act_cell,
-            'candidate_activation': self.act_cand
+            'candidate_activation': self.act_cand,
+            'share_cell_act': self.share_cell_act
         }
 
     def test_check_output(self):
@@ -340,9 +341,25 @@ class TestLstmOpNotUsePeepholes(TestLstmOp):
 
         self.share_cell_act = True
         self.has_initial_state = False
-        self.is_reverse = True
+        self.is_reverse = False
         self.use_peepholes = False
 
 
+class TestLstmOpNotShareCellAct(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[0, 2, 5, 7]]
+        self.D = 16
+        self.P = 10
+
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+
+        self.share_cell_act = False
+        self.has_initial_state = False
+        self.is_reverse = False
+        self.use_peepholes = True
+
+
 if __name__ == '__main__':
     unittest.main()

From db1f6a591ae9291de9877099e6801f101e679969 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 23 Jan 2018 19:29:12 -0800
Subject: [PATCH 029/314] Update doc in lstmp_op

---
 paddle/operators/lstmp_op.cc | 86 ++++++++++++++++++++----------------
 1 file changed, 49 insertions(+), 37 deletions(-)

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
index 932e76e913..85be64f44c 100644
--- a/paddle/operators/lstmp_op.cc
+++ b/paddle/operators/lstmp_op.cc
@@ -120,7 +120,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
   LSTMPOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("Input",
-             "(LoDTensor) the first input is a LodTensor, which support "
+             "(LoDTensor) the input for sequence data, which supports "
              "variable-time length input sequence. The underlying tensor in "
              "this LoDTensor is a matrix with shape (T X 4D), where T is the "
              "total time steps in this mini-batch, D is the hidden size.");
@@ -132,21 +132,23 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("C0",
              "(Tensor, optional) the initial cell state is an optional "
              "input. This is a tensor with shape (N x D), where N is the "
-             "batch size. `H0` and `C0` can be NULL but only at the same time")
+             "batch size. Only one of `H0` and `C0` can be NULL at the same "
+             "time.")
         .AsDispensable();
     AddInput("Weight",
              "(Tensor) the learnable hidden-hidden weights."
-             " - The shape is (P x 4D), where P is the recurrent projection "
-             "layer size and  D is the hidden size. "
+             " - The shape is (P x 4D), where P is the projection layer size "
+             "and  D is the hidden size."
              " - Weight = {W_cr, W_ir, W_fr, W_or}");
     AddInput("ProjWeight",
-             "(Tensor) the learnable weight `W_rh` of the projection layer."
+             "(Tensor) the learnable weight of the projection layer."
              " - The shape is (D x P), where P is the recurrent projection "
-             "layer size and  D is the hidden size.");
+             "layer size and  D is the hidden size."
+             " - ProjWeight = {W_rh}");
     AddInput("Bias",
-             "(Tensor) the learnable weights, which contains two parts: "
-             "input-hidden bias weight and peephole connections weight if "
-             "setting `use_peepholes` True. "
+             "(Tensor) the learnable biases, which contains two parts: "
+             "input-hidden biases and peephole connections weights if "
+             "setting `use_peepholes` to `True`. "
              "1. `use_peepholes = False` "
              " - The shape is (1 x 4D). "
              " - Bias = {b_c, b_i, b_f, b_o}."
@@ -155,27 +157,28 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
              " - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
     AddOutput("Projection",
               "(LoDTensor) the projection of the hidden state of LSTMP "
-              "operator. The shape is (T x P), and lod is the same with the "
+              "operator. The shape is (T x P), and LoD is the same with the "
               "`Input`.");
     AddOutput("Cell",
               "(LoDTensor) the cell state of LSTMP operator. "
               "The shape is (T x D), and lod is the same with the `Input`.");
     AddOutput("BatchGate",
               "(LoDTensor) This LoDTensor contains input gate, forget gate "
-              "and output gate after the nonlinear computation. This "
-              "LoDTensor has the same shape as the reorganized input, which "
-              "is also be called batch input. The LoD size is 2. The first "
-              "LoD is the batch offsets and the second LoD contains the "
-              "indexes, which denote the position of reorganized sequence "
-              "in the raw input.")
+              "and output gate after the activations. This LoDTensor has the "
+              "same shape as the reorganized input, which is also be called "
+              "batch input. The LoD size is 2. The first-level LoD is the "
+              "batch offsets and the second contains the indices, which "
+              "denotes the position of reorganized sequence in the raw input.")
         .AsIntermediate();
     AddOutput("BatchCellPreAct",
-              "(LoDTensor) This LoDTensor is obtained in the forward and used "
-              "in the backward.")
+              "(LoDTensor) the pre-activation cell state reorganized in batch. "
+              "This LoDTensor is obtained in the forward and used in the "
+              "backward.")
         .AsIntermediate();
     AddOutput("BatchHidden",
-              "(LoDTensor) This LoDTensor is obtained in the forward and used "
-              "in the backward.")
+              "(LoDTensor) the hidden state reorganized in batch. "
+              "This LoDTensor is obtained in the forward and used in the "
+              "backward.")
         .AsIntermediate();
     AddOutput("OrderedP0",
               "(Tensor) the projection of the initial hidden state "
@@ -190,12 +193,6 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool, defalut: False) "
                   "whether to compute reversed LSTMP.")
         .SetDefault(false);
-    AddAttr<bool>("share_cell_act",
-                  "(bool, defalut: True) "
-                  "whether to share activation with cell output. "
-                  "If false, the projection would be linear, else "
-                  "through an activation same with the cell output.")
-        .SetDefault(true);
     AddAttr<std::string>(
         "gate_activation",
         "(string, default: sigmoid)"
@@ -214,11 +211,21 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
                          "`tanh` by default.")
         .SetDefault("tanh")
         .InEnum({"sigmoid", "tanh", "relu", "identity"});
+    AddAttr<bool>("share_cell_act",
+                  "(bool, defalut: True) "
+                  "whether to share the activation of cell output with the "
+                  "projection layer. When set to `False`, the projection "
+                  "is simple linear, otherwise it will go through an "
+                  "activation function same as `cell_activation`.")
+        .SetDefault(true);
     AddComment(R"DOC(
-Long-Short Term Memory with Recurrent Projection (LSTMP) Operator.
+Long-Short Term Memory with recurrent Projection layer (LSTMP) Operator.
 
-LSTMP is stand LSTM appended by a recurrent projection layer to reduce the
-number of parameters, espeacially when the output size is relative large. 
+LSTMP has a separate projection layer after the LSTM layer, projecting the 
+original hidden state to a lower-dimensional one, which is proposed to reduce 
+the number of total parameters and furthermore computational complexity for 
+the LSTM, espeacially for the case that the size of output units is relative 
+large (https://research.google.com/pubs/archive/43905.pdf). 
 The formula is as follows:
 
 $$
@@ -226,13 +233,15 @@ i_t = \sigma(W_{ix}x_{t} + W_{ih}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
 
 f_t = \sigma(W_{fx}x_{t} + W_{fh}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
 
-c_t = f_t \odot c_{t-1} + i_t \odot act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
+\tilde{c_t} = act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
 
 o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
 
+c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}
+
 h_t = o_t \odot act_h(c_t)
 
-r_t = act_{h'}(W_{rh}h_t)
+r_t = \overline{act_h}(W_{rh}h_t)
 $$
 
 where the W terms denote weight matrices (e.g. $W_{xi}$ is the matrix
@@ -240,20 +249,23 @@ of weights from the input gate to the input), $W_{ic}, W_{fc}, W_{oc}$
 are diagonal weight matrices for peephole connections. In our implementation,
 we use vectors to reprenset these diagonal weight matrices. The b terms
 denote bias vectors ($b_i$ is the input gate bias vector), $\sigma$
-is the non-line activations, such as logistic sigmoid function, and
+is the activation, such as logistic sigmoid function, and
 $i, f, o$ and $c$ are the input gate, forget gate, output gate,
 and cell activation vectors, respectively, all of which have the same size as
-the cell output activation vector $h$. $r$ denotes the recurrent projection 
-layer.
+the cell output activation vector $h$. Here $h$ is usually called the hidden 
+state and $r$ denotes its recurrent projection. And $\tilde{c_t}$ is also 
+called the candidate hidden state, whose computation is based on the current 
+input and previous hidden state.
 
 The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
 are the cell input and cell output activation functions and `tanh` is usually
-used for them. If `share_cell_act` setted to `False`, $act_h'$ will be linear
-else will be same with $act_h$.
+used for them. $\overline{act_h}$ is the activation function for the projection 
+layer. When `share_cell_act` set to `False`, $\overline{act_h}$ is an
+identity activation, otherwise it will be same as $act_h$.
 
 Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
 operations on the input $x_{t}$ are NOT included in this operator.
-Users can choose to use fully-connect operator before LSTMP operator.
+Users can choose to use fully-connected operator before LSTMP operator.
 
 )DOC");
   }

From ca0177190f75a4f39482b8fe1c8e929ab8e1a381 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 22 Jan 2018 15:18:47 +0800
Subject: [PATCH 030/314] add layer_norm

---
 paddle/operators/layer_norm_op.cc             | 283 ++++++++++++++++++
 paddle/operators/layer_norm_op.h              |  35 +++
 .../v2/fluid/tests/test_layer_norm_op.py      |  81 +++++
 3 files changed, 399 insertions(+)
 create mode 100644 paddle/operators/layer_norm_op.cc
 create mode 100644 paddle/operators/layer_norm_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_layer_norm_op.py

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
new file mode 100644
index 0000000000..f1ddcd8210
--- /dev/null
+++ b/paddle/operators/layer_norm_op.cc
@@ -0,0 +1,283 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/layer_norm_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
+template <typename T>
+using EigenMatrixMapRowMajor = Eigen::Map<
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
+template <typename T>
+using ConstEigenMatrixMapRowMajor = Eigen::Map<
+    const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
+
+class LayerNormOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "");
+    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
+    PADDLE_ENFORCE(ctx->HasInput("Bias"), "");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
+
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], 1);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], 1);
+
+    ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
+    ctx->SetOutputDim("Mean", {ctx->GetInputDim("X")[0]});
+    ctx->SetOutputDim("Variance", {ctx->GetInputDim("X")[0]});
+
+    ctx->ShareLoD("X", "Y");
+  }
+};
+
+class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LayerNormOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input tensor");
+    AddInput("Scale",
+             "Scale is a 1-dimensional tensor of size 1 "
+             "that is applied to the output");
+    AddInput("Bias",
+             "Bias is a 1-dimensional tensor of size 1 "
+             "that is applied to the output");
+    AddOutput("Y", "result after normalization");
+    AddOutput("Mean", "Mean of the current mini batch.");
+    AddOutput("Variance", "Variance of the current mini batch.");
+
+    AddAttr<float>("epsilon", "")
+        .SetDefault(1e-5)
+        .AddCustomChecker([](const float &epsilon) {
+          PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
+                         "'epsilon' should be between 0.0 and 0.001.");
+        });
+    AddAttr<std::vector<int>>("axis",
+                              "(vector<int> default:{1, 1, 1}), the "
+                              "axis to normalize.")
+        .SetDefault({1, 2, 3});  // todo(zcd) : who to set axis
+
+    AddComment(R"DOC(
+Layer Normalization.
+
+Layer Norm has been implemented as discussed in the paper:
+https://arxiv.org/abs/1607.06450
+...
+)DOC");
+  }
+};
+
+template <typename T>
+class LayerNormKernel<platform::CPUDeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    const auto *scale = ctx.Input<Tensor>("Scale");
+    const auto *bias = ctx.Input<Tensor>("Bias");
+    const auto *x = ctx.Input<Tensor>("X");
+    const auto &x_dims = x->dims();
+
+    const int N = x_dims[0];
+    const int sample_size = x->numel() / N;
+
+    auto scale_data = scale->data<T>()[0];
+    auto bias_data = bias->data<T>()[0];
+
+    auto *output = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    output->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+
+    int left = N, right = sample_size;
+    auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
+    auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
+    auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
+    auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
+
+    auto squre = [](T ele) { return ele * ele; };
+    auto add_epslion = [epsilon](T ele) { return ele + epsilon; };
+
+    mean_map = input_map.rowwise().mean();
+    var_map = (input_map - mean_map.replicate(1, right))
+                  .unaryExpr(squre)
+                  .rowwise()
+                  .mean()
+                  .unaryExpr(add_epslion);
+
+    auto scale_inv_std = [scale_data](T ele) {
+      return std::sqrt(1 / ele) * scale_data;
+    };
+    auto sub_bias = [bias_data](T ele) { return bias_data - ele; };
+
+    output_map = (var_map.unaryExpr(scale_inv_std).replicate(1, right))
+                     .cwiseProduct(input_map) +
+                 var_map.unaryExpr(scale_inv_std)
+                     .cwiseProduct(mean_map)
+                     .unaryExpr(sub_bias)
+                     .replicate(1, right);
+  }
+};
+
+class LayerNormGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    // check input
+    PADDLE_ENFORCE(ctx->HasInput("X"));
+    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
+    PADDLE_ENFORCE(ctx->HasInput("Mean"), "");
+    PADDLE_ENFORCE(ctx->HasInput("Variance"), "");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "");
+
+    const auto x_dims = ctx->GetInputDim("X");
+
+    // check output
+    if (ctx->HasOutput(framework::GradVarName("X"))) {
+      ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+    }
+    if (ctx->HasOutput(framework::GradVarName("Scale"))) {
+      ctx->SetOutputDim(framework::GradVarName("Scale"), {1});
+    }
+    if (ctx->HasOutput(framework::GradVarName("Bias"))) {
+      ctx->SetOutputDim(framework::GradVarName("Bias"), {1});
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    const auto *var = ctx.InputVar(framework::GradVarName("Y"));
+    if (var == nullptr) {
+      PADDLE_THROW("can't find Y@GRAD");
+    }
+    const Tensor *t = nullptr;
+    if (var->IsType<Tensor>()) {
+      t = &var->Get<Tensor>();
+    } else if (var->IsType<LoDTensor>()) {
+      t = &var->Get<LoDTensor>();
+    }
+    if (t == nullptr) {
+      PADDLE_THROW("can't find Y@GRAD");
+    }
+    return framework::OpKernelType(framework::ToDataType(t->type()),
+                                   ctx.GetPlace());
+  }
+};
+
+template <typename T>
+class LayerNormGradKernel<platform::CPUDeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const auto *x = ctx.Input<Tensor>("X");
+    const auto *mean = ctx.Input<Tensor>("Mean");
+    const auto *var = ctx.Input<Tensor>("Variance");
+    const auto *scale = ctx.Input<Tensor>("Scale");
+    const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+
+    const auto &x_dims = x->dims();
+    const int N = x_dims[0];
+    const int sample_size = x->numel() / N;
+    int left = N, right = sample_size;
+
+    auto scale_data = scale->data<T>()[0];
+
+    // init output
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
+    auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
+    auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
+    auto var_map = ConstEigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
+
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      d_bias->data<T>()[0] = d_y_map.sum();
+    }
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      auto inv_std = [](T ele) { return std::sqrt(1 / ele); };
+      d_scale->data<T>()[0] =
+          ((x_map - mean_map.replicate(1, right))
+               .cwiseProduct(var_map.unaryExpr(inv_std).replicate(1, right))
+               .cwiseProduct(d_y_map))
+              .sum();  // also can use `y` to get d_scale_map
+    }
+
+    if (d_x) {
+      d_x->mutable_data<T>(ctx.GetPlace());
+      auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
+      auto triple_product = [](T ele) { return ele * ele * ele; };
+      auto neg_inv_std = [](T ele) { return T(-1.0) * std::sqrt(1 / ele); };
+      auto inv_std_scale_func = [scale_data](T ele) {
+        return std::sqrt(1 / ele) * scale_data;
+      };
+      auto neg_inv_std_scale_func = [scale_data](T ele) {
+        return T(-1.0) * std::sqrt(1 / ele) * scale_data;
+      };
+      // dy_dx
+      auto dx_end = var_map.unaryExpr(inv_std_scale_func)
+                        .replicate(1, right)
+                        .cwiseProduct(d_y_map);
+      // dy_dmean_dx
+      auto dmean_end = var_map.unaryExpr(neg_inv_std_scale_func)
+                           .replicate(1, right)
+                           .cwiseProduct(d_y_map)
+                           .rowwise()
+                           .sum();
+      auto dx_mean = (T(1.0) / right) * dmean_end.replicate(1, right);
+      // dy_var_dx
+      auto dvar_end_0 = (x_map - mean_map.replicate(1, right))
+                            .cwiseProduct(d_y_map)
+                            .rowwise()
+                            .sum();
+      auto dvar_end = var_map.unaryExpr(neg_inv_std)
+                          .unaryExpr(triple_product)
+                          .cwiseProduct(dvar_end_0);
+      auto dx_var = (1.0f / right) *
+                    (x_map - mean_map.replicate(1, right))
+                        .cwiseProduct(dvar_end.replicate(1, right));
+
+      d_x_map = dx_end + dx_mean + dx_var;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(layer_norm, ops::LayerNormOp, ops::LayerNormOpMaker,
+            layer_norm_grad, ops::LayerNormGradOp);
+REGISTER_OP_CPU_KERNEL(
+    layer_norm,
+    ops::LayerNormKernel<paddle::platform::CPUDeviceContext, float>);
+REGISTER_OP_CPU_KERNEL(
+    layer_norm_grad,
+    ops::LayerNormGradKernel<paddle::platform::CPUDeviceContext, float>);
diff --git a/paddle/operators/layer_norm_op.h b/paddle/operators/layer_norm_op.h
new file mode 100644
index 0000000000..bca35b91e6
--- /dev/null
+++ b/paddle/operators/layer_norm_op.h
@@ -0,0 +1,35 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class LayerNormKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override;
+};
+
+template <typename DeviceContext, typename T>
+class LayerNormGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override;
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
new file mode 100644
index 0000000000..73450c599d
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -0,0 +1,81 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+
+from op_test import OpTest
+
+
+def layer_norm_naive(x, scale, beta, epsilon):
+    n, c, h, w = x.shape
+    mean = np.mean(x, axis=(1, 2, 3))
+    var = np.var(x, axis=(1, 2, 3)) + epsilon
+    output = scale * np.divide((x - mean.reshape([n, 1, 1, 1])),
+                               (np.sqrt(var)).reshape([n, 1, 1, 1])) + beta
+    return output, mean, var
+
+
+class TestLayerNormdOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+
+        input = np.random.random(self.input_size).astype("float32")
+        self.inputs = {
+            'X': input,
+            'Scale': np.array([self.scale]).astype("float32"),
+            'Bias': np.array([self.bias]).astype("float32")
+        }
+        output, mean, var = layer_norm_naive(input, self.scale, self.bias,
+                                             self.epsilon)
+        self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
+
+    def test_check_output(self):
+        self.check_output()
+
+    # def test_check_grad(self):
+    #     self.check_grad(
+    #         ['Scale', 'Bias', 'X'], ['Y', 'Mean', 'Variance'],
+    #         max_relative_error=0.02)
+
+    def test_check_grad_no_x(self):
+        self.check_grad(
+            ['Scale', 'Bias'], ['Y', 'Mean', 'Variance'],
+            max_relative_error=0.02,
+            no_grad_set=set(['X']))
+
+    # def test_check_grad_no_scale(self):
+    #     self.check_grad(
+    #         ['Bias','X'],
+    #         'Y',
+    #         max_relative_error=0.02,
+    #         no_grad_set=set(['Scale']))
+    #
+    # def test_check_grad_no_bias(self):
+    #     self.check_grad(
+    #         ['Scale','X'],
+    #         'Y',
+    #         max_relative_error=0.02,
+    #         no_grad_set=set(['Bias']))
+
+    def init_test_case(self):
+        self.op_type = "layer_norm"
+        self.input_size = [2, 3, 4, 5]
+        self.scale = 0.21
+        self.bias = 0.1
+        self.epsilon = 0.00001
+
+
+if __name__ == '__main__':
+    unittest.main()

From f3fe41078a047790b247be238468a9047e2bb691 Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Wed, 24 Jan 2018 14:15:17 +0800
Subject: [PATCH 031/314] Fix conflicts and add more supported dtype.

---
 paddle/operators/multiplex_op.cc | 10 ++++++++--
 paddle/operators/multiplex_op.cu | 10 ++++++++--
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/paddle/operators/multiplex_op.cc b/paddle/operators/multiplex_op.cc
index 78263da2fb..d275fa5cbb 100644
--- a/paddle/operators/multiplex_op.cc
+++ b/paddle/operators/multiplex_op.cc
@@ -119,7 +119,13 @@ REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
 REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
 REGISTER_OP_CPU_KERNEL(
     multiplex,
-    ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::MultiplexCPUKernel<paddle::platform::CPUDeviceContext, int64_t>);
 REGISTER_OP_CPU_KERNEL(
     multiplex_grad,
-    ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::MultiplexGradCPUKernel<paddle::platform::CPUDeviceContext, int64_t>);
diff --git a/paddle/operators/multiplex_op.cu b/paddle/operators/multiplex_op.cu
index 4372dc2c65..546e6e7a24 100644
--- a/paddle/operators/multiplex_op.cu
+++ b/paddle/operators/multiplex_op.cu
@@ -90,7 +90,13 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
     multiplex,
-    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::MultiplexGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     multiplex_grad,
-    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, float>);
+    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::MultiplexGradGPUKernel<paddle::platform::CUDADeviceContext, int64_t>);

From 9a8517fd8b909baddac7945f403763ec1e7bd0c7 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 24 Jan 2018 14:43:56 +0800
Subject: [PATCH 032/314] daemonize the server process

---
 python/paddle/v2/fluid/layers/io.py          | 19 ++++++---
 python/paddle/v2/fluid/tests/test_recv_op.py | 44 ++++++++++++++------
 2 files changed, 46 insertions(+), 17 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/io.py b/python/paddle/v2/fluid/layers/io.py
index be581531d1..bc804a4043 100644
--- a/python/paddle/v2/fluid/layers/io.py
+++ b/python/paddle/v2/fluid/layers/io.py
@@ -136,17 +136,26 @@ class ListenAndServ(object):
                 # simple recv mode, recv operators inputs.
                 for iname in op.input_names:
                     for in_var_name in op.input(iname):
-                        params.append(parent_block.var(name))
-                        grads.append(parent_block.var(name))
+                        params.append(parent_block.var(in_var_name))
+                        grads.append(parent_block.var(in_var_name))
 
         return params, grads
 
+    def parent_block(self):
+        prog = self.helper.main_program
+        parent_idx = prog.current_block().parent_idx
+        assert parent_idx >= 0
+        parent_block = prog.block(parent_idx)
+        return parent_block
+
     def complete_op(self):
         main_program = self.helper.main_program
         current_block = main_program.current_block()
         parent_block = self.parent_block()
 
         params, grads = self.get_params_and_grads()
+        param_names = [p.name for p in params]
+        grad_names = [g.name for g in grads]
         parent_block.append_op(
             type='recv',
             inputs={},
@@ -154,8 +163,8 @@ class ListenAndServ(object):
             attrs={
                 'endpoint': self.endpoint,
                 'Fanin': self.fan_in,
-                'ParamList': params,
-                'GradList': grads,
+                'ParamList': param_names,
+                'GradList': grad_names,
                 'OptimizeBlock': current_block
             })
 
@@ -177,7 +186,7 @@ def Send(endpoints, send_vars, get_vars):
     assert (type(get_vars) == list)
 
     epmap = endpoints.split(",")
-    endpoints = set(epmap)
+    endpoints = list(set(epmap))
 
     helper = LayerHelper("Send", **locals())
     helper.append_op(
diff --git a/python/paddle/v2/fluid/tests/test_recv_op.py b/python/paddle/v2/fluid/tests/test_recv_op.py
index e06f468648..6ebb58ed33 100644
--- a/python/paddle/v2/fluid/tests/test_recv_op.py
+++ b/python/paddle/v2/fluid/tests/test_recv_op.py
@@ -17,40 +17,60 @@ import unittest
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.layers as layers
 import numpy
-import threading
+from multiprocessing import Process
+import os, sys
 
 
 class TestRecvOp(unittest.TestCase):
     def test_send(self):
         # Run init_serv in a thread
         place = fluid.CPUPlace()
-        t = threading.Thread(target=self.init_serv, args=(place, ))
-        t.start()
+        p = Process(target=self.init_serv, args=(place, ))
+        p.daemon = True
+        p.start()
         self.init_client(place)
-        t.join()
+        # FIXME(typhoonzero): find a way to gracefully shutdown the server.
+        os.system("kill -9 %d" % p.pid)
+        p.join()
 
     def init_serv(self, place):
         main = fluid.Program()
         with fluid.program_guard(main):
-            x = layers.data(shape=[32, 32], dtype='float32', name='X')
-            i = fluid.initializer.Constant(value=1.0)
-            y = i(x, main.global_block())
-            serv = layers.ListenAndServ("127.0.0.1:6174")
+            x = layers.data(
+                shape=[32, 32],
+                dtype='float32',
+                name="X",
+                append_batch_size=False)
+            fluid.initializer.Constant(value=1.0)(x, main.global_block())
+            serv = layers.ListenAndServ("127.0.0.1:6174", optimizer_mode=False)
             with serv.do():
-                layers.scale(input=y, scale=10.0)
+                o = layers.scale(x=x, scale=10.0)
+            main.global_block().create_var(
+                name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
+            print main
         exe = fluid.Executor(place)
         exe.run(main)
 
     def init_client(self, place):
         main = fluid.Program()
         with fluid.program_guard(main):
-            x = layers.data(shape=[32, 32], dtype='float32', name='X')
-            i = fluid.initializer.Constant(value=1.0)
-            i(x, main.global_block())
+            x = layers.data(
+                shape=[32, 32],
+                dtype='float32',
+                name='X',
+                append_batch_size=False)
+            fluid.initializer.Constant(value=1.0)(x, main.global_block())
             layers.Send("127.0.0.1:6174", [x], [x])
+            print main
         exe = fluid.Executor(place)
         exe.run(main)
 
 
 if __name__ == "__main__":
     unittest.main()
+    # test = TestRecvOp()
+    # place = fluid.CPUPlace()
+    # if sys.argv[1] == "server":
+    #     test.init_serv(place)
+    # else:
+    #     test.init_client(place)

From b9d9b11c804c5f5efe645acd10fde7bb6641a317 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 24 Jan 2018 15:00:46 +0800
Subject: [PATCH 033/314] remove recv_op input

---
 paddle/operators/recv_op.cc | 1 -
 1 file changed, 1 deletion(-)

diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index e3c86966b8..381890d30b 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -161,7 +161,6 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   RecvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    // AddInput("RX", "(Tensor) Input tensor to be optimized").AsDuplicable();
     AddComment(R"DOC(
 Recv operator
 

From fa285a9d29701c24ade67da3eeb293b37eb2efb9 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 15:23:38 +0800
Subject: [PATCH 034/314] Fit a line with parallel.do

---
 .../tests/book/test_fit_a_line_parallel_do.py | 57 +++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py

diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
new file mode 100644
index 0000000000..9693b26d54
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
@@ -0,0 +1,57 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+places = fluid.layers.get_places()
+pd = fluid.layers.ParallelDo(places=places)
+with pd.do():
+    x_ = pd.read_input(x)
+    y_ = pd.read_input(y)
+    y_predict = fluid.layers.fc(input=x_, size=1, act=None)
+    cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
+    pd.write_output(fluid.layers.mean(x=cost))
+
+avg_cost = fluid.layers.mean(x=pd())
+
+sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+sgd_optimizer.minimize(avg_cost)
+
+BATCH_SIZE = 20
+
+train_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.train(), buf_size=500),
+    batch_size=BATCH_SIZE)
+
+place = fluid.CPUPlace()
+feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+exe = fluid.Executor(place)
+
+exe.run(fluid.default_startup_program())
+
+PASS_NUM = 100
+for pass_id in range(PASS_NUM):
+    for data in train_reader():
+        avg_loss_value, = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+        print(avg_loss_value)
+        if avg_loss_value[0] < 10.0:
+            exit(0)  # if avg cost less than 10.0, we think our code is good.
+exit(1)

From d0a047573687d78010147833d729da5988fd5e53 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 15:28:08 +0800
Subject: [PATCH 035/314] Rename is_compile_gpu to is_compiled_with_cuda

The English of the previous API is bad.
---
 paddle/pybind/pybind.cc                                     | 4 ++--
 python/paddle/v2/fluid/__init__.py                          | 2 +-
 python/paddle/v2/fluid/tests/op_test.py                     | 4 ++--
 python/paddle/v2/fluid/tests/test_adagrad_op.py             | 2 +-
 python/paddle/v2/fluid/tests/test_adam_op.py                | 2 +-
 python/paddle/v2/fluid/tests/test_batch_norm_op.py          | 2 +-
 python/paddle/v2/fluid/tests/test_gaussian_random_op.py     | 2 +-
 python/paddle/v2/fluid/tests/test_normalization_wrapper.py  | 2 +-
 python/paddle/v2/fluid/tests/test_op_support_gpu.py         | 3 ++-
 python/paddle/v2/fluid/tests/test_parallel_op.py            | 2 +-
 python/paddle/v2/fluid/tests/test_profiler.py               | 4 ++--
 python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py     | 2 +-
 python/paddle/v2/fluid/tests/test_sgd_op.py                 | 2 +-
 python/paddle/v2/fluid/tests/test_split_selected_rows_op.py | 2 +-
 python/paddle/v2/fluid/tests/test_uniform_random_op.py      | 2 +-
 15 files changed, 19 insertions(+), 18 deletions(-)

diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 82f5b1922c..b4fd2a8989 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -53,7 +53,7 @@ static size_t UniqueIntegerGenerator(const std::string &prefix) {
   return generators[prefix].fetch_add(1);
 }
 
-bool IsCompileGPU() {
+bool IsCompiledWithCUDA() {
 #ifndef PADDLE_WITH_CUDA
   return false;
 #else
@@ -431,7 +431,7 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("init_glog", framework::InitGLOG);
   m.def("init_devices", &framework::InitDevices);
 
-  m.def("is_compile_gpu", IsCompileGPU);
+  m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
 
   m.def("set_feed_variable", framework::SetFeedVariable);
   m.def("get_fetch_variable", framework::GetFetchVariable);
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index 1f041c7459..787416aed1 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -89,7 +89,7 @@ def __bootstrap__():
     read_env_flags = [
         'use_pinned_memory', 'check_nan_inf', 'do_memory_benchmark'
     ]
-    if core.is_compile_gpu():
+    if core.is_compiled_with_cuda():
         read_env_flags += ['fraction_of_gpu_memory_to_use', 'op_sync']
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])
diff --git a/python/paddle/v2/fluid/tests/op_test.py b/python/paddle/v2/fluid/tests/op_test.py
index 56f54de86f..3f6d7070c2 100644
--- a/python/paddle/v2/fluid/tests/op_test.py
+++ b/python/paddle/v2/fluid/tests/op_test.py
@@ -334,7 +334,7 @@ class OpTest(unittest.TestCase):
 
     def check_output(self, atol=1e-5):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
+        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
             places.append(core.CUDAPlace(0))
         for place in places:
             self.check_output_with_place(place, atol)
@@ -367,7 +367,7 @@ class OpTest(unittest.TestCase):
                    max_relative_error=0.005,
                    user_defined_grads=None):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu() and core.op_support_gpu(self.op_type):
+        if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
             places.append(core.CUDAPlace(0))
         for place in places:
             self.check_grad_with_place(place, inputs_to_check, output_names,
diff --git a/python/paddle/v2/fluid/tests/test_adagrad_op.py b/python/paddle/v2/fluid/tests/test_adagrad_op.py
index 86b0567ce1..3556bcf8ba 100644
--- a/python/paddle/v2/fluid/tests/test_adagrad_op.py
+++ b/python/paddle/v2/fluid/tests/test_adagrad_op.py
@@ -180,7 +180,7 @@ class TestSparseAdagradOp(unittest.TestCase):
 
     def test_sparse_adagrad(self):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
             self.check_with_place(place)
diff --git a/python/paddle/v2/fluid/tests/test_adam_op.py b/python/paddle/v2/fluid/tests/test_adam_op.py
index 10580adca7..df1fa8983c 100644
--- a/python/paddle/v2/fluid/tests/test_adam_op.py
+++ b/python/paddle/v2/fluid/tests/test_adam_op.py
@@ -305,7 +305,7 @@ class TestSparseAdamOp(unittest.TestCase):
 
     def test_sparse_sgd(self):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
             self.check_with_place(place)
diff --git a/python/paddle/v2/fluid/tests/test_batch_norm_op.py b/python/paddle/v2/fluid/tests/test_batch_norm_op.py
index 371bd42678..cf13166f25 100644
--- a/python/paddle/v2/fluid/tests/test_batch_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_batch_norm_op.py
@@ -352,7 +352,7 @@ class TestBatchNormOp(OpTest):
             print "op test backward passed: ", str(place), data_layout
 
         places = [core.CPUPlace()]
-        if core.is_compile_gpu() and core.op_support_gpu("batch_norm"):
+        if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
             places.append(core.CUDAPlace(0))
 
         for place in places:
diff --git a/python/paddle/v2/fluid/tests/test_gaussian_random_op.py b/python/paddle/v2/fluid/tests/test_gaussian_random_op.py
index 82842534d4..79beb8b1fc 100644
--- a/python/paddle/v2/fluid/tests/test_gaussian_random_op.py
+++ b/python/paddle/v2/fluid/tests/test_gaussian_random_op.py
@@ -33,7 +33,7 @@ class TestGaussianRandomOp(unittest.TestCase):
         self.gaussian_random_test(place=fluid.CPUPlace())
 
     def test_gpu(self):
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             self.gaussian_random_test(place=fluid.CUDAPlace(0))
 
     def gaussian_random_test(self, place):
diff --git a/python/paddle/v2/fluid/tests/test_normalization_wrapper.py b/python/paddle/v2/fluid/tests/test_normalization_wrapper.py
index 57f14f6b9c..6b71f2a923 100644
--- a/python/paddle/v2/fluid/tests/test_normalization_wrapper.py
+++ b/python/paddle/v2/fluid/tests/test_normalization_wrapper.py
@@ -46,7 +46,7 @@ class TestNormalization(unittest.TestCase):
         """Run the test program.
         """
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
 
         for place in places:
diff --git a/python/paddle/v2/fluid/tests/test_op_support_gpu.py b/python/paddle/v2/fluid/tests/test_op_support_gpu.py
index 3493981812..7de02a8fda 100644
--- a/python/paddle/v2/fluid/tests/test_op_support_gpu.py
+++ b/python/paddle/v2/fluid/tests/test_op_support_gpu.py
@@ -18,7 +18,8 @@ import paddle.v2.fluid.core as core
 
 class TestOpSupportGPU(unittest.TestCase):
     def test_case(self):
-        self.assertEqual(core.is_compile_gpu(), core.op_support_gpu("sum"))
+        self.assertEqual(core.is_compiled_with_cuda(),
+                         core.op_support_gpu("sum"))
 
 
 if __name__ == '__main__':
diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index dfde492c7c..3b86ccb617 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -53,7 +53,7 @@ class BaseParallelForTest(unittest.TestCase):
             fetch=fetch,
             place=cpu,
             use_parallel=True)
-        if fluid.core.is_compile_gpu():
+        if fluid.core.is_compiled_with_cuda():
             gpu = fluid.CUDAPlace(0)
             result_gpu = self._run_test_impl_(
                 callback=callback,
diff --git a/python/paddle/v2/fluid/tests/test_profiler.py b/python/paddle/v2/fluid/tests/test_profiler.py
index 34700df37d..09b2d08401 100644
--- a/python/paddle/v2/fluid/tests/test_profiler.py
+++ b/python/paddle/v2/fluid/tests/test_profiler.py
@@ -23,7 +23,7 @@ import paddle.v2.fluid.core as core
 
 class TestProfiler(unittest.TestCase):
     def test_nvprof(self):
-        if not fluid.core.is_compile_gpu():
+        if not fluid.core.is_compiled_with_cuda():
             return
         epoc = 8
         dshape = [4, 3, 28, 28]
@@ -42,7 +42,7 @@ class TestProfiler(unittest.TestCase):
         os.remove(output_file)
 
     def net_profiler(self, state):
-        if state == 'GPU' and not core.is_compile_gpu():
+        if state == 'GPU' and not core.is_compiled_with_cuda():
             return
         startup_program = fluid.Program()
         main_program = fluid.Program()
diff --git a/python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py b/python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
index 74cd6de9e6..0a223bac0c 100644
--- a/python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
+++ b/python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
@@ -45,7 +45,7 @@ class TestReorderLoDTensor(unittest.TestCase):
         outputs = []
         input_grads = []
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
             self.set_inputs(place)
diff --git a/python/paddle/v2/fluid/tests/test_sgd_op.py b/python/paddle/v2/fluid/tests/test_sgd_op.py
index f87927968b..ba2ca1683f 100644
--- a/python/paddle/v2/fluid/tests/test_sgd_op.py
+++ b/python/paddle/v2/fluid/tests/test_sgd_op.py
@@ -91,7 +91,7 @@ class TestSparseSGDOp(unittest.TestCase):
 
     def test_sparse_sgd(self):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
             self.check_with_place(place)
diff --git a/python/paddle/v2/fluid/tests/test_split_selected_rows_op.py b/python/paddle/v2/fluid/tests/test_split_selected_rows_op.py
index 37c6587c41..343aa20066 100644
--- a/python/paddle/v2/fluid/tests/test_split_selected_rows_op.py
+++ b/python/paddle/v2/fluid/tests/test_split_selected_rows_op.py
@@ -21,7 +21,7 @@ from paddle.v2.fluid.op import Operator
 class TestSpliteSelectedRows(unittest.TestCase):
     def get_places(self):
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         return places
 
diff --git a/python/paddle/v2/fluid/tests/test_uniform_random_op.py b/python/paddle/v2/fluid/tests/test_uniform_random_op.py
index b2a39f975e..94cf416fad 100644
--- a/python/paddle/v2/fluid/tests/test_uniform_random_op.py
+++ b/python/paddle/v2/fluid/tests/test_uniform_random_op.py
@@ -36,7 +36,7 @@ class TestUniformRandomOp(unittest.TestCase):
         self.uniform_random_test(place=core.CPUPlace())
 
     def test_gpu(self):
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             self.uniform_random_test(place=core.CUDAPlace(0))
 
     def uniform_random_test(self, place):

From a06205f569564c44e2aded4b82ee0d0f7ae93f9a Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 16:06:13 +0800
Subject: [PATCH 036/314] Add demo for parallel.do

Unify the recognize_digits
---
 paddle/operators/parallel_do_op.cc            |  35 ++++-
 .../paddle/v2/fluid/tests/book/CMakeLists.txt |  26 +++-
 python/paddle/v2/fluid/tests/book/__init__.py |  13 ++
 .../tests/book/test_fit_a_line_parallel_do.py |  57 -------
 .../fluid/tests/book/test_recognize_digits.py | 140 ++++++++++++++++++
 .../tests/book/test_recognize_digits_conv.py  |  74 ---------
 .../tests/book/test_recognize_digits_mlp.py   |  96 ------------
 7 files changed, 208 insertions(+), 233 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/book/__init__.py
 delete mode 100644 python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
 create mode 100644 python/paddle/v2/fluid/tests/book/test_recognize_digits.py
 delete mode 100644 python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
 delete mode 100644 python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py

diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index 09e808902f..f9cf40bb57 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/framework/executor.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/threadpool.h"
+#include "paddle/operators/detail/safe_ref.h"
 
 namespace paddle {
 namespace operators {
@@ -39,8 +40,10 @@ static void SplitTensorAndMoveTensorToScopes(
     const std::vector<std::string> &names) {
   size_t num_sub_scopes = 0;
   for (auto &argu : names) {
-    auto *var = scope.FindVar(argu);
-    const auto &tensor = var->Get<LoDTensor>();
+    const auto &tensor =
+        detail::Ref(scope.FindVar(argu),
+                    "Cannot find variable %s in the parent scope", argu)
+            .Get<LoDTensor>();
     auto lod_tensors = tensor.SplitLoDTensor(places);
 
     for (auto &lod : lod_tensors) {
@@ -60,7 +63,9 @@ static void SplitTensorAndMoveTensorToScopes(
     }
 
     for (size_t i = 0; i < lod_tensors.size(); ++i) {
-      *(*sub_scopes)[i]->Var(argu)->GetMutable<LoDTensor>() = lod_tensors[i];
+      *detail::Ref(sub_scopes->at(i)->Var(argu),
+                   "Cannot find variable in the sub-scope", argu)
+           .GetMutable<LoDTensor>() = lod_tensors[i];
     }
   }
 }
@@ -287,6 +292,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
                         this->InputGrad(input_param, false));
       }
     }
+    auto *g_block = this->grad_block_[0];
+
+    // All variable name that needed by gradient operators
+    std::unordered_set<std::string> all_inputs_in_grad_blocks;
+
+    for (size_t i = 0; i < g_block->OpSize(); ++i) {
+      auto *op = g_block->Op(i);
+      for (auto &var_name : op->InputArgumentNames()) {
+        all_inputs_in_grad_blocks.insert(var_name);
+      }
+    }
 
     for (auto &output_param : this->OutputNames()) {
       if (output_param == kParallelScopes) {
@@ -295,8 +311,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
                        this->Output(output_param));
       } else {
         grad->SetInput(output_param, this->Output(output_param));
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->OutputGrad(output_param));
+        std::vector<std::string> og_names;
+        for (auto &og_name : this->OutputGrad(output_param)) {
+          if (all_inputs_in_grad_blocks.count(og_name) != 0) {
+            // there is some gradient operator needs the og, make this og as the
+            // input of parallel.do
+            // if there is no operator need this og, just do not make this og as
+            // input.
+            og_names.push_back(og_name);
+          }
+        }
+        grad->SetInput(framework::GradVarName(output_param), og_names);
       }
     }
     grad->SetAttrMap(this->Attrs());
diff --git a/python/paddle/v2/fluid/tests/book/CMakeLists.txt b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
index a35abe3e0c..dda02c03fd 100644
--- a/python/paddle/v2/fluid/tests/book/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
@@ -1,9 +1,33 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-list(REMOVE_ITEM TEST_OPS test_image_classification_train)
+list(REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits)
 py_test(test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet)
 py_test(test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg)
+py_test(test_recognize_digits_mlp_cpu
+  SRCS test_recognize_digits.py
+  ARGS mlp)
+py_test(test_recognize_digits_mlp_cuda
+  SRCS test_recognize_digits.py
+  ARGS mlp --use_cuda)
+py_test(test_recognize_digits_conv_cpu
+  SRCS test_recognize_digits.py
+  ARGS conv)
+py_test(test_recognize_digits_conv_cuda
+  SRCS test_recognize_digits.py
+  ARGS conv --use_cuda)
+py_test(test_recognize_digits_mlp_cpu_parallel
+  SRCS test_recognize_digits.py
+  ARGS mlp --parallel)
+py_test(test_recognize_digits_mlp_cuda_parallel
+  SRCS test_recognize_digits.py
+  ARGS mlp --use_cuda --parallel)
+py_test(test_recognize_digits_conv_cpu_parallel
+  SRCS test_recognize_digits.py
+  ARGS conv --parallel)
+py_test(test_recognize_digits_conv_cuda_parallel
+  SRCS test_recognize_digits.py
+  ARGS conv --use_cuda --parallel)
 
 # default test
 foreach(src ${TEST_OPS})
diff --git a/python/paddle/v2/fluid/tests/book/__init__.py b/python/paddle/v2/fluid/tests/book/__init__.py
new file mode 100644
index 0000000000..b94a21a7e4
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book/__init__.py
@@ -0,0 +1,13 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
deleted file mode 100644
index 9693b26d54..0000000000
--- a/python/paddle/v2/fluid/tests/book/test_fit_a_line_parallel_do.py
+++ /dev/null
@@ -1,57 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-places = fluid.layers.get_places()
-pd = fluid.layers.ParallelDo(places=places)
-with pd.do():
-    x_ = pd.read_input(x)
-    y_ = pd.read_input(y)
-    y_predict = fluid.layers.fc(input=x_, size=1, act=None)
-    cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
-    pd.write_output(fluid.layers.mean(x=cost))
-
-avg_cost = fluid.layers.mean(x=pd())
-
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
-
-BATCH_SIZE = 20
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
-
-place = fluid.CPUPlace()
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
-
-exe.run(fluid.default_startup_program())
-
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_loss_value, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[avg_cost])
-        print(avg_loss_value)
-        if avg_loss_value[0] < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
new file mode 100644
index 0000000000..4ecdcdc632
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -0,0 +1,140 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+import argparse
+import paddle.v2.fluid as fluid
+import paddle.v2 as paddle
+import sys
+
+
+def parse_arg():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "nn_type",
+        help="The neural network type, in ['mlp', 'conv']",
+        type=str,
+        choices=['mlp', 'conv'])
+    parser.add_argument(
+        "--parallel",
+        help='Run in parallel or not',
+        default=False,
+        action="store_true")
+    parser.add_argument(
+        "--use_cuda",
+        help="Run the program by using CUDA",
+        default=False,
+        action="store_true")
+    return parser.parse_args()
+
+
+BATCH_SIZE = 64
+
+
+def loss_net(hidden, label):
+    prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
+    loss = fluid.layers.cross_entropy(input=prediction, label=label)
+    return fluid.layers.mean(x=loss), fluid.layers.accuracy(
+        input=prediction, label=label)
+
+
+def mlp(img, label):
+    hidden = fluid.layers.fc(input=img, size=200, act='tanh')
+    hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
+    return loss_net(hidden, label)
+
+
+def conv_net(img, label):
+    conv_pool_1 = fluid.nets.simple_img_conv_pool(
+        input=img,
+        filter_size=5,
+        num_filters=20,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    conv_pool_2 = fluid.nets.simple_img_conv_pool(
+        input=conv_pool_1,
+        filter_size=5,
+        num_filters=50,
+        pool_size=2,
+        pool_stride=2,
+        act="relu")
+    return loss_net(conv_pool_2, label)
+
+
+def main():
+    args = parse_arg()
+    print("recognize digits with args: {0}".format(" ".join(sys.argv[1:])))
+
+    img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    if args.nn_type == 'mlp':
+        net_conf = mlp
+    else:
+        net_conf = conv_net
+
+    if args.parallel:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            img_ = pd.read_input(img)
+            label_ = pd.read_input(label)
+            for o in net_conf(img_, label_):
+                pd.write_output(o)
+
+        avg_loss, acc = pd()
+        # get mean loss and acc through every devices.
+        avg_loss = fluid.layers.mean(x=avg_loss)
+        acc = fluid.layers.mean(x=acc)
+    else:
+        avg_loss, acc = net_conf(img, label)
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    optimizer.minimize(avg_loss)
+
+    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
+
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.mnist.train(), buf_size=500),
+        batch_size=BATCH_SIZE)
+    feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
+
+    PASS_NUM = 100
+    for pass_id in range(PASS_NUM):
+        for batch_id, data in enumerate(train_reader()):
+            need_check = (batch_id + 1) % 10 == 0
+
+            if need_check:
+                fetch_list = [avg_loss, acc]
+            else:
+                fetch_list = []
+
+            outs = exe.run(feed=feeder.feed(data), fetch_list=fetch_list)
+            if need_check:
+                avg_loss_np, acc_np = outs
+                if float(acc_np) > 0.9:
+                    exit(0)
+                else:
+                    print(
+                        'PassID {0:1}, BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.
+                        format(pass_id, batch_id + 1,
+                               float(avg_loss_np), float(acc_np)))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
deleted file mode 100644
index 4710d16c24..0000000000
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
+++ /dev/null
@@ -1,74 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-import numpy as np
-import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-
-images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
-label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-conv_pool_1 = fluid.nets.simple_img_conv_pool(
-    input=images,
-    filter_size=5,
-    num_filters=20,
-    pool_size=2,
-    pool_stride=2,
-    act="relu")
-conv_pool_2 = fluid.nets.simple_img_conv_pool(
-    input=conv_pool_1,
-    filter_size=5,
-    num_filters=50,
-    pool_size=2,
-    pool_stride=2,
-    act="relu")
-
-predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
-cost = fluid.layers.cross_entropy(input=predict, label=label)
-avg_cost = fluid.layers.mean(x=cost)
-optimizer = fluid.optimizer.Adam(learning_rate=0.01)
-optimizer.minimize(avg_cost)
-
-accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-
-BATCH_SIZE = 50
-PASS_NUM = 3
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.mnist.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    accuracy.reset(exe)
-    for data in train_reader():
-        loss, acc = exe.run(fluid.default_main_program(),
-                            feed=feeder.feed(data),
-                            fetch_list=[avg_cost] + accuracy.metrics)
-        pass_acc = accuracy.eval(exe)
-        print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
-              str(pass_acc))
-        # print loss, acc
-        if loss < 10.0 and pass_acc > 0.9:
-            # if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
-            exit(0)
-
-    pass_acc = accuracy.eval(exe)
-    print("pass_id=" + str(pass_id) + " pass_acc=" + str(pass_acc))
-
-exit(1)
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
deleted file mode 100644
index 8776a65bf8..0000000000
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-import numpy as np
-import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-
-BATCH_SIZE = 128
-image = fluid.layers.data(name='x', shape=[784], dtype='float32')
-
-regularizer = fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
-
-hidden1 = fluid.layers.fc(input=image,
-                          size=128,
-                          act='relu',
-                          param_attr=fluid.ParamAttr(
-                              regularizer=regularizer,
-                              gradient_clip=fluid.clip.ClipByValue(10)))
-
-hidden2 = fluid.layers.fc(input=hidden1,
-                          size=64,
-                          act='relu',
-                          param_attr=regularizer)
-
-predict = fluid.layers.fc(input=hidden2,
-                          size=10,
-                          act='softmax',
-                          param_attr=regularizer)
-
-label = fluid.layers.data(name='y', shape=[1], dtype='int64')
-
-cost = fluid.layers.cross_entropy(input=predict, label=label)
-avg_cost = fluid.layers.mean(x=cost)
-
-optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
-opts = optimizer.minimize(avg_cost)
-
-accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-
-inference_program = fluid.default_main_program().clone()
-with fluid.program_guard(inference_program):
-    test_accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-    test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
-    inference_program = fluid.io.get_inference_program(test_target)
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.mnist.train(), buf_size=8192),
-    batch_size=BATCH_SIZE)
-
-test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
-exe.run(fluid.default_startup_program())
-
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    accuracy.reset(exe)
-    for data in train_reader():
-        out, acc = exe.run(fluid.default_main_program(),
-                           feed=feeder.feed(data),
-                           fetch_list=[avg_cost] + accuracy.metrics)
-        pass_acc = accuracy.eval(exe)
-
-        test_accuracy.reset(exe)
-        for data in test_reader():
-            out, acc = exe.run(inference_program,
-                               feed=feeder.feed(data),
-                               fetch_list=[avg_cost] + test_accuracy.metrics)
-
-        test_pass_acc = test_accuracy.eval(exe)
-        print("pass_id=" + str(pass_id) + " train_cost=" + str(
-            out) + " train_acc=" + str(acc) + " train_pass_acc=" + str(pass_acc)
-              + " test_acc=" + str(test_pass_acc))
-
-        if test_pass_acc > 0.7:
-            fluid.io.save_inference_model(
-                "./recognize_digits_mlp.inference.model/", ["x"], [predict],
-                exe)
-            exit(0)
-
-exit(1)

From ef55a8f6089f195f105fa28c49f125fadc5babc6 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 16:35:35 +0800
Subject: [PATCH 037/314] Polish english

---
 paddle/operators/parallel_do_op.cc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index f9cf40bb57..67f9854c02 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -314,12 +314,12 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
         std::vector<std::string> og_names;
         for (auto &og_name : this->OutputGrad(output_param)) {
           if (all_inputs_in_grad_blocks.count(og_name) != 0) {
-            // there is some gradient operator needs the og, make this og as the
-            // input of parallel.do
-            // if there is no operator need this og, just do not make this og as
-            // input.
+            // there are some gradient operators who need the OG. So make this
+            // OG as an input of parallel.do
             og_names.push_back(og_name);
           }
+          // else, there is no operator who need the OG. Do not use this OG as
+          // an input
         }
         grad->SetInput(framework::GradVarName(output_param), og_names);
       }

From e206c636fd154435973f9eeb5a8800e500bea0fa Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 24 Jan 2018 16:39:35 +0800
Subject: [PATCH 038/314] update by comment, add WITH_DISTRIBUTE switch

---
 python/paddle/v2/fluid/tests/CMakeLists.txt  | 5 +++++
 python/paddle/v2/fluid/tests/test_recv_op.py | 8 --------
 2 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/CMakeLists.txt b/python/paddle/v2/fluid/tests/CMakeLists.txt
index 8305316082..628ce60b40 100644
--- a/python/paddle/v2/fluid/tests/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/CMakeLists.txt
@@ -1,5 +1,10 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+if(NOT WITH_DISTRIBUTE)
+    list(REMOVE_ITEM TEST_OPS test_recv_op)
+endif(NOT WITH_DISTRIBUTE)
+
 foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()
diff --git a/python/paddle/v2/fluid/tests/test_recv_op.py b/python/paddle/v2/fluid/tests/test_recv_op.py
index 6ebb58ed33..5c4cec028d 100644
--- a/python/paddle/v2/fluid/tests/test_recv_op.py
+++ b/python/paddle/v2/fluid/tests/test_recv_op.py
@@ -47,7 +47,6 @@ class TestRecvOp(unittest.TestCase):
                 o = layers.scale(x=x, scale=10.0)
             main.global_block().create_var(
                 name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)
-            print main
         exe = fluid.Executor(place)
         exe.run(main)
 
@@ -61,16 +60,9 @@ class TestRecvOp(unittest.TestCase):
                 append_batch_size=False)
             fluid.initializer.Constant(value=1.0)(x, main.global_block())
             layers.Send("127.0.0.1:6174", [x], [x])
-            print main
         exe = fluid.Executor(place)
         exe.run(main)
 
 
 if __name__ == "__main__":
     unittest.main()
-    # test = TestRecvOp()
-    # place = fluid.CPUPlace()
-    # if sys.argv[1] == "server":
-    #     test.init_serv(place)
-    # else:
-    #     test.init_client(place)

From d815ec2354ed722d6a0e5d6102318ca0b5d9e687 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 16:51:50 +0800
Subject: [PATCH 039/314] Use test accuracy to exit(0)

---
 .../fluid/tests/book/test_recognize_digits.py | 31 +++++++++++++------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index 4ecdcdc632..19b93a66f4 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -16,6 +16,7 @@ import argparse
 import paddle.v2.fluid as fluid
 import paddle.v2 as paddle
 import sys
+import numpy
 
 
 def parse_arg():
@@ -100,6 +101,8 @@ def main():
     else:
         avg_loss, acc = net_conf(img, label)
 
+    test_program = fluid.default_main_program().clone()
+
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
     optimizer.minimize(avg_loss)
 
@@ -112,6 +115,8 @@ def main():
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=500),
         batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
     feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
 
     PASS_NUM = 100
@@ -119,21 +124,27 @@ def main():
         for batch_id, data in enumerate(train_reader()):
             need_check = (batch_id + 1) % 10 == 0
 
+            # train a mini-batch, fetch nothing
+            exe.run(feed=feeder.feed(data))
             if need_check:
-                fetch_list = [avg_loss, acc]
-            else:
-                fetch_list = []
-
-            outs = exe.run(feed=feeder.feed(data), fetch_list=fetch_list)
-            if need_check:
-                avg_loss_np, acc_np = outs
-                if float(acc_np) > 0.9:
+                acc_set = []
+                avg_loss_set = []
+                for test_data in test_reader():
+                    acc_np, avg_loss_np = exe.run(program=test_program,
+                                                  feed=feeder.feed(test_data),
+                                                  fetch_list=[acc, avg_loss])
+                    acc_set.append(float(acc_np))
+                    avg_loss_set.append(float(avg_loss_np))
+                # get test acc and loss
+                acc_val = numpy.array(acc_set).mean()
+                avg_loss_val = numpy.array(avg_loss_set).mean()
+                if float(acc_val) > 0.85:  # test acc > 85%
                     exit(0)
                 else:
                     print(
-                        'PassID {0:1}, BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.
+                        'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
                         format(pass_id, batch_id + 1,
-                               float(avg_loss_np), float(acc_np)))
+                               float(avg_loss_val), float(acc_val)))
 
 
 if __name__ == '__main__':

From 35b4d42ab69ee598df0c973c8884a053c42adb21 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 24 Jan 2018 16:54:35 +0800
Subject: [PATCH 040/314] merge doc fixes

---
 paddle/operators/recv_op.cc | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 381890d30b..5d1df566af 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -49,7 +49,7 @@ static void CreateTensorFromMessageType(framework::Variable *var,
     var->GetMutable<framework::SelectedRows>();
   } else {
     PADDLE_THROW(
-        "VraibleMessage type %d is not in "
+        "VariableMessage type %d is not in "
         "[LoDTensor, SelectedRows]",
         var_type);
   }
@@ -121,17 +121,17 @@ class RecvOp : public framework::OperatorBase {
         if (it != grad_list.end()) {
           param_var_name = param_list[it - grad_list.begin()];
         } else {
-          LOG(ERROR) << "grad have no paired param:" << grad_var_name;
+          LOG(ERROR) << "grad has no paired param:" << grad_var_name;
         }
-        VLOG(3) << "recved grad: " << grad_var_name
+        VLOG(3) << "received grad: " << grad_var_name
                 << " updating param: " << param_var_name;
         if (fan_in > 1) {
           grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
         }
         auto *var = recv_scope.FindVar(grad_var_name);
         if (var == nullptr) {
-          LOG(ERROR) << "can not find server side var: " << grad_var_name;
-          PADDLE_THROW("can not find server side var");
+          LOG(ERROR) << "Can not find server side var: " << grad_var_name;
+          PADDLE_THROW("Can not find server side var");
         }
         detail::DeserializeFromMessage(v.second, dev_ctx, var);
       }
@@ -164,7 +164,7 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Recv operator
 
-This operator will recv tensor from send_op
+This operator will recieve tensor from send_op
 )DOC");
     AddAttr<std::string>("endpoint",
                          "(string, default 127.0.0.1:6164)"
@@ -175,11 +175,11 @@ This operator will recv tensor from send_op
         kOptimizeBlock, "Serialized ProgramDesc string for recv to run.");
     AddAttr<std::vector<std::string>>(
         "ParamList", "type list of string",
-        "grad->param name mapping to find which param to optimize.")
+        "grad->param name mapping to find which parameters to optimize.")
         .SetDefault({});
     AddAttr<std::vector<std::string>>(
         "GradList", "type list of string",
-        "grad->param name mapping to find which param to optimize.")
+        "grad->param name mapping to find which parameters to optimize.")
         .SetDefault({});
     AddAttr<int>("Fanin", "type int",
                  "Number of trainers in the current cluster job")

From ef9098a325eb7ed6d443a3241575754e8c559883 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 24 Jan 2018 17:16:59 +0800
Subject: [PATCH 041/314] fix a bug

---
 python/paddle/v2/fluid/backward.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/backward.py b/python/paddle/v2/fluid/backward.py
index ae81d68baf..29243c90e8 100644
--- a/python/paddle/v2/fluid/backward.py
+++ b/python/paddle/v2/fluid/backward.py
@@ -178,7 +178,7 @@ def _remove_no_grad_branch_(op_descs, no_grad_set):
         if _all_in_set_(
                 filter(lambda name: name.find(core.grad_var_suffix()) != -1,
                        op_desc.input_arg_names()), no_grad_set):
-            no_grad_set.union(out_arg_names)
+            no_grad_set.update(out_arg_names)
             return True
         return False
 

From 76beff86a0f8e0d6856691b2968bafa52bf3a859 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Wed, 24 Jan 2018 01:34:54 -0800
Subject: [PATCH 042/314] Make the projection activation configurable

---
 paddle/operators/lstmp_op.cc                  | 76 +++++++++----------
 paddle/operators/lstmp_op.h                   | 14 ++--
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 41 +++++-----
 3 files changed, 66 insertions(+), 65 deletions(-)

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
index 85be64f44c..14469c708d 100644
--- a/paddle/operators/lstmp_op.cc
+++ b/paddle/operators/lstmp_op.cc
@@ -23,27 +23,29 @@ class LSTMPOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Input"),
-                   "Input(Input) of LSTMP should not be null.");
+                   "Input(Input) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Weight"),
-                   "Input(Weight) of LSTMP should not be null.");
+                   "Input(Weight) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
-                   "Input(ProjWeight) of LSTMP should not be null.");
+                   "Input(ProjWeight) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Bias"),
-                   "Input(Bias) of LSTMP should not be null.");
+                   "Input(Bias) of LSTMP operator should not be null.");
 
     PADDLE_ENFORCE(ctx->HasOutput("Projection"),
-                   "Output(Projection) of LSTMP should not be null.");
+                   "Output(Projection) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Cell"),
-                   "Output(Cell) of LSTMP should not be null.");
+                   "Output(Cell) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
-                   "Output(BatchGate) of LSTMP should not be null.");
+                   "Output(BatchGate) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
-                   "Output(BatchGate) of LSTMP should not be null.");
+                   "Output(BatchCellPreAct) of LSTMP operator should not be "
+                   "null.");
     PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"),
-                   "Output(BatchHidden) of LSTMP should not be null.");
+                   "Output(BatchHidden) of LSTMP operator should not be null.");
 
     auto in_dims = ctx->GetInputDim("Input");
-    PADDLE_ENFORCE_EQ(in_dims.size(), 2, "Input(X)'s rank must be 2.");
+    PADDLE_ENFORCE_EQ(in_dims.size(), 2,
+                      "Input(X)'s rank of LSTMP operator must be 2.");
 
     int frame_size = in_dims[1] / 4;
     auto w_dims = ctx->GetInputDim("Weight");
@@ -68,8 +70,8 @@ class LSTMPOp : public framework::OperatorWithKernel {
 
     if (ctx->HasInput("H0")) {
       PADDLE_ENFORCE(ctx->HasInput("C0"),
-                     "Input(C0) and Input(H0) of LSTMP should not "
-                     "be null at the same time.");
+                     "Input(C0) of LSTMP operator should not be null after "
+                     "Input(H0) provided.");
       auto h_dims = ctx->GetInputDim("H0");
       auto c_dims = ctx->GetInputDim("C0");
       PADDLE_ENFORCE(h_dims == c_dims,
@@ -132,8 +134,7 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("C0",
              "(Tensor, optional) the initial cell state is an optional "
              "input. This is a tensor with shape (N x D), where N is the "
-             "batch size. Only one of `H0` and `C0` can be NULL at the same "
-             "time.")
+             "batch size. `C0` should not be null if `H0` provided.")
         .AsDispensable();
     AddInput("Weight",
              "(Tensor) the learnable hidden-hidden weights."
@@ -211,13 +212,12 @@ class LSTMPOpMaker : public framework::OpProtoAndCheckerMaker {
                          "`tanh` by default.")
         .SetDefault("tanh")
         .InEnum({"sigmoid", "tanh", "relu", "identity"});
-    AddAttr<bool>("share_cell_act",
-                  "(bool, defalut: True) "
-                  "whether to share the activation of cell output with the "
-                  "projection layer. When set to `False`, the projection "
-                  "is simple linear, otherwise it will go through an "
-                  "activation function same as `cell_activation`.")
-        .SetDefault(true);
+    AddAttr<std::string>("proj_activation",
+                         "(string, default: tanh)"
+                         "The activation for projection output, "
+                         "`tanh` by defalut.")
+        .SetDefault("tanh")
+        .InEnum({"sigmoid", "tanh", "relu", "identity"});
     AddComment(R"DOC(
 Long-Short Term Memory with recurrent Projection layer (LSTMP) Operator.
 
@@ -226,20 +226,21 @@ original hidden state to a lower-dimensional one, which is proposed to reduce
 the number of total parameters and furthermore computational complexity for 
 the LSTM, espeacially for the case that the size of output units is relative 
 large (https://research.google.com/pubs/archive/43905.pdf). 
+
 The formula is as follows:
 
 $$
-i_t = \sigma(W_{ix}x_{t} + W_{ih}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
+i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
 
-f_t = \sigma(W_{fx}x_{t} + W_{fh}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
+f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
 
-\tilde{c_t} = act_g(W_{cx}x_t + W_{ch}r_{t-1} + b_c) \\
+\tilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c) \\
 
-o_t = \sigma(W_{ox}x_{t} + W_{oh}r_{t-1} + W_{oc}c_t + b_o) \\
+o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o) \\
 
-c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t}
+c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
 
-h_t = o_t \odot act_h(c_t)
+h_t = o_t \odot act_h(c_t) \\
 
 r_t = \overline{act_h}(W_{rh}h_t)
 $$
@@ -259,9 +260,8 @@ input and previous hidden state.
 
 The $\odot$ is the element-wise product of the vectors. $act_g$ and $act_h$
 are the cell input and cell output activation functions and `tanh` is usually
-used for them. $\overline{act_h}$ is the activation function for the projection 
-layer. When `share_cell_act` set to `False`, $\overline{act_h}$ is an
-identity activation, otherwise it will be same as $act_h$.
+used for them. $\overline{act_h}$ is the activation function for the 
+projection output, usually using `identity` or same as $act_h$.
 
 Note that these $W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}$
 operations on the input $x_{t}$ are NOT included in this operator.
@@ -277,22 +277,22 @@ class LSTMPGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Input"),
-                   "Input(Input) of LSTMP should not be null.");
+                   "Input(Input) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Projection"),
-                   "Input(Projection) of LSTMP should not be null.");
+                   "Input(Projection) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Cell"),
-                   "Input(Cell) of LSTMP should not be null.");
+                   "Input(Cell) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Weight"),
-                   "Input(Weight) of LSTMP should not be null.");
+                   "Input(Weight) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("ProjWeight"),
-                   "Input(ProjWeight) of LSTMP should not be null.");
+                   "Input(ProjWeight) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Bias"),
-                   "Input(Bias) of LSTMP should not be null.");
+                   "Input(Bias) of LSTMP operator should not be null.");
 
     PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
-                   "Input(BatchGate) of LSTMP should not be null.");
+                   "Input(BatchGate) of LSTMP operator should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
-                   "Input(BatchGate) of LSTMP should not be null.");
+                   "Input(BatchGate) of LSTMP operator should not be null.");
 
     auto SetOutGradDim = [&ctx](const std::string& name) {
       auto g_name = framework::GradVarName(name);
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
index 0048f7e1c6..9dc37615f0 100644
--- a/paddle/operators/lstmp_op.h
+++ b/paddle/operators/lstmp_op.h
@@ -136,7 +136,8 @@ class LSTMPKernel : public framework::OpKernel<T> {
         ctx.Attr<std::string>("cell_activation"));
     auto cand_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("candidate_activation"));
-    auto share_cell_act = ctx.Attr<bool>("share_cell_act");
+    auto proj_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("proj_activation"));
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
     for (size_t n = 0; n < num_batch; n++) {
@@ -174,7 +175,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
         math::matmul<DeviceContext, T>(device_ctx, ordered_h0, false,
                                        *proj_weight, false, static_cast<T>(1.0),
                                        ordered_proj0, static_cast<T>(0.0));
-        if (share_cell_act) {
+        if (proj_act != math::detail::ActivationType::kIdentity) {
           auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
           ActCompute(cell_act, place, proj0_dev, proj0_dev);
         }
@@ -194,7 +195,7 @@ class LSTMPKernel : public framework::OpKernel<T> {
       math::matmul<DeviceContext, T>(device_ctx, hidden_t, false, *proj_weight,
                                      false, static_cast<T>(1.0), &proj_t,
                                      static_cast<T>(0.0));
-      if (share_cell_act) {
+      if (proj_act != math::detail::ActivationType::kIdentity) {
         auto proj_t_dev = EigenMatrix<T>::From(proj_t);
         ActCompute(cell_act, place, proj_t_dev, proj_t_dev);
       }
@@ -348,7 +349,8 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
         ctx.Attr<std::string>("cell_activation"));
     auto cand_act = math::detail::GetActivationType(
         ctx.Attr<std::string>("candidate_activation"));
-    auto share_cell_act = ctx.Attr<bool>("share_cell_act");
+    auto proj_act = math::detail::GetActivationType(
+        ctx.Attr<std::string>("proj_activation"));
     auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
     auto batch_starts = batch_gate->lod()[0];
@@ -359,7 +361,7 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
 
       Tensor cur_proj = batch_proj.Slice(bstart, bend);
       Tensor proj_g = batch_proj_g.Slice(bstart, bend);
-      if (share_cell_act) {
+      if (proj_act != math::detail::ActivationType::kIdentity) {
         auto cur_proj_dev = EigenMatrix<T>::From(cur_proj);
         auto proj_g_dev = EigenMatrix<T>::From(proj_g);
         ActGradCompute(cell_act, place, cur_proj_dev, cur_proj_dev, proj_g_dev,
@@ -439,7 +441,7 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
           math::matmul<DeviceContext, T>(device_ctx, gate_g, false, *weight,
                                          true, static_cast<T>(1.0), &proj0_g,
                                          static_cast<T>(0.0));
-          if (share_cell_act) {
+          if (proj_act != math::detail::ActivationType::kIdentity) {
             auto proj0_dev = EigenMatrix<T>::From(*ordered_proj0);
             auto proj0_g_dev = EigenMatrix<T>::From(proj0_g);
             ActGradCompute(cell_act, place, proj0_dev, proj0_dev, proj0_g_dev,
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index 8835cae504..08fc32e117 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -41,7 +41,7 @@ def relu(x):
     return np.maximum(x, 0)
 
 
-ACTVATION = {
+ACTIVATION = {
     'identity': identity,
     'sigmoid': sigmoid,
     'tanh': tanh,
@@ -63,8 +63,9 @@ def lstmp(
         act_gate=None,
         act_cell=None,
         act_cand=None,
-        share_cell_act=True):
-    def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand):
+        act_proj=None):
+    def _step(x, w_r, w_rh, w_c, r_pre, c_pre, act_gate, act_cell, act_cand,
+              act_proj):
         g = np.dot(r_pre, w_r)  # 1 x 4D
         g = g + x
         g = np.reshape(g, (1, g.size))
@@ -86,8 +87,7 @@ def lstmp(
         h = g_o * act_cell(c)
         # projection
         r = np.dot(h, w_rh)
-        if share_cell_act:
-            r = act_cell(r)
+        r = act_proj(r)
         return r, c
 
     def _reverse(x, lod):
@@ -110,13 +110,12 @@ def lstmp(
         seq_len = offset[i + 1] - offset[i]
         x = input[offset[i]:offset[i + 1], :]
         r_pre = np.dot(h0[i], w_rh)  # 1 x P
-        if share_cell_act:
-            r_pre = act_cell(r_pre)
+        r_pre = act_proj(r_pre)
         c_pre = c0[i]  # 1 x D
         for j in range(seq_len):
             # compute one step
             r_pre, c_pre = _step(x[j], w_r, w_rh, w_c, r_pre, c_pre, act_gate,
-                                 act_cell, act_cand)
+                                 act_cell, act_cand, act_proj)
             projection.append(r_pre.flatten())
             cell.append(c_pre.flatten())
 
@@ -131,7 +130,7 @@ def lstmp(
     return projection, cell
 
 
-class TestLstmOp(OpTest):
+class TestLstmpOp(OpTest):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         # hidden size
@@ -142,8 +141,8 @@ class TestLstmOp(OpTest):
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
+        self.act_proj = self.act_cell
 
-        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = False
         self.use_peepholes = True
@@ -172,8 +171,8 @@ class TestLstmOp(OpTest):
         w_c = b[:, 4 * self.D:] if self.use_peepholes else None
         w_rh = np.random.normal(size=(self.D, self.P)).astype('float64')
         r, c = lstmp(x, self.lod, h0, c0, w, w_rh, w_b, w_c, self.is_reverse,
-                     ACTVATION[self.act_gate], ACTVATION[self.act_cell],
-                     ACTVATION[self.act_cand], self.share_cell_act)
+                     ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
+                     ACTIVATION[self.act_cand], ACTIVATION[self.act_proj])
 
         self.inputs = {'Input': (x, self.lod), 'Weight': w, 'ProjWeight': w_rh}
 
@@ -193,7 +192,7 @@ class TestLstmOp(OpTest):
             'gate_activation': self.act_gate,
             'cell_activation': self.act_cell,
             'candidate_activation': self.act_cand,
-            'share_cell_act': self.share_cell_act
+            'proj_activation': self.act_proj
         }
 
     def test_check_output(self):
@@ -212,7 +211,7 @@ class TestLstmOp(OpTest):
             max_relative_error=1e-2)
 
 
-class TestLstmOpHasInitial(TestLstmOp):
+class TestLstmpOpHasInitial(TestLstmpOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -221,8 +220,8 @@ class TestLstmOpHasInitial(TestLstmOp):
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
+        self.act_proj = self.act_cell
 
-        self.share_cell_act = True
         self.has_initial_state = True
         self.is_reverse = True
         self.use_peepholes = True
@@ -313,7 +312,7 @@ class TestLstmOpHasInitial(TestLstmOp):
             no_grad_set=set('C0'))
 
 
-class TestLstmOpRerverse(TestLstmOp):
+class TestLstmpOpRerverse(TestLstmpOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -322,14 +321,14 @@ class TestLstmOpRerverse(TestLstmOp):
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
+        self.act_proj = self.act_cell
 
-        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = True
         self.use_peepholes = True
 
 
-class TestLstmOpNotUsePeepholes(TestLstmOp):
+class TestLstmpOpNotUsePeepholes(TestLstmpOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -338,14 +337,14 @@ class TestLstmOpNotUsePeepholes(TestLstmOp):
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
+        self.act_proj = self.act_cell
 
-        self.share_cell_act = True
         self.has_initial_state = False
         self.is_reverse = False
         self.use_peepholes = False
 
 
-class TestLstmOpNotShareCellAct(TestLstmOp):
+class TestLstmpOpLinearProjection(TestLstmpOp):
     def set_argument(self):
         self.lod = [[0, 2, 5, 7]]
         self.D = 16
@@ -354,8 +353,8 @@ class TestLstmOpNotShareCellAct(TestLstmOp):
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
         self.act_cand = 'tanh'
+        self.act_proj = 'identity'
 
-        self.share_cell_act = False
         self.has_initial_state = False
         self.is_reverse = False
         self.use_peepholes = True

From 7a935b17011b1021478a0c8a2115c8cf7da67f44 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 24 Jan 2018 17:48:59 +0800
Subject: [PATCH 043/314] Polish code

---
 python/paddle/v2/fluid/tests/book/test_recognize_digits.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index 19b93a66f4..ac7ef4046f 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -122,11 +122,9 @@ def main():
     PASS_NUM = 100
     for pass_id in range(PASS_NUM):
         for batch_id, data in enumerate(train_reader()):
-            need_check = (batch_id + 1) % 10 == 0
-
             # train a mini-batch, fetch nothing
             exe.run(feed=feeder.feed(data))
-            if need_check:
+            if (batch_id + 1) % 10 == 0:
                 acc_set = []
                 avg_loss_set = []
                 for test_data in test_reader():

From 77a7bba8c516cf437558febf0270eb0365140f04 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Wed, 24 Jan 2018 17:53:51 +0800
Subject: [PATCH 044/314] Add `ProgramDesc` storage. (#7822)

Add `ProgramDesc` storage
---
 .../dist_refactor/distributed_architecture.md |   4 ++--
 .../dist_refactor/src/remote_executor.graffle | Bin 6865 -> 10248 bytes
 .../dist_refactor/src/remote_executor.png     | Bin 137681 -> 120818 bytes
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/design/dist_refactor/distributed_architecture.md b/doc/design/dist_refactor/distributed_architecture.md
index 3a741f9586..9368c5780d 100644
--- a/doc/design/dist_refactor/distributed_architecture.md
+++ b/doc/design/dist_refactor/distributed_architecture.md
@@ -152,12 +152,12 @@ for data in train_reader():
 `JobDesc` object describe the distributed job resource specification to run on
 Cluster environment.
 
-<img src="src/remote_executor.png"/>
+<img src="src/remote_executor.png" width="500" align="center" />
 
 `RemoteExecutor.run` sends the `ProgramDesc` and
 [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/develop/doc/autoscale/README.md#training-job-resource)
 to a server in the cluster which executes `RemoteExecutor.listen`. This server is responsible
-to start the final Kubernetes Jobs to run the different role of `ProgramDesc`.
+to start the final Kubernetes Jobs to run the different role of `ProgramDesc` from `ConfigMap`.
 
 
 ### Placement Algorithm
diff --git a/doc/design/dist_refactor/src/remote_executor.graffle b/doc/design/dist_refactor/src/remote_executor.graffle
index ce2c18fee5687732053c48af9c8c290a994a8090..41b2067311694b56d211a4f32d1b76884eeffd2d 100644
GIT binary patch
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z2amVPw|5EOvfArv&EEi>eM2qL%0HQy>;Z}=f%3sQaAN#h@a&g9zkUk42sv`CHC*d=
zX^1>y0u`^kJf1u6-s*I>FH`*+f5-?P!#>k@qy5O1>p#CUh41tBSF1Z=W9J$#umt~m
z_?h@5<Ctdc_<t@({{06S{ZU9UvGm@l7bEfC->r#L=6PgDth3_ZYl)FmR8(|wa)K9z
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From 1ab11814601443c3b71c6db894265fa90f5cbb4a Mon Sep 17 00:00:00 2001
From: helinwang <ustc.harry@gmail.com>
Date: Wed, 24 Jan 2018 02:08:26 -0800
Subject: [PATCH 045/314] aysnc send/recv, seriliaze/deserialize using
 threadpool. (#7705)

* aysnc send/recv, seriliaze/deserialize using threadpool

* implement paralell deserialization correctly
---
 paddle/operators/detail/grpc_client.cc | 118 +++++++++++++++----------
 1 file changed, 71 insertions(+), 47 deletions(-)

diff --git a/paddle/operators/detail/grpc_client.cc b/paddle/operators/detail/grpc_client.cc
index 1e41587c41..d699dabf2f 100644
--- a/paddle/operators/detail/grpc_client.cc
+++ b/paddle/operators/detail/grpc_client.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "grpc_client.h"
+#include "paddle/framework/threadpool.h"
 namespace paddle {
 namespace operators {
 namespace detail {
@@ -22,25 +23,32 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
                                   const framework::Scope& scope,
                                   const std::string& var_name,
                                   int64_t time_out) {
-  sendrecv::VariableMessage req;
-  auto* var = scope.FindVar(var_name);
-  SerializeToMessage(var_name, var, ctx, &req);
-
-  // varhandle
-  VarHandle var_h;
-  var_h.ep = ep;
-  var_h.scope = &scope;
-  var_h.name = var_name;
-  var_h.ctx = &ctx;
-
-  // stub context
-  auto ch = GetChannel(ep);
-  SendProcessor* s = new SendProcessor(ch);
-  s->Prepare(var_h, time_out);
-  s->response_call_back_ = NULL;
-
-  auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
-  rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  const platform::DeviceContext* p_ctx = &ctx;
+  const std::string ep_val = ep;
+  const std::string var_name_val = var_name;
+  const framework::Scope* p_scope = &scope;
+  const auto ch = GetChannel(ep_val);
+
+  framework::Async([var_name_val, p_ctx, ep_val, p_scope, time_out, ch, this] {
+    auto* var = p_scope->FindVar(var_name_val);
+    sendrecv::VariableMessage req;
+    SerializeToMessage(var_name_val, var, *p_ctx, &req);
+
+    // varhandle
+    VarHandle var_h;
+    var_h.ep = ep_val;
+    var_h.scope = p_scope;
+    var_h.name = var_name_val;
+    var_h.ctx = p_ctx;
+
+    // stub context
+    SendProcessor* s = new SendProcessor(ch);
+    s->Prepare(var_h, time_out);
+    s->response_call_back_ = NULL;
+
+    auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
+    rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  });
 
   req_count_++;
 
@@ -50,8 +58,6 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
 void ProcGetResponse(const VarHandle& var_h,
                      const sendrecv::VariableMessage& ret_msg) {
   auto* outvar = var_h.scope->FindVar(var_h.name);
-
-  std::istringstream iss(ret_msg.serialized());
   DeserializeFromMessage(ret_msg, *var_h.ctx, outvar);
 }
 
@@ -60,24 +66,31 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
                                  const framework::Scope& scope,
                                  const std::string& var_name,
                                  int64_t time_out) {
-  sendrecv::VariableMessage req;
-  req.set_varname(var_name);
-
-  // varhandle
-  VarHandle var_h;
-  var_h.ep = ep;
-  var_h.scope = &scope;
-  var_h.name = var_name;
-  var_h.ctx = &ctx;
-
-  // stub context
-  auto ch = GetChannel(ep);
-  GetProcessor* s = new GetProcessor(ch);
-  s->Prepare(var_h, time_out);
-  s->response_call_back_ = ProcGetResponse;
-
-  auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
-  rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  const platform::DeviceContext* p_ctx = &ctx;
+  const std::string ep_val = ep;
+  const std::string var_name_val = var_name;
+  const framework::Scope* p_scope = &scope;
+  const auto ch = GetChannel(ep_val);
+
+  framework::Async([var_name_val, ep_val, p_scope, p_ctx, time_out, ch, this] {
+    sendrecv::VariableMessage req;
+    req.set_varname(var_name_val);
+
+    // varhandle
+    VarHandle var_h;
+    var_h.ep = ep_val;
+    var_h.scope = p_scope;
+    var_h.name = var_name_val;
+    var_h.ctx = p_ctx;
+
+    // stub context
+    GetProcessor* s = new GetProcessor(ch);
+    s->Prepare(var_h, time_out);
+    s->response_call_back_ = ProcGetResponse;
+
+    auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
+    rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  });
 
   req_count_++;
 
@@ -85,19 +98,31 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
 }
 
 bool RPCClient::Wait() {
-  bool ok = true;
+  if (req_count_ <= 0) {
+    return true;
+  }
 
-  while (true) {
-    if (req_count_ <= 0) {
-      break;
-    }
+  std::vector<bool> a(req_count_);
+  std::vector<std::future<void>> waits(req_count_);
 
-    if (!Proceed()) {
+  for (int i = 0; i < req_count_; i++) {
+    waits[i] = framework::Async([i, &a, this] { a[i] = Proceed(); });
+  }
+
+  for (int i = 0; i < req_count_; i++) {
+    waits[i].wait();
+  }
+
+  int last_req_count = req_count_;
+  req_count_ = 0;
+
+  for (int i = 0; i < last_req_count; i++) {
+    if (!a[i]) {
       return false;
     }
   }
 
-  return ok;
+  return true;
 }
 
 bool RPCClient::Proceed() {
@@ -124,7 +149,6 @@ bool RPCClient::Proceed() {
 
   c->Process();
   delete c;
-  req_count_--;
   return true;
 }
 

From fd0dd6e63a2c85077f436da77146447453a5d278 Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Wed, 24 Jan 2018 19:49:06 +0800
Subject: [PATCH 046/314] Use extend instead of appending.

---
 python/paddle/v2/fluid/io.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 5b02d2495d..606033af43 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -191,8 +191,8 @@ def get_inference_program(target_vars, main_program=None):
     vars = []
     for var in target_vars:
         if isinstance(var, Evaluator):
-            vars.append(var.states)
-            vars.append(var.metrics)
+            vars.extend(var.states)
+            vars.extend(var.metrics)
         else:
             vars.append(var)
     pruned_program = main_program.prune(targets=vars)

From a249c0cae97e64c89c4db480d0092c19a44d3dfd Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Wed, 24 Jan 2018 19:51:37 +0800
Subject: [PATCH 047/314] Refine doc and fix dtype.

---
 python/paddle/v2/fluid/layers/nn.py | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 477ae7cea9..d87cedca89 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -2719,22 +2719,22 @@ def multiplex(inputs, index):
     input variables and let :math:`I_i` represents the i-th input variable and i
     is in [0, :math:`m`). All input variables are tensors with same shape
     [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. Please note that rank of the
-    input tensor should be at least 2. Each input variable will be viewed as a
+    input tensor should be at least 2. Each input variable will be treated as a
     2-D matrix with shape [:math:`M`, :math:`N`] where :math:`M` for :math:`d_0`
     and :math:`N` for :math:`d_1` * :math:`d_2` * ... * :math:`d_R`. Let
     :math:`I_i[j]` be the j-th row of the i-th input variable. The given index
     variable should be a 2-D tensor with shape [:math:`M`, 1]. Let `ID[i]` be
-    the i-th index value of index variable. Then the output variable will be a
-    tensor with shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. If we view
-    the output tensor as a 2-D matrix with shape [:math:`M`, :math:`N`] and let
-    :math:`O[i]` be the i-th row of the matrix, then values of `O[i]` come from
+    the i-th index value of the index variable. Then the output variable will
+    be a tensor with shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. If we
+    treat the output tensor as a 2-D matrix with shape [:math:`M`, :math:`N`]
+    and let :math:`O[i]` be the i-th row of the matrix, then `O[i]` is equal to
     :math:`I_{ID[i]}[i]`.
 
     Args:
-       inputs (list): Input variables which are tensors with same shape and the
-                rank is at least 2.
+       inputs (list): A list of variables to gather from. All variables have the
+                same shape and the rank is at least 2.
        index (Variable): Tensor<int32>, index variable which is a 2-D tensor
-                with shape [M, 1] where M for batch size.
+                with shape [M, 1] where M is the batch size.
 
     Returns:
         Variable: Multiplex variable gathered from input variables.
@@ -2748,7 +2748,12 @@ def multiplex(inputs, index):
             out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
     """
     helper = LayerHelper('multiplex', **locals())
-    out = helper.create_tmp_variable(helper.input_dtype())
+
+    if not isinstance(inputs, list) and len(inputs) < 2:
+        raise ValueError("inputs should be a list object and contains at least "
+                         "2 elements.")
+
+    out = helper.create_tmp_variable(inputs[0].dtype)
     helper.append_op(
         type='multiplex',
         inputs={'X': inputs,

From 9ecc54a11b61e09c3c503b51049394e61f8e1fa3 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Wed, 24 Jan 2018 09:00:03 -0800
Subject: [PATCH 048/314] Remove redundant code in unit test

---
 paddle/operators/lstmp_op.cc                  |  2 +-
 paddle/operators/lstmp_op.h                   |  2 +-
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 60 +++----------------
 3 files changed, 11 insertions(+), 53 deletions(-)

diff --git a/paddle/operators/lstmp_op.cc b/paddle/operators/lstmp_op.cc
index 14469c708d..c96b30ba35 100644
--- a/paddle/operators/lstmp_op.cc
+++ b/paddle/operators/lstmp_op.cc
@@ -4,7 +4,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
index 9dc37615f0..ee82d5c10a 100644
--- a/paddle/operators/lstmp_op.h
+++ b/paddle/operators/lstmp_op.h
@@ -4,7 +4,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index 08fc32e117..f137fc61b3 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -131,7 +131,10 @@ def lstmp(
 
 
 class TestLstmpOp(OpTest):
-    def set_argument(self):
+    def reset_argument(self):
+        pass
+
+    def setUp(self):
         self.lod = [[0, 2, 5, 7]]
         # hidden size
         self.D = 16
@@ -147,8 +150,7 @@ class TestLstmpOp(OpTest):
         self.is_reverse = False
         self.use_peepholes = True
 
-    def setUp(self):
-        self.set_argument()
+        self.reset_argument()
         self.op_type = 'lstmp'
 
         T = self.lod[0][-1]
@@ -212,19 +214,8 @@ class TestLstmpOp(OpTest):
 
 
 class TestLstmpOpHasInitial(TestLstmpOp):
-    def set_argument(self):
-        self.lod = [[0, 2, 5, 7]]
-        self.D = 16
-        self.P = 5
-
-        self.act_gate = 'sigmoid'
-        self.act_cell = 'tanh'
-        self.act_cand = 'tanh'
-        self.act_proj = self.act_cell
-
+    def reset_argument(self):
         self.has_initial_state = True
-        self.is_reverse = True
-        self.use_peepholes = True
 
     def test_check_grad(self):
         # TODO(qingqing) remove folowing lines after the check_grad is refined.
@@ -313,52 +304,19 @@ class TestLstmpOpHasInitial(TestLstmpOp):
 
 
 class TestLstmpOpRerverse(TestLstmpOp):
-    def set_argument(self):
-        self.lod = [[0, 2, 5, 7]]
-        self.D = 16
-        self.P = 10
-
-        self.act_gate = 'sigmoid'
-        self.act_cell = 'tanh'
-        self.act_cand = 'tanh'
-        self.act_proj = self.act_cell
-
-        self.has_initial_state = False
+    def reset_argument(self):
         self.is_reverse = True
-        self.use_peepholes = True
 
 
 class TestLstmpOpNotUsePeepholes(TestLstmpOp):
-    def set_argument(self):
-        self.lod = [[0, 2, 5, 7]]
-        self.D = 16
-        self.P = 10
-
-        self.act_gate = 'sigmoid'
-        self.act_cell = 'tanh'
-        self.act_cand = 'tanh'
-        self.act_proj = self.act_cell
-
-        self.has_initial_state = False
-        self.is_reverse = False
+    def reset_argument(self):
         self.use_peepholes = False
 
 
 class TestLstmpOpLinearProjection(TestLstmpOp):
-    def set_argument(self):
-        self.lod = [[0, 2, 5, 7]]
-        self.D = 16
-        self.P = 10
-
-        self.act_gate = 'sigmoid'
-        self.act_cell = 'tanh'
-        self.act_cand = 'tanh'
+    def reset_argument(self):
         self.act_proj = 'identity'
 
-        self.has_initial_state = False
-        self.is_reverse = False
-        self.use_peepholes = True
-
 
 if __name__ == '__main__':
     unittest.main()

From 8360ff0b328414f1ff4bb58f504ea218fe0b6e59 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Wed, 24 Jan 2018 13:25:48 -0800
Subject: [PATCH 049/314] fix openblas build

---
 paddle/operators/math/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
index c607704efa..28c5aec199 100644
--- a/paddle/operators/math/CMakeLists.txt
+++ b/paddle/operators/math/CMakeLists.txt
@@ -11,7 +11,7 @@ if(WITH_GPU)
     nv_library(sequence_pooling SRCS sequence_pooling.cc sequence_pooling.cu DEPS device_context math_function)
     nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context tensor)
     nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context math_function)
-    nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor)
+    nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context tensor math_function)
     nv_library(sequence_padding SRCS sequence_padding.cc sequence_padding.cu DEPS lod_tensor device_context)
     nv_library(sequence_scale SRCS sequence_scale.cc sequence_scale.cu DEPS lod_tensor device_context)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
@@ -28,7 +28,7 @@ else()
     cc_library(sequence_pooling SRCS sequence_pooling.cc DEPS device_context math_function)
     cc_library(vol2col SRCS vol2col.cc DEPS device_context tensor)
     cc_library(context_project SRCS context_project.cc DEPS device_context math_function)
-    cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context tensor)
+    cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context tensor math_function)
     cc_library(sequence_padding SRCS sequence_padding.cc DEPS lod_tensor device_context)
     cc_library(sequence_scale SRCS sequence_scale.cc DEPS lod_tensor device_context)
     cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)

From 5009f7c12fd51e6fab7182971c664049bddadbbc Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Wed, 24 Jan 2018 13:32:19 -0800
Subject: [PATCH 050/314] Fix save load inference model and remove pickle 
 (#7712)

* remove pick dependency

* fix bug

* small fix

* modify executor.py for save and load

* clean code

* Add usage example

* refine executor run function

* fix bug

* refine executor code

* fix block bug

* fix comments

* fix bug

* fix pass num
---
 paddle/inference/inference.cc                 |  38 +----
 paddle/inference/inference.h                  |   3 -
 python/paddle/v2/fluid/executor.py            | 149 ++++++++++++++----
 python/paddle/v2/fluid/io.py                  |  76 +++++----
 .../tests/book/test_recognize_digits_mlp.py   |  21 ++-
 5 files changed, 193 insertions(+), 94 deletions(-)

diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 4900177880..09268ffb3a 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -19,14 +19,10 @@ limitations under the License. */
 #include "paddle/framework/init.h"
 #include "paddle/framework/scope.h"
 
-#ifdef PADDLE_USE_PTOOLS
-#include "chooseser.h"
-#endif
-
 namespace paddle {
 
 void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
-  std::string model_filename = dirname + "/__model__.dat";
+  std::string model_filename = dirname + "/__model__";
   LOG(INFO) << "loading model from " << model_filename;
   std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
   std::string program_desc_str;
@@ -52,39 +48,15 @@ void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
   }
 }
 
-void InferenceEngine::LoadInferenceModel(
-    const std::string& dirname,
-    const std::vector<std::string>& feed_var_names,
-    const std::vector<std::string>& fetch_var_names) {
-  std::string model_filename = dirname + "/__model__.dat";
-  LOG(INFO) << "loading model from " << model_filename;
-  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
-  std::string program_desc_str;
-  inputfs.seekg(0, std::ios::end);
-  program_desc_str.resize(inputfs.tellg());
-  inputfs.seekg(0, std::ios::beg);
-  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
-  inputfs.read(&program_desc_str[0], program_desc_str.size());
-  inputfs.close();
-
-  program_ = new framework::ProgramDesc(program_desc_str);
-  GenerateLoadProgram(dirname);
-
-  if (feed_var_names.empty() || fetch_var_names.empty()) {
-    LOG(FATAL) << "Please specify the feed_var_names and fetch_var_names.";
-  }
-  feed_var_names_ = feed_var_names;
-  fetch_var_names_ = fetch_var_names;
-  PrependFeedOp();
-  AppendFetchOp();
-}
-
 bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
-  if (var->Persistable() && var->Name() != "feed" && var->Name() != "fetch") {
+  if (var->Persistable()) {
     // There are many unreachable variables in the program
     for (size_t i = 0; i < program_->Size(); ++i) {
       const framework::BlockDesc& block = program_->Block(i);
       for (auto* op : block.AllOps()) {
+        if (op->Type() == "feed") {
+          continue;
+        }
         for (auto input_argument_name : op->InputArgumentNames()) {
           if (input_argument_name == var->Name()) {
             return true;
diff --git a/paddle/inference/inference.h b/paddle/inference/inference.h
index 7fc09cb9e5..26f259824b 100644
--- a/paddle/inference/inference.h
+++ b/paddle/inference/inference.h
@@ -29,9 +29,6 @@ public:
   }
 
   void LoadInferenceModel(const std::string& dirname);
-  void LoadInferenceModel(const std::string& dirname,
-                          const std::vector<std::string>& feed_var_names,
-                          const std::vector<std::string>& fetch_var_names);
   void Execute(const std::vector<framework::LoDTensor>& feeds,
                std::vector<framework::LoDTensor>& fetchs);
 
diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py
index 9d5ed9571a..9f48815b8b 100644
--- a/python/paddle/v2/fluid/executor.py
+++ b/python/paddle/v2/fluid/executor.py
@@ -68,6 +68,84 @@ def as_numpy(tensor):
     return ans
 
 
+def has_feed_operators(block, feed_targets, feed_holder_name):
+    """ Check whether the block already has feed operators.
+
+    Return false if the block does not have any feed operators.
+    If some feed operators have been prepended to the block, check that
+    the info contained in these feed operators matches the feed_targets
+    and feed_holder_name. Raise exception when any mismatch is found.
+    Return true when the block has feed operators with matching info.
+
+    Args:
+        block: a block instance (typically global block of a program)
+        feed_targets: a dictionary of {feed_target_name: feed_target_data}
+        feed_holder_name: the name of the variable that holds the data of 
+            all feed targets. The type of this feed_holder variable is 
+            FEED_MINIBATCH, which is essentially vector<LoDTensor>.
+
+    Returns:
+        A boolean value that indicates whether a block has feed operators 
+        that match the info contained in feed_targets and feed_holder_name.
+    """
+
+    feed_count = 0
+    for op in block.ops:
+        if op.desc.type() == 'feed':
+            feed_count += 1
+            assert op.desc.input('X')[0] == feed_holder_name
+            feed_target_name = op.desc.output('Out')[0]
+            if feed_target_name not in feed_targets:
+                raise Exception("'feed_targets' does not have {} variable".
+                                format(feed_target_name))
+        else:
+            break
+    if feed_count > 0 and feed_count != len(feed_targets):
+        raise Exception(
+            "Feed operators in program desc do not match 'feed_targets'")
+    return feed_count > 0
+
+
+def has_fetch_operators(block, fetch_targets, fetch_holder_name):
+    """ Check whether the block already has fetch operators.
+    
+    Return false if the block does not have any fetch operators.
+    If some fetch operators have been appended to the block, check that
+    the info contained in these fetch operators matches the fetch_targets
+    and fetch_holder_name. Raise exception when any mismatch is found.
+    Return true when the block has fetch operators with matching info.
+
+    Args:
+        block: a block instance (typically global block of a program)
+        fetch_targets: a dictionary of {fetch_target_name: fetch_target_data}
+        fetch_holder_name: the name of the variable that holds the data of 
+            all fetch targets. The type of this fetch_holder variable is 
+            FETCH_LIST, which is essentially vector<LoDTensor>.    
+
+    Return:    
+        A boolean value that indicates whether a block has fetch operators 
+        that match the info contained in fetch_targets and fetch_holder_name.     
+    """
+
+    fetch_count = 0
+    for op in block.ops:
+        if op.desc.type() == 'fetch':
+            fetch_count += 1
+            assert op.desc.output('Out')[0] == fetch_holder_name
+            fetch_target_name = op.desc.input('X')[0]
+            if fetch_target_name not in [
+                    var.desc.name() for var in fetch_targets
+            ]:
+                raise Exception("'fetch_targets' does not have {} variable".
+                                format(fetch_target_name))
+            idx = op.desc.attr('col')
+            assert fetch_target_name == fetch_targets[idx].desc.name()
+    if fetch_count > 0 and fetch_count != len(fetch_targets):
+        raise Exception(
+            "Fetch operators in program desc do not match 'fetch_targets'")
+    return fetch_count > 0
+
+
 class Executor(object):
     def __init__(self, places):
         if not isinstance(places, list) and not isinstance(places, tuple):
@@ -147,33 +225,50 @@ class Executor(object):
 
         program = program.clone()
         global_block = program.global_block()
-        feed_var = global_block.create_var(
-            name=feed_var_name,
-            type=core.VarDesc.VarType.FEED_MINIBATCH,
-            persistable=True)
-
-        for i, name in enumerate(feed):
-            out = global_block.var(name)
-            global_block.prepend_op(
-                'feed',
-                inputs={'X': [feed_var]},
-                outputs={'Out': [out]},
-                attrs={'col': i})
-            cur_feed = feed[name]
-            if not isinstance(cur_feed, core.LoDTensor):
-                cur_feed = self.aslodtensor(cur_feed)
-            core.set_feed_variable(scope, cur_feed, feed_var.name, i)
-
-        fetch_var = global_block.create_var(
-            name=fetch_var_name,
-            type=core.VarDesc.VarType.FETCH_LIST,
-            persistable=True)
-        for i, var in enumerate(fetch_list):
-            global_block.append_op(
-                type='fetch',
-                inputs={'X': [var]},
-                outputs={'Out': [fetch_var]},
-                attrs={'col': i})
+
+        if feed_var_name in global_block.vars:
+            feed_var = global_block.var(feed_var_name)
+        else:
+            feed_var = global_block.create_var(
+                name=feed_var_name,
+                type=core.VarDesc.VarType.FEED_MINIBATCH,
+                persistable=True)
+
+        if fetch_var_name in global_block.vars:
+            fetch_var = global_block.var(fetch_var_name)
+        else:
+            fetch_var = global_block.create_var(
+                name=fetch_var_name,
+                type=core.VarDesc.VarType.FETCH_LIST,
+                persistable=True)
+
+        if not has_feed_operators(global_block, feed, feed_var_name):
+            for i, name in enumerate(feed):
+                out = global_block.var(name)
+                global_block.prepend_op(
+                    type='feed',
+                    inputs={'X': [feed_var]},
+                    outputs={'Out': [out]},
+                    attrs={'col': i})
+
+        for op in global_block.ops:
+            if op.desc.type() == 'feed':
+                feed_target_name = op.desc.output('Out')[0]
+                cur_feed = feed[feed_target_name]
+                if not isinstance(cur_feed, core.LoDTensor):
+                    cur_feed = self.aslodtensor(cur_feed)
+                idx = op.desc.attr('col')
+                core.set_feed_variable(scope, cur_feed, feed_var_name, idx)
+            else:
+                break
+
+        if not has_fetch_operators(global_block, fetch_list, fetch_var_name):
+            for i, var in enumerate(fetch_list):
+                global_block.append_op(
+                    type='fetch',
+                    inputs={'X': [var]},
+                    outputs={'Out': [fetch_var]},
+                    attrs={'col': i})
 
         self.executor.run(program.desc, scope, 0, True, True)
         outs = [
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 606033af43..d56ec45c53 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import os
-import cPickle as pickle
 
 from paddle.v2.fluid.evaluator import Evaluator
 from paddle.v2.fluid.framework import Program, Parameter, default_main_program, Variable
@@ -200,12 +199,16 @@ def get_inference_program(target_vars, main_program=None):
     return inference_program
 
 
-def prepend_feed_ops(inference_program, feeded_var_names):
+def prepend_feed_ops(inference_program,
+                     feed_target_names,
+                     feed_holder_name='feed'):
     global_block = inference_program.global_block()
     feed_var = global_block.create_var(
-        name='feed', type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True)
+        name=feed_holder_name,
+        type=core.VarDesc.VarType.FEED_MINIBATCH,
+        persistable=True)
 
-    for i, name in enumerate(feeded_var_names):
+    for i, name in enumerate(feed_target_names):
         out = global_block.var(name)
         global_block.prepend_op(
             type='feed',
@@ -214,12 +217,16 @@ def prepend_feed_ops(inference_program, feeded_var_names):
             attrs={'col': i})
 
 
-def append_fetch_ops(inference_program, fetch_var_names):
+def append_fetch_ops(inference_program,
+                     fetch_target_names,
+                     fetch_holder_name='fetch'):
     global_block = inference_program.global_block()
     fetch_var = global_block.create_var(
-        name='fetch', type=core.VarDesc.VarType.FETCH_LIST, persistable=True)
+        name=fetch_holder_name,
+        type=core.VarDesc.VarType.FETCH_LIST,
+        persistable=True)
 
-    for i, name in enumerate(fetch_var_names):
+    for i, name in enumerate(fetch_target_names):
         global_block.append_op(
             type='fetch',
             inputs={'X': [name]},
@@ -269,21 +276,12 @@ def save_inference_model(dirname,
     inference_program = pruned_program.inference_optimize()
     fetch_var_names = [v.name for v in target_vars]
 
-    model_file_name = dirname + "/__model__"
-    with open(model_file_name, "w") as f:
-        pickle.dump({
-            "program_desc_str": inference_program.desc.serialize_to_string(),
-            "feed_var_names": feeded_var_names,
-            "fetch_var_names": fetch_var_names
-        }, f, -1)
-
     prepend_feed_ops(inference_program, feeded_var_names)
     append_fetch_ops(inference_program, fetch_var_names)
 
-    # Save only programDesc of inference_program in binary format
-    # in another file: __model__.dat
-    with open(model_file_name + ".dat", "wb") as fp:
-        fp.write(inference_program.desc.serialize_to_string())
+    model_file_name = dirname + "/__model__"
+    with open(model_file_name, "wb") as f:
+        f.write(inference_program.desc.serialize_to_string())
 
     save_params(executor, dirname, main_program)
 
@@ -306,6 +304,24 @@ def load_persistables_if_exist(executor, dirname, main_program=None):
         predicate=_is_presistable_and_exist_)
 
 
+def get_feed_targets_names(program):
+    feed_targets_names = []
+    global_block = program.global_block()
+    for op in global_block.ops:
+        if op.desc.type() == 'feed':
+            feed_targets_names.insert(0, op.desc.output('Out')[0])
+    return feed_targets_names
+
+
+def get_fetch_targets_names(program):
+    fetch_targets_names = []
+    global_block = program.global_block()
+    for op in global_block.ops:
+        if op.desc.type() == 'fetch':
+            fetch_targets_names.append(op.desc.input('X')[0])
+    return fetch_targets_names
+
+
 def load_inference_model(dirname, executor):
     """
     Load inference model from a directory
@@ -313,24 +329,28 @@ def load_inference_model(dirname, executor):
     :param dirname: directory path
     :param executor: executor that load inference model
 
-    :return: [program, feed_var_names, fetch_var_names]
+    :return: [program, feed_target_names, fetch_targets]
              program: program especially for inference.
-             feeded_var_names: Names of variables that need to feed data
-             fetch_vars: Variables from which we can get inference results.
+             feed_target_names: Names of variables that need to feed data
+             fetch_targets: Variables from which we can get inference results.
     """
     if not os.path.isdir(dirname):
         raise ValueError("There is no directory named '%s'", dirname)
 
     model_file_name = dirname + "/__model__"
-    model = pickle.load(open(model_file_name, "r"))
-    program_desc_str = model["program_desc_str"]
-    feed_var_names = model["feed_var_names"]
-    fetch_var_names = model["fetch_var_names"]
+    with open(model_file_name, "rb") as f:
+        program_desc_str = f.read()
+
     program = Program.parse_from_string(program_desc_str)
     load_persistables_if_exist(executor, dirname, program)
-    fetch_vars = [program.global_block().var(name) for name in fetch_var_names]
 
-    return [program, feed_var_names, fetch_vars]
+    feed_target_names = get_feed_targets_names(program)
+    fetch_target_names = get_fetch_targets_names(program)
+    fetch_targets = [
+        program.global_block().var(name) for name in fetch_target_names
+    ]
+
+    return [program, feed_target_names, fetch_targets]
 
 
 def get_parameter_value(para, executor):
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
index 8776a65bf8..236ee4f339 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
@@ -91,6 +91,21 @@ for pass_id in range(PASS_NUM):
             fluid.io.save_inference_model(
                 "./recognize_digits_mlp.inference.model/", ["x"], [predict],
                 exe)
-            exit(0)
-
-exit(1)
+            break
+
+# Use load_inference_model to obtain the inference program desc,
+# the feed_target_names (the names of variables that will be feeded 
+# data using feed operators), and the fetch_targets (variables that 
+# we want to obtain data from using fetch operators).
+[infer_prog, feed_target_names, fetch_targets] = fluid.io.load_inference_model(
+    "./recognize_digits_mlp.inference.model/", exe)
+
+tensor_x = np.random.rand(1, 784).astype("float32")
+# Construct feed as a dictionary of {feed_target_name: feed_target_data}
+# and results will contain a list of data corresponding to fetch_targets.
+results = exe.run(infer_prog,
+                  feed={feed_target_names[0]: tensor_x},
+                  fetch_list=fetch_targets)
+print(results[0])
+
+exit(0)

From 605ef6ea2eb542d90c77b048a97b2d0058290ba6 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Wed, 24 Jan 2018 13:58:51 -0800
Subject: [PATCH 051/314] Adding more details to cluster_train (#7831)

---
 doc/howto/usage/cluster/fluid_cluster_train_en.md | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/doc/howto/usage/cluster/fluid_cluster_train_en.md b/doc/howto/usage/cluster/fluid_cluster_train_en.md
index 11904a6f71..ae825d9a51 100644
--- a/doc/howto/usage/cluster/fluid_cluster_train_en.md
+++ b/doc/howto/usage/cluster/fluid_cluster_train_en.md
@@ -16,6 +16,12 @@ PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes
 
 PaddlePaddle build and installation guide can be found  [here](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html).
 
+In addition to above, the `cmake` command should be run with the option `WITH_DISTRIBUTE` set to on. An example bare minimum `cmake` command would look as follows:
+
+``` bash
+cmake .. -DWITH_DOC=OFF -DWITH_GPU=OFF -DWITH_DISTRIBUTE=ON -DWITH_SWIG_PY=ON -DWITH_PYTHON=ON
+```
+
 ### Update the training script
 
 #### Non-cluster training script
@@ -119,7 +125,14 @@ for pass_id in range(100):
 
 ### E2E demo
 
-Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py). In parameter server node run the following in the command line:
+Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py).
+First `cd` into the folder that contains the `python` files. In this case:
+
+```bash
+cd /paddle/python/paddle/v2/fluid/tests/book_distribute
+```
+
+In parameter server node run the following in the command line:
 
 ``` bash
 PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py

From 925b44464a19a77967b5e3366dafe1231a347235 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Wed, 24 Jan 2018 14:22:38 -0800
Subject: [PATCH 052/314] fix test due to api change

---
 python/paddle/v2/fluid/tests/test_multihead_attention.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/tests/test_multihead_attention.py b/python/paddle/v2/fluid/tests/test_multihead_attention.py
index 54ec3e3d6e..a2b300a645 100644
--- a/python/paddle/v2/fluid/tests/test_multihead_attention.py
+++ b/python/paddle/v2/fluid/tests/test_multihead_attention.py
@@ -58,7 +58,7 @@ class TestMultiheadAttention(unittest.TestCase):
         """Run the test program.
         """
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
 
         for place in places:

From 39b0fdc3afca31f89224534f921f5fdcb48778d2 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Wed, 24 Jan 2018 15:14:54 -0800
Subject: [PATCH 053/314] Transpiler: fix pserver crash due to split var name
 check.

In notest_dist_label_semantic_roles.py, "emb" is matched with
"embedding_1.w_0", but they are two irrevalent vars.

Fixes: https://github.com/PaddlePaddle/Paddle/issues/7701
---
 python/paddle/v2/fluid/distribute_transpiler.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index abcad899bf..934eba73b8 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -33,6 +33,10 @@ class VarBlock:
         return "%s:%d:%d" % (self.varname, self.offset, self.size)
 
 
+def same_or_split_var(p_name, var_name):
+    return p_name == var_name or p_name.startswith(var_name + ".block")
+
+
 def split_dense_variable(var_list,
                          pserver_count,
                          min_block_size=1024,
@@ -303,8 +307,8 @@ class DistributeTranspiler:
                 return True
             else:
                 for n in param_names:
-                    if n.startswith(op.inputs["Param"].name+".block") and \
-                       n != op.inputs["Param"].name:
+                    if same_or_split_var(n, op.inputs[
+                            "Param"].name) and n != op.inputs["Param"].name:
                         return True
                 return False
         else:
@@ -335,7 +339,7 @@ class DistributeTranspiler:
             if key == "Grad":
                 grad_block = None
                 for g in self.param_grad_ep_mapping[endpoint]["grads"]:
-                    if g.name.startswith(var.name):
+                    if same_or_split_var(g.name, var.name):
                         grad_block = g
                         break
                 if not grad_block:
@@ -365,7 +369,7 @@ class DistributeTranspiler:
                 # param is already created on global program
                 param_block = None
                 for p in self.param_grad_ep_mapping[endpoint]["params"]:
-                    if p.name.startswith(var.name):
+                    if same_or_split_var(p.name, var.name):
                         param_block = p
                         break
                 if not param_block:
@@ -502,7 +506,7 @@ class DistributeTranspiler:
         def _get_splited_name_and_shape(varname):
             for idx, splited_param in enumerate(params):
                 pname = splited_param.name
-                if pname.startswith(varname) and varname != pname:
+                if same_or_split_var(pname, varname) and varname != pname:
                     return pname, splited_param.shape
             return "", []
 

From 08b736c6d4b2a85c0069e060b6058cee51a7e1b3 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Wed, 24 Jan 2018 18:33:16 -0800
Subject: [PATCH 054/314] Add distributed implementation for recommender system
 (#7810)

* Add distributed implementation for recommender system

* Addressing code review feedback
---
 .../notest_recommender_system_dist.py         | 216 ++++++++++++++++++
 1 file changed, 216 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/notest_recommender_system_dist.py

diff --git a/python/paddle/v2/fluid/tests/book_distribute/notest_recommender_system_dist.py b/python/paddle/v2/fluid/tests/book_distribute/notest_recommender_system_dist.py
new file mode 100644
index 0000000000..2d8885e377
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/notest_recommender_system_dist.py
@@ -0,0 +1,216 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import os
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.nets as nets
+from paddle.v2.fluid.optimizer import SGDOptimizer
+
+IS_SPARSE = True
+BATCH_SIZE = 256
+PASS_NUM = 100
+
+
+def get_usr_combined_features():
+    USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
+    uid = layers.data(name='user_id', shape=[1], dtype='int64')
+    usr_emb = layers.embedding(
+        input=uid,
+        dtype='float32',
+        size=[USR_DICT_SIZE, 32],
+        param_attr='user_table',
+        is_sparse=IS_SPARSE)
+    usr_fc = layers.fc(input=usr_emb, size=32)
+    USR_GENDER_DICT_SIZE = 2
+
+    usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
+    usr_gender_emb = layers.embedding(
+        input=usr_gender_id,
+        size=[USR_GENDER_DICT_SIZE, 16],
+        param_attr='gender_table',
+        is_sparse=IS_SPARSE)
+    usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
+
+    USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
+    usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
+    usr_age_emb = layers.embedding(
+        input=usr_age_id,
+        size=[USR_AGE_DICT_SIZE, 16],
+        is_sparse=IS_SPARSE,
+        param_attr='age_table')
+    usr_age_fc = layers.fc(input=usr_age_emb, size=16)
+
+    USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
+    usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
+    usr_job_emb = layers.embedding(
+        input=usr_job_id,
+        size=[USR_JOB_DICT_SIZE, 16],
+        param_attr='job_table',
+        is_sparse=IS_SPARSE)
+    usr_job_fc = layers.fc(input=usr_job_emb, size=16)
+
+    concat_embed = layers.concat(
+        input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
+
+    usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
+    return usr_combined_features
+
+
+def get_mov_combined_features():
+    MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1
+    mov_id = layers.data(name='movie_id', shape=[1], dtype='int64')
+    mov_emb = layers.embedding(
+        input=mov_id,
+        dtype='float32',
+        size=[MOV_DICT_SIZE, 32],
+        param_attr='movie_table',
+        is_sparse=IS_SPARSE)
+    mov_fc = layers.fc(input=mov_emb, size=32)
+
+    CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
+    category_id = layers.data(name='category_id', shape=[1], dtype='int64')
+    mov_categories_emb = layers.embedding(
+        input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
+    mov_categories_hidden = layers.sequence_pool(
+        input=mov_categories_emb, pool_type="sum")
+
+    MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
+    mov_title_id = layers.data(name='movie_title', shape=[1], dtype='int64')
+    mov_title_emb = layers.embedding(
+        input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
+    mov_title_conv = nets.sequence_conv_pool(
+        input=mov_title_emb,
+        num_filters=32,
+        filter_size=3,
+        act="tanh",
+        pool_type="sum")
+
+    concat_embed = layers.concat(
+        input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
+
+    mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
+    return mov_combined_features
+
+
+def model():
+    usr_combined_features = get_usr_combined_features()
+    mov_combined_features = get_mov_combined_features()
+
+    # need cos sim
+    inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
+    scale_infer = layers.scale(x=inference, scale=5.0)
+
+    label = layers.data(name='score', shape=[1], dtype='float32')
+    square_cost = layers.square_error_cost(input=scale_infer, label=label)
+    avg_cost = layers.mean(x=square_cost)
+
+    return avg_cost
+
+
+def func_feed(feeding, data, place):
+    feed_tensors = {}
+    for (key, idx) in feeding.iteritems():
+        tensor = core.LoDTensor()
+        if key != "category_id" and key != "movie_title":
+            if key == "score":
+                numpy_data = np.array(map(lambda x: x[idx], data)).astype(
+                    "float32")
+            else:
+                numpy_data = np.array(map(lambda x: x[idx], data)).astype(
+                    "int64")
+        else:
+            numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data)
+            lod_info = [len(item) for item in numpy_data]
+            offset = 0
+            lod = [offset]
+            for item in lod_info:
+                offset += item
+                lod.append(offset)
+            numpy_data = np.concatenate(numpy_data, axis=0)
+            tensor.set_lod([lod])
+
+        numpy_data = numpy_data.reshape([numpy_data.shape[0], 1])
+        tensor.set(numpy_data, place)
+        feed_tensors[key] = tensor
+    return feed_tensors
+
+
+def main():
+    cost = model()
+    optimizer = SGDOptimizer(learning_rate=0.2)
+    optimize_ops, params_grads = optimizer.minimize(cost)
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.movielens.train(), buf_size=8192),
+        batch_size=BATCH_SIZE)
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    t = fluid.DistributeTranspiler()
+
+    # all parameter server endpoints list for spliting parameters
+    pserver_endpoints = os.getenv("PSERVERS")
+    # server endpoint for current node
+    current_endpoint = os.getenv("SERVER_ENDPOINT")
+    # run as trainer or parameter server
+    training_role = os.getenv("TRAINING_ROLE", "TRAINER")
+    t.transpile(
+        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+    if training_role == "PSERVER":
+        if not current_endpoint:
+            print("need env SERVER_ENDPOINT")
+            exit(1)
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+        exe.run(pserver_startup)
+        exe.run(pserver_prog)
+    elif training_role == "TRAINER":
+        exe.run(fluid.default_startup_program())
+        trainer_prog = t.get_trainer_program()
+
+        feeding = {
+            'user_id': 0,
+            'gender_id': 1,
+            'age_id': 2,
+            'job_id': 3,
+            'movie_id': 4,
+            'category_id': 5,
+            'movie_title': 6,
+            'score': 7
+        }
+
+        for pass_id in range(PASS_NUM):
+            for data in train_reader():
+                outs = exe.run(trainer_prog,
+                               feed=func_feed(feeding, data, place),
+                               fetch_list=[cost])
+                out = np.array(outs[0])
+                print("cost=" + str(out[0]))
+                if out[0] < 6.0:
+                    print("Training complete. Average cost is less than 6.0.")
+                    # if avg cost less than 6.0, we think our code is good.
+                    exit(0)
+    else:
+        print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+if __name__ == '__main__':
+    main()

From dc7073ded23126bde7a1e7d514b9174cce23a19c Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 25 Jan 2018 10:52:25 +0800
Subject: [PATCH 055/314] update by comment.

---
 python/paddle/v2/fluid/layers/io.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/layers/io.py b/python/paddle/v2/fluid/layers/io.py
index bc804a4043..b7b2cf2296 100644
--- a/python/paddle/v2/fluid/layers/io.py
+++ b/python/paddle/v2/fluid/layers/io.py
@@ -113,7 +113,8 @@ class ListenAndServ(object):
         self.outputs = []
         self.endpoint = endpoint
         self.fan_in = fan_in
-        # FIXME(typhoonzero): Add this switch is stupid
+        # FIXME(typhoonzero): add optimizer_mode is stupid, should make it more
+        # general.
         self.optimizer_mode = optimizer_mode
 
     def do(self):

From 5c6fc3f92ff05edbc77284a1ec34666eac34646e Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Wed, 24 Jan 2018 19:03:12 -0800
Subject: [PATCH 056/314] Make TestLstmpOp inherit from TestLstmOp

---
 python/paddle/v2/fluid/tests/test_lstm_op.py  |  6 +--
 python/paddle/v2/fluid/tests/test_lstmp_op.py | 52 +++----------------
 2 files changed, 11 insertions(+), 47 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_lstm_op.py b/python/paddle/v2/fluid/tests/test_lstm_op.py
index d9fa01e247..3e79f9d8e1 100644
--- a/python/paddle/v2/fluid/tests/test_lstm_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstm_op.py
@@ -42,7 +42,7 @@ def relu(x):
     return np.maximum(x, 0)
 
 
-ACTVATION = {
+ACTIVATION = {
     'identity': identity,
     'sigmoid': sigmoid,
     'tanh': tanh,
@@ -158,8 +158,8 @@ class TestLstmOp(OpTest):
         w_b = b[:, 0:4 * self.D]
         w_c = b[:, 4 * self.D:] if self.use_peepholes else None
         h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
-                    ACTVATION[self.act_gate], ACTVATION[self.act_cell],
-                    ACTVATION[self.act_cand])
+                    ACTIVATION[self.act_gate], ACTIVATION[self.act_cell],
+                    ACTIVATION[self.act_cand])
 
         self.inputs = {'Input': (x, self.lod), 'Weight': w}
 
diff --git a/python/paddle/v2/fluid/tests/test_lstmp_op.py b/python/paddle/v2/fluid/tests/test_lstmp_op.py
index f137fc61b3..92a954a9aa 100644
--- a/python/paddle/v2/fluid/tests/test_lstmp_op.py
+++ b/python/paddle/v2/fluid/tests/test_lstmp_op.py
@@ -13,39 +13,13 @@
 #limitations under the License.
 import unittest
 import numpy as np
-from op_test import OpTest
-
-SIGMOID_THRESHOLD_MIN = -40.0
-SIGMOID_THRESHOLD_MAX = 13.0
-EXP_MAX_INPUT = 40.0
-
-
-def identity(x):
-    return x
-
-
-def sigmoid(x):
-    y = np.copy(x)
-    y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN
-    y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX
-    return 1. / (1. + np.exp(-y))
-
-
-def tanh(x):
-    y = -2. * x
-    y[y > EXP_MAX_INPUT] = EXP_MAX_INPUT
-    return (2. / (1. + np.exp(y))) - 1.
-
-
-def relu(x):
-    return np.maximum(x, 0)
-
+import test_lstm_op as LstmTest
 
 ACTIVATION = {
-    'identity': identity,
-    'sigmoid': sigmoid,
-    'tanh': tanh,
-    'relu': relu
+    'identity': LstmTest.identity,
+    'sigmoid': LstmTest.sigmoid,
+    'tanh': LstmTest.tanh,
+    'relu': LstmTest.relu
 }
 
 
@@ -55,7 +29,7 @@ def lstmp(
         lod,  # 1 x N
         h0=None,  # N x D
         c0=None,  # N x D
-        w_r=None,  # P x 5D
+        w_r=None,  # P x 4D
         w_rh=None,  # D x P
         w_b=None,  # 1 x 4D
         w_c=None,  # 1 x 3D
@@ -130,26 +104,16 @@ def lstmp(
     return projection, cell
 
 
-class TestLstmpOp(OpTest):
+class TestLstmpOp(LstmTest.TestLstmOp):
     def reset_argument(self):
         pass
 
     def setUp(self):
-        self.lod = [[0, 2, 5, 7]]
-        # hidden size
-        self.D = 16
+        self.set_argument()
         # projection size
         self.P = 10
-
-        self.act_gate = 'sigmoid'
-        self.act_cell = 'tanh'
-        self.act_cand = 'tanh'
         self.act_proj = self.act_cell
 
-        self.has_initial_state = False
-        self.is_reverse = False
-        self.use_peepholes = True
-
         self.reset_argument()
         self.op_type = 'lstmp'
 

From 25cb906403b5bd85c539055947eb1226f5939571 Mon Sep 17 00:00:00 2001
From: "Yang Yang(Tony)" <yangyang62@baidu.com>
Date: Wed, 24 Jan 2018 19:06:37 -0800
Subject: [PATCH 057/314] Fix call once logic (#7839)

* fix call once logic

* clean up

* further clean up
---
 paddle/platform/call_once.h | 31 ++++++++++++++++++-------------
 1 file changed, 18 insertions(+), 13 deletions(-)

diff --git a/paddle/platform/call_once.h b/paddle/platform/call_once.h
index 00337a7f05..44a4d38f67 100644
--- a/paddle/platform/call_once.h
+++ b/paddle/platform/call_once.h
@@ -29,20 +29,25 @@ namespace platform {
 */
 template <typename Callable, typename... Args>
 inline void call_once(std::once_flag& flag, Callable&& f, Args&&... args) {
-  bool good = false;
+  bool good = true;
   std::exception ex;
-  std::call_once(flag,
-                 [&](Args&&... args) {
-                   try {
-                     f(args...);
-                     good = true;
-                   } catch (const std::exception& e) {
-                     ex = e;
-                   } catch (...) {
-                     ex = std::runtime_error("excption caught in call_once");
-                   }
-                 },
-                 args...);
+  try {
+    std::call_once(flag,
+                   [&](Args&&... args) {
+                     try {
+                       f(args...);
+                     } catch (const std::exception& e) {
+                       ex = e;
+                       good = false;
+                     } catch (...) {
+                       ex = std::runtime_error("excption caught in call_once");
+                       good = false;
+                     }
+                   },
+                   args...);
+  } catch (std::system_error& x) {
+    throw std::runtime_error("call once failed");
+  }
   if (!good) {
     throw std::exception(ex);
   }

From 1d2c99c336c156e502a081b89e8f6937839e86e5 Mon Sep 17 00:00:00 2001
From: "Yang Yang(Tony)" <yangyang62@baidu.com>
Date: Wed, 24 Jan 2018 19:08:22 -0800
Subject: [PATCH 058/314] fix nccl root number (#7842)

* fix call once logic

* clean up

* further clean up

* fix root number
---
 paddle/operators/nccl_op_test.cu.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/operators/nccl_op_test.cu.cc b/paddle/operators/nccl_op_test.cu.cc
index 6546096069..072e4eb2ef 100644
--- a/paddle/operators/nccl_op_test.cu.cc
+++ b/paddle/operators/nccl_op_test.cu.cc
@@ -241,7 +241,7 @@ TEST_F(NCCLTester, ncclReduceOp) {
 // ncclBcastOp with desc
 TEST_F(NCCLTester, ncclBcastOp) {
   std::unique_ptr<f::OpDesc> op2(new f::OpDesc);
-  const int kRoot = 5;
+  const int kRoot = 0;
   op2->SetType("ncclBcast");
   op2->SetInput("X", {"st"});
   op2->SetInput("Communicator", {"comm"});

From 876b32ae6a151534d5f09528a1460bb9e641370e Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Thu, 25 Jan 2018 12:38:20 +0800
Subject: [PATCH 059/314] "fix typo" (#7846)

---
 .copyright.hook | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.copyright.hook b/.copyright.hook
index dc1b096a0a..09afff2072 100644
--- a/.copyright.hook
+++ b/.copyright.hook
@@ -9,7 +9,7 @@ import subprocess
 import platform
 
 COPYRIGHT = '''
-  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

From 7eb19abc76d58ffc2a4968732a08545b3f8cecb5 Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Thu, 25 Jan 2018 13:00:22 +0800
Subject: [PATCH 060/314] Refine the doc.

---
 python/paddle/v2/fluid/layers/nn.py | 30 ++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index d87cedca89..bae33f6a15 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -2714,21 +2714,21 @@ def multiplex(inputs, index):
     """
     **Multiplex Layer**
 
-    Referring to the given index variable, this layer gathers from the input
-    variables to output a multiplex variable. Assuming that there are :math:`m`
-    input variables and let :math:`I_i` represents the i-th input variable and i
-    is in [0, :math:`m`). All input variables are tensors with same shape
-    [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. Please note that rank of the
-    input tensor should be at least 2. Each input variable will be treated as a
-    2-D matrix with shape [:math:`M`, :math:`N`] where :math:`M` for :math:`d_0`
-    and :math:`N` for :math:`d_1` * :math:`d_2` * ... * :math:`d_R`. Let
-    :math:`I_i[j]` be the j-th row of the i-th input variable. The given index
-    variable should be a 2-D tensor with shape [:math:`M`, 1]. Let `ID[i]` be
-    the i-th index value of the index variable. Then the output variable will
-    be a tensor with shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`]. If we
-    treat the output tensor as a 2-D matrix with shape [:math:`M`, :math:`N`]
-    and let :math:`O[i]` be the i-th row of the matrix, then `O[i]` is equal to
-    :math:`I_{ID[i]}[i]`.
+    Referring to the given index variable, this layer selects rows from the
+    input variables to construct a multiplex variable. Assuming that there are
+    :math:`m` input variables and :math:`I_i` represents the i-th input
+    variable and :math:`i` is in [0, :math:`m`). All input variables are
+    tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
+    Please note that rank of the input tensor should be at least 2. Each input
+    variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
+    where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
+    * ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
+    variable. The given index variable should be a 2-D tensor with shape
+    [:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
+    Then the output variable will be a tensor with shape [:math:`d_0`,
+    :math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
+    matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
+    row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
 
     Args:
        inputs (list): A list of variables to gather from. All variables have the

From 250206d1cfeafa74c353ed167b6b5852f8ccec3e Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Tue, 23 Jan 2018 10:44:28 +0000
Subject: [PATCH 061/314] Change the example of inference to a unittest.

---
 paddle/inference/CMakeLists.txt               | 17 ++--------
 paddle/inference/tests/book/CMakeLists.txt    | 13 +++++++
 .../test_inference_recognize_digits_mlp.cc}   | 34 +++++++++----------
 3 files changed, 31 insertions(+), 33 deletions(-)
 create mode 100644 paddle/inference/tests/book/CMakeLists.txt
 rename paddle/inference/{example.cc => tests/book/test_inference_recognize_digits_mlp.cc} (72%)

diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index ae4d3fd2f5..fedf9e4cb8 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -24,19 +24,6 @@ if(NOT WITH_C_API AND WITH_FLUID)
   install(TARGETS paddle_fluid_shared DESTINATION lib)
 endif()
 
-add_executable(example example.cc)
-if(APPLE)
-  set(OPTIONAL_LINK_FLAGS)
-  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang" OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang")
-    set(OPTIONAL_LINK_FLAGS "-undefined dynamic_lookup")
-  endif()
-  target_link_libraries(example
-      -Wl,-force_load paddle_fluid
-      ${OPTIONAL_LINK_FLAGS}
-      ${PTOOLS_LIB})
-else()
-  target_link_libraries(example
-      -Wl,--start-group -Wl,--whole-archive paddle_fluid
-      -Wl,--no-whole-archive -Wl,--end-group
-      ${PTOOLS_LIB})
+if(WITH_TESTING)
+  add_subdirectory(tests/book)
 endif()
diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
new file mode 100644
index 0000000000..31e6796fdb
--- /dev/null
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -0,0 +1,13 @@
+add_executable(test_inference_recognize_digits_mlp test_inference_recognize_digits_mlp.cc)
+target_circle_link_libraries(
+    test_inference_recognize_digits_mlp
+    ARCHIVE_START
+    paddle_fluid
+    ARCHIVE_END
+    gtest
+    gflags)
+add_test(
+    NAME test_inference_recognize_digits_mlp
+    COMMAND test_inference_recognize_digits_mlp
+           --dirname=${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests/book/recognize_digits_mlp.inference.model
+    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
diff --git a/paddle/inference/example.cc b/paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc
similarity index 72%
rename from paddle/inference/example.cc
rename to paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc
index 0c18b45624..e96af21344 100644
--- a/paddle/inference/example.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc
@@ -12,20 +12,17 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <gtest/gtest.h>
 #include <time.h>
-#include <iostream>
+#include <sstream>
 #include "gflags/gflags.h"
 #include "paddle/inference/inference.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-int main(int argc, char** argv) {
-  google::ParseCommandLineFlags(&argc, &argv, true);
+TEST(inference, recognize_digits_mlp) {
   if (FLAGS_dirname.empty()) {
-    // Example:
-    //   ./example --dirname=recognize_digits_mlp.inference.model
-    std::cout << "Usage: ./example --dirname=path/to/your/model" << std::endl;
-    exit(1);
+    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
   }
 
   std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
@@ -48,20 +45,21 @@ int main(int argc, char** argv) {
   engine->Execute(feeds, fetchs);
 
   for (size_t i = 0; i < fetchs.size(); ++i) {
-    auto dims_i = fetchs[i].dims();
-    std::cout << "dims_i:";
-    for (int j = 0; j < dims_i.size(); ++j) {
-      std::cout << " " << dims_i[j];
-    }
-    std::cout << std::endl;
-    std::cout << "result:";
+    LOG(INFO) << fetchs[i].dims();
+    std::stringstream ss;
+    ss << "result:";
     float* output_ptr = fetchs[i].data<float>();
-    for (int j = 0; j < paddle::framework::product(dims_i); ++j) {
-      std::cout << " " << output_ptr[j];
+    for (int j = 0; j < fetchs[i].numel(); ++j) {
+      ss << " " << output_ptr[j];
     }
-    std::cout << std::endl;
+    LOG(INFO) << ss.str();
   }
 
   delete engine;
-  return 0;
+}
+
+int main(int argc, char** argv) {
+  google::ParseCommandLineFlags(&argc, &argv, false);
+  testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
 }

From f9a11f51ce4b815c8c456d5d699c675726abdb91 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Thu, 25 Jan 2018 14:32:53 +0800
Subject: [PATCH 062/314] Add noavx-openblas whl package download link

---
 doc/getstarted/build_and_install/pip_install_cn.rst | 1 +
 doc/getstarted/build_and_install/pip_install_en.rst | 1 +
 2 files changed, 2 insertions(+)

diff --git a/doc/getstarted/build_and_install/pip_install_cn.rst b/doc/getstarted/build_and_install/pip_install_cn.rst
index 0c741e936b..8e4165da6b 100644
--- a/doc/getstarted/build_and_install/pip_install_cn.rst
+++ b/doc/getstarted/build_and_install/pip_install_cn.rst
@@ -39,6 +39,7 @@ PaddlePaddle可以使用常用的Python包管理工具
 
     "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
+    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
     "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
diff --git a/doc/getstarted/build_and_install/pip_install_en.rst b/doc/getstarted/build_and_install/pip_install_en.rst
index 285ed09805..c1e806c0fe 100644
--- a/doc/getstarted/build_and_install/pip_install_en.rst
+++ b/doc/getstarted/build_and_install/pip_install_en.rst
@@ -42,6 +42,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
 
     "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
+    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
     "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"

From 5594456a4bd6df3771f2d6d736a80adda83dd431 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Thu, 25 Jan 2018 15:13:00 +0800
Subject: [PATCH 063/314] Add build_type to build.sh

---
 paddle/scripts/docker/build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index e70d04d901..fbae37b2ca 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -32,7 +32,7 @@ function cmake_gen() {
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
-        -DCMAKE_BUILD_TYPE=Release
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release}
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
@@ -54,7 +54,7 @@ EOF
     # docker environment is fully controlled by this script.
     # See /Paddle/CMakeLists.txt, UNITTEST_USE_VIRTUALENV option.
     cmake .. \
-        -DCMAKE_BUILD_TYPE=Release \
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release} \
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \

From 900f411d629dd4fe18417455055529b86f4455f2 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 25 Jan 2018 17:11:52 +0800
Subject: [PATCH 064/314] fix dist transpiler bug

---
 python/paddle/v2/fluid/distribute_transpiler.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 934eba73b8..908810c8be 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -225,7 +225,7 @@ class DistributeTranspiler:
             if len(splited_vars) <= 1:
                 continue
             orig_var = program.global_block().vars[varname]
-            if orig_var == core.VarDesc.VarType.SELECTED_ROWS:
+            if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS:
                 height_sections = []
                 for v in splited_vars:
                     height_sections.append(v.shape[0])
@@ -234,7 +234,7 @@ class DistributeTranspiler:
                     inputs={"X": orig_var},
                     outputs={"Out": splited_vars},
                     attrs={"height_sections": height_sections})
-            elif orig_var == core.VarDesc.VarType.LOD_TENSOR:
+            elif orig_var.type == core.VarDesc.VarType.LOD_TENSOR:
                 sections = []
                 for v in splited_vars:
                     sections.append(v.shape[0])

From 7a2e6dead9a8d13ade81e699932b5e78cb6ea64b Mon Sep 17 00:00:00 2001
From: peterzhang2029 <zhangchao41@baidu.com>
Date: Thu, 25 Jan 2018 18:59:59 +0800
Subject: [PATCH 065/314] fix test_rnn_encoder_decoder

---
 ...encoder_context.py => test_rnn_encoder_decoder.py} | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)
 rename python/paddle/v2/fluid/tests/book/{test_machine_translation_encoder_context.py => test_rnn_encoder_decoder.py} (95%)

diff --git a/python/paddle/v2/fluid/tests/book/test_machine_translation_encoder_context.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
similarity index 95%
rename from python/paddle/v2/fluid/tests/book/test_machine_translation_encoder_context.py
rename to python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index 53ed912c6f..3fd3dbaf77 100644
--- a/python/paddle/v2/fluid/tests/book/test_machine_translation_encoder_context.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -118,12 +118,13 @@ def seq_to_seq_net():
     src_forward, src_backward = bi_lstm_encoder(
         input_seq=src_embedding, hidden_size=encoder_size)
 
-    encoded_vector = fluid.layers.concat(
-        input=[src_forward, src_backward], axis=1)
+    src_forward_last = fluid.layers.sequence_last_step(input=src_forward)
+    src_backward_first = fluid.layers.sequence_first_step(input=src_backward)
 
-    enc_vec_last = fluid.layers.sequence_last_step(input=encoded_vector)
+    encoded_vector = fluid.layers.concat(
+        input=[src_forward_last, src_backward_first], axis=1)
 
-    decoder_boot = fluid.layers.fc(input=enc_vec_last,
+    decoder_boot = fluid.layers.fc(input=encoded_vector,
                                    size=decoder_size,
                                    bias_attr=False,
                                    act='tanh')
@@ -137,7 +138,7 @@ def seq_to_seq_net():
         dtype='float32')
 
     prediction = lstm_decoder_without_attention(trg_embedding, decoder_boot,
-                                                enc_vec_last, decoder_size)
+                                                encoded_vector, decoder_size)
     label = fluid.layers.data(
         name='label_sequence', shape=[1], dtype='int64', lod_level=1)
     cost = fluid.layers.cross_entropy(input=prediction, label=label)

From 20d3af62d048f3fa8793f929c91f887ca52e556b Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Thu, 25 Jan 2018 09:47:41 +0000
Subject: [PATCH 066/314] Enable the dependency.

---
 paddle/inference/inference.cc                 |  2 +-
 paddle/inference/tests/book/CMakeLists.txt    | 11 ++--
 ....cc => test_inference_recognize_digits.cc} |  2 +-
 .../fluid/tests/book/test_recognize_digits.py | 53 ++++++++++++++++---
 4 files changed, 54 insertions(+), 14 deletions(-)
 rename paddle/inference/tests/book/{test_inference_recognize_digits_mlp.cc => test_inference_recognize_digits.cc} (98%)

diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 09268ffb3a..37d9776cd0 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -75,7 +75,7 @@ void InferenceEngine::GenerateLoadProgram(const std::string& dirname) {
   framework::BlockDesc* load_block = load_program_->MutableBlock(0);
   for (auto* var : global_block->AllVars()) {
     if (IsParameter(var)) {
-      LOG(INFO) << "parameter's name: " << var->Name();
+      VLOG(3) << "parameter's name: " << var->Name();
 
       framework::VarDesc* new_var = load_block->Var(var->Name());
       new_var->SetShape(var->Shape());
diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index 31e6796fdb..78083cc218 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -1,6 +1,7 @@
-add_executable(test_inference_recognize_digits_mlp test_inference_recognize_digits_mlp.cc)
+set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests)
+add_executable(test_inference_recognize_digits test_inference_recognize_digits.cc)
 target_circle_link_libraries(
-    test_inference_recognize_digits_mlp
+    test_inference_recognize_digits
     ARCHIVE_START
     paddle_fluid
     ARCHIVE_END
@@ -8,6 +9,8 @@ target_circle_link_libraries(
     gflags)
 add_test(
     NAME test_inference_recognize_digits_mlp
-    COMMAND test_inference_recognize_digits_mlp
-           --dirname=${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests/book/recognize_digits_mlp.inference.model
+    COMMAND test_inference_recognize_digits
+            --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model
     WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+set_tests_properties(test_inference_recognize_digits_mlp
+    PROPERTIES DEPENDS test_recognize_digits_mlp_cpu)
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
similarity index 98%
rename from paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc
rename to paddle/inference/tests/book/test_inference_recognize_digits.cc
index e96af21344..d0e811914c 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits_mlp.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -20,7 +20,7 @@ limitations under the License. */
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-TEST(inference, recognize_digits_mlp) {
+TEST(inference, recognize_digits) {
   if (FLAGS_dirname.empty()) {
     LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
   }
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index ac7ef4046f..d6e4675a24 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -45,8 +45,9 @@ BATCH_SIZE = 64
 def loss_net(hidden, label):
     prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
     loss = fluid.layers.cross_entropy(input=prediction, label=label)
-    return fluid.layers.mean(x=loss), fluid.layers.accuracy(
-        input=prediction, label=label)
+    avg_loss = fluid.layers.mean(x=loss)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+    return prediction, avg_loss, acc
 
 
 def mlp(img, label):
@@ -73,8 +74,7 @@ def conv_net(img, label):
     return loss_net(conv_pool_2, label)
 
 
-def main():
-    args = parse_arg()
+def train(args, save_dirname=None):
     print("recognize digits with args: {0}".format(" ".join(sys.argv[1:])))
 
     img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
@@ -91,7 +91,8 @@ def main():
         with pd.do():
             img_ = pd.read_input(img)
             label_ = pd.read_input(label)
-            for o in net_conf(img_, label_):
+            prediction, avg_loss, acc = net_conf(img_, label_)
+            for o in [avg_loss, acc]:
                 pd.write_output(o)
 
         avg_loss, acc = pd()
@@ -99,7 +100,7 @@ def main():
         avg_loss = fluid.layers.mean(x=avg_loss)
         acc = fluid.layers.mean(x=acc)
     else:
-        avg_loss, acc = net_conf(img, label)
+        prediction, avg_loss, acc = net_conf(img, label)
 
     test_program = fluid.default_main_program().clone()
 
@@ -137,7 +138,10 @@ def main():
                 acc_val = numpy.array(acc_set).mean()
                 avg_loss_val = numpy.array(avg_loss_set).mean()
                 if float(acc_val) > 0.85:  # test acc > 85%
-                    exit(0)
+                    if save_dirname is not None:
+                        fluid.io.save_inference_model(save_dirname, ["img"],
+                                                      [prediction], exe)
+                    return
                 else:
                     print(
                         'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
@@ -145,5 +149,38 @@ def main():
                                float(avg_loss_val), float(acc_val)))
 
 
+def infer(args, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Use fluid.io.load_inference_model to obtain the inference program desc,
+    # the feed_target_names (the names of variables that will be feeded 
+    # data using feed operators), and the fetch_targets (variables that 
+    # we want to obtain data from using fetch operators).
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+    if args.nn_type == 'mlp':
+        tensor_img = numpy.random.rand(1, 28, 28).astype("float32")
+    else:
+        tensor_img = numpy.random.rand(1, 1, 28, 28).astype("float32")
+
+    # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+    # and results will contain a list of data corresponding to fetch_targets.
+    results = exe.run(inference_program,
+                      feed={feed_target_names[0]: tensor_img},
+                      fetch_list=fetch_targets)
+    print("infer results: ", results[0])
+
+
 if __name__ == '__main__':
-    main()
+    args = parse_arg()
+    if not args.use_cuda and not args.parallel:
+        save_dirname = "recognize_digits_" + args.nn_type + ".inference.model"
+    else:
+        save_dirname = None
+    train(args, save_dirname)
+    infer(args, save_dirname)

From 020bee03a914d1a904bad02e99981125fa4deb03 Mon Sep 17 00:00:00 2001
From: peterzhang2029 <zhangchao41@baidu.com>
Date: Thu, 25 Jan 2018 20:28:20 +0800
Subject: [PATCH 067/314] fix the decoder_boot

---
 .../v2/fluid/tests/book/test_rnn_encoder_decoder.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index 3fd3dbaf77..fdc6086176 100644
--- a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -49,7 +49,11 @@ def bi_lstm_encoder(input_seq, hidden_size):
         size=hidden_size * 4,
         is_reverse=True,
         use_peepholes=USE_PEEPHOLES)
-    return forward, backward
+
+    forward_last = fluid.layers.sequence_last_step(input=forward)
+    backward_first = fluid.layers.sequence_first_step(input=backward)
+
+    return forward_last, backward_first
 
 
 # FIXME(peterzhang2029): Replace this function with the lstm_unit_op.
@@ -115,16 +119,13 @@ def seq_to_seq_net():
         size=[source_dict_dim, embedding_dim],
         dtype='float32')
 
-    src_forward, src_backward = bi_lstm_encoder(
+    src_forward_last, src_backward_first = bi_lstm_encoder(
         input_seq=src_embedding, hidden_size=encoder_size)
 
-    src_forward_last = fluid.layers.sequence_last_step(input=src_forward)
-    src_backward_first = fluid.layers.sequence_first_step(input=src_backward)
-
     encoded_vector = fluid.layers.concat(
         input=[src_forward_last, src_backward_first], axis=1)
 
-    decoder_boot = fluid.layers.fc(input=encoded_vector,
+    decoder_boot = fluid.layers.fc(input=src_backward_first,
                                    size=decoder_size,
                                    bias_attr=False,
                                    act='tanh')

From 7333df8510953a0c99a1c0702666ff090c005378 Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Thu, 25 Jan 2018 21:16:45 +0800
Subject: [PATCH 068/314] fix pool_op bug (#7879)

---
 paddle/operators/pool_op.h | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/paddle/operators/pool_op.h b/paddle/operators/pool_op.h
index c3d82ecbde..d6ba5e298a 100644
--- a/paddle/operators/pool_op.h
+++ b/paddle/operators/pool_op.h
@@ -139,10 +139,8 @@ class PoolGradKernel : public framework::OpKernel<T> {
     auto& dev_ctx = context.template device_context<DeviceContext>();
     if (in_x_grad) {
       in_x_grad->mutable_data<T>(context.GetPlace());
-      auto temp = framework::EigenVector<T>::Flatten(*in_x_grad);
-      temp.device(
-          *context.template device_context<DeviceContext>().eigen_device()) =
-          temp.constant(static_cast<T>(0));
+      paddle::operators::math::SetConstant<DeviceContext, T> set_constant;
+      set_constant(dev_ctx, in_x_grad, 0.0);
 
       switch (ksize.size()) {
         case 2: {

From a8c46f33d8ede25d92bda492acca5358d8b01ebe Mon Sep 17 00:00:00 2001
From: "yi.wu" <yi.wu@baifendian.com>
Date: Thu, 25 Jan 2018 21:17:43 +0800
Subject: [PATCH 069/314] downgrade boost to fit manylinux

---
 cmake/external/boost.cmake | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/cmake/external/boost.cmake b/cmake/external/boost.cmake
index 137f11da7f..a2c979b2e8 100644
--- a/cmake/external/boost.cmake
+++ b/cmake/external/boost.cmake
@@ -15,8 +15,8 @@
 include(ExternalProject)
 
 set(BOOST_PROJECT       "extern_boost")
-set(BOOST_VER           "1.66.0")
-set(BOOST_TAR           "boost_1_66_0")
+set(BOOST_VER           "1.41.0")
+set(BOOST_TAR           "boost_1_41_0")
 set(BOOST_URL           "https://dl.bintray.com/boostorg/release/${BOOST_VER}/source/${BOOST_TAR}.tar.gz")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")

From 4ca19d4e18c73cd84e1787a79704f2a1da3c8c0a Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Thu, 25 Jan 2018 05:26:50 -0800
Subject: [PATCH 070/314] Add python api for lstmp operator

---
 doc/api/v2/fluid/layers.rst         |   7 +-
 python/paddle/v2/fluid/layers/nn.py | 178 +++++++++++++++++++++++++++-
 2 files changed, 183 insertions(+), 2 deletions(-)

diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index f738bf1564..6c6af106db 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -18,7 +18,12 @@ dynamic_lstm
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
     :noindex:
 
-dynamic_gru
+dynamic_lstmp
+------------
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
+    :noindex:
+
+ dynamic_gru
 -----------
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
     :noindex:
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index bae33f6a15..fa03a64291 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -26,6 +26,7 @@ __all__ = [
     'fc',
     'embedding',
     'dynamic_lstm',
+    'dynamic_lstmp',
     'dynamic_gru',
     'gru_unit',
     'linear_chain_crf',
@@ -282,7 +283,7 @@ def dynamic_lstm(input,
     W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
     our implementation, we use vectors to reprenset these diagonal weight
     matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
-    gate bias vector), :math:`\sigma` is the non-line activations, such as
+    gate bias vector), :math:`\sigma` is the non-linear activations, such as
     logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
     gate, forget gate, output gate, and cell activation vectors, respectively,
     all of which have the same size as the cell output activation vector :math:`h`.
@@ -389,6 +390,181 @@ def dynamic_lstm(input,
     return hidden, cell
 
 
+def dynamic_lstmp(input,
+                  size,
+                  proj_size,
+                  param_attr=None,
+                  bias_attr=None,
+                  use_peepholes=True,
+                  is_reverse=False,
+                  gate_activation='sigmoid',
+                  cell_activation='tanh',
+                  candidate_activation='tanh',
+                  proj_activation='tanh',
+                  dtype='float32'):
+    """
+    **Dynamic LSTMP Layer**
+
+    LSTMP (LSTM with recurrent projection) layer has a separate projection 
+    layer after the LSTM layer, projecting the original hidden state to a 
+    lower-dimensional one, which is proposed to reduce the number of total 
+    parameters and furthermore computational complexity for the LSTM, 
+    espeacially for the case that the size of output units is relative 
+    large (https://research.google.com/pubs/archive/43905.pdf). 
+
+    The formula is as follows:
+
+    .. math::
+
+        i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
+
+        f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
+
+        \tilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c) \\
+
+        o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o) \\
+
+        c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
+
+        h_t = o_t \odot act_h(c_t) \\
+
+        r_t = \overline{act_h}(W_{rh}h_t)
+
+    where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is 
+    the matrix of weights from the input gate to the input), :math:`W_{ic}`, 
+    :math:`W_{fc}`, :math:`W_{oc}` are diagonal weight matrices for peephole 
+    connections. In our implementation, we use vectors to reprenset these 
+    diagonal weight matrices. The :math:`b` terms denote bias vectors 
+    (:math:`b_i` is the input gate bias vector), :math:`\sigma` is the 
+    activation, such as logistic sigmoid function, and :math:`i, f, o` and 
+    :math:`c` are the input gate, forget gate, output gate, and cell activation 
+    vectors, respectively, all of which have the same size as the cell output 
+    activation vector :math:`h`. Here :math:`h` is usually called the hidden 
+    state and :math:`r` denotes its recurrent projection. And 
+    :math:`\tilde{c_t}` is also called the candidate hidden state, whose 
+    computation is based on the current input and previous hidden state.
+
+    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g` 
+    and :math:`act_h` are the cell input and cell output activation functions 
+    and `tanh` is usually used for them. :math:`\overline{act_h}` is the 
+    activation function for the projection output, usually using `identity` or 
+    same as :math:`act_h`.
+
+    Set `use_peepholes` to `False` to disable peephole connection. The formula
+    is omitted here, please refer to the paper
+    http://www.bioinf.jku.at/publications/older/2604.pdf for details.
+    
+    Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
+    operations on the input :math:`x_{t}` are NOT included in this operator.
+    Users can choose to use fully-connected layer before LSTMP layer.
+
+    Args:
+        input(Variable): The input of dynamic_lstmp layer, which supports
+                         variable-time length input sequence. The underlying
+                         tensor in this Variable is a matrix with shape
+                         (T X 4D), where T is the total time steps in this
+                         mini-batch, D is the hidden size.
+        size(int): 4 * hidden size.
+        proj_size(int): The size of projection output.
+        param_attr(ParamAttr): The parameter attribute for the learnable
+                               hidden-hidden weight and projection weight.
+
+                               - The shape of hidden-hidden weight is (P x 4D), 
+                                 where P is the projection size and D the hidden 
+                                 size.
+                               - The shape of projection weight is (D x P).
+                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
+                                                W_{fh}, W_{oh}`}.
+                               - Projection weight = {:math:`W_{rh}`}.
+        bias_attr(ParamAttr): The bias attribute for the learnable bias
+                              weights, which contains two parts, input-hidden
+                              bias weights and peephole connections weights if
+                              setting `use_peepholes` to `True`.
+
+                              1. `use_peepholes = False`
+                                - The shape is (1 x 4D).
+                                - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                              2. `use_peepholes = True`
+                                - The shape is (1 x 7D).
+                                - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
+                                                 W_{fc}, W_{oc}`}.
+        use_peepholes(bool): Whether to enable diagonal/peephole connections,
+                             default `True`.
+        is_reverse(bool): Whether to compute reversed LSTM, default `False`.
+        gate_activation(str): The activation for input gate, forget gate and
+                              output gate. Choices = ["sigmoid", "tanh", "relu",
+                              "identity"], default "sigmoid".
+        cell_activation(str): The activation for cell output. Choices = ["sigmoid",
+                              "tanh", "relu", "identity"], default "tanh".
+        candidate_activation(str): The activation for candidate hidden state.
+                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              default "tanh".
+        proj_activation(str): The activation for projection output.
+                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              default "tanh".
+        dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
+
+    Returns:
+        tuple: The projection of hidden state, and cell state of LSTMP. The 
+               shape of projection is (T x P), for the cell state which is 
+               (T x D), and both LoD is the same with the `input`.
+
+    Examples:
+        .. code-block:: python
+
+            hidden_dim = 512
+            proj_dim = 256
+            fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
+                                     act=None, bias_attr=None)
+            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out, 
+                size=hidden_dim * 4, proj_size=proj_dim, use_peepholes=False)
+    """
+    helper = LayerHelper('lstmp', **locals())
+    size = size / 4
+    weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
+    proj_weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
+    bias_size = [1, 7 * size]
+    if not use_peepholes:
+        bias_size[1] = 4 * size
+    bias = helper.create_parameter(
+        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+
+    projection = helper.create_tmp_variable(dtype)
+    cell = helper.create_tmp_variable(dtype)
+    ordered_proj0 = helper.create_tmp_variable(dtype)
+    batch_hidden = helper.create_tmp_variable(dtype)
+    batch_gate = helper.create_tmp_variable(dtype)
+    batch_cell_pre_act = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='lstmp',
+        inputs={
+            'Input': input,
+            'Weight': weight,
+            'ProjWeight': proj_weight,
+            'Bias': bias
+        },
+        outputs={
+            'Projection': projection,
+            'Cell': cell,
+            'OrderedP0': ordered_proj0,
+            'BatchHidden': batch_hidden,
+            'BatchGate': batch_gate,
+            'BatchCellPreAct': batch_cell_pre_act
+        },
+        attrs={
+            'use_peepholes': use_peepholes,
+            'is_reverse': is_reverse,
+            'gate_activation': gate_activation,
+            'cell_activation': cell_activation,
+            'candidate_activation': candidate_activation,
+            'proj_activation': proj_activation
+        })
+    return projection, cell
+
+
 def dynamic_gru(input,
                 size,
                 param_attr=None,

From ae0ea5415902f3187c7883016c3798ee5ec64fab Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 24 Jan 2018 14:14:42 +0800
Subject: [PATCH 071/314] fix unit test

---
 paddle/operators/layer_norm_op.cc             |  11 +-
 .../v2/fluid/tests/test_layer_norm_op.py      | 261 ++++++++++++++----
 2 files changed, 215 insertions(+), 57 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index f1ddcd8210..0808192565 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -233,13 +233,13 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     if (d_x) {
       d_x->mutable_data<T>(ctx.GetPlace());
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
-      auto triple_product = [](T ele) { return ele * ele * ele; };
-      auto neg_inv_std = [](T ele) { return T(-1.0) * std::sqrt(1 / ele); };
+      auto triple_product = [](T ele) { return ele * ele; };
+      auto neg_inv_std = [](T ele) { return -std::sqrt(1 / ele); };
       auto inv_std_scale_func = [scale_data](T ele) {
         return std::sqrt(1 / ele) * scale_data;
       };
       auto neg_inv_std_scale_func = [scale_data](T ele) {
-        return T(-1.0) * std::sqrt(1 / ele) * scale_data;
+        return -std::sqrt(1 / ele) * scale_data;
       };
       // dy_dx
       auto dx_end = var_map.unaryExpr(inv_std_scale_func)
@@ -260,10 +260,13 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto dvar_end = var_map.unaryExpr(neg_inv_std)
                           .unaryExpr(triple_product)
                           .cwiseProduct(dvar_end_0);
-      auto dx_var = (1.0f / right) *
+      auto dx_var = (T(1.0) / right) *
                     (x_map - mean_map.replicate(1, right))
                         .cwiseProduct(dvar_end.replicate(1, right));
 
+      // d_x = (1. / N) * scale * inv_var * (N * d_y - np.sum(d_y, axis=0)
+      //   - (X - mean) * inv_var * inv_var * np.sum(d_y * (X - mean), axis=0))
+
       d_x_map = dx_end + dx_mean + dx_var;
     }
   }
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 73450c599d..4ca9754f32 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -15,66 +15,221 @@
 import unittest
 import numpy as np
 
+from operator import mul
 from op_test import OpTest
+import paddle.v2.fluid.core as core
+from paddle.v2.fluid.op import Operator
+from paddle.v2.fluid.framework import grad_var_name
 
 
-def layer_norm_naive(x, scale, beta, epsilon):
-    n, c, h, w = x.shape
-    mean = np.mean(x, axis=(1, 2, 3))
-    var = np.var(x, axis=(1, 2, 3)) + epsilon
-    output = scale * np.divide((x - mean.reshape([n, 1, 1, 1])),
-                               (np.sqrt(var)).reshape([n, 1, 1, 1])) + beta
+def get_backward_op(scope, op, no_grad_set):
+    backward_op = core.Operator.backward(op, no_grad_set)
+    for input in backward_op.input_vars():
+        var = scope.var(input)
+        var.get_tensor()
+    for output in backward_op.output_vars():
+        var = scope.var(output)
+        var.get_tensor()
+    return backward_op
+
+
+def _reference_layer_norm_naive(x, scale, beta, epsilon):
+    old_shape = x.shape
+    N = x.shape[0]
+    D = reduce(mul, old_shape, 1) / N
+    x.shape = [N, D]
+    mean = np.mean(x, axis=1)
+    var = np.var(x, axis=1) + epsilon
+    output = scale * np.divide((x - mean.reshape([N, 1])),
+                               (np.sqrt(var)).reshape([N, 1])) + beta
+    output.shape = old_shape
     return output, mean, var
 
 
+def _reference_layer_norm_grad(x, grad_y, scale, mean, var, epsilon):
+    x_shape = x.shape
+    N = x_shape[0]
+    D = reduce(mul, x_shape, 1) / N
+    grad_y.shape = [N, D]
+    x.shape = [N, D]
+    grad_offset = np.sum(grad_y)
+    mean.shape = [N, 1]
+    var.shape = [N, 1]
+    grad_scale = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y)
+
+    dx_end = np.sqrt(1.0 / var) * grad_y
+
+    d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y, axis=1).reshape([N, 1])
+    d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
+        [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
+                   np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
+    d_mean = 1.0 / D * (d_mean_0 + d_mean_1)
+
+    d_std = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape([N, 1]) * (
+        1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
+
+    grad_x = scale * (dx_end + d_mean + d_std)
+
+    grad_y.shape = x_shape
+    x.shape = x_shape
+
+    return grad_x, grad_scale, grad_offset
+
+
+def create_or_get_tensor(scope, var_name, var, place):
+    tensor = scope.var(var_name).get_tensor()
+    if var is not None:
+        assert isinstance(var, np.ndarray)
+        tensor.set_lod([[]])
+        tensor.set_dims(var.shape)
+        tensor.set(var, place)
+    return tensor
+
+
+def set_output_grad(scope, outputs, place, feed_dict=None):
+    def __set_tensor__(name, data=None):
+        out_tensor = scope.find_var(name).get_tensor()
+        grad_tensor = scope.var(grad_var_name(name)).get_tensor()
+        out_dtype = out_tensor.dtype()
+        if data is None:
+            if out_dtype == core.DataType.FP64:
+                data = np.ones(out_tensor.shape(), dtype=np.float64)
+            elif out_dtype == core.DataType.FP32:
+                data = np.ones(out_tensor.shape(), dtype=np.float32)
+            else:
+                raise ValueError("Not supported data type " + str(out_dtype))
+        grad_tensor.set(data, place)
+
+    for output in outputs:
+        data = None
+        if output in feed_dict:
+            data = feed_dict[output]
+        __set_tensor__(output, data)
+
+
 class TestLayerNormdOp(OpTest):
-    def setUp(self):
-        self.init_test_case()
-
-        input = np.random.random(self.input_size).astype("float32")
-        self.inputs = {
-            'X': input,
-            'Scale': np.array([self.scale]).astype("float32"),
-            'Bias': np.array([self.bias]).astype("float32")
-        }
-        output, mean, var = layer_norm_naive(input, self.scale, self.bias,
-                                             self.epsilon)
-        self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
-
-    def test_check_output(self):
-        self.check_output()
-
-    # def test_check_grad(self):
-    #     self.check_grad(
-    #         ['Scale', 'Bias', 'X'], ['Y', 'Mean', 'Variance'],
-    #         max_relative_error=0.02)
-
-    def test_check_grad_no_x(self):
-        self.check_grad(
-            ['Scale', 'Bias'], ['Y', 'Mean', 'Variance'],
-            max_relative_error=0.02,
-            no_grad_set=set(['X']))
-
-    # def test_check_grad_no_scale(self):
-    #     self.check_grad(
-    #         ['Bias','X'],
-    #         'Y',
-    #         max_relative_error=0.02,
-    #         no_grad_set=set(['Scale']))
-    #
-    # def test_check_grad_no_bias(self):
-    #     self.check_grad(
-    #         ['Scale','X'],
-    #         'Y',
-    #         max_relative_error=0.02,
-    #         no_grad_set=set(['Bias']))
-
-    def init_test_case(self):
-        self.op_type = "layer_norm"
-        self.input_size = [2, 3, 4, 5]
-        self.scale = 0.21
-        self.bias = 0.1
-        self.epsilon = 0.00001
+    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
+        self.assertTrue(
+            np.allclose(
+                np.array(tensor).reshape(np_array.shape), np_array, atol=atol),
+            msg)
+
+    def __assert_grad_close(self,
+                            tensor,
+                            np_array,
+                            name,
+                            place,
+                            max_relative_error=0.02):
+        a = np.array(tensor).reshape(np_array.shape)
+        b = np_array
+        abs_a = np.abs(a)
+        abs_a[abs_a < 1e-5] = 1
+
+        diff_mat = np.abs(a - b) / abs_a
+        max_diff = np.max(diff_mat)
+
+        def err_msg():
+            offset = np.argmax(diff_mat > max_relative_error)
+            return ("%s Variable %s max gradient diff %f over limit %f, "
+                    "the first error element is %d, %f, %f") % (
+                        "Gradient Check On %s" % str(place), name, max_diff,
+                        max_relative_error, offset, a.flatten()[offset],
+                        b.flatten()[offset])
+
+        self.assertLessEqual(max_diff, max_relative_error, err_msg())
+
+    def test_forward_backward(self):
+        def test_with_place(place, shape):
+            # attr
+            epsilon = 0.00001
+            x_shape = shape
+            scale_shape = [1]
+
+            x_val = np.random.random_sample(x_shape).astype(np.float32)
+            scale_val = np.random.random_sample(scale_shape).astype(np.float32)
+            bias_val = np.random.random_sample(scale_shape).astype(np.float32)
+
+            # run forward
+            y_out, saved_mean, var_ref = _reference_layer_norm_naive(
+                x_val, scale_val, bias_val, epsilon)
+
+            #  for gradient test
+            # y_grad = np.ones(x_shape).astype(np.float32) * 0.00277778
+            y_grad = np.random.random_sample(x_shape).astype(np.float32)
+
+            x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_layer_norm_grad(
+                x_val, y_grad, scale_val, saved_mean, var_ref, epsilon)
+
+            scope = core.Scope()
+
+            # create input
+            x_tensor = create_or_get_tensor(scope, "X", x_val, place)
+            scale_tensor = create_or_get_tensor(scope, "Scale", scale_val,
+                                                place)
+            bias_tensor = create_or_get_tensor(scope, "Bias", bias_val, place)
+
+            # create output
+            y_tensor = create_or_get_tensor(scope, "Y", None, place)
+            mean_tensor = create_or_get_tensor(scope, "Mean", None, place)
+            variance_tensor = create_or_get_tensor(scope, "Variance", None,
+                                                   place)
+
+            layer_norm_op = Operator(
+                "layer_norm",
+                # inputs
+                X="X",
+                Scale="Scale",
+                Bias="Bias",
+                # outputs
+                Y="Y",
+                Mean="Mean",
+                Variance="Variance",
+                # attrs
+                epsilon=epsilon)
+
+            layer_norm_op.run(scope, place)
+
+            # check forward result
+            if isinstance(place, core.CUDAPlace):
+                atol = 5e-2
+            else:
+                atol = 1e-4
+            self.__assert_close(y_tensor, y_out, "Y", atol)
+            self.__assert_close(mean_tensor, saved_mean, "Mean", atol)
+            self.__assert_close(variance_tensor, var_ref, "Variance", atol)
+
+            # run backward
+            layer_norm_op_grad = get_backward_op(scope, layer_norm_op, set())
+            set_output_grad(
+                scope, ["Y", "Mean", "Variance"],
+                place,
+                feed_dict={"Y": y_grad})
+            layer_norm_op_grad.run(scope, place)
+
+            x_grad_tensor = create_or_get_tensor(scope,
+                                                 grad_var_name("X"), None,
+                                                 place)
+            scale_grad_tensor = create_or_get_tensor(scope,
+                                                     grad_var_name("Scale"),
+                                                     None, place)
+            bias_grad_tensor = create_or_get_tensor(scope,
+                                                    grad_var_name("Bias"), None,
+                                                    place)
+
+            # check gradient output
+            self.__assert_grad_close(x_grad_tensor, x_grad_ref, "x_grad", place)
+            self.__assert_grad_close(scale_grad_tensor, scale_grad_ref,
+                                     "scale_grad", place)
+            self.__assert_grad_close(bias_grad_tensor, bias_grad_ref,
+                                     "bias_grad", place)
+
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu() and core.op_support_gpu("layer_norm"):
+            places.append(core.CUDAPlace(0))
+
+        for place in places:
+            test_with_place(place, [2, 3, 4, 5])
+            test_with_place(place, [2, 3])
 
 
 if __name__ == '__main__':

From 0e1109cdf303b2da69f416733f4c08159e43c491 Mon Sep 17 00:00:00 2001
From: Helin Wang <ustc.harry@gmail.com>
Date: Thu, 25 Jan 2018 14:54:06 -0800
Subject: [PATCH 072/314] Fix send op data race

std::vector<bool> is not safe for concurrent write, even to difference indices.
More discussion:
https://stackoverflow.com/questions/48452611/is-stdfuturewait-a-memory-barrier-i-can-not-explain-this-data-race
---
 paddle/operators/detail/grpc_client.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/operators/detail/grpc_client.cc b/paddle/operators/detail/grpc_client.cc
index d699dabf2f..90e2b29659 100644
--- a/paddle/operators/detail/grpc_client.cc
+++ b/paddle/operators/detail/grpc_client.cc
@@ -102,7 +102,7 @@ bool RPCClient::Wait() {
     return true;
   }
 
-  std::vector<bool> a(req_count_);
+  bool a[req_count_];
   std::vector<std::future<void>> waits(req_count_);
 
   for (int i = 0; i < req_count_; i++) {

From 6592ea156810ab053ff8926f629815b113647814 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Thu, 25 Jan 2018 15:02:42 -0800
Subject: [PATCH 073/314] initial commit

---
 paddle/framework/prune.cc | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 25eb813ffb..59947470e7 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -102,6 +102,34 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
       *op_field->Add() = input.blocks(block_id).ops(i);
     }
   }
+
+  // remove the vars in ProgramDesc that are not referenced in
+  // the pruned ops
+  std::unordered_map<std::string name, proto::VarDesc> var_map;
+  auto* var_field = output->mutable_blocks(block_id)->mutable_vars();
+  for (auto* var : *var_field) {
+    var_map[var->name()] = *var;
+  }
+
+  //  for (size_t i = 0; i < var_field->size(); ++i) {
+  //    auto* var = (*var_field)[i];
+  //    var_map[var->name()] = *var;
+  //  }
+
+  //  var_field->Clear();
+  //  for (size_t i = 0; i < op_field->size(); ++i) {
+  //    auto* op = (*op_field)[i];
+
+  //    auto* input_field = op->mutable_inputs();
+  //    for (size_t j = 0; j < input_field->size(); ++j) {
+  //        auto* input_names = (*input_field)[j]->arguments();
+  //        for ()
+  //        *var_field->Add() = var_map[]
+  //    }
+  //    auto* ouput_field = op->mutable_outputs();
+  //    for (size_t k = 0; k < output_field->size(); ++k) {
+  //    }
+  //  }
 }
 
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies

From a6718797599943788cd2f10d9adc05dd2c2ef781 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Thu, 25 Jan 2018 15:50:32 -0800
Subject: [PATCH 074/314] remove unreferenced vars

---
 paddle/framework/prune.cc | 47 +++++++++++++++++++--------------------
 1 file changed, 23 insertions(+), 24 deletions(-)

diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 59947470e7..6117cbb79f 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -17,6 +17,7 @@ limitations under the License. */
 #include <algorithm>
 #include <set>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #include <glog/logging.h>
@@ -103,33 +104,31 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     }
   }
 
-  // remove the vars in ProgramDesc that are not referenced in
-  // the pruned ops
-  std::unordered_map<std::string name, proto::VarDesc> var_map;
+  // remove the VarDescs in BlockDesc that are not referenced in
+  // the pruned OpDescs
+  std::unordered_map<std::string, proto::VarDesc> var_map;
   auto* var_field = output->mutable_blocks(block_id)->mutable_vars();
-  for (auto* var : *var_field) {
-    var_map[var->name()] = *var;
+  for (const auto& var : *var_field) {
+    var_map[var.name()] = var;
   }
 
-  //  for (size_t i = 0; i < var_field->size(); ++i) {
-  //    auto* var = (*var_field)[i];
-  //    var_map[var->name()] = *var;
-  //  }
-
-  //  var_field->Clear();
-  //  for (size_t i = 0; i < op_field->size(); ++i) {
-  //    auto* op = (*op_field)[i];
-
-  //    auto* input_field = op->mutable_inputs();
-  //    for (size_t j = 0; j < input_field->size(); ++j) {
-  //        auto* input_names = (*input_field)[j]->arguments();
-  //        for ()
-  //        *var_field->Add() = var_map[]
-  //    }
-  //    auto* ouput_field = op->mutable_outputs();
-  //    for (size_t k = 0; k < output_field->size(); ++k) {
-  //    }
-  //  }
+  var_field->Clear();
+  for (const auto& op : *op_field) {
+    // add VarDescs of all input arguments for each OpDesc
+    auto& input_field = op.inputs();
+    for (auto& input : input_field) {
+      for (auto& arg : input.arguments()) {
+        *var_field->Add() = var_map[arg];
+      }
+    }
+    // add VarDescs of all output arguments for each OpDesc
+    auto& output_field = op.outputs();
+    for (auto& output : output_field) {
+      for (auto& arg : output.arguments()) {
+        *var_field->Add() = var_map[arg];
+      }
+    }
+  }
 }
 
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies

From c3cebc0f04ddc3b51fa1f36d3691fd820e03dd20 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Thu, 25 Jan 2018 16:11:56 -0800
Subject: [PATCH 075/314] fix #5902

---
 python/paddle/trainer/config_parser.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 4fdf409021..24a7e94e3f 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -140,8 +140,13 @@ def init_config_environment(
         g_submodel_stack=[],
         g_add_submodel_suffix=False, ):
 
-    for k, v in locals().iteritems():
-        globals()[k] = copy.deepcopy(v)
+    # directly iterate through locals().iteritems() will change
+    # the size of locals() due to introducting k, v into scope
+    # which will break the process in some env
+
+    local_vars = copy.deepcopy(locals())
+    for k, v in local_vars.iteritems():
+        globals()[k] = v
 
 
 # Because type is widely used as a variable name in this code.

From ce54d5e834e85cc46c2ac020dbbfe651d7005000 Mon Sep 17 00:00:00 2001
From: Xi Chen <putcncx@gmail.com>
Date: Thu, 25 Jan 2018 16:13:59 -0800
Subject: [PATCH 076/314] fix typo

---
 python/paddle/trainer/config_parser.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py
index 24a7e94e3f..186b91c226 100644
--- a/python/paddle/trainer/config_parser.py
+++ b/python/paddle/trainer/config_parser.py
@@ -141,7 +141,7 @@ def init_config_environment(
         g_add_submodel_suffix=False, ):
 
     # directly iterate through locals().iteritems() will change
-    # the size of locals() due to introducting k, v into scope
+    # the size of locals() due to introducing k, v into scope
     # which will break the process in some env
 
     local_vars = copy.deepcopy(locals())

From 788f5c6d439f2795d9882697be1e257eedfc5c5a Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Thu, 25 Jan 2018 18:20:12 -0800
Subject: [PATCH 077/314] New Run() method for framework::Executor (#7807)

* initial commit

* add new executor run function

* fix bug

* fix multiple definition of feed_fetch_method issue

* fix cmake

* fix tensor copy error

* refine executor code

* add comments

* temporary modification

* address comments

* fix bug
---
 paddle/framework/CMakeLists.txt       |   4 +-
 paddle/framework/executor.cc          | 164 ++++++++++++++++++++++++++
 paddle/framework/executor.h           |   6 +
 paddle/framework/feed_fetch_method.cc |  56 +++++++++
 paddle/framework/feed_fetch_method.h  |  34 +-----
 paddle/inference/inference.cc         |  14 ++-
 paddle/pybind/CMakeLists.txt          |   2 +-
 paddle/pybind/pybind.cc               |   4 +-
 8 files changed, 244 insertions(+), 40 deletions(-)
 create mode 100644 paddle/framework/feed_fetch_method.cc

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 8d9260811a..2804969842 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -74,8 +74,10 @@ cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context fill_constant_op)
 cc_library(lod_rank_table SRCS lod_rank_table.cc DEPS lod_tensor)
 
+cc_library(feed_fetch_method SRCS feed_fetch_method.cc DEPS lod_tensor scope glog)
+
 cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
-framework_proto backward glog lod_rank_table profiler)
+framework_proto backward glog lod_rank_table profiler feed_fetch_method)
 
 cc_library(prune SRCS prune.cc DEPS framework_proto)
 cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index c28ffefdd0..50a70d723e 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -17,6 +17,7 @@ limitations under the License. */
 #include <set>
 
 #include "gflags/gflags.h"
+#include "paddle/framework/feed_fetch_method.h"
 #include "paddle/framework/feed_fetch_type.h"
 #include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor_array.h"
@@ -149,5 +150,168 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
   }
 }
 
+// Check whether the block already has feed operators and feed_holder.
+// Return false if the block does not have any feed operators.
+// If some feed operators have been prepended to the block, check that
+// the info contained in these feed operators matches the feed_targets
+// and feed_holder_name. Raise exception when any mismatch is found.
+// Return true if the block has feed operators and holder of matching info.
+static bool has_feed_operators(
+    BlockDesc* block, std::map<std::string, const LoDTensor*>& feed_targets,
+    const std::string& feed_holder_name) {
+  size_t feed_count = 0;
+  for (auto* op : block->AllOps()) {
+    if (op->Type() == kFeedOpType) {
+      feed_count++;
+      PADDLE_ENFORCE_EQ(op->Input("X")[0], feed_holder_name,
+                        "Input to feed op should be '%s'", feed_holder_name);
+      std::string feed_target_name = op->Output("Out")[0];
+      PADDLE_ENFORCE(
+          feed_targets.find(feed_target_name) != feed_targets.end(),
+          "Feed operator output name '%s' cannot be found in 'feed_targets'",
+          feed_target_name);
+    } else {
+      break;
+    }
+  }
+
+  if (feed_count > 0) {
+    PADDLE_ENFORCE_EQ(
+        feed_count, feed_targets.size(),
+        "The number of feed operators should match 'feed_targets'");
+
+    // When feed operator are present, so should be feed_holder
+    auto var = block->FindVar(feed_holder_name);
+    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
+                            feed_holder_name);
+    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FEED_MINIBATCH,
+                      "'%s' variable should be 'FEED_MINIBATCH' type",
+                      feed_holder_name);
+  }
+
+  return feed_count > 0;
+}
+
+// Check whether the block already has fetch operators and fetch_holder.
+// Return false if the block does not have any fetch operators.
+// If some fetch operators have been appended to the block, check that
+// the info contained in these fetch operators matches the fetch_targets
+// and fetch_holder_name. Raise exception when any mismatch is found.
+// Return true if the block has fetch operators and holder of matching info.
+static bool has_fetch_operators(
+    BlockDesc* block, std::map<std::string, LoDTensor*>& fetch_targets,
+    const std::string& fetch_holder_name) {
+  size_t fetch_count = 0;
+  for (auto* op : block->AllOps()) {
+    if (op->Type() == kFetchOpType) {
+      fetch_count++;
+      PADDLE_ENFORCE_EQ(op->Output("Out")[0], fetch_holder_name,
+                        "Output of fetch op should be '%s'", fetch_holder_name);
+      std::string fetch_target_name = op->Input("X")[0];
+      PADDLE_ENFORCE(
+          fetch_targets.find(fetch_target_name) != fetch_targets.end(),
+          "Fetch operator input name '%s' cannot be found in 'fetch_targets'",
+          fetch_target_name);
+    }
+  }
+
+  if (fetch_count > 0) {
+    PADDLE_ENFORCE_EQ(
+        fetch_count, fetch_targets.size(),
+        "The number of fetch operators should match 'fetch_targets'");
+
+    // When fetch operator are present, so should be fetch_holder
+    auto var = block->FindVar(fetch_holder_name);
+    PADDLE_ENFORCE_NOT_NULL(var, "Block should already have a '%s' variable",
+                            fetch_holder_name);
+    PADDLE_ENFORCE_EQ(var->GetType(), proto::VarDesc::FETCH_LIST,
+                      "'%s' variable should be 'FETCH_LIST' type",
+                      fetch_holder_name);
+  }
+
+  return fetch_count > 0;
+}
+
+void Executor::Run(const ProgramDesc& program, Scope* scope,
+                   std::map<std::string, const LoDTensor*>& feed_targets,
+                   std::map<std::string, LoDTensor*>& fetch_targets,
+                   const std::string& feed_holder_name,
+                   const std::string& fetch_holder_name) {
+  auto* copy_program = new ProgramDesc(program);
+  auto* global_block = copy_program->MutableBlock(0);
+
+  if (!has_feed_operators(global_block, feed_targets, feed_holder_name)) {
+    // create feed_holder variable
+    auto* feed_holder = global_block->Var(feed_holder_name);
+    feed_holder->SetType(proto::VarDesc::FEED_MINIBATCH);
+    feed_holder->SetPersistable(true);
+
+    int i = 0;
+    for (auto& feed_target : feed_targets) {
+      std::string var_name = feed_target.first;
+      VLOG(3) << "feed target's name: " << var_name;
+
+      // prepend feed op
+      auto* op = global_block->PrependOp();
+      op->SetType(kFeedOpType);
+      op->SetInput("X", {feed_holder_name});
+      op->SetOutput("Out", {var_name});
+      op->SetAttr("col", {static_cast<int>(i)});
+      op->CheckAttrs();
+
+      i++;
+    }
+  }
+
+  // map the data of feed_targets to feed_holder
+  for (auto* op : global_block->AllOps()) {
+    if (op->Type() == kFeedOpType) {
+      std::string feed_target_name = op->Output("Out")[0];
+      int idx = boost::get<int>(op->GetAttr("col"));
+      SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
+                      idx);
+    } else {
+      break;
+    }
+  }
+
+  if (!has_fetch_operators(global_block, fetch_targets, fetch_holder_name)) {
+    // create fetch_holder variable
+    auto* fetch_holder = global_block->Var(fetch_holder_name);
+    fetch_holder->SetType(proto::VarDesc::FETCH_LIST);
+    fetch_holder->SetPersistable(true);
+
+    int i = 0;
+    for (auto& fetch_target : fetch_targets) {
+      std::string var_name = fetch_target.first;
+      VLOG(3) << "fetch target's name: " << var_name;
+
+      // append fetch op
+      auto* op = global_block->AppendOp();
+      op->SetType(kFetchOpType);
+      op->SetInput("X", {var_name});
+      op->SetOutput("Out", {fetch_holder_name});
+      op->SetAttr("col", {static_cast<int>(i)});
+      op->CheckAttrs();
+
+      i++;
+    }
+  }
+
+  Run(*copy_program, scope, 0, true, true);
+
+  // obtain the data of fetch_targets from fetch_holder
+  for (auto* op : global_block->AllOps()) {
+    if (op->Type() == kFetchOpType) {
+      std::string fetch_target_name = op->Input("X")[0];
+      int idx = boost::get<int>(op->GetAttr("col"));
+      *fetch_targets[fetch_target_name] =
+          GetFetchVariable(*scope, fetch_holder_name, idx);
+    }
+  }
+
+  delete copy_program;
+}
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/executor.h b/paddle/framework/executor.h
index d869e18901..035ff48a52 100644
--- a/paddle/framework/executor.h
+++ b/paddle/framework/executor.h
@@ -41,6 +41,12 @@ class Executor {
   void Run(const ProgramDesc&, Scope*, int, bool create_local_scope = true,
            bool create_vars = true);
 
+  void Run(const ProgramDesc& program, Scope* scope,
+           std::map<std::string, const LoDTensor*>& feed_targets,
+           std::map<std::string, LoDTensor*>& fetch_targets,
+           const std::string& feed_holder_name = "feed",
+           const std::string& fetch_holder_name = "fetch");
+
  private:
   const platform::Place place_;
 };
diff --git a/paddle/framework/feed_fetch_method.cc b/paddle/framework/feed_fetch_method.cc
new file mode 100644
index 0000000000..21201b6755
--- /dev/null
+++ b/paddle/framework/feed_fetch_method.cc
@@ -0,0 +1,56 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/feed_fetch_method.h"
+#include "glog/logging.h"
+#include "paddle/framework/variable.h"
+
+namespace paddle {
+namespace framework {
+
+void SetFeedVariable(Scope* scope, const LoDTensor& input,
+                     const std::string& var_name, size_t index) {
+  // If var_name Variable is not found in GlobalScope, a new variable will
+  // be created.
+  VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
+  Variable* g_feed_value = scope->Var(var_name);
+  auto& feed_inputs =
+      *(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
+  if (index >= feed_inputs.size()) {
+    feed_inputs.resize(index + 1);
+  }
+  // shared data with input tensor
+  feed_inputs[index].ShareDataWith(input);
+  // set lod
+  feed_inputs[index].set_lod(input.lod());
+}
+
+LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
+                            size_t index) {
+  // Since we want to fetch LodTensor from a variable, the variable must
+  // be created alreadly.
+  Variable* g_fetch_value = scope.FindVar(var_name);
+  PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
+                 "Only %s can be invoked by GetFetchVariable",
+                 typeid(FeedFetchList).name());
+  auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
+  auto& tensor = fetch_outputs[index];
+  VLOG(3) << "Fetch " << var_name << " with index " << index
+          << " shape= " << tensor.dims();
+  PADDLE_ENFORCE_LT(index, fetch_outputs.size());
+  return tensor;
+}
+
+}  // namespace framework
+}  // namespace paddle
diff --git a/paddle/framework/feed_fetch_method.h b/paddle/framework/feed_fetch_method.h
index 7feacb1e24..b71945fcc8 100644
--- a/paddle/framework/feed_fetch_method.h
+++ b/paddle/framework/feed_fetch_method.h
@@ -13,46 +13,18 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "glog/logging.h"
+
 #include "paddle/framework/feed_fetch_type.h"
 #include "paddle/framework/scope.h"
-#include "paddle/framework/variable.h"
 
 namespace paddle {
 namespace framework {
 
 void SetFeedVariable(Scope* scope, const LoDTensor& input,
-                     const std::string& var_name, size_t index) {
-  // If var_name Variable is not found in GlobalScope, a new variable will
-  // be created.
-  VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
-  Variable* g_feed_value = scope->Var(var_name);
-  auto& feed_inputs =
-      *(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
-  if (index >= feed_inputs.size()) {
-    feed_inputs.resize(index + 1);
-  }
-  // shared data with input tensor
-  feed_inputs[index].ShareDataWith(input);
-  // set lod
-  feed_inputs[index].set_lod(input.lod());
-}
+                     const std::string& var_name, size_t index);
 
 LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
-                            size_t index) {
-  // Since we want to fetch LodTensor from a variable, the variable must
-  // be created alreadly.
-  Variable* g_fetch_value = scope.FindVar(var_name);
-  PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
-                 "Only %s can be invoked by GetFetchVariable",
-                 typeid(FeedFetchList).name());
-  auto& fetch_outputs = *g_fetch_value->GetMutable<FeedFetchList>();
-  auto& tensor = fetch_outputs[index];
-  VLOG(3) << "Fetch " << var_name << " with index " << index
-          << " shape= " << tensor.dims();
-  PADDLE_ENFORCE_LT(index, fetch_outputs.size());
-  return tensor;
-}
+                            size_t index);
 
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 09268ffb3a..b43c359ed1 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -15,7 +15,6 @@ limitations under the License. */
 #include "inference.h"
 #include <fstream>
 #include "paddle/framework/executor.h"
-#include "paddle/framework/feed_fetch_method.h"
 #include "paddle/framework/init.h"
 #include "paddle/framework/scope.h"
 
@@ -154,7 +153,7 @@ void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
     LOG(FATAL) << "Please initialize the program_ and load_program_ first.";
   }
 
-  if (feeds.size() < feed_var_names_.size()) {
+  if (feeds.size() != feed_var_names_.size()) {
     LOG(FATAL) << "Please feed " << feed_var_names_.size() << " input Tensors.";
   }
 
@@ -165,19 +164,22 @@ void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
 
   executor->Run(*load_program_, scope, 0, true, true);
 
+  std::map<std::string, const framework::LoDTensor*> feed_targets;
+  std::map<std::string, framework::LoDTensor*> fetch_targets;
+
   // set_feed_variable
   for (size_t i = 0; i < feed_var_names_.size(); ++i) {
-    framework::SetFeedVariable(scope, feeds[i], "feed", i);
+    feed_targets[feed_var_names_[i]] = &feeds[i];
   }
 
-  executor->Run(*program_, scope, 0, true, true);
-
   // get_fetch_variable
   fetchs.resize(fetch_var_names_.size());
   for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
-    fetchs[i] = framework::GetFetchVariable(*scope, "fetch", i);
+    fetch_targets[fetch_var_names_[i]] = &fetchs[i];
   }
 
+  executor->Run(*program_, scope, feed_targets, fetch_targets);
+
   delete place;
   delete scope;
   delete executor;
diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt
index e78673e0ba..de53fea0dd 100644
--- a/paddle/pybind/CMakeLists.txt
+++ b/paddle/pybind/CMakeLists.txt
@@ -1,7 +1,7 @@
 if(WITH_PYTHON)
   cc_library(paddle_pybind SHARED
     SRCS pybind.cc exception.cc protobuf.cc const_value.cc
-    DEPS pybind python backward proto_desc paddle_memory executor prune init profiler
+    DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
     ${GLOB_OP_LIB})
   if(NOT APPLE AND NOT ANDROID)
     target_link_libraries(paddle_pybind rt)
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index b4fd2a8989..490397afdd 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -424,7 +424,9 @@ All parameter, weight, gradient are variables in Paddle.
 
   py::class_<framework::Executor>(m, "Executor")
       .def(py::init<const platform::Place &>())
-      .def("run", &Executor::Run);
+      .def("run",
+           (void (Executor::*)(const ProgramDesc &, Scope *, int, bool, bool)) &
+               Executor::Run);
 
   m.def("unique_integer", UniqueIntegerGenerator);
   m.def("init_gflags", framework::InitGflags);

From 438aad24a5a82d5e5302543a7f56bfd8f414aaf6 Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Fri, 26 Jan 2018 04:07:02 +0000
Subject: [PATCH 078/314] Update the inference unittest using the new
 Executor.Run().

---
 paddle/inference/inference.cc                 | 103 ++----------------
 paddle/inference/inference.h                  |  18 ++-
 .../book/test_inference_recognize_digits.cc   |  56 +++++++---
 3 files changed, 59 insertions(+), 118 deletions(-)

diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
index 2c4d717a13..51d43a63ee 100644
--- a/paddle/inference/inference.cc
+++ b/paddle/inference/inference.cc
@@ -14,13 +14,13 @@ limitations under the License. */
 
 #include "inference.h"
 #include <fstream>
-#include "paddle/framework/executor.h"
-#include "paddle/framework/init.h"
-#include "paddle/framework/scope.h"
 
 namespace paddle {
 
-void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
+framework::ProgramDesc* InferenceEngine::LoadInferenceModel(
+    framework::Executor& exe,
+    framework::Scope* scope,
+    const std::string& dirname) {
   std::string model_filename = dirname + "/__model__";
   LOG(INFO) << "loading model from " << model_filename;
   std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
@@ -34,6 +34,7 @@ void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
 
   program_ = new framework::ProgramDesc(program_desc_str);
   GenerateLoadProgram(dirname);
+  exe.Run(*load_program_, scope, 0, true, true);
 
   framework::BlockDesc* global_block = program_->MutableBlock(0);
   feed_var_names_.clear();
@@ -45,6 +46,8 @@ void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
       fetch_var_names_.push_back(op->Input("X")[0]);
     }
   }
+
+  return program_;
 }
 
 bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
@@ -92,96 +95,4 @@ void InferenceEngine::GenerateLoadProgram(const std::string& dirname) {
     }
   }
 }
-
-void InferenceEngine::PrependFeedOp() {
-  if (!program_) {
-    LOG(FATAL) << "Please initialize the program_ first.";
-  }
-
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-
-  // create_var
-  framework::VarDesc* feed_var = global_block->Var("feed");
-  feed_var->SetType(framework::proto::VarDesc::FEED_MINIBATCH);
-  feed_var->SetPersistable(true);
-
-  // prepend feed_op
-  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
-    std::string var_name = feed_var_names_[i];
-    LOG(INFO) << "feed var's name: " << var_name;
-
-    // prepend_op
-    framework::OpDesc* op = global_block->PrependOp();
-    op->SetType("feed");
-    op->SetInput("X", {"feed"});
-    op->SetOutput("Out", {var_name});
-    op->SetAttr("col", {static_cast<int>(i)});
-    op->CheckAttrs();
-  }
-}
-
-void InferenceEngine::AppendFetchOp() {
-  if (!program_) {
-    LOG(FATAL) << "Please initialize the program_ first.";
-  }
-
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-
-  // create_var
-  framework::VarDesc* fetch_var = global_block->Var("fetch");
-  fetch_var->SetType(framework::proto::VarDesc::FETCH_LIST);
-  fetch_var->SetPersistable(true);
-
-  // append fetch_op
-  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
-    std::string var_name = fetch_var_names_[i];
-    LOG(INFO) << "fetch var's name: " << var_name;
-
-    // append_op
-    framework::OpDesc* op = global_block->AppendOp();
-    op->SetType("fetch");
-    op->SetInput("X", {var_name});
-    op->SetOutput("Out", {"fetch"});
-    op->SetAttr("col", {static_cast<int>(i)});
-    op->CheckAttrs();
-  }
-}
-
-void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
-                              std::vector<framework::LoDTensor>& fetchs) {
-  if (!program_ || !load_program_) {
-    LOG(FATAL) << "Please initialize the program_ and load_program_ first.";
-  }
-
-  if (feeds.size() != feed_var_names_.size()) {
-    LOG(FATAL) << "Please feed " << feed_var_names_.size() << " input Tensors.";
-  }
-
-  auto* place = new platform::CPUPlace();
-  framework::InitDevices();
-  framework::Executor* executor = new framework::Executor(*place);
-  framework::Scope* scope = new framework::Scope();
-
-  executor->Run(*load_program_, scope, 0, true, true);
-
-  std::map<std::string, const framework::LoDTensor*> feed_targets;
-  std::map<std::string, framework::LoDTensor*> fetch_targets;
-
-  // set_feed_variable
-  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
-    feed_targets[feed_var_names_[i]] = &feeds[i];
-  }
-
-  // get_fetch_variable
-  fetchs.resize(fetch_var_names_.size());
-  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
-    fetch_targets[fetch_var_names_[i]] = &fetchs[i];
-  }
-
-  executor->Run(*program_, scope, feed_targets, fetch_targets);
-
-  delete place;
-  delete scope;
-  delete executor;
-}
 }  // namespace paddle
diff --git a/paddle/inference/inference.h b/paddle/inference/inference.h
index 26f259824b..60caa41c70 100644
--- a/paddle/inference/inference.h
+++ b/paddle/inference/inference.h
@@ -15,8 +15,10 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/framework/block_desc.h"
+#include "paddle/framework/executor.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/program_desc.h"
+#include "paddle/framework/scope.h"
 
 namespace paddle {
 
@@ -28,15 +30,21 @@ public:
     delete load_program_;
   }
 
-  void LoadInferenceModel(const std::string& dirname);
-  void Execute(const std::vector<framework::LoDTensor>& feeds,
-               std::vector<framework::LoDTensor>& fetchs);
+  framework::ProgramDesc* LoadInferenceModel(framework::Executor& exe,
+                                             framework::Scope* scope,
+                                             const std::string& dirname);
+
+  const std::vector<std::string>& GetFeedVarNames() const {
+    return feed_var_names_;
+  }
+
+  const std::vector<std::string>& GetFetchVarNames() const {
+    return fetch_var_names_;
+  }
 
 private:
   bool IsParameter(const framework::VarDesc* var);
   void GenerateLoadProgram(const std::string& dirname);
-  void PrependFeedOp();
-  void AppendFetchOp();
 
 private:
   framework::ProgramDesc* program_;
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index d0e811914c..0dfaf9a0ee 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -16,11 +16,12 @@ limitations under the License. */
 #include <time.h>
 #include <sstream>
 #include "gflags/gflags.h"
+#include "paddle/framework/init.h"
 #include "paddle/inference/inference.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-TEST(inference, recognize_digits) {
+TEST(recognize_digits, CPU) {
   if (FLAGS_dirname.empty()) {
     LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
   }
@@ -28,33 +29,54 @@ TEST(inference, recognize_digits) {
   std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
   std::string dirname = FLAGS_dirname;
 
+  // 0. Initialize all the devices
+  paddle::framework::InitDevices();
+
+  // 1. Define place, executor and scope
+  auto place = paddle::platform::CPUPlace();
+  auto executor = paddle::framework::Executor(place);
+  auto* scope = new paddle::framework::Scope();
+
+  // 2. Initialize the inference_program and load all parameters from file
   paddle::InferenceEngine* engine = new paddle::InferenceEngine();
-  engine->LoadInferenceModel(dirname);
+  paddle::framework::ProgramDesc* inference_program =
+      engine->LoadInferenceModel(executor, scope, dirname);
+
+  // 3. Get the feed_var_names and fetch_var_names
+  const std::vector<std::string>& feed_target_names = engine->GetFeedVarNames();
+  const std::vector<std::string>& fetch_target_names =
+      engine->GetFetchVarNames();
 
+  // 4. Prepare inputs
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
   paddle::framework::LoDTensor input;
   srand(time(0));
   float* input_ptr =
-      input.mutable_data<float>({1, 784}, paddle::platform::CPUPlace());
+      input.mutable_data<float>({1, 28, 28}, paddle::platform::CPUPlace());
   for (int i = 0; i < 784; ++i) {
     input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
   }
+  feed_targets[feed_target_names[0]] = &input;
+
+  // 5. Define Tensor to get the outputs
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+  paddle::framework::LoDTensor output;
+  fetch_targets[fetch_target_names[0]] = &output;
+
+  // 6. Run the inference program
+  executor.Run(*inference_program, scope, feed_targets, fetch_targets);
 
-  std::vector<paddle::framework::LoDTensor> feeds;
-  feeds.push_back(input);
-  std::vector<paddle::framework::LoDTensor> fetchs;
-  engine->Execute(feeds, fetchs);
-
-  for (size_t i = 0; i < fetchs.size(); ++i) {
-    LOG(INFO) << fetchs[i].dims();
-    std::stringstream ss;
-    ss << "result:";
-    float* output_ptr = fetchs[i].data<float>();
-    for (int j = 0; j < fetchs[i].numel(); ++j) {
-      ss << " " << output_ptr[j];
-    }
-    LOG(INFO) << ss.str();
+  // 7. Use the output as your expect.
+  LOG(INFO) << output.dims();
+  std::stringstream ss;
+  ss << "result:";
+  float* output_ptr = output.data<float>();
+  for (int j = 0; j < output.numel(); ++j) {
+    ss << " " << output_ptr[j];
   }
+  LOG(INFO) << ss.str();
 
+  delete scope;
   delete engine;
 }
 

From 84c12c6edc2bc0b6b410c4385e928c8d061ea18e Mon Sep 17 00:00:00 2001
From: Yang yaming <mxscmxsc@gmail.com>
Date: Fri, 26 Jan 2018 15:35:23 +0800
Subject: [PATCH 079/314] Add one_hot operator. (#7819)

* Add one_hot operator.

* Add more unit tests.
---
 paddle/operators/one_hot_op.cc                |  95 +++++++++++++++
 paddle/operators/one_hot_op.cu                |  80 +++++++++++++
 paddle/operators/one_hot_op.h                 |  68 +++++++++++
 .../paddle/v2/fluid/tests/test_one_hot_op.py  | 110 ++++++++++++++++++
 4 files changed, 353 insertions(+)
 create mode 100644 paddle/operators/one_hot_op.cc
 create mode 100644 paddle/operators/one_hot_op.cu
 create mode 100644 paddle/operators/one_hot_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_one_hot_op.py

diff --git a/paddle/operators/one_hot_op.cc b/paddle/operators/one_hot_op.cc
new file mode 100644
index 0000000000..e78b7468de
--- /dev/null
+++ b/paddle/operators/one_hot_op.cc
@@ -0,0 +1,95 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/operators/one_hot_op.h"
+#include "paddle/framework/framework.pb.h"
+
+namespace paddle {
+namespace operators {
+
+class OneHotOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of OneHotOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of OneHotOp should not be null.");
+
+    auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(x_dims.size(), 2,
+                      "Rank of Input(X) should be at least 2.");
+    PADDLE_ENFORCE_GE(x_dims[x_dims.size() - 1], 1U,
+                      "Last dimension of Input(X) should be 1.");
+
+    int depth = ctx->Attrs().Get<int>("depth");
+
+    PADDLE_ENFORCE_GT(depth, 0, "Should provide a positive depth (%d).", depth);
+
+    framework::DDim out_dims(x_dims);
+    out_dims[out_dims.size() - 1] = depth;
+    ctx->SetOutputDim("Out", out_dims);
+    ctx->ShareLoD("X", /* --> */ "Out");
+  }
+};
+
+class OneHotOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  OneHotOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
+             "The last dimension of X should be 1. Each value of X is an index "
+             "to indicate the position.");
+    AddOutput("Out",
+              "(Tensor, Tensor<float>) Output tensor with same rank as X. "
+              "The tensor consists of one-hot representations of values in X.");
+    AddAttr<int>("depth",
+                 "A positive integer to specify the length of one-hot vector.");
+    AddAttr<int>("dtype",
+                 "An integer to specify the data type of one-hot "
+                 "vector. The default value is FP32.")
+        .SetDefault(paddle::framework::proto::DataType::FP32);
+    AddComment(R"DOC(
+One Hot Operator. This operator creates the one-hot representations for input
+index values. The following example will help to explain the function of this
+operator:
+
+X is a LoDTensor:
+  X.lod = [[0, 1, 4]]
+  X.shape = [4, 1]
+  X.data = [[1], [1], [3], [0]]
+
+set depth = 4
+
+Out is a LoDTensor:
+  Out.lod = [[0, 1, 4]]
+  Out.shape = [4, 4]
+  Out.data = [[0., 1., 0., 0.],
+              [0., 1., 0., 0.],
+              [0., 0., 0., 1.],
+              [1., 0., 0., 0.]]
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(one_hot, ops::OneHotOp, ops::OneHotOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    one_hot, ops::OneHotKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::OneHotKernel<paddle::platform::CPUDeviceContext, int64_t>);
diff --git a/paddle/operators/one_hot_op.cu b/paddle/operators/one_hot_op.cu
new file mode 100644
index 0000000000..16f6d9433e
--- /dev/null
+++ b/paddle/operators/one_hot_op.cu
@@ -0,0 +1,80 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/operators/one_hot_op.h"
+#include "paddle/platform/cuda_helper.h"
+#include "paddle/platform/gpu_info.h"
+
+namespace paddle {
+namespace operators {
+using platform::PADDLE_CUDA_NUM_THREADS;
+
+template <typename InT, typename OutT>
+__global__ void FillOutputKernel(const InT* p_in_data, OutT* p_out_data,
+                                 const int64_t numel, const int depth) {
+  int idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (idx < numel) {
+    *(p_out_data + (idx * depth) + p_in_data[idx]) = 1.0;
+  }
+}
+
+template <typename DeviceContext, typename InT>
+struct OneHotOpCUDAFunctor {
+  const framework::LoDTensor* in_;
+  framework::LoDTensor* out_;
+  const DeviceContext& ctx_;
+  int depth_;
+
+  OneHotOpCUDAFunctor(const framework::LoDTensor* in, framework::LoDTensor* out,
+                      int depth, const DeviceContext& ctx)
+      : in_(in), out_(out), depth_(depth), ctx_(ctx) {}
+
+  template <typename OutT>
+  void operator()() const {
+    auto* p_in_data = in_->data<InT>();
+    auto numel = in_->numel();
+    auto* p_out_data = out_->mutable_data<OutT>(ctx_.GetPlace());
+    auto stream = ctx_.stream();
+    math::set_constant(ctx_, out_, 0.0);
+
+    FillOutputKernel<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) /
+                           PADDLE_CUDA_NUM_THREADS,
+                       PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
+        p_in_data, p_out_data, numel, depth_);
+  }
+};
+
+using LoDTensor = framework::LoDTensor;
+template <typename DeviceContext, typename T>
+class OneHotCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int depth = context.Attr<int>("depth");
+
+    framework::VisitDataType(
+        static_cast<framework::proto::DataType>(context.Attr<int>("dtype")),
+        OneHotOpCUDAFunctor<DeviceContext, T>(
+            in, out, depth, context.template device_context<DeviceContext>()));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    one_hot, ops::OneHotCUDAKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::OneHotCUDAKernel<paddle::platform::CUDADeviceContext, int64_t>);
diff --git a/paddle/operators/one_hot_op.h b/paddle/operators/one_hot_op.h
new file mode 100644
index 0000000000..12031ede2c
--- /dev/null
+++ b/paddle/operators/one_hot_op.h
@@ -0,0 +1,68 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename InT>
+struct OneHotOpFunctor {
+  const framework::LoDTensor* in_;
+  framework::LoDTensor* out_;
+  int depth_;
+  const DeviceContext& ctx_;
+
+  OneHotOpFunctor(const framework::LoDTensor* in, framework::LoDTensor* out,
+                  int depth, const DeviceContext& ctx)
+      : in_(in), out_(out), depth_(depth), ctx_(ctx) {}
+
+  template <typename OutT>
+  void operator()() const {
+    auto* p_in_data = in_->data<InT>();
+    auto numel = in_->numel();
+    auto* p_out_data = out_->mutable_data<OutT>(ctx_.GetPlace());
+    math::set_constant(ctx_, out_, 0.0);
+
+    for (int i = 0; i < numel; ++i) {
+      PADDLE_ENFORCE_GE(p_in_data[i], 0,
+                        "Illegal index value, should be at least 0.");
+      PADDLE_ENFORCE_LT(p_in_data[i], depth_,
+                        "Illegal index value, should be less than depth (%d).",
+                        depth_);
+      *(p_out_data + i * depth_ + p_in_data[i]) = 1.0;
+    }
+  }
+};
+
+using LoDTensor = framework::LoDTensor;
+template <typename DeviceContext, typename T>
+class OneHotKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<LoDTensor>("X");
+    auto* out = context.Output<LoDTensor>("Out");
+    int depth = context.Attr<int>("depth");
+
+    framework::VisitDataType(
+        static_cast<framework::proto::DataType>(context.Attr<int>("dtype")),
+        OneHotOpFunctor<DeviceContext, T>(
+            in, out, depth, context.template device_context<DeviceContext>()));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_one_hot_op.py b/python/paddle/v2/fluid/tests/test_one_hot_op.py
new file mode 100644
index 0000000000..e51ea27d14
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_one_hot_op.py
@@ -0,0 +1,110 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+import math
+from op_test import OpTest
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.framework as framework
+from paddle.v2.fluid.framework import Program, program_guard
+
+
+class TestOneHotOp(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        dimension = 12
+        x_lod = [[0, 4, 5, 8, 11]]
+        x = [np.random.randint(0, depth - 1) for i in xrange(x_lod[0][-1])]
+        x = np.array(x).astype('int').reshape([x_lod[0][-1], 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in xrange(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth, 'dtype': int(core.DataType.FP32)}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestOneHotOp_default_dtype(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        depth = 10
+        dimension = 12
+        x_lod = [[0, 4, 5, 8, 11]]
+        x = [np.random.randint(0, depth - 1) for i in xrange(x_lod[0][-1])]
+        x = np.array(x).astype('int').reshape([x_lod[0][-1], 1])
+
+        out = np.zeros(shape=(np.product(x.shape[:-1]),
+                              depth)).astype('float32')
+
+        for i in xrange(np.product(x.shape)):
+            out[i, x[i]] = 1.0
+
+        self.inputs = {'X': (x, x_lod)}
+        self.attrs = {'depth': depth}
+        self.outputs = {'Out': (out, x_lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestOneHotOp_exception(OpTest):
+    def setUp(self):
+        self.op_type = 'one_hot'
+        self.depth = 10
+        self.place = core.CPUPlace()
+        self.dimension = 12
+        self.x = core.LoDTensor()
+        x_lod = [[0, 4, 5, 8, 11]]
+        data = [np.random.randint(11, 20) for i in xrange(x_lod[0][-1])]
+        data = np.array(data).astype('int').reshape([x_lod[0][-1], 1])
+        self.x.set(data, self.place)
+        self.x.set_lod(x_lod)
+
+    def test_check_output(self):
+        program = Program()
+        with program_guard(program):
+            x = fluid.layers.data(
+                name='x', shape=[self.dimension], dtype='float32', lod_level=1)
+            block = program.current_block()
+            one_hot_out = block.create_var(
+                name="one_hot_out",
+                type=core.VarDesc.VarType.LOD_TENSOR,
+                dtype='float32')
+            block.append_op(
+                type='one_hot',
+                inputs={'X': x},
+                attrs={'depth': self.depth},
+                outputs={'Out': one_hot_out})
+            exe = fluid.Executor(self.place)
+
+            def run():
+                exe.run(feed={'x': self.x},
+                        fetch_list=[one_hot_out],
+                        return_numpy=False)
+
+            self.assertRaises(core.EnforceNotMet, run)
+
+
+if __name__ == '__main__':
+    unittest.main()

From eca58a62000b76d4aa218c6a12a42cefeb547a23 Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Fri, 26 Jan 2018 08:58:12 +0000
Subject: [PATCH 080/314] Add unittest for GPU.

---
 .../book/test_inference_recognize_digits.cc   | 97 +++++++++++++------
 1 file changed, 65 insertions(+), 32 deletions(-)

diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index 0dfaf9a0ee..de15167ac3 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -21,19 +21,12 @@ limitations under the License. */
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-TEST(recognize_digits, CPU) {
-  if (FLAGS_dirname.empty()) {
-    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
-  }
-
-  std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
-  std::string dirname = FLAGS_dirname;
-
-  // 0. Initialize all the devices
-  paddle::framework::InitDevices();
-
+template <typename Place, typename T>
+void TestInference(const std::string& dirname,
+                   const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
+                   std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
   // 1. Define place, executor and scope
-  auto place = paddle::platform::CPUPlace();
+  auto place = Place();
   auto executor = paddle::framework::Executor(place);
   auto* scope = new paddle::framework::Scope();
 
@@ -49,37 +42,77 @@ TEST(recognize_digits, CPU) {
 
   // 4. Prepare inputs
   std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
-  paddle::framework::LoDTensor input;
-  srand(time(0));
-  float* input_ptr =
-      input.mutable_data<float>({1, 28, 28}, paddle::platform::CPUPlace());
-  for (int i = 0; i < 784; ++i) {
-    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
+  for (size_t i = 0; i < feed_target_names.size(); ++i) {
+    // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
+    feed_targets[feed_target_names[i]] = cpu_feeds[i];
   }
-  feed_targets[feed_target_names[0]] = &input;
 
   // 5. Define Tensor to get the outputs
   std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
-  paddle::framework::LoDTensor output;
-  fetch_targets[fetch_target_names[0]] = &output;
+  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
+    fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
+  }
 
   // 6. Run the inference program
   executor.Run(*inference_program, scope, feed_targets, fetch_targets);
 
-  // 7. Use the output as your expect.
-  LOG(INFO) << output.dims();
-  std::stringstream ss;
-  ss << "result:";
-  float* output_ptr = output.data<float>();
-  for (int j = 0; j < output.numel(); ++j) {
-    ss << " " << output_ptr[j];
-  }
-  LOG(INFO) << ss.str();
-
   delete scope;
   delete engine;
 }
 
+TEST(inference, recognize_digits) {
+  if (FLAGS_dirname.empty()) {
+    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
+  }
+
+  LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
+
+  // 0. Initialize all the devices
+  paddle::framework::InitDevices();
+
+  paddle::framework::LoDTensor input;
+  srand(time(0));
+  float* input_ptr =
+      input.mutable_data<float>({1, 28, 28}, paddle::platform::CPUPlace());
+  for (int i = 0; i < 784; ++i) {
+    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
+  }
+  std::vector<paddle::framework::LoDTensor*> cpu_feeds;
+  cpu_feeds.push_back(&input);
+
+  paddle::framework::LoDTensor output1;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
+  cpu_fetchs1.push_back(&output1);
+
+  // Run inference on CPU
+  TestInference<paddle::platform::CPUPlace, float>(
+      FLAGS_dirname, cpu_feeds, cpu_fetchs1);
+  LOG(INFO) << output1.dims();
+
+#ifdef PADDLE_WITH_CUDA
+  paddle::framework::LoDTensor output2;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
+  cpu_fetchs2.push_back(&output2);
+
+  // Run inference on CUDA GPU
+  TestInference<paddle::platform::CUDAPlace, float>(
+      FLAGS_dirname, cpu_feeds, cpu_fetchs2);
+  LOG(INFO) << output2.dims();
+
+  EXPECT_EQ(output1.dims(), output2.dims());
+  EXPECT_EQ(output1.numel(), output2.numel());
+
+  float err = 1E-3;
+  int count = 0;
+  for (int64_t i = 0; i < output1.numel(); ++i) {
+    if (fabs(output1.data<float>()[i] - output2.data<float>()[i]) > err) {
+      count++;
+    }
+  }
+  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+#endif
+}
+
 int main(int argc, char** argv) {
   google::ParseCommandLineFlags(&argc, &argv, false);
   testing::InitGoogleTest(&argc, argv);

From 9beec1212bb895eabf4604f6231db52920fd61d0 Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Sat, 27 Jan 2018 02:23:02 +0800
Subject: [PATCH 081/314] Add Channel (#7442)

* add Channle

* refine Channel
---
 paddle/operators/detail/channel.h | 89 +++++++++++++++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 paddle/operators/detail/channel.h

diff --git a/paddle/operators/detail/channel.h b/paddle/operators/detail/channel.h
new file mode 100644
index 0000000000..cbfdf80040
--- /dev/null
+++ b/paddle/operators/detail/channel.h
@@ -0,0 +1,89 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <condition_variable>
+#include <mutex>
+#include <queue>
+
+namespace paddle {
+namespace operators {
+namespace detail {
+
+template <typename T>
+class Channel {
+ public:
+  explicit Channel(std::size_t capacity) : capacity_(capacity) {}
+
+  void Send(T* channel_element) {
+    std::unique_lock<std::mutex> lock(mu_);
+
+    if (IsBounded()) {
+      full_cond_var_.wait(lock, [this]() {
+        bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true;
+        return capacity_valid;
+      });
+    }
+    channel_.push_back(std::move(*channel_element));
+
+    lock.unlock();
+    empty_cond_var_.notify_all();
+  }
+
+  T* Receive() {
+    std::unique_lock<std::mutex> lock(mu_);
+    empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
+
+    T* channel_element = std::move(channel_.front());
+    channel_.pop_front();
+
+    NotifyAllSenders(&lock);
+    return channel_element;
+  }
+
+  size_t Size() {
+    std::unique_lock<std::mutex> lock(mu_);
+    return channel_.size();
+  }
+
+  void Clear() {
+    std::unique_lock<std::mutex> lock(mu_);
+    channel_.clear();
+
+    NotifyAllSenders(&lock);
+  }
+
+ private:
+  std::size_t capacity_;
+  std::mutex mu_;
+  std::condition_variable empty_cond_var_;
+  std::condition_variable full_cond_var_;
+  std::deque<T> channel_;
+
+ private:
+  void NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
+    if (IsBounded()) {
+      lock->unlock();
+      full_cond_var_.notify_all();
+    }
+  }
+
+  bool IsBounded() const { return capacity_ > 0; }
+
+  bool IsCapacityFull() const { return channel_.size() >= capacity_; }
+};
+
+}  // namespace detail
+}  // namespace operator
+}  // namespace paddle

From 2f9cf7ccae6101fa5f14ac05a5364e2e22507993 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Fri, 26 Jan 2018 12:21:17 -0800
Subject: [PATCH 082/314] address comments

---
 paddle/framework/prune.cc | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 6117cbb79f..bff8e0bcea 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -116,15 +116,15 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
   for (const auto& op : *op_field) {
     // add VarDescs of all input arguments for each OpDesc
     auto& input_field = op.inputs();
-    for (auto& input : input_field) {
-      for (auto& arg : input.arguments()) {
+    for (auto& input_var : input_field) {
+      for (auto& arg : input_var.arguments()) {
         *var_field->Add() = var_map[arg];
       }
     }
     // add VarDescs of all output arguments for each OpDesc
     auto& output_field = op.outputs();
-    for (auto& output : output_field) {
-      for (auto& arg : output.arguments()) {
+    for (auto& output_var : output_field) {
+      for (auto& arg : output_var.arguments()) {
         *var_field->Add() = var_map[arg];
       }
     }

From 06e226378fb70d02d50b023c29f28dedb560fc5f Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Fri, 26 Jan 2018 14:14:32 -0800
Subject: [PATCH 083/314] Fix Latex (#7901)

---
 paddle/operators/gru_op.cc | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/paddle/operators/gru_op.cc b/paddle/operators/gru_op.cc
index 76f2adefed..fb901b6394 100644
--- a/paddle/operators/gru_op.cc
+++ b/paddle/operators/gru_op.cc
@@ -135,14 +135,14 @@ class GRUOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 GRU Operator implements part calculations of the complete GRU as following:
 
-\f[
-update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
-reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\
-output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
+$$
+update\_gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
+reset\_gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\
+output\_candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
 output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
-\f]
+$$
 
-@note To implement the complete GRU, fully-connected operator must be used  
+@note To implement the complete GRU, fully-connected operator must be used
 before to feed xu, xr and xc as the Input of GRU operator.
 )DOC");
   }

From 1f3caaa8a492e926b168586efaf89b3ef3228cc2 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Fri, 26 Jan 2018 14:14:47 -0800
Subject: [PATCH 084/314] Make notest_dist_image_classification  consistent
 with distributed implementation in others. (#7899)

* Make this file consistent with others

* fixed style
---
 .../notest_dist_image_classification.py       | 19 ++++++++-----------
 1 file changed, 8 insertions(+), 11 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
index 218dea31e1..0c51ccf306 100644
--- a/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
+++ b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
@@ -14,8 +14,6 @@
 
 from __future__ import print_function
 
-import sys
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import os
@@ -106,10 +104,10 @@ if len(sys.argv) >= 2:
     net_type = sys.argv[1]
 
 if net_type == "vgg":
-    print("train vgg net")
+    print("training vgg net")
     net = vgg16_bn_drop(images)
 elif net_type == "resnet":
-    print("train resnet")
+    print("training resnet")
     net = resnet_cifar10(images, 32)
 else:
     raise ValueError("%s network is not supported" % net_type)
@@ -129,6 +127,7 @@ train_reader = paddle.batch(
     batch_size=BATCH_SIZE)
 
 place = fluid.CPUPlace()
+feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
 exe = fluid.Executor(place)
 
 t = fluid.DistributeTranspiler()
@@ -146,17 +145,14 @@ if training_role == "PSERVER":
     if not current_endpoint:
         print("need env SERVER_ENDPOINT")
         exit(1)
-    print("start pserver at:", current_endpoint)
     pserver_prog = t.get_pserver_program(current_endpoint)
     pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
     exe.run(pserver_startup)
     exe.run(pserver_prog)
-    print("pserver run end")
 elif training_role == "TRAINER":
-    print("start trainer")
     trainer_prog = t.get_trainer_program()
-    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
     exe.run(fluid.default_startup_program())
+
     for pass_id in range(PASS_NUM):
         accuracy.reset(exe)
         for data in train_reader():
@@ -164,9 +160,10 @@ elif training_role == "TRAINER":
                                 feed=feeder.feed(data),
                                 fetch_list=[avg_cost] + accuracy.metrics)
             pass_acc = accuracy.eval(exe)
-            print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-                pass_acc))
-            # this model is slow, so if we can train two mini batch, we think it works properly.
+            print("pass_id:" + str(pass_id) + "loss:" + str(loss) + " pass_acc:"
+                  + str(pass_acc))
+            # this model is slow, so if we can train two mini batches,
+            # we think it works properly.
     print("trainer run end")
 else:
     print("environment var TRAINER_ROLE should be TRAINER os PSERVER")

From 9b6387e7aeca545260562fdee4021e040f5294e4 Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Fri, 26 Jan 2018 14:25:24 -0800
Subject: [PATCH 085/314] address comments (#7900)

---
 paddle/framework/executor.cc | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index 50a70d723e..cbf3ec7526 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -170,8 +170,6 @@ static bool has_feed_operators(
           feed_targets.find(feed_target_name) != feed_targets.end(),
           "Feed operator output name '%s' cannot be found in 'feed_targets'",
           feed_target_name);
-    } else {
-      break;
     }
   }
 
@@ -270,8 +268,6 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
       int idx = boost::get<int>(op->GetAttr("col"));
       SetFeedVariable(scope, *feed_targets[feed_target_name], feed_holder_name,
                       idx);
-    } else {
-      break;
     }
   }
 

From 0531edf9da3a089ab1667cd1fb8d9760c7b3503a Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Fri, 26 Jan 2018 16:28:31 -0800
Subject: [PATCH 086/314] Adding distributed training for dynamic_lstm (#7903)

---
 ...otest_understand_sentiment_dynamic_lstm.py | 135 ++++++++++++++++++
 1 file changed, 135 insertions(+)
 create mode 100644 python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py

diff --git a/python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py b/python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py
new file mode 100644
index 0000000000..bff376a0e2
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book_distribute/notest_understand_sentiment_dynamic_lstm.py
@@ -0,0 +1,135 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import os
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+
+
+def stacked_lstm_net(data,
+                     label,
+                     input_dim,
+                     class_dim=2,
+                     emb_dim=128,
+                     hid_dim=512,
+                     stacked_num=3):
+    assert stacked_num % 2 == 1
+
+    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
+    # add bias attr
+
+    # TODO(qijun) linear act
+    fc1 = fluid.layers.fc(input=emb, size=hid_dim)
+    lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
+
+    inputs = [fc1, lstm1]
+
+    for i in range(2, stacked_num + 1):
+        fc = fluid.layers.fc(input=inputs, size=hid_dim)
+        lstm, cell = fluid.layers.dynamic_lstm(
+            input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
+        inputs = [fc, lstm]
+
+    fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
+    lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
+
+    prediction = fluid.layers.fc(input=[fc_last, lstm_last],
+                                 size=class_dim,
+                                 act='softmax')
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
+    optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
+    accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
+    return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
+
+
+def to_lodtensor(data, place):
+    seq_lens = [len(seq) for seq in data]
+    cur_len = 0
+    lod = [cur_len]
+    for l in seq_lens:
+        cur_len += l
+        lod.append(cur_len)
+    flattened_data = np.concatenate(data, axis=0).astype("int64")
+    flattened_data = flattened_data.reshape([len(flattened_data), 1])
+    res = fluid.LoDTensor()
+    res.set(flattened_data, place)
+    res.set_lod([lod])
+    return res
+
+
+def main():
+    BATCH_SIZE = 100
+    PASS_NUM = 5
+
+    word_dict = paddle.dataset.imdb.word_dict()
+    print "loaded word dict successfully"
+    dict_dim = len(word_dict)
+    class_dim = 2
+
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+    cost, accuracy, acc_out, optimize_ops, params_grads = stacked_lstm_net(
+        data, label, input_dim=dict_dim, class_dim=class_dim)
+
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=1000),
+        batch_size=BATCH_SIZE)
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
+
+    t = fluid.DistributeTranspiler()
+    # all parameter server endpoints list for spliting parameters
+    pserver_endpoints = os.getenv("PSERVERS")
+    # server endpoint for current node
+    current_endpoint = os.getenv("SERVER_ENDPOINT")
+    # run as trainer or parameter server
+    training_role = os.getenv(
+        "TRAINING_ROLE", "TRAINER")  # get the training role: trainer/pserver
+    t.transpile(
+        optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+
+    if training_role == "PSERVER":
+        if not current_endpoint:
+            print("need env SERVER_ENDPOINT")
+            exit(1)
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
+        exe.run(pserver_startup)
+        exe.run(pserver_prog)
+    elif training_role == "TRAINER":
+        exe.run(fluid.default_startup_program())
+        trainer_prog = t.get_trainer_program()
+        for pass_id in xrange(PASS_NUM):
+            accuracy.reset(exe)
+            for data in train_data():
+                cost_val, acc_val = exe.run(trainer_prog,
+                                            feed=feeder.feed(data),
+                                            fetch_list=[cost, acc_out])
+                pass_acc = accuracy.eval(exe)
+                print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
+                      " pass_acc=" + str(pass_acc))
+                if cost_val < 1.0 and acc_val > 0.8:
+                    exit(0)
+    else:
+        print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+if __name__ == '__main__':
+    main()

From 90a5fd26a9688dcbb09d2f18659296174dcbe31b Mon Sep 17 00:00:00 2001
From: "yi.wu" <yi.wu@baifendian.com>
Date: Sat, 27 Jan 2018 10:26:02 +0800
Subject: [PATCH 087/314] fix boost down link

---
 cmake/external/boost.cmake | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/cmake/external/boost.cmake b/cmake/external/boost.cmake
index a2c979b2e8..c70d83b3f4 100644
--- a/cmake/external/boost.cmake
+++ b/cmake/external/boost.cmake
@@ -17,7 +17,7 @@ include(ExternalProject)
 set(BOOST_PROJECT       "extern_boost")
 set(BOOST_VER           "1.41.0")
 set(BOOST_TAR           "boost_1_41_0")
-set(BOOST_URL           "https://dl.bintray.com/boostorg/release/${BOOST_VER}/source/${BOOST_TAR}.tar.gz")
+set(BOOST_URL           "http://sourceforge.net/projects/boost/files/boost/${BOOST_VER}/${BOOST_TAR}.tar.gz")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)

From 4ce397964b788f1e9bbbe08a8e5f4d4ce21dd2f8 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 27 Jan 2018 12:31:30 +0800
Subject: [PATCH 088/314] fix unit test and c++ code

---
 paddle/operators/layer_norm_op.cc             | 44 +++++++++----------
 .../v2/fluid/tests/test_layer_norm_op.py      | 19 ++++----
 2 files changed, 30 insertions(+), 33 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 0808192565..0b0c760e57 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -233,39 +233,37 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     if (d_x) {
       d_x->mutable_data<T>(ctx.GetPlace());
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
-      auto triple_product = [](T ele) { return ele * ele; };
-      auto neg_inv_std = [](T ele) { return -std::sqrt(1 / ele); };
+      auto triple_product_func = [](T ele) { return ele * ele * ele; };
+      auto scale_func = [scale_data](T ele) { return ele * scale_data; };
+      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
       auto inv_std_scale_func = [scale_data](T ele) {
         return std::sqrt(1 / ele) * scale_data;
       };
-      auto neg_inv_std_scale_func = [scale_data](T ele) {
-        return -std::sqrt(1 / ele) * scale_data;
-      };
       // dy_dx
       auto dx_end = var_map.unaryExpr(inv_std_scale_func)
                         .replicate(1, right)
                         .cwiseProduct(d_y_map);
       // dy_dmean_dx
-      auto dmean_end = var_map.unaryExpr(neg_inv_std_scale_func)
-                           .replicate(1, right)
-                           .cwiseProduct(d_y_map)
-                           .rowwise()
-                           .sum();
-      auto dx_mean = (T(1.0) / right) * dmean_end.replicate(1, right);
+      auto dx_mean = (T(-1.0) / right) *
+                     var_map.unaryExpr(inv_std_scale_func)
+                         .replicate(1, right)
+                         .cwiseProduct(d_y_map)
+                         .rowwise()
+                         .sum()
+                         .replicate(1, right);
       // dy_var_dx
-      auto dvar_end_0 = (x_map - mean_map.replicate(1, right))
-                            .cwiseProduct(d_y_map)
-                            .rowwise()
-                            .sum();
-      auto dvar_end = var_map.unaryExpr(neg_inv_std)
-                          .unaryExpr(triple_product)
-                          .cwiseProduct(dvar_end_0);
-      auto dx_var = (T(1.0) / right) *
+      auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                               .cwiseProduct(d_y_map)
+                               .rowwise()
+                               .sum();
+      auto dvar_end = var_map.unaryExpr(inv_std_func)
+                          .unaryExpr(triple_product_func)
+                          .cwiseProduct(dvar_end_part)
+                          .replicate(1, right);
+      auto dx_var = (T(-1.0) / right) *
                     (x_map - mean_map.replicate(1, right))
-                        .cwiseProduct(dvar_end.replicate(1, right));
-
-      // d_x = (1. / N) * scale * inv_var * (N * d_y - np.sum(d_y, axis=0)
-      //   - (X - mean) * inv_var * inv_var * np.sum(d_y * (X - mean), axis=0))
+                        .cwiseProduct(dvar_end)
+                        .unaryExpr(scale_func);
 
       d_x_map = dx_end + dx_mean + dx_var;
     }
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 4ca9754f32..caa3b944eb 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -52,18 +52,19 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, epsilon):
     D = reduce(mul, x_shape, 1) / N
     grad_y.shape = [N, D]
     x.shape = [N, D]
-    grad_offset = np.sum(grad_y)
     mean.shape = [N, 1]
     var.shape = [N, 1]
-    grad_scale = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y)
+
+    d_scale = np.sum(grad_y).reshape([1, ])
+    d_bias = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y).reshape([1, ])
 
     dx_end = np.sqrt(1.0 / var) * grad_y
 
     d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y, axis=1).reshape([N, 1])
-    d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
-        [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
-                   np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
-    d_mean = 1.0 / D * (d_mean_0 + d_mean_1)
+    # d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
+    #     [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
+    #                np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
+    d_mean = 1.0 / D * (d_mean_0)
 
     d_std = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape([N, 1]) * (
         1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
@@ -73,7 +74,7 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, epsilon):
     grad_y.shape = x_shape
     x.shape = x_shape
 
-    return grad_x, grad_scale, grad_offset
+    return grad_x, d_bias, d_scale
 
 
 def create_or_get_tensor(scope, var_name, var, place):
@@ -144,7 +145,7 @@ class TestLayerNormdOp(OpTest):
             epsilon = 0.00001
             x_shape = shape
             scale_shape = [1]
-
+            np.random.random(123)
             x_val = np.random.random_sample(x_shape).astype(np.float32)
             scale_val = np.random.random_sample(scale_shape).astype(np.float32)
             bias_val = np.random.random_sample(scale_shape).astype(np.float32)
@@ -154,7 +155,6 @@ class TestLayerNormdOp(OpTest):
                 x_val, scale_val, bias_val, epsilon)
 
             #  for gradient test
-            # y_grad = np.ones(x_shape).astype(np.float32) * 0.00277778
             y_grad = np.random.random_sample(x_shape).astype(np.float32)
 
             x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_layer_norm_grad(
@@ -229,7 +229,6 @@ class TestLayerNormdOp(OpTest):
 
         for place in places:
             test_with_place(place, [2, 3, 4, 5])
-            test_with_place(place, [2, 3])
 
 
 if __name__ == '__main__':

From a026f52b73b1d9ec80b95346de8671b77effa95c Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Sun, 28 Jan 2018 03:59:44 +0800
Subject: [PATCH 089/314] refine channel (#7910)

---
 paddle/{operators/detail => framework}/channel.h | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)
 rename paddle/{operators/detail => framework}/channel.h (94%)

diff --git a/paddle/operators/detail/channel.h b/paddle/framework/channel.h
similarity index 94%
rename from paddle/operators/detail/channel.h
rename to paddle/framework/channel.h
index cbfdf80040..9ba0fc5c55 100644
--- a/paddle/operators/detail/channel.h
+++ b/paddle/framework/channel.h
@@ -18,8 +18,7 @@ limitations under the License. */
 #include <queue>
 
 namespace paddle {
-namespace operators {
-namespace detail {
+namespace framework {
 
 template <typename T>
 class Channel {
@@ -38,7 +37,7 @@ class Channel {
     channel_.push_back(std::move(*channel_element));
 
     lock.unlock();
-    empty_cond_var_.notify_all();
+    empty_cond_var_.notify_one();
   }
 
   T* Receive() {
@@ -75,7 +74,7 @@ class Channel {
   void NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
     if (IsBounded()) {
       lock->unlock();
-      full_cond_var_.notify_all();
+      full_cond_var_.notify_one();
     }
   }
 
@@ -84,6 +83,5 @@ class Channel {
   bool IsCapacityFull() const { return channel_.size() >= capacity_; }
 };
 
-}  // namespace detail
 }  // namespace operator
 }  // namespace paddle

From 0f0ce4e5ec66d7661fac57c1ab6aa4cd71d7e2c3 Mon Sep 17 00:00:00 2001
From: Yancey <yanxu05@baidu.com>
Date: Sun, 28 Jan 2018 10:30:44 +0800
Subject: [PATCH 090/314] Fix cpplint (#7914)

---
 paddle/operators/detail/grpc_client.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/operators/detail/grpc_client.cc b/paddle/operators/detail/grpc_client.cc
index 90e2b29659..c433945542 100644
--- a/paddle/operators/detail/grpc_client.cc
+++ b/paddle/operators/detail/grpc_client.cc
@@ -101,8 +101,8 @@ bool RPCClient::Wait() {
   if (req_count_ <= 0) {
     return true;
   }
-
-  bool a[req_count_];
+  const size_t kReqCnt = req_count_;
+  bool a[kReqCnt];
   std::vector<std::future<void>> waits(req_count_);
 
   for (int i = 0; i < req_count_; i++) {

From 3646be7bfca135fc20eb291dcbe14e13c93d8429 Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Sat, 27 Jan 2018 23:24:05 -0800
Subject: [PATCH 091/314] Fix some typos in support new device doc (#7895)

* fix some typos

* fix
---
 doc/design/support_new_device.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/doc/design/support_new_device.md b/doc/design/support_new_device.md
index 4c5f10e2ec..b154f01d32 100644
--- a/doc/design/support_new_device.md
+++ b/doc/design/support_new_device.md
@@ -2,9 +2,9 @@
 
 ## Background
 
-Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries flexibly and efficiently.
+Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries in a flexible and efficient manner.
 
-On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example,Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
+On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example, Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
 
 On the other hand, users usually do not want to care about the low-level hardware and computing libraries when writing a neural network configuration. In Fluid, `Layer` is exposed in `Python`, and `Operator` is exposed in `C++`. Both `Layer` and `Operator` are hardware independent.
 
@@ -17,7 +17,7 @@ For a general overview of fluid, please refer to the [overview doc](https://gith
 
 There are mainly three parts that we have to consider while integrating a new device/library:
 
-- Place and DeviceContext: indicates the device id and manages hardware resources
+- Place and DeviceContext: indicate the device id and manage hardware resources
 
 - Memory and Tensor: malloc/free data on certain device
 
@@ -25,10 +25,10 @@ There are mainly three parts that we have to consider while integrating a new de
 
 ### Place and DeviceContext
 
-Please remind that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
+Please note that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
 
 #### Place
-Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add corresponding `DevicePlace`.
+Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add the corresponding `DevicePlace`.
 
 ```
         |   CPUPlace
@@ -144,7 +144,7 @@ class Tensor {
 };
 ```
 
-`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configure its shape, and then call `mutuable_data` to allocate the actual memory.
+`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configurate its shape, and then call `mutuable_data` to allocate the actual memory.
 
 ```cpp
 paddle::framework::Tensor t;
@@ -163,7 +163,7 @@ Fluid implements computing units based on different DeviceContexts. Some computi
 
 Let's take [MaxOutFunctor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/math/maxouting.h#L27) as an example:
 
-The interface is defined in header file.
+The interface is defined in the header file.
 
 ```
 template <typename DeviceContext, typename T>
@@ -203,7 +203,7 @@ class MaxOutFunctor<platform::CUDADeviceContext, T> {
 ```
 
 
-We get computing handle from a concrete DeviceContext, and make compution on tensors.
+We first obtain the computing handle from a concrete DeviceContext, and then compute on tensors.
 
 The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
 
@@ -231,7 +231,7 @@ REGISTER_OP_CUDA_KERNEL(
 
 ## Advanced topics: How to switch between different Device/Library
 
-Generally, we will impelement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not sutibale on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run at GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
+Generally, we will implement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not suitable on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run on GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
 
 
 For more details, please refer to following docs:

From 72eccb238ebea3bb0509da7bf6e9ff18db088763 Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Sun, 28 Jan 2018 15:38:54 +0800
Subject: [PATCH 092/314] add box coder op

---
 paddle/operators/box_coder_op.cc              | 106 ++++++++++++
 paddle/operators/box_coder_op.cu              | 145 ++++++++++++++++
 paddle/operators/box_coder_op.h               | 163 ++++++++++++++++++
 .../v2/fluid/tests/test_box_coder_op.py       | 117 +++++++++++++
 4 files changed, 531 insertions(+)
 create mode 100644 paddle/operators/box_coder_op.cc
 create mode 100644 paddle/operators/box_coder_op.cu
 create mode 100644 paddle/operators/box_coder_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_box_coder_op.py

diff --git a/paddle/operators/box_coder_op.cc b/paddle/operators/box_coder_op.cc
new file mode 100644
index 0000000000..0cb20a4182
--- /dev/null
+++ b/paddle/operators/box_coder_op.cc
@@ -0,0 +1,106 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/box_coder_op.h"
+
+namespace paddle {
+namespace operators {
+
+class BoxCoderOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
+                   "Input(PriorBox) of BoxCoderOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("PriorBoxVar"),
+                   "Input(PriorBoxVar) of BoxCoderOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
+                   "Input(TargetBox) of BoxCoderOp should not be null.");
+
+    auto prior_box_dims = ctx->GetInputDim("PriorBox");
+    auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
+    auto target_box_dims = ctx->GetInputDim("TargetBox");
+
+    PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2UL,
+                      "The shape of PriorBox is [N, 4]");
+    PADDLE_ENFORCE_EQ(prior_box_dims[1], 4UL,
+                      "The shape of PriorBox is [N, 4]");
+    PADDLE_ENFORCE_EQ(prior_box_var_dims.size(), 2UL,
+                      "The shape of PriorBoxVar is [N, 4]");
+    PADDLE_ENFORCE_EQ(prior_box_var_dims[1], 4UL,
+                      "The shape of PriorBoxVar is [N, 4]");
+    PADDLE_ENFORCE_EQ(target_box_dims.size(), 2UL,
+                      "The shape of TargetBox is [M, 4]");
+    PADDLE_ENFORCE_EQ(target_box_dims[1], 4UL,
+                      "The shape of TargetBox is [M, 4]");
+
+    GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
+
+    ctx->SetOutputDim("OutputBox", framework::make_ddim({target_box_dims[0],
+                                                         target_box_dims[1]}));
+  }
+};
+
+class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  BoxCoderOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "PriorBox",
+        "(Tensor, default Tensor<float>) "
+        "Box list PriorBox is a 2-D Tensor with shape [M, 4] holds N boxes, "
+        "each box is represented as [xmin, ymin, xmax, ymax], "
+        "[xmin, ymin] is the left top coordinate of the anchor box, "
+        "if the input is image feature map, they are close to the origin "
+        "of the coordinate system. [xmax, ymax] is the right bottom "
+        "coordinate of the anchor box.");
+    AddInput("PriorBoxVar",
+             "(Tensor, default Tensor<float>) "
+             "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds N group "
+             "of variance.");
+    AddInput(
+        "TargetBox",
+        "(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape "
+        "[N, 4], each box is represented as [xmin, ymin, xmax, ymax], "
+        "[xmin, ymin] is the left top coordinate of the box if the input "
+        "is image feature map, they are close to the origin of the coordinate "
+        "system. [xmax, ymax] is the right bottom coordinate of the box. "
+        "This tensor can contain LoD information to represent a batch "
+        "of inputs. One instance of this batch can contain different "
+        "numbers of entities.");
+    AddAttr<std::string>("code_type",
+                         "(string, default encode_center_size) "
+                         "the code type used with the target box")
+        .SetDefault("encode_center_size")
+        .InEnum({"encode_center_size", "decode_center_size"});
+    AddOutput(
+        "OutputBox",
+        "(Tensor, default Tensor<float>)"
+        "(Tensor) The output of box_coder_op, a tensor with shape [N, M, 4] "
+        "representing the result of N target boxes encoded/decoded with "
+        "M Prior boxes and variances.");
+
+    AddComment(R"DOC(
+Bounding Box Coder Operator.
+Encode/Decode the priorbox information with the target bounding box.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker);
+REGISTER_OP_CPU_KERNEL(box_coder, ops::BoxCoderKernel<float>,
+                       ops::BoxCoderKernel<double>);
diff --git a/paddle/operators/box_coder_op.cu b/paddle/operators/box_coder_op.cu
new file mode 100644
index 0000000000..4055ded1f8
--- /dev/null
+++ b/paddle/operators/box_coder_op.cu
@@ -0,0 +1,145 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/box_coder_op.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using platform::PADDLE_CUDA_NUM_THREADS;
+
+template <typename T>
+__global__ void EncodeCenterSizeKernel(const T* prior_box_data,
+                                       const T* prior_box_var_data,
+                                       const T* target_box_data, int row,
+                                       int col, T* output) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < row * col) {
+    const int row_idx = idx / col;
+    const int col_idx = idx % col;
+    T prior_box_width =
+        prior_box_data[col_idx * 4 + 2] - prior_box_data[col_idx * 4];
+    T prior_box_height =
+        prior_box_data[col_idx * 4 + 3] - prior_box_data[col_idx * 4 + 1];
+    T prior_box_center_x =
+        (prior_box_data[col_idx * 4 + 2] + prior_box_data[col_idx * 4]) / 2;
+    T prior_box_center_y =
+        (prior_box_data[col_idx * 4 + 3] + prior_box_data[col_idx * 4 + 1]) / 2;
+
+    T target_box_center_x =
+        (target_box_data[row_idx * 4 + 2] + target_box_data[row_idx * 4]) / 2;
+    T target_box_center_y =
+        (target_box_data[row_idx * 4 + 3] + target_box_data[row_idx * 4 + 1]) /
+        2;
+    T target_box_width =
+        target_box_data[row_idx * 4 + 2] - target_box_data[row_idx * 4];
+    T target_box_height =
+        target_box_data[row_idx * 4 + 3] - target_box_data[row_idx * 4 + 1];
+
+    output[idx * 4] = (target_box_center_x - prior_box_center_x) /
+                      prior_box_width / prior_box_var_data[col_idx * 4];
+    output[idx * 4 + 1] = (target_box_center_y - prior_box_center_y) /
+                          prior_box_height /
+                          prior_box_var_data[col_idx * 4 + 1];
+    output[idx * 4 + 2] = log(fabs(target_box_width / prior_box_width)) /
+                          prior_box_var_data[col_idx * 4 + 2];
+    output[idx * 4 + 3] = log(fabs(target_box_height / prior_box_height)) /
+                          prior_box_var_data[col_idx * 4 + 3];
+  }
+}
+
+template <typename T>
+__global__ void DecodeCenterSizeKernel(const T* prior_box_data,
+                                       const T* prior_box_var_data,
+                                       const T* target_box_data, int row,
+                                       int col, T* output) {
+  const int idx = threadIdx.x + blockIdx.x * blockDim.x;
+  if (idx < row * col) {
+    const int row_idx = idx / col;
+    const int col_idx = idx % col;
+    T prior_box_width =
+        prior_box_data[col_idx * 4 + 2] - prior_box_data[col_idx * 4];
+    T prior_box_height =
+        prior_box_data[col_idx * 4 + 3] - prior_box_data[col_idx * 4 + 1];
+    T prior_box_center_x =
+        (prior_box_data[col_idx * 4 + 2] + prior_box_data[col_idx * 4]) / 2;
+    T prior_box_center_y =
+        (prior_box_data[col_idx * 4 + 3] + prior_box_data[col_idx * 4 + 1]) / 2;
+
+    T target_box_width = exp(prior_box_var_data[col_idx * 4 + 2] *
+                             target_box_data[row_idx * 4 + 2]) *
+                         prior_box_width;
+    T target_box_height = exp(prior_box_var_data[col_idx * 4 + 3] *
+                              target_box_data[row_idx * 4 + 3]) *
+                          prior_box_height;
+    T target_box_center_x = prior_box_var_data[col_idx * 4] *
+                                target_box_data[row_idx * 4] * prior_box_width +
+                            prior_box_center_x;
+    T target_box_center_y = prior_box_var_data[col_idx * 4 + 1] *
+                                target_box_data[row_idx * 4 + 1] *
+                                prior_box_height +
+                            prior_box_center_y;
+
+    output[idx * 4] = target_box_center_x - target_box_width / 2;
+    output[idx * 4 + 1] = target_box_center_y - target_box_height / 2;
+    output[idx * 4 + 2] = target_box_center_x + target_box_width / 2;
+    output[idx * 4 + 3] = target_box_center_y + target_box_height / 2;
+  }
+}
+
+template <typename T>
+class BoxCoderCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
+                   "This kernel only runs on GPU device.");
+    auto* prior_box = context.Input<framework::Tensor>("PriorBox");
+    auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
+    auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
+    auto* output_box = context.Output<Tensor>("OutputBox");
+
+    if (target_box->lod().size()) {
+      PADDLE_ENFORCE_EQ(target_box->lod().size(), 1UL,
+                        "Only support 1 level of LoD.");
+    }
+    auto row = target_box->dims()[0];
+    auto col = prior_box->dims()[0];
+    int block = 512;
+    int grid = (row * col + block - 1) / block;
+    auto& device_ctx = context.cuda_device_context();
+
+    const T* prior_box_data = prior_box->data<T>();
+    const T* prior_box_var_data = prior_box_var->data<T>();
+    const T* target_box_data = target_box->data<T>();
+
+    output_box->mutable_data<T>({row, col, 4}, context.GetPlace());
+    T* output = output_box->data<T>();
+
+    auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    if (code_type == BoxCodeType::kEncodeCenterSize) {
+      EncodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
+          prior_box_data, prior_box_var_data, target_box_data, row, col,
+          output);
+    } else if (code_type == BoxCodeType::kDecodeCenterSize) {
+      DecodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
+          prior_box_data, prior_box_var_data, target_box_data, row, col,
+          output);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(box_coder, ops::BoxCoderCUDAKernel<float>,
+                        ops::BoxCoderCUDAKernel<double>);
diff --git a/paddle/operators/box_coder_op.h b/paddle/operators/box_coder_op.h
new file mode 100644
index 0000000000..3865da40c3
--- /dev/null
+++ b/paddle/operators/box_coder_op.h
@@ -0,0 +1,163 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };
+
+inline BoxCodeType GetBoxCodeType(const std::string& type) {
+  if (type == "encode_center_size") {
+    return BoxCodeType::kEncodeCenterSize;
+  } else if (type == "decode_center_size") {
+    return BoxCodeType::kDecodeCenterSize;
+  }
+  PADDLE_THROW("Not support type %s.", type);
+}
+
+template <typename T>
+class BoxCoderKernel : public framework::OpKernel<T> {
+ public:
+  void EncodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
+                        const Tensor& prior_box_var, T* output) const {
+    PADDLE_ENFORCE_EQ(target_box.dims().size(), 2,
+                      "The rank of target_box must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box.dims().size(), 2,
+                      "The rank of prior_box must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box_var.dims().size(), 2,
+                      "The rank of prior_box_var must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box.dims()[0], prior_box_var.dims()[0],
+                      "The dims of prior_box must equal to prior_box_var.");
+
+    int64_t row = target_box.dims()[0];
+    int64_t col = prior_box.dims()[0];
+    auto* target_box_data = target_box.data<T>();
+    auto* prior_box_data = prior_box.data<T>();
+    auto* prior_box_var_data = prior_box_var.data<T>();
+
+    for (int64_t i = 0; i < row; ++i) {
+      for (int64_t j = 0; j < col; ++j) {
+        T prior_box_width = prior_box_data[j * 4 + 2] - prior_box_data[j * 4];
+        T prior_box_height =
+            prior_box_data[j * 4 + 3] - prior_box_data[j * 4 + 1];
+        T prior_box_center_x =
+            (prior_box_data[j * 4 + 2] + prior_box_data[j * 4]) / 2;
+        T prior_box_center_y =
+            (prior_box_data[j * 4 + 3] + prior_box_data[j * 4 + 1]) / 2;
+
+        T target_box_center_x =
+            (target_box_data[i * 4 + 2] + target_box_data[i * 4]) / 2;
+        T target_box_center_y =
+            (target_box_data[i * 4 + 3] + target_box_data[i * 4 + 1]) / 2;
+        T target_box_width =
+            target_box_data[i * 4 + 2] - target_box_data[i * 4];
+        T target_box_height =
+            target_box_data[i * 4 + 3] - target_box_data[i * 4 + 1];
+
+        size_t offset = i * col * 4 + j * 4;
+        output[offset] = (target_box_center_x - prior_box_center_x) /
+                         prior_box_width / prior_box_var_data[j * 4];
+        output[offset + 1] = (target_box_center_y - prior_box_center_y) /
+                             prior_box_height / prior_box_var_data[j * 4 + 1];
+        output[offset + 2] =
+            std::log(std::fabs(target_box_width / prior_box_width)) /
+            prior_box_var_data[j * 4 + 2];
+        output[offset + 3] =
+            std::log(std::fabs(target_box_height / prior_box_height)) /
+            prior_box_var_data[j * 4 + 3];
+      }
+    }
+  }
+  void DecodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
+                        const Tensor& prior_box_var, T* output) const {
+    PADDLE_ENFORCE_EQ(target_box.dims().size(), 2,
+                      "The rank of target_box must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box.dims().size(), 2,
+                      "The rank of prior_box must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box_var.dims().size(), 2,
+                      "The rank of prior_box_var must be 2.");
+    PADDLE_ENFORCE_EQ(prior_box.dims()[0], prior_box_var.dims()[0],
+                      "The dims of prior_box must equal to prior_box_var.");
+
+    int64_t row = target_box.dims()[0];
+    int64_t col = prior_box.dims()[0];
+
+    auto* target_box_data = target_box.data<T>();
+    auto* prior_box_data = prior_box.data<T>();
+    auto* prior_box_var_data = prior_box_var.data<T>();
+
+    for (int64_t i = 0; i < row; ++i) {
+      for (int64_t j = 0; j < col; ++j) {
+        T prior_box_width = prior_box_data[j * 4 + 2] - prior_box_data[j * 4];
+        T prior_box_height =
+            prior_box_data[j * 4 + 3] - prior_box_data[j * 4 + 1];
+        T prior_box_center_x =
+            (prior_box_data[j * 4 + 2] + prior_box_data[j * 4]) / 2;
+        T prior_box_center_y =
+            (prior_box_data[j * 4 + 3] + prior_box_data[j * 4 + 1]) / 2;
+
+        T target_box_center_x = prior_box_var_data[j * 4] *
+                                    target_box_data[i * 4] * prior_box_width +
+                                prior_box_center_x;
+        T target_box_center_y = prior_box_var_data[j * 4 + 1] *
+                                    target_box_data[i * 4 + 1] *
+                                    prior_box_height +
+                                prior_box_center_y;
+        T target_box_width = std::exp(prior_box_var_data[j * 4 + 2] *
+                                      target_box_data[i * 4 + 2]) *
+                             prior_box_width;
+        T target_box_height = std::exp(prior_box_var_data[j * 4 + 3] *
+                                       target_box_data[i * 4 + 3]) *
+                              prior_box_height;
+
+        size_t offset = i * col * 4 + j * 4;
+        output[offset] = target_box_center_x - target_box_width / 2;
+        output[offset + 1] = target_box_center_y - target_box_height / 2;
+        output[offset + 2] = target_box_center_x + target_box_width / 2;
+        output[offset + 3] = target_box_center_y + target_box_height / 2;
+      }
+    }
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* prior_box = context.Input<framework::Tensor>("PriorBox");
+    auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
+    auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
+    auto* output_box = context.Output<Tensor>("OutputBox");
+
+    if (target_box->lod().size()) {
+      PADDLE_ENFORCE_EQ(target_box->lod().size(), 1UL,
+                        "Only support 1 level of LoD.");
+    }
+    auto row = target_box->dims()[0];
+    auto col = prior_box->dims()[0];
+
+    output_box->mutable_data<T>({row, col, 4}, context.GetPlace());
+
+    auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
+    T* output = output_box->data<T>();
+    if (code_type == BoxCodeType::kEncodeCenterSize) {
+      EncodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+    } else if (code_type == BoxCodeType::kDecodeCenterSize) {
+      DecodeCenterSize(*target_box, *prior_box, *prior_box_var, output);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_box_coder_op.py b/python/paddle/v2/fluid/tests/test_box_coder_op.py
new file mode 100644
index 0000000000..fcf5da01ce
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_box_coder_op.py
@@ -0,0 +1,117 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
+    prior_box_x = (prior_box[:, 2] + prior_box[:, 0]) / 2
+    prior_box_y = (prior_box[:, 3] + prior_box[:, 1]) / 2
+    prior_box_width = (prior_box[:, 2] - prior_box[:, 0])
+    prior_box_height = (prior_box[:, 3] - prior_box[:, 1])
+
+    if (code_type == "EncodeCenterSize"):
+        target_box_x = (target_box[:, 2] + target_box[:, 0]) / 2
+        target_box_y = (target_box[:, 3] + target_box[:, 1]) / 2
+        target_box_width = (target_box[:, 2] - target_box[:, 0])
+        target_box_height = (target_box[:, 3] - target_box[:, 1])
+
+        for i in range(target_box.shape[0]):
+            output_box[i,:,0] = (target_box_x[i] - prior_box_x) / prior_box_width / \
+                                prior_box_var[:,0]
+            output_box[i,:,1] = (target_box_y[i] - prior_box_y) / prior_box_height / \
+                                prior_box_var[:,1]
+            output_box[i,:,2] = np.log(np.fabs(target_box_width[i] / prior_box_width)) / \
+                                prior_box_var[:,2]
+            output_box[i,:,3] = np.log(np.fabs(target_box_height[i] / prior_box_height)) / \
+                                prior_box_var[:,3]
+
+    elif (code_type == "DecodeCenterSize"):
+        for i in range(target_box.shape[0]):
+            target_box_x = prior_box_var[:,0] * target_box[i][0] * \
+                           prior_box_width[:] + prior_box_x[:]
+            target_box_y = prior_box_var[:,1] * target_box[i][1] * \
+                           prior_box_height[:] + prior_box_y[:]
+            target_box_width = np.exp(prior_box_var[:,2] * target_box[i][2]) * \
+                               prior_box_width[:]
+            target_box_height = np.exp(prior_box_var[:,3] * target_box[i][3]) * \
+                                prior_box_height[:]
+            output_box[i, :, 0] = target_box_x - target_box_width / 2
+            output_box[i, :, 1] = target_box_y - target_box_height / 2
+            output_box[i, :, 2] = target_box_x + target_box_width / 2
+            output_box[i, :, 3] = target_box_y + target_box_height / 2
+
+
+def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):
+    n = target_box.shape[0]
+    m = prior_box.shape[0]
+    output_box = np.zeros((n, m, 4), dtype=np.float32)
+    for i in range(len(lod) - 1):
+        box_coder(target_box[lod[i]:lod[i + 1], :], prior_box, prior_box_var,
+                  output_box[lod[i]:lod[i + 1], :, :], code_type)
+    return output_box
+
+
+class TestBoxCoderOp(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        lod = [[0, 20]]
+        prior_box = np.random.random((10, 4)).astype('float32')
+        prior_box_var = np.random.random((10, 4)).astype('float32')
+        target_box = np.random.random((20, 4)).astype('float32')
+        code_type = "DecodeCenterSize"
+        output_box = batch_box_coder(prior_box, prior_box_var, target_box,
+                                     lod[0], code_type)
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': target_box,
+        }
+        self.attrs = {'code_type': 'decode_center_size'}
+        self.outputs = {'OutputBox': output_box}
+
+
+class TestBoxCoderOpWithLoD(OpTest):
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "box_coder"
+        lod = [[0, 4, 12, 20]]
+        prior_box = np.random.random((10, 4)).astype('float32')
+        prior_box_var = np.random.random((10, 4)).astype('float32')
+        target_box = np.random.random((20, 4)).astype('float32')
+        code_type = "EncodeCenterSize"
+        output_box = batch_box_coder(prior_box, prior_box_var, target_box,
+                                     lod[0], code_type)
+
+        self.inputs = {
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': (target_box, lod),
+        }
+        self.attrs = {'code_type': 'encode_center_size'}
+        self.outputs = {'OutputBox': output_box}
+
+
+if __name__ == '__main__':
+    unittest.main()

From bc6c4dbb9fa06ade3bc6da36fbd6265cec163be4 Mon Sep 17 00:00:00 2001
From: Yuan Gao <yuan.gao.gavin@gmail.com>
Date: Sun, 28 Jan 2018 16:00:55 +0800
Subject: [PATCH 093/314] Update box_coder_op.cc

---
 paddle/operators/box_coder_op.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/operators/box_coder_op.cc b/paddle/operators/box_coder_op.cc
index 0cb20a4182..7d0d28aaba 100644
--- a/paddle/operators/box_coder_op.cc
+++ b/paddle/operators/box_coder_op.cc
@@ -32,15 +32,15 @@ class BoxCoderOp : public framework::OperatorWithKernel {
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
     PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2UL,
-                      "The shape of PriorBox is [N, 4]");
+                      "The rank of Input of PriorBox must be 2");
     PADDLE_ENFORCE_EQ(prior_box_dims[1], 4UL,
                       "The shape of PriorBox is [N, 4]");
     PADDLE_ENFORCE_EQ(prior_box_var_dims.size(), 2UL,
-                      "The shape of PriorBoxVar is [N, 4]");
+                      "The rank of Input of PriorBoxVar must be 2");
     PADDLE_ENFORCE_EQ(prior_box_var_dims[1], 4UL,
                       "The shape of PriorBoxVar is [N, 4]");
     PADDLE_ENFORCE_EQ(target_box_dims.size(), 2UL,
-                      "The shape of TargetBox is [M, 4]");
+                      "The rank of Input of TargetBox must be 2");
     PADDLE_ENFORCE_EQ(target_box_dims[1], 4UL,
                       "The shape of TargetBox is [M, 4]");
 

From 634faab1c06f9700297444789aa4336738a8100d Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Sun, 28 Jan 2018 00:53:15 -0800
Subject: [PATCH 094/314] Format doc & add unit test for dynamic_lstmp api

---
 doc/api/v2/fluid/layers.rst                 |  4 +-
 paddle/operators/CMakeLists.txt             |  1 +
 python/paddle/v2/fluid/layers/nn.py         | 58 ++++++++++++---------
 python/paddle/v2/fluid/tests/test_layers.py | 12 +++++
 4 files changed, 48 insertions(+), 27 deletions(-)

diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index 6c6af106db..231ec2d4ba 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -19,11 +19,11 @@ dynamic_lstm
     :noindex:
 
 dynamic_lstmp
-------------
+-------------
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
     :noindex:
 
- dynamic_gru
+dynamic_gru
 -----------
 ..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
     :noindex:
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 15f7cb6b56..48cf5816cc 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -147,6 +147,7 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
 op_library(sequence_conv_op DEPS context_project)
 op_library(sequence_pool_op DEPS sequence_pooling)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
+op_library(lstmp_op DEPS sequence2batch lstm_compute)
 op_library(gru_op DEPS sequence2batch gru_compute)
 op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function)
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index fa03a64291..c23cd733f1 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -257,7 +257,8 @@ def dynamic_lstm(input,
                  gate_activation='sigmoid',
                  cell_activation='tanh',
                  candidate_activation='tanh',
-                 dtype='float32'):
+                 dtype='float32',
+                 name=None):
     """
     **Dynamic LSTM Layer**
 
@@ -309,25 +310,25 @@ def dynamic_lstm(input,
                          (T X 4D), where T is the total time steps in this
                          mini-batch, D is the hidden size.
         size(int): 4 * hidden size.
-        param_attr(ParamAttr): The parameter attribute for the learnable
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
                                hidden-hidden weights.
 
-                               - The shape is (D x 4D), where D is the hidden
-                                 size.
                                - Weights = {:math:`W_{ch}, W_{ih}, \
                                                 W_{fh}, W_{oh}`}
-        bias_attr(ParamAttr): The bias attribute for the learnable bias
+                               - The shape is (D x 4D), where D is the hidden
+                                 size.
+        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
                               weights, which contains two parts, input-hidden
                               bias weights and peephole connections weights if
                               setting `use_peepholes` to `True`.
 
                               1. `use_peepholes = False`
-                                - The shape is (1 x 4D).
                                 - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                                - The shape is (1 x 4D).
                               2. `use_peepholes = True`
-                                - The shape is (1 x 7D).
                                 - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                  W_{fc}, W_{oc}`}.
+                                - The shape is (1 x 7D).
         use_peepholes(bool): Whether to enable diagonal/peephole connections,
                              default `True`.
         is_reverse(bool): Whether to compute reversed LSTM, default `False`.
@@ -340,6 +341,8 @@ def dynamic_lstm(input,
                               Choices = ["sigmoid", "tanh", "relu", "identity"],
                               default "tanh".
         dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
 
     Returns:
         tuple: The hidden state, and cell state of LSTM. The shape of both \
@@ -354,6 +357,7 @@ def dynamic_lstm(input,
             forward, _ = fluid.layers.dynamic_lstm(
                 input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
     """
+
     helper = LayerHelper('lstm', **locals())
     size = size / 4
     weight = helper.create_parameter(
@@ -401,7 +405,8 @@ def dynamic_lstmp(input,
                   cell_activation='tanh',
                   candidate_activation='tanh',
                   proj_activation='tanh',
-                  dtype='float32'):
+                  dtype='float32',
+                  name=None):
     """
     **Dynamic LSTMP Layer**
 
@@ -416,19 +421,19 @@ def dynamic_lstmp(input,
 
     .. math::
 
-        i_t = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i) \\
+        i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
 
-        f_t = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f) \\
+        f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
 
-        \tilde{c_t} = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c) \\
+        \\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
 
-        o_t = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o) \\
+        o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
 
-        c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c_t} \\
+        c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
 
-        h_t = o_t \odot act_h(c_t) \\
+        h_t & = o_t \odot act_h(c_t)
 
-        r_t = \overline{act_h}(W_{rh}h_t)
+        r_t & = \overline{act_h}(W_{rh}h_t)
 
     where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is 
     the matrix of weights from the input gate to the input), :math:`W_{ic}`, 
@@ -441,7 +446,7 @@ def dynamic_lstmp(input,
     vectors, respectively, all of which have the same size as the cell output 
     activation vector :math:`h`. Here :math:`h` is usually called the hidden 
     state and :math:`r` denotes its recurrent projection. And 
-    :math:`\tilde{c_t}` is also called the candidate hidden state, whose 
+    :math:`\\tilde{c_t}` is also called the candidate hidden state, whose 
     computation is based on the current input and previous hidden state.
 
     The :math:`\odot` is the element-wise product of the vectors. :math:`act_g` 
@@ -466,28 +471,28 @@ def dynamic_lstmp(input,
                          mini-batch, D is the hidden size.
         size(int): 4 * hidden size.
         proj_size(int): The size of projection output.
-        param_attr(ParamAttr): The parameter attribute for the learnable
+        param_attr(ParamAttr|None): The parameter attribute for the learnable
                                hidden-hidden weight and projection weight.
 
+                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
+                                                W_{fh}, W_{oh}`}.
                                - The shape of hidden-hidden weight is (P x 4D), 
                                  where P is the projection size and D the hidden 
                                  size.
-                               - The shape of projection weight is (D x P).
-                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
-                                                W_{fh}, W_{oh}`}.
                                - Projection weight = {:math:`W_{rh}`}.
-        bias_attr(ParamAttr): The bias attribute for the learnable bias
+                               - The shape of projection weight is (D x P).
+        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
                               weights, which contains two parts, input-hidden
                               bias weights and peephole connections weights if
                               setting `use_peepholes` to `True`.
 
                               1. `use_peepholes = False`
-                                - The shape is (1 x 4D).
                                 - Biases = {:math:`b_c, b_i, b_f, b_o`}.
+                                - The shape is (1 x 4D).
                               2. `use_peepholes = True`
-                                - The shape is (1 x 7D).
                                 - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                  W_{fc}, W_{oc}`}.
+                                - The shape is (1 x 7D).
         use_peepholes(bool): Whether to enable diagonal/peephole connections,
                              default `True`.
         is_reverse(bool): Whether to compute reversed LSTM, default `False`.
@@ -503,10 +508,12 @@ def dynamic_lstmp(input,
                               Choices = ["sigmoid", "tanh", "relu", "identity"],
                               default "tanh".
         dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
+        name(str|None): A name for this layer(optional). If set None, the layer
+                        will be named automatically.
 
     Returns:
-        tuple: The projection of hidden state, and cell state of LSTMP. The 
-               shape of projection is (T x P), for the cell state which is 
+        tuple: The projection of hidden state, and cell state of LSTMP. The \
+               shape of projection is (T x P), for the cell state which is \
                (T x D), and both LoD is the same with the `input`.
 
     Examples:
@@ -519,6 +526,7 @@ def dynamic_lstmp(input,
             proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out, 
                 size=hidden_dim * 4, proj_size=proj_dim, use_peepholes=False)
     """
+
     helper = LayerHelper('lstmp', **locals())
     size = size / 4
     weight = helper.create_parameter(
diff --git a/python/paddle/v2/fluid/tests/test_layers.py b/python/paddle/v2/fluid/tests/test_layers.py
index 4e86362542..3f54e28def 100644
--- a/python/paddle/v2/fluid/tests/test_layers.py
+++ b/python/paddle/v2/fluid/tests/test_layers.py
@@ -202,6 +202,18 @@ class TestBook(unittest.TestCase):
                     x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
         print(str(program))
 
+    def test_dynamic_lstmp(self):
+        program = Program()
+        with program_guard(program):
+            hidden_dim, proj_dim = 16, 8
+            seq_data = layers.data(
+                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
+            fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
+            self.assertIsNotNone(
+                layers.dynamic_lstmp(
+                    input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
+        print(str(program))
+
     def test_sequence_softmax(self):
         program = Program()
         with program_guard(program):

From a0669e387b1b4f5564ae51fdc561689b1d86cd04 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sun, 28 Jan 2018 17:43:09 +0800
Subject: [PATCH 095/314] dtype2str

---
 paddle/framework/data_type.h | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/paddle/framework/data_type.h b/paddle/framework/data_type.h
index 6a372ac32e..98eb3e857d 100644
--- a/paddle/framework/data_type.h
+++ b/paddle/framework/data_type.h
@@ -79,5 +79,33 @@ inline void VisitDataType(proto::DataType type, Visitor visitor) {
   }
 }
 
+inline std::string DataTypeToString(const proto::DataType type) {
+  using namespace paddle::framework::proto;
+  switch (type) {
+    case DataType::FP16:
+      return "float16";
+    case DataType::FP32:
+      return "float32";
+    case DataType::FP64:
+      return "float64";
+    case DataType::INT16:
+      return "int16";
+    case DataType::INT32:
+      return "int32";
+    case DataType::INT64:
+      return "int64";
+    case DataType::BOOL:
+      return "bool";
+    default:
+      PADDLE_THROW("Not support type %d", type);
+  }
+}
+
+inline std::ostream& operator<<(std::ostream& out,
+                                const proto::DataType& type) {
+  out << DataTypeToString(type);
+  return out;
+}
+
 }  // namespace framework
 }  // namespace paddle

From 0f47703dd5db01a7510031e810f963e09a8c9c13 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sun, 28 Jan 2018 16:41:18 +0800
Subject: [PATCH 096/314] add begin_norm_axis

---
 paddle/operators/layer_norm_op.cc             | 47 ++++++++++++-------
 .../v2/fluid/tests/test_layer_norm_op.py      | 28 ++++++-----
 2 files changed, 47 insertions(+), 28 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 0b0c760e57..9e618d10d2 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -42,10 +42,17 @@ class LayerNormOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], 1);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], 1);
+    auto x_dim = ctx->GetInputDim("X");
+    auto begin_norm_axis = ctx->Attrs().Get<int>("begin_norm_axis");
+    PADDLE_ENFORCE_LT(begin_norm_axis, x_dim.size(),
+                      "'begin_norm_axis' must be less than the rank of X");
+
+    auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
 
     ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
-    ctx->SetOutputDim("Mean", {ctx->GetInputDim("X")[0]});
-    ctx->SetOutputDim("Variance", {ctx->GetInputDim("X")[0]});
+    ctx->SetOutputDim("Mean", {left});
+    ctx->SetOutputDim("Variance", {left});
 
     ctx->ShareLoD("X", "Y");
   }
@@ -72,10 +79,14 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
           PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
                          "'epsilon' should be between 0.0 and 0.001.");
         });
-    AddAttr<std::vector<int>>("axis",
-                              "(vector<int> default:{1, 1, 1}), the "
-                              "axis to normalize.")
-        .SetDefault({1, 2, 3});  // todo(zcd) : who to set axis
+    AddAttr<int>("begin_norm_axis",
+                 "(int default:1), the "
+                 "axis of `begin_norm_axis ... Rank(X) - 1` will be normalized")
+        .SetDefault(1)
+        .AddCustomChecker([](const int &begin_norm_axis) {
+          PADDLE_ENFORCE_GT(begin_norm_axis, 0,
+                            "'begin_norm_axis' should be greater than zero.");
+        });
 
     AddComment(R"DOC(
 Layer Normalization.
@@ -97,9 +108,7 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     const auto *bias = ctx.Input<Tensor>("Bias");
     const auto *x = ctx.Input<Tensor>("X");
     const auto &x_dims = x->dims();
-
-    const int N = x_dims[0];
-    const int sample_size = x->numel() / N;
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
 
     auto scale_data = scale->data<T>()[0];
     auto bias_data = bias->data<T>()[0];
@@ -111,7 +120,9 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     mean->mutable_data<T>(ctx.GetPlace());
     var->mutable_data<T>(ctx.GetPlace());
 
-    int left = N, right = sample_size;
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
     auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
     auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
     auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
@@ -131,7 +142,8 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
       return std::sqrt(1 / ele) * scale_data;
     };
     auto sub_bias = [bias_data](T ele) { return bias_data - ele; };
-
+    // TODO(zcd): Some thinking about output_map, is it appropriate that
+    // `output_map` and `input_map` point to the same memory.
     output_map = (var_map.unaryExpr(scale_inv_std).replicate(1, right))
                      .cwiseProduct(input_map) +
                  var_map.unaryExpr(scale_inv_std)
@@ -198,13 +210,14 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     const auto *var = ctx.Input<Tensor>("Variance");
     const auto *scale = ctx.Input<Tensor>("Scale");
     const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+    auto scale_data = scale->data<T>()[0];
 
     const auto &x_dims = x->dims();
-    const int N = x_dims[0];
-    const int sample_size = x->numel() / N;
-    int left = N, right = sample_size;
 
-    auto scale_data = scale->data<T>()[0];
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]),
+        right = static_cast<int>(matrix_dim[1]);
 
     // init output
     auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
@@ -223,11 +236,13 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     if (d_scale) {
       d_scale->mutable_data<T>(ctx.GetPlace());
       auto inv_std = [](T ele) { return std::sqrt(1 / ele); };
+      // There are two equation to compute d_scale. One uses "Y" and the other
+      // does not use "Y"
       d_scale->data<T>()[0] =
           ((x_map - mean_map.replicate(1, right))
                .cwiseProduct(var_map.unaryExpr(inv_std).replicate(1, right))
                .cwiseProduct(d_y_map))
-              .sum();  // also can use `y` to get d_scale_map
+              .sum();
     }
 
     if (d_x) {
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index caa3b944eb..8ce327436f 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import unittest
 import numpy as np
 
@@ -33,23 +32,24 @@ def get_backward_op(scope, op, no_grad_set):
     return backward_op
 
 
-def _reference_layer_norm_naive(x, scale, beta, epsilon):
+def _reference_layer_norm_naive(x, scale, beta, epsilon, begin_norm_axis=1):
     old_shape = x.shape
-    N = x.shape[0]
-    D = reduce(mul, old_shape, 1) / N
+    N = reduce(mul, old_shape[0:begin_norm_axis], 1)
+    D = reduce(mul, old_shape[begin_norm_axis:len(old_shape)], 1)
     x.shape = [N, D]
     mean = np.mean(x, axis=1)
     var = np.var(x, axis=1) + epsilon
     output = scale * np.divide((x - mean.reshape([N, 1])),
                                (np.sqrt(var)).reshape([N, 1])) + beta
     output.shape = old_shape
+    x.shape = old_shape
     return output, mean, var
 
 
-def _reference_layer_norm_grad(x, grad_y, scale, mean, var, epsilon):
+def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     x_shape = x.shape
-    N = x_shape[0]
-    D = reduce(mul, x_shape, 1) / N
+    N = reduce(mul, x_shape[0:begin_norm_axis], 1)
+    D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
     grad_y.shape = [N, D]
     x.shape = [N, D]
     mean.shape = [N, 1]
@@ -140,7 +140,9 @@ class TestLayerNormdOp(OpTest):
         self.assertLessEqual(max_diff, max_relative_error, err_msg())
 
     def test_forward_backward(self):
-        def test_with_place(place, shape):
+        def test_with_place(place, shape, begin_norm_axis=1):
+            assert begin_norm_axis > 0 and begin_norm_axis < len(
+                shape), 'begin_norm_axis must be between 0 and len(shape)-1.'
             # attr
             epsilon = 0.00001
             x_shape = shape
@@ -152,13 +154,13 @@ class TestLayerNormdOp(OpTest):
 
             # run forward
             y_out, saved_mean, var_ref = _reference_layer_norm_naive(
-                x_val, scale_val, bias_val, epsilon)
+                x_val, scale_val, bias_val, epsilon, begin_norm_axis)
 
             #  for gradient test
             y_grad = np.random.random_sample(x_shape).astype(np.float32)
 
             x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_layer_norm_grad(
-                x_val, y_grad, scale_val, saved_mean, var_ref, epsilon)
+                x_val, y_grad, scale_val, saved_mean, var_ref, begin_norm_axis)
 
             scope = core.Scope()
 
@@ -185,7 +187,8 @@ class TestLayerNormdOp(OpTest):
                 Mean="Mean",
                 Variance="Variance",
                 # attrs
-                epsilon=epsilon)
+                epsilon=epsilon,
+                begin_norm_axis=begin_norm_axis)
 
             layer_norm_op.run(scope, place)
 
@@ -228,7 +231,8 @@ class TestLayerNormdOp(OpTest):
             places.append(core.CUDAPlace(0))
 
         for place in places:
-            test_with_place(place, [2, 3, 4, 5])
+            test_with_place(place, [2, 3, 4, 5], begin_norm_axis=1)
+            test_with_place(place, [2, 3, 4, 5], begin_norm_axis=3)
 
 
 if __name__ == '__main__':

From e3952b9fae456c88aee758976d494467258cfea1 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sun, 28 Jan 2018 23:31:06 +0800
Subject: [PATCH 097/314] fix unit test

---
 paddle/framework/op_kernel_type_test.cc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/framework/op_kernel_type_test.cc b/paddle/framework/op_kernel_type_test.cc
index 649afeee8a..cb23bbde01 100644
--- a/paddle/framework/op_kernel_type_test.cc
+++ b/paddle/framework/op_kernel_type_test.cc
@@ -26,9 +26,9 @@ TEST(OpKernelType, ToString) {
   OpKernelType op_kernel_type(DataType::FP32, CPUPlace(), DataLayout::kNCHW,
                               LibraryType::kCUDNN);
 
-  ASSERT_EQ(
-      paddle::framework::KernelTypeToString(op_kernel_type),
-      "data_type[5]:data_layout[NCHW]:place[CPUPlace]:library_type[CUDNN]");
+  ASSERT_EQ(paddle::framework::KernelTypeToString(op_kernel_type),
+            "data_type[float32]:data_layout[NCHW]:place[CPUPlace]:library_type["
+            "CUDNN]");
 }
 
 TEST(OpKernelType, Hash) {

From 4fb3c676a8f229684f0369d5e2e0d549c97565a8 Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Sun, 28 Jan 2018 08:50:55 -0800
Subject: [PATCH 098/314] Polish threadpool (#7918)

* Polish threadpool

* Add #include <vector>

* Rename variables

* Rename variables

* clang-format
---
 paddle/framework/threadpool.cc      |  91 ++++++++++++++++---
 paddle/framework/threadpool.h       | 134 ++++++++--------------------
 paddle/framework/threadpool_test.cc |   6 +-
 3 files changed, 118 insertions(+), 113 deletions(-)

diff --git a/paddle/framework/threadpool.cc b/paddle/framework/threadpool.cc
index 109a7e7dc4..b2f5ae4a96 100644
--- a/paddle/framework/threadpool.cc
+++ b/paddle/framework/threadpool.cc
@@ -1,24 +1,93 @@
 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
 
-    http://www.apache.org/licenses/LICENSE-2.0
+   http://www.apache.org/licenses/LICENSE-2.0
 
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
 
 #include "paddle/framework/threadpool.h"
 
 namespace paddle {
 namespace framework {
 
-std::unique_ptr<ThreadPool> ThreadPool::threadpool(nullptr);
-std::once_flag ThreadPool::init_flag;
+std::unique_ptr<ThreadPool> ThreadPool::threadpool_(nullptr);
+std::once_flag ThreadPool::init_flag_;
+
+ThreadPool* ThreadPool::GetInstance() {
+  std::call_once(init_flag_, &ThreadPool::Init);
+  return threadpool_.get();
+}
+
+void ThreadPool::Init() {
+  if (threadpool_.get() == nullptr) {
+    // TODO(Yancey1989): specify the max threads number
+    int num_threads = std::thread::hardware_concurrency();
+    PADDLE_ENFORCE_GT(num_threads, 0);
+    threadpool_.reset(new ThreadPool(num_threads));
+  }
+}
+
+ThreadPool::ThreadPool(int num_threads)
+    : total_threads_(num_threads), idle_threads_(num_threads), running_(true) {
+  threads_.resize(num_threads);
+  for (auto& thread : threads_) {
+    // TODO(Yancey1989): binding the thread on the specify CPU number
+    thread.reset(new std::thread(std::bind(&ThreadPool::TaskLoop, this)));
+  }
+}
+
+ThreadPool::~ThreadPool() {
+  {
+    // notify all threads to stop running
+    running_ = false;
+    scheduled_.notify_all();
+  }
+
+  for (auto& t : threads_) {
+    t->join();
+    t.reset(nullptr);
+  }
+}
+
+void ThreadPool::Wait() {
+  std::unique_lock<std::mutex> lock(mutex_);
+  completed_.wait(lock, [=] { return Done() == true; });
+}
+
+void ThreadPool::TaskLoop() {
+  while (running_) {
+    std::unique_lock<std::mutex> lock(mutex_);
+    scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
+
+    if (!running_) {
+      break;
+    }
+    // pop a task from the task queue
+    auto task = std::move(tasks_.front());
+    tasks_.pop();
+
+    --idle_threads_;
+    lock.unlock();
+
+    // run the task
+    task();
+
+    {
+      std::unique_lock<std::mutex> lock(mutex_);
+      ++idle_threads_;
+      if (Done()) {
+        completed_.notify_all();
+      }
+    }
+  }
+}
 
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/threadpool.h b/paddle/framework/threadpool.h
index 3ac345851c..8912b1a43a 100644
--- a/paddle/framework/threadpool.h
+++ b/paddle/framework/threadpool.h
@@ -20,52 +20,36 @@ limitations under the License. */
 #include <mutex>
 #include <queue>
 #include <thread>
+#include <vector>
 
 #include "paddle/platform/enforce.h"
 
 namespace paddle {
 namespace framework {
 
+// ThreadPool maintains a queue of tasks, and runs them using a fixed
+// number of threads.
 class ThreadPool {
  public:
   typedef std::packaged_task<void()> Task;
 
-  /**
-   * @brief   Get a instance of threadpool, the thread number will
-   *          be specified as the number of hardware thread contexts
-   */
-  static ThreadPool* GetInstance() {
-    std::call_once(init_flag, &ThreadPool::Init);
-    return threadpool.get();
-  }
+  // Returns the singleton of ThreadPool.
+  static ThreadPool* GetInstance();
 
-  ~ThreadPool() {
-    {
-      // notify all threads to stop running
-      running_ = false;
-      scheduled_.notify_all();
-    }
-
-    for (auto& t : threads_) {
-      t->join();
-      t.reset(nullptr);
-    }
-  }
+  ~ThreadPool();
 
-  int GetNumThreads() const { return num_threads_; }
+  // Returns the number of threads created by the constructor.
+  size_t Threads() const { return total_threads_; }
 
-  int GetAvailable() {
+  // Returns the number of currently idle threads.
+  size_t IdleThreads() {
     std::unique_lock<std::mutex> lock(mutex_);
-    return available_;
+    return idle_threads_;
   }
 
-  /**
-   * @brief   Push a function to the queue, and will be scheduled and
-   *          executed if a thread is available.
-   * @param[in] Task, will be pushed to the task queue.
-   * @return    std::future<void>, we could wait for the task finished by
-   *            f.wait().
-   */
+  // Run pushes a function to the task queue and returns a std::future
+  // object.  To wait for the completion of the task, call
+  // std::future::wait().
   template <typename Callback>
   std::future<void> Run(Callback fn) {
     std::unique_lock<std::mutex> lock(mutex_);
@@ -77,84 +61,40 @@ class ThreadPool {
     return f;
   }
 
-  /**
-   * @brief   Wait until all the tasks are completed.
-   */
-  void Wait() {
-    std::unique_lock<std::mutex> lock(mutex_);
-    completed_.wait(lock, [=] { return Done() == true; });
-  }
+  // Wait until all the tasks are completed.
+  void Wait();
 
  private:
   DISABLE_COPY_AND_ASSIGN(ThreadPool);
 
-  explicit ThreadPool(int num_threads)
-      : num_threads_(num_threads), available_(num_threads), running_(true) {
-    threads_.resize(num_threads);
-    for (auto& thread : threads_) {
-      // TODO(Yancey1989): binding the thread on the specify CPU number
-      thread.reset(new std::thread(std::bind(&ThreadPool::TaskLoop, this)));
-    }
-  }
+  explicit ThreadPool(int num_threads);
 
-  /**
-   * @brief   If the task queue is empty and avaialbe
-   *          is equal to the number of threads, means that
-   *          all tasks are completed.
-   *
-   *          Note: this function is not thread-safe.
-   *
-   * @return true if all tasks are completed.
-   */
-  bool Done() { return tasks_.empty() && available_ == num_threads_; }
-
-  void TaskLoop() {
-    while (running_) {
-      std::unique_lock<std::mutex> lock(mutex_);
-      scheduled_.wait(lock, [=] { return !tasks_.empty() || !running_; });
-
-      if (!running_) {
-        break;
-      }
-      // pop a task from the task queue
-      auto task = std::move(tasks_.front());
-      tasks_.pop();
-
-      --available_;
-      lock.unlock();
-
-      // run the task
-      task();
-
-      {
-        std::unique_lock<std::mutex> lock(mutex_);
-        ++available_;
-        if (Done()) {
-          completed_.notify_all();
-        }
-      }
-    }
-  }
+  // If the task queue is empty and avaialbe is equal to the number of
+  // threads, means that all tasks are completed.  Note: this function
+  // is not thread-safe.  Returns true if all tasks are completed.
+  // Note: don't delete the data member total_threads_ and use
+  // threads_.size() instead; because you'd need to lock the mutex
+  // before accessing threads_.
+  bool Done() { return tasks_.empty() && idle_threads_ == total_threads_; }
 
-  static void Init() {
-    if (threadpool.get() == nullptr) {
-      // TODO(Yancey1989): specify the max threads number
-      int num_threads = std::thread::hardware_concurrency();
-      PADDLE_ENFORCE_GT(num_threads, 0);
-      threadpool.reset(new ThreadPool(num_threads));
-    }
-  }
+  // The constructor starts threads to run TaskLoop, which retrieves
+  // and runs tasks from the queue.
+  void TaskLoop();
+
+  // Init is called by GetInstance.
+  static void Init();
 
  private:
-  static std::unique_ptr<ThreadPool> threadpool;
-  static std::once_flag init_flag;
+  static std::unique_ptr<ThreadPool> threadpool_;
+  static std::once_flag init_flag_;
 
-  int num_threads_;
-  int available_;
-  bool running_;
-  std::queue<Task> tasks_;
   std::vector<std::unique_ptr<std::thread>> threads_;
+  const size_t total_threads_;
+  size_t idle_threads_;
+
+  std::queue<Task> tasks_;
   std::mutex mutex_;
+  bool running_;
   std::condition_variable scheduled_;
   std::condition_variable completed_;
 };
diff --git a/paddle/framework/threadpool_test.cc b/paddle/framework/threadpool_test.cc
index 50b6238cd8..3fbfe7efc8 100644
--- a/paddle/framework/threadpool_test.cc
+++ b/paddle/framework/threadpool_test.cc
@@ -22,11 +22,7 @@ namespace framework = paddle::framework;
 void do_sum(framework::ThreadPool* pool, std::atomic<int>& sum, int cnt) {
   std::vector<std::future<void>> fs;
   for (int i = 0; i < cnt; ++i) {
-    auto f = pool->Run([&sum]() { sum.fetch_add(1); });
-    fs.push_back(std::move(f));
-  }
-  for (auto& f : fs) {
-    f.wait();
+    fs.push_back(framework::Async([&sum]() { sum.fetch_add(1); }));
   }
 }
 

From 59357f4fb94d4589b9b51a33d1f3febb00653779 Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Mon, 29 Jan 2018 07:15:44 +0800
Subject: [PATCH 099/314] fix floor_op (#7926)

---
 paddle/operators/activation_op.h                   | 2 +-
 python/paddle/v2/fluid/tests/test_activation_op.py | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/paddle/operators/activation_op.h b/paddle/operators/activation_op.h
index 88c3d1c597..c0809abc05 100644
--- a/paddle/operators/activation_op.h
+++ b/paddle/operators/activation_op.h
@@ -323,7 +323,7 @@ template <typename T>
 struct FloorFunctor : public BaseActivationFunctor<T> {
   template <typename Device, typename X, typename Out>
   void operator()(Device d, X x, Out out) const {
-    out.device(d) = x.ceil();
+    out.device(d) = x.floor();
   }
 };
 
diff --git a/python/paddle/v2/fluid/tests/test_activation_op.py b/python/paddle/v2/fluid/tests/test_activation_op.py
index 18605e6065..1de5d446b8 100644
--- a/python/paddle/v2/fluid/tests/test_activation_op.py
+++ b/python/paddle/v2/fluid/tests/test_activation_op.py
@@ -186,8 +186,7 @@ class TestFloor(OpTest):
         self.op_type = "floor"
         x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
         self.inputs = {'X': x}
-        # numpy floor need +1
-        self.outputs = {'Out': np.floor(self.inputs['X']) + 1.0}
+        self.outputs = {'Out': np.floor(self.inputs['X'])}
 
     def test_check_output(self):
         self.check_output()

From e27a030072d23701aeda91cc6c1b76c84ca361d2 Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Wed, 24 Jan 2018 19:17:49 +0800
Subject: [PATCH 100/314] Add weight normalization

---
 python/paddle/v2/fluid/layer_helper.py        | 186 +++++++++++++++++-
 python/paddle/v2/fluid/param_attr.py          |  17 ++
 .../fluid/tests/test_weight_normalization.py  | 122 ++++++++++++
 3 files changed, 321 insertions(+), 4 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_weight_normalization.py

diff --git a/python/paddle/v2/fluid/layer_helper.py b/python/paddle/v2/fluid/layer_helper.py
index 0b0064ade9..368cf8ed2e 100644
--- a/python/paddle/v2/fluid/layer_helper.py
+++ b/python/paddle/v2/fluid/layer_helper.py
@@ -18,7 +18,7 @@ import itertools
 from framework import Variable, Parameter, default_main_program, default_startup_program, \
     unique_name, dtype_is_floating
 from paddle.v2.fluid.initializer import Constant, Xavier
-from param_attr import ParamAttr
+from param_attr import ParamAttr, WeightNormParamAttr
 
 
 class LayerHelper(object):
@@ -103,6 +103,177 @@ class LayerHelper(object):
                 raise ValueError("Data Type mismatch")
         return dtype
 
+    def _create_weight_normalize(self, attr, shape, dtype):
+        from .layers import elementwise_mul, elementwise_div, reshape
+
+        # Remove these ops when LayerHelper and layers support indicating
+        # program and block.
+        def __norm_op(x,
+                      out=None,
+                      p=2,
+                      dim=None,
+                      keep_dim=False,
+                      block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join([self.name, 'weight_norm_norm'])),
+                    dtype=dtype,
+                    persistable=False)
+            abs_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_abs'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='abs', inputs={'X': x}, outputs={'Out': abs_out})
+            pow_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_pow'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='pow',
+                inputs={'X': abs_out},
+                outputs={'Out': pow_out},
+                attrs={'factor': float(p)})
+            sum_out = block.create_var(
+                name=unique_name(".".join([self.name, 'weight_norm_sum'])),
+                dtype=dtype,
+                persistable=False)
+            block.append_op(
+                type='reduce_sum',
+                inputs={'X': pow_out},
+                outputs={'Out': sum_out},
+                attrs={
+                    'dim': dim,
+                    'keep_dim': keep_dim,
+                    'reduce_all': True if dim is None else False
+                })
+            block.append_op(
+                type='pow',
+                inputs={'X': sum_out},
+                outputs={'Out': out},
+                attrs={'factor': 1. / p})
+            return out
+
+        def __reshape_op(x,
+                         shape,
+                         out=None,
+                         block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join(
+                        [self.name, 'weight_norm_reshape'])),
+                    dtype=dtype,
+                    persistable=False)
+            block.append_op(
+                type='reshape',
+                inputs={'X': x},
+                outputs={'Out': out},
+                attrs={'shape': shape})
+            return out
+
+        def __transpose_op(x,
+                           axis,
+                           out=None,
+                           block=self.startup_program.global_block()):
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join(
+                        [self.name, 'weight_norm_transpose'])),
+                    dtype=dtype,
+                    persistable=False)
+            block.append_op(
+                type='transpose',
+                inputs={'X': x},
+                outputs={'Out': out},
+                attrs={'axis': axis})
+            return out
+
+        def __norm_except_dim(x,
+                              out=None,
+                              dim=None,
+                              block=self.startup_program.global_block()):
+            """Computes the norm over all dimensions except dim"""
+            if out is None:
+                out = block.create_var(
+                    name=unique_name(".".join([self.name, 'weight_norm_norm'])),
+                    dtype=dtype,
+                    persistable=False)
+            if dim is None:
+                __norm_op(x, out, dim=dim, block=block)
+            elif dim == 0:
+                out_shape = [x.shape[0]] + [1] * (len(x.shape) - 1)
+                reshape = __reshape_op(x, shape=[x.shape[0], -1], block=block)
+                norm = __norm_op(reshape, dim=1, block=block)
+                __reshape_op(norm, out=out, shape=out_shape, block=block)
+            elif dim == len(x.shape) - 1:
+                out_shape = [1] * (len(x.shape) - 1) + [x.shape[-1]]
+                reshape = __reshape_op(x, shape=[-1, x.shape[-1]], block=block)
+                norm = __norm_op(reshape, dim=0, block=block)
+                __reshape_op(norm, out=out, shape=out_shape, block=block)
+            else:
+                perm = range(len(x.shape))
+                perm[0], perm[dim] = dim, 0
+                transpose = __transpose_op(x, perm, block=block)
+                norm = __norm_op(transpose, dim=0, block=block)
+                __transpose_op(norm, perm, out=out, block=block)
+            return out
+
+        def __weight_normalize(g, v, dim):
+            """Calculations for weight normalization"""
+            norm = __norm_except_dim(
+                v, dim=dim, block=self.main_program.current_block())
+            scale = elementwise_div(
+                x=g, y=norm)  # The shapes of g and norm are the same.
+            # Currently, elementwise_mul only support broadcast when the shape
+            # of y is a subset of x. Thus, we should reshape y to squeeze to
+            # achive it.
+            w = elementwise_mul(
+                x=v,
+                y=scale if dim is None else reshape(
+                    x=scale, shape=[v.shape[dim]]),
+                axis=-1 if dim is None else dim)
+            # To serialize the original parameter for inference, maybe a
+            # parameter rather than a variable should be returned.
+            return w
+
+        g_param_attr = copy.deepcopy(attr)
+        g_param_attr.name = attr.name + '_g'
+        g_param_shape = [1] * len(shape)
+        if attr.dim is not None:
+            g_param_shape[attr.dim] = shape[attr.dim]
+        v_param_attr = copy.deepcopy(attr)
+        v_param_attr.name = attr.name + '_v'
+        v_param_shape = shape
+
+        # Add to startup_program to initialize g and v.
+        # Try to reconstruct the initializer of w by initializing g and v.
+        # Set the initializers of g and v as below, then the distribution
+        # of w is the same as initializing w with the given initializer.
+        # For Data-Dependent Initialization, please compute the init-values
+        # of g and v in external and then feed the values to g and v by
+        # executing an extra program.
+        g_param = self.startup_program.global_block().create_parameter(
+            dtype=dtype,
+            shape=g_param_shape,
+            **g_param_attr.to_kwargs(with_initializer=False))
+        v_param = self.startup_program.global_block().create_parameter(
+            dtype=dtype,
+            shape=v_param_shape,
+            **v_param_attr.to_kwargs(with_initializer=True))
+        __norm_except_dim(
+            x=v_param,
+            out=g_param,
+            dim=attr.dim,
+            block=self.startup_program.global_block())
+
+        # Add weight normalization to main_program
+        g_param = self.main_program.global_block().create_parameter(
+            dtype=dtype, shape=g_param_shape, **g_param_attr.to_kwargs())
+        v_param = self.main_program.global_block().create_parameter(
+            dtype=dtype, shape=v_param_shape, **v_param_attr.to_kwargs())
+        w_param = __weight_normalize(g_param, v_param, dim=attr.dim)
+        return w_param
+
     def create_parameter(self,
                          attr,
                          shape,
@@ -112,16 +283,23 @@ class LayerHelper(object):
         # Deepcopy the attr so that parameters can be shared in program
         assert isinstance(attr, ParamAttr)
         suffix = 'b' if is_bias else 'w'
+        if attr.name is None:
+            attr.name = unique_name(".".join([self.name, suffix]))
 
-        if default_initializer is None:
+        if default_initializer is None and attr.initializer is None:
             if is_bias:
                 attr.set_default_bias_initializer()
             else:
                 attr.set_default_param_initializer()
         else:
             attr.set_default_initializer(default_initializer)
-        if attr.name is None:
-            attr.name = unique_name(".".join([self.name, suffix]))
+
+        # If weight normalization is set, insert extra parameters and ops.
+        # Refer to https://arxiv.org/pdf/1602.07868.pdf
+        if isinstance(attr, WeightNormParamAttr):
+            param = self._create_weight_normalize(attr, shape, dtype)
+            WeightNormParamAttr.params_with_weight_norm.append(param)
+            return param
 
         self.startup_program.global_block().create_parameter(
             dtype=dtype, shape=shape, **attr.to_kwargs(with_initializer=True))
diff --git a/python/paddle/v2/fluid/param_attr.py b/python/paddle/v2/fluid/param_attr.py
index dcca8b6c54..1218e71ca1 100644
--- a/python/paddle/v2/fluid/param_attr.py
+++ b/python/paddle/v2/fluid/param_attr.py
@@ -82,3 +82,20 @@ class ParamAttr(object):
         if with_initializer:
             kwargs['initializer'] = self.initializer
         return kwargs
+
+
+class WeightNormParamAttr(ParamAttr):
+    """
+    Used for weight normalization. Any field in ParamAttr can also be set here.
+    Besides, an extra field dim can be set to indicate the dimension except 
+    which to normalize.
+    """
+    # List to record the parameters reparameterized by weight normalization.
+    # If these parameters are treated as Variable rather than Parameter,
+    # it can be used to discriminate these parameters and help to serialize 
+    # these paramters for inference.
+    params_with_weight_norm = []
+
+    def __init__(self, dim=None, **kwargs):
+        super(WeightNormParamAttr, self).__init__(**kwargs)
+        self.dim = dim
diff --git a/python/paddle/v2/fluid/tests/test_weight_normalization.py b/python/paddle/v2/fluid/tests/test_weight_normalization.py
new file mode 100644
index 0000000000..200b5b9dc0
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_weight_normalization.py
@@ -0,0 +1,122 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy
+import collections
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+from paddle.v2.fluid.initializer import ConstantInitializer
+from paddle.v2.fluid.param_attr import WeightNormParamAttr
+
+
+class TestWeightNormalization(unittest.TestCase):
+    batch_size = 3
+    hidden_size = 5
+    data_desc = (['x', [10], 0], )
+
+    @classmethod
+    def setUpClass(cls):
+        cls.set_program()
+
+    @classmethod
+    def set_program(cls):
+        data = fluid.layers.data(
+            name=cls.data_desc[0][0], shape=cls.data_desc[0][1])
+        out = fluid.layers.fc(input=data,
+                              size=cls.hidden_size,
+                              param_attr=WeightNormParamAttr(
+                                  dim=None,
+                                  name='weight_norm_param',
+                                  initializer=ConstantInitializer(1.0)),
+                              bias_attr=False,
+                              act=None)
+        loss = fluid.layers.reduce_sum(out)
+        fluid.backward.append_backward(loss=loss)
+        cls.fetch_list = [
+            'weight_norm_param_g', 'weight_norm_param_v',
+            'weight_norm_param_g@GRAD'
+        ]
+
+    def run_program(self):
+        outputs = []
+        places = [core.CPUPlace()]
+        if core.is_compile_gpu():
+            places.append(core.CUDAPlace(0))
+        for place in places:
+            self.set_inputs(place)
+            exe = fluid.Executor(place)
+            exe.run(fluid.default_startup_program())
+            output = exe.run(fluid.default_main_program(),
+                             feed=self.inputs,
+                             fetch_list=self.fetch_list,
+                             return_numpy=False)
+            outputs.append(output)
+        self.actual_outputs = outputs
+
+    def set_data(self):
+        self.data = collections.OrderedDict()
+        for desc in self.data_desc:
+            data_name = desc[0]
+            data_shape = desc[1]
+            data_lod_level = desc[2]
+            data_lod = []
+            for i in range(data_lod_level):
+                lod_level_i = numpy.random.randint(
+                    low=1,
+                    high=5,
+                    size=self.batch_size if i == 0 else lod_level_i[-1])
+                lod_level_i = [0] + numpy.cumsum(lod_level_i).tolist()
+                data_lod.append(lod_level_i)
+            data_value = numpy.random.random(
+                size=[data_lod[-1][-1] if data_lod else self.batch_size
+                      ] + data_shape).astype('float32')
+            self.data[data_name] = (data_value, data_lod)
+
+    def set_inputs(self, place):
+        self.inputs = {}
+        for desc in self.data_desc:
+            tensor = fluid.Tensor()
+            tensor.set(self.data[desc[0]][0], place)
+            if self.data[desc[0]][1]:
+                tensor.set_lod(self.data[desc[0]][1])
+            self.inputs[desc[0]] = tensor
+
+    def weight_normalize(self):
+        v = numpy.ones((self.data[self.data_desc[0][0]][0].shape[-1],
+                        self.hidden_size))
+        g = numpy.linalg.norm(v, axis=None, keepdims=True)
+        w = g * v / numpy.linalg.norm(v, axis=None, keepdims=True)
+        x = self.data[self.data_desc[0][0]][0]
+        out = numpy.dot(x, w)
+        g_grad = (numpy.dot(x.T, numpy.ones_like(out)) * (v / numpy.linalg.norm(
+            v, axis=None, keepdims=True))).sum(axis=None, keepdims=True)
+        return g, v, g_grad
+
+    def test_weight_normalization(self):
+        self.set_data()
+        self.run_program()
+        expect_output = self.weight_normalize()
+        for actual_output in self.actual_outputs:
+            [
+                self.assertTrue(
+                    numpy.allclose(
+                        numpy.array(actual_output), expect_output, atol=0.001))
+                for expect_output, actual_output in zip(expect_output,
+                                                        actual_output)
+            ]
+
+
+if __name__ == '__main__':
+    unittest.main()

From 0065548cd312c643d42f2bfae59348cd5f471811 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Sun, 28 Jan 2018 18:29:44 -0800
Subject: [PATCH 101/314] Update copyright for notest_dist_image_classification
 (#7898)

Update copyright for notest_dist_image_classification
---
 .../notest_dist_image_classification.py       | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
index 0c51ccf306..298ecfc386 100644
--- a/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
+++ b/python/paddle/v2/fluid/tests/book_distribute/notest_dist_image_classification.py
@@ -1,16 +1,16 @@
-#Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 #
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-#    http://www.apache.org/licenses/LICENSE-2.0
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import print_function
 

From 6b9f1d343eac40d71b5275071a9e47a04546a356 Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Mon, 29 Jan 2018 10:33:41 +0800
Subject: [PATCH 102/314] Make weight normalization adapt to the up-to-date
 code

---
 python/paddle/v2/fluid/layer_helper.py                    | 4 ++--
 python/paddle/v2/fluid/param_attr.py                      | 7 +++++--
 python/paddle/v2/fluid/tests/test_weight_normalization.py | 2 +-
 3 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/python/paddle/v2/fluid/layer_helper.py b/python/paddle/v2/fluid/layer_helper.py
index cc4822735a..2119ca12c8 100644
--- a/python/paddle/v2/fluid/layer_helper.py
+++ b/python/paddle/v2/fluid/layer_helper.py
@@ -226,8 +226,8 @@ class LayerHelper(object):
             scale = elementwise_div(
                 x=g, y=norm)  # The shapes of g and norm are the same.
             # Currently, elementwise_mul only support broadcast when the shape
-            # of y is a subset of x. Thus, we should reshape y to squeeze to
-            # achive it.
+            # of y is a subset of the shape of x. Thus, we reshape y to squeeze
+            # to achive the subset.
             w = elementwise_mul(
                 x=v,
                 y=scale if dim is None else reshape(
diff --git a/python/paddle/v2/fluid/param_attr.py b/python/paddle/v2/fluid/param_attr.py
index 1218e71ca1..fc566b8a24 100644
--- a/python/paddle/v2/fluid/param_attr.py
+++ b/python/paddle/v2/fluid/param_attr.py
@@ -15,7 +15,10 @@
 from initializer import Initializer, Xavier, Constant
 from regularizer import WeightDecayRegularizer
 
-__all__ = ['ParamAttr']
+__all__ = [
+    'ParamAttr',
+    'WeightNormParamAttr',
+]
 
 
 class ParamAttr(object):
@@ -92,7 +95,7 @@ class WeightNormParamAttr(ParamAttr):
     """
     # List to record the parameters reparameterized by weight normalization.
     # If these parameters are treated as Variable rather than Parameter,
-    # it can be used to discriminate these parameters and help to serialize 
+    # it can be used to discriminate these parameters and help to serialize
     # these paramters for inference.
     params_with_weight_norm = []
 
diff --git a/python/paddle/v2/fluid/tests/test_weight_normalization.py b/python/paddle/v2/fluid/tests/test_weight_normalization.py
index 200b5b9dc0..1ac54ea305 100644
--- a/python/paddle/v2/fluid/tests/test_weight_normalization.py
+++ b/python/paddle/v2/fluid/tests/test_weight_normalization.py
@@ -52,7 +52,7 @@ class TestWeightNormalization(unittest.TestCase):
     def run_program(self):
         outputs = []
         places = [core.CPUPlace()]
-        if core.is_compile_gpu():
+        if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
             self.set_inputs(place)

From 0fbfd2dc7024badf2bbfc3ebc23bf13c50dca56a Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Sun, 28 Jan 2018 20:03:10 -0800
Subject: [PATCH 103/314] Simplify the symbol description

---
 python/paddle/v2/fluid/layers/nn.py | 51 ++++++++++++++++-------------
 1 file changed, 29 insertions(+), 22 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index c23cd733f1..d11dccfd22 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -435,25 +435,28 @@ def dynamic_lstmp(input,
 
         r_t & = \overline{act_h}(W_{rh}h_t)
 
-    where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is 
-    the matrix of weights from the input gate to the input), :math:`W_{ic}`, 
-    :math:`W_{fc}`, :math:`W_{oc}` are diagonal weight matrices for peephole 
-    connections. In our implementation, we use vectors to reprenset these 
-    diagonal weight matrices. The :math:`b` terms denote bias vectors 
-    (:math:`b_i` is the input gate bias vector), :math:`\sigma` is the 
-    activation, such as logistic sigmoid function, and :math:`i, f, o` and 
-    :math:`c` are the input gate, forget gate, output gate, and cell activation 
-    vectors, respectively, all of which have the same size as the cell output 
-    activation vector :math:`h`. Here :math:`h` is usually called the hidden 
-    state and :math:`r` denotes its recurrent projection. And 
-    :math:`\\tilde{c_t}` is also called the candidate hidden state, whose 
-    computation is based on the current input and previous hidden state.
-
-    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g` 
-    and :math:`act_h` are the cell input and cell output activation functions 
-    and `tanh` is usually used for them. :math:`\overline{act_h}` is the 
-    activation function for the projection output, usually using `identity` or 
-    same as :math:`act_h`.
+    In the above formula:
+
+    * :math:`W`: Denotes weight matrices (e.g. :math:`W_{xi}` is \
+          the matrix of weights from the input gate to the input).
+    * :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
+          matrices for peephole connections. In our implementation, \
+          we use vectors to reprenset these diagonal weight matrices. 
+    * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
+          bias vector). 
+    * :math:`\sigma`: The activation, such as logistic sigmoid function.
+    * :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
+          gate, and cell activation vectors, respectively, all of which have \
+          the same size as the cell output activation vector :math:`h`. 
+    * :math:`h`: The hidden state.
+    * :math:`r`: The recurrent projection of the hidden state. 
+    * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
+          computation is based on the current input and previous hidden state.
+    * :math:`\odot`: The element-wise product of the vectors. 
+    * :math:`act_g` and :math:`act_h`: The cell input and cell output \
+          activation functions and `tanh` is usually used for them. 
+    * :math:`\overline{act_h}`: The activation function for the projection \
+          output, usually using `identity` or same as :math:`act_h`.
 
     Set `use_peepholes` to `False` to disable peephole connection. The formula
     is omitted here, please refer to the paper
@@ -519,12 +522,16 @@ def dynamic_lstmp(input,
     Examples:
         .. code-block:: python
 
-            hidden_dim = 512
-            proj_dim = 256
+            hidden_dim, proj_dim = 512, 256
             fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
                                      act=None, bias_attr=None)
             proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out, 
-                size=hidden_dim * 4, proj_size=proj_dim, use_peepholes=False)
+                                                     size=hidden_dim * 4, 
+                                                     proj_size=proj_dim, 
+                                                     use_peepholes=False,
+                                                     is_reverse=True,
+                                                     cell_activation="tanh",
+                                                     proj_activation="tanh")
     """
 
     helper = LayerHelper('lstmp', **locals())

From cce18c9fe6ae763c3cd93038d09a390a5a0ac99e Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Mon, 29 Jan 2018 12:50:56 +0800
Subject: [PATCH 104/314] Fix typo (#7933)

* fix typo

* fix typo
---
 doc/design/support_new_device.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/design/support_new_device.md b/doc/design/support_new_device.md
index b154f01d32..8983df9004 100644
--- a/doc/design/support_new_device.md
+++ b/doc/design/support_new_device.md
@@ -174,7 +174,7 @@ class MaxOutFunctor {
 };
 ```
 
-CPU implemention is in .cc file
+CPU implementation is in .cc file
 
 ```
 template <typename T>
@@ -188,7 +188,7 @@ class MaxOutFunctor<platform::CPUDeviceContext, T> {
 };
 ```
 
-CUDA implemention is in .cu file
+CUDA implementation is in .cu file
 
 ```
 template <typename T>
@@ -203,9 +203,9 @@ class MaxOutFunctor<platform::CUDADeviceContext, T> {
 ```
 
 
-We first obtain the computing handle from a concrete DeviceContext, and then compute on tensors.
+We first obtain the computing handle from a concrete DeviceContext and then compute on tensors.
 
-The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
+The implementation of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
 
 Fluid provides different register interfaces in op_registry.h
 

From 52e17bf50c2e89e3a29136e4679d312d9c1aa995 Mon Sep 17 00:00:00 2001
From: guosheng <guosheng@baidu.com>
Date: Mon, 29 Jan 2018 13:56:36 +0800
Subject: [PATCH 105/314] Fix the unit test of weight normalization

---
 paddle/operators/reduce_op.cc                            | 9 ++++++---
 .../paddle/v2/fluid/tests/test_weight_normalization.py   | 5 ++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/paddle/operators/reduce_op.cc b/paddle/operators/reduce_op.cc
index 4a06babeda..84f24a9095 100644
--- a/paddle/operators/reduce_op.cc
+++ b/paddle/operators/reduce_op.cc
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/reduce_op.h"
-#include "paddle/operators/net_op.h"
 
 namespace paddle {
 namespace operators {
@@ -38,10 +37,14 @@ class ReduceOp : public framework::OperatorWithKernel {
         dim, x_rank,
         "The dim should be in the range [-rank(input), rank(input)).");
     bool reduce_all = ctx->Attrs().Get<bool>("reduce_all");
+    bool keep_dim = ctx->Attrs().Get<bool>("keep_dim");
     if (reduce_all) {
-      ctx->SetOutputDim("Out", {1});
+      if (keep_dim)
+        ctx->SetOutputDim(
+            "Out", framework::make_ddim(std::vector<int64_t>(x_rank, 1)));
+      else
+        ctx->SetOutputDim("Out", {1});
     } else {
-      bool keep_dim = ctx->Attrs().Get<bool>("keep_dim");
       auto dims_vector = vectorize(x_dims);
       if (keep_dim || x_rank == 1) {
         dims_vector[dim] = 1;
diff --git a/python/paddle/v2/fluid/tests/test_weight_normalization.py b/python/paddle/v2/fluid/tests/test_weight_normalization.py
index 1ac54ea305..80ad8285d8 100644
--- a/python/paddle/v2/fluid/tests/test_weight_normalization.py
+++ b/python/paddle/v2/fluid/tests/test_weight_normalization.py
@@ -112,9 +112,8 @@ class TestWeightNormalization(unittest.TestCase):
             [
                 self.assertTrue(
                     numpy.allclose(
-                        numpy.array(actual_output), expect_output, atol=0.001))
-                for expect_output, actual_output in zip(expect_output,
-                                                        actual_output)
+                        numpy.array(actual), expect, atol=0.001))
+                for expect, actual in zip(expect_output, actual_output)
             ]
 
 

From 82d924984f75cecce2626ecb7376f2424b50aaae Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 29 Jan 2018 14:45:10 +0800
Subject: [PATCH 106/314] update dist transpiler

---
 python/paddle/v2/fluid/distribute_transpiler.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index abcad899bf..4e54ab806b 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -221,7 +221,7 @@ class DistributeTranspiler:
             if len(splited_vars) <= 1:
                 continue
             orig_var = program.global_block().vars[varname]
-            if orig_var == core.VarDesc.VarType.SELECTED_ROWS:
+            if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS:
                 height_sections = []
                 for v in splited_vars:
                     height_sections.append(v.shape[0])
@@ -230,7 +230,7 @@ class DistributeTranspiler:
                     inputs={"X": orig_var},
                     outputs={"Out": splited_vars},
                     attrs={"height_sections": height_sections})
-            elif orig_var == core.VarDesc.VarType.LOD_TENSOR:
+            elif orig_var.type == core.VarDesc.VarType.LOD_TENSOR:
                 sections = []
                 for v in splited_vars:
                     sections.append(v.shape[0])
@@ -470,8 +470,7 @@ class DistributeTranspiler:
         # Append the recv op
         pserver_program.global_block().append_op(
             type="recv",
-            inputs={"RX": self.param_grad_ep_mapping[endpoint]["grads"]
-                    },  # grads to recv
+            inputs={},
             outputs={},
             attrs={
                 "OptimizeBlock": optimize_sub_program.global_block(),

From d082f3a911721231ac321ccbafa571ae8a44af7f Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Sun, 28 Jan 2018 23:29:53 -0800
Subject: [PATCH 107/314] Rewrite class Channel to implement buffered and
 unbuffered channels (#7915)

* Remove IsBounded as buffered channels have to be bounded

* Add derived classes Buffered and UnBuffered"

* Implement buffered and unbuffered channels

* Correct the syntax of Channel::Receive

* clang-format

* clang-format 3.8

* clang 3.8
---
 paddle/framework/CMakeLists.txt               |  2 +
 paddle/framework/channel.h                    | 91 +++++++------------
 paddle/framework/channel_test.cc              | 26 ++++++
 paddle/framework/details/buffered_channel.h   | 82 +++++++++++++++++
 paddle/framework/details/unbuffered_channel.h | 52 +++++++++++
 5 files changed, 196 insertions(+), 57 deletions(-)
 create mode 100644 paddle/framework/channel_test.cc
 create mode 100644 paddle/framework/details/buffered_channel.h
 create mode 100644 paddle/framework/details/unbuffered_channel.h

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 2804969842..318661af8b 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -98,3 +98,5 @@ if(NOT WITH_C_API AND WITH_FLUID)
   install(FILES ${CMAKE_CURRENT_BINARY_DIR}/framework.pb.h DESTINATION include/paddle/framework)
   install(FILES details/cow_ptr.h details/op_registry.h DESTINATION include/paddle/framework/details)
 endif()
+
+cc_test(channel_test SRCS channel_test.cc)
diff --git a/paddle/framework/channel.h b/paddle/framework/channel.h
index 9ba0fc5c55..70ecccc1a1 100644
--- a/paddle/framework/channel.h
+++ b/paddle/framework/channel.h
@@ -13,75 +13,52 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include <condition_variable>
-#include <mutex>
-#include <queue>
+
+#include <stddef.h>  // for size_t
 
 namespace paddle {
 namespace framework {
 
+// Channel is the abstract class of buffered and un-buffered channels.
 template <typename T>
 class Channel {
  public:
-  explicit Channel(std::size_t capacity) : capacity_(capacity) {}
-
-  void Send(T* channel_element) {
-    std::unique_lock<std::mutex> lock(mu_);
-
-    if (IsBounded()) {
-      full_cond_var_.wait(lock, [this]() {
-        bool capacity_valid = capacity_ > 0 ? !IsCapacityFull() : true;
-        return capacity_valid;
-      });
-    }
-    channel_.push_back(std::move(*channel_element));
-
-    lock.unlock();
-    empty_cond_var_.notify_one();
-  }
+  virtual void Send(T*) = 0;
+  virtual void Receive(T*) = 0;
+  virtual size_t Cap() = 0;
 
-  T* Receive() {
-    std::unique_lock<std::mutex> lock(mu_);
-    empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
-
-    T* channel_element = std::move(channel_.front());
-    channel_.pop_front();
-
-    NotifyAllSenders(&lock);
-    return channel_element;
-  }
-
-  size_t Size() {
-    std::unique_lock<std::mutex> lock(mu_);
-    return channel_.size();
-  }
+  // Don't delete channels; instead, call Channel::Close.
+ protected:
+  virtual ~Channel() {}
+};
 
-  void Clear() {
-    std::unique_lock<std::mutex> lock(mu_);
-    channel_.clear();
+// Forward declaration of channel implementations.
+namespace details {
+template <typename T>
+class Buffered;
+template <typename T>
+class UnBuffered;
+}  // namespace details
 
-    NotifyAllSenders(&lock);
+template <typename T>
+Channel<T>* MakeChannel(size_t buffer_size) {
+  if (buffer_size > 0) {
+    return new details::Buffered<T>(buffer_size);
   }
+  return new details::UnBuffered<T>();
+}
 
- private:
-  std::size_t capacity_;
-  std::mutex mu_;
-  std::condition_variable empty_cond_var_;
-  std::condition_variable full_cond_var_;
-  std::deque<T> channel_;
-
- private:
-  void NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
-    if (IsBounded()) {
-      lock->unlock();
-      full_cond_var_.notify_one();
-    }
+template <typename T>
+void CloseChannel(Channel<T>* ch) {
+  if (ch->Cap() > 0) {
+    delete dynamic_cast<details::Buffered<T>*>(ch);
+  } else {
+    delete dynamic_cast<details::UnBuffered<T>*>(ch);
   }
+}
 
-  bool IsBounded() const { return capacity_ > 0; }
-
-  bool IsCapacityFull() const { return channel_.size() >= capacity_; }
-};
-
-}  // namespace operator
+}  // namespace framework
 }  // namespace paddle
+
+#include "paddle/framework/details/buffered_channel.h"
+#include "paddle/framework/details/unbuffered_channel.h"
diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
new file mode 100644
index 0000000000..9efc017265
--- /dev/null
+++ b/paddle/framework/channel_test.cc
@@ -0,0 +1,26 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/channel.h"
+
+#include "gtest/gtest.h"
+
+TEST(Channel, MakeAndClose) {
+  using paddle::framework::Channel;
+  using paddle::framework::MakeChannel;
+  using paddle::framework::CloseChannel;
+
+  Channel<int>* ch = MakeChannel<int>(10);
+  CloseChannel(ch);
+}
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
new file mode 100644
index 0000000000..572e29d44a
--- /dev/null
+++ b/paddle/framework/details/buffered_channel.h
@@ -0,0 +1,82 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+
+#include "paddle/framework/channel.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+template <typename T>
+class Buffered : public paddle::framework::Channel<T> {
+  friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
+  friend void paddle::framework::CloseChannel<T>(Channel<T>*);
+
+ public:
+  virtual void Send(T*);
+  virtual void Receive(T*);
+  virtual size_t Cap() { return cap_; }
+
+ private:
+  size_t cap_;
+  std::mutex mu_;
+  std::condition_variable empty_cond_var_;
+  std::condition_variable full_cond_var_;
+  std::deque<T> channel_;
+
+  Buffered(size_t cap) : cap_(cap) {}
+  virtual ~Buffered();
+
+  void NotifyAllSenders(std::unique_lock<std::mutex>*);
+};
+
+template <typename T>
+void Buffered<T>::Send(T* item) {
+  std::unique_lock<std::mutex> lock(mu_);
+  full_cond_var_.wait(lock, [this]() { return channel_.size() < cap_; });
+  channel_.push_back(std::move(*item));
+  lock.unlock();
+  empty_cond_var_.notify_one();
+}
+
+template <typename T>
+void Buffered<T>::Receive(T* item) {
+  std::unique_lock<std::mutex> lock(mu_);
+  empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
+  *item = std::move(channel_.front());
+  channel_.pop_front();
+  NotifyAllSenders(&lock);
+}
+
+template <typename T>
+Buffered<T>::~Buffered() {
+  std::unique_lock<std::mutex> lock(mu_);
+  channel_.clear();
+  NotifyAllSenders(&lock);
+}
+
+template <typename T>
+void Buffered<T>::NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
+  lock->unlock();
+  full_cond_var_.notify_one();
+}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
new file mode 100644
index 0000000000..7ecced1fba
--- /dev/null
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -0,0 +1,52 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+
+#include "paddle/framework/channel.h"
+
+namespace paddle {
+namespace framework {
+namespace details {
+
+template <typename T>
+class UnBuffered : public paddle::framework::Channel<T> {
+  friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
+  friend void paddle::framework::CloseChannel<T>(Channel<T>*);
+
+ public:
+  virtual void Send(T*);
+  virtual void Receive(T*);
+  virtual size_t Cap() { return 0; }
+
+ private:
+  UnBuffered() {}
+  virtual ~UnBuffered();
+};
+
+template <typename T>
+void UnBuffered<T>::Send(T* channel_element) {}
+
+template <typename T>
+void UnBuffered<T>::Receive(T*) {}
+
+template <typename T>
+UnBuffered<T>::~UnBuffered() {}
+
+}  // namespace details
+}  // namespace framework
+}  // namespace paddle

From 5a3b06bef7d02366c5fc1055c5d5b6fe1a241c75 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 29 Jan 2018 16:05:25 +0800
Subject: [PATCH 108/314] update debug string

---
 python/paddle/v2/fluid/clip.py        | 24 +++++++++
 python/paddle/v2/fluid/framework.py   | 71 ++++++++++++++++++++++++---
 python/paddle/v2/fluid/regularizer.py | 11 +++++
 3 files changed, 98 insertions(+), 8 deletions(-)

diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index 3028029e60..fcf070fd3d 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -30,6 +30,9 @@ __all__ = [
 
 
 class BaseErrorClipAttr(object):
+    def __str__(self):
+        raise NotImplementedError()
+
     def append_clip_op(self, block, grad_name):
         raise NotImplementedError()
 
@@ -44,6 +47,9 @@ class ErrorClipByValue(BaseErrorClipAttr):
         self.max = max
         self.min = min
 
+    def __str__(self):
+        return "ByValue, min=%f, max=%f" % (self.min, self.max)
+
     def append_clip_op(self, block, grad_name):
         clip_op_desc = block.desc.append_op()
         clip_op_desc.set_type("clip")
@@ -71,6 +77,9 @@ def error_clip_callback(block, context):
 
 
 class BaseGradientClipAttr(object):
+    def __str__(self):
+        raise NotImplementedError()
+
     def process_context(self, context, param, grad):
         raise NotImplementedError()
 
@@ -79,6 +88,9 @@ class BaseGradientClipAttr(object):
 
 
 class NullGradientClipAttr(BaseGradientClipAttr):
+    def __str__(self):
+        return "Null"
+
     def process_context(self, context, param, grad):
         pass
 
@@ -96,6 +108,9 @@ class GradientClipByValue(BaseGradientClipAttr):
         self.max = max
         self.min = min
 
+    def __str__(self):
+        return "ByValue, min=%f, max=%f" % (self.min, self.max)
+
     def process_context(self, context, param, grad):
         pass
 
@@ -108,6 +123,9 @@ class GradientClipByNorm(BaseGradientClipAttr):
     def __init__(self, clip_norm):
         self.clip_norm = clip_norm
 
+    def __str__(self):
+        return "ByNorm, clip_norm=%f" % self.clip_norm
+
     def process_context(self, context, param, grad):
         pass
 
@@ -124,6 +142,10 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         self.clip_norm = clip_norm
         self.group_name = group_name
 
+    def __str__(self):
+        return "ByGlobalNorm, group_name=%s, clip_norm=%f" % (self.group_name,
+                                                              self.clip_norm)
+
     def process_context(self, context, param, grad):
         if self.group_name not in context:
             context[self.group_name] = []
@@ -199,3 +221,5 @@ def append_gradient_clip_ops(param_grad):
 
 
 ClipByValue = GradientClipByValue
+ClipByNorm = GradientClipByNorm
+ClipByGlobalNorm = GradientClipByGlobalNorm
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index 4d8343e7de..ff9bc7239c 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -629,10 +629,34 @@ class Block(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
-        protostr = self.desc.serialize_to_string()
-        proto = framework_pb2.BlockDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about paramters(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
+                self.idx, self.parent_idx)
+            for var in self.vars.itervalues():
+                res_str += "\n  vars {\n    %s  }" % var.to_string(
+                    throw_on_error).replace("\n", "\n    ")
+            for op in self.ops:
+                res_str += "\n  ops {\n    %s  }" % op.to_string(
+                    throw_on_error).replace("\n", "\n    ")
+            res_str += "\n}"
+        else:
+            protostr = self.desc.serialize_to_string()
+            proto = framework_pb2.BlockDesc.FromString(str(protostr))
+            res_str = _debug_string_(proto, throw_on_error)
+        return res_str
 
     __repr__ = __str__
 
@@ -796,10 +820,28 @@ class Program(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
-        protostr = self.desc.serialize_to_string()
-        proto = framework_pb2.ProgramDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about paramters(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            res_str = ""
+            for block in self.blocks:
+                res_str += block.to_string(throw_on_error, with_details)
+        else:
+            protostr = self.desc.serialize_to_string()
+            proto = framework_pb2.ProgramDesc.FromString(str(protostr))
+            res_str = _debug_string_(proto, throw_on_error)
+        return res_str
 
     def get_desc(self):
         return self.desc
@@ -950,6 +992,19 @@ class Parameter(Variable):
 
         self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
 
+    def __str__(self):
+        return self.to_string(True)
+
+    def to_string(self, throw_on_error):
+        res_str = Variable.to_string(self, throw_on_error)
+        additional_attr = ("trainable", "optimize_attr", "regularizer",
+                           "gradient_clip_attr")
+        for attr_name in additional_attr:
+            res_str += "%s: %s\n" % (attr_name, str(getattr(self, attr_name)))
+        return res_str
+
+    __repr__ = __str__
+
 
 # program is a global instance.
 _main_program_ = Program()
diff --git a/python/paddle/v2/fluid/regularizer.py b/python/paddle/v2/fluid/regularizer.py
index c2f28eecfd..0273da647a 100644
--- a/python/paddle/v2/fluid/regularizer.py
+++ b/python/paddle/v2/fluid/regularizer.py
@@ -87,6 +87,11 @@ class WeightDecayRegularizer(object):
         """
         raise NotImplementedError()
 
+    def __str__(self):
+        """Debug string
+        """
+        raise NotImplementedError()
+
 
 class L2DecayRegularizer(WeightDecayRegularizer):
     """Implements the L2 Weight Decay Regularization
@@ -123,6 +128,9 @@ class L2DecayRegularizer(WeightDecayRegularizer):
 
         return decay
 
+    def __str__(self):
+        return "L2Decay, regularization_coeff=%f" % self._regularization_coeff
+
 
 class L1DecayRegularizer(WeightDecayRegularizer):
     """Implements the L1 Weight Decay Regularization
@@ -163,6 +171,9 @@ class L1DecayRegularizer(WeightDecayRegularizer):
 
         return decay
 
+    def __str__(self):
+        return "L1Decay, regularization_coeff=%f" % self._regularization_coeff
+
 
 # We short the class name, since users will use the regulaizer with the package
 # name. The sample code:

From e3b499605ad55c088326b9649c6ca23885320c0c Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 29 Jan 2018 16:42:50 +0800
Subject: [PATCH 109/314] add docstring

---
 python/paddle/v2/fluid/clip.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/python/paddle/v2/fluid/clip.py b/python/paddle/v2/fluid/clip.py
index 3028029e60..92081a47d4 100644
--- a/python/paddle/v2/fluid/clip.py
+++ b/python/paddle/v2/fluid/clip.py
@@ -160,6 +160,17 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
 
 
 def set_gradient_clip(clip, param_list=None, program=None):
+    """
+        To specify parameters that require gradient clip.
+        Args:
+            clip(BaseGradientClipAttr): An instance of some derived class of BaseGradientClipAttr, 
+                    which describes the type and detailed attributes of required gradient clip.
+            param_list(list, None by default): Parameters that require gradient clip. 
+                    It can be a list of parameter or a list of parameter's name. 
+                    When it's None, all parameters in the program will be included. 
+            program(Program, None by default): The program where parameters are. 
+                    Will be the default main program when assigned with None.
+    """
     if not isinstance(clip, BaseGradientClipAttr):
         raise TypeError(
             "'clip' should be an instance of BaseGradientClipAttr's derived class"

From a585b585dd3f16c45d57a88bf2f6f0bb101d0398 Mon Sep 17 00:00:00 2001
From: Yancey <yanxu05@baidu.com>
Date: Mon, 29 Jan 2018 16:48:05 +0800
Subject: [PATCH 110/314] Batch barrier in send/recv op (#7847)

* initialize batch barrier

* add some comments

* update

* fix batch barrier

* use sendvariable rpc interface to send batch barrier

* fix comment

* fix method

* fix by comment

* fix by comment
---
 paddle/operators/detail/grpc_client.cc     | 15 ++++++
 paddle/operators/detail/grpc_client.h      | 24 ++++++++++
 paddle/operators/detail/grpc_server.cc     | 13 ++---
 paddle/operators/detail/grpc_server.h      |  3 +-
 paddle/operators/detail/sendrecvop_utils.h |  3 ++
 paddle/operators/recv_op.cc                | 55 +++++++++++++---------
 paddle/operators/send_op.cc                | 12 ++++-
 7 files changed, 92 insertions(+), 33 deletions(-)

diff --git a/paddle/operators/detail/grpc_client.cc b/paddle/operators/detail/grpc_client.cc
index c433945542..9b5f7afc6a 100644
--- a/paddle/operators/detail/grpc_client.cc
+++ b/paddle/operators/detail/grpc_client.cc
@@ -97,6 +97,21 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
   return true;
 }
 
+bool RPCClient::AsyncSendBatchBarrier(const std::string& ep, int64_t time_out) {
+  const auto ch = GetChannel(ep);
+
+  BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
+  s->Prepare(time_out);
+
+  sendrecv::VariableMessage req;
+  req.set_varname(BATCH_BARRIER_MESSAGE);
+  auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
+  rpc->Finish(&s->reply_, &s->status_, (void*)s);
+  req_count_++;
+
+  return true;
+}
+
 bool RPCClient::Wait() {
   if (req_count_ <= 0) {
     return true;
diff --git a/paddle/operators/detail/grpc_client.h b/paddle/operators/detail/grpc_client.h
index a62e70a253..f9499f6dc7 100644
--- a/paddle/operators/detail/grpc_client.h
+++ b/paddle/operators/detail/grpc_client.h
@@ -71,6 +71,15 @@ class ClientBase {
     context_->set_deadline(deadline);
   }
 
+  virtual void Prepare(int64_t time_out) {
+    context_.reset(new grpc::ClientContext());
+
+    std::chrono::system_clock::time_point deadline =
+        std::chrono::system_clock::now() + std::chrono::milliseconds(time_out);
+
+    context_->set_deadline(deadline);
+  }
+
   virtual void Process() = 0;
 
   std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
@@ -117,6 +126,17 @@ class GetProcessor : public ClientBase {
   RequestGetCallBack response_call_back_ = ProcGetResponse;
 };
 
+class BatchBarrierProcessor : public ClientBase {
+ public:
+  explicit BatchBarrierProcessor(std::shared_ptr<grpc::Channel> ch)
+      : ClientBase(ch) {}
+
+  virtual ~BatchBarrierProcessor() {}
+
+  virtual void Process() {}
+  sendrecv::VoidMessage reply_;
+};
+
 class RPCClient {
  public:
   bool AsyncSendVariable(const std::string& ep,
@@ -130,6 +150,10 @@ class RPCClient {
                         const framework::Scope& scope,
                         const std::string& var_name,
                         int64_t time_out = 600 * 1000);
+
+  bool AsyncSendBatchBarrier(const std::string& ep,
+                             int64_t time_out = 600 * 1000);
+
   bool Wait();
 
  private:
diff --git a/paddle/operators/detail/grpc_server.cc b/paddle/operators/detail/grpc_server.cc
index 3ddcd839bd..4f94e1315f 100644
--- a/paddle/operators/detail/grpc_server.cc
+++ b/paddle/operators/detail/grpc_server.cc
@@ -132,6 +132,7 @@ void AsyncGRPCServer::RunSyncUpdate() {
 
   cq_send_ = builder.AddCompletionQueue();
   cq_get_ = builder.AddCompletionQueue();
+
   server_ = builder.BuildAndStart();
   LOG(INFO) << "Server listening on " << address_ << std::endl;
 
@@ -141,11 +142,11 @@ void AsyncGRPCServer::RunSyncUpdate() {
       std::bind(&AsyncGRPCServer::TryToRegisterNewGetOne, this);
 
   t_send_.reset(
-      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, false,
+      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_send_.get(), "cq_send", send_register)));
 
   t_get_.reset(
-      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, true,
+      new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this,
                                 cq_get_.get(), "cq_get", get_register)));
 
   // wait server
@@ -174,7 +175,7 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() {
   }
   RequestSend* send =
       new RequestSend(&service_, cq_send_.get(), &var_recv_queue_);
-  VLOG(4) << "create RequestSend status:" << send->Status();
+  VLOG(4) << "Create RequestSend status:" << send->Status();
 }
 
 void AsyncGRPCServer::TryToRegisterNewGetOne() {
@@ -184,11 +185,11 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() {
   }
   RequestGet* get = new RequestGet(&service_, cq_get_.get(), scope_, dev_ctx_,
                                    &var_get_queue_);
-  VLOG(4) << "create Requestget status:" << get->Status();
+  VLOG(4) << "Create RequestGet status:" << get->Status();
 }
 
-// FIXME(typhoonzero): remove wait argument and change cq_name to enum.
-void AsyncGRPCServer::HandleRequest(bool wait, grpc::ServerCompletionQueue* cq,
+// FIXME(typhoonzero): change cq_name to enum.
+void AsyncGRPCServer::HandleRequest(grpc::ServerCompletionQueue* cq,
                                     std::string cq_name,
                                     std::function<void()> TryToRegisterNewOne) {
   TryToRegisterNewOne();
diff --git a/paddle/operators/detail/grpc_server.h b/paddle/operators/detail/grpc_server.h
index 1ca9086c74..3f8b9d9317 100644
--- a/paddle/operators/detail/grpc_server.h
+++ b/paddle/operators/detail/grpc_server.h
@@ -57,8 +57,7 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
   void ShutDown();
 
  protected:
-  void HandleRequest(bool wait, grpc::ServerCompletionQueue *cq,
-                     std::string cq_name,
+  void HandleRequest(grpc::ServerCompletionQueue *cq, std::string cq_name,
                      std::function<void()> TryToRegisterNewOne);
   void TryToRegisterNewSendOne();
   void TryToRegisterNewGetOne();
diff --git a/paddle/operators/detail/sendrecvop_utils.h b/paddle/operators/detail/sendrecvop_utils.h
index bc6581afab..8e66f7299c 100644
--- a/paddle/operators/detail/sendrecvop_utils.h
+++ b/paddle/operators/detail/sendrecvop_utils.h
@@ -30,6 +30,9 @@ namespace paddle {
 namespace operators {
 namespace detail {
 
+#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
+#define BATCH_BARRIER_MESSAGE "BATCH_BARRIER@RECV"
+
 void SerializeToMessage(const std::string& name, const framework::Variable* var,
                         const platform::DeviceContext& ctx,
                         sendrecv::VariableMessage* msg);
diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 5d1df566af..49e1eb3402 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -29,8 +29,6 @@ limitations under the License. */
 #include "paddle/operators/detail/simple_block_queue.h"
 #include "paddle/string/printf.h"
 
-#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
-
 namespace paddle {
 namespace operators {
 
@@ -95,7 +93,6 @@ class RecvOp : public framework::OperatorBase {
     auto param_list = Attr<std::vector<std::string>>("ParamList");
     auto grad_list = Attr<std::vector<std::string>>("GradList");
     auto fan_in = Attr<int>("Fanin");
-    size_t param_count = param_list.size();
 
     auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
     auto *program = block->Program();
@@ -103,38 +100,50 @@ class RecvOp : public framework::OperatorBase {
 
     // TODO(typhoonzero): change this to a while_op for every cluster-batch.
     bool exit_flag = false;
-    size_t barrier_size = param_count * fan_in;
     while (!exit_flag) {
       // Get from multiple trainers, we don't care about the order in which
       // the gradients arrives, just add suffix 0~n and merge the gradient.
       rpc_service_->SetCond(0);
-      for (size_t i = 0; i < barrier_size; ++i) {
+      size_t recv_var_cnt = 0;
+      int batch_barrier = 0;
+      while (batch_barrier != fan_in) {
         const detail::MessageWithName &v = rpc_service_->Get();
         auto grad_var_name = v.first;
         if (grad_var_name == LISTEN_TERMINATE_MESSAGE) {
           LOG(INFO) << "received terminate message and exit";
           exit_flag = true;
           break;
-        }
-        auto it = std::find(grad_list.begin(), grad_list.end(), grad_var_name);
-        std::string param_var_name;
-        if (it != grad_list.end()) {
-          param_var_name = param_list[it - grad_list.begin()];
+        } else if (grad_var_name == BATCH_BARRIER_MESSAGE) {
+          VLOG(3) << "recv batch barrier message";
+          batch_barrier++;
+          continue;
         } else {
-          LOG(ERROR) << "grad has no paired param:" << grad_var_name;
-        }
-        VLOG(3) << "received grad: " << grad_var_name
-                << " updating param: " << param_var_name;
-        if (fan_in > 1) {
-          grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
-        }
-        auto *var = recv_scope.FindVar(grad_var_name);
-        if (var == nullptr) {
-          LOG(ERROR) << "Can not find server side var: " << grad_var_name;
-          PADDLE_THROW("Can not find server side var");
+          // receive a variable
+          recv_var_cnt++;
+          auto it =
+              std::find(grad_list.begin(), grad_list.end(), grad_var_name);
+          std::string param_var_name;
+          if (it != grad_list.end()) {
+            param_var_name = param_list[it - grad_list.begin()];
+          } else {
+            LOG(ERROR) << "grad has no paired param:" << grad_var_name;
+          }
+          VLOG(3) << "received grad: " << grad_var_name
+                  << " updating param: " << param_var_name;
+
+          if (fan_in > 1) {
+            grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
+          }
+          auto *var = recv_scope.FindVar(grad_var_name);
+          if (var == nullptr) {
+            LOG(ERROR) << "Can not find server side var: " << grad_var_name;
+            PADDLE_THROW("Can not find server side var");
+          }
+          detail::DeserializeFromMessage(v.second, dev_ctx, var);
         }
-        detail::DeserializeFromMessage(v.second, dev_ctx, var);
       }
+      VLOG(3) << "recv " << recv_var_cnt << " parmeters for one barrier.";
+      // TODO(Yancey1989): merge SelectedRows variables here
       if (exit_flag) {
         break;
       }
@@ -146,7 +155,7 @@ class RecvOp : public framework::OperatorBase {
         LOG(ERROR) << "run sub program error " << e.what();
       }
       rpc_service_->SetCond(1);
-      rpc_service_->WaitClientGet(barrier_size);
+      rpc_service_->WaitClientGet(recv_var_cnt);
       grads_counter_.clear();
     }  // while(true)
   }
diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index 5aa66c20ea..bb719dc2a8 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -37,17 +37,25 @@ class SendOp : public framework::OperatorBase {
     auto ins = Inputs("X");
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
+    std::vector<std::string> endpoints =
+        Attr<std::vector<std::string>>("endpoints");
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
     for (size_t i = 0; i < ins.size(); i++) {
-      VLOG(3) << "sending " << ins[i];
+      VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
       client_.AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
     }
     PADDLE_ENFORCE(client_.Wait());
 
+    for (auto& ep : endpoints) {
+      VLOG(3) << "batch barrier, ep: " << ep;
+      client_.AsyncSendBatchBarrier(ep);
+    }
+    PADDLE_ENFORCE(client_.Wait());
+
     for (size_t i = 0; i < outs.size(); i++) {
-      VLOG(3) << "getting " << outs[i];
+      VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
       client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
 

From 1b8bc3c5b3d3884dad4310d58eb949c0cfbf0d60 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 29 Jan 2018 16:58:01 +0800
Subject: [PATCH 111/314] rename rpc ops

---
 paddle/operators/recv_op.cc           | 188 ++++++--------------------
 paddle/operators/send_op.cc           |  13 +-
 paddle/operators/send_recv_op_test.cc |  19 +--
 3 files changed, 58 insertions(+), 162 deletions(-)

diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 593c35879a..f8af4a290b 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -12,179 +12,67 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include <stdint.h>
-#include <sys/stat.h>
 #include <ostream>
-#include <thread>
 
-#include <unistd.h>
-
-#include "paddle/framework/executor.h"
+#include "paddle/framework/data_type.h"
 #include "paddle/framework/framework.pb.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/op_registry.h"
-#include "paddle/framework/proto_desc.h"
-#include "paddle/operators/detail/grpc_server.h"
-#include "paddle/operators/detail/sendrecvop_utils.h"
-#include "paddle/operators/detail/simple_block_queue.h"
-#include "paddle/string/printf.h"
 
-#define LISTEN_TERMINATE_MESSAGE "TERMINATE@RECV"
+#include <future>
+#include "paddle/operators/detail/grpc_client.h"
 
 namespace paddle {
 namespace operators {
 
-constexpr char kOptimizeBlock[] = "OptimizeBlock";
-
-void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
-  service->RunSyncUpdate();
-  VLOG(4) << "RunServer thread end";
-}
-
-static void CreateTensorFromMessageType(framework::Variable *var,
-                                        sendrecv::VarType var_type) {
-  if (var_type == sendrecv::VarType::LOD_TENSOR) {
-    var->GetMutable<framework::LoDTensor>();
-  } else if (var_type == sendrecv::VarType::SELECTED_ROWS) {
-    var->GetMutable<framework::SelectedRows>();
-  } else {
-    PADDLE_THROW(
-        "VariableMessage type %d is not in "
-        "[LoDTensor, SelectedRows]",
-        var_type);
-  }
-}
-
-class RecvOp : public framework::OperatorBase {
+class SendOp : public framework::OperatorBase {
  public:
-  RecvOp(const std::string &type, const framework::VariableNameMap &inputs,
-         const framework::VariableNameMap &outputs,
-         const framework::AttributeMap &attrs)
-      : OperatorBase(type, inputs, outputs, attrs) {
-    if (!rpc_service_) {
-      std::string endpoint = Attr<std::string>("endpoint");
-      rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
-      server_thread_.reset(new std::thread(RunServer, rpc_service_));
+  SendOp(const std::string& type, const framework::VariableNameMap& inputs,
+         const framework::VariableNameMap& outputs,
+         const framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void Run(const framework::Scope& scope,
+           const platform::Place& place) const override {
+    auto outs = Outputs("Out");
+    std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
+
+    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+    auto& ctx = *pool.Get(place);
+
+    for (size_t i = 0; i < outs.size(); i++) {
+      VLOG(3) << "getting " << outs[i];
+      client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
-  }
-
-  void Stop() override {
-    detail::MessageWithName term_msg;
-    term_msg.first = LISTEN_TERMINATE_MESSAGE;
-    rpc_service_->Push(term_msg);
-    rpc_service_->ShutDown();
-    server_thread_->join();
-  }
-
-  std::string GetGradVarNameForTrainer(const std::string &varname) const {
-    if (grads_counter_.find(varname) == grads_counter_.end()) {
-      grads_counter_[varname] = 0;
-    }
-    return string::Sprintf("%s.trainer_%d", varname, grads_counter_[varname]++);
-  }
 
-  void Run(const framework::Scope &scope,
-           const platform::Place &dev_place) const override {
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    auto &dev_ctx = *pool.Get(dev_place);
-    framework::Scope &recv_scope = scope.NewScope();
-
-    // FIXME(Yancey1989): initialize rpc server with laze mode.
-    rpc_service_->SetScope(&recv_scope);
-    rpc_service_->SetDevCtx(&dev_ctx);
-    auto param_list = Attr<std::vector<std::string>>("ParamList");
-    auto grad_list = Attr<std::vector<std::string>>("GradList");
-    auto fan_in = Attr<int>("Fanin");
-    size_t param_count = param_list.size();
-
-    auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
-    auto *program = block->Program();
-    framework::Executor executor(dev_place);
-
-    // TODO(typhoonzero): change this to a while_op for every cluster-batch.
-    bool exit_flag = false;
-    size_t barrier_size = param_count * fan_in;
-    while (!exit_flag) {
-      // Get from multiple trainers, we don't care about the order in which
-      // the gradients arrives, just add suffix 0~n and merge the gradient.
-      rpc_service_->SetCond(0);
-      for (size_t i = 0; i < barrier_size; ++i) {
-        const detail::MessageWithName &v = rpc_service_->Get();
-        auto grad_var_name = v.first;
-        if (grad_var_name == LISTEN_TERMINATE_MESSAGE) {
-          LOG(INFO) << "received terminate message and exit";
-          exit_flag = true;
-          break;
-        }
-        auto it = std::find(grad_list.begin(), grad_list.end(), grad_var_name);
-        std::string param_var_name;
-        if (it != grad_list.end()) {
-          param_var_name = param_list[it - grad_list.begin()];
-        } else {
-          LOG(ERROR) << "grad has no paired param:" << grad_var_name;
-        }
-        VLOG(3) << "received grad: " << grad_var_name
-                << " updating param: " << param_var_name;
-        if (fan_in > 1) {
-          grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
-        }
-        auto *var = recv_scope.FindVar(grad_var_name);
-        if (var == nullptr) {
-          LOG(ERROR) << "Can not find server side var: " << grad_var_name;
-          PADDLE_THROW("Can not find server side var");
-        }
-        detail::DeserializeFromMessage(v.second, dev_ctx, var);
-      }
-      if (exit_flag) {
-        break;
-      }
-
-      try {
-        executor.Run(*program, &recv_scope, block->ID(), /*global_block*/
-                     false /*create_local_scope*/, false /*create_vars*/);
-      } catch (std::exception &e) {
-        LOG(ERROR) << "run sub program error " << e.what();
-      }
-      rpc_service_->SetCond(1);
-      rpc_service_->WaitClientGet(barrier_size);
-      grads_counter_.clear();
-    }  // while(true)
+    PADDLE_ENFORCE(client_.Wait());
   }
 
- protected:
-  std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
-  std::shared_ptr<std::thread> server_thread_;
-  mutable std::unordered_map<std::string, int> grads_counter_;
+ private:
+  mutable detail::RPCClient client_;
 };
 
-class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
+class SendOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  RecvOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  SendOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("RX", "(Tensor) Input tensor to be optimized").AsDuplicable();
+    AddInput("X", "(Tensor) Input tensor to be sent").AsDuplicable();
+    AddOutput("Out", "(Tensor) Output tensor to be received from server")
+        .AsDuplicable();
     AddComment(R"DOC(
-Recv operator
+Send operator
 
-This operator will recieve tensor from send_op
+This operator will send tensor to recv_op at the parameter server.
 )DOC");
-    AddAttr<std::string>("endpoint",
-                         "(string, default 127.0.0.1:6164)"
-                         "IP address to listen on.")
-        .SetDefault("127.0.0.1:6164")
-        .AddCustomChecker([](const std::string &ip) { return !ip.empty(); });
-    AddAttr<framework::BlockDesc *>(
-        kOptimizeBlock, "Serialized ProgramDesc string for recv to run.");
-    AddAttr<std::vector<std::string>>(
-        "ParamList", "type list of string",
-        "grad->param name mapping to find which parameters to optimize.")
+    AddAttr<std::vector<std::string>>("endpoints",
+                                      "(string vector, default 127.0.0.1:6164)"
+                                      "Server endpoints to send variables to.")
         .SetDefault({});
-    AddAttr<std::vector<std::string>>(
-        "GradList", "type list of string",
-        "grad->param name mapping to find which parameters to optimize.")
+    AddAttr<std::vector<std::string>>("epmap",
+                                      "(string vector, default 127.0.0.1:6164)"
+                                      "Server endpoints in the order of input "
+                                      "variables for mapping")
         .SetDefault({});
-    AddAttr<int>("Fanin", "type int",
-                 "Number of trainers in the current cluster job")
-        .SetDefault(1);
   }
 };
 
@@ -193,4 +81,4 @@ This operator will recieve tensor from send_op
 
 namespace ops = paddle::operators;
 
-REGISTER_OPERATOR(recv, ops::RecvOp, ops::RecvOpMaker);
+REGISTER_OPERATOR(send, ops::SendOp, ops::SendOpMaker);
diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index 5aa66c20ea..c90e4d8ef0 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -37,6 +37,7 @@ class SendOp : public framework::OperatorBase {
     auto ins = Inputs("X");
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
+    bool do_get = Attr<bool>("DoGet");
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
@@ -46,9 +47,11 @@ class SendOp : public framework::OperatorBase {
     }
     PADDLE_ENFORCE(client_.Wait());
 
-    for (size_t i = 0; i < outs.size(); i++) {
-      VLOG(3) << "getting " << outs[i];
-      client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
+    if (do_get) {
+      for (size_t i = 0; i < outs.size(); i++) {
+        VLOG(3) << "getting " << outs[i];
+        client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
+      }
     }
 
     PADDLE_ENFORCE(client_.Wait());
@@ -79,6 +82,10 @@ This operator will send tensor to recv_op at the parameter server.
                                       "Server endpoints in the order of input "
                                       "variables for mapping")
         .SetDefault({});
+    AddAttr<bool>("DoGet",
+                  "(bool, default true)"
+                  "Whether do GetVariable call after send")
+        .SetDefault(true);
   }
 };
 
diff --git a/paddle/operators/send_recv_op_test.cc b/paddle/operators/send_recv_op_test.cc
index 045a0f5434..874eac9711 100644
--- a/paddle/operators/send_recv_op_test.cc
+++ b/paddle/operators/send_recv_op_test.cc
@@ -25,7 +25,7 @@ limitations under the License. */
 #include "paddle/string/printf.h"
 
 USE_NO_KERNEL_OP(send);
-USE_NO_KERNEL_OP(recv);
+USE_NO_KERNEL_OP(listen_and_serv);
 USE_OP(sum);
 
 namespace f = paddle::framework;
@@ -33,7 +33,7 @@ namespace p = paddle::platform;
 namespace m = paddle::operators::math;
 
 // global for simplicity.
-std::unique_ptr<f::OperatorBase> recv_op;
+std::unique_ptr<f::OperatorBase> listen_and_serv_op;
 
 void InitTensorsInScope(f::Scope &scope, p::CPUPlace &place) {
   p::CPUDeviceContext ctx(place);
@@ -120,7 +120,7 @@ void StartServerNet(bool is_sparse) {
     InitTensorsInScope(scope, place);
   }
 
-  // sub program run in recv_op, for simple test we use sum
+  // sub program run in listen_and_serv_op, for simple test we use sum
   f::ProgramDesc program;
   f::BlockDesc *block = program.MutableBlock(0);
   // X for server side tensors, RX for received tensers, must be of same shape.
@@ -131,8 +131,9 @@ void StartServerNet(bool is_sparse) {
   attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
   attrs.insert({"GradList", std::vector<std::string>({"x1"})});
   attrs.insert({"OptimizeBlock", block});
-  recv_op = f::OpRegistry::CreateOp("recv", {{"RX", {"x1"}}}, {}, attrs);
-  recv_op->Run(scope, place);
+  listen_and_serv_op =
+      f::OpRegistry::CreateOp("listen_and_serv", {{"RX", {"x1"}}}, {}, attrs);
+  listen_and_serv_op->Run(scope, place);
 }
 
 TEST(SendRecvOp, CPUDense) {
@@ -161,9 +162,9 @@ TEST(SendRecvOp, CPUDense) {
   for (int64_t i = 0; i < target->numel(); ++i) {
     EXPECT_EQ(expected[i] * 2, actual[i]);
   }
-  recv_op->Stop();
+  listen_and_serv_op->Stop();
   server_thread.join();
-  recv_op.reset(nullptr);
+  listen_and_serv_op.reset(nullptr);
 }
 
 TEST(SendRecvOp, CPUSparse) {
@@ -200,7 +201,7 @@ TEST(SendRecvOp, CPUSparse) {
     EXPECT_EQ(expect_value->mutable_data<float>(place)[i],
               actual->mutable_data<float>(place)[i]);
   }
-  recv_op->Stop();
+  listen_and_serv_op->Stop();
   server_thread.join();
-  recv_op.reset();
+  listen_and_serv_op.reset();
 }

From 369e2ba0d7f0045f710982df182ab947079dfc27 Mon Sep 17 00:00:00 2001
From: yangyaming <mxscmxsc@gmail.com>
Date: Mon, 29 Jan 2018 17:22:56 +0800
Subject: [PATCH 112/314] Bug fix for sequence_reshape operator.

---
 paddle/operators/sequence_reshape_op.cc | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/paddle/operators/sequence_reshape_op.cc b/paddle/operators/sequence_reshape_op.cc
index 57cca13105..d89a46a712 100644
--- a/paddle/operators/sequence_reshape_op.cc
+++ b/paddle/operators/sequence_reshape_op.cc
@@ -30,8 +30,13 @@ class SequenceReshapeOp : public framework::OperatorWithKernel {
     auto x_numel = product(x_dims);
     PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
     int new_dim = ctx->Attrs().Get<int>("new_dim");
-    ctx->SetOutputDim("Out",
-                      {x_numel / new_dim, static_cast<int64_t>(new_dim)});
+    if (ctx->IsRuntime()) {
+      ctx->SetOutputDim("Out",
+                        {x_numel / new_dim, static_cast<int64_t>(new_dim)});
+    } else {
+      // when compiling, the batch size is undetermined, just set to -1
+      ctx->SetOutputDim("Out", {-1, static_cast<int64_t>(new_dim)});
+    }
   }
 };
 

From 33ae13313142d23774367a1925cc45684b44d00d Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 29 Jan 2018 17:42:41 +0800
Subject: [PATCH 113/314] refine indent

---
 python/paddle/v2/fluid/framework.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index ff9bc7239c..cd4179fb12 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -14,6 +14,7 @@
 
 import collections
 import contextlib
+import re
 
 import numpy as np
 
@@ -643,14 +644,15 @@ class Block(object):
         assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                bool)
         if with_details:
+            re_add_indent = re.compile(r"\n(.)")
             res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
                 self.idx, self.parent_idx)
             for var in self.vars.itervalues():
-                res_str += "\n  vars {\n    %s  }" % var.to_string(
-                    throw_on_error).replace("\n", "\n    ")
+                res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
+                    r"\n    \1", var.to_string(throw_on_error))
             for op in self.ops:
-                res_str += "\n  ops {\n    %s  }" % op.to_string(
-                    throw_on_error).replace("\n", "\n    ")
+                res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
+                    r"\n    \1", op.to_string(throw_on_error))
             res_str += "\n}"
         else:
             protostr = self.desc.serialize_to_string()

From 361bd29670c4f3a1618b8395c8aafaee5484ef5d Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 29 Jan 2018 18:13:06 +0800
Subject: [PATCH 114/314] update

---
 python/paddle/v2/fluid/framework.py | 51 ++++++++++++++++++++++-------
 1 file changed, 40 insertions(+), 11 deletions(-)

diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index cd4179fb12..8bf545e2ec 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -240,20 +240,30 @@ class Variable(object):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
+    def to_string(self, throw_on_error, with_details=False):
         """
         Get debug string.
 
         Args:
             throw_on_error(bool): True if raise an exception when self is not
                 intialized.
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
 
         Returns(str): The debug string.
 
         """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
         protostr = self.desc.serialize_to_string()
         proto = framework_pb2.VarDesc.FromString(str(protostr))
-        return _debug_string_(proto, throw_on_error)
+        res_str = _debug_string_(proto, throw_on_error)
+        if with_details:
+            additional_attr = ("error_clip", "stop_gradient")
+            for attr_name in additional_attr:
+                res_str += "%s: %s\n" % (attr_name,
+                                         str(getattr(self, attr_name)))
+        return res_str
 
     __repr__ = __str__
 
@@ -636,7 +646,8 @@ class Block(object):
         Args:
             throw_on_error(bool): raise exception when self is not initialized
                 when throw_on_error is True
-            with_details(bool): more details about paramters(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
 
         Returns(str): The debug string.
 
@@ -649,7 +660,7 @@ class Block(object):
                 self.idx, self.parent_idx)
             for var in self.vars.itervalues():
                 res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
-                    r"\n    \1", var.to_string(throw_on_error))
+                    r"\n    \1", var.to_string(throw_on_error, with_details))
             for op in self.ops:
                 res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                     r"\n    \1", op.to_string(throw_on_error))
@@ -828,7 +839,8 @@ class Program(object):
         Args:
             throw_on_error(bool): raise exception when self is not initialized
                 when throw_on_error is True
-            with_details(bool): more details about paramters(e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
 
         Returns(str): The debug string.
 
@@ -997,12 +1009,29 @@ class Parameter(Variable):
     def __str__(self):
         return self.to_string(True)
 
-    def to_string(self, throw_on_error):
-        res_str = Variable.to_string(self, throw_on_error)
-        additional_attr = ("trainable", "optimize_attr", "regularizer",
-                           "gradient_clip_attr")
-        for attr_name in additional_attr:
-            res_str += "%s: %s\n" % (attr_name, str(getattr(self, attr_name)))
+    def to_string(self, throw_on_error, with_details=False):
+        """
+        To debug string.
+        Args:
+            throw_on_error(bool): raise exception when self is not initialized
+                when throw_on_error is True
+            with_details(bool): more details about variables and parameters
+                (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
+
+        Returns(str): The debug string.
+
+        """
+        assert isinstance(throw_on_error, bool) and isinstance(with_details,
+                                                               bool)
+        if with_details:
+            res_str = Variable.to_string(self, throw_on_error, True)
+            additional_attr = ("trainable", "optimize_attr", "regularizer",
+                               "gradient_clip_attr")
+            for attr_name in additional_attr:
+                res_str += "%s: %s\n" % (attr_name,
+                                         str(getattr(self, attr_name)))
+        else:
+            res_str = Variable.to_string(self, throw_on_error, False)
         return res_str
 
     __repr__ = __str__

From bd64719a2f012af82dcac731179a998764d432b9 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 29 Jan 2018 20:42:29 +0800
Subject: [PATCH 115/314] update for today

---
 benchmark/cluster/vgg16/README.md          | 44 +++++++++++++++-------
 benchmark/cluster/vgg16/fluid_trainer.yaml |  2 +-
 benchmark/cluster/vgg16/v2_trainer.yaml    |  2 +-
 benchmark/cluster/vgg16/vgg16_fluid.py     | 39 ++++++++++---------
 benchmark/cluster/vgg16/vgg16_v2.py        |  1 +
 5 files changed, 54 insertions(+), 34 deletions(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 18128e5276..c1e85a2c40 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -2,41 +2,57 @@
 
 ## Test Result
 
-### Single node single thread
+### Hardware Infomation
+
+- CPU: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
+- cpu MHz		: 2101.000
+- cache size	: 20480 KB
+
+### Single Node Single Thread
+
+- PServer Count: 10
+- Trainer Count: 20
+- Metrics: samples / sec
 
 | Batch Size | 32 | 64 | 128 | 256 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | - | - | 16.74 | - |
-| PaddlePaddle v2 | - | - | 17.60 | - |
+| PaddlePaddle Fluid | 15.44 | 16.32 | 16.74 | 16.79 |
+| PaddlePaddle v2 | 15.97 | 17.04 | 17.60 | 17.83 |
 | TensorFlow | - | - | - | - |
 
 ### different batch size
 
 - PServer Count: 10
 - Trainer Count: 20
+- Per trainer CPU Core: 1
 - Metrics: samples / sec
 
 | Batch Size | 32 | 64 | 128 | 256 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | - | 247.40 | - | - |
-| PaddlePaddle v2 | - | - | 256.14 | - |
+| PaddlePaddle Fluid | 190.20 | 222.15 | 247.40 | 258.18 |
+| PaddlePaddle v2 | 170.96 | 233.71 | 256.14 | 329.23 |
 | TensorFlow | - | - | - | - |
 
-### different pserver number
 
-- Trainer Count: 100
-- Batch Size: 64
-- Metrics: mini-batch / sec
+### Accelerate rate
 
-| PServer Count | 10 | 20 | 40 | 60 |
+- Pserver Count: 20
+- Batch Size: 128
+- Metrics: samples / sec
+
+| Trainer Counter | 20 | 40 | 80 | 100 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | - | - | - | - |
-| PaddlePaddle v2 | - | - | - | - |
+| PaddlePaddle Fluid | 291.06 | 518.80 | 836.26 | 1019.29 |
+| PaddlePaddle v2 | 356.28 | - | - | 1041.99 |
 | TensorFlow | - | - | - | - |
 
-### Accelerate rate
+### different pserver number
 
-| Trainer Counter | 20 | 40 | 80 | 100 |
+- Trainer Count: 100
+- Batch Size: 128
+- Metrics: mini-batch / sec
+
+| PServer Count | 10 | 20 | 40 | 60 |
 | -- | -- | -- | -- | -- |
 | PaddlePaddle Fluid | - | - | - | - |
 | PaddlePaddle v2 | - | - | - | - |
diff --git a/benchmark/cluster/vgg16/fluid_trainer.yaml b/benchmark/cluster/vgg16/fluid_trainer.yaml
index 0a0ed25ebe..2f6a87ab02 100644
--- a/benchmark/cluster/vgg16/fluid_trainer.yaml
+++ b/benchmark/cluster/vgg16/fluid_trainer.yaml
@@ -30,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 128"
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 256"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/v2_trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
index 9d52e231f0..997bbc81c9 100644
--- a/benchmark/cluster/vgg16/v2_trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -22,7 +22,7 @@ spec:
         - name: PADDLE_JOB_NAME
           value: vgg16v2job
         - name: BATCH_SIZE
-          value: "128"
+          value: "256"
         - name: TRAINERS
           value: "20"
         - name: PSERVERS
diff --git a/benchmark/cluster/vgg16/vgg16_fluid.py b/benchmark/cluster/vgg16/vgg16_fluid.py
index 88d6d79cc0..51a01af672 100644
--- a/benchmark/cluster/vgg16/vgg16_fluid.py
+++ b/benchmark/cluster/vgg16/vgg16_fluid.py
@@ -20,6 +20,7 @@ import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.core as core
+import paddle.v2.fluid.profiler as profiler
 import argparse
 import functools
 import os
@@ -160,24 +161,25 @@ def main():
             start_time = time.time()
             num_samples = 0
             accuracy.reset(exe)
-            for batch_id, data in enumerate(train_reader()):
-                ts = time.time()
-                img_data = np.array(
-                    map(lambda x: x[0].reshape(data_shape), data)).astype(
-                        "float32")
-                y_data = np.array(map(lambda x: x[1], data)).astype("int64")
-                y_data = y_data.reshape([-1, 1])
-
-                loss, acc = exe.run(trainer_prog,
-                                    feed={"pixel": img_data,
-                                          "label": y_data},
-                                    fetch_list=[avg_cost] + accuracy.metrics)
-                iters += 1
-                num_samples += len(data)
-                print(
-                    "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
-                    % (pass_id, iters, loss, acc, time.time() - ts)
-                )  # The accuracy is the accumulation of batches, but not the current batch.
+            with profiler.profiler("CPU", 'total') as prof:
+                for batch_id, data in enumerate(train_reader()):
+                    ts = time.time()
+                    img_data = np.array(
+                        map(lambda x: x[0].reshape(data_shape), data)).astype(
+                            "float32")
+                    y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+                    y_data = y_data.reshape([-1, 1])
+
+                    loss, acc = exe.run(trainer_prog,
+                                        feed={"pixel": img_data,
+                                            "label": y_data},
+                                        fetch_list=[avg_cost] + accuracy.metrics)
+                    iters += 1
+                    num_samples += len(data)
+                    print(
+                        "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
+                        % (pass_id, iters, loss, acc, time.time() - ts)
+                    )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time
             pass_train_acc = accuracy.eval(exe)
@@ -211,6 +213,7 @@ def main():
         pserver_endpoints = ",".join(eplist)
         print("pserver endpoints: ", pserver_endpoints)
         trainers = int(os.getenv("TRAINERS"))  # total trainer count
+        print("trainers total: ", trainers)
         current_endpoint = os.getenv(
             "POD_IP") + ":6174"  # current pserver endpoint
         training_role = os.getenv(
diff --git a/benchmark/cluster/vgg16/vgg16_v2.py b/benchmark/cluster/vgg16/vgg16_v2.py
index 284dbec48d..81ddeb0332 100644
--- a/benchmark/cluster/vgg16/vgg16_v2.py
+++ b/benchmark/cluster/vgg16/vgg16_v2.py
@@ -26,6 +26,7 @@ if BATCH_SIZE:
     BATCH_SIZE = int(BATCH_SIZE)
 else:
     BATCH_SIZE = 128
+print "batch_size", BATCH_SIZE
 NODE_COUNT = int(os.getenv("TRAINERS"))
 ts = 0
 

From 912a4f2511ad118d7a989cbe4e7f634503670e34 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Mon, 29 Jan 2018 23:49:56 +0800
Subject: [PATCH 116/314] Add multi-class non-maximum suppression operator.

---
 paddle/operators/multiclass_nms_op.cc         | 353 ++++++++++++++++++
 .../v2/fluid/tests/test_bipartite_match_op.py |   2 +-
 .../v2/fluid/tests/test_multiclass_nms_op.py  | 199 ++++++++++
 3 files changed, 553 insertions(+), 1 deletion(-)
 create mode 100644 paddle/operators/multiclass_nms_op.cc
 create mode 100644 python/paddle/v2/fluid/tests/test_multiclass_nms_op.py

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
new file mode 100644
index 0000000000..19c5b7efd6
--- /dev/null
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -0,0 +1,353 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+constexpr int64_t kOutputDim = 6;
+constexpr int64_t kBBoxSize = 4;
+
+class MulticlassNMSOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Bboxes"),
+                   "Input(Bboxes) of MulticlassNMS should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Scores"),
+                   "Input(Scores) of MulticlassNMS should not be null.");
+
+    auto box_dims = ctx->GetInputDim("Bboxes");
+    auto score_dims = ctx->GetInputDim("Scores");
+
+    PADDLE_ENFORCE_EQ(box_dims.size(), 3,
+                      "The rank of Input(Bboxes) must be 3.");
+    PADDLE_ENFORCE_EQ(score_dims.size(), 3,
+                      "The rank of Input(Scores) must be 3.");
+    PADDLE_ENFORCE_EQ(box_dims[0], score_dims[0]);
+    PADDLE_ENFORCE_EQ(box_dims[2], 4);
+    PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2]);
+
+    // Here the box_dims[0] is not the real dimension of output.
+    // It will be rewritten in the computing kernel.
+    ctx->SetOutputDim("Out", {box_dims[0], 6});
+  }
+};
+
+template <class T>
+bool SortScorePairDescend(const std::pair<float, T>& pair1,
+                          const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+template <class T>
+static inline void GetMaxScoreIndex(
+    const std::vector<T>& scores, const T threshold, int top_k,
+    std::vector<std::pair<T, int>>* sorted_indices) {
+  for (size_t i = 0; i < scores.size(); ++i) {
+    if (scores[i] > threshold) {
+      sorted_indices->push_back(std::make_pair(scores[i], i));
+    }
+  }
+  // Sort the score pair according to the scores in descending order
+  std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
+                   SortScorePairDescend<int>);
+  // Keep top_k scores if needed.
+  if (top_k > -1 && top_k < sorted_indices->size()) {
+    sorted_indices->resize(top_k);
+  }
+}
+
+template <class T>
+T BBoxArea(const T* box, const bool normalized) {
+  if (box[2] < box[0] || box[3] < box[1]) {
+    // If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0.
+    return T(0.);
+  } else {
+    const T w = box[2] - box[0];
+    const T h = box[3] - box[1];
+    if (normalized) {
+      return w * h;
+    } else {
+      // If bbox is not within range [0, 1].
+      return (w + 1) * (h + 1);
+    }
+  }
+}
+
+template <class T>
+static inline T JaccardOverlap(const T* box1, const T* box2,
+                               const bool normalized) {
+  if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
+      box2[3] < box1[1]) {
+    return static_cast<T>(0.);
+  } else {
+    const T inter_xmin = std::max(box1[0], box2[0]);
+    const T inter_ymin = std::max(box1[1], box2[1]);
+    const T inter_xmax = std::min(box1[2], box2[2]);
+    const T inter_ymax = std::min(box1[3], box2[3]);
+    const T inter_w = inter_xmax - inter_xmin;
+    const T inter_h = inter_ymax - inter_ymin;
+    const T inter_area = inter_w * inter_h;
+    const T bbox1_area = BBoxArea<T>(box1, normalized);
+    const T bbox2_area = BBoxArea<T>(box2, normalized);
+    return inter_area / (bbox1_area + bbox2_area - inter_area);
+  }
+}
+
+template <typename T>
+class MulticlassNMSKernel : public framework::OpKernel<T> {
+ public:
+  void NMSFast(const Tensor& bbox, const Tensor& scores,
+               const T score_threshold, const T nms_threshold, const T eta,
+               const int64_t top_k, std::vector<int>* selected_indices) const {
+    // The total boxes for each instance.
+    int64_t num_boxes = bbox.dims()[0];
+    // 4: [xmin ymin xmax ymax]
+    int64_t box_size = bbox.dims()[1];
+
+    std::vector<T> scores_data(num_boxes);
+    std::copy_n(scores.data<T>(), num_boxes, scores_data.begin());
+    std::vector<std::pair<T, int>> sorted_indices;
+    GetMaxScoreIndex(scores_data, score_threshold, top_k, &sorted_indices);
+
+    selected_indices->clear();
+    T adaptive_threshold = nms_threshold;
+    const T* bbox_data = bbox.data<T>();
+
+    while (sorted_indices.size() != 0) {
+      const int idx = sorted_indices.front().second;
+      bool keep = true;
+      for (int k = 0; k < selected_indices->size(); ++k) {
+        if (keep) {
+          const int kept_idx = (*selected_indices)[k];
+          T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
+                                        bbox_data + kept_idx * box_size, true);
+          keep = overlap <= adaptive_threshold;
+        } else {
+          break;
+        }
+      }
+      if (keep) {
+        selected_indices->push_back(idx);
+      }
+      sorted_indices.erase(sorted_indices.begin());
+      if (keep && eta < 1 && adaptive_threshold > 0.5) {
+        adaptive_threshold *= eta;
+      }
+    }
+  }
+
+  void MulticlassNMS(const framework::ExecutionContext& ctx,
+                     const Tensor& scores, const Tensor& bboxes,
+                     std::map<int, std::vector<int>>* indices,
+                     int* num_nmsed_out) const {
+    int64_t background_label = ctx.Attr<int64_t>("background_label");
+    int64_t nms_top_k = ctx.Attr<int64_t>("nms_top_k");
+    int64_t keep_top_k = ctx.Attr<int64_t>("keep_top_k");
+    T nms_threshold = static_cast<T>(ctx.Attr<float>("nms_threshold"));
+    T nms_eta = static_cast<T>(ctx.Attr<float>("nms_eta"));
+    T score_threshold = static_cast<T>(ctx.Attr<float>("confidence_threshold"));
+
+    int64_t class_num = scores.dims()[0];
+    int64_t predict_dim = scores.dims()[1];
+    int num_det = 0;
+    for (int64_t c = 0; c < class_num; ++c) {
+      if (c == background_label) continue;
+      Tensor score = scores.Slice(c, c + 1);
+      NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
+              &((*indices)[c]));
+      num_det += indices[c].size();
+    }
+
+    *num_nmsed_out = num_det;
+    const T* scores_data = scores.data<T>();
+    if (keep_top_k > -1 && num_det > keep_top_k) {
+      std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
+      for (const auto& it : *indices) {
+        int label = it.first;
+        const T* sdata = scores_data + label * predict_dim;
+        const std::vector<int>& label_indices = it.second;
+        for (int j = 0; j < label_indices.size(); ++j) {
+          int idx = label_indices[j];
+          PADDLE_ENFORCE_LT(idx, predict_dim);
+          score_index_pairs.push_back(
+              std::make_pair(sdata[idx], std::make_pair(label, idx)));
+        }
+      }
+      // Keep top k results per image.
+      std::sort(score_index_pairs.begin(), score_index_pairs.end(),
+                SortScorePairDescend<std::pair<int, int>>);
+      score_index_pairs.resize(keep_top_k);
+
+      // Store the new indices.
+      std::map<int, std::vector<int>> new_indices;
+      for (int j = 0; j < score_index_pairs.size(); ++j) {
+        int label = score_index_pairs[j].second.first;
+        int idx = score_index_pairs[j].second.second;
+        new_indices[label].push_back(idx);
+      }
+      new_indices.swap(*indices);
+      *num_nmsed_out = keep_top_k;
+    }
+  }
+
+  void MulticlassOutput(const Tensor& scores, const Tensor& bboxes,
+                        std::map<int, std::vector<int>>& selected_indices,
+                        Tensor* outs) const {
+    int predict_dim = scores.dims()[1];
+    auto* scores_data = scores.data<T>();
+    auto* bboxes_data = bboxes.data<T>();
+    auto* odata = outs->data<T>();
+
+    int count = 0;
+    for (const auto& it : selected_indices) {
+      int label = it.first;
+      const T* sdata = scores_data + label * predict_dim;
+      std::vector<int> indices = it.second;
+      for (int j = 0; j < indices.size(); ++j) {
+        int idx = indices[j];
+        const T* bdata = bboxes_data + idx * kBBoxSize;
+        odata[count * kOutputDim] = label;           // label
+        odata[count * kOutputDim + 1] = sdata[idx];  // score
+        odata[count * kOutputDim + 2] = bdata[0];    // xmin
+        odata[count * kOutputDim + 3] = bdata[1];    // ymin
+        odata[count * kOutputDim + 4] = bdata[2];    // xmax
+        odata[count * kOutputDim + 5] = bdata[3];    // ymax
+      }
+      count++;
+    }
+  }
+
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* boxes = ctx.Input<Tensor>("Bboxes");
+    auto* scores = ctx.Input<Tensor>("Scores");
+    auto* outs = ctx.Output<LoDTensor>("Out");
+
+    auto box_dims = boxes->dims();
+    auto score_dims = scores->dims();
+
+    int64_t batch_size = box_dims[0];
+    int64_t class_num = score_dims[1];
+    int64_t predict_dim = score_dims[2];
+
+    std::vector<std::map<int, std::vector<int>>> all_indices;
+    std::vector<size_t> batch_starts = {0};
+    for (int64_t i = 0; i < batch_size; ++i) {
+      Tensor ins_score = scores->Slice(i, i + 1);
+      ins_score.Resize({class_num, predict_dim});
+      std::map<int, std::vector<int>> indices;
+      int num_nmsed_out = 0;
+      MulticlassNMS(ctx, ins_score, *boxes, &indices, &num_nmsed_out);
+      all_indices.push_back(indices);
+      batch_starts.push_back(batch_starts.back() + num_nmsed_out);
+    }
+
+    int num_kept = batch_starts.back();
+    if (num_kept == 0) {
+      outs->Resize({0, 0});
+    } else {
+      outs->mutable_data<T>({num_kept, kOutputDim}, ctx.GetPlace());
+      for (int64_t i = 0; i < batch_size; ++i) {
+        Tensor ins_score = scores->Slice(i, i + 1);
+        ins_score.Resize({class_num, predict_dim});
+        int64_t s = batch_starts[i];
+        int64_t e = batch_starts[i + 1];
+        if (e > s) {
+          Tensor out = outs->Slice(s, e);
+          MulticlassOutput(ins_score, *boxes, all_indices[i], &out);
+        }
+      }
+    }
+
+    framework::LoD lod;
+    lod.emplace_back(batch_starts);
+
+    outs->set_lod(lod);
+  }
+};
+
+class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MulticlassNMSOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Bboxes",
+             "(Tensor) A 2-D Tensor with shape [M, 4] represents the location "
+             "predictions with M bboxes. 4 is the number of "
+             "each location coordinates.");
+    AddOutput("Scores",
+              "(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
+              "confidence predictions. N is the batch size, C is the class "
+              "number, M is number of predictions for each class, which is "
+              "the same with Bboxes.");
+    AddAttr<int64_t>(
+        "background_label",
+        "(int64_t, defalut: 0) "
+        "The index of background label, the background label will be ignored.")
+        .SetDefault(0);
+    AddAttr<float>("nms_threshold",
+                   "(float, defalut: 0.3) "
+                   "The threshold to be used in nms.")
+        .SetDefault(0.3);
+    AddAttr<int64_t>("nms_top_k",
+                     "(int64_t) "
+                     " .");
+    AddAttr<float>("nms_eta",
+                   "(float) "
+                   "The parameter for adaptive nms.")
+        .SetDefault(1.0);
+    AddAttr<int64_t>("keep_top_k",
+                     "(int64_t) "
+                     ".");
+    AddAttr<float>("confidence_threshold",
+                   "(float) "
+                   ".");
+    AddOutput("Out",
+              "(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
+              "detections. Each row has 6 values: "
+              "[label, confidence, xmin, ymin, xmax, ymax], No is the total "
+              "number of detections in this mini-batch. For each instance, "
+              "the offsets in first dimension are called LoD, the number of "
+              "offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
+              "no detected bbox.");
+    AddComment(R"DOC(
+This operators is to do multi-class non maximum suppression (nms) on a batched
+of boxes and scores.
+
+This op greedily selects a subset of detection bounding boxes, pruning
+away boxes that have high IOU (intersection over union) overlap (> thresh)
+with already selected boxes.  It operates independently for each class for
+which scores are provided (via the scores field of the input box_list),
+pruning boxes with score less than a provided threshold prior to
+applying NMS.
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(multiclass_nms, ops::MulticlassNMSOp,
+                  ops::MulticlassNMSOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(multiclass_nms, ops::MulticlassNMSKernel<float>,
+                       ops::MulticlassNMSKernel<double>);
diff --git a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
index 7413829897..c35fb20b10 100644
--- a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
+++ b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
@@ -62,7 +62,7 @@ def batch_bipartite_match(distance, lod):
     return match_indices, match_dist
 
 
-class TestBipartiteMatchOpForWithLoD(OpTest):
+class TestBipartiteMatchOpWithLoD(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
         lod = [[0, 5, 11, 23]]
diff --git a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
new file mode 100644
index 0000000000..60c6488f84
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
@@ -0,0 +1,199 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#Licensed under the Apache License, Version 2.0 (the "License");
+#you may not use this file except in compliance with the License.
+#You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+#Unless required by applicable law or agreed to in writing, software
+#distributed under the License is distributed on an "AS IS" BASIS,
+#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#See the License for the specific language governing permissions and
+#limitations under the License.
+import unittest
+import numpy as np
+import copy
+from op_test import OpTest
+
+
+def iou(box_a, box_b):
+    """Apply intersection-over-union overlap between box_a and box_b
+    """
+    xmin_a = min(box_a[0], box_a[2])
+    ymin_a = min(box_a[1], box_a[3])
+    xmax_a = max(box_a[0], box_a[2])
+    ymax_a = max(box_a[1], box_a[3])
+
+    xmin_b = min(box_b[0], box_b[2])
+    ymin_b = min(box_b[1], box_b[3])
+    xmax_b = max(box_b[0], box_b[2])
+    ymax_b = max(box_b[1], box_b[3])
+
+    area_a = (ymax_a - ymin_a) * (xmax_a - xmin_a)
+    area_b = (ymax_b - ymin_b) * (xmax_b - xmin_b)
+    if area_a <= 0 and area_b <= 0:
+        return 0.0
+
+    xa = max(xmin_a, xmin_b)
+    ya = max(ymin_a, ymin_b)
+    xb = min(xmax_a, xmax_b)
+    yb = min(ymax_a, ymax_b)
+
+    inter_area = max(xb - xa, 0.0) * max(yb - ya, 0.0)
+
+    box_a_area = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1])
+    box_b_area = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1])
+
+    iou_ratio = inter_area / (area_a + area_b - inter_area)
+
+    return iou_ratio
+
+
+def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
+    """Apply non-maximum suppression at test time to avoid detecting too many
+    overlapping bounding boxes for a given object.
+    Args:
+        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+        scores: (tensor) The class predscores for the img, Shape:[num_priors].
+        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+        top_k: (int) The Maximum number of box preds to consider.
+    Return:
+        The indices of the kept boxes with respect to num_priors.
+    """
+    all_scores = copy.deepcopy(scores)
+    all_scores = all_scores.flatten()
+    selected_indices = np.argwhere(all_scores > score_threshold)
+    selected_indices = selected_indices.flatten()
+    all_scores = all_scores[selected_indices]
+
+    sorted_indices = np.argsort(-all_scores, axis=0)
+    sorted_scores = all_scores[sorted_indices]
+    if top_k < -1 and top_k < sorted_indices.shape[0]:
+        sorted_indices = sorted_indices[:top_k]
+        sorted_scores = sorted_scores[:top_k]
+
+    selected_indices = []
+    adaptive_threshold = nms_threshold
+    for i in range(sorted_scores.shape[0]):
+        idx = sorted_indices[i]
+        keep = True
+        for k in range(len(selected_indices)):
+            if keep:
+                kept_idx = selected_indices[k]
+                overlap = iou(boxes[idx], boxes[kept_idx])
+                keep = overlap <= adaptive_threshold
+            else:
+                break
+        if keep:
+            selected_indices.append(idx)
+        if keep and eta < 1 and adaptive_threshold > 0.5:
+            adaptive_threshold *= eta
+    return selected_indices
+
+
+def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
+                   nms_top_k, keep_top_k):
+    class_num = scores.shape[0]
+    priorbox_num = scores.shape[1]
+
+    selected_indices = []
+    num_det = 0
+    for c in range(class_num):
+        if c == background: continue
+        indices = nms(boxes, scores[c], score_threshold, nms_threshold,
+                      nms_top_k)
+        selected_indices.append((c, indices))
+        num_det += len(indices)
+
+    if keep_top_k > -1 and num_det > keep_top_k:
+        score_index = []
+        for c, indices in selected_indices:
+            for idx in indices:
+                score_index.append((scores[c][idx], c, idx))
+
+        sorted_score_index = sorted(
+            score_index, key=lambda tup: tup[0], reverse=True)
+        sorted_score_index = sorted_score_index[:keep_top_k]
+        selected_indices = []
+        for s, c, idx in sorted_score_index:
+            selected_indices.append((c, idx))
+
+    return selected_indices
+
+
+def batched_multiclass_nms(boxes, scores, background, score_threshold,
+                           nms_threshold, nms_top_k, keep_top_k):
+    batch_size = scores.shape[0]
+
+    det_outs = []
+    lod = [0]
+    for n in range(batch_size):
+        nmsed_outs = multiclass_nms(boxes, scores[n], background,
+                                    score_threshold, nms_threshold, nms_top_k,
+                                    keep_top_k)
+        lod.append(lod[-1] + len(nmsed_outs))
+        if len(nmsed_outs) == 0: continue
+        for c, indices in nmsed_outs:
+            for idx in indices:
+                xmin, ymin, xmax, ymax = boxes[idx][:]
+                det_outs.append(
+                    (c, scores[n][c][idx], c, xmin, ymin, xmax, ymax))
+    return det_outs, lod
+
+
+class TestMulticlassNMSOp(OpTest):
+    def setUp(self):
+        self.op_type = 'multiclass_nms'
+        N = 7
+        M = 1230
+        C = 21
+        BOX_SIZE = 4
+        background = 0
+        nms_threshold = 0.3
+        nms_top_k = 400
+        keep_top_k = 200
+        score_threshold = 0.01
+
+        scores = np.random.random((N, C, M)).astype('float32')
+        boxes = np.random.random((M, BOX_SIZE)).astype('float32')
+        boxes[:, 0:2] = boxes[:, 0:2] * 0.5
+        boxes[:, 2:4] = boxes[:, 0:2] * 0.5 + 0.5
+
+        nmsed_outs, lod = batched_multiclass_nms(boxes, scores, background,
+                                                 score_threshold, nms_threshold,
+                                                 nms_top_k, keep_top_k)
+        self.inputs = {'Bboxes': boxes, 'Scores': scores}
+        self.outputs = {'Out': (nmsed_outs, [lod])}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestIOU(unittest.TestCase):
+    def test_iou(self):
+        box1 = np.array([4.0, 3.0, 7.0, 5.0]).astype('float32')
+        box2 = np.array([3.0, 4.0, 6.0, 8.0]).astype('float32')
+
+        expt_output = np.array([2.0 / 16.0]).astype('float32')
+        calc_output = np.array([iou(box1, box2)]).astype('float32')
+        self.assertTrue(np.allclose(calc_output, expt_output))
+
+
+if __name__ == '__main__':
+    unittest.main()
+    # N = 7
+    # M = 8
+    # C = 5
+    # BOX_SIZE = 4
+    # background = 0
+    # nms_threshold = 0.3
+    # nms_top_k = 400
+    # keep_top_k = 200
+    # score_threshold = 0.5
+
+    # scores = np.random.random((N, C, M)).astype('float32')
+    # boxes = np.random.random((M, BOX_SIZE)).astype('float32')
+    # boxes[:, 0 : 2] = boxes[:, 0 : 2] * 0.5
+    # boxes[:, 2 : 4] = boxes[:, 0 : 2] * 0.5 + 0.5
+    # print nmsed_outs, lod

From 2731fd96606b18411b485269e36fd44ae8909650 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Tue, 30 Jan 2018 00:19:28 +0800
Subject: [PATCH 117/314] Update doc for multiclass_nms_op.

---
 paddle/operators/multiclass_nms_op.cc | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index 19c5b7efd6..5da553a6cc 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -37,13 +37,12 @@ class MulticlassNMSOp : public framework::OperatorWithKernel {
     auto box_dims = ctx->GetInputDim("Bboxes");
     auto score_dims = ctx->GetInputDim("Scores");
 
-    PADDLE_ENFORCE_EQ(box_dims.size(), 3,
+    PADDLE_ENFORCE_EQ(box_dims.size(), 2,
                       "The rank of Input(Bboxes) must be 3.");
     PADDLE_ENFORCE_EQ(score_dims.size(), 3,
                       "The rank of Input(Scores) must be 3.");
-    PADDLE_ENFORCE_EQ(box_dims[0], score_dims[0]);
     PADDLE_ENFORCE_EQ(box_dims[2], 4);
-    PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2]);
+    PADDLE_ENFORCE_EQ(box_dims[0], score_dims[2]);
 
     // Here the box_dims[0] is not the real dimension of output.
     // It will be rewritten in the computing kernel.
@@ -308,17 +307,19 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(0.3);
     AddAttr<int64_t>("nms_top_k",
                      "(int64_t) "
-                     " .");
+                     "Maximum number of results to be kept.");
     AddAttr<float>("nms_eta",
                    "(float) "
                    "The parameter for adaptive nms.")
         .SetDefault(1.0);
     AddAttr<int64_t>("keep_top_k",
                      "(int64_t) "
-                     ".");
+                     "Number of total bboxes to be kept per image after nms "
+                     "step. -1 means keeping all bboxes after nms step.");
     AddAttr<float>("confidence_threshold",
                    "(float) "
-                   ".");
+                   "Only consider detections whose confidences are larger than "
+                   "a threshold. If not provided, consider all boxes.");
     AddOutput("Out",
               "(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
               "detections. Each row has 6 values: "
@@ -328,15 +329,14 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
               "offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
               "no detected bbox.");
     AddComment(R"DOC(
-This operators is to do multi-class non maximum suppression (nms) on a batched
+This operators is to do multi-class non maximum suppression (NMS) on a batched
 of boxes and scores.
 
 This op greedily selects a subset of detection bounding boxes, pruning
 away boxes that have high IOU (intersection over union) overlap (> thresh)
 with already selected boxes.  It operates independently for each class for
-which scores are provided (via the scores field of the input box_list),
-pruning boxes with score less than a provided threshold prior to
-applying NMS.
+which scores are provided, pruning boxes with score less than a provided
+threshold prior to applying NMS.
 
 )DOC");
   }

From 970147505a393d5739e44bd6913e64b07b1ec1da Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Mon, 29 Jan 2018 18:04:44 -0800
Subject: [PATCH 118/314] Correct deps of threadpool (#7955)

* refine channel test

* follow comments

* Add dependency enforce to threadpool

* Revert changes to channel_test.cc

* Revert changes to channel_test.cc

* Add #include "paddle/framework/macros.h"
---
 paddle/framework/CMakeLists.txt | 2 +-
 paddle/framework/threadpool.cc  | 2 ++
 paddle/framework/threadpool.h   | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 318661af8b..8c28709a68 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -26,7 +26,7 @@ nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
 
 cc_test(variable_test SRCS variable_test.cc)
 
-cc_library(threadpool SRCS threadpool.cc)
+cc_library(threadpool SRCS threadpool.cc DEPS enforce)
 cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
 
 cc_library(scope SRCS scope.cc DEPS glog threadpool)
diff --git a/paddle/framework/threadpool.cc b/paddle/framework/threadpool.cc
index b2f5ae4a96..b7d7c00bcf 100644
--- a/paddle/framework/threadpool.cc
+++ b/paddle/framework/threadpool.cc
@@ -14,6 +14,8 @@
 
 #include "paddle/framework/threadpool.h"
 
+#include "paddle/platform/enforce.h"
+
 namespace paddle {
 namespace framework {
 
diff --git a/paddle/framework/threadpool.h b/paddle/framework/threadpool.h
index 8912b1a43a..4e9b58679d 100644
--- a/paddle/framework/threadpool.h
+++ b/paddle/framework/threadpool.h
@@ -22,7 +22,7 @@ limitations under the License. */
 #include <thread>
 #include <vector>
 
-#include "paddle/platform/enforce.h"
+#include "paddle/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
 
 namespace paddle {
 namespace framework {

From 6ac2e079b37d0b3fd7165362f1749437aea8df5a Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Mon, 29 Jan 2018 09:53:19 +0000
Subject: [PATCH 119/314] Enable whole-archive flag in cc_test and use cc_test
 to rewrite the CMakeLists.txt of inference unittest.

---
 cmake/generic.cmake                             | 14 ++++++++++----
 paddle/inference/tests/book/CMakeLists.txt      | 17 ++++-------------
 .../book/test_inference_recognize_digits.cc     | 15 +++++----------
 paddle/testing/paddle_gtest_main.cc             |  4 +++-
 .../fluid/tests/book/test_recognize_digits.py   |  6 ++----
 5 files changed, 24 insertions(+), 32 deletions(-)

diff --git a/cmake/generic.cmake b/cmake/generic.cmake
index 585db019d5..18770fe286 100644
--- a/cmake/generic.cmake
+++ b/cmake/generic.cmake
@@ -224,12 +224,18 @@ function(cc_test TARGET_NAME)
   if(WITH_TESTING)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS)
+    set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_executable(${TARGET_NAME} ${cc_test_SRCS})
-    target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    # Support linking flags: --whole-archive (Linux) / -force_load (MacOS)
+    target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    if("${cc_test_DEPS}" MATCHES "ARCHIVE_START")
+      list(REMOVE_ITEM cc_test_DEPS ARCHIVE_START ARCHIVE_END)
+    endif()
     add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
-    add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME} WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+    add_test(NAME ${TARGET_NAME}
+             COMMAND ${TARGET_NAME} ${cc_test_ARGS}
+             WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
   endif()
 endfunction(cc_test)
 
@@ -457,7 +463,7 @@ endfunction()
 
 function(py_test TARGET_NAME)
   if(WITH_TESTING)
-    set(options STATIC static SHARED shared)
+    set(options "")
     set(oneValueArgs "")
     set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index 78083cc218..d3798fb8fd 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -1,16 +1,7 @@
 set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests)
-add_executable(test_inference_recognize_digits test_inference_recognize_digits.cc)
-target_circle_link_libraries(
-    test_inference_recognize_digits
-    ARCHIVE_START
-    paddle_fluid
-    ARCHIVE_END
-    gtest
-    gflags)
-add_test(
-    NAME test_inference_recognize_digits_mlp
-    COMMAND test_inference_recognize_digits
-            --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model
-    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+cc_test(test_inference_recognize_digits_mlp
+    SRCS test_inference_recognize_digits.cc
+    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+    ARGS --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model)
 set_tests_properties(test_inference_recognize_digits_mlp
     PROPERTIES DEPENDS test_recognize_digits_mlp_cpu)
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index de15167ac3..45fbfe27a7 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -66,9 +66,10 @@ TEST(inference, recognize_digits) {
   }
 
   LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
+  std::string dirname = FLAGS_dirname;
 
-  // 0. Initialize all the devices
-  paddle::framework::InitDevices();
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
 
   paddle::framework::LoDTensor input;
   srand(time(0));
@@ -86,7 +87,7 @@ TEST(inference, recognize_digits) {
 
   // Run inference on CPU
   TestInference<paddle::platform::CPUPlace, float>(
-      FLAGS_dirname, cpu_feeds, cpu_fetchs1);
+      dirname, cpu_feeds, cpu_fetchs1);
   LOG(INFO) << output1.dims();
 
 #ifdef PADDLE_WITH_CUDA
@@ -96,7 +97,7 @@ TEST(inference, recognize_digits) {
 
   // Run inference on CUDA GPU
   TestInference<paddle::platform::CUDAPlace, float>(
-      FLAGS_dirname, cpu_feeds, cpu_fetchs2);
+      dirname, cpu_feeds, cpu_fetchs2);
   LOG(INFO) << output2.dims();
 
   EXPECT_EQ(output1.dims(), output2.dims());
@@ -112,9 +113,3 @@ TEST(inference, recognize_digits) {
   EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
 #endif
 }
-
-int main(int argc, char** argv) {
-  google::ParseCommandLineFlags(&argc, &argv, false);
-  testing::InitGoogleTest(&argc, argv);
-  return RUN_ALL_TESTS();
-}
diff --git a/paddle/testing/paddle_gtest_main.cc b/paddle/testing/paddle_gtest_main.cc
index a7fb50ee41..a2f21e37e4 100644
--- a/paddle/testing/paddle_gtest_main.cc
+++ b/paddle/testing/paddle_gtest_main.cc
@@ -22,7 +22,9 @@ limitations under the License. */
 int main(int argc, char** argv) {
   std::vector<char*> new_argv;
   std::string gflags_env;
-  new_argv.push_back(argv[0]);
+  for (int i = 0; i < argc; ++i) {
+    new_argv.push_back(argv[i]);
+  }
 #ifdef PADDLE_WITH_CUDA
   new_argv.push_back(
       strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"));
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index d6e4675a24..b4b6020f58 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -163,10 +163,8 @@ def infer(args, save_dirname=None):
     [inference_program, feed_target_names,
      fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
-    if args.nn_type == 'mlp':
-        tensor_img = numpy.random.rand(1, 28, 28).astype("float32")
-    else:
-        tensor_img = numpy.random.rand(1, 1, 28, 28).astype("float32")
+    # The input's dimension of conv should be 4-D or 5-D.
+    tensor_img = numpy.random.rand(1, 1, 28, 28).astype("float32")
 
     # Construct feed as a dictionary of {feed_target_name: feed_target_data}
     # and results will contain a list of data corresponding to fetch_targets.

From fcff9758ed7f15bc832fa505557bbf94974f687c Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Mon, 29 Jan 2018 18:12:49 -0800
Subject: [PATCH 120/314] Add label smooth operator

---
 paddle/operators/label_smooth_op.cc           | 85 +++++++++++++++++++
 paddle/operators/label_smooth_op.cu           | 26 ++++++
 paddle/operators/label_smooth_op.h            | 58 +++++++++++++
 .../v2/fluid/tests/test_label_smooth_op.py    | 41 +++++++++
 4 files changed, 210 insertions(+)
 create mode 100644 paddle/operators/label_smooth_op.cc
 create mode 100644 paddle/operators/label_smooth_op.cu
 create mode 100644 paddle/operators/label_smooth_op.h
 create mode 100644 python/paddle/v2/fluid/tests/test_label_smooth_op.py

diff --git a/paddle/operators/label_smooth_op.cc b/paddle/operators/label_smooth_op.cc
new file mode 100644
index 0000000000..99a0a005a1
--- /dev/null
+++ b/paddle/operators/label_smooth_op.cc
@@ -0,0 +1,85 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/label_smooth_op.h"
+
+namespace paddle {
+namespace operators {
+
+class LabelSmoothOp : public framework::OperatorWithKernel {
+ public:
+  LabelSmoothOp(const std::string &type,
+                const framework::VariableNameMap &inputs,
+                const framework::VariableNameMap &outputs,
+                const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LabelSmoothOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of LabelSmoothOp should not be null.");
+    auto in_dims = ctx->GetInputDim("X");
+    ctx->ShareLoD("X", /*->*/ "Out");
+    ctx->SetOutputDim("Out", in_dims);
+  }
+};
+
+class LabelSmoothOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LabelSmoothOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "The input label of LabelSmooth operator.");
+    AddOutput("Out", "The smoothed label of LabelSmooth operator.");
+    AddAttr<float>("epsilon",
+                   "(float, default 0.0f)"
+                   "The smoothing parameter of LabelSmooth operator.")
+        .SetDefault(0.0f);
+    AddComment(R"DOC(
+LabelSmooth Operator.
+
+)DOC");
+  }
+};
+
+class LabelSmoothGradOp : public framework::OperatorWithKernel {
+ public:
+  LabelSmoothGradOp(const std::string &type,
+                    const framework::VariableNameMap &inputs,
+                    const framework::VariableNameMap &outputs,
+                    const framework::AttributeMap &attrs)
+      : OperatorWithKernel(type, inputs, outputs, attrs) {}
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) shouldn't be null.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OP(label_smooth, ops::LabelSmoothOp, ops::LabelSmoothOpMaker,
+            label_smooth_grad, ops::LabelSmoothGradOp);
+REGISTER_OP_CPU_KERNEL(
+    label_smooth,
+    ops::LabelSmoothKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LabelSmoothKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    label_smooth_grad,
+    ops::LabelSmoothGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LabelSmoothGradKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/label_smooth_op.cu b/paddle/operators/label_smooth_op.cu
new file mode 100644
index 0000000000..5a0cec12bc
--- /dev/null
+++ b/paddle/operators/label_smooth_op.cu
@@ -0,0 +1,26 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/label_smooth_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    label_smooth,
+    ops::LabelSmoothKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LabelSmoothKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    label_smooth_grad,
+    ops::LabelSmoothGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LabelSmoothGradKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/label_smooth_op.h b/paddle/operators/label_smooth_op.h
new file mode 100644
index 0000000000..d94ff43d5a
--- /dev/null
+++ b/paddle/operators/label_smooth_op.h
@@ -0,0 +1,58 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class LabelSmoothKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* out_t = ctx.Output<framework::LoDTensor>("Out");
+    auto* in_t = ctx.Input<framework::LoDTensor>("X");
+    auto label_dim = in_t->dims()[1];
+    out_t->mutable_data<T>(ctx.GetPlace());
+
+    auto epsilon = ctx.Attr<float>("epsilon");
+    auto out = framework::EigenVector<T>::Flatten(*out_t);
+    auto in = framework::EigenVector<T>::Flatten(*in_t);
+    auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
+    out.device(dev) =
+        static_cast<T>(1 - epsilon) * in + static_cast<T>(epsilon / label_dim);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class LabelSmoothGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* d_in_t = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    d_in_t->mutable_data<T>(ctx.GetPlace());
+
+    auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
+    auto d_in = framework::EigenVector<T>::Flatten(*d_in_t);
+
+    auto epsilon = ctx.Attr<float>("epsilon");
+    auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
+    d_in.device(dev) = static_cast<T>(1 - epsilon) * d_out;
+  }
+};
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_label_smooth_op.py b/python/paddle/v2/fluid/tests/test_label_smooth_op.py
new file mode 100644
index 0000000000..d156e2c35f
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_label_smooth_op.py
@@ -0,0 +1,41 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestLabelSmoothOp(OpTest):
+    def setUp(self):
+        self.op_type = "label_smooth"
+        epsilon = 0.1
+        batch_size, label_dim = 5, 10
+        label = np.zeros((batch_size, label_dim)).astype("float64")
+        nonzero_index = np.random.randint(label_dim, size=(batch_size))
+        label[np.arange(batch_size), nonzero_index] = 1
+        smoothed_label = (1 - epsilon) * label + epsilon / label_dim
+        self.inputs = {'X': label}
+        self.attrs = {'epsilon': epsilon}
+        self.outputs = {'Out': smoothed_label}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+
+if __name__ == '__main__':
+    unittest.main()

From 87b5559cd15a28d515b16f3ad04ca9919c7edd32 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 29 Jan 2018 20:41:08 +0800
Subject: [PATCH 121/314] fix scale and bias dim

---
 paddle/operators/layer_norm_op.cc             | 84 +++++++++----------
 .../v2/fluid/tests/test_layer_norm_op.py      | 16 ++--
 2 files changed, 52 insertions(+), 48 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 9e618d10d2..07ca8ac222 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -38,10 +38,6 @@ class LayerNormOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("Bias"), "");
     PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
 
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], 1);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], 1);
     auto x_dim = ctx->GetInputDim("X");
     auto begin_norm_axis = ctx->Attrs().Get<int>("begin_norm_axis");
     PADDLE_ENFORCE_LT(begin_norm_axis, x_dim.size(),
@@ -50,6 +46,11 @@ class LayerNormOp : public framework::OperatorWithKernel {
     auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
 
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], left);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], left);
+
     ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
     ctx->SetOutputDim("Mean", {left});
     ctx->SetOutputDim("Variance", {left});
@@ -64,10 +65,10 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The input tensor");
     AddInput("Scale",
-             "Scale is a 1-dimensional tensor of size 1 "
+             "Scale is a 1-dimensional tensor of size H "
              "that is applied to the output");
     AddInput("Bias",
-             "Bias is a 1-dimensional tensor of size 1 "
+             "Bias is a 1-dimensional tensor of size H "
              "that is applied to the output");
     AddOutput("Y", "result after normalization");
     AddOutput("Mean", "Mean of the current mini batch.");
@@ -110,9 +111,6 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     const auto &x_dims = x->dims();
     const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
 
-    auto scale_data = scale->data<T>()[0];
-    auto bias_data = bias->data<T>()[0];
-
     auto *output = ctx.Output<Tensor>("Y");
     auto *mean = ctx.Output<Tensor>("Mean");
     auto *var = ctx.Output<Tensor>("Variance");
@@ -123,7 +121,10 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
     int right = static_cast<int>(matrix_dim[1]);
+
     auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
+    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), left, 1);
+    auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), left, 1);
     auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
     auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
     auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
@@ -138,18 +139,15 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
                   .mean()
                   .unaryExpr(add_epslion);
 
-    auto scale_inv_std = [scale_data](T ele) {
-      return std::sqrt(1 / ele) * scale_data;
-    };
-    auto sub_bias = [bias_data](T ele) { return bias_data - ele; };
+    auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+
     // TODO(zcd): Some thinking about output_map, is it appropriate that
     // `output_map` and `input_map` point to the same memory.
-    output_map = (var_map.unaryExpr(scale_inv_std).replicate(1, right))
-                     .cwiseProduct(input_map) +
-                 var_map.unaryExpr(scale_inv_std)
-                     .cwiseProduct(mean_map)
-                     .unaryExpr(sub_bias)
-                     .replicate(1, right);
+    auto inv_std_scale =
+        var_map.unaryExpr(inv_std_func).cwiseProduct(scale_map);
+    output_map =
+        inv_std_scale.replicate(1, right).cwiseProduct(input_map) +
+        (bias_map - inv_std_scale.cwiseProduct(mean_map)).replicate(1, right);
   }
 };
 
@@ -165,17 +163,17 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("Variance"), "");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "");
 
-    const auto x_dims = ctx->GetInputDim("X");
-
     // check output
     if (ctx->HasOutput(framework::GradVarName("X"))) {
-      ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
+      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
     }
     if (ctx->HasOutput(framework::GradVarName("Scale"))) {
-      ctx->SetOutputDim(framework::GradVarName("Scale"), {1});
+      ctx->SetOutputDim(framework::GradVarName("Scale"),
+                        ctx->GetInputDim("Scale"));
     }
     if (ctx->HasOutput(framework::GradVarName("Bias"))) {
-      ctx->SetOutputDim(framework::GradVarName("Bias"), {1});
+      ctx->SetOutputDim(framework::GradVarName("Bias"),
+                        ctx->GetInputDim("Bias"));
     }
   }
 
@@ -210,20 +208,20 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     const auto *var = ctx.Input<Tensor>("Variance");
     const auto *scale = ctx.Input<Tensor>("Scale");
     const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
-    auto scale_data = scale->data<T>()[0];
 
     const auto &x_dims = x->dims();
 
     const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
     auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]),
-        right = static_cast<int>(matrix_dim[1]);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
 
     // init output
     auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
     auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
+    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), left, 1);
     auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
     auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
     auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
@@ -231,36 +229,38 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
 
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
-      d_bias->data<T>()[0] = d_y_map.sum();
+      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), left, 1);
+      d_bias_map = d_y_map.colwise().mean();
     }
     if (d_scale) {
       d_scale->mutable_data<T>(ctx.GetPlace());
-      auto inv_std = [](T ele) { return std::sqrt(1 / ele); };
+      auto d_scale_map = EigenMatrixMapRowMajor<T>(d_scale->data<T>(), left, 1);
+      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
       // There are two equation to compute d_scale. One uses "Y" and the other
       // does not use "Y"
-      d_scale->data<T>()[0] =
+      d_scale_map =
           ((x_map - mean_map.replicate(1, right))
-               .cwiseProduct(var_map.unaryExpr(inv_std).replicate(1, right))
+               .cwiseProduct(
+                   var_map.unaryExpr(inv_std_func).replicate(1, right))
                .cwiseProduct(d_y_map))
-              .sum();
+              .colwise()
+              .mean();
     }
 
     if (d_x) {
       d_x->mutable_data<T>(ctx.GetPlace());
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
       auto triple_product_func = [](T ele) { return ele * ele * ele; };
-      auto scale_func = [scale_data](T ele) { return ele * scale_data; };
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-      auto inv_std_scale_func = [scale_data](T ele) {
-        return std::sqrt(1 / ele) * scale_data;
-      };
       // dy_dx
-      auto dx_end = var_map.unaryExpr(inv_std_scale_func)
+      auto dx_end = var_map.unaryExpr(inv_std_func)
+                        .cwiseProduct(scale_map)
                         .replicate(1, right)
                         .cwiseProduct(d_y_map);
       // dy_dmean_dx
       auto dx_mean = (T(-1.0) / right) *
-                     var_map.unaryExpr(inv_std_scale_func)
+                     var_map.unaryExpr(inv_std_func)
+                         .cwiseProduct(scale_map)
                          .replicate(1, right)
                          .cwiseProduct(d_y_map)
                          .rowwise()
@@ -274,11 +274,11 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto dvar_end = var_map.unaryExpr(inv_std_func)
                           .unaryExpr(triple_product_func)
                           .cwiseProduct(dvar_end_part)
+                          .cwiseProduct(scale_map)
                           .replicate(1, right);
-      auto dx_var = (T(-1.0) / right) *
-                    (x_map - mean_map.replicate(1, right))
-                        .cwiseProduct(dvar_end)
-                        .unaryExpr(scale_func);
+      auto dx_var =
+          (T(-1.0) / right) *
+          (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
 
       d_x_map = dx_end + dx_mean + dx_var;
     }
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 8ce327436f..9264cf4b79 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -39,8 +39,9 @@ def _reference_layer_norm_naive(x, scale, beta, epsilon, begin_norm_axis=1):
     x.shape = [N, D]
     mean = np.mean(x, axis=1)
     var = np.var(x, axis=1) + epsilon
-    output = scale * np.divide((x - mean.reshape([N, 1])),
-                               (np.sqrt(var)).reshape([N, 1])) + beta
+    output = scale.reshape([1, D]) * np.divide(
+        (x - mean.reshape([N, 1])),
+        (np.sqrt(var)).reshape([N, 1])) + beta.reshape([1, D])
     output.shape = old_shape
     x.shape = old_shape
     return output, mean, var
@@ -55,8 +56,10 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     mean.shape = [N, 1]
     var.shape = [N, 1]
 
-    d_scale = np.sum(grad_y).reshape([1, ])
-    d_bias = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y).reshape([1, ])
+    d_scale = np.sum(grad_y, axis=1).reshape([1, D])
+    d_bias = scale.reshape([1, D]) * np.sum((
+        (x - mean) * np.sqrt(1 / var)) * grad_y,
+                                            axis=1).reshape([1, D])
 
     dx_end = np.sqrt(1.0 / var) * grad_y
 
@@ -69,7 +72,7 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     d_std = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape([N, 1]) * (
         1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
 
-    grad_x = scale * (dx_end + d_mean + d_std)
+    grad_x = scale.reshape([1, D]) * (dx_end + d_mean + d_std)
 
     grad_y.shape = x_shape
     x.shape = x_shape
@@ -146,7 +149,8 @@ class TestLayerNormdOp(OpTest):
             # attr
             epsilon = 0.00001
             x_shape = shape
-            scale_shape = [1]
+            D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
+            scale_shape = [D]
             np.random.random(123)
             x_val = np.random.random_sample(x_shape).astype(np.float32)
             scale_val = np.random.random_sample(scale_shape).astype(np.float32)

From 7e0d21de6d3352c1238d35d2586f40e48b6da27f Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 11:11:04 +0800
Subject: [PATCH 122/314] fix scale and bias dim

---
 paddle/operators/layer_norm_op.cc | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 07ca8ac222..125ac9f53f 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -123,8 +123,8 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     int right = static_cast<int>(matrix_dim[1]);
 
     auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), left, 1);
-    auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), left, 1);
+    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+    auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
     auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
     auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
     auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
@@ -143,11 +143,11 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
 
     // TODO(zcd): Some thinking about output_map, is it appropriate that
     // `output_map` and `input_map` point to the same memory.
-    auto inv_std_scale =
-        var_map.unaryExpr(inv_std_func).cwiseProduct(scale_map);
-    output_map =
-        inv_std_scale.replicate(1, right).cwiseProduct(input_map) +
-        (bias_map - inv_std_scale.cwiseProduct(mean_map)).replicate(1, right);
+    auto inv_std_scale = var_map.unaryExpr(inv_std_func);
+    output_map = (input_map - mean_map.replicate(1, right))
+                     .cwiseProduct(inv_std_scale.replicate(1, right))
+                     .cwiseProduct(scale_map.replicate(left, 1)) -
+                 bias_map.replicate(left, 1);
   }
 };
 
@@ -221,7 +221,7 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
     auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), left, 1);
+    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
     auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
     auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
     auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
@@ -229,12 +229,13 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
 
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
-      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), left, 1);
+      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
       d_bias_map = d_y_map.colwise().mean();
     }
     if (d_scale) {
       d_scale->mutable_data<T>(ctx.GetPlace());
-      auto d_scale_map = EigenMatrixMapRowMajor<T>(d_scale->data<T>(), left, 1);
+      auto d_scale_map =
+          EigenMatrixMapRowMajor<T>(d_scale->data<T>(), 1, right);
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
       // There are two equation to compute d_scale. One uses "Y" and the other
       // does not use "Y"
@@ -254,15 +255,15 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
       // dy_dx
       auto dx_end = var_map.unaryExpr(inv_std_func)
-                        .cwiseProduct(scale_map)
                         .replicate(1, right)
-                        .cwiseProduct(d_y_map);
+                        .cwiseProduct(d_y_map)
+                        .cwiseProduct(scale_map.replicate(left, 1));
       // dy_dmean_dx
       auto dx_mean = (T(-1.0) / right) *
                      var_map.unaryExpr(inv_std_func)
-                         .cwiseProduct(scale_map)
                          .replicate(1, right)
                          .cwiseProduct(d_y_map)
+                         .cwiseProduct(scale_map.replicate(left, 1))
                          .rowwise()
                          .sum()
                          .replicate(1, right);
@@ -274,8 +275,8 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto dvar_end = var_map.unaryExpr(inv_std_func)
                           .unaryExpr(triple_product_func)
                           .cwiseProduct(dvar_end_part)
-                          .cwiseProduct(scale_map)
-                          .replicate(1, right);
+                          .replicate(1, right)
+                          .cwiseProduct(scale_map.replicate(left, 1));
       auto dx_var =
           (T(-1.0) / right) *
           (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);

From ccefde203adb1a5af99fb3ce30ba553f0aec1680 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 30 Jan 2018 11:30:31 +0800
Subject: [PATCH 123/314] follow comments

---
 paddle/operators/listen_and_serv_op.cc       | 207 +++++++++++++++++++
 paddle/operators/recv_op.cc                  |   9 +-
 paddle/operators/send_op.cc                  |  12 +-
 python/paddle/v2/fluid/tests/test_recv_op.py |   2 +-
 4 files changed, 214 insertions(+), 16 deletions(-)
 create mode 100644 paddle/operators/listen_and_serv_op.cc

diff --git a/paddle/operators/listen_and_serv_op.cc b/paddle/operators/listen_and_serv_op.cc
new file mode 100644
index 0000000000..5745938ed9
--- /dev/null
+++ b/paddle/operators/listen_and_serv_op.cc
@@ -0,0 +1,207 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <stdint.h>
+#include <sys/stat.h>
+#include <ostream>
+#include <thread>
+
+#include <unistd.h>
+
+#include "paddle/framework/executor.h"
+#include "paddle/framework/framework.pb.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/proto_desc.h"
+#include "paddle/operators/detail/grpc_server.h"
+#include "paddle/operators/detail/sendrecvop_utils.h"
+#include "paddle/operators/detail/simple_block_queue.h"
+#include "paddle/string/printf.h"
+
+namespace paddle {
+namespace operators {
+
+constexpr char kOptimizeBlock[] = "OptimizeBlock";
+
+void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
+  service->RunSyncUpdate();
+  VLOG(4) << "RunServer thread end";
+}
+
+static void CreateTensorFromMessageType(framework::Variable *var,
+                                        sendrecv::VarType var_type) {
+  if (var_type == sendrecv::VarType::LOD_TENSOR) {
+    var->GetMutable<framework::LoDTensor>();
+  } else if (var_type == sendrecv::VarType::SELECTED_ROWS) {
+    var->GetMutable<framework::SelectedRows>();
+  } else {
+    PADDLE_THROW(
+        "VariableMessage type %d is not in "
+        "[LoDTensor, SelectedRows]",
+        var_type);
+  }
+}
+
+class ListenAndServOp : public framework::OperatorBase {
+ public:
+  ListenAndServOp(const std::string &type,
+                  const framework::VariableNameMap &inputs,
+                  const framework::VariableNameMap &outputs,
+                  const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {
+    if (!rpc_service_) {
+      std::string endpoint = Attr<std::string>("endpoint");
+      rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
+      server_thread_.reset(new std::thread(RunServer, rpc_service_));
+    }
+  }
+
+  void Stop() override {
+    detail::MessageWithName term_msg;
+    term_msg.first = LISTEN_TERMINATE_MESSAGE;
+    rpc_service_->Push(term_msg);
+    rpc_service_->ShutDown();
+    server_thread_->join();
+  }
+
+  std::string GetGradVarNameForTrainer(const std::string &varname) const {
+    if (grads_counter_.find(varname) == grads_counter_.end()) {
+      grads_counter_[varname] = 0;
+    }
+    return string::Sprintf("%s.trainer_%d", varname, grads_counter_[varname]++);
+  }
+
+  void Run(const framework::Scope &scope,
+           const platform::Place &dev_place) const override {
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(dev_place);
+    framework::Scope &recv_scope = scope.NewScope();
+
+    // FIXME(Yancey1989): initialize rpc server with lazy mode.
+    rpc_service_->SetScope(&recv_scope);
+    rpc_service_->SetDevCtx(&dev_ctx);
+    auto param_list = Attr<std::vector<std::string>>("ParamList");
+    auto grad_list = Attr<std::vector<std::string>>("GradList");
+    auto fan_in = Attr<int>("Fanin");
+
+    auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
+    auto *program = block->Program();
+    framework::Executor executor(dev_place);
+
+    // TODO(typhoonzero): change this to a while_op for every cluster-batch.
+    bool exit_flag = false;
+    while (!exit_flag) {
+      // Get from multiple trainers, we don't care about the order in which
+      // the gradients arrives, just add suffix 0~n and merge the gradient.
+      rpc_service_->SetCond(0);
+      size_t recv_var_cnt = 0;
+      int batch_barrier = 0;
+      while (batch_barrier != fan_in) {
+        const detail::MessageWithName &v = rpc_service_->Get();
+        auto grad_var_name = v.first;
+        if (grad_var_name == LISTEN_TERMINATE_MESSAGE) {
+          LOG(INFO) << "received terminate message and exit";
+          exit_flag = true;
+          break;
+        } else if (grad_var_name == BATCH_BARRIER_MESSAGE) {
+          VLOG(3) << "recv batch barrier message";
+          batch_barrier++;
+          continue;
+        } else {
+          // receive a variable
+          recv_var_cnt++;
+          auto it =
+              std::find(grad_list.begin(), grad_list.end(), grad_var_name);
+          std::string param_var_name;
+          if (it != grad_list.end()) {
+            param_var_name = param_list[it - grad_list.begin()];
+          } else {
+            LOG(ERROR) << "grad has no paired param:" << grad_var_name;
+          }
+          VLOG(3) << "received grad: " << grad_var_name
+                  << " updating param: " << param_var_name;
+
+          if (fan_in > 1) {
+            grad_var_name = this->GetGradVarNameForTrainer(grad_var_name);
+          }
+          auto *var = recv_scope.FindVar(grad_var_name);
+          if (var == nullptr) {
+            LOG(ERROR) << "Can not find server side var: " << grad_var_name;
+            PADDLE_THROW("Can not find server side var");
+          }
+          detail::DeserializeFromMessage(v.second, dev_ctx, var);
+        }
+      }
+      VLOG(3) << "recv " << recv_var_cnt << " parmeters for one barrier.";
+      // TODO(Yancey1989): merge SelectedRows variables here
+      if (exit_flag) {
+        rpc_service_->ShutDown();
+      }
+
+      try {
+        executor.Run(*program, &recv_scope, block->ID(), /*global_block*/
+                     false /*create_local_scope*/, false /*create_vars*/);
+      } catch (std::exception &e) {
+        LOG(ERROR) << "run sub program error " << e.what();
+      }
+      rpc_service_->SetCond(1);
+      rpc_service_->WaitClientGet(recv_var_cnt);
+      grads_counter_.clear();
+    }  // while(true)
+  }
+
+ protected:
+  std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
+  std::shared_ptr<std::thread> server_thread_;
+  mutable std::unordered_map<std::string, int> grads_counter_;
+};
+
+class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ListenAndServOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddComment(R"DOC(
+ListenAndServ operator
+
+This operator will start a RPC server which can receive variables
+from send_op and send back variables to recv_op.
+)DOC");
+    AddAttr<std::string>("endpoint",
+                         "(string, default 127.0.0.1:6164)"
+                         "IP address to listen on.")
+        .SetDefault("127.0.0.1:6164")
+        .AddCustomChecker([](const std::string &ip) { return !ip.empty(); });
+    AddAttr<framework::BlockDesc *>(kOptimizeBlock,
+                                    "BlockID to run on server side.");
+    AddAttr<std::vector<std::string>>(
+        "ParamList", "type list of string",
+        "grad->param name mapping to find which parameters to optimize.")
+        .SetDefault({});
+    AddAttr<std::vector<std::string>>(
+        "GradList", "type list of string",
+        "grad->param name mapping to find which parameters to optimize.")
+        .SetDefault({});
+    AddAttr<int>("Fanin", "type int",
+                 "Number of trainers in the current cluster job")
+        .SetDefault(1);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(listen_and_serv, ops::ListenAndServOp,
+                  ops::ListenAndServOpMaker);
\ No newline at end of file
diff --git a/paddle/operators/recv_op.cc b/paddle/operators/recv_op.cc
index 1e64d5c65c..ba71094219 100644
--- a/paddle/operators/recv_op.cc
+++ b/paddle/operators/recv_op.cc
@@ -55,19 +55,12 @@ class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   RecvOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "(Tensor) Input tensor to be sent").AsDuplicable();
-    AddOutput("Out", "(Tensor) Output tensor to be received from server")
-        .AsDuplicable();
+    AddOutput("Out", "(Tensor) Variables to get from server.").AsDuplicable();
     AddComment(R"DOC(
 Recv operator
 
 This operator can get variables from server side.
 )DOC");
-    AddAttr<std::vector<std::string>>("endpoints",
-                                      "(string vector, default 127.0.0.1:6164)"
-                                      "Server endpoints to recv variables"
-                                      "from.")
-        .SetDefault({});
     AddAttr<std::vector<std::string>>("epmap",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints in the order of input "
diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index 9c180a7244..51e6b9de58 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -37,7 +37,6 @@ class SendOp : public framework::OperatorBase {
     auto ins = Inputs("X");
     auto outs = Outputs("Out");
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
-    bool do_get = Attr<bool>("DoGet");
     std::vector<std::string> endpoints =
         Attr<std::vector<std::string>>("endpoints");
 
@@ -55,7 +54,7 @@ class SendOp : public framework::OperatorBase {
     }
     PADDLE_ENFORCE(client_.Wait());
 
-    if (do_get) {
+    if (outs.size() > 0) {
       for (size_t i = 0; i < outs.size(); i++) {
         VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
         client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
@@ -65,7 +64,8 @@ class SendOp : public framework::OperatorBase {
   }
 
  private:
-  // TODO(typhoonzero): put RPCClient in a Variable.
+  // TODO(typhoonzero): put RPCClient in a Variable, so that
+  // send and recv can use the same connection.
   mutable detail::RPCClient client_;
 };
 
@@ -81,6 +81,8 @@ Send operator
 
 This operator will send tensor to recv_op at the parameter server.
 )DOC");
+    // TODO(typhoonzero): remove this attr generate de-duplicated vector from
+    // epmap when initializing.
     AddAttr<std::vector<std::string>>("endpoints",
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")
@@ -90,10 +92,6 @@ This operator will send tensor to recv_op at the parameter server.
                                       "Server endpoints in the order of input "
                                       "variables for mapping")
         .SetDefault({});
-    AddAttr<bool>("DoGet",
-                  "(bool, default true)"
-                  "Whether do GetVariable call after send")
-        .SetDefault(true);
   }
 };
 
diff --git a/python/paddle/v2/fluid/tests/test_recv_op.py b/python/paddle/v2/fluid/tests/test_recv_op.py
index def5ca9442..3a02b88241 100644
--- a/python/paddle/v2/fluid/tests/test_recv_op.py
+++ b/python/paddle/v2/fluid/tests/test_recv_op.py
@@ -29,7 +29,7 @@ class TestRecvOp(unittest.TestCase):
         p = Process(target=self.init_serv, args=(place, ))
         p.daemon = True
         p.start()
-        time.sleep(5)
+        time.sleep(1)
         self.init_client(place)
         # FIXME(typhoonzero): find a way to gracefully shutdown the server.
         os.system("kill -9 %d" % p.pid)

From a96ac4f54d37fe225f3ca5b9075a114d22dbe1d6 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Tue, 30 Jan 2018 12:57:29 +0800
Subject: [PATCH 124/314] Refine code

---
 .../v2/fluid/tests/book/test_word2vec.py      | 168 ++++++++++--------
 1 file changed, 98 insertions(+), 70 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index 8cf54846fe..cdfa910fcd 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -12,76 +12,104 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import unittest
 
-PASS_NUM = 100
-EMBED_SIZE = 32
-HIDDEN_SIZE = 256
-N = 5
-BATCH_SIZE = 32
-IS_SPARSE = True
-
-word_dict = paddle.dataset.imikolov.build_dict()
-dict_size = len(word_dict)
-
-first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
-second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
-third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
-forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
-next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
-
-embed_first = fluid.layers.embedding(
-    input=first_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_second = fluid.layers.embedding(
-    input=second_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_third = fluid.layers.embedding(
-    input=third_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-embed_forth = fluid.layers.embedding(
-    input=forth_word,
-    size=[dict_size, EMBED_SIZE],
-    dtype='float32',
-    is_sparse=IS_SPARSE,
-    param_attr='shared_w')
-
-concat_embed = fluid.layers.concat(
-    input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
-hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
-predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
-avg_cost = fluid.layers.mean(x=cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
-
-train_reader = paddle.batch(
-    paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(
-    feed_list=[first_word, second_word, third_word, forth_word, next_word],
-    place=place)
-
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_cost_np = exe.run(fluid.default_main_program(),
-                              feed=feeder.feed(data),
-                              fetch_list=[avg_cost])
-        if avg_cost_np[0] < 5.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
+
+def main_impl(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    PASS_NUM = 100
+    EMBED_SIZE = 32
+    HIDDEN_SIZE = 256
+    N = 5
+    BATCH_SIZE = 32
+    IS_SPARSE = True
+
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+
+    first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
+    second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
+    third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
+    forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
+    next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
+
+    embed_first = fluid.layers.embedding(
+        input=first_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_second = fluid.layers.embedding(
+        input=second_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_third = fluid.layers.embedding(
+        input=third_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+    embed_forth = fluid.layers.embedding(
+        input=forth_word,
+        size=[dict_size, EMBED_SIZE],
+        dtype='float32',
+        is_sparse=IS_SPARSE,
+        param_attr='shared_w')
+
+    concat_embed = fluid.layers.concat(
+        input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+    hidden1 = fluid.layers.fc(input=concat_embed,
+                              size=HIDDEN_SIZE,
+                              act='sigmoid')
+    predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
+    avg_cost = fluid.layers.mean(x=cost)
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_cost)
+
+    train_reader = paddle.batch(
+        paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(
+        feed_list=[first_word, second_word, third_word, forth_word, next_word],
+        place=place)
+
+    exe.run(fluid.default_startup_program())
+
+    for pass_id in range(PASS_NUM):
+        for data in train_reader():
+            avg_cost_np = exe.run(fluid.default_main_program(),
+                                  feed=feeder.feed(data),
+                                  fetch_list=[avg_cost])
+            if avg_cost_np[0] < 5.0:
+                return
+    raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
+
+
+def main(*args, **kwargs):
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            main_impl(*args, **kwargs)
+
+
+class W2VTest(unittest.TestCase):
+    def test_cpu_normal(self):
+        main(use_cuda=False)
+
+    def test_gpu_normal(self):
+        main(use_cuda=True)
+
+
+if __name__ == '__main__':
+    unittest.main()

From 7c0cc113d92dfebb11bf689924a47f7ae520e739 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Tue, 30 Jan 2018 14:56:01 +0800
Subject: [PATCH 125/314] Test word2vec for parallel.do

* Polish sum_op support SelectedRows in_place
---
 paddle/operators/sum_op.h                     |  49 +++++--
 .../v2/fluid/tests/book/test_word2vec.py      | 133 ++++++++++++------
 2 files changed, 125 insertions(+), 57 deletions(-)

diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h
index 48201b344d..3d8102c3ae 100644
--- a/paddle/operators/sum_op.h
+++ b/paddle/operators/sum_op.h
@@ -68,7 +68,32 @@ class SumKernel : public framework::OpKernel<T> {
         }
       }
     } else if (out_var->IsType<framework::SelectedRows>()) {
-      PADDLE_ENFORCE(!in_place, "SelectedRows not support inplace sum now");
+      std::unique_ptr<framework::SelectedRows> in0;
+      if (in_place) {
+        // If is in_place, we store the input[0] to in0
+        auto &in_sel0 = in_vars[0]->Get<SelectedRows>();
+        auto &rows = in_sel0.rows();
+#ifdef PADDLE_WITH_CUDA
+        std::vector<int64_t> rows_in_cpu;
+        rows_in_cpu.reserve(rows.size());
+        for (auto item : rows) {
+          rows_in_cpu.push_back(item);
+        }
+        in0.reset(new framework::SelectedRows(rows_in_cpu, in_sel0.height()));
+#else
+        in0.reset(new framework::SelectedRows(rows, in_sel0.height()));
+#endif
+        in0->mutable_value()->ShareDataWith(in_sel0.value());
+      }
+
+      auto get_selected_row = [&](size_t i) -> const SelectedRows & {
+        if (i == 0 && in0) {
+          return *in0.get();
+        } else {
+          return in_vars[i]->Get<SelectedRows>();
+        }
+      };
+
       auto *out = context.Output<SelectedRows>("Out");
       out->mutable_rows()->clear();
       auto *out_value = out->mutable_value();
@@ -76,24 +101,26 @@ class SumKernel : public framework::OpKernel<T> {
       // Runtime InferShape
       size_t first_dim = 0;
       for (int i = 0; i < N; i++) {
-        first_dim += in_vars[i]->Get<SelectedRows>().rows().size();
+        auto &sel_row = get_selected_row(i);
+        first_dim += sel_row.rows().size();
       }
-      auto in_dim = in_vars[0]->Get<SelectedRows>().value().dims();
-      auto in_dim_vec = framework::vectorize(in_dim);
-      in_dim_vec[0] = static_cast<int64_t>(first_dim);
+      auto in_dim =
+          framework::vectorize(get_selected_row(N - 1).value().dims());
+      in_dim[0] = static_cast<int64_t>(first_dim);
 
-      out_value->Resize(framework::make_ddim(in_dim_vec));
+      out_value->Resize(framework::make_ddim(in_dim));
       out_value->mutable_data<T>(context.GetPlace());
 
       math::SelectedRowsAddTo<DeviceContext, T> functor;
 
       int64_t offset = 0;
       for (int i = 0; i < N; i++) {
-        PADDLE_ENFORCE_EQ(out->height(),
-                          in_vars[i]->Get<SelectedRows>().height());
-        functor(context.template device_context<DeviceContext>(),
-                in_vars[i]->Get<SelectedRows>(), offset, out);
-        offset += in_vars[i]->Get<SelectedRows>().value().numel();
+        auto &sel_row = get_selected_row(i);
+
+        PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
+        functor(context.template device_context<DeviceContext>(), sel_row,
+                offset, out);
+        offset += sel_row.value().numel();
       }
     } else if (out_var->IsType<framework::LoDTensorArray>()) {
       auto &out_array = *out_var->GetMutable<framework::LoDTensorArray>();
diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index cdfa910fcd..cfa8d9580d 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -15,9 +15,10 @@
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import unittest
+import os
 
 
-def main_impl(use_cuda):
+def main(use_cuda, is_sparse, parallel):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
 
@@ -26,7 +27,45 @@ def main_impl(use_cuda):
     HIDDEN_SIZE = 256
     N = 5
     BATCH_SIZE = 32
-    IS_SPARSE = True
+    IS_SPARSE = is_sparse
+
+    def __network__(words):
+        embed_first = fluid.layers.embedding(
+            input=words[0],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_second = fluid.layers.embedding(
+            input=words[1],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_third = fluid.layers.embedding(
+            input=words[2],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+        embed_forth = fluid.layers.embedding(
+            input=words[3],
+            size=[dict_size, EMBED_SIZE],
+            dtype='float32',
+            is_sparse=IS_SPARSE,
+            param_attr='shared_w')
+
+        concat_embed = fluid.layers.concat(
+            input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
+        hidden1 = fluid.layers.fc(input=concat_embed,
+                                  size=HIDDEN_SIZE,
+                                  act='sigmoid')
+        predict_word = fluid.layers.fc(input=hidden1,
+                                       size=dict_size,
+                                       act='softmax')
+        cost = fluid.layers.cross_entropy(input=predict_word, label=words[4])
+        avg_cost = fluid.layers.mean(x=cost)
+        return avg_cost
 
     word_dict = paddle.dataset.imikolov.build_dict()
     dict_size = len(word_dict)
@@ -37,39 +76,21 @@ def main_impl(use_cuda):
     forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
     next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
 
-    embed_first = fluid.layers.embedding(
-        input=first_word,
-        size=[dict_size, EMBED_SIZE],
-        dtype='float32',
-        is_sparse=IS_SPARSE,
-        param_attr='shared_w')
-    embed_second = fluid.layers.embedding(
-        input=second_word,
-        size=[dict_size, EMBED_SIZE],
-        dtype='float32',
-        is_sparse=IS_SPARSE,
-        param_attr='shared_w')
-    embed_third = fluid.layers.embedding(
-        input=third_word,
-        size=[dict_size, EMBED_SIZE],
-        dtype='float32',
-        is_sparse=IS_SPARSE,
-        param_attr='shared_w')
-    embed_forth = fluid.layers.embedding(
-        input=forth_word,
-        size=[dict_size, EMBED_SIZE],
-        dtype='float32',
-        is_sparse=IS_SPARSE,
-        param_attr='shared_w')
-
-    concat_embed = fluid.layers.concat(
-        input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
-    hidden1 = fluid.layers.fc(input=concat_embed,
-                              size=HIDDEN_SIZE,
-                              act='sigmoid')
-    predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
-    cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
-    avg_cost = fluid.layers.mean(x=cost)
+    if not parallel:
+        avg_cost = __network__(
+            [first_word, second_word, third_word, forth_word, next_word])
+    else:
+        places = fluid.layers.get_places()
+        pd = fluid.layers.ParallelDo(places)
+        with pd.do():
+            avg_cost = __network__(
+                map(pd.read_input, [
+                    first_word, second_word, third_word, forth_word, next_word
+                ]))
+            pd.write_output(avg_cost)
+
+        avg_cost = fluid.layers.mean(x=pd())
+
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
     sgd_optimizer.minimize(avg_cost)
 
@@ -94,22 +115,42 @@ def main_impl(use_cuda):
     raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
 
 
-def main(*args, **kwargs):
-    prog = fluid.Program()
-    startup_prog = fluid.Program()
-    scope = fluid.core.Scope()
-    with fluid.scope_guard(scope):
-        with fluid.program_guard(prog, startup_prog):
-            main_impl(*args, **kwargs)
+FULL_TEST = os.getenv('FULL_TEST',
+                      '1').lower() in ['true', '1', 't', 'y', 'yes', 'on']
+SKIP_REASON = "Only run minimum number of tests in CI server, to make CI faster"
 
 
 class W2VTest(unittest.TestCase):
-    def test_cpu_normal(self):
-        main(use_cuda=False)
+    pass
+
+
+def inject_test_method(use_cuda, is_sparse, parallel):
+    fn_name = "test_{0}_{1}_{2}".format("cuda" if use_cuda else "cpu", "sparse"
+                                        if is_sparse else "dense", "parallel"
+                                        if parallel else "normal")
+
+    def __impl__(*args, **kwargs):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                main(use_cuda=use_cuda, is_sparse=is_sparse, parallel=parallel)
+
+    if use_cuda and is_sparse and parallel:
+        fn = __impl__
+    else:
+        # skip the other test when on CI server
+        fn = unittest.skipUnless(
+            condition=FULL_TEST, reason=SKIP_REASON)(__impl__)
+
+    setattr(W2VTest, fn_name, fn)
 
-    def test_gpu_normal(self):
-        main(use_cuda=True)
 
+for use_cuda in (False, True):
+    for is_sparse in (False, True):
+        for parallel in (False, True):
+            inject_test_method(use_cuda, is_sparse, parallel)
 
 if __name__ == '__main__':
     unittest.main()

From 311334e083dca3117bbc41bd11a3649ec7580ef0 Mon Sep 17 00:00:00 2001
From: Siddharth Goyal <vi.siddharth78@gmail.com>
Date: Mon, 29 Jan 2018 23:27:20 -0800
Subject: [PATCH 126/314] Implement basic `Load()` and modify example based on
 updated inference design (#7690)

* Initial commit

* Remove resolution bug

* Modify IsParam

* Remove small bugs

* First commit unifying Run and Load

* Fix bugs

* Fix Cmake

* Modify Cmake and dir structure

* Add io.* files to inference dir

* Fix include in example

* Address review comments: part 1

* Address review comments: round 2

* Address review comments: round 3

* Address review comments: round 4
---
 paddle/framework/program_desc.cc |  25 +++++
 paddle/framework/program_desc.h  |   4 +
 paddle/inference/CMakeLists.txt  |   6 +-
 paddle/inference/example.cc      |  50 ++++++++-
 paddle/inference/inference.cc    | 187 -------------------------------
 paddle/inference/inference.h     |  48 --------
 paddle/inference/io.cc           |  97 ++++++++++++++++
 paddle/inference/io.h            |  41 +++++++
 8 files changed, 214 insertions(+), 244 deletions(-)
 delete mode 100644 paddle/inference/inference.cc
 delete mode 100644 paddle/inference/inference.h
 create mode 100644 paddle/inference/io.cc
 create mode 100644 paddle/inference/io.h

diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index b5d9e5e385..e59e392dfd 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -18,6 +18,9 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+const std::string kFeedOpType = "feed";
+const std::string kFetchOpType = "fetch";
+
 BlockDesc *ProgramDesc::AppendBlock(const BlockDesc &parent) {
   auto *b = desc_.add_blocks();
   b->set_parent_idx(parent.ID());
@@ -64,5 +67,27 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
   }
 }
 
+const std::vector<std::string> ProgramDesc::GetFeedVarNames() {
+  BlockDesc *global_block = blocks_[0].get();
+  std::vector<std::string> feed_var_names;
+  for (auto *op : global_block->AllOps()) {
+    if (op->Type() == "feed") {
+      feed_var_names.insert(feed_var_names.begin(), op->Output("Out")[0]);
+    }
+  }
+  return feed_var_names;
+}
+
+const std::vector<std::string> ProgramDesc::GetFetchVarNames() {
+  BlockDesc *global_block = blocks_[0].get();
+  std::vector<std::string> fetch_var_names;
+  for (auto *op : global_block->AllOps()) {
+    if (op->Type() == "fetch") {
+      fetch_var_names.push_back(op->Input("X")[0]);
+    }
+  }
+  return fetch_var_names;
+}
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h
index 15a962bb69..2c3883275a 100644
--- a/paddle/framework/program_desc.h
+++ b/paddle/framework/program_desc.h
@@ -45,6 +45,10 @@ class ProgramDesc {
 
   proto::ProgramDesc *Proto();
 
+  const std::vector<std::string> GetFeedVarNames();
+
+  const std::vector<std::string> GetFetchVarNames();
+
  private:
   proto::ProgramDesc desc_;
 
diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index ae4d3fd2f5..683aaee42a 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -1,14 +1,14 @@
 set(FLUID_CORE_MODULES proto_desc paddle_memory executor prune init)
 
 cc_library(paddle_fluid_api
-    SRCS inference.cc
+    SRCS io.cc
     DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
 
 # Merge all modules into a single static library
 cc_library(paddle_fluid DEPS paddle_fluid_api ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
 
 # Create shared library
-add_library(paddle_fluid_shared SHARED inference.cc)
+add_library(paddle_fluid_shared SHARED io.cc)
 
 target_circle_link_libraries(paddle_fluid_shared
   ARCHIVE_START
@@ -20,7 +20,7 @@ SET_TARGET_PROPERTIES(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 
 # install library & headers
 if(NOT WITH_C_API AND WITH_FLUID)
-  install(FILES inference.h DESTINATION include/paddle/inference)
+  install(FILES io.h DESTINATION include/paddle/inference)
   install(TARGETS paddle_fluid_shared DESTINATION lib)
 endif()
 
diff --git a/paddle/inference/example.cc b/paddle/inference/example.cc
index 0c18b45624..5173779c62 100644
--- a/paddle/inference/example.cc
+++ b/paddle/inference/example.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -15,7 +15,9 @@ limitations under the License. */
 #include <time.h>
 #include <iostream>
 #include "gflags/gflags.h"
-#include "paddle/inference/inference.h"
+#include "paddle/framework/init.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/inference/io.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
@@ -28,12 +30,27 @@ int main(int argc, char** argv) {
     exit(1);
   }
 
+  // 1. Define place, executor, scope
+  auto place = paddle::platform::CPUPlace();
+  paddle::framework::InitDevices();
+  auto* executor = new paddle::framework::Executor(place);
+  auto* scope = new paddle::framework::Scope();
+
   std::cout << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
   std::string dirname = FLAGS_dirname;
 
-  paddle::InferenceEngine* engine = new paddle::InferenceEngine();
-  engine->LoadInferenceModel(dirname);
+  // 2. Initialize the inference program
+  auto* inference_program = paddle::inference::Load(*executor, *scope, dirname);
+
+  // 3. Optional: perform optimization on the inference_program
+
+  // 4. Get the feed_var_names and fetch_var_names
+  const std::vector<std::string>& feed_var_names =
+      inference_program->GetFeedVarNames();
+  const std::vector<std::string>& fetch_var_names =
+      inference_program->GetFetchVarNames();
 
+  // 5. Generate input
   paddle::framework::LoDTensor input;
   srand(time(0));
   float* input_ptr =
@@ -45,8 +62,26 @@ int main(int argc, char** argv) {
   std::vector<paddle::framework::LoDTensor> feeds;
   feeds.push_back(input);
   std::vector<paddle::framework::LoDTensor> fetchs;
-  engine->Execute(feeds, fetchs);
 
+  // Set up maps for feed and fetch targets
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+
+  // set_feed_variable
+  for (size_t i = 0; i < feed_var_names.size(); ++i) {
+    feed_targets[feed_var_names[i]] = &feeds[i];
+  }
+
+  // get_fetch_variable
+  fetchs.resize(fetch_var_names.size());
+  for (size_t i = 0; i < fetch_var_names.size(); ++i) {
+    fetch_targets[fetch_var_names[i]] = &fetchs[i];
+  }
+
+  // Run the inference program
+  executor->Run(*inference_program, scope, feed_targets, fetch_targets);
+
+  // Get outputs
   for (size_t i = 0; i < fetchs.size(); ++i) {
     auto dims_i = fetchs[i].dims();
     std::cout << "dims_i:";
@@ -62,6 +97,9 @@ int main(int argc, char** argv) {
     std::cout << std::endl;
   }
 
-  delete engine;
+  delete inference_program;
+  delete scope;
+  delete executor;
+
   return 0;
 }
diff --git a/paddle/inference/inference.cc b/paddle/inference/inference.cc
deleted file mode 100644
index b43c359ed1..0000000000
--- a/paddle/inference/inference.cc
+++ /dev/null
@@ -1,187 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#include "inference.h"
-#include <fstream>
-#include "paddle/framework/executor.h"
-#include "paddle/framework/init.h"
-#include "paddle/framework/scope.h"
-
-namespace paddle {
-
-void InferenceEngine::LoadInferenceModel(const std::string& dirname) {
-  std::string model_filename = dirname + "/__model__";
-  LOG(INFO) << "loading model from " << model_filename;
-  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
-  std::string program_desc_str;
-  inputfs.seekg(0, std::ios::end);
-  program_desc_str.resize(inputfs.tellg());
-  inputfs.seekg(0, std::ios::beg);
-  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
-  inputfs.read(&program_desc_str[0], program_desc_str.size());
-  inputfs.close();
-
-  program_ = new framework::ProgramDesc(program_desc_str);
-  GenerateLoadProgram(dirname);
-
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-  feed_var_names_.clear();
-  fetch_var_names_.clear();
-  for (auto* op : global_block->AllOps()) {
-    if (op->Type() == "feed") {
-      feed_var_names_.insert(feed_var_names_.begin(), op->Output("Out")[0]);
-    } else if (op->Type() == "fetch") {
-      fetch_var_names_.push_back(op->Input("X")[0]);
-    }
-  }
-}
-
-bool InferenceEngine::IsParameter(const framework::VarDesc* var) {
-  if (var->Persistable()) {
-    // There are many unreachable variables in the program
-    for (size_t i = 0; i < program_->Size(); ++i) {
-      const framework::BlockDesc& block = program_->Block(i);
-      for (auto* op : block.AllOps()) {
-        if (op->Type() == "feed") {
-          continue;
-        }
-        for (auto input_argument_name : op->InputArgumentNames()) {
-          if (input_argument_name == var->Name()) {
-            return true;
-          }
-        }
-      }
-    }
-  }
-  return false;
-}
-
-void InferenceEngine::GenerateLoadProgram(const std::string& dirname) {
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-
-  load_program_ = new framework::ProgramDesc();
-  framework::BlockDesc* load_block = load_program_->MutableBlock(0);
-  for (auto* var : global_block->AllVars()) {
-    if (IsParameter(var)) {
-      LOG(INFO) << "parameter's name: " << var->Name();
-
-      framework::VarDesc* new_var = load_block->Var(var->Name());
-      new_var->SetShape(var->Shape());
-      new_var->SetDataType(var->GetDataType());
-      new_var->SetType(var->GetType());
-      new_var->SetLoDLevel(var->GetLoDLevel());
-      new_var->SetPersistable(true);
-
-      // append_op
-      framework::OpDesc* op = load_block->AppendOp();
-      op->SetType("load");
-      op->SetOutput("Out", {new_var->Name()});
-      op->SetAttr("file_path", {dirname + "/" + new_var->Name()});
-      op->CheckAttrs();
-    }
-  }
-}
-
-void InferenceEngine::PrependFeedOp() {
-  if (!program_) {
-    LOG(FATAL) << "Please initialize the program_ first.";
-  }
-
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-
-  // create_var
-  framework::VarDesc* feed_var = global_block->Var("feed");
-  feed_var->SetType(framework::proto::VarDesc::FEED_MINIBATCH);
-  feed_var->SetPersistable(true);
-
-  // prepend feed_op
-  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
-    std::string var_name = feed_var_names_[i];
-    LOG(INFO) << "feed var's name: " << var_name;
-
-    // prepend_op
-    framework::OpDesc* op = global_block->PrependOp();
-    op->SetType("feed");
-    op->SetInput("X", {"feed"});
-    op->SetOutput("Out", {var_name});
-    op->SetAttr("col", {static_cast<int>(i)});
-    op->CheckAttrs();
-  }
-}
-
-void InferenceEngine::AppendFetchOp() {
-  if (!program_) {
-    LOG(FATAL) << "Please initialize the program_ first.";
-  }
-
-  framework::BlockDesc* global_block = program_->MutableBlock(0);
-
-  // create_var
-  framework::VarDesc* fetch_var = global_block->Var("fetch");
-  fetch_var->SetType(framework::proto::VarDesc::FETCH_LIST);
-  fetch_var->SetPersistable(true);
-
-  // append fetch_op
-  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
-    std::string var_name = fetch_var_names_[i];
-    LOG(INFO) << "fetch var's name: " << var_name;
-
-    // append_op
-    framework::OpDesc* op = global_block->AppendOp();
-    op->SetType("fetch");
-    op->SetInput("X", {var_name});
-    op->SetOutput("Out", {"fetch"});
-    op->SetAttr("col", {static_cast<int>(i)});
-    op->CheckAttrs();
-  }
-}
-
-void InferenceEngine::Execute(const std::vector<framework::LoDTensor>& feeds,
-                              std::vector<framework::LoDTensor>& fetchs) {
-  if (!program_ || !load_program_) {
-    LOG(FATAL) << "Please initialize the program_ and load_program_ first.";
-  }
-
-  if (feeds.size() != feed_var_names_.size()) {
-    LOG(FATAL) << "Please feed " << feed_var_names_.size() << " input Tensors.";
-  }
-
-  auto* place = new platform::CPUPlace();
-  framework::InitDevices();
-  framework::Executor* executor = new framework::Executor(*place);
-  framework::Scope* scope = new framework::Scope();
-
-  executor->Run(*load_program_, scope, 0, true, true);
-
-  std::map<std::string, const framework::LoDTensor*> feed_targets;
-  std::map<std::string, framework::LoDTensor*> fetch_targets;
-
-  // set_feed_variable
-  for (size_t i = 0; i < feed_var_names_.size(); ++i) {
-    feed_targets[feed_var_names_[i]] = &feeds[i];
-  }
-
-  // get_fetch_variable
-  fetchs.resize(fetch_var_names_.size());
-  for (size_t i = 0; i < fetch_var_names_.size(); ++i) {
-    fetch_targets[fetch_var_names_[i]] = &fetchs[i];
-  }
-
-  executor->Run(*program_, scope, feed_targets, fetch_targets);
-
-  delete place;
-  delete scope;
-  delete executor;
-}
-}  // namespace paddle
diff --git a/paddle/inference/inference.h b/paddle/inference/inference.h
deleted file mode 100644
index 26f259824b..0000000000
--- a/paddle/inference/inference.h
+++ /dev/null
@@ -1,48 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include "paddle/framework/block_desc.h"
-#include "paddle/framework/lod_tensor.h"
-#include "paddle/framework/program_desc.h"
-
-namespace paddle {
-
-class InferenceEngine {
-public:
-  InferenceEngine() : program_(nullptr), load_program_(nullptr) {}
-  ~InferenceEngine() {
-    delete program_;
-    delete load_program_;
-  }
-
-  void LoadInferenceModel(const std::string& dirname);
-  void Execute(const std::vector<framework::LoDTensor>& feeds,
-               std::vector<framework::LoDTensor>& fetchs);
-
-private:
-  bool IsParameter(const framework::VarDesc* var);
-  void GenerateLoadProgram(const std::string& dirname);
-  void PrependFeedOp();
-  void AppendFetchOp();
-
-private:
-  framework::ProgramDesc* program_;
-  framework::ProgramDesc* load_program_;
-  std::vector<std::string> feed_var_names_;
-  std::vector<std::string> fetch_var_names_;
-};
-
-}  // namespace paddle
diff --git a/paddle/inference/io.cc b/paddle/inference/io.cc
new file mode 100644
index 0000000000..98b33d656d
--- /dev/null
+++ b/paddle/inference/io.cc
@@ -0,0 +1,97 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/inference/io.h"
+#include <fstream>
+
+namespace paddle {
+namespace inference {
+
+const std::string kFeedOpType = "feed";
+
+bool IsParameter(const framework::VarDesc* var,
+                 const framework::ProgramDesc* main_program) {
+  if (var->Persistable()) {
+    // There are many unreachable variables in the program
+    for (size_t i = 0; i < main_program->Size(); ++i) {
+      const framework::BlockDesc& block = main_program->Block(i);
+      for (auto* op : block.AllOps()) {
+        if (op->Type() == kFeedOpType) {
+          continue;
+        }
+        for (auto input_argument_name : op->InputArgumentNames()) {
+          if (input_argument_name == var->Name()) {
+            return true;
+          }
+        }
+      }
+    }
+  }
+  return false;
+}
+
+void LoadPersistables(framework::Executor& executor,
+                      framework::Scope& scope,
+                      const std::string& dirname,
+                      framework::ProgramDesc* main_program) {
+  framework::BlockDesc* global_block = main_program->MutableBlock(0);
+
+  framework::ProgramDesc* load_program = new framework::ProgramDesc();
+  framework::BlockDesc* load_block = load_program->MutableBlock(0);
+  for (auto* var : global_block->AllVars()) {
+    if (IsParameter(var, main_program)) {
+      LOG(INFO) << "parameter's name: " << var->Name();
+
+      framework::VarDesc* new_var = load_block->Var(var->Name());
+      new_var->SetShape(var->Shape());
+      new_var->SetDataType(var->GetDataType());
+      new_var->SetType(var->GetType());
+      new_var->SetLoDLevel(var->GetLoDLevel());
+      new_var->SetPersistable(true);
+
+      // append_op
+      framework::OpDesc* op = load_block->AppendOp();
+      op->SetType("load");
+      op->SetOutput("Out", {new_var->Name()});
+      op->SetAttr("file_path", {dirname + "/" + new_var->Name()});
+      op->CheckAttrs();
+    }
+  }
+  executor.Run(*load_program, &scope, 0, true, true);
+  delete load_program;
+}
+
+framework::ProgramDesc* Load(framework::Executor& executor,
+                             framework::Scope& scope,
+                             const std::string& dirname) {
+  std::string model_filename = dirname + "/__model__";
+  LOG(INFO) << "loading model from " << model_filename;
+  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
+  std::string program_desc_str;
+  inputfs.seekg(0, std::ios::end);
+  program_desc_str.resize(inputfs.tellg());
+  inputfs.seekg(0, std::ios::beg);
+  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
+  inputfs.read(&program_desc_str[0], program_desc_str.size());
+  inputfs.close();
+
+  framework::ProgramDesc* main_program =
+      new framework::ProgramDesc(program_desc_str);
+
+  LoadPersistables(executor, scope, dirname, main_program);
+  return main_program;
+}
+
+}  // namespace inference
+}  // namespace paddle
diff --git a/paddle/inference/io.h b/paddle/inference/io.h
new file mode 100644
index 0000000000..400f5af8c5
--- /dev/null
+++ b/paddle/inference/io.h
@@ -0,0 +1,41 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "paddle/framework/block_desc.h"
+#include "paddle/framework/executor.h"
+#include "paddle/framework/program_desc.h"
+#include "paddle/framework/scope.h"
+#include "paddle/framework/var_desc.h"
+
+namespace paddle {
+namespace inference {
+
+bool IsParameter(const framework::VarDesc* var,
+                 const framework::ProgramDesc* main_program);
+
+void LoadPersistables(framework::Executor& executor,
+                      framework::Scope& scope,
+                      const std::string& dirname,
+                      framework::ProgramDesc* main_program);
+
+framework::ProgramDesc* Load(framework::Executor& executor,
+                             framework::Scope& scope,
+                             const std::string& dirname);
+
+}  // namespace inference
+}  // namespace paddle

From f1e32e241971b3603dbe9e1d094656f89c177497 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Tue, 30 Jan 2018 16:12:49 +0800
Subject: [PATCH 127/314] Skip tests of word2vec on CI

---
 python/paddle/v2/fluid/tests/book/test_word2vec.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index cfa8d9580d..766ba9681d 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -116,7 +116,7 @@ def main(use_cuda, is_sparse, parallel):
 
 
 FULL_TEST = os.getenv('FULL_TEST',
-                      '1').lower() in ['true', '1', 't', 'y', 'yes', 'on']
+                      '0').lower() in ['true', '1', 't', 'y', 'yes', 'on']
 SKIP_REASON = "Only run minimum number of tests in CI server, to make CI faster"
 
 

From 4fee15e86003b38973a1fdd943e4a6ef96bd9bbe Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Tue, 30 Jan 2018 16:59:54 +0800
Subject: [PATCH 128/314] Merge test_understand_sentiment together

Into one unit test file
---
 ...c_lstm.py => test_understand_sentiment.py} | 115 +++++++++----
 .../book/test_understand_sentiment_conv.py    | 101 -----------
 .../book/test_understand_sentiment_lstm.py    | 160 ------------------
 3 files changed, 78 insertions(+), 298 deletions(-)
 rename python/paddle/v2/fluid/tests/book/{test_understand_sentiment_dynamic_lstm.py => test_understand_sentiment.py} (52%)
 delete mode 100644 python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
 delete mode 100644 python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py

diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
similarity index 52%
rename from python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
rename to python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
index 529223eba8..2ba9077a26 100644
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
+++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
@@ -1,4 +1,4 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +12,36 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
-import paddle.v2 as paddle
+import unittest
 import paddle.v2.fluid as fluid
+import paddle.v2 as paddle
+import contextlib
+
+
+def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
+                    hid_dim=32):
+    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
+    conv_3 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=3,
+        act="tanh",
+        pool_type="sqrt")
+    conv_4 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=4,
+        act="tanh",
+        pool_type="sqrt")
+    prediction = fluid.layers.fc(input=[conv_3, conv_4],
+                                 size=class_dim,
+                                 act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
+    adam_optimizer.minimize(avg_cost)
+    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    return avg_cost, accuracy
 
 
 def stacked_lstm_net(data,
@@ -51,63 +78,77 @@ def stacked_lstm_net(data,
     avg_cost = fluid.layers.mean(x=cost)
     adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
     adam_optimizer.minimize(avg_cost)
-    accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
-    return avg_cost, accuracy, accuracy.metrics[0]
-
-
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = fluid.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res
-
-
-def main():
-    BATCH_SIZE = 100
-    PASS_NUM = 5
+    accuracy = fluid.layers.accuracy(input=prediction, label=label)
+    return avg_cost, accuracy
 
-    word_dict = paddle.dataset.imdb.word_dict()
-    print "load word dict successfully"
+
+def main(word_dict, net_method, use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    BATCH_SIZE = 128
+    PASS_NUM = 5
     dict_dim = len(word_dict)
     class_dim = 2
 
     data = fluid.layers.data(
         name="words", shape=[1], dtype="int64", lod_level=1)
     label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-    cost, accuracy, acc_out = stacked_lstm_net(
+    cost, acc_out = net_method(
         data, label, input_dim=dict_dim, class_dim=class_dim)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.imdb.train(word_dict), buf_size=1000),
         batch_size=BATCH_SIZE)
-    place = fluid.CPUPlace()
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
     feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
 
     exe.run(fluid.default_startup_program())
 
     for pass_id in xrange(PASS_NUM):
-        accuracy.reset(exe)
         for data in train_data():
             cost_val, acc_val = exe.run(fluid.default_main_program(),
                                         feed=feeder.feed(data),
                                         fetch_list=[cost, acc_out])
-            pass_acc = accuracy.eval(exe)
-            print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
-                  " pass_acc=" + str(pass_acc))
-            if cost_val < 1.0 and acc_val > 0.8:
-                exit(0)
-    exit(1)
+            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
+            if cost_val < 0.4 and acc_val > 0.8:
+                return
+    raise AssertionError("Cost is too large for {0}".format(
+        net_method.__name__))
+
+
+class TestUnderstandSentiment(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.word_dict = paddle.dataset.imdb.word_dict()
+
+    @contextlib.contextmanager
+    def new_program_scope(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+    def test_conv_cpu(self):
+        with self.new_program_scope():
+            main(self.word_dict, net_method=convolution_net, use_cuda=False)
+
+    def test_stacked_lstm_cpu(self):
+        with self.new_program_scope():
+            main(self.word_dict, net_method=stacked_lstm_net, use_cuda=False)
+
+    def test_conv_gpu(self):
+        with self.new_program_scope():
+            main(self.word_dict, net_method=convolution_net, use_cuda=True)
+
+    def test_stacked_lstm_gpu(self):
+        with self.new_program_scope():
+            main(self.word_dict, net_method=stacked_lstm_net, use_cuda=True)
 
 
 if __name__ == '__main__':
-    main()
+    unittest.main()
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
deleted file mode 100644
index df27399dd2..0000000000
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-import numpy as np
-import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-
-
-def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
-                    hid_dim=32):
-    emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
-    conv_3 = fluid.nets.sequence_conv_pool(
-        input=emb,
-        num_filters=hid_dim,
-        filter_size=3,
-        act="tanh",
-        pool_type="sqrt")
-    conv_4 = fluid.nets.sequence_conv_pool(
-        input=emb,
-        num_filters=hid_dim,
-        filter_size=4,
-        act="tanh",
-        pool_type="sqrt")
-    prediction = fluid.layers.fc(input=[conv_3, conv_4],
-                                 size=class_dim,
-                                 act="softmax")
-    cost = fluid.layers.cross_entropy(input=prediction, label=label)
-    avg_cost = fluid.layers.mean(x=cost)
-    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
-    adam_optimizer.minimize(avg_cost)
-    accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
-    return avg_cost, accuracy, accuracy.metrics[0]
-
-
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = fluid.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res
-
-
-def main():
-    BATCH_SIZE = 100
-    PASS_NUM = 5
-
-    word_dict = paddle.dataset.imdb.word_dict()
-    dict_dim = len(word_dict)
-    class_dim = 2
-
-    data = fluid.layers.data(
-        name="words", shape=[1], dtype="int64", lod_level=1)
-    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-    cost, accuracy, acc_out = convolution_net(
-        data, label, input_dim=dict_dim, class_dim=class_dim)
-
-    train_data = paddle.batch(
-        paddle.reader.shuffle(
-            paddle.dataset.imdb.train(word_dict), buf_size=1000),
-        batch_size=BATCH_SIZE)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-    feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
-
-    exe.run(fluid.default_startup_program())
-
-    for pass_id in xrange(PASS_NUM):
-        accuracy.reset(exe)
-        for data in train_data():
-            cost_val, acc_val = exe.run(fluid.default_main_program(),
-                                        feed=feeder.feed(data),
-                                        fetch_list=[cost, acc_out])
-            pass_acc = accuracy.eval(exe)
-            print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
-                  " pass_acc=" + str(pass_acc))
-            if cost_val < 1.0 and pass_acc > 0.8:
-                exit(0)
-    exit(1)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
deleted file mode 100644
index 117f74c59a..0000000000
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
+++ /dev/null
@@ -1,160 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import paddle.v2 as paddle
-import paddle.v2.fluid as fluid
-from paddle.v2.fluid.layer_helper import LayerHelper
-
-
-def lstm(x, c_pre_init, hidden_dim, forget_bias=None):
-    """
-    This function helps create an operator for the LSTM (Long Short Term
-    Memory) cell that can be used inside an RNN.
-    """
-    helper = LayerHelper('lstm_unit', **locals())
-    rnn = fluid.layers.StaticRNN()
-    with rnn.step():
-        c_pre = rnn.memory(init=c_pre_init)
-        x_t = rnn.step_input(x)
-
-        before_fc = fluid.layers.concat(input=[x_t, c_pre], axis=1)
-        after_fc = fluid.layers.fc(input=before_fc, size=hidden_dim * 4)
-
-        dtype = x.dtype
-        c = helper.create_tmp_variable(dtype)
-        h = helper.create_tmp_variable(dtype)
-
-        helper.append_op(
-            type='lstm_unit',
-            inputs={"X": after_fc,
-                    "C_prev": c_pre},
-            outputs={"C": c,
-                     "H": h},
-            attrs={"forget_bias": forget_bias})
-
-        rnn.update_memory(c_pre, c)
-        rnn.output(h)
-
-    return rnn()
-
-
-def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
-    data = fluid.layers.data(
-        name="words",
-        shape=[seq_len * batch_size, 1],
-        append_batch_size=False,
-        dtype="int64",
-        lod_level=1)
-    label = fluid.layers.data(
-        name="label",
-        shape=[batch_size, 1],
-        append_batch_size=False,
-        dtype="int64")
-
-    emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
-    emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
-    emb = fluid.layers.transpose(x=emb, perm=[1, 0, 2])
-
-    c_pre_init = fluid.layers.fill_constant(
-        dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
-    c_pre_init.stop_gradient = False
-    layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
-    layer_1_out = fluid.layers.transpose(x=layer_1_out, perm=[1, 0, 2])
-
-    prediction = fluid.layers.fc(input=layer_1_out,
-                                 size=class_dim,
-                                 act="softmax")
-    cost = fluid.layers.cross_entropy(input=prediction, label=label)
-
-    avg_cost = fluid.layers.mean(x=cost)
-    adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
-    adam_optimizer.minimize(avg_cost)
-    acc = fluid.layers.accuracy(input=prediction, label=label)
-
-    return avg_cost, acc
-
-
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = fluid.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res
-
-
-def chop_data(data, chop_len=80, batch_size=50):
-    data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
-
-    return data[:batch_size]
-
-
-def prepare_feed_data(data, place):
-    tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
-
-    label = np.array(map(lambda x: x[1], data)).astype("int64")
-    label = label.reshape([len(label), 1])
-    tensor_label = fluid.LoDTensor()
-    tensor_label.set(label, place)
-
-    return tensor_words, tensor_label
-
-
-def main():
-    BATCH_SIZE = 100
-    PASS_NUM = 5
-
-    word_dict = paddle.dataset.imdb.word_dict()
-    print "load word dict successfully"
-    dict_dim = len(word_dict)
-    class_dim = 2
-
-    cost, acc = lstm_net(dict_dim=dict_dim, class_dim=class_dim)
-
-    train_data = paddle.batch(
-        paddle.reader.shuffle(
-            paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
-        batch_size=BATCH_SIZE)
-    place = fluid.CPUPlace()
-    exe = fluid.Executor(place)
-
-    exe.run(fluid.default_startup_program())
-
-    for pass_id in xrange(PASS_NUM):
-        for data in train_data():
-            chopped_data = chop_data(data)
-            tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
-
-            outs = exe.run(fluid.default_main_program(),
-                           feed={"words": tensor_words,
-                                 "label": tensor_label},
-                           fetch_list=[cost, acc])
-            cost_val = np.array(outs[0])
-            acc_val = np.array(outs[1])
-
-            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
-            if acc_val > 0.7:
-                exit(0)
-    exit(1)
-
-
-if __name__ == '__main__':
-    main()

From 9b5d41b63697ea9f126b57d28e8d3940d09ce55a Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 30 Jan 2018 17:37:26 +0800
Subject: [PATCH 129/314] make inference_lib_dist

---
 cmake/external/eigen.cmake      | 12 +++++++-----
 cmake/external/gflags.cmake     |  9 ++++++++-
 cmake/external/glog.cmake       |  9 ++++++++-
 cmake/external/protobuf.cmake   |  9 ++++++++-
 paddle/framework/CMakeLists.txt | 13 +++++++------
 paddle/inference/CMakeLists.txt | 13 +++++++++----
 paddle/memory/CMakeLists.txt    | 12 ++++++------
 paddle/platform/CMakeLists.txt  | 14 +++++++-------
 paddle/string/CMakeLists.txt    | 11 ++++++-----
 9 files changed, 66 insertions(+), 36 deletions(-)

diff --git a/cmake/external/eigen.cmake b/cmake/external/eigen.cmake
index d49c8d6011..eb6c0cef57 100644
--- a/cmake/external/eigen.cmake
+++ b/cmake/external/eigen.cmake
@@ -29,8 +29,10 @@ add_dependencies(eigen3 extern_eigen3)
 
 LIST(APPEND external_project_dependencies eigen3)
 
-IF(NOT WITH_C_API AND WITH_FLUID)
-    INSTALL(FILES ${EIGEN_INCLUDE_DIR}/Eigen/Core DESTINATION third_party/eigen3/Eigen)
-    INSTALL(DIRECTORY ${EIGEN_INCLUDE_DIR}/Eigen/src DESTINATION third_party/eigen3/Eigen)
-    INSTALL(DIRECTORY ${EIGEN_INCLUDE_DIR}/unsupported/Eigen DESTINATION third_party/eigen3/unsupported)
-ENDIF()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
+add_custom_target(eigen3_lib
+    COMMAND mkdir -p "${lib_dir}/Eigen" "${lib_dir}/unsupported"
+    COMMAND cp "${EIGEN_INCLUDE_DIR}/Eigen/Core" "${lib_dir}/Eigen"
+    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/Eigen/src" "${lib_dir}/Eigen"
+    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/unsupported/Eigen" "${lib_dir}/unsupported"
+)
diff --git a/cmake/external/gflags.cmake b/cmake/external/gflags.cmake
index 6094630454..9cbc376ba0 100644
--- a/cmake/external/gflags.cmake
+++ b/cmake/external/gflags.cmake
@@ -52,7 +52,7 @@ ADD_DEPENDENCIES(gflags extern_gflags)
 
 LIST(APPEND external_project_dependencies gflags)
 
-IF(WITH_C_API OR WITH_FLUID)
+IF(WITH_C_API)
   INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags)
   IF(ANDROID)
     INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI})
@@ -60,3 +60,10 @@ IF(WITH_C_API OR WITH_FLUID)
     INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
   ENDIF()
 ENDIF()
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
+add_custom_target(gflags_lib
+    COMMAND mkdir -p "${lib_dir}/lib"
+    COMMAND cp -r "${GFLAGS_INCLUDE_DIR}" "${lib_dir}"
+    COMMAND cp "${GFLAGS_LIBRARIES}" "${lib_dir}/lib"
+)
diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake
index 382fbda3b5..0031225a6c 100644
--- a/cmake/external/glog.cmake
+++ b/cmake/external/glog.cmake
@@ -68,7 +68,7 @@ LINK_LIBRARIES(glog gflags)
 
 LIST(APPEND external_project_dependencies glog)
 
-IF(WITH_C_API OR WITH_FLUID)
+IF(WITH_C_API)
   INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog)
   IF(ANDROID)
     INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI})
@@ -76,3 +76,10 @@ IF(WITH_C_API OR WITH_FLUID)
     INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib)
   ENDIF()
 ENDIF()
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
+add_custom_target(glog_lib
+    COMMAND mkdir -p "${lib_dir}/lib"
+    COMMAND cp -r "${GLOG_INCLUDE_DIR}" "${lib_dir}"
+    COMMAND cp "${GLOG_LIBRARIES}" "${lib_dir}/lib"
+)
diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake
index 365a370a9c..ff3d38a691 100644
--- a/cmake/external/protobuf.cmake
+++ b/cmake/external/protobuf.cmake
@@ -250,7 +250,7 @@ IF(NOT PROTOBUF_FOUND)
     SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY}
         CACHE FILEPATH "protoc library." FORCE)
 
-    IF(WITH_C_API OR WITH_FLUID)
+    IF(WITH_C_API)
         INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
         IF(ANDROID)
             INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
@@ -259,6 +259,13 @@ IF(NOT PROTOBUF_FOUND)
         ENDIF()
     ENDIF()
 
+    set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
+    add_custom_target(protobuf_lib
+        COMMAND mkdir -p "${lib_dir}/lib"
+        COMMAND cp -r "${PROTOBUF_INCLUDE_DIR}" "${lib_dir}"
+        COMMAND cp "${PROTOBUF_LITE_LIBRARY}" "${lib_dir}/lib"
+    )
+
     IF(CMAKE_CROSSCOMPILING)
         PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf)
     ELSE()
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 8c28709a68..d394fa5d10 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -92,11 +92,12 @@ cc_test(init_test SRCS init_test.cc DEPS init)
 cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
 cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
       
-if(NOT WITH_C_API AND WITH_FLUID)
-  file(GLOB FRAMEWORK_HEADERS *.h)
-  install(FILES ${FRAMEWORK_HEADERS} DESTINATION include/paddle/framework)
-  install(FILES ${CMAKE_CURRENT_BINARY_DIR}/framework.pb.h DESTINATION include/paddle/framework)
-  install(FILES details/cow_ptr.h details/op_registry.h DESTINATION include/paddle/framework/details)
-endif()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/framework")
+add_custom_target(framework_lib DEPENDS framework_py_proto
+    COMMAND mkdir -p "${lib_dir}/details"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/details/*.h" "${lib_dir}/details"
+    COMMAND cp "${CMAKE_CURRENT_BINARY_DIR}/framework.pb.h" "${lib_dir}"
+)
 
 cc_test(channel_test SRCS channel_test.cc)
diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index 683aaee42a..58c0c59380 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -19,10 +19,15 @@ target_circle_link_libraries(paddle_fluid_shared
 SET_TARGET_PROPERTIES(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 
 # install library & headers
-if(NOT WITH_C_API AND WITH_FLUID)
-  install(FILES io.h DESTINATION include/paddle/inference)
-  install(TARGETS paddle_fluid_shared DESTINATION lib)
-endif()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/inference")
+add_custom_target(inference_lib DEPENDS paddle_fluid_shared
+    COMMAND mkdir -p "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_BINARY_DIR}/libpaddle_fluid.so" "${lib_dir}"
+)
+add_custom_target(inference_lib_dist DEPENDS 
+  inference_lib framework_lib memory_lib platform_lib string_lib
+  gflags_lib glog_lib protobuf_lib eigen3_lib)
 
 add_executable(example example.cc)
 if(APPLE)
diff --git a/paddle/memory/CMakeLists.txt b/paddle/memory/CMakeLists.txt
index 496098f804..fad49346f2 100644
--- a/paddle/memory/CMakeLists.txt
+++ b/paddle/memory/CMakeLists.txt
@@ -15,9 +15,9 @@ cc_library(paddle_memory
 
 cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory)
 
-if(NOT WITH_C_API AND WITH_FLUID)
-  file(GLOB MEMORY_HEADERS *.h)
-  file(GLOB MEMORY_DETAIL_HEADERS detail/*.h)
-  install(FILES ${MEMORY_HEADERS} DESTINATION include/paddle/memory)
-  install(FILES ${MEMORY_DETAIL_HEADERS} DESTINATION include/paddle/memory/detail)
-endif()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/memory")
+add_custom_target(memory_lib
+    COMMAND mkdir -p "${lib_dir}/detail"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/detail/*.h" "${lib_dir}/detail"
+)
diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt
index d68caea997..d70530aadb 100644
--- a/paddle/platform/CMakeLists.txt
+++ b/paddle/platform/CMakeLists.txt
@@ -40,10 +40,10 @@ nv_test(nccl_test SRCS nccl_test.cu DEPS dynload_cuda gpu_info device_context)
 cc_library(profiler SRCS profiler.cc DEPS device_context)
 cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
 
-if(NOT WITH_C_API AND WITH_FLUID)
-  file(GLOB PLATFORM_HEADERS *.h)
-  file(GLOB PLATFORM_dynload_HEADERS dynload/*.h)
-  install(FILES ${PLATFORM_HEADERS} DESTINATION include/paddle/platform)
-  install(FILES ${PLATFORM_HEADERS} DESTINATION include/paddle/platform/dynload)
-  install(FILES details/device_ptr_cast.h DESTINATION include/paddle/platform/details)
-endif()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/platform")
+add_custom_target(platform_lib
+    COMMAND mkdir -p "${lib_dir}/dynload" "${lib_dir}/details"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/dynload/*.h" "${lib_dir}/dynload"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/details/*.h" "${lib_dir}/details"
+)    
diff --git a/paddle/string/CMakeLists.txt b/paddle/string/CMakeLists.txt
index 751776dbb5..234a9a6d03 100644
--- a/paddle/string/CMakeLists.txt
+++ b/paddle/string/CMakeLists.txt
@@ -3,8 +3,9 @@ cc_test(stringpiece_test SRCS piece_test.cc DEPS stringpiece glog gflags)
 cc_test(stringprintf_test SRCS printf_test.cc DEPS glog gflags)
 cc_test(to_string_test SRCS to_string_test.cc)
 
-if(NOT WITH_C_API AND WITH_FLUID)
-  file(GLOB STRING_HEADERS *.h)
-  install(FILES ${STRING_HEADERS} DESTINATION include/paddle/string)
-  install(FILES tinyformat/tinyformat.h DESTINATION include/paddle/string/tinyformat)
-endif()
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/string")
+add_custom_target(string_lib
+    COMMAND mkdir -p "${lib_dir}/tinyformat"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
+    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/tinyformat/*.h" "${lib_dir}/tinyformat"
+)

From 35dec3d7228e2f924ccc6549a420604110640337 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Tue, 30 Jan 2018 17:59:48 +0800
Subject: [PATCH 130/314] Fix bug in unit test.

---
 paddle/operators/multiclass_nms_op.cc         | 84 +++++++++++--------
 .../v2/fluid/tests/test_multiclass_nms_op.py  | 61 +++++++-------
 2 files changed, 82 insertions(+), 63 deletions(-)

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index 5da553a6cc..93c8b5216f 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -41,13 +41,22 @@ class MulticlassNMSOp : public framework::OperatorWithKernel {
                       "The rank of Input(Bboxes) must be 3.");
     PADDLE_ENFORCE_EQ(score_dims.size(), 3,
                       "The rank of Input(Scores) must be 3.");
-    PADDLE_ENFORCE_EQ(box_dims[2], 4);
+    PADDLE_ENFORCE_EQ(box_dims[1], 4);
     PADDLE_ENFORCE_EQ(box_dims[0], score_dims[2]);
 
     // Here the box_dims[0] is not the real dimension of output.
     // It will be rewritten in the computing kernel.
     ctx->SetOutputDim("Out", {box_dims[0], 6});
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::LoDTensor>("Scores")->type()),
+        ctx.device_context());
+  }
 };
 
 template <class T>
@@ -158,12 +167,12 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
                      const Tensor& scores, const Tensor& bboxes,
                      std::map<int, std::vector<int>>* indices,
                      int* num_nmsed_out) const {
-    int64_t background_label = ctx.Attr<int64_t>("background_label");
-    int64_t nms_top_k = ctx.Attr<int64_t>("nms_top_k");
-    int64_t keep_top_k = ctx.Attr<int64_t>("keep_top_k");
+    int64_t background_label = ctx.Attr<int>("background_label");
+    int64_t nms_top_k = ctx.Attr<int>("nms_top_k");
+    int64_t keep_top_k = ctx.Attr<int>("keep_top_k");
     T nms_threshold = static_cast<T>(ctx.Attr<float>("nms_threshold"));
     T nms_eta = static_cast<T>(ctx.Attr<float>("nms_eta"));
-    T score_threshold = static_cast<T>(ctx.Attr<float>("confidence_threshold"));
+    T score_threshold = static_cast<T>(ctx.Attr<float>("score_threshold"));
 
     int64_t class_num = scores.dims()[0];
     int64_t predict_dim = scores.dims()[1];
@@ -173,7 +182,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
       Tensor score = scores.Slice(c, c + 1);
       NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
               &((*indices)[c]));
-      num_det += indices[c].size();
+      num_det += (*indices)[c].size();
     }
 
     *num_nmsed_out = num_det;
@@ -230,8 +239,8 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
         odata[count * kOutputDim + 3] = bdata[1];    // ymin
         odata[count * kOutputDim + 4] = bdata[2];    // xmax
         odata[count * kOutputDim + 5] = bdata[3];    // ymax
+        count++;
       }
-      count++;
     }
   }
 
@@ -240,10 +249,9 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
     auto* scores = ctx.Input<Tensor>("Scores");
     auto* outs = ctx.Output<LoDTensor>("Out");
 
-    auto box_dims = boxes->dims();
     auto score_dims = scores->dims();
 
-    int64_t batch_size = box_dims[0];
+    int64_t batch_size = score_dims[0];
     int64_t class_num = score_dims[1];
     int64_t predict_dim = score_dims[2];
 
@@ -291,35 +299,37 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
              "(Tensor) A 2-D Tensor with shape [M, 4] represents the location "
              "predictions with M bboxes. 4 is the number of "
              "each location coordinates.");
-    AddOutput("Scores",
-              "(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
-              "confidence predictions. N is the batch size, C is the class "
-              "number, M is number of predictions for each class, which is "
-              "the same with Bboxes.");
-    AddAttr<int64_t>(
+    AddInput("Scores",
+             "(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
+             "confidence predictions. N is the batch size, C is the class "
+             "number, M is number of predictions for each class, which is "
+             "the same with Bboxes.");
+    AddAttr<int>(
         "background_label",
         "(int64_t, defalut: 0) "
         "The index of background label, the background label will be ignored.")
         .SetDefault(0);
+    AddAttr<float>("score_threshold",
+                   "(float) "
+                   "Only consider detections whose confidences are larger than "
+                   "a threshold. If not provided, consider all boxes.");
+    AddAttr<int>("nms_top_k",
+                 "(int64_t) "
+                 "Maximum number of detections to be kept according to the "
+                 "confidences aftern the filtering detections based on "
+                 "score_threshold");
     AddAttr<float>("nms_threshold",
                    "(float, defalut: 0.3) "
-                   "The threshold to be used in nms.")
+                   "The threshold to be used in NMS.")
         .SetDefault(0.3);
-    AddAttr<int64_t>("nms_top_k",
-                     "(int64_t) "
-                     "Maximum number of results to be kept.");
     AddAttr<float>("nms_eta",
                    "(float) "
-                   "The parameter for adaptive nms.")
+                   "The parameter for adaptive NMS.")
         .SetDefault(1.0);
-    AddAttr<int64_t>("keep_top_k",
-                     "(int64_t) "
-                     "Number of total bboxes to be kept per image after nms "
-                     "step. -1 means keeping all bboxes after nms step.");
-    AddAttr<float>("confidence_threshold",
-                   "(float) "
-                   "Only consider detections whose confidences are larger than "
-                   "a threshold. If not provided, consider all boxes.");
+    AddAttr<int>("keep_top_k",
+                 "(int64_t) "
+                 "Number of total bboxes to be kept per image after NMS "
+                 "step. -1 means keeping all bboxes after NMS step.");
     AddOutput("Out",
               "(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
               "detections. Each row has 6 values: "
@@ -329,15 +339,21 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
               "offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
               "no detected bbox.");
     AddComment(R"DOC(
-This operators is to do multi-class non maximum suppression (NMS) on a batched
+This operator is to do multi-class non maximum suppression (NMS) on a batched
 of boxes and scores.
 
-This op greedily selects a subset of detection bounding boxes, pruning
-away boxes that have high IOU (intersection over union) overlap (> thresh)
-with already selected boxes.  It operates independently for each class for
-which scores are provided, pruning boxes with score less than a provided
-threshold prior to applying NMS.
+In the NMS step, this operator greedily selects a subset of detection bounding
+boxes that have high scores larger than score_threshold, if providing this
+threshold, then selects the largest nms_top_k confidences scores if nms_top_k
+is larger than -1. Then this operator pruns away boxes that have high IOU
+(intersection over union) overlap with already selected boxes by adaptive
+threshold NMS based on parameters of nms_threshold and nms_eta.
+
+Aftern NMS step, only at most keep_top_k number of total bboxes are to be kept
+per image if keep_top_k is larger than -1.
 
+This operator support multi-class and batched inputs. It applying NMS
+independently for each class.
 )DOC");
   }
 };
diff --git a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
index 60c6488f84..b619c52e55 100644
--- a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
+++ b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
@@ -69,7 +69,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
 
     sorted_indices = np.argsort(-all_scores, axis=0)
     sorted_scores = all_scores[sorted_indices]
-    if top_k < -1 and top_k < sorted_indices.shape[0]:
+    if top_k > -1 and top_k < sorted_indices.shape[0]:
         sorted_indices = sorted_indices[:top_k]
         sorted_scores = sorted_scores[:top_k]
 
@@ -82,7 +82,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
             if keep:
                 kept_idx = selected_indices[k]
                 overlap = iou(boxes[idx], boxes[kept_idx])
-                keep = overlap <= adaptive_threshold
+                keep = True if overlap <= adaptive_threshold else False
             else:
                 break
         if keep:
@@ -103,14 +103,14 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
         if c == background: continue
         indices = nms(boxes, scores[c], score_threshold, nms_threshold,
                       nms_top_k)
-        selected_indices.append((c, indices))
+        for idx in indices:
+            selected_indices.append((c, idx))
         num_det += len(indices)
 
     if keep_top_k > -1 and num_det > keep_top_k:
         score_index = []
-        for c, indices in selected_indices:
-            for idx in indices:
-                score_index.append((scores[c][idx], c, idx))
+        for c, idx in selected_indices:
+            score_index.append((scores[c][idx], c, idx))
 
         sorted_score_index = sorted(
             score_index, key=lambda tup: tup[0], reverse=True)
@@ -134,19 +134,16 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
                                     keep_top_k)
         lod.append(lod[-1] + len(nmsed_outs))
         if len(nmsed_outs) == 0: continue
-        for c, indices in nmsed_outs:
-            for idx in indices:
-                xmin, ymin, xmax, ymax = boxes[idx][:]
-                det_outs.append(
-                    (c, scores[n][c][idx], c, xmin, ymin, xmax, ymax))
+        for c, idx in nmsed_outs:
+            xmin, ymin, xmax, ymax = boxes[idx][:]
+            det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
     return det_outs, lod
 
 
 class TestMulticlassNMSOp(OpTest):
     def setUp(self):
-        self.op_type = 'multiclass_nms'
         N = 7
-        M = 1230
+        M = 1240
         C = 21
         BOX_SIZE = 4
         background = 0
@@ -155,7 +152,17 @@ class TestMulticlassNMSOp(OpTest):
         keep_top_k = 200
         score_threshold = 0.01
 
-        scores = np.random.random((N, C, M)).astype('float32')
+        scores = np.random.random((N * M, C)).astype('float32')
+
+        def softmax(x):
+            shiftx = x - np.max(x).clip(-64.)
+            exps = np.exp(shiftx)
+            return exps / np.sum(exps)
+
+        scores = np.apply_along_axis(softmax, 1, scores)
+        scores = np.reshape(scores, (N, M, C))
+        scores = np.transpose(scores, (0, 2, 1))
+
         boxes = np.random.random((M, BOX_SIZE)).astype('float32')
         boxes[:, 0:2] = boxes[:, 0:2] * 0.5
         boxes[:, 2:4] = boxes[:, 0:2] * 0.5 + 0.5
@@ -163,8 +170,19 @@ class TestMulticlassNMSOp(OpTest):
         nmsed_outs, lod = batched_multiclass_nms(boxes, scores, background,
                                                  score_threshold, nms_threshold,
                                                  nms_top_k, keep_top_k)
+        nmsed_outs = np.array(nmsed_outs).astype('float32')
+
+        self.op_type = 'multiclass_nms'
         self.inputs = {'Bboxes': boxes, 'Scores': scores}
         self.outputs = {'Out': (nmsed_outs, [lod])}
+        self.attrs = {
+            'background_label': 0,
+            'nms_threshold': nms_threshold,
+            'nms_top_k': nms_top_k,
+            'keep_top_k': keep_top_k,
+            'score_threshold': score_threshold,
+            'nms_eta': 1.0,
+        }
 
     def test_check_output(self):
         self.check_output()
@@ -182,18 +200,3 @@ class TestIOU(unittest.TestCase):
 
 if __name__ == '__main__':
     unittest.main()
-    # N = 7
-    # M = 8
-    # C = 5
-    # BOX_SIZE = 4
-    # background = 0
-    # nms_threshold = 0.3
-    # nms_top_k = 400
-    # keep_top_k = 200
-    # score_threshold = 0.5
-
-    # scores = np.random.random((N, C, M)).astype('float32')
-    # boxes = np.random.random((M, BOX_SIZE)).astype('float32')
-    # boxes[:, 0 : 2] = boxes[:, 0 : 2] * 0.5
-    # boxes[:, 2 : 4] = boxes[:, 0 : 2] * 0.5 + 0.5
-    # print nmsed_outs, lod

From 6e17babe49a7fdeb4f345c83d347f217d05e7e77 Mon Sep 17 00:00:00 2001
From: xzl <zlx_hg@163.com>
Date: Tue, 30 Jan 2018 19:05:53 +0800
Subject: [PATCH 131/314] More efficient, add check on python side

---
 paddle/operators/CMakeLists.txt         |  1 -
 paddle/operators/math/depthwise_conv.cu | 52 ++++++++++++-------------
 python/paddle/v2/fluid/layers/nn.py     |  3 +-
 3 files changed, 26 insertions(+), 30 deletions(-)

diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 8b442af45b..f7d600414f 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -159,7 +159,6 @@ if (WITH_GPU)
 op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS
     vol2col depthwise_conv)
 
-# op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS vol2col)
 op_library(edit_distance_op SRCS edit_distance_op.cc edit_distance_op.cu DEPS math_function)
 op_library(pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling)
 op_library(conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
diff --git a/paddle/operators/math/depthwise_conv.cu b/paddle/operators/math/depthwise_conv.cu
index 23e26e8827..4aa38151e6 100644
--- a/paddle/operators/math/depthwise_conv.cu
+++ b/paddle/operators/math/depthwise_conv.cu
@@ -46,16 +46,18 @@ __global__ void KernelDepthwiseConv(
         -padding_height + h_out * stride_height + filter_height - 1;
     const int w_in_end =
         -padding_width + w_out * stride_width + filter_width - 1;
+
+    const int in_offset =
+        ((batch * input_channels + c_in) * input_height) * input_width;
+
     if ((h_in_start >= 0) && (h_in_end < input_height) && (w_in_start >= 0) &&
         (w_in_end < input_width)) {
       for (int kh = 0; kh < filter_height; ++kh) {
         for (int kw = 0; kw < filter_width; ++kw) {
-          const int h_in = -padding_height + h_out * stride_height + kh;
-          const int w_in = -padding_width + w_out * stride_width + kw;
-          const int offset =
-              ((batch * input_channels + c_in) * input_height + h_in) *
-                  input_width +
-              w_in;
+          const int h_in = h_in_start + kh;
+          const int w_in = w_in_start + kw;
+          const int offset = in_offset + h_in * input_width + w_in;
+
           value += (*weight) * input_data[offset];
           ++weight;
         }
@@ -63,14 +65,11 @@ __global__ void KernelDepthwiseConv(
     } else {
       for (int kh = 0; kh < filter_height; ++kh) {
         for (int kw = 0; kw < filter_width; ++kw) {
-          const int h_in = -padding_height + h_out * stride_height + kh;
-          const int w_in = -padding_width + w_out * stride_width + kw;
+          const int h_in = h_in_start + kh;
+          const int w_in = w_in_start + kw;
           if ((h_in >= 0) && (h_in < input_height) && (w_in >= 0) &&
               (w_in < input_width)) {
-            const int offset =
-                ((batch * input_channels + c_in) * input_height + h_in) *
-                    input_width +
-                w_in;
+            const int offset = in_offset + h_in * input_width + w_in;
             value += (*weight) * input_data[offset];
           }
           ++weight;
@@ -159,36 +158,33 @@ __global__ void KernelDepthwiseConvFilterGrad(
     const int h_in_end =
         -padding_height + h_out * stride_height + filter_height;
     const int w_in_end = -padding_width + w_out * stride_width + filter_width;
+    const int in_offset =
+        (batch * input_channels + c_in) * input_height * input_width;
+
+    T* addr_offset = filter_grad_data + c_out * filter_height * filter_width;
+
     if ((h_in_start >= 0) && (h_in_end < input_height) && (w_in_start >= 0) &&
         (w_in_end < input_width)) {
       for (int kw = 0; kw < filter_width; kw++) {
         for (int kh = 0; kh < filter_height; kh++) {
-          const int h_in = -padding_height + h_out * stride_height + kh;
-          const int w_in = -padding_width + w_out * stride_width + kw;
-          const int offset =
-              ((batch * input_channels + c_in) * input_height + h_in) *
-                  input_width +
-              w_in;
+          const int h_in = h_in_start + kh;
+          const int w_in = w_in_start + kw;
+          const int offset = in_offset + h_in * input_width + w_in;
           const T diff_temp = output_grad_data[index] * input_data[offset];
-          T* addr = filter_grad_data + c_out * filter_height * filter_width +
-                    kh * filter_width + kw;
+          T* addr = addr_offset + kh * filter_width + kw;
           paddle::platform::CudaAtomicAdd(addr, diff_temp);
         }
       }
     } else {
       for (int kw = 0; kw < filter_width; kw++) {
         for (int kh = 0; kh < filter_height; kh++) {
-          const int h_in = -padding_height + h_out * stride_height + kh;
-          const int w_in = -padding_width + w_out * stride_width + kw;
+          const int h_in = h_in_start + kh;
+          const int w_in = w_in_start + kw;
           if ((h_in >= 0) && (h_in < input_height) && (w_in >= 0) &&
               (w_in < input_width)) {
-            const int offset =
-                ((batch * input_channels + c_in) * input_height + h_in) *
-                    input_width +
-                w_in;
+            const int offset = in_offset + h_in * input_width + w_in;
             const T diff_temp = output_grad_data[index] * input_data[offset];
-            T* addr = filter_grad_data + c_out * filter_height * filter_width +
-                      kh * filter_width + kw;
+            T* addr = addr_offset + kh * filter_width + kw;
             paddle::platform::CudaAtomicAdd(addr, diff_temp);
           }
         }
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 40c7ec5866..a047cc4eec 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -1013,7 +1013,8 @@ def conv2d(input,
     num_channels = input.shape[1]
 
     l_type = 'conv2d'
-    if num_channels == groups and not use_cudnn:
+    if (num_channels == groups and num_filters % num_channels == 0 and
+            not use_cudnn):
         l_type = 'depthwise_conv'
 
     helper = LayerHelper(l_type, **locals())

From f5d9336825e8f27fd02c260b5687c78ded61ed67 Mon Sep 17 00:00:00 2001
From: Liu Yiqun <liuyiqun01@baidu.com>
Date: Tue, 30 Jan 2018 10:55:07 +0000
Subject: [PATCH 132/314] Unify the definition of kFeedOpType and kFetchOpType.

---
 paddle/framework/executor.cc                  |  3 ---
 paddle/framework/feed_fetch_type.h            |  4 ++++
 paddle/framework/program_desc.cc              | 24 +++++++++----------
 paddle/framework/program_desc.h               |  5 ++--
 paddle/framework/prune.cc                     |  3 +--
 paddle/inference/io.cc                        |  7 +++---
 paddle/inference/io.h                         |  5 ----
 .../book/test_inference_recognize_digits.cc   |  6 ++---
 8 files changed, 25 insertions(+), 32 deletions(-)

diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index cbf3ec7526..4f87cf8b95 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -33,9 +33,6 @@ DEFINE_bool(check_nan_inf, false,
 namespace paddle {
 namespace framework {
 
-const std::string kFeedOpType = "feed";
-const std::string kFetchOpType = "fetch";
-
 Executor::Executor(const platform::Place& place) : place_(place) {}
 
 static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
diff --git a/paddle/framework/feed_fetch_type.h b/paddle/framework/feed_fetch_type.h
index 9bc4a90c44..168f456675 100644
--- a/paddle/framework/feed_fetch_type.h
+++ b/paddle/framework/feed_fetch_type.h
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <string>
 #include <vector>
 #include "paddle/framework/lod_tensor.h"
 
@@ -20,5 +21,8 @@ namespace paddle {
 namespace framework {
 using FeedFetchType = LoDTensor;
 using FeedFetchList = std::vector<FeedFetchType>;
+
+static const std::string kFeedOpType = "feed";
+static const std::string kFetchOpType = "fetch";
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index e59e392dfd..15ea4035c6 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -14,13 +14,11 @@ limitations under the License. */
 
 #include "paddle/framework/program_desc.h"
 #include "paddle/framework/block_desc.h"
+#include "paddle/framework/feed_fetch_type.h"
 
 namespace paddle {
 namespace framework {
 
-const std::string kFeedOpType = "feed";
-const std::string kFetchOpType = "fetch";
-
 BlockDesc *ProgramDesc::AppendBlock(const BlockDesc &parent) {
   auto *b = desc_.add_blocks();
   b->set_parent_idx(parent.ID());
@@ -67,26 +65,26 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
   }
 }
 
-const std::vector<std::string> ProgramDesc::GetFeedVarNames() {
+const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
   BlockDesc *global_block = blocks_[0].get();
-  std::vector<std::string> feed_var_names;
+  std::vector<std::string> feed_target_names;
   for (auto *op : global_block->AllOps()) {
-    if (op->Type() == "feed") {
-      feed_var_names.insert(feed_var_names.begin(), op->Output("Out")[0]);
+    if (op->Type() == kFeedOpType) {
+      feed_target_names.insert(feed_target_names.begin(), op->Output("Out")[0]);
     }
   }
-  return feed_var_names;
+  return feed_target_names;
 }
 
-const std::vector<std::string> ProgramDesc::GetFetchVarNames() {
+const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
   BlockDesc *global_block = blocks_[0].get();
-  std::vector<std::string> fetch_var_names;
+  std::vector<std::string> fetch_target_names;
   for (auto *op : global_block->AllOps()) {
-    if (op->Type() == "fetch") {
-      fetch_var_names.push_back(op->Input("X")[0]);
+    if (op->Type() == kFetchOpType) {
+      fetch_target_names.push_back(op->Input("X")[0]);
     }
   }
-  return fetch_var_names;
+  return fetch_target_names;
 }
 
 }  // namespace framework
diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h
index 2c3883275a..b9741b3139 100644
--- a/paddle/framework/program_desc.h
+++ b/paddle/framework/program_desc.h
@@ -45,9 +45,8 @@ class ProgramDesc {
 
   proto::ProgramDesc *Proto();
 
-  const std::vector<std::string> GetFeedVarNames();
-
-  const std::vector<std::string> GetFetchVarNames();
+  const std::vector<std::string> GetFeedTargetNames();
+  const std::vector<std::string> GetFetchTargetNames();
 
  private:
   proto::ProgramDesc desc_;
diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index bff8e0bcea..db63cd12c9 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -21,12 +21,11 @@ limitations under the License. */
 #include <vector>
 
 #include <glog/logging.h>
+#include "paddle/framework/feed_fetch_type.h"
 
 namespace paddle {
 namespace framework {
 
-const std::string kFeedOpType = "feed";
-const std::string kFetchOpType = "fetch";
 const std::string kDropOutOpType = "dropout";
 const std::string kBatchNormOpType = "batch_norm";
 
diff --git a/paddle/inference/io.cc b/paddle/inference/io.cc
index d1842ec938..556f235d16 100644
--- a/paddle/inference/io.cc
+++ b/paddle/inference/io.cc
@@ -13,13 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/inference/io.h"
+
 #include <fstream>
+#include "paddle/framework/block_desc.h"
+#include "paddle/framework/feed_fetch_type.h"
 
 namespace paddle {
 namespace inference {
 
-const std::string kFeedOpType = "feed";
-
 bool IsParameter(const framework::VarDesc* var,
                  const framework::ProgramDesc* main_program) {
   if (var->Persistable()) {
@@ -27,7 +28,7 @@ bool IsParameter(const framework::VarDesc* var,
     for (size_t i = 0; i < main_program->Size(); ++i) {
       const framework::BlockDesc& block = main_program->Block(i);
       for (auto* op : block.AllOps()) {
-        if (op->Type() == kFeedOpType) {
+        if (op->Type() == framework::kFeedOpType) {
           continue;
         }
         for (auto input_argument_name : op->InputArgumentNames()) {
diff --git a/paddle/inference/io.h b/paddle/inference/io.h
index 400f5af8c5..fa9a620764 100644
--- a/paddle/inference/io.h
+++ b/paddle/inference/io.h
@@ -16,18 +16,13 @@ limitations under the License. */
 
 #include <string>
 #include <vector>
-#include "paddle/framework/block_desc.h"
 #include "paddle/framework/executor.h"
 #include "paddle/framework/program_desc.h"
 #include "paddle/framework/scope.h"
-#include "paddle/framework/var_desc.h"
 
 namespace paddle {
 namespace inference {
 
-bool IsParameter(const framework::VarDesc* var,
-                 const framework::ProgramDesc* main_program);
-
 void LoadPersistables(framework::Executor& executor,
                       framework::Scope& scope,
                       const std::string& dirname,
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index d8e4c4d7ee..a2cdd60752 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -33,11 +33,11 @@ void TestInference(const std::string& dirname,
   // 2. Initialize the inference_program and load all parameters from file
   auto* inference_program = paddle::inference::Load(executor, *scope, dirname);
 
-  // 3. Get the feed_var_names and fetch_var_names
+  // 3. Get the feed_target_names and fetch_target_names
   const std::vector<std::string>& feed_target_names =
-      inference_program->GetFeedVarNames();
+      inference_program->GetFeedTargetNames();
   const std::vector<std::string>& fetch_target_names =
-      inference_program->GetFetchVarNames();
+      inference_program->GetFetchTargetNames();
 
   // 4. Prepare inputs
   std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;

From d93959f0fb6580af95aea2645f99b163d69c82f5 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 30 Jan 2018 19:07:40 +0800
Subject: [PATCH 133/314] perf enhance reuse connection

---
 paddle/operators/send_op.cc                   | 27 +++++++++++++------
 .../paddle/v2/fluid/distribute_transpiler.py  |  9 ++++++-
 2 files changed, 27 insertions(+), 9 deletions(-)

diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index bb719dc2a8..0be3b37859 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/op_registry.h"
 
+#include <sys/time.h>
 #include <future>
 #include "paddle/operators/detail/grpc_client.h"
 
@@ -42,28 +43,35 @@ class SendOp : public framework::OperatorBase {
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);
+
+    auto client_var_name = Output("RPCClient");
+    PADDLE_ENFORCE_NOT_NULL(scope.FindVar(client_var_name),
+                            "Can not find variable '%s' in the scope.",
+                            client_var_name);
+    auto* client_var = scope.FindVar(client_var_name);
+    detail::RPCClient* rpc_client = client_var->GetMutable<detail::RPCClient>();
+
     for (size_t i = 0; i < ins.size(); i++) {
       VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
-      client_.AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
+      rpc_client->AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
     }
-    PADDLE_ENFORCE(client_.Wait());
+    PADDLE_ENFORCE(rpc_client->Wait());
 
     for (auto& ep : endpoints) {
       VLOG(3) << "batch barrier, ep: " << ep;
-      client_.AsyncSendBatchBarrier(ep);
+      rpc_client->AsyncSendBatchBarrier(ep);
     }
-    PADDLE_ENFORCE(client_.Wait());
+    PADDLE_ENFORCE(rpc_client->Wait());
 
     for (size_t i = 0; i < outs.size(); i++) {
       VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
-      client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
+      rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
     }
-
-    PADDLE_ENFORCE(client_.Wait());
+    PADDLE_ENFORCE(rpc_client->Wait());
   }
 
  private:
-  mutable detail::RPCClient client_;
+  // mutable detail::RPCClient client_;
 };
 
 class SendOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -73,6 +81,9 @@ class SendOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X", "(Tensor) Input tensor to be sent").AsDuplicable();
     AddOutput("Out", "(Tensor) Output tensor to be received from server")
         .AsDuplicable();
+    AddOutput("RPCClient",
+              "(RPCClient) The RPC client object which is"
+              "initialized at most once.");
     AddComment(R"DOC(
 Send operator
 
diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 77f80442e0..a4464a281a 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -153,11 +153,18 @@ class DistributeTranspiler:
             self.param_grad_ep_mapping[ep]["params"].append(param)
             self.param_grad_ep_mapping[ep]["grads"].append(grad)
 
+        rpc_client_var = program.global_block().create_var(
+            name="RPC_CLIENT_VAR",
+            psersistable=True,
+            dtype='float32',  # dtype and shape is not used in fact
+            shape=[0])
+
         # create send_op
         send_op = program.global_block().append_op(
             type="send",
             inputs={"X": send_inputs},
-            outputs={"Out": send_outputs},
+            outputs={"Out": send_outputs,
+                     "RPCClient": rpc_client_var},
             attrs={"endpoints": pserver_endpoints,
                    "epmap": eplist})
         # step4

From 683c5a3eb58ad3c75644a822b2e159e6b37b5b49 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Tue, 30 Jan 2018 19:09:19 +0800
Subject: [PATCH 134/314] clean up code

---
 paddle/operators/send_op.cc | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index 0be3b37859..be41b527f2 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -19,7 +19,6 @@ limitations under the License. */
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/op_registry.h"
 
-#include <sys/time.h>
 #include <future>
 #include "paddle/operators/detail/grpc_client.h"
 
@@ -69,9 +68,6 @@ class SendOp : public framework::OperatorBase {
     }
     PADDLE_ENFORCE(rpc_client->Wait());
   }
-
- private:
-  // mutable detail::RPCClient client_;
 };
 
 class SendOpMaker : public framework::OpProtoAndCheckerMaker {

From 7d303bdc69232846adc0a0ae3a2b27b168bf6367 Mon Sep 17 00:00:00 2001
From: caoying03 <caoying03@baidu.com>
Date: Tue, 30 Jan 2018 18:12:52 +0800
Subject: [PATCH 135/314] fix the bug that dropout always use a fixed seed.

---
 paddle/operators/dropout_op.cc                |  7 ++++
 paddle/operators/dropout_op.cu                |  6 ++-
 paddle/operators/dropout_op.h                 |  8 +++-
 python/paddle/v2/fluid/layers/nn.py           | 39 +++++++++++++++++--
 .../paddle/v2/fluid/tests/test_dropout_op.py  |  8 ++--
 5 files changed, 58 insertions(+), 10 deletions(-)

diff --git a/paddle/operators/dropout_op.cc b/paddle/operators/dropout_op.cc
index 35cb18797f..5274aa204e 100644
--- a/paddle/operators/dropout_op.cc
+++ b/paddle/operators/dropout_op.cc
@@ -51,6 +51,13 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
                          "'dropout_prob' must be between 0.0 and 1.0.");
         });
     AddAttr<bool>("is_test", "True if in test phase.").SetDefault(false);
+    AddAttr<bool>("fix_seed",
+                  "A flag indicating whether to use a fixed seed to generate "
+                  "random mask. NOTE: DO NOT set this flag to true in "
+                  "training. Setting this flag to true is only useful in "
+                  "unittest or for debug that always the same output units "
+                  "will be dropped.")
+        .SetDefault(false);
     AddAttr<int>("seed", "Dropout random seed.").SetDefault(0);
 
     AddComment(R"DOC(
diff --git a/paddle/operators/dropout_op.cu b/paddle/operators/dropout_op.cu
index c56930336e..84d78445a4 100644
--- a/paddle/operators/dropout_op.cu
+++ b/paddle/operators/dropout_op.cu
@@ -62,7 +62,11 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
       int size = framework::product(mask->dims());
-      int seed = context.Attr<int>("seed");
+
+      std::random_device rnd;
+      int seed =
+          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+
       thrust::counting_iterator<unsigned int> index_sequence_begin(0);
       thrust::transform(index_sequence_begin, index_sequence_begin + size,
                         thrust::device_ptr<T>(mask_data),
diff --git a/paddle/operators/dropout_op.h b/paddle/operators/dropout_op.h
index c90b8d277e..46e5dbc64f 100644
--- a/paddle/operators/dropout_op.h
+++ b/paddle/operators/dropout_op.h
@@ -38,9 +38,15 @@ class CPUDropoutKernel : public framework::OpKernel<T> {
     if (!context.Attr<bool>("is_test")) {
       auto* mask = context.Output<Tensor>("Mask");
       auto* mask_data = mask->mutable_data<T>(context.GetPlace());
-      int seed = context.Attr<int>("seed");
+
+      // NOTE: fixed seed should only be used in unittest or for debug.
+      // Guarantee to use random seed in training.
+      std::random_device rnd;
       std::minstd_rand engine;
+      int seed =
+          context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
       engine.seed(seed);
+
       std::uniform_real_distribution<float> dist(0, 1);
       size_t size = framework::product(mask->dims());
       for (size_t i = 0; i < size; ++i) {
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index d11dccfd22..c38e21087d 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -847,7 +847,35 @@ def cos_sim(X, Y, **kwargs):
     return out
 
 
-def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs):
+def dropout(x, dropout_prob, is_test=False, seed=None, **kwargs):
+    """
+    Computes dropout.
+
+    Drop or keep each element of `x` independently. Dropout is a regularization
+    technique for reducing overfitting by preventing neuron co-adaption during
+    training. The dropout operator randomly set (according to the given dropout
+    probability) the outputs of some units to zero, while others are remain
+    unchanged.
+
+    Args:
+       x(variable): The input tensor.
+       dropout_prob(float): Probability of setting units to zero.
+       is_test(bool): A flag indicating whether it is in test phrase or not.
+       seed(int): A Python integer used to create random seeds. If this
+                  parameter is set to None, a random seed is used.
+                  NOTE: If an integer seed is given, always the same output
+                  units will be dropped. DO NOT use a fixed seed in training.
+
+    Returns:
+        Variable: A tensor variable.
+
+    Examples:
+        .. code-block:: python
+
+          x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+          droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
+    """
+
     helper = LayerHelper('dropout', **kwargs)
     out = helper.create_tmp_variable(dtype=x.dtype)
     mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
@@ -856,9 +884,12 @@ def dropout(x, dropout_prob, is_test=False, seed=0, **kwargs):
         inputs={'X': [x]},
         outputs={'Out': [out],
                  'Mask': [mask]},
-        attrs={'dropout_prob': dropout_prob,
-               'is_test': is_test,
-               'seed': seed})
+        attrs={
+            'dropout_prob': dropout_prob,
+            'is_test': is_test,
+            'fix_seed': seed is not None,
+            'seed': seed if seed is not None else 0
+        })
     return out
 
 
diff --git a/python/paddle/v2/fluid/tests/test_dropout_op.py b/python/paddle/v2/fluid/tests/test_dropout_op.py
index 107b9567dc..b0c55df9f5 100644
--- a/python/paddle/v2/fluid/tests/test_dropout_op.py
+++ b/python/paddle/v2/fluid/tests/test_dropout_op.py
@@ -21,7 +21,7 @@ class TestDropoutOp(OpTest):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64)).astype('float32')
@@ -38,7 +38,7 @@ class TestDropoutOp2(TestDropoutOp):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 1.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 1.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': np.zeros((32, 64)).astype('float32'),
             'Mask': np.zeros((32, 64)).astype('float32')
@@ -49,7 +49,7 @@ class TestDropoutOp3(TestDropoutOp):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64, 2)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.0, 'is_test': False}
+        self.attrs = {'dropout_prob': 0.0, 'fix_seed': True, 'is_test': False}
         self.outputs = {
             'Out': self.inputs['X'],
             'Mask': np.ones((32, 64, 2)).astype('float32')
@@ -60,7 +60,7 @@ class TestDropoutOp4(OpTest):
     def setUp(self):
         self.op_type = "dropout"
         self.inputs = {'X': np.random.random((32, 64)).astype("float32")}
-        self.attrs = {'dropout_prob': 0.35, 'is_test': True}
+        self.attrs = {'dropout_prob': 0.35, 'fix_seed': True, 'is_test': True}
         self.outputs = {
             'Out': self.inputs['X'] * (1.0 - self.attrs['dropout_prob'])
         }

From 09570b48dd40a52009b66e93af6108cb308e361d Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 15:22:52 +0800
Subject: [PATCH 136/314] layer norm -> scale + bias

---
 paddle/operators/layer_norm_op.cc             | 19 ++++++-------
 .../v2/fluid/tests/test_layer_norm_op.py      | 27 ++++++++++---------
 2 files changed, 25 insertions(+), 21 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 125ac9f53f..5821afe9f6 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -45,11 +45,12 @@ class LayerNormOp : public framework::OperatorWithKernel {
 
     auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
 
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], left);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], left);
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right);
 
     ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
     ctx->SetOutputDim("Mean", {left});
@@ -143,10 +144,10 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
 
     // TODO(zcd): Some thinking about output_map, is it appropriate that
     // `output_map` and `input_map` point to the same memory.
-    auto inv_std_scale = var_map.unaryExpr(inv_std_func);
+    auto inv_std = var_map.unaryExpr(inv_std_func);
     output_map = (input_map - mean_map.replicate(1, right))
-                     .cwiseProduct(inv_std_scale.replicate(1, right))
-                     .cwiseProduct(scale_map.replicate(left, 1)) -
+                     .cwiseProduct(inv_std.replicate(1, right))
+                     .cwiseProduct(scale_map.replicate(left, 1)) +
                  bias_map.replicate(left, 1);
   }
 };
@@ -230,7 +231,7 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
       auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
-      d_bias_map = d_y_map.colwise().mean();
+      d_bias_map = d_y_map.colwise().sum();
     }
     if (d_scale) {
       d_scale->mutable_data<T>(ctx.GetPlace());
@@ -245,7 +246,7 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
                    var_map.unaryExpr(inv_std_func).replicate(1, right))
                .cwiseProduct(d_y_map))
               .colwise()
-              .mean();
+              .sum();
     }
 
     if (d_x) {
@@ -269,14 +270,14 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
                          .replicate(1, right);
       // dy_var_dx
       auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                               .cwiseProduct(scale_map.replicate(left, 1))
                                .cwiseProduct(d_y_map)
                                .rowwise()
                                .sum();
       auto dvar_end = var_map.unaryExpr(inv_std_func)
                           .unaryExpr(triple_product_func)
                           .cwiseProduct(dvar_end_part)
-                          .replicate(1, right)
-                          .cwiseProduct(scale_map.replicate(left, 1));
+                          .replicate(1, right);
       auto dx_var =
           (T(-1.0) / right) *
           (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 9264cf4b79..d27d1d8138 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -49,35 +49,38 @@ def _reference_layer_norm_naive(x, scale, beta, epsilon, begin_norm_axis=1):
 
 def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     x_shape = x.shape
+    scale_shape = scale.shape
     N = reduce(mul, x_shape[0:begin_norm_axis], 1)
     D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
     grad_y.shape = [N, D]
     x.shape = [N, D]
     mean.shape = [N, 1]
     var.shape = [N, 1]
+    scale.shape = [1, D]
 
-    d_scale = np.sum(grad_y, axis=1).reshape([1, D])
-    d_bias = scale.reshape([1, D]) * np.sum((
-        (x - mean) * np.sqrt(1 / var)) * grad_y,
-                                            axis=1).reshape([1, D])
+    d_bias = np.sum(grad_y, axis=0).reshape([1, D])
+    d_scale = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y,
+                     axis=0).reshape([1, D])
 
-    dx_end = np.sqrt(1.0 / var) * grad_y
+    dx_end = scale * np.sqrt(1.0 / var) * grad_y
 
-    d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y, axis=1).reshape([N, 1])
+    d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y * scale, axis=1).reshape(
+        [N, 1])
     # d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
     #     [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
     #                np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
-    d_mean = 1.0 / D * (d_mean_0)
+    d_mean = 1.0 / D * d_mean_0
 
-    d_std = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape([N, 1]) * (
-        1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
+    d_std = np.sum(
+        -1.0 / var * (x - mean) * grad_y * scale, axis=1).reshape([N, 1]) * (
+            1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
 
-    grad_x = scale.reshape([1, D]) * (dx_end + d_mean + d_std)
+    grad_x = dx_end + d_mean + d_std
 
     grad_y.shape = x_shape
     x.shape = x_shape
-
-    return grad_x, d_bias, d_scale
+    scale.shape = scale_shape
+    return grad_x, d_scale, d_bias
 
 
 def create_or_get_tensor(scope, var_name, var, place):

From db8da0259abaf72ff8b1c42390ce03c091bbf17a Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Tue, 30 Jan 2018 04:05:57 -0800
Subject: [PATCH 137/314] fix comments (#7978)

---
 paddle/framework/program_desc.cc | 20 ++++++++++----------
 paddle/framework/program_desc.h  |  5 ++---
 paddle/inference/example.cc      | 23 +++++++++++------------
 paddle/inference/io.cc           | 24 ++++++++++++------------
 paddle/inference/io.h            | 11 ++++++-----
 5 files changed, 41 insertions(+), 42 deletions(-)

diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index e59e392dfd..b2368e3a27 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -67,26 +67,26 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
   }
 }
 
-const std::vector<std::string> ProgramDesc::GetFeedVarNames() {
+const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
   BlockDesc *global_block = blocks_[0].get();
-  std::vector<std::string> feed_var_names;
+  std::vector<std::string> feed_target_names;
   for (auto *op : global_block->AllOps()) {
-    if (op->Type() == "feed") {
-      feed_var_names.insert(feed_var_names.begin(), op->Output("Out")[0]);
+    if (op->Type() == kFeedOpType) {
+      feed_target_names.insert(feed_target_names.begin(), op->Output("Out")[0]);
     }
   }
-  return feed_var_names;
+  return feed_target_names;
 }
 
-const std::vector<std::string> ProgramDesc::GetFetchVarNames() {
+const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
   BlockDesc *global_block = blocks_[0].get();
-  std::vector<std::string> fetch_var_names;
+  std::vector<std::string> fetch_target_names;
   for (auto *op : global_block->AllOps()) {
-    if (op->Type() == "fetch") {
-      fetch_var_names.push_back(op->Input("X")[0]);
+    if (op->Type() == kFetchOpType) {
+      fetch_target_names.push_back(op->Input("X")[0]);
     }
   }
-  return fetch_var_names;
+  return fetch_target_names;
 }
 
 }  // namespace framework
diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h
index 2c3883275a..b9741b3139 100644
--- a/paddle/framework/program_desc.h
+++ b/paddle/framework/program_desc.h
@@ -45,9 +45,8 @@ class ProgramDesc {
 
   proto::ProgramDesc *Proto();
 
-  const std::vector<std::string> GetFeedVarNames();
-
-  const std::vector<std::string> GetFetchVarNames();
+  const std::vector<std::string> GetFeedTargetNames();
+  const std::vector<std::string> GetFetchTargetNames();
 
  private:
   proto::ProgramDesc desc_;
diff --git a/paddle/inference/example.cc b/paddle/inference/example.cc
index 5173779c62..ac2aedd88b 100644
--- a/paddle/inference/example.cc
+++ b/paddle/inference/example.cc
@@ -40,15 +40,15 @@ int main(int argc, char** argv) {
   std::string dirname = FLAGS_dirname;
 
   // 2. Initialize the inference program
-  auto* inference_program = paddle::inference::Load(*executor, *scope, dirname);
+  auto inference_program = paddle::inference::Load(*executor, *scope, dirname);
 
   // 3. Optional: perform optimization on the inference_program
 
-  // 4. Get the feed_var_names and fetch_var_names
-  const std::vector<std::string>& feed_var_names =
-      inference_program->GetFeedVarNames();
-  const std::vector<std::string>& fetch_var_names =
-      inference_program->GetFetchVarNames();
+  // 4. Get the feed_target_names and fetch_target_names
+  const std::vector<std::string>& feed_target_names =
+      inference_program->GetFeedTargetNames();
+  const std::vector<std::string>& fetch_target_names =
+      inference_program->GetFetchTargetNames();
 
   // 5. Generate input
   paddle::framework::LoDTensor input;
@@ -68,14 +68,14 @@ int main(int argc, char** argv) {
   std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
 
   // set_feed_variable
-  for (size_t i = 0; i < feed_var_names.size(); ++i) {
-    feed_targets[feed_var_names[i]] = &feeds[i];
+  for (size_t i = 0; i < feed_target_names.size(); ++i) {
+    feed_targets[feed_target_names[i]] = &feeds[i];
   }
 
   // get_fetch_variable
-  fetchs.resize(fetch_var_names.size());
-  for (size_t i = 0; i < fetch_var_names.size(); ++i) {
-    fetch_targets[fetch_var_names[i]] = &fetchs[i];
+  fetchs.resize(fetch_target_names.size());
+  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
+    fetch_targets[fetch_target_names[i]] = &fetchs[i];
   }
 
   // Run the inference program
@@ -97,7 +97,6 @@ int main(int argc, char** argv) {
     std::cout << std::endl;
   }
 
-  delete inference_program;
   delete scope;
   delete executor;
 
diff --git a/paddle/inference/io.cc b/paddle/inference/io.cc
index 98b33d656d..f6d901381e 100644
--- a/paddle/inference/io.cc
+++ b/paddle/inference/io.cc
@@ -21,11 +21,11 @@ namespace inference {
 const std::string kFeedOpType = "feed";
 
 bool IsParameter(const framework::VarDesc* var,
-                 const framework::ProgramDesc* main_program) {
+                 const framework::ProgramDesc& main_program) {
   if (var->Persistable()) {
     // There are many unreachable variables in the program
-    for (size_t i = 0; i < main_program->Size(); ++i) {
-      const framework::BlockDesc& block = main_program->Block(i);
+    for (size_t i = 0; i < main_program.Size(); ++i) {
+      const framework::BlockDesc& block = main_program.Block(i);
       for (auto* op : block.AllOps()) {
         if (op->Type() == kFeedOpType) {
           continue;
@@ -44,12 +44,12 @@ bool IsParameter(const framework::VarDesc* var,
 void LoadPersistables(framework::Executor& executor,
                       framework::Scope& scope,
                       const std::string& dirname,
-                      framework::ProgramDesc* main_program) {
-  framework::BlockDesc* global_block = main_program->MutableBlock(0);
+                      const framework::ProgramDesc& main_program) {
+  const framework::BlockDesc& global_block = main_program.Block(0);
 
   framework::ProgramDesc* load_program = new framework::ProgramDesc();
   framework::BlockDesc* load_block = load_program->MutableBlock(0);
-  for (auto* var : global_block->AllVars()) {
+  for (auto* var : global_block.AllVars()) {
     if (IsParameter(var, main_program)) {
       LOG(INFO) << "parameter's name: " << var->Name();
 
@@ -72,9 +72,9 @@ void LoadPersistables(framework::Executor& executor,
   delete load_program;
 }
 
-framework::ProgramDesc* Load(framework::Executor& executor,
-                             framework::Scope& scope,
-                             const std::string& dirname) {
+std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
+                                             framework::Scope& scope,
+                                             const std::string& dirname) {
   std::string model_filename = dirname + "/__model__";
   LOG(INFO) << "loading model from " << model_filename;
   std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
@@ -86,10 +86,10 @@ framework::ProgramDesc* Load(framework::Executor& executor,
   inputfs.read(&program_desc_str[0], program_desc_str.size());
   inputfs.close();
 
-  framework::ProgramDesc* main_program =
-      new framework::ProgramDesc(program_desc_str);
+  std::unique_ptr<framework::ProgramDesc> main_program(
+      new framework::ProgramDesc(program_desc_str));
 
-  LoadPersistables(executor, scope, dirname, main_program);
+  LoadPersistables(executor, scope, dirname, *main_program);
   return main_program;
 }
 
diff --git a/paddle/inference/io.h b/paddle/inference/io.h
index 400f5af8c5..dccb700e95 100644
--- a/paddle/inference/io.h
+++ b/paddle/inference/io.h
@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <memory>
 #include <string>
 #include <vector>
 #include "paddle/framework/block_desc.h"
@@ -26,16 +27,16 @@ namespace paddle {
 namespace inference {
 
 bool IsParameter(const framework::VarDesc* var,
-                 const framework::ProgramDesc* main_program);
+                 const framework::ProgramDesc& main_program);
 
 void LoadPersistables(framework::Executor& executor,
                       framework::Scope& scope,
                       const std::string& dirname,
-                      framework::ProgramDesc* main_program);
+                      const framework::ProgramDesc& main_program);
 
-framework::ProgramDesc* Load(framework::Executor& executor,
-                             framework::Scope& scope,
-                             const std::string& dirname);
+std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
+                                             framework::Scope& scope,
+                                             const std::string& dirname);
 
 }  // namespace inference
 }  // namespace paddle

From 263e01970d4f1923a5ee92e8d9b615a529bfb29e Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 19:43:26 +0800
Subject: [PATCH 138/314] follow comments

---
 paddle/operators/layer_norm_op.cc | 197 ++++++++++++++++++++----------
 1 file changed, 133 insertions(+), 64 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 5821afe9f6..1c6d2ae4d0 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -33,29 +33,35 @@ class LayerNormOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Bias"), "");
-    PADDLE_ENFORCE(ctx->HasOutput("Y"), "");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Y"),
+                   "Output(Y) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Mean"),
+                   "Output(Mean) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Variance"),
+                   "Output(Variance) of LayerNormOp should not be null.");
 
     auto x_dim = ctx->GetInputDim("X");
     auto begin_norm_axis = ctx->Attrs().Get<int>("begin_norm_axis");
     PADDLE_ENFORCE_LT(begin_norm_axis, x_dim.size(),
-                      "'begin_norm_axis' must be less than the rank of X");
+                      "'begin_norm_axis' must be less than the rank of X.");
 
     auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
     int right = static_cast<int>(matrix_dim[1]);
-
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right);
+    if (ctx->HasInput("Scale")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right);
+    }
+    if (ctx->HasInput("Bias")) {
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right);
+    }
 
     ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
     ctx->SetOutputDim("Mean", {left});
     ctx->SetOutputDim("Variance", {left});
-
     ctx->ShareLoD("X", "Y");
   }
 };
@@ -64,18 +70,26 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LayerNormOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The input tensor");
+    AddInput("X", "(LoDTensor) The input tensor.");
     AddInput("Scale",
-             "Scale is a 1-dimensional tensor of size H "
-             "that is applied to the output");
+             "(Tensor, optional) Scale is a 1-dimensional tensor of size "
+             "H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
+             "It is applied to the output.")
+        .AsDispensable();
     AddInput("Bias",
-             "Bias is a 1-dimensional tensor of size H "
-             "that is applied to the output");
-    AddOutput("Y", "result after normalization");
-    AddOutput("Mean", "Mean of the current mini batch.");
-    AddOutput("Variance", "Variance of the current mini batch.");
-
-    AddAttr<float>("epsilon", "")
+             "(Tensor, optional) Bias is a 1-dimensional tensor of size "
+             "H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
+             "It is applied to the output.")
+        .AsDispensable();
+    AddOutput("Y", "(LoDTensor) Result after normalization.");
+    AddOutput("Mean", "(Tensor) Mean of the current mini batch.")
+        .AsIntermediate();
+    AddOutput("Variance", "(Tensor) Variance of the current mini batch.")
+        .AsIntermediate();
+
+    AddAttr<float>("epsilon",
+                   "(float, default 1e-5) Constant for "
+                   "numerical stability")
         .SetDefault(1e-5)
         .AddCustomChecker([](const float &epsilon) {
           PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
@@ -83,7 +97,9 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
         });
     AddAttr<int>("begin_norm_axis",
                  "(int default:1), the "
-                 "axis of `begin_norm_axis ... Rank(X) - 1` will be normalized")
+                 "axis of `begin_norm_axis ... Rank(X) - 1` will be "
+                 "normalized. `begin_norm_axis` splits the tensor(`X`) to a "
+                 "matrix [N,H].")
         .SetDefault(1)
         .AddCustomChecker([](const int &begin_norm_axis) {
           PADDLE_ENFORCE_GT(begin_norm_axis, 0,
@@ -124,8 +140,7 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
     int right = static_cast<int>(matrix_dim[1]);
 
     auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-    auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+
     auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
     auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
     auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
@@ -141,14 +156,32 @@ class LayerNormKernel<platform::CPUDeviceContext, T>
                   .unaryExpr(add_epslion);
 
     auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-
     // TODO(zcd): Some thinking about output_map, is it appropriate that
     // `output_map` and `input_map` point to the same memory.
     auto inv_std = var_map.unaryExpr(inv_std_func);
-    output_map = (input_map - mean_map.replicate(1, right))
-                     .cwiseProduct(inv_std.replicate(1, right))
-                     .cwiseProduct(scale_map.replicate(left, 1)) +
-                 bias_map.replicate(left, 1);
+    if (scale && bias) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1)) +
+                   bias_map.replicate(left, 1);
+    } else if (scale) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1));
+    } else if (bias) {
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right)) +
+                   bias_map.replicate(left, 1);
+    } else {
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right));
+    }
   }
 };
 
@@ -158,11 +191,16 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     // check input
-    PADDLE_ENFORCE(ctx->HasInput("X"));
-    PADDLE_ENFORCE(ctx->HasInput("Scale"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Mean"), "");
-    PADDLE_ENFORCE(ctx->HasInput("Variance"), "");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Scale"),
+                   "Input(Scale) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Mean"),
+                   "Input(Mean) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Variance"),
+                   "Input(Variance) of LayerNormOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
+                   "Input(Y@GRAD) of LayerNormOp should not be null.");
 
     // check output
     if (ctx->HasOutput(framework::GradVarName("X"))) {
@@ -222,7 +260,6 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
     auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
     auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
 
-    auto scale_map = ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
     auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
     auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
     auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
@@ -254,35 +291,67 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
       auto triple_product_func = [](T ele) { return ele * ele * ele; };
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-      // dy_dx
-      auto dx_end = var_map.unaryExpr(inv_std_func)
-                        .replicate(1, right)
-                        .cwiseProduct(d_y_map)
-                        .cwiseProduct(scale_map.replicate(left, 1));
-      // dy_dmean_dx
-      auto dx_mean = (T(-1.0) / right) *
-                     var_map.unaryExpr(inv_std_func)
-                         .replicate(1, right)
-                         .cwiseProduct(d_y_map)
-                         .cwiseProduct(scale_map.replicate(left, 1))
-                         .rowwise()
-                         .sum()
-                         .replicate(1, right);
-      // dy_var_dx
-      auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                               .cwiseProduct(scale_map.replicate(left, 1))
-                               .cwiseProduct(d_y_map)
-                               .rowwise()
-                               .sum();
-      auto dvar_end = var_map.unaryExpr(inv_std_func)
-                          .unaryExpr(triple_product_func)
-                          .cwiseProduct(dvar_end_part)
-                          .replicate(1, right);
-      auto dx_var =
-          (T(-1.0) / right) *
-          (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-      d_x_map = dx_end + dx_mean + dx_var;
+      // TODO(zcd): these code can be refined
+      if (d_scale) {
+        auto scale_map =
+            ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+        // dy_dx
+        auto dx_end = var_map.unaryExpr(inv_std_func)
+                          .replicate(1, right)
+                          .cwiseProduct(d_y_map)
+                          .cwiseProduct(scale_map.replicate(left, 1));
+        // dy_dmean_dx
+        auto dx_mean = (T(-1.0) / right) *
+                       var_map.unaryExpr(inv_std_func)
+                           .replicate(1, right)
+                           .cwiseProduct(d_y_map)
+                           .cwiseProduct(scale_map.replicate(left, 1))
+                           .rowwise()
+                           .sum()
+                           .replicate(1, right);
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(scale_map.replicate(left, 1))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = var_map.unaryExpr(inv_std_func)
+                            .unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      } else {
+        // dy_dx
+        auto dx_end = var_map.unaryExpr(inv_std_func)
+                          .replicate(1, right)
+                          .cwiseProduct(d_y_map);
+        // dy_dmean_dx
+        auto dx_mean = (T(-1.0) / right) *
+                       var_map.unaryExpr(inv_std_func)
+                           .replicate(1, right)
+                           .cwiseProduct(d_y_map)
+                           .rowwise()
+                           .sum()
+                           .replicate(1, right);
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = var_map.unaryExpr(inv_std_func)
+                            .unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      }
     }
   }
 };

From 1acad21bbf7a7eea1dc5cb9a68057d35210f7cdb Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 30 Jan 2018 20:27:38 +0800
Subject: [PATCH 139/314] init reader.h and reader.cc files

---
 paddle/framework/reader.cc | 51 ++++++++++++++++++++++++++++++
 paddle/framework/reader.h  | 65 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 116 insertions(+)
 create mode 100644 paddle/framework/reader.cc
 create mode 100644 paddle/framework/reader.h

diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
new file mode 100644
index 0000000000..7f80dd7fc1
--- /dev/null
+++ b/paddle/framework/reader.cc
@@ -0,0 +1,51 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/framework/reader.h"
+
+namespace paddle {
+namespace framework {
+
+DDim Reader::shape(int idx) const {
+  PADDLE_ENFORCE_LT(
+      idx, shapes_.size(),
+      "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
+      shapes_.size());
+}
+
+int RandomReader::ReadNext(std::vector<LoDTensor>* outs) {
+  PADDLE_ENFORCE_EQ(
+      shapes_.size(), outs.size(),
+      "shapes_.size() is %d, while outs.size() is %d. They are not equal.",
+      shapes_.size(), outs.size());
+  std::minstd_rand engine;
+  unsigned int seed = std::random_device()();
+  engine.seed(seed);
+  std::uniform_real_distribution<float> dist(min_, max_);
+  for (int idx = 0; idx < shapes_.size(); ++idx) {
+    DDim shape = shapes_[idx];
+    LoDTensor* out = outs[idx];
+    int64_t numel = out->numel();
+    PADDLE_ENFORCE_EQ(product(shape), numel,
+                      "The product of %d'th shape is %lld, while the "
+                      "corresponding out's numel is %lld. They are not equal.",
+                      idx, product(shape), numel);
+    for (int64_t i = 0; i < numel, ++i) {
+      out[i] = dist(engine);
+    }
+  }
+  return 0;
+}
+}  // namespace framework
+}  // namespace paddle
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
new file mode 100644
index 0000000000..eed9c18d08
--- /dev/null
+++ b/paddle/framework/reader.h
@@ -0,0 +1,65 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/framework/ddim.h"
+#include "paddle/framework/lod_tensor.h"
+
+namespace paddle {
+namespace framework {
+
+class Reader {
+ public:
+  virtual int ReadNext(std::vector<LoDTensor>* outs) = 0;
+  DDim shape(int idx) const;
+
+ private:
+  std::vector<DDim> shapes_;
+};
+
+// file readers
+
+class RandomReader : public Reader {
+ public:
+  RandomReader(const std::vector<DDim>& shapes, float min, float max)
+      : shapes_(shapes), min_(min), max_(max) {}
+  int ReadNext(std::vector<LoDTensor>* outs) override;
+
+ private:
+  float min_;
+  float max_;
+};
+
+// decorators
+
+class BatchReader : public Reader {
+ public:
+  BatchReader(const Reader* reader) : reader_(reader) {}
+  int ReadNext(std::vector<LoDTensor>* outs) override;
+
+ private:
+  const Reader* reader_;
+};
+
+class ShuffleReader : public Reader {
+ public:
+  ShuffleReader(const Reader* reader) : reader_(reader) {}
+  int ReadNext(std::vector<LoDTensor>* outs) override;
+
+ private:
+  const Reader* reader_;
+};
+}  // namespace framework
+}  // namespace paddle

From 5ed07ef1d1d1b91b158f7b3fe622eeaac00b5ad5 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 30 Jan 2018 04:36:00 -0800
Subject: [PATCH 140/314] Add more comments and enable the distribution's
 outside setting

---
 paddle/operators/label_smooth_op.cc           | 48 ++++++++++++++++++-
 paddle/operators/label_smooth_op.h            | 12 ++++-
 .../v2/fluid/tests/test_label_smooth_op.py    | 32 +++++++++----
 3 files changed, 79 insertions(+), 13 deletions(-)

diff --git a/paddle/operators/label_smooth_op.cc b/paddle/operators/label_smooth_op.cc
index 99a0a005a1..432d4c7d01 100644
--- a/paddle/operators/label_smooth_op.cc
+++ b/paddle/operators/label_smooth_op.cc
@@ -31,6 +31,14 @@ class LabelSmoothOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of LabelSmoothOp should not be null.");
     auto in_dims = ctx->GetInputDim("X");
+    if (ctx->HasInput("PriorDist")) {
+      auto noise_dims = ctx->GetInputDim("PriorDist");
+      auto noise_numel = paddle::framework::product(noise_dims);
+      PADDLE_ENFORCE(
+          in_dims[1] == noise_numel,
+          "The number of elements in Input(PriorDist) must be equal to the "
+          "dimension of each label.");
+    }
     ctx->ShareLoD("X", /*->*/ "Out");
     ctx->SetOutputDim("Out", in_dims);
   }
@@ -40,8 +48,22 @@ class LabelSmoothOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   LabelSmoothOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "The input label of LabelSmooth operator.");
-    AddOutput("Out", "The smoothed label of LabelSmooth operator.");
+    AddInput("X",
+             "(LoDTensor) The input labels of LabelSmooth operator. This "
+             "input can be batched labels in one-hot encoding or output from "
+             "softmax, with shape [N x K], where N is the batch size and K is "
+             "the number of classes");
+    AddInput("PriorDist",
+             "(Tensor, optional)"
+             "The prior distribution to be added to the smoothed label. It is "
+             "fixed during training and the number of elements should be equal "
+             "to the dimension K of each label. Default is uniform "
+             "distribution and each element will be set to 1/K if not provided "
+             "in input.")
+        .AsDispensable();
+    AddOutput("Out",
+              "(loDTensor) The smoothed label of LabelSmooth operator. It has"
+              "the same shape and LoD with the Input(LoDTensor).");
     AddAttr<float>("epsilon",
                    "(float, default 0.0f)"
                    "The smoothing parameter of LabelSmooth operator.")
@@ -49,6 +71,28 @@ class LabelSmoothOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 LabelSmooth Operator.
 
+Label smoothing is a mechanism to regularize the classifier layer. In machine 
+learning, optimizing the log-likelihood of the correct label directly may 
+cause two problems. First, it may result in overfitting: if the model learns 
+to assign full probability to the ground-truth label for each training example,
+it is not guaranteed to generalize. Second, it encourages the differences 
+between the largest logit and all others to become large, reducing the ability 
+of the model to adapt. Label smoothing is proposed to encourage the model to 
+be less confident, which replaces the ground-truth label $y$ with the weighted 
+sum of itselft and some fixed distribution $\mu$, 
+i.e.
+
+$$
+    \tilde{y} = (1 - \epsilon) * y + \epsilon * \mu,
+$$
+
+where $(1 - \epsilon)$ and $\epsilon$ are the weights respectively, and 
+$\tilde{y}$ is the smoothed label. Usually uniform distribution is used for 
+$\mu$. This change in the ground-truth label is called label-smoothing 
+regularization or LSR.
+
+See more details about label smoothing in https://arxiv.org/abs/1512.00567.
+
 )DOC");
   }
 };
diff --git a/paddle/operators/label_smooth_op.h b/paddle/operators/label_smooth_op.h
index d94ff43d5a..87bc9f793e 100644
--- a/paddle/operators/label_smooth_op.h
+++ b/paddle/operators/label_smooth_op.h
@@ -26,6 +26,7 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const {
     auto* out_t = ctx.Output<framework::LoDTensor>("Out");
     auto* in_t = ctx.Input<framework::LoDTensor>("X");
+    auto* dist_t = ctx.Input<framework::Tensor>("PriorDist");
     auto label_dim = in_t->dims()[1];
     out_t->mutable_data<T>(ctx.GetPlace());
 
@@ -33,8 +34,15 @@ class LabelSmoothKernel : public framework::OpKernel<T> {
     auto out = framework::EigenVector<T>::Flatten(*out_t);
     auto in = framework::EigenVector<T>::Flatten(*in_t);
     auto& dev = *ctx.template device_context<DeviceContext>().eigen_device();
-    out.device(dev) =
-        static_cast<T>(1 - epsilon) * in + static_cast<T>(epsilon / label_dim);
+    if (dist_t) {
+      auto dist = framework::EigenVector<T>::Flatten(*dist_t);
+      out.device(dev) =
+          static_cast<T>(1 - epsilon) * in +
+          epsilon * dist.broadcast(Eigen::DSizes<int, 1>(in_t->numel()));
+    } else {
+      out.device(dev) = static_cast<T>(1 - epsilon) * in +
+                        static_cast<T>(epsilon / label_dim);
+    }
   }
 };
 
diff --git a/python/paddle/v2/fluid/tests/test_label_smooth_op.py b/python/paddle/v2/fluid/tests/test_label_smooth_op.py
index d156e2c35f..19a4df5744 100644
--- a/python/paddle/v2/fluid/tests/test_label_smooth_op.py
+++ b/python/paddle/v2/fluid/tests/test_label_smooth_op.py
@@ -18,16 +18,20 @@ from op_test import OpTest
 
 
 class TestLabelSmoothOp(OpTest):
-    def setUp(self):
+    def config(self):
         self.op_type = "label_smooth"
-        epsilon = 0.1
-        batch_size, label_dim = 5, 10
-        label = np.zeros((batch_size, label_dim)).astype("float64")
-        nonzero_index = np.random.randint(label_dim, size=(batch_size))
-        label[np.arange(batch_size), nonzero_index] = 1
-        smoothed_label = (1 - epsilon) * label + epsilon / label_dim
-        self.inputs = {'X': label}
-        self.attrs = {'epsilon': epsilon}
+        self.epsilon = 0.1
+        batch_size, self.label_dim = 5, 10
+        self.label = np.zeros((batch_size, self.label_dim)).astype("float64")
+        nonzero_index = np.random.randint(self.label_dim, size=(batch_size))
+        self.label[np.arange(batch_size), nonzero_index] = 1
+
+    def setUp(self):
+        self.config()
+        smoothed_label = (1 - self.epsilon
+                          ) * self.label + self.epsilon / self.label_dim
+        self.inputs = {'X': self.label}
+        self.attrs = {'epsilon': self.epsilon}
         self.outputs = {'Out': smoothed_label}
 
     def test_check_output(self):
@@ -37,5 +41,15 @@ class TestLabelSmoothOp(OpTest):
         self.check_grad(["X"], "Out")
 
 
+class TestLabelSmoothOpWithPriorDist(TestLabelSmoothOp):
+    def setUp(self):
+        self.config()
+        dist = np.random.random((1, self.label_dim))
+        smoothed_label = (1 - self.epsilon) * self.label + self.epsilon * dist
+        self.inputs = {'X': self.label, 'PriorDist': dist}
+        self.attrs = {'epsilon': self.epsilon}
+        self.outputs = {'Out': smoothed_label}
+
+
 if __name__ == '__main__':
     unittest.main()

From a10caf7c2360cb18c788c19dc73ec7a1e055866a Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 30 Jan 2018 04:41:50 -0800
Subject: [PATCH 141/314] Fix typos in label_smooth_op

---
 paddle/operators/label_smooth_op.cc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/paddle/operators/label_smooth_op.cc b/paddle/operators/label_smooth_op.cc
index 432d4c7d01..c89082f44b 100644
--- a/paddle/operators/label_smooth_op.cc
+++ b/paddle/operators/label_smooth_op.cc
@@ -79,8 +79,7 @@ it is not guaranteed to generalize. Second, it encourages the differences
 between the largest logit and all others to become large, reducing the ability 
 of the model to adapt. Label smoothing is proposed to encourage the model to 
 be less confident, which replaces the ground-truth label $y$ with the weighted 
-sum of itselft and some fixed distribution $\mu$, 
-i.e.
+sum of itself and some fixed distribution $\mu$, i.e.
 
 $$
     \tilde{y} = (1 - \epsilon) * y + \epsilon * \mu,

From acb907878a0e3a66f56371aa82088233f6dc9aaf Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 20:33:54 +0800
Subject: [PATCH 142/314] refine unit test

---
 .../v2/fluid/tests/test_layer_norm_op.py      | 118 +++++++++---------
 1 file changed, 59 insertions(+), 59 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index d27d1d8138..ac94dfb92a 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -21,29 +21,19 @@ from paddle.v2.fluid.op import Operator
 from paddle.v2.fluid.framework import grad_var_name
 
 
-def get_backward_op(scope, op, no_grad_set):
-    backward_op = core.Operator.backward(op, no_grad_set)
-    for input in backward_op.input_vars():
-        var = scope.var(input)
-        var.get_tensor()
-    for output in backward_op.output_vars():
-        var = scope.var(output)
-        var.get_tensor()
-    return backward_op
-
-
 def _reference_layer_norm_naive(x, scale, beta, epsilon, begin_norm_axis=1):
-    old_shape = x.shape
-    N = reduce(mul, old_shape[0:begin_norm_axis], 1)
-    D = reduce(mul, old_shape[begin_norm_axis:len(old_shape)], 1)
+    x_shape = x.shape
+    N = reduce(mul, x_shape[0:begin_norm_axis], 1)
+    D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
     x.shape = [N, D]
+
     mean = np.mean(x, axis=1)
     var = np.var(x, axis=1) + epsilon
     output = scale.reshape([1, D]) * np.divide(
         (x - mean.reshape([N, 1])),
         (np.sqrt(var)).reshape([N, 1])) + beta.reshape([1, D])
-    output.shape = old_shape
-    x.shape = old_shape
+
+    x.shape, output.shape = x_shape, x_shape
     return output, mean, var
 
 
@@ -52,27 +42,25 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     scale_shape = scale.shape
     N = reduce(mul, x_shape[0:begin_norm_axis], 1)
     D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
-    grad_y.shape = [N, D]
-    x.shape = [N, D]
-    mean.shape = [N, 1]
-    var.shape = [N, 1]
+    x.shape, grad_y.shape = [N, D], [N, D]
+    var.shape, mean.shape = [N, 1], [N, 1]
     scale.shape = [1, D]
 
+    # d_bias
     d_bias = np.sum(grad_y, axis=0).reshape([1, D])
+    # d_scale
     d_scale = np.sum(((x - mean) * np.sqrt(1 / var)) * grad_y,
                      axis=0).reshape([1, D])
-
+    # dx
     dx_end = scale * np.sqrt(1.0 / var) * grad_y
-
     d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y * scale, axis=1).reshape(
         [N, 1])
     # d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
     #     [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
     #                np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
     d_mean = 1.0 / D * d_mean_0
-
     d_std = np.sum(
-        -1.0 / var * (x - mean) * grad_y * scale, axis=1).reshape([N, 1]) * (
+        -(1.0 / var) * (x - mean) * grad_y * scale, axis=1).reshape([N, 1]) * (
             1.0 / D * np.sqrt(1.0 / var).reshape([N, 1]) * (x - mean))
 
     grad_x = dx_end + d_mean + d_std
@@ -83,6 +71,17 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     return grad_x, d_scale, d_bias
 
 
+def get_backward_op(scope, op, no_grad_set):
+    backward_op = core.Operator.backward(op, no_grad_set)
+    for input in backward_op.input_vars():
+        var = scope.var(input)
+        var.get_tensor()
+    for output in backward_op.output_vars():
+        var = scope.var(output)
+        var.get_tensor()
+    return backward_op
+
+
 def create_or_get_tensor(scope, var_name, var, place):
     tensor = scope.var(var_name).get_tensor()
     if var is not None:
@@ -145,8 +144,9 @@ class TestLayerNormdOp(OpTest):
 
         self.assertLessEqual(max_diff, max_relative_error, err_msg())
 
-    def test_forward_backward(self):
+    def check_forward_backward(self, shape, begin_norm_axis):
         def test_with_place(place, shape, begin_norm_axis=1):
+            # setUp
             assert begin_norm_axis > 0 and begin_norm_axis < len(
                 shape), 'begin_norm_axis must be between 0 and len(shape)-1.'
             # attr
@@ -158,30 +158,35 @@ class TestLayerNormdOp(OpTest):
             x_val = np.random.random_sample(x_shape).astype(np.float32)
             scale_val = np.random.random_sample(scale_shape).astype(np.float32)
             bias_val = np.random.random_sample(scale_shape).astype(np.float32)
+            y_grad = np.random.random_sample(x_shape).astype(np.float32)
 
             # run forward
             y_out, saved_mean, var_ref = _reference_layer_norm_naive(
                 x_val, scale_val, bias_val, epsilon, begin_norm_axis)
+            naive_fw = {"Y": y_out, "Mean": saved_mean, "Variance": var_ref}
 
-            #  for gradient test
-            y_grad = np.random.random_sample(x_shape).astype(np.float32)
-
+            # get gradient
             x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_layer_norm_grad(
                 x_val, y_grad, scale_val, saved_mean, var_ref, begin_norm_axis)
+            naive_grad = {
+                "X": x_grad_ref,
+                "Scale": scale_grad_ref,
+                "Bias": bias_grad_ref
+            }
 
             scope = core.Scope()
 
             # create input
-            x_tensor = create_or_get_tensor(scope, "X", x_val, place)
-            scale_tensor = create_or_get_tensor(scope, "Scale", scale_val,
-                                                place)
-            bias_tensor = create_or_get_tensor(scope, "Bias", bias_val, place)
+            input_map = {"X": x_val, "Scale": scale_val, "Bias": bias_val}
+            for i_name in input_map:
+                create_or_get_tensor(scope, i_name, input_map[i_name], place)
 
             # create output
-            y_tensor = create_or_get_tensor(scope, "Y", None, place)
-            mean_tensor = create_or_get_tensor(scope, "Mean", None, place)
-            variance_tensor = create_or_get_tensor(scope, "Variance", None,
-                                                   place)
+            output_map = {"Y": None, "Mean": None, "Variance": None}
+            output_tensor = {}
+            for o_name in output_map:
+                output_tensor[o_name] = create_or_get_tensor(
+                    scope, o_name, output_map[o_name], place)
 
             layer_norm_op = Operator(
                 "layer_norm",
@@ -200,13 +205,10 @@ class TestLayerNormdOp(OpTest):
             layer_norm_op.run(scope, place)
 
             # check forward result
-            if isinstance(place, core.CUDAPlace):
-                atol = 5e-2
-            else:
-                atol = 1e-4
-            self.__assert_close(y_tensor, y_out, "Y", atol)
-            self.__assert_close(mean_tensor, saved_mean, "Mean", atol)
-            self.__assert_close(variance_tensor, var_ref, "Variance", atol)
+            atol = 5e-2 if isinstance(place, core.CUDAPlace) else 1e-4
+            for o_tensor in output_tensor:
+                self.__assert_close(output_tensor[o_tensor], naive_fw[o_tensor],
+                                    o_tensor, atol)
 
             # run backward
             layer_norm_op_grad = get_backward_op(scope, layer_norm_op, set())
@@ -216,30 +218,28 @@ class TestLayerNormdOp(OpTest):
                 feed_dict={"Y": y_grad})
             layer_norm_op_grad.run(scope, place)
 
-            x_grad_tensor = create_or_get_tensor(scope,
-                                                 grad_var_name("X"), None,
-                                                 place)
-            scale_grad_tensor = create_or_get_tensor(scope,
-                                                     grad_var_name("Scale"),
-                                                     None, place)
-            bias_grad_tensor = create_or_get_tensor(scope,
-                                                    grad_var_name("Bias"), None,
-                                                    place)
+            # get output
+            grad_tensor = {}
+            for o_name in naive_grad:
+                grad_tensor[o_name] = x_ = create_or_get_tensor(
+                    scope, grad_var_name(o_name), None, place)
 
             # check gradient output
-            self.__assert_grad_close(x_grad_tensor, x_grad_ref, "x_grad", place)
-            self.__assert_grad_close(scale_grad_tensor, scale_grad_ref,
-                                     "scale_grad", place)
-            self.__assert_grad_close(bias_grad_tensor, bias_grad_ref,
-                                     "bias_grad", place)
+            for o_grad in naive_grad:
+                self.__assert_grad_close(grad_tensor[o_grad],
+                                         naive_grad[o_grad], o_grad + "@GRAD",
+                                         place)
 
         places = [core.CPUPlace()]
         if core.is_compile_gpu() and core.op_support_gpu("layer_norm"):
             places.append(core.CUDAPlace(0))
 
         for place in places:
-            test_with_place(place, [2, 3, 4, 5], begin_norm_axis=1)
-            test_with_place(place, [2, 3, 4, 5], begin_norm_axis=3)
+            test_with_place(place, shape, begin_norm_axis)
+
+    def test_check_forward_backward(self):
+        self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=1)
+        self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=3)
 
 
 if __name__ == '__main__':

From 55b5f29ea44cc5e94061dd4a92e6cbf11d7f2346 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 30 Jan 2018 21:14:48 +0800
Subject: [PATCH 143/314] refine paddle_fluid_shared library

---
 paddle/inference/CMakeLists.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index 58c0c59380..3f587fa790 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -13,8 +13,8 @@ add_library(paddle_fluid_shared SHARED io.cc)
 target_circle_link_libraries(paddle_fluid_shared
   ARCHIVE_START
   ${GLOB_OP_LIB}
-  ARCHIVE_END
-  ${FLUID_CORE_MODULES})
+  ${FLUID_CORE_MODULES}
+  ARCHIVE_END)
 
 SET_TARGET_PROPERTIES(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 

From 537886408863f68d7863e8245d746d2c15ef55dd Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Tue, 30 Jan 2018 21:30:16 +0800
Subject: [PATCH 144/314] Fix the output order and add more unit test cases.

---
 paddle/operators/multiclass_nms_op.cc         | 16 +++--
 .../v2/fluid/tests/test_multiclass_nms_op.py  | 68 +++++++++++++------
 2 files changed, 57 insertions(+), 27 deletions(-)

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index 93c8b5216f..4689306d24 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -201,8 +201,8 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
         }
       }
       // Keep top k results per image.
-      std::sort(score_index_pairs.begin(), score_index_pairs.end(),
-                SortScorePairDescend<std::pair<int, int>>);
+      std::stable_sort(score_index_pairs.begin(), score_index_pairs.end(),
+                       SortScorePairDescend<std::pair<int, int>>);
       score_index_pairs.resize(keep_top_k);
 
       // Store the new indices.
@@ -269,7 +269,8 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
 
     int num_kept = batch_starts.back();
     if (num_kept == 0) {
-      outs->Resize({0, 0});
+      T* od = outs->mutable_data<T>({1}, ctx.GetPlace());
+      od[0] = -1;
     } else {
       outs->mutable_data<T>({num_kept, kOutputDim}, ctx.GetPlace());
       for (int64_t i = 0; i < batch_size; ++i) {
@@ -349,11 +350,16 @@ is larger than -1. Then this operator pruns away boxes that have high IOU
 (intersection over union) overlap with already selected boxes by adaptive
 threshold NMS based on parameters of nms_threshold and nms_eta.
 
-Aftern NMS step, only at most keep_top_k number of total bboxes are to be kept
+Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
 per image if keep_top_k is larger than -1.
 
 This operator support multi-class and batched inputs. It applying NMS
-independently for each class.
+independently for each class. The outputs is a 2-D LoDTenosr, for each
+image, the offsets in first dimension of LoDTensor are called LoD, the number
+of offset is N + 1, where N is the batch size. If LoD[i + 1] - LoD[i] == 0,
+means there is no detected bbox for this image. If there is no detected boxes
+for all images, all the elements in LoD are 0, and the Out only contains one
+value which is -1.
 )DOC");
   }
 };
diff --git a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
index b619c52e55..3097b8388c 100644
--- a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
+++ b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
@@ -56,8 +56,12 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
     Args:
         boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
         scores: (tensor) The class predscores for the img, Shape:[num_priors].
-        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
-        top_k: (int) The Maximum number of box preds to consider.
+        score_threshold: (float) The confidence thresh for filtering low
+            confidence boxes.
+        nms_threshold: (float) The overlap thresh for suppressing unnecessary
+            boxes.
+        top_k: (int) The maximum number of box preds to consider.
+        eta: (float) The parameter for adaptive NMS.
     Return:
         The indices of the kept boxes with respect to num_priors.
     """
@@ -67,7 +71,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
     selected_indices = selected_indices.flatten()
     all_scores = all_scores[selected_indices]
 
-    sorted_indices = np.argsort(-all_scores, axis=0)
+    sorted_indices = np.argsort(-all_scores, axis=0, kind='mergesort')
     sorted_scores = all_scores[sorted_indices]
     if top_k > -1 and top_k < sorted_indices.shape[0]:
         sorted_indices = sorted_indices[:top_k]
@@ -97,29 +101,33 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
     class_num = scores.shape[0]
     priorbox_num = scores.shape[1]
 
-    selected_indices = []
+    selected_indices = {}
     num_det = 0
     for c in range(class_num):
         if c == background: continue
         indices = nms(boxes, scores[c], score_threshold, nms_threshold,
                       nms_top_k)
-        for idx in indices:
-            selected_indices.append((c, idx))
+        selected_indices[c] = indices
         num_det += len(indices)
 
     if keep_top_k > -1 and num_det > keep_top_k:
         score_index = []
-        for c, idx in selected_indices:
-            score_index.append((scores[c][idx], c, idx))
+        for c, indices in selected_indices.iteritems():
+            for idx in indices:
+                score_index.append((scores[c][idx], c, idx))
 
         sorted_score_index = sorted(
             score_index, key=lambda tup: tup[0], reverse=True)
         sorted_score_index = sorted_score_index[:keep_top_k]
-        selected_indices = []
+        selected_indices = {}
+
+        for _, c, _ in sorted_score_index:
+            selected_indices[c] = []
         for s, c, idx in sorted_score_index:
-            selected_indices.append((c, idx))
+            selected_indices[c].append(idx)
+        num_det = keep_top_k
 
-    return selected_indices
+    return selected_indices, num_det
 
 
 def batched_multiclass_nms(boxes, scores, background, score_threshold,
@@ -129,28 +137,36 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
     det_outs = []
     lod = [0]
     for n in range(batch_size):
-        nmsed_outs = multiclass_nms(boxes, scores[n], background,
-                                    score_threshold, nms_threshold, nms_top_k,
-                                    keep_top_k)
-        lod.append(lod[-1] + len(nmsed_outs))
-        if len(nmsed_outs) == 0: continue
-        for c, idx in nmsed_outs:
-            xmin, ymin, xmax, ymax = boxes[idx][:]
-            det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
+        nmsed_outs, nmsed_num = multiclass_nms(boxes, scores[n], background,
+                                               score_threshold, nms_threshold,
+                                               nms_top_k, keep_top_k)
+        lod.append(lod[-1] + nmsed_num)
+        if nmsed_num == 0: continue
+
+        for c, indices in nmsed_outs.iteritems():
+            for idx in indices:
+                xmin, ymin, xmax, ymax = boxes[idx][:]
+                det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
+
     return det_outs, lod
 
 
 class TestMulticlassNMSOp(OpTest):
+    def set_argument(self):
+        self.score_threshold = 0.01
+
     def setUp(self):
+        self.set_argument()
         N = 7
-        M = 1240
+        M = 1200
         C = 21
         BOX_SIZE = 4
+
         background = 0
         nms_threshold = 0.3
         nms_top_k = 400
         keep_top_k = 200
-        score_threshold = 0.01
+        score_threshold = self.score_threshold
 
         scores = np.random.random((N * M, C)).astype('float32')
 
@@ -165,11 +181,12 @@ class TestMulticlassNMSOp(OpTest):
 
         boxes = np.random.random((M, BOX_SIZE)).astype('float32')
         boxes[:, 0:2] = boxes[:, 0:2] * 0.5
-        boxes[:, 2:4] = boxes[:, 0:2] * 0.5 + 0.5
+        boxes[:, 2:4] = boxes[:, 2:4] * 0.5 + 0.5
 
         nmsed_outs, lod = batched_multiclass_nms(boxes, scores, background,
                                                  score_threshold, nms_threshold,
                                                  nms_top_k, keep_top_k)
+        nmsed_outs = [-1] if not nmsed_outs else nmsed_outs
         nmsed_outs = np.array(nmsed_outs).astype('float32')
 
         self.op_type = 'multiclass_nms'
@@ -188,6 +205,13 @@ class TestMulticlassNMSOp(OpTest):
         self.check_output()
 
 
+class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
+    def set_argument(self):
+        # Here set 2.0 to test the case there is no outputs.
+        # In practical use, 0.0 < score_threshold < 1.0 
+        self.score_threshold = 2.0
+
+
 class TestIOU(unittest.TestCase):
     def test_iou(self):
         box1 = np.array([4.0, 3.0, 7.0, 5.0]).astype('float32')

From e5058ed1f14b8fe16be8055bb819e0a101cf2ade Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 22:13:06 +0800
Subject: [PATCH 145/314] Add unit test for with_scale and with_bias

---
 .../paddle/v2/fluid/tests/test_layer_norm_op.py  | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index ac94dfb92a..7d5dc7d1a6 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -54,10 +54,7 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
     # dx
     dx_end = scale * np.sqrt(1.0 / var) * grad_y
     d_mean_0 = np.sum(-np.sqrt(1.0 / var) * grad_y * scale, axis=1).reshape(
-        [N, 1])
-    # d_mean_1 = np.sum(-1.0 / var * (x - mean) * grad_y, axis=1).reshape(
-    #     [N, 1]) * (-1.0 / D * np.sqrt(1.0 / var) *
-    #                np.sum(x - mean, axis=1).reshape([N, 1])).reshape([N, 1])
+        [N, 1])  # the second part equals to zero.
     d_mean = 1.0 / D * d_mean_0
     d_std = np.sum(
         -(1.0 / var) * (x - mean) * grad_y * scale, axis=1).reshape([N, 1]) * (
@@ -237,10 +234,19 @@ class TestLayerNormdOp(OpTest):
         for place in places:
             test_with_place(place, shape, begin_norm_axis)
 
-    def test_check_forward_backward(self):
+    def test_check_forward_backward_with_scale_and_bias(self):
         self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=1)
         self.check_forward_backward(shape=[2, 3, 4, 5], begin_norm_axis=3)
 
+    def test_check_forward_backward_with_scale(self):
+        pass  # TODO(zcd)
+
+    def test_check_forward_backward_with_bias(self):
+        pass  # TODO(zcd)
+
+    def test_check_forward_backward(self):
+        pass  # TODO(zcd)
+
 
 if __name__ == '__main__':
     unittest.main()

From 2e907c3613abfd68ebe8bf4c9d7b2bc42816105a Mon Sep 17 00:00:00 2001
From: Siddharth Goyal <vi.siddharth78@gmail.com>
Date: Tue, 30 Jan 2018 15:20:40 -0800
Subject: [PATCH 146/314] Add variant of new load and save ops for storing
 model params in a single file (#7909)

* Add save_combine_op

* Add load_combine_op and test

* Add unit-test

* Add a delete to free buffer memory

* Add new variant of load/save

* Fix unit-test

* Add another unit test for compatibility with original save/load

* Address review comments and simplify logic

* Address review comments and simplify code - part 2

* Fix naming issues and CMake problems

* Address review comments

* Fix LoD information in tests

* Address review comments: round 2
---
 paddle/operators/CMakeLists.txt               |   3 +
 paddle/operators/load_combine_op.cc           | 108 +++++++++++
 paddle/operators/save_combine_op.cc           | 141 ++++++++++++++
 paddle/operators/save_load_combine_op_test.cc | 180 ++++++++++++++++++
 paddle/operators/save_load_op_test.cc         |   2 +-
 5 files changed, 433 insertions(+), 1 deletion(-)
 create mode 100644 paddle/operators/load_combine_op.cc
 create mode 100644 paddle/operators/save_combine_op.cc
 create mode 100644 paddle/operators/save_load_combine_op_test.cc

diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 48cf5816cc..b2e73b6f23 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -173,6 +173,8 @@ endif()
 # FIXME(typhoonzero): save/load depends lodtensor serialization functions
 op_library(save_op DEPS lod_tensor)
 op_library(load_op DEPS lod_tensor)
+op_library(save_combine_op DEPS lod_tensor)
+op_library(load_combine_op DEPS lod_tensor)
 
 list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})
@@ -192,3 +194,4 @@ if(WITH_GPU)
     cc_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
 endif()
 cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
+cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
diff --git a/paddle/operators/load_combine_op.cc b/paddle/operators/load_combine_op.cc
new file mode 100644
index 0000000000..f4be793d7b
--- /dev/null
+++ b/paddle/operators/load_combine_op.cc
@@ -0,0 +1,108 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+#include <fstream>
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+
+class LoadCombineOp : public framework::OperatorBase {
+ public:
+  LoadCombineOp(const std::string &type,
+                const framework::VariableNameMap &inputs,
+                const framework::VariableNameMap &outputs,
+                const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::Place &place) const override {
+    auto filename = Attr<std::string>("file_path");
+
+    std::ifstream fin(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fin),
+                   "Cannot open file %s for load_combine op", filename);
+
+    auto out_var_names = Outputs("Out");
+    PADDLE_ENFORCE_GT(
+        static_cast<int>(out_var_names.size()), 0,
+        "The number of output variables should be greater than 0.");
+
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(place);
+
+    for (size_t i = 0; i < out_var_names.size(); i++) {
+      auto *out_var = scope.FindVar(out_var_names[i]);
+
+      PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
+                     out_var_names[i]);
+
+      auto *tensor = out_var->GetMutable<framework::LoDTensor>();
+
+      // Error checking
+      PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot read more from file %s",
+                     filename);
+
+      // Get data from fin to tensor
+      DeserializeFromStream(fin, tensor, dev_ctx);
+
+      if (platform::is_gpu_place(place)) {
+        // copy CPU to GPU
+        framework::LoDTensor cpu_tensor;
+        cpu_tensor.ShareDataWith(*tensor);
+        cpu_tensor.set_lod(tensor->lod());
+
+        // reset tensor
+        out_var->Clear();
+        tensor = out_var->GetMutable<framework::LoDTensor>();
+        tensor->set_lod(cpu_tensor.lod());
+        Copy(cpu_tensor, place, dev_ctx, tensor);
+      }
+    }
+  }
+};
+
+class LoadCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  LoadCombineOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddOutput(
+        "Out",
+        "(vector) The output LoDTensors that will be read from the input file.")
+        .AsDuplicable();
+    AddAttr<std::string>("file_path",
+                         "(string) "
+                         "LoDTensors will be loaded from \"file_path\".")
+        .AddCustomChecker(
+            [](const std::string &path) { return !path.empty(); });
+    AddComment(R"DOC(
+LoadCombine Operator.
+
+LoadCombine operator loads LoDTensor variables from a file. The file should 
+contain one or more LoDTensors serialized using the SaveCombine operator. The 
+LoadCombine operator applies a deserialization strategy to appropriately load 
+the LodTensors, and this strategy complements the serialization strategy used 
+in the SaveCombine operator. Hence, the LoadCombine operator is tightly coupled
+with the SaveCombine operator, and can only deserialize one or more LoDTensors 
+that were saved using the SaveCombine operator.
+
+)DOC");
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(load_combine, ops::LoadCombineOp,
+                  ops::LoadCombineOpProtoMaker);
diff --git a/paddle/operators/save_combine_op.cc b/paddle/operators/save_combine_op.cc
new file mode 100644
index 0000000000..bffa2908bc
--- /dev/null
+++ b/paddle/operators/save_combine_op.cc
@@ -0,0 +1,141 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <stdint.h>
+#include <sys/stat.h>
+#include <fstream>
+#include <numeric>
+#include <sstream>
+#include "paddle/framework/data_type.h"
+#include "paddle/framework/framework.pb.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+
+// TODO(sidgoyal78): These function are needed by other files (save_op), move
+// them to paddle::filesystem namespace. (as noted by yuyang18 in save_op).
+constexpr char kSEP = '/';
+static bool FileExists(const std::string &filepath) {
+  struct stat buffer;
+  return (stat(filepath.c_str(), &buffer) == 0);
+}
+
+static std::string DirName(const std::string &filepath) {
+  auto pos = filepath.rfind(kSEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static void MkDir(const char *path) {
+  if (mkdir(path, 0755)) {
+    PADDLE_ENFORCE_EQ(errno, EEXIST, "%s mkdir failed!", path);
+  }
+}
+
+static void MkDirRecursively(const char *fullpath) {
+  if (*fullpath == '\0') return;  // empty string
+  if (FileExists(fullpath)) return;
+
+  MkDirRecursively(DirName(fullpath).c_str());
+  MkDir(fullpath);
+}
+
+class SaveCombineOp : public framework::OperatorBase {
+ public:
+  SaveCombineOp(const std::string &type,
+                const framework::VariableNameMap &inputs,
+                const framework::VariableNameMap &outputs,
+                const framework::AttributeMap &attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+  void Run(const framework::Scope &scope,
+           const platform::Place &place) const override {
+    auto filename = Attr<std::string>("file_path");
+    auto overwrite = Attr<bool>("overwrite");
+
+    bool is_present = FileExists(filename);
+    if (is_present && !overwrite) {
+      PADDLE_THROW("%s exists!, cannot save_combine to it when overwrite=false",
+                   filename, overwrite);
+    }
+
+    MkDirRecursively(DirName(filename).c_str());
+    std::ofstream fout(filename);
+    PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
+                   filename);
+
+    auto inp_var_names = Inputs("X");
+    PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
+                      "The number of input variables should be greater than 0");
+
+    // get device context from pool
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(place);
+
+    for (size_t i = 0; i < inp_var_names.size(); i++) {
+      auto *var = scope.FindVar(inp_var_names[i]);
+
+      PADDLE_ENFORCE(var != nullptr,
+                     "Cannot find variable %s for save_combine_op",
+                     inp_var_names[i]);
+      PADDLE_ENFORCE(var->IsType<framework::LoDTensor>(),
+                     "SaveCombineOp only supports LoDTensor, %s has wrong type",
+                     inp_var_names[i]);
+
+      auto &tensor = var->Get<framework::LoDTensor>();
+      // Serialize tensor
+      framework::SerializeToStream(fout, tensor, dev_ctx);
+    }
+    fout.close();
+  }
+};
+
+class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SaveCombineOpProtoMaker(OpProto *proto, OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput(
+        "X",
+        "(vector) Input LoDTensors that need to be saved together in a file.")
+        .AsDuplicable();
+    AddComment(R"DOC(
+SaveCombine operator
+
+This operator will serialize and write a list of input LoDTensor variables 
+to a file on disk.
+)DOC");
+    AddAttr<bool>("overwrite",
+                  "(boolean, default true)"
+                  "Overwrite the output file if it exists.")
+        .SetDefault(true);
+    AddAttr<std::string>(
+        "file_path",
+        "(string)"
+        "The \"file_path\" where the LoDTensor variables will be saved.")
+        .AddCustomChecker(
+            [](const std::string &path) { return !path.empty(); });
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
+                  ops::SaveCombineOpProtoMaker);
diff --git a/paddle/operators/save_load_combine_op_test.cc b/paddle/operators/save_load_combine_op_test.cc
new file mode 100644
index 0000000000..f3ddc4a6c5
--- /dev/null
+++ b/paddle/operators/save_load_combine_op_test.cc
@@ -0,0 +1,180 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <iostream>
+#include <string>
+#include <vector>
+#include "gtest/gtest.h"
+#include "paddle/framework/op_registry.h"
+
+USE_NO_KERNEL_OP(save_combine);
+USE_NO_KERNEL_OP(load_combine);
+
+int* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
+                            std::string var_name,
+                            paddle::platform::CPUPlace& place,
+                            paddle::framework::Scope& scope,
+                            paddle::framework::LoD& expect_lod) {
+  auto var = scope.Var(var_name);
+  auto tensor = var->GetMutable<paddle::framework::LoDTensor>();
+  tensor->Resize({x, y});
+  expect_lod.resize(1);
+  for (size_t i = 0; i < lod_info.size(); i++) {
+    expect_lod[0].push_back(lod_info[i]);
+  }
+  tensor->set_lod(expect_lod);
+  int* expect = tensor->mutable_data<int>(place);
+  for (int64_t i = 0; i < tensor->numel(); ++i) {
+    expect[i] = static_cast<int>(i);
+  }
+  return expect;
+}
+
+paddle::framework::LoDTensor* GeneratePlaceholderBeforeLoad(
+    const std::string out_var_name, paddle::framework::Scope& scope) {
+  auto load_var = scope.Var(out_var_name);
+  auto target = load_var->GetMutable<paddle::framework::LoDTensor>();
+  return target;
+}
+
+int* GetValuesAfterLoadCombineOp(paddle::framework::LoDTensor* target,
+                                 paddle::framework::Scope& scope,
+                                 paddle::framework::LoD& actual_lod) {
+  int* actual = target->data<int>();
+  actual_lod = target->lod();
+  return actual;
+}
+
+void CheckValues(int* expect, int* actual, paddle::framework::LoD expect_lod,
+                 paddle::framework::LoD actual_lod, const int& numel) {
+  for (int64_t i = 0; i < numel; ++i) {
+    EXPECT_EQ(expect[i], actual[i]);
+  }
+  EXPECT_EQ(expect_lod.size(), actual_lod.size());
+  for (size_t i = 0; i < expect_lod.size(); ++i) {
+    for (size_t j = 0; j < expect_lod[i].size(); ++j) {
+      EXPECT_EQ(expect_lod[i][j], actual_lod[i][j]);
+    }
+  }
+}
+
+// Here, we create 4 LoDTensors and use save_combine_op to first save these
+// in a single file. Then, we use load_combine_op to load these sequentially
+TEST(SaveLoadCombineOp, CPU) {
+  paddle::framework::Scope scope;
+  paddle::platform::CPUPlace place;
+
+  std::vector<int> lod1 = {0, 1, 2, 3, 10};
+  int numel1 = 100;
+  paddle::framework::LoD expect_lod1;
+  int* expect1 = CreateForSaveCombineOp(10, 10, lod1, "test_var1", place, scope,
+                                        expect_lod1);
+
+  std::vector<int> lod2 = {0, 2, 5, 10};
+  int numel2 = 200;
+  paddle::framework::LoD expect_lod2;
+  int* expect2 = CreateForSaveCombineOp(10, 20, lod2, "test_var2", place, scope,
+                                        expect_lod2);
+
+  std::vector<int> lod3 = {0, 2, 3, 20};
+  int numel3 = 4000;
+  paddle::framework::LoD expect_lod3;
+  int* expect3 = CreateForSaveCombineOp(20, 200, lod3, "test_var3", place,
+                                        scope, expect_lod3);
+
+  std::vector<int> lod4 = {0, 1, 20};
+  int numel4 = 1000;
+  paddle::framework::LoD expect_lod4;
+  int* expect4 = CreateForSaveCombineOp(20, 50, lod4, "test_var4", place, scope,
+                                        expect_lod4);
+
+  // Set attributes
+  std::string filename = "check_tensor.ls";
+  paddle::framework::AttributeMap attrs;
+  attrs.insert({"file_path", std::string(filename)});
+
+  // Run the save_combine_op
+  auto save_combine_op = paddle::framework::OpRegistry::CreateOp(
+      "save_combine",
+      {{"X", {"test_var1", "test_var2", "test_var3", "test_var4"}}}, {}, attrs);
+  save_combine_op->Run(scope, place);
+
+  // Set up output vars
+  auto target1 = GeneratePlaceholderBeforeLoad("out_var1", scope);
+  auto target2 = GeneratePlaceholderBeforeLoad("out_var2", scope);
+  auto target3 = GeneratePlaceholderBeforeLoad("out_var3", scope);
+  auto target4 = GeneratePlaceholderBeforeLoad("out_var4", scope);
+
+  // Run the load_combine_op
+  auto load_combine_op = paddle::framework::OpRegistry::CreateOp(
+      "load_combine", {},
+      {{"Out", {"out_var1", "out_var2", "out_var3", "out_var4"}}}, attrs);
+  load_combine_op->Run(scope, place);
+
+  paddle::framework::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4;
+  int* actual1 = GetValuesAfterLoadCombineOp(target1, scope, actual_lod1);
+  int* actual2 = GetValuesAfterLoadCombineOp(target2, scope, actual_lod2);
+  int* actual3 = GetValuesAfterLoadCombineOp(target3, scope, actual_lod3);
+  int* actual4 = GetValuesAfterLoadCombineOp(target4, scope, actual_lod4);
+
+  CheckValues(expect1, actual1, expect_lod1, actual_lod1, numel1);
+  CheckValues(expect2, actual2, expect_lod2, actual_lod2, numel2);
+  CheckValues(expect3, actual3, expect_lod3, actual_lod3, numel3);
+  CheckValues(expect4, actual4, expect_lod4, actual_lod4, numel4);
+}
+
+// Test with original SaveLoadTest
+TEST(SaveLoadTestWithCombineOp, CPU) {
+  paddle::framework::Scope scope;
+  paddle::platform::CPUPlace place;
+
+  auto var = scope.Var("test_var");
+  auto tensor = var->GetMutable<paddle::framework::LoDTensor>();
+  tensor->Resize({3, 10});
+  paddle::framework::LoD expect_lod;
+  expect_lod.resize(1);
+  expect_lod[0].push_back(0);
+  expect_lod[0].push_back(1);
+  expect_lod[0].push_back(2);
+  expect_lod[0].push_back(3);
+
+  tensor->set_lod(expect_lod);
+  int* expect = tensor->mutable_data<int>(place);
+  for (int64_t i = 0; i < tensor->numel(); ++i) {
+    expect[i] = static_cast<int>(i);
+  }
+  paddle::framework::AttributeMap attrs;
+  attrs.insert({"file_path", std::string("check_t.save")});
+
+  auto save_op = paddle::framework::OpRegistry::CreateOp(
+      "save_combine", {{"X", {"test_var"}}}, {}, attrs);
+  save_op->Run(scope, place);
+
+  auto load_var = scope.Var("out_var");
+  auto target = load_var->GetMutable<paddle::framework::LoDTensor>();
+  auto load_op = paddle::framework::OpRegistry::CreateOp(
+      "load_combine", {}, {{"Out", {"out_var"}}}, attrs);
+  load_op->Run(scope, place);
+  int* actual = target->data<int>();
+  for (int64_t i = 0; i < tensor->numel(); ++i) {
+    EXPECT_EQ(expect[i], actual[i]);
+  }
+  auto& actual_lod = target->lod();
+  EXPECT_EQ(expect_lod.size(), actual_lod.size());
+  for (size_t i = 0; i < expect_lod.size(); ++i) {
+    for (size_t j = 0; j < expect_lod[i].size(); ++j) {
+      EXPECT_EQ(expect_lod[i][j], actual_lod[i][j]);
+    }
+  }
+}
diff --git a/paddle/operators/save_load_op_test.cc b/paddle/operators/save_load_op_test.cc
index 40103d864f..d829d5da17 100644
--- a/paddle/operators/save_load_op_test.cc
+++ b/paddle/operators/save_load_op_test.cc
@@ -24,7 +24,7 @@ TEST(SaveLoadOp, CPU) {
 
   auto var = scope.Var("test_var");
   auto tensor = var->GetMutable<paddle::framework::LoDTensor>();
-  tensor->Resize({10, 10});
+  tensor->Resize({3, 10});
   paddle::framework::LoD expect_lod;
   expect_lod.resize(1);
   expect_lod[0].push_back(0);

From 80eff2662b4889ee169ee1efb0b2533d764b22d2 Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Wed, 31 Jan 2018 10:11:38 +0800
Subject: [PATCH 147/314] "unify flags" (#7973)

* "unify flags"

* "fix init"
---
 paddle/framework/executor.cc       | 6 +++---
 paddle/framework/operator.cc       | 6 ++----
 paddle/framework/scope.cc          | 8 +++++---
 python/paddle/v2/fluid/__init__.py | 6 ++----
 4 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index cbf3ec7526..fe1ca27eb3 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -25,7 +25,7 @@ limitations under the License. */
 #include "paddle/platform/place.h"
 #include "paddle/platform/profiler.h"
 
-DECLARE_bool(do_memory_benchmark);
+DECLARE_bool(benchmark);
 DEFINE_bool(check_nan_inf, false,
             "Checking whether operator produce NAN/INF or not. It will be "
             "extremely slow so please use this flag wisely.");
@@ -125,7 +125,7 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
 
     op->Run(*local_scope, place_);
     VLOG(3) << op->DebugStringEx(local_scope);
-    if (FLAGS_do_memory_benchmark) {
+    if (FLAGS_benchmark) {
       VLOG(2) << "Memory used after operator " + op->Type() + " running: "
               << memory::memory_usage(place_);
     }
@@ -142,7 +142,7 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
   if (create_vars && create_local_scope) {
     scope->DeleteScope(local_scope);
   }
-  if (FLAGS_do_memory_benchmark) {
+  if (FLAGS_benchmark) {
     VLOG(2) << "-------------------------------------------------------";
     VLOG(2) << "Memory used after deleting local scope: "
             << memory::memory_usage(place_);
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 831b1e2a1e..4e854f54dd 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -22,9 +22,7 @@ limitations under the License. */
 #include "paddle/framework/shape_inference.h"
 #include "paddle/framework/var_type.h"
 
-DEFINE_bool(op_sync, false,
-            "Default cuda is asynchronous device, set to True will"
-            "force op run in synchronous mode.");
+DECLARE_bool(benchmark);
 
 namespace paddle {
 namespace framework {
@@ -531,7 +529,7 @@ void OperatorWithKernel::Run(const Scope& scope,
       ExecutionContext(*this, new_scope, *new_dev_ctx));
 
   /*For profiling/benchmark only*/
-  if (FLAGS_op_sync) {
+  if (FLAGS_benchmark) {
     new_dev_ctx->Wait();
   }
 }
diff --git a/paddle/framework/scope.cc b/paddle/framework/scope.cc
index a67ff91009..af08b2ab81 100644
--- a/paddle/framework/scope.cc
+++ b/paddle/framework/scope.cc
@@ -20,9 +20,11 @@ limitations under the License. */
 #include "paddle/framework/threadpool.h"
 #include "paddle/string/printf.h"
 
-DEFINE_bool(do_memory_benchmark, false,
+DEFINE_bool(benchmark, false,
             "Doing memory benchmark. It will make deleting scope synchronized, "
-            "and add some memory usage logs");
+            "and add some memory usage logs."
+            "Default cuda is asynchronous device, set to True will"
+            "force op run in synchronous mode.");
 
 namespace paddle {
 namespace framework {
@@ -93,7 +95,7 @@ void Scope::DeleteScope(Scope* scope) {
   PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
   this->kids_.erase(it);
   // When making memory benchmark on Fluid, we have to delete scope sync.
-  if (FLAGS_do_memory_benchmark) {
+  if (FLAGS_benchmark) {
     delete scope;
   } else {
     Async([scope] { delete scope; });
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index 787416aed1..a542e3dbab 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -86,11 +86,9 @@ def __bootstrap__():
 
     os.environ['OMP_NUM_THREADS'] = str(num_threads)
 
-    read_env_flags = [
-        'use_pinned_memory', 'check_nan_inf', 'do_memory_benchmark'
-    ]
+    read_env_flags = ['use_pinned_memory', 'check_nan_inf', 'benchmark']
     if core.is_compiled_with_cuda():
-        read_env_flags += ['fraction_of_gpu_memory_to_use', 'op_sync']
+        read_env_flags += ['fraction_of_gpu_memory_to_use']
     core.init_gflags([sys.argv[0]] +
                      ["--tryfromenv=" + ",".join(read_env_flags)])
     core.init_glog(sys.argv[0])

From e100b37e30d23e11c9091cc7161eaa030985bfdb Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Wed, 31 Jan 2018 10:25:05 +0800
Subject: [PATCH 148/314] change the default option of `WITH_TESTING` to OFF

---
 CMakeLists.txt                                            | 2 +-
 doc/getstarted/build_and_install/build_from_source_cn.rst | 2 +-
 doc/getstarted/build_and_install/build_from_source_en.rst | 2 +-
 paddle/scripts/docker/README.md                           | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 7c7eb260ae..e8ea828dd2 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -39,7 +39,7 @@ option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_F
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
 option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
-option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
+option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
 option(WITH_STYLE_CHECK "Compile PaddlePaddle with style check"         ON)
 option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
diff --git a/doc/getstarted/build_and_install/build_from_source_cn.rst b/doc/getstarted/build_and_install/build_from_source_cn.rst
index 71904dc41e..ff904b1022 100644
--- a/doc/getstarted/build_and_install/build_from_source_cn.rst
+++ b/doc/getstarted/build_and_install/build_from_source_cn.rst
@@ -115,7 +115,7 @@ PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种B
     "WITH_AVX", "是否编译含有AVX指令集的PaddlePaddle二进制文件", "ON"
     "WITH_PYTHON", "是否内嵌PYTHON解释器", "ON"
     "WITH_STYLE_CHECK", "是否编译时进行代码风格检查", "ON"
-    "WITH_TESTING", "是否开启单元测试", "ON"
+    "WITH_TESTING", "是否开启单元测试", "OFF"
     "WITH_DOC", "是否编译中英文文档", "OFF"
     "WITH_SWIG_PY", "是否编译PYTHON的SWIG接口，该接口可用于预测和定制化训练", "Auto"
     "WITH_GOLANG", "是否编译go语言的可容错parameter server", "ON"
diff --git a/doc/getstarted/build_and_install/build_from_source_en.rst b/doc/getstarted/build_and_install/build_from_source_en.rst
index 27f73b2e2c..718fb869c2 100644
--- a/doc/getstarted/build_and_install/build_from_source_en.rst
+++ b/doc/getstarted/build_and_install/build_from_source_en.rst
@@ -126,7 +126,7 @@ You can add :code:`-D` argument to pass such options, like:
     "WITH_AVX", "Build with AVX support", "ON"
     "WITH_PYTHON", "Build with integrated Python interpreter", "ON"
     "WITH_STYLE_CHECK", "Check code style when building", "ON"
-    "WITH_TESTING", "Build unit tests", "ON"
+    "WITH_TESTING", "Build unit tests", "OFF"
     "WITH_DOC", "Build documentations", "OFF"
     "WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto"
     "WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON"
diff --git a/paddle/scripts/docker/README.md b/paddle/scripts/docker/README.md
index f0620498cf..65c4674555 100644
--- a/paddle/scripts/docker/README.md
+++ b/paddle/scripts/docker/README.md
@@ -56,7 +56,7 @@ Users can specify the following Docker build arguments with either "ON" or "OFF"
 | ------ | -------- | ----------- |
 | `WITH_GPU` | OFF | Generates NVIDIA CUDA GPU code and relies on CUDA libraries. |
 | `WITH_AVX` | OFF | Set to "ON" to enable AVX support. |
-| `WITH_TESTING` | ON | Build unit tests binaries. |
+| `WITH_TESTING` | OFF | Build unit tests binaries. |
 | `WITH_MKL` | ON | Build with [Intel® MKL](https://software.intel.com/en-us/mkl) and [Intel® MKL-DNN](https://github.com/01org/mkl-dnn) support. |
 | `WITH_GOLANG` | ON | Build fault-tolerant parameter server written in go. |
 | `WITH_SWIG_PY` | ON | Build with SWIG python API support. |

From be801d6c056c3435922e345d9d2ea105120b812d Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Wed, 31 Jan 2018 10:37:09 +0800
Subject: [PATCH 149/314] Add learning rate decay (#7892)

* add basic interface for learning rate decay
* add exponential_decay
* add natural_exp_decay
* add inverse_time_decay
---
 paddle/operators/elementwise_pow_op.cc        |  37 ++++++
 paddle/operators/elementwise_pow_op.cu        |  20 +++
 paddle/operators/elementwise_pow_op.h         |  37 ++++++
 python/paddle/v2/fluid/__init__.py            |   2 +
 .../paddle/v2/fluid/layers/math_op_patch.py   |   4 +-
 python/paddle/v2/fluid/layers/ops.py          |   1 +
 python/paddle/v2/fluid/layers/tensor.py       |  13 +-
 python/paddle/v2/fluid/learning_rate_decay.py | 125 ++++++++++++++++++
 python/paddle/v2/fluid/optimizer.py           |  66 +++++----
 .../tests/book/test_label_semantic_roles.py   |   2 +-
 .../v2/fluid/tests/test_elementwise_pow_op.py |  43 ++++++
 .../fluid/tests/test_learning_rate_decay.py   | 110 +++++++++++++++
 12 files changed, 432 insertions(+), 28 deletions(-)
 create mode 100644 paddle/operators/elementwise_pow_op.cc
 create mode 100644 paddle/operators/elementwise_pow_op.cu
 create mode 100644 paddle/operators/elementwise_pow_op.h
 create mode 100644 python/paddle/v2/fluid/learning_rate_decay.py
 create mode 100644 python/paddle/v2/fluid/tests/test_elementwise_pow_op.py
 create mode 100644 python/paddle/v2/fluid/tests/test_learning_rate_decay.py

diff --git a/paddle/operators/elementwise_pow_op.cc b/paddle/operators/elementwise_pow_op.cc
new file mode 100644
index 0000000000..5293cc7dd3
--- /dev/null
+++ b/paddle/operators/elementwise_pow_op.cc
@@ -0,0 +1,37 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/elementwise_pow_op.h"
+#include "paddle/operators/elementwise_op.h"
+
+namespace paddle {
+namespace operators {
+class ElementwisePowOpMaker : public ElementwiseOpMaker {
+ public:
+  ElementwisePowOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : ElementwiseOpMaker(proto, op_checker) {
+    SetComment("Pow", "Out = X ^ Y");
+    AddComment(comment_);
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(elementwise_pow, ops::ElementwiseOp,
+                             ops::ElementwisePowOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    elementwise_pow,
+    ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwisePowKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/elementwise_pow_op.cu b/paddle/operators/elementwise_pow_op.cu
new file mode 100644
index 0000000000..643c978e63
--- /dev/null
+++ b/paddle/operators/elementwise_pow_op.cu
@@ -0,0 +1,20 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#define EIGEN_USE_GPU
+#include "paddle/operators/elementwise_pow_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    elementwise_pow,
+    ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::ElementwisePowKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/elementwise_pow_op.h b/paddle/operators/elementwise_pow_op.h
new file mode 100644
index 0000000000..6019e709e0
--- /dev/null
+++ b/paddle/operators/elementwise_pow_op.h
@@ -0,0 +1,37 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <cmath>
+#include "paddle/operators/elementwise_op_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct PowFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return std::pow(a, b); }
+};
+
+template <typename DeviceContext, typename T>
+class ElementwisePowKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index a542e3dbab..18c8343d09 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -26,6 +26,7 @@ import initializer
 import layers
 import nets
 import optimizer
+import learning_rate_decay
 import backward
 import regularizer
 from param_attr import ParamAttr
@@ -44,6 +45,7 @@ __all__ = framework.__all__ + executor.__all__ + [
     'layers',
     'nets',
     'optimizer',
+    'learning_rate_decay',
     'backward',
     'regularizer',
     'LoDTensor',
diff --git a/python/paddle/v2/fluid/layers/math_op_patch.py b/python/paddle/v2/fluid/layers/math_op_patch.py
index f359e70126..79a130a3eb 100644
--- a/python/paddle/v2/fluid/layers/math_op_patch.py
+++ b/python/paddle/v2/fluid/layers/math_op_patch.py
@@ -145,7 +145,9 @@ def monkey_patch_variable():
             # a*b == b*a. Do not need to reverse explicitly
         ("__rmul__", "elementwise_mul", False),
         ("__div__", "elementwise_div", False),
-        ("__rdiv__", "elementwise_div", True)):
+        ("__rdiv__", "elementwise_div", True),
+        ("__pow__", "elementwise_pow", False),
+        ("__rpow__", "elementwise_pow", True)):
         setattr(Variable, method_name,
                 _elemwise_method_creator_(method_name, op_type, reverse))
 
diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py
index 022a94cad4..ee3172c7b8 100644
--- a/python/paddle/v2/fluid/layers/ops.py
+++ b/python/paddle/v2/fluid/layers/ops.py
@@ -56,6 +56,7 @@ __all__ = [
     'elementwise_mul',
     'elementwise_max',
     'elementwise_min',
+    'elementwise_pow',
     'clip',
     'clip_by_norm',
     'sequence_softmax',
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index 6e7d09459c..c435c5206d 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -16,12 +16,14 @@ from ..layer_helper import LayerHelper
 from ..param_attr import ParamAttr
 from ..framework import convert_np_dtype_to_dtype_
 from ..framework import Variable
+from ..initializer import Constant
 from ..core import DataType
 import numpy
 
 __all__ = [
     'create_tensor',
     'create_parameter',
+    'create_global_var',
     'cast',
     'concat',
     'sums',
@@ -58,13 +60,22 @@ def create_parameter(shape,
     Returns:
         Parameter: the created parameter
     """
-    helper = LayerHelper("create_parameter")
+    helper = LayerHelper("create_parameter", **locals())
     if attr is None:
         attr = ParamAttr()
     return helper.create_parameter(attr, shape, dtype, is_bias,
                                    default_initializer)
 
 
+def create_global_var(shape, value, dtype, persistable=False, name=None):
+    helper = LayerHelper("global_var", **locals())
+    var = helper.create_global_variable(
+        dtype=dtype, shape=shape, persistable=persistable, name=name)
+    helper.set_variable_initializer(
+        var, initializer=Constant(value=float(value)))
+    return var
+
+
 def cast(x, dtype):
     """
     This function takes in the input with input_dtype
diff --git a/python/paddle/v2/fluid/learning_rate_decay.py b/python/paddle/v2/fluid/learning_rate_decay.py
new file mode 100644
index 0000000000..96b3e9a0d7
--- /dev/null
+++ b/python/paddle/v2/fluid/learning_rate_decay.py
@@ -0,0 +1,125 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import layers
+from framework import Variable
+
+__all__ = ['exponential_decay', 'natural_exp_decay', 'inverse_time_decay']
+"""
+When training a model, it's often useful to decay the
+learning rate during training process, this is called
+learning_rate_decay. There are many strategies to do
+this, this module will provide some classical method.
+User can also implement their own learning_rate_decay
+strategy according to this module.
+"""
+
+
+def exponential_decay(learning_rate,
+                      global_step,
+                      decay_steps,
+                      decay_rate,
+                      staircase=False):
+    """Applies exponential decay to the learning rate.
+
+    ```python
+    decayed_learning_rate = learning_rate *
+            decay_rate ^ (global_step / decay_steps)
+    ```
+    Args:
+        learning_rate: A scalar float32 value or a Variable. This
+          will be the initial learning rate during training
+        global_step: A Variable that record the training step.
+        decay_steps: A Python `int32` number.
+        decay_rate: A Python `float` number.
+        staircase: Boolean. If set true, decay the learning rate every decay_steps.
+
+    Returns:
+        The decayed learning rate
+    """
+    if not isinstance(global_step, Variable):
+        raise ValueError("global_step is required for exponential_decay.")
+
+    # update learning_rate
+    div_res = global_step / decay_steps
+    if staircase:
+        div_res = layers.floor(x=div_res)
+    return learning_rate * (decay_rate**div_res)
+
+
+def natural_exp_decay(learning_rate,
+                      global_step,
+                      decay_steps,
+                      decay_rate,
+                      staircase=False):
+    """Applies natural exponential decay to the initial learning rate.
+
+    ```python
+    if not staircase:
+        decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
+    else:
+        decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
+    ```
+    Args:
+        learning_rate: A scalar float32 value or a Variable. This
+          will be the initial learning rate during training
+        global_step: A Variable that record the training step.
+        decay_steps: A Python `int32` number.
+        decay_rate: A Python `float` number.
+        staircase: Boolean. If set true, decay the learning rate every decay_steps.
+
+    Returns:
+        The decayed learning rate
+    """
+    if not isinstance(global_step, Variable):
+        raise ValueError("global_step is required for natural_exp_decay.")
+
+    div_res = global_step / decay_steps
+    if staircase:
+        div_res = layers.floor(x=div_res)
+    return learning_rate * layers.exp(x=(-1 * decay_rate * div_res))
+
+
+def inverse_time_decay(learning_rate,
+                       global_step,
+                       decay_steps,
+                       decay_rate,
+                       staircase=False):
+    """Applies inverse time decay to the initial learning rate.
+
+    ```python
+    if staircase:
+      decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
+    else
+      decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
+    ```
+    Args:
+        learning_rate: A scalar float32 value or a Variable. This
+          will be the initial learning rate during training
+        global_step: A Variable that record the training step.
+        decay_steps: A Python `int32` number.
+        decay_rate: A Python `float` number.
+        staircase: Boolean. If set true, decay the learning rate every decay_steps.
+
+    Returns:
+        The decayed learning rate
+    """
+    if not isinstance(global_step, Variable):
+        raise ValueError("global_step is required for inverse_time_decay.")
+
+    div_res = global_step / decay_steps
+    if staircase:
+        div_res = layers.floor(x=div_res)
+
+    return learning_rate / (1 + decay_rate * div_res)
diff --git a/python/paddle/v2/fluid/optimizer.py b/python/paddle/v2/fluid/optimizer.py
index 0c3533b892..7844a4e2df 100644
--- a/python/paddle/v2/fluid/optimizer.py
+++ b/python/paddle/v2/fluid/optimizer.py
@@ -15,6 +15,7 @@
 from collections import defaultdict
 
 import framework
+import layers
 from backward import append_backward
 from framework import unique_name, program_guard
 from initializer import Constant
@@ -33,9 +34,11 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
-    def __init__(self, global_step=None, regularization=None):
+    def __init__(self, learning_rate, global_step=None, regularization=None):
+        assert learning_rate is not None
         self._global_step = global_step
         self.regularization = regularization
+        self._global_learning_rate = learning_rate
         # Dictionary of accumulators. Some optimizer subclasses need to
         # allocate and manage extra variables associated with the parameters
         # to train. These variables are called accumulators.
@@ -43,6 +46,28 @@ class Optimizer(object):
         self._accumulators = defaultdict(lambda: dict())
         self.helper = None
 
+    def _create_global_learning_rate(self):
+        if isinstance(self._global_learning_rate, float):
+            self._global_learning_rate = layers.create_global_var(
+                name=unique_name("learning_rate"),
+                shape=[1],
+                value=float(self._global_learning_rate),
+                dtype='float32',
+                persistable=True)
+
+        if not isinstance(self._global_learning_rate, framework.Variable):
+            raise ValueError("learning rate should be a Variable, "
+                             "actual type is %s",
+                             type(self._global_learning_rate))
+
+    @property
+    def global_learning_rate(self):
+        """
+        get global decayed learning rate
+        :return:
+        """
+        return self._global_learning_rate
+
     def _append_optimize_op(self, block, param_and_grad):
         """ append optimize operator to block and return all the added optimize_op
         """
@@ -52,17 +77,7 @@ class Optimizer(object):
         # create learning rate variable for every parameter
         param = param_and_grad[0]
         param_lr = param.optimize_attr['learning_rate']
-        param_lr_shape = [1]
-        param_lr_var = self.helper.create_global_variable(
-            name=unique_name("learning_rate"),
-            dtype='float32',
-            shape=param_lr_shape,
-            lod_level=1,
-            persistable=True)
-        param_lr = param_lr * self._learning_rate
-        self.helper.set_variable_initializer(
-            var=param_lr_var, initializer=Constant(param_lr))
-        return param_lr_var
+        return self._global_learning_rate * param_lr
 
     def _create_accumulators(self, block, parameters):
         """Create all accumulators needed by the parameters
@@ -163,7 +178,7 @@ class Optimizer(object):
           optimization. This will include parameter update ops, global step
           update ops and any other custom ops required by subclasses to manage
           their internal state.
-          :param startup_program: 
+          :param startup_program:
         """
         # This is a default implementation of create_optimization_pass that
         # can be shared by most optimizers. This implementation assumes that
@@ -178,6 +193,7 @@ class Optimizer(object):
             self.helper = LayerHelper(self.__class__.__name__)
             self._create_accumulators(loss.block,
                                       [p[0] for p in parameters_and_grads])
+            self._create_global_learning_rate()
 
             optimize_ops = []
             for param_and_grad in parameters_and_grads:
@@ -231,9 +247,9 @@ class SGDOptimizer(Optimizer):
 
     def __init__(self, learning_rate, **kwargs):
         assert learning_rate is not None
-        super(SGDOptimizer, self).__init__(**kwargs)
+        super(SGDOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "sgd"
-        self._learning_rate = learning_rate
 
     def _append_optimize_op(self, block, param_and_grad):
         assert isinstance(block, framework.Block)
@@ -259,9 +275,9 @@ class MomentumOptimizer(Optimizer):
     def __init__(self, learning_rate, momentum, use_nesterov=False, **kwargs):
         assert learning_rate is not None
         assert momentum is not None
-        super(MomentumOptimizer, self).__init__(**kwargs)
+        super(MomentumOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "momentum"
-        self._learning_rate = learning_rate
         self._momentum = momentum
         self._use_nesterov = bool(use_nesterov)
 
@@ -303,9 +319,9 @@ class AdagradOptimizer(Optimizer):
     def __init__(self, learning_rate, epsilon=1.0e-6, **kwargs):
         assert learning_rate is not None
         assert epsilon is not None
-        super(AdagradOptimizer, self).__init__(**kwargs)
+        super(AdagradOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "adagrad"
-        self._learning_rate = learning_rate
         self._epsilon = epsilon
 
     def _create_accumulators(self, block, parameters):
@@ -352,9 +368,9 @@ class AdamOptimizer(Optimizer):
         assert beta1 is not None
         assert beta2 is not None
         assert epsilon is not None
-        super(AdamOptimizer, self).__init__(**kwargs)
+        super(AdamOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "adam"
-        self._learning_rate = learning_rate
         self._beta1 = beta1
         self._beta2 = beta2
         self._epsilon = epsilon
@@ -457,9 +473,9 @@ class AdamaxOptimizer(Optimizer):
         assert beta1 is not None
         assert beta2 is not None
         assert epsilon is not None
-        super(AdamaxOptimizer, self).__init__(**kwargs)
+        super(AdamaxOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "adamax"
-        self._learning_rate = learning_rate
         self._beta1 = beta1
         self._beta2 = beta2
         self._epsilon = epsilon
@@ -535,9 +551,9 @@ class DecayedAdagradOptimizer(Optimizer):
         assert decay is not None
         assert epsilon is not None
 
-        super(DecayedAdagradOptimizer, self).__init__(**kwargs)
+        super(DecayedAdagradOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
         self.type = "decayed_adagrad"
-        self._learning_rate = learning_rate
         self._decay = decay
         self._epsilon = epsilon
 
diff --git a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
index 1a342bf1fb..f85768de99 100644
--- a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
+++ b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
@@ -175,7 +175,7 @@ def main():
         paddle.reader.shuffle(
             paddle.dataset.conll05.test(), buf_size=8192),
         batch_size=BATCH_SIZE)
-    #place = fluid.CPUPlace()
+    # place = fluid.CPUPlace()
     place = fluid.CUDAPlace(0)
     feeder = fluid.DataFeeder(
         feed_list=[
diff --git a/python/paddle/v2/fluid/tests/test_elementwise_pow_op.py b/python/paddle/v2/fluid/tests/test_elementwise_pow_op.py
new file mode 100644
index 0000000000..e31749df9b
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_elementwise_pow_op.py
@@ -0,0 +1,43 @@
+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestElementwisePowOp(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_pow"
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
+            'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
+        }
+        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestElementwisePowOp_scalar(TestElementwisePowOp):
+    def setUp(self):
+        self.op_type = "elementwise_pow"
+        self.inputs = {
+            'X': np.random.rand(2, 3, 4).astype('float32'),
+            'Y': np.random.rand(1).astype('float32')
+        }
+        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_learning_rate_decay.py b/python/paddle/v2/fluid/tests/test_learning_rate_decay.py
new file mode 100644
index 0000000000..dc348cf2d2
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_learning_rate_decay.py
@@ -0,0 +1,110 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import math
+import paddle.v2.fluid.framework as framework
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.learning_rate_decay as lr_decay
+
+
+def exponential_decay(learning_rate,
+                      global_step,
+                      decay_steps,
+                      decay_rate,
+                      staircase=False):
+    exponent = float(global_step) / float(decay_steps)
+    if staircase:
+        exponent = math.floor(exponent)
+    return learning_rate * decay_rate**exponent
+
+
+def natural_exp_decay(learning_rate,
+                      global_step,
+                      decay_steps,
+                      decay_rate,
+                      staircase=False):
+    exponent = float(global_step) / float(decay_steps)
+    if staircase:
+        exponent = math.floor(exponent)
+    return learning_rate * math.exp(-1 * decay_rate * exponent)
+
+
+def inverse_time_decay(learning_rate,
+                       global_step,
+                       decay_steps,
+                       decay_rate,
+                       staircase=False):
+    temp = float(global_step) / float(decay_steps)
+    if staircase:
+        temp = math.floor(temp)
+    return learning_rate / (1 + decay_rate * temp)
+
+
+class TestLearningRateDecay(unittest.TestCase):
+    def check_decay(self, python_decay_fn, fluid_decay_fn, staircase):
+        init_lr = 1.0
+        decay_steps = 5
+        decay_rate = 0.5
+
+        global_step = layers.create_global_var(
+            shape=[1], value=0.0, dtype='float32', persistable=True)
+
+        decayed_lr = fluid_decay_fn(
+            learning_rate=init_lr,
+            global_step=global_step,
+            decay_steps=decay_steps,
+            decay_rate=decay_rate,
+            staircase=staircase)
+        layers.increment(global_step, 1.0)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+
+        exe.run(fluid.default_startup_program())
+        for step in range(10):
+            step_val, lr_val = exe.run(fluid.default_main_program(),
+                                       feed=[],
+                                       fetch_list=[global_step, decayed_lr])
+            python_decayed_lr = python_decay_fn(
+                learning_rate=init_lr,
+                global_step=step,
+                decay_steps=decay_steps,
+                decay_rate=decay_rate,
+                staircase=staircase)
+            self.assertAlmostEqual(python_decayed_lr, lr_val[0])
+
+    def test_decay(self):
+        decay_fns = [
+            (exponential_decay, lr_decay.exponential_decay, True),
+            (exponential_decay, lr_decay.exponential_decay, False),
+            (natural_exp_decay, lr_decay.natural_exp_decay, True),
+            (natural_exp_decay, lr_decay.natural_exp_decay, False),
+            (inverse_time_decay, lr_decay.inverse_time_decay, True),
+            (inverse_time_decay, lr_decay.inverse_time_decay, False),
+        ]
+
+        for py_decay_fn, fluid_decay_fn, staircase in decay_fns:
+            print("decay_fn=" + str(py_decay_fn) + " staircase=" + str(
+                staircase))
+            main_program = framework.Program()
+            startup_program = framework.Program()
+            with framework.program_guard(main_program, startup_program):
+                self.check_decay(py_decay_fn, fluid_decay_fn, staircase)
+
+
+if __name__ == '__main__':
+    unittest.main()

From e1611eb48485e4b62f0186277ac4d67218a2d77a Mon Sep 17 00:00:00 2001
From: Yancey <yanxu05@baidu.com>
Date: Wed, 31 Jan 2018 10:56:28 +0800
Subject: [PATCH 150/314] Add mirror registry server for book image (#7988)

---
 doc/getstarted/build_and_install/docker_install_cn.rst | 6 ++++++
 doc/getstarted/build_and_install/docker_install_en.rst | 6 ++++++
 2 files changed, 12 insertions(+)

diff --git a/doc/getstarted/build_and_install/docker_install_cn.rst b/doc/getstarted/build_and_install/docker_install_cn.rst
index 98fada7bdb..79d214635a 100644
--- a/doc/getstarted/build_and_install/docker_install_cn.rst
+++ b/doc/getstarted/build_and_install/docker_install_cn.rst
@@ -95,6 +95,12 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
 
      docker run -p 8888:8888 paddlepaddle/book
 
+国内用户可以使用下面的镜像源来加速访问：
+
+  .. code-block: bash
+
+    docker run -p 8888:8888 docker.paddlepaddlehub.com/book
+
 然后在浏览器中输入以下网址：
 
   .. code-block:: text
diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst
index b1d0890b4c..e0e0559fb8 100644
--- a/doc/getstarted/build_and_install/docker_install_en.rst
+++ b/doc/getstarted/build_and_install/docker_install_en.rst
@@ -102,6 +102,12 @@ We provide a packaged book image, simply issue the command:
 
      docker run -p 8888:8888 paddlepaddle/book
 
+For users in China, we provide a faster mirror:
+
+  .. code-block: bash
+
+    docker run -p 8888:8888 docker.paddlepaddlehub.com/book
+
 Then, you would back and paste the address into the local browser:
 
   .. code-block:: text

From 6f7eb0d5f3201dc107181aed570e66c502dbfc02 Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Wed, 31 Jan 2018 13:11:01 +0800
Subject: [PATCH 151/314] "fix gpu init" (#7528)

* "fix gpu init"

* "set env variable default value for share gpu"

* "fix ci"

* "removed CUDA_VISIBLE_DEVICES default"

* "removed"
---
 paddle/framework/init.cc      | 15 +++++++++++----
 paddle/framework/init_test.cc | 16 +++++++++++++++-
 2 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/paddle/framework/init.cc b/paddle/framework/init.cc
index 4ef82a541e..3f6ea121b3 100644
--- a/paddle/framework/init.cc
+++ b/paddle/framework/init.cc
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 #include <string.h>  // for strdup
 #include <algorithm>
+#include <stdexcept>
 #include <string>
 
 #include "paddle/framework/init.h"
@@ -46,17 +47,23 @@ void InitDevices() {
 
   std::vector<platform::Place> places;
   places.emplace_back(platform::CPUPlace());
+  int count = 0;
 
 #ifdef PADDLE_WITH_CUDA
-  int count = platform::GetCUDADeviceCount();
-  for (int i = 0; i < count; ++i) {
-    places.emplace_back(platform::CUDAPlace(i));
+  try {
+    count = platform::GetCUDADeviceCount();
+  } catch (const std::exception &exp) {
+    LOG(WARNING) << "Compiled with WITH_GPU, but no GPU found in runtime.";
   }
 #else
   LOG(WARNING)
-      << "'GPU' is not supported, Please re-compile with WITH_GPU option";
+      << "'CUDA' is not supported, Please re-compile with WITH_GPU option";
 #endif
 
+  for (int i = 0; i < count; ++i) {
+    places.emplace_back(platform::CUDAPlace(i));
+  }
+
   platform::DeviceContextPool::Init(places);
 }
 
diff --git a/paddle/framework/init_test.cc b/paddle/framework/init_test.cc
index f837a965d3..01e076dd8e 100644
--- a/paddle/framework/init_test.cc
+++ b/paddle/framework/init_test.cc
@@ -20,7 +20,21 @@ TEST(InitDevices, CPU) {
   using paddle::framework::InitDevices;
   using paddle::platform::DeviceContextPool;
 
+#ifndef PADDLE_WITH_CUDA
   InitDevices();
   DeviceContextPool& pool = DeviceContextPool::Instance();
-  ASSERT_GE(pool.size(), 1U);
+  ASSERT_EQ(pool.size(), 1U);
+#endif
+}
+
+TEST(InitDevices, CUDA) {
+  using paddle::framework::InitDevices;
+  using paddle::platform::DeviceContextPool;
+
+#ifdef PADDLE_WITH_CUDA
+  int count = paddle::platform::GetCUDADeviceCount();
+  InitDevices();
+  DeviceContextPool& pool = DeviceContextPool::Instance();
+  ASSERT_EQ(pool.size(), 1U + static_cast<unsigned>(count));
+#endif
 }

From adf14b0c708e20a39cb2f22cedd47be093129cae Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Wed, 31 Jan 2018 14:01:40 +0800
Subject: [PATCH 152/314] Refine channel test (#7946)

* refine channel test

* follow comments

* Add dependency enforce to threadpool

* Revert changes to channel_test.cc

* Revert changes to channel_test.cc

* Add #include "paddle/framework/macros.h"

* Add unit tests

* fix code format

* refine close channel

* follow comments

* use delete to destroy channel
---
 paddle/framework/channel.h                    | 10 +--
 paddle/framework/channel_test.cc              | 62 +++++++++++++++++--
 paddle/framework/details/buffered_channel.h   | 40 +++++++++---
 paddle/framework/details/unbuffered_channel.h |  6 +-
 4 files changed, 95 insertions(+), 23 deletions(-)

diff --git a/paddle/framework/channel.h b/paddle/framework/channel.h
index 70ecccc1a1..0570980c5a 100644
--- a/paddle/framework/channel.h
+++ b/paddle/framework/channel.h
@@ -26,9 +26,7 @@ class Channel {
   virtual void Send(T*) = 0;
   virtual void Receive(T*) = 0;
   virtual size_t Cap() = 0;
-
-  // Don't delete channels; instead, call Channel::Close.
- protected:
+  virtual void Close() = 0;
   virtual ~Channel() {}
 };
 
@@ -50,11 +48,7 @@ Channel<T>* MakeChannel(size_t buffer_size) {
 
 template <typename T>
 void CloseChannel(Channel<T>* ch) {
-  if (ch->Cap() > 0) {
-    delete dynamic_cast<details::Buffered<T>*>(ch);
-  } else {
-    delete dynamic_cast<details::UnBuffered<T>*>(ch);
-  }
+  ch->Close();
 }
 
 }  // namespace framework
diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 9efc017265..1510fb8abf 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -14,13 +14,67 @@ limitations under the License. */
 
 #include "paddle/framework/channel.h"
 
+#include <chrono>
+#include <thread>
+
 #include "gtest/gtest.h"
 
+using paddle::framework::Channel;
+using paddle::framework::MakeChannel;
+using paddle::framework::CloseChannel;
+
 TEST(Channel, MakeAndClose) {
-  using paddle::framework::Channel;
-  using paddle::framework::MakeChannel;
-  using paddle::framework::CloseChannel;
+  using paddle::framework::details::Buffered;
+  using paddle::framework::details::UnBuffered;
+  {
+    // MakeChannel should return a buffered channel is buffer_size > 0.
+    auto ch = MakeChannel<int>(10);
+    EXPECT_NE(dynamic_cast<Buffered<int>*>(ch), nullptr);
+    EXPECT_EQ(dynamic_cast<UnBuffered<int>*>(ch), nullptr);
+    CloseChannel(ch);
+    delete ch;
+  }
+  {
+    // MakeChannel should return an un-buffered channel is buffer_size = 0.
+    auto ch = MakeChannel<int>(0);
+    EXPECT_EQ(dynamic_cast<Buffered<int>*>(ch), nullptr);
+    EXPECT_NE(dynamic_cast<UnBuffered<int>*>(ch), nullptr);
+    CloseChannel(ch);
+    delete ch;
+  }
+}
+
+TEST(Channel, SufficientBufferSizeDoesntBlock) {
+  const size_t buffer_size = 10;
+  auto ch = MakeChannel<size_t>(buffer_size);
+  for (size_t i = 0; i < buffer_size; ++i) {
+    ch->Send(&i);  // should not block
+  }
+
+  size_t out;
+  for (size_t i = 0; i < buffer_size; ++i) {
+    ch->Receive(&out);  // should not block
+    EXPECT_EQ(out, i);
+  }
+  CloseChannel(ch);
+  delete ch;
+}
+
+TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
+  const size_t buffer_size = 10;
+  auto ch = MakeChannel<size_t>(buffer_size);
+  size_t sum = 0;
+  std::thread t([&]() {
+    // Try to write more than buffer size.
+    for (size_t i = 0; i < 2 * buffer_size; ++i) {
+      ch->Send(&i);  // should not block
+      sum += i;
+    }
+  });
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.5 sec
+  EXPECT_EQ(sum, 45U);
 
-  Channel<int>* ch = MakeChannel<int>(10);
   CloseChannel(ch);
+  t.join();
+  delete ch;
 }
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 572e29d44a..b093e15892 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -18,6 +18,7 @@ limitations under the License. */
 #include <mutex>
 
 #include "paddle/framework/channel.h"
+#include "paddle/platform/enforce.h"
 
 namespace paddle {
 namespace framework {
@@ -32,6 +33,8 @@ class Buffered : public paddle::framework::Channel<T> {
   virtual void Send(T*);
   virtual void Receive(T*);
   virtual size_t Cap() { return cap_; }
+  virtual void Close();
+  virtual ~Buffered();
 
  private:
   size_t cap_;
@@ -39,9 +42,11 @@ class Buffered : public paddle::framework::Channel<T> {
   std::condition_variable empty_cond_var_;
   std::condition_variable full_cond_var_;
   std::deque<T> channel_;
+  bool closed_;
 
-  Buffered(size_t cap) : cap_(cap) {}
-  virtual ~Buffered();
+  Buffered(size_t cap) : cap_(cap), closed_(false) {
+    PADDLE_ENFORCE_GT(cap, 0);
+  }
 
   void NotifyAllSenders(std::unique_lock<std::mutex>*);
 };
@@ -49,24 +54,39 @@ class Buffered : public paddle::framework::Channel<T> {
 template <typename T>
 void Buffered<T>::Send(T* item) {
   std::unique_lock<std::mutex> lock(mu_);
-  full_cond_var_.wait(lock, [this]() { return channel_.size() < cap_; });
-  channel_.push_back(std::move(*item));
-  lock.unlock();
-  empty_cond_var_.notify_one();
+  full_cond_var_.wait(lock,
+                      [this]() { return channel_.size() < cap_ || closed_; });
+  if (!closed_) {
+    channel_.push_back(std::move(*item));
+    lock.unlock();
+    empty_cond_var_.notify_one();
+  }
 }
 
 template <typename T>
 void Buffered<T>::Receive(T* item) {
   std::unique_lock<std::mutex> lock(mu_);
-  empty_cond_var_.wait(lock, [this]() { return !channel_.empty(); });
-  *item = std::move(channel_.front());
-  channel_.pop_front();
+  empty_cond_var_.wait(lock, [this]() { return !channel_.empty() || closed_; });
+  if (!closed_) {
+    *item = std::move(channel_.front());
+    channel_.pop_front();
+    NotifyAllSenders(&lock);
+  } else {
+    item = nullptr;
+  }
+}
+
+template <typename T>
+void Buffered<T>::Close() {
+  std::unique_lock<std::mutex> lock(mu_);
+  closed_ = true;
   NotifyAllSenders(&lock);
 }
 
 template <typename T>
 Buffered<T>::~Buffered() {
   std::unique_lock<std::mutex> lock(mu_);
+  closed_ = true;
   channel_.clear();
   NotifyAllSenders(&lock);
 }
@@ -74,7 +94,7 @@ Buffered<T>::~Buffered() {
 template <typename T>
 void Buffered<T>::NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
   lock->unlock();
-  full_cond_var_.notify_one();
+  full_cond_var_.notify_all();
 }
 
 }  // namespace details
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index 7ecced1fba..cc2d2e587e 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -32,10 +32,11 @@ class UnBuffered : public paddle::framework::Channel<T> {
   virtual void Send(T*);
   virtual void Receive(T*);
   virtual size_t Cap() { return 0; }
+  virtual void Close();
+  virtual ~UnBuffered();
 
  private:
   UnBuffered() {}
-  virtual ~UnBuffered();
 };
 
 template <typename T>
@@ -44,6 +45,9 @@ void UnBuffered<T>::Send(T* channel_element) {}
 template <typename T>
 void UnBuffered<T>::Receive(T*) {}
 
+template <typename T>
+void UnBuffered<T>::Close() {}
+
 template <typename T>
 UnBuffered<T>::~UnBuffered() {}
 

From 7c53d72719c6f964e81363fb502f757501c99446 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 30 Jan 2018 22:12:57 -0800
Subject: [PATCH 153/314] Refine the design doc for ctc_beam_search_decoder

---
 doc/design/speech/README.MD | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/doc/design/speech/README.MD b/doc/design/speech/README.MD
index cc03aac7b4..4509d6453d 100644
--- a/doc/design/speech/README.MD
+++ b/doc/design/speech/README.MD
@@ -142,13 +142,15 @@ TODO by Assignees
 
 <div align="center">
 <img src="image/beam_search.png" width=400><br/>
-Figure 2. Algorithm for Beam Search Decoder.
+Figure 2. Algorithm for CTC Beam Search Decoder.
 </div>
 
-- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] with a modification for the ambiguous part, as shown in Figure 2.
-- An **external defined scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a space character appended.
-- Such scorer is a unified class, may consisting of language model, word count or any customed evaluators.
-- The **language model** is built from Task 5, with a parameter should be carefully tuned to achieve minimum WER/CER (c.f. Task 7)
+- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] as shown in Figure 2, with two important modifications for the ambiguous parts: 
+   - 1) in the iterative computation of probabilities, the assignment operation is changed to accumulation for one prefix may comes from different paths; 
+   - 2) the if condition ```if l^+ not in A_prev then``` after probabilities' computation is deprecated for it is hard to understand and seems unnecessary.
+- An **external scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a white space appended in English decoding and any character appended in Mandarin decoding.
+- Such external scorer consists of language model, word count or any other customed scorers.
+- The **language model** is built from Task 5, with parameters should be carefully tuned to achieve minimum WER/CER (c.f. Task 7)
 - This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. 
  
 

From 535f6bdf7bec8903829cd2020ad80bb948f471bf Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 30 Jan 2018 22:19:01 -0800
Subject: [PATCH 154/314] Rename the DS2' design doc

---
 doc/design/speech/{README.MD => deep_speech_2.md} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename doc/design/speech/{README.MD => deep_speech_2.md} (99%)

diff --git a/doc/design/speech/README.MD b/doc/design/speech/deep_speech_2.md
similarity index 99%
rename from doc/design/speech/README.MD
rename to doc/design/speech/deep_speech_2.md
index 4509d6453d..5e72c7e201 100644
--- a/doc/design/speech/README.MD
+++ b/doc/design/speech/deep_speech_2.md
@@ -149,7 +149,7 @@ Figure 2. Algorithm for CTC Beam Search Decoder.
    - 1) in the iterative computation of probabilities, the assignment operation is changed to accumulation for one prefix may comes from different paths; 
    - 2) the if condition ```if l^+ not in A_prev then``` after probabilities' computation is deprecated for it is hard to understand and seems unnecessary.
 - An **external scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a white space appended in English decoding and any character appended in Mandarin decoding.
-- Such external scorer consists of language model, word count or any other customed scorers.
+- Such external scorer consists of language model, word count or any other custom scorers.
 - The **language model** is built from Task 5, with parameters should be carefully tuned to achieve minimum WER/CER (c.f. Task 7)
 - This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. 
  

From de1a70147ed97055a1ad595208a696e86ea6f183 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Tue, 30 Jan 2018 22:25:29 -0800
Subject: [PATCH 155/314] Adjust the width of figure 2

---
 doc/design/speech/deep_speech_2.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/design/speech/deep_speech_2.md b/doc/design/speech/deep_speech_2.md
index 5e72c7e201..cfdc4d6df0 100644
--- a/doc/design/speech/deep_speech_2.md
+++ b/doc/design/speech/deep_speech_2.md
@@ -141,7 +141,7 @@ TODO by Assignees
 ### Beam Search with CTC and LM
 
 <div align="center">
-<img src="image/beam_search.png" width=400><br/>
+<img src="image/beam_search.png" width=600><br/>
 Figure 2. Algorithm for CTC Beam Search Decoder.
 </div>
 

From 419e4c49d07afbbb9abd3c323ce66794410b4ed8 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Wed, 31 Jan 2018 06:38:17 +0000
Subject: [PATCH 156/314] modify some

---
 benchmark/cluster/vgg16/v2_pserver.yaml | 4 ++--
 benchmark/cluster/vgg16/v2_trainer.yaml | 8 ++++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/benchmark/cluster/vgg16/v2_pserver.yaml b/benchmark/cluster/vgg16/v2_pserver.yaml
index dd1271e0cf..857e2ff455 100644
--- a/benchmark/cluster/vgg16/v2_pserver.yaml
+++ b/benchmark/cluster/vgg16/v2_pserver.yaml
@@ -23,13 +23,13 @@ spec:
         - name: PADDLE_JOB_NAME
           value: vgg16v2job
         - name: TRAINERS
-          value: "20"
+          value: "60"
         - name: PSERVERS
           value: "10"
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "python train.py"
+          value: "python -u train.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/v2_trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
index 997bbc81c9..be0f741b34 100644
--- a/benchmark/cluster/vgg16/v2_trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -3,8 +3,8 @@ kind: Job
 metadata:
   name: vgg16v2job-trainer
 spec:
-  parallelism: 20
-  completions: 20
+  parallelism: 60
+  completions: 60
   template:
     metadata:
       labels:
@@ -24,13 +24,13 @@ spec:
         - name: BATCH_SIZE
           value: "256"
         - name: TRAINERS
-          value: "20"
+          value: "60"
         - name: PSERVERS
           value: "10"
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16_v2.py"
+          value: "cd /workspace && MKL_NUM_THREADS=1 python -u /workspace/vgg16_v2.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT

From e3b0af43a05ac04159aa9d2d9777c0f5a2486d49 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 31 Jan 2018 14:42:20 +0800
Subject: [PATCH 157/314] Fix CI Error

---
 python/paddle/v2/fluid/tests/test_layer_norm_op.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 7d5dc7d1a6..68cf8673cd 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -228,7 +228,7 @@ class TestLayerNormdOp(OpTest):
                                          place)
 
         places = [core.CPUPlace()]
-        if core.is_compile_gpu() and core.op_support_gpu("layer_norm"):
+        if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
             places.append(core.CUDAPlace(0))
 
         for place in places:

From ae7d1c1f8c7175d60420dcfefba37c1a2518a536 Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Wed, 31 Jan 2018 15:46:59 +0800
Subject: [PATCH 158/314] Fix/lod (#7714)

* "Need to re-design LoD "

* "add lod design"

* "fix lod gpu ptr pointer"

* "removed commented code"

* "fix CI"

* "remove set lod in pybind"

* "fix style check"

* "fix CI"

* "fix long type template error"

* "pybind reorder to use Place"

* "fix ci"

* "fix ci"

* fix ci

* "sperate as a new file"

* "fix CI"

* "fix ci"

* small fix

* "add test"

* "fix adam op"

* "fix lstmp op"

* "fix adam op"

* "follow comments"

* "fix ci"
---
 paddle/framework/CMakeLists.txt               |   2 +-
 paddle/framework/lod_tensor.cc                |   2 -
 paddle/framework/lod_tensor.h                 |  26 +--
 paddle/framework/lod_tensor_test.cc           |  11 ++
 paddle/framework/lod_tensor_test.cu           |  46 +++++-
 paddle/framework/mixed_vector.h               | 154 ++++++++++++++++++
 paddle/framework/tensor.h                     |   7 +
 paddle/inference/CMakeLists.txt               |   2 +-
 paddle/operators/adagrad_op.cu                |   6 +-
 paddle/operators/adam_op.h                    |   7 +-
 paddle/operators/ctc_align_op.cu              |   5 +-
 paddle/operators/gru_op.h                     |  13 +-
 paddle/operators/lookup_table_op.cu           |   4 +-
 paddle/operators/lstm_op.h                    |  12 +-
 paddle/operators/lstmp_op.h                   |  11 +-
 .../operators/math/selected_rows_functor.cu   |  24 +--
 paddle/operators/math/sequence2batch.cc       |   6 +-
 paddle/operators/math/sequence2batch.cu       |   6 +-
 paddle/operators/math/sequence2batch.h        |   9 +-
 paddle/operators/math/sequence_padding.cu     |  20 ++-
 paddle/operators/math/sequence_pooling.cu     |   2 +-
 paddle/operators/math/sequence_scale.cu       |   2 +-
 paddle/operators/row_conv_op.cu               |   4 +-
 paddle/operators/sequence_erase_op.cu         |   3 +-
 paddle/operators/sgd_op.cu                    |   4 +-
 paddle/pybind/pybind.cc                       |  37 +----
 python/paddle/v2/fluid/tests/test_tensor.py   |  24 ++-
 27 files changed, 346 insertions(+), 103 deletions(-)
 create mode 100644 paddle/framework/mixed_vector.h

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 8c28709a68..8b71f73c36 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -22,7 +22,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
 
 cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
-nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
+nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor init)
 
 cc_test(variable_test SRCS variable_test.cc)
 
diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc
index 53b0d0fe08..cb27de6991 100644
--- a/paddle/framework/lod_tensor.cc
+++ b/paddle/framework/lod_tensor.cc
@@ -24,8 +24,6 @@ limitations under the License. */
 #include <algorithm>
 #include <iterator>
 
-#include <glog/logging.h>
-
 namespace paddle {
 namespace framework {
 
diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 9d1294fdeb..d0ab640485 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -18,11 +18,11 @@ limitations under the License. */
 #ifdef PADDLE_WITH_CUDA
 #include <thrust/device_vector.h>
 #include <thrust/host_vector.h>
-#include <thrust/system/cuda/experimental/pinned_allocator.h>
 #endif
 
 #include <glog/logging.h>
 #include "paddle/framework/ddim.h"
+#include "paddle/framework/mixed_vector.h"
 #include "paddle/framework/tensor.h"
 #include "paddle/framework/tensor_util.h"
 #include "paddle/platform/enforce.h"
@@ -31,15 +31,6 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-#ifndef PADDLE_WITH_CUDA
-template <typename T>
-using Vector = std::vector<T>;
-#else
-template <typename T>
-using Vector = thrust::host_vector<
-    T, thrust::system::cuda::experimental::pinned_allocator<T>>;
-#endif
-
 /*
  * LoD is short for Level of Details.
  *
@@ -55,7 +46,15 @@ using Vector = thrust::host_vector<
  *    0 2 4 7
  *    0 2 5 7 10 12 15 20
  */
-using LoD = std::vector<Vector<size_t>>;
+struct LoD : public std::vector<Vector<size_t>> {
+  using std::vector<Vector<size_t>>::vector;
+
+  void CopyFromCUDA() {
+    for (auto it = this->begin(); it != this->end(); ++it) {
+      it->CopyFromCUDA();
+    }
+  }
+};
 
 std::ostream& operator<<(std::ostream& os, const LoD& lod);
 std::ostream& operator<<(std::ostream& os, const LoDTensor& t);
@@ -109,7 +108,10 @@ bool CheckAbsLoD(const LoD& in, int tensor_height = -1);
  */
 class LoDTensor : public Tensor {
  public:
-  LoDTensor() {}
+  LoDTensor() : Tensor() {}
+
+  /* Constructor with place should only be used in pybind */
+  explicit LoDTensor(const platform::Place& place) : Tensor(place) {}
 
   explicit LoDTensor(const LoD& lod) : lod_(lod) {}
 
diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc
index 4d172c43c7..3b63020e68 100644
--- a/paddle/framework/lod_tensor_test.cc
+++ b/paddle/framework/lod_tensor_test.cc
@@ -23,6 +23,17 @@
 namespace paddle {
 namespace framework {
 
+TEST(LoD, data) {
+  LoD lod{{0, 1, 2}};
+  lod.push_back({0, 2, 4, 5});
+  lod.push_back(std::vector<size_t>({0, 1, 6, 8, 10, 11}));
+
+  auto& v = lod[0];
+  for (size_t i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(v[i], i);
+  }
+}
+
 TEST(LodExpand, test) {
   LoD lod{{0, 2}};
   LoDTensor tensor;
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
index 1e253a2f6f..d4c9f00bd9 100644
--- a/paddle/framework/lod_tensor_test.cu
+++ b/paddle/framework/lod_tensor_test.cu
@@ -14,6 +14,8 @@
 
 #include <cuda.h>
 #include <cuda_runtime.h>
+#include <stdio.h>
+#include "paddle/framework/init.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/platform/assert.h"
 
@@ -26,7 +28,48 @@ __global__ void test(size_t* a, int size) {
   }
 }
 
+TEST(Vector, Normal) {
+  using namespace paddle::framework;
+  using namespace paddle::platform;
+  using namespace paddle::memory;
+
+  paddle::framework::InitDevices();
+
+  paddle::framework::Vector<size_t> vec({1, 2, 3});
+  size_t* ptr = vec.data();
+  for (size_t i = 0; i < vec.size(); ++i) {
+    EXPECT_EQ(vec[i], *(ptr + i));
+  }
+
+  vec.clear();
+  vec.CopyFromCUDA();
+
+  std::vector<size_t> v = {1, 2, 3};
+  for (size_t i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(v[i], vec[i]);
+  }
+}
+
+TEST(LoD, data) {
+  paddle::framework::InitDevices();
+
+  paddle::framework::LoD lod{{0, 1, 2}};
+  lod.push_back({0, 2, 4, 5});
+  lod.push_back(std::vector<size_t>({0, 1, 6, 8, 10, 11}));
+
+  auto& v = lod[0];
+  test<<<1, 1>>>(v.cuda_data(), v.size());
+  cudaDeviceSynchronize();
+
+  v.CopyFromCUDA();
+  for (size_t i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(v[i], i * 2);
+  }
+}
+
 TEST(LoDTensor, LoDInGPU) {
+  paddle::framework::InitDevices();
+
   paddle::framework::LoDTensor lod_tensor;
   paddle::platform::CUDAPlace place(0);
 
@@ -42,8 +85,9 @@ TEST(LoDTensor, LoDInGPU) {
 
   auto lod = lod_tensor.lod();
 
-  test<<<1, 8>>>(lod[0].data(), lod[0].size());
+  test<<<1, 8>>>(lod[0].cuda_data(), lod[0].size());
   cudaDeviceSynchronize();
+  lod.CopyFromCUDA();
 
   for (size_t i = 0; i < src_lod[0].size(); ++i) {
     EXPECT_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
new file mode 100644
index 0000000000..0e0e239586
--- /dev/null
+++ b/paddle/framework/mixed_vector.h
@@ -0,0 +1,154 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+
+#pragma once
+
+#include <initializer_list>
+#include <vector>
+
+#include "paddle/memory/memcpy.h"
+#include "paddle/memory/memory.h"
+#include "paddle/platform/device_context.h"
+#include "paddle/platform/enforce.h"
+#include "paddle/platform/place.h"
+
+namespace paddle {
+namespace framework {
+
+/**
+ * @brief Vector support both cpu and gpu.
+ * host vector lifetime is same with Vector
+ * device vector is lazily malloc and modified.
+ */
+
+template <typename T>
+class Vector : public std::vector<T> {
+ public:
+  /* NOTE(dzhwinter):
+   * Data always store and modified on Host.
+   * If the data is modified when use cuda_data interface,
+   * You need to call the CopyFromCUDA explicitly to synchronize data.
+   *
+   */
+  enum class kDataPosition {
+    kDataOnHost = 0,
+    kDataOnDevice = 1,
+  };
+
+ public:
+  using std::vector<T>::vector;
+
+  Vector() {}
+  Vector(const std::vector<T> &v) : std::vector<T>(v) {}  // NOLINT
+
+  virtual ~Vector() {
+#ifdef PADDLE_WITH_CUDA
+    if (cuda_ptr_ != nullptr) {
+      memory::Free<platform::CUDAPlace>(place_, static_cast<void *>(cuda_ptr_));
+    }
+#endif
+  }
+
+  T *cuda_data() {
+    CopyToCUDA();
+    PADDLE_ENFORCE_NOT_NULL(
+        cuda_ptr_, "No data or Insufficient CUDA memory to allocation");
+    return static_cast<T *>(cuda_ptr_);
+  }
+
+  T *data() { return std::vector<T>::data(); }
+
+  const T *data() const { return std::vector<T>::data(); }
+
+  void CopyToCUDA();
+
+  void CopyFromCUDA();
+
+  void CopyToPeer(platform::Place);
+
+ private:
+  void *cuda_ptr_ = nullptr;
+  size_t cuda_size_ = 0;
+  /*The DataPosition is unused now,
+    if we want support random access from cpu and cuda,
+    we need to overload all the vector method */
+
+  kDataPosition position_ = kDataPosition::kDataOnHost;
+  platform::CUDAPlace place_;
+};
+
+template <typename T>
+void Vector<T>::CopyToCUDA() {
+#ifdef PADDLE_WITH_CUDA
+  if (cuda_ptr_ == nullptr) {
+    cuda_ptr_ =
+        memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T));
+  }
+  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+  auto *cuda_ctx = pool.GetByPlace(place_);
+
+  memory::Copy(place_, static_cast<void *>(cuda_ptr_), platform::CPUPlace(),
+               static_cast<const void *>(this->data()),
+               this->size() * sizeof(T), cuda_ctx->stream());
+  cuda_ctx->Wait();
+
+  cuda_size_ = this->size();
+#endif
+}
+
+template <typename T>
+void Vector<T>::CopyFromCUDA() {
+#ifdef PADDLE_WITH_CUDA
+  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+  auto *cuda_ctx = pool.GetByPlace(place_);
+  if (cuda_ptr_ == nullptr) {
+    LOG(WARNING) << "No uncommited cuda data.";
+    return;
+  }
+  this->resize(cuda_size_);
+  memory::Copy(platform::CPUPlace(), static_cast<void *>(this->data()), place_,
+               static_cast<const void *>(cuda_ptr_), this->size() * sizeof(T),
+               cuda_ctx->stream());
+  cuda_ctx->Wait();
+
+#endif
+}
+
+template <typename T>
+void Vector<T>::CopyToPeer(platform::Place peer_place) {
+  if (platform::is_cpu_place(peer_place)) {
+    return;
+  }
+#ifdef PADDLE_WITH_CUDA
+  auto *cuda_ctx = platform::DeviceContextPool::Instance().GetByPlace(place_);
+  void *peer_cuda_ptr_ = memory::Alloc<platform::CUDAPlace>(
+      boost::get<platform::CUDAPlace>(peer_place), this->size() * sizeof(T));
+  memory::Copy(boost::get<platform::CUDAPlace>(peer_place),
+               static_cast<void *>(peer_cuda_ptr_), place_,
+               static_cast<const void *>(cuda_ptr_), this->size() * sizeof(T),
+               cuda_ctx->stream());
+  cuda_ctx->Wait();
+  memory::Free<platform::CUDAPlace>(place_, static_cast<void *>(cuda_ptr_));
+  place_ = boost::get<platform::CUDAPlace>(peer_place);
+  cuda_ptr_ = peer_cuda_ptr_;
+#endif
+}
+
+template class Vector<int>;
+template class Vector<unsigned>;
+template class Vector<size_t>;
+template class Vector<int64_t>;
+
+}  // namespace framework
+}  // namespace paddle
diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h
index 4aaa29d794..f0ea709a5c 100644
--- a/paddle/framework/tensor.h
+++ b/paddle/framework/tensor.h
@@ -47,6 +47,11 @@ class Tensor {
  public:
   Tensor() : offset_(0) {}
 
+  /*! Constructor with place should only be used in pybind. */
+  explicit Tensor(const platform::Place& place) : offset_(0) {
+    holder_->set_place(place);
+  }
+
   /*! Return a pointer to mutable memory block. */
   template <typename T>
   inline T* data();
@@ -137,6 +142,7 @@ class Tensor {
     virtual std::type_index type() const = 0;
     virtual platform::Place place() const = 0;
     virtual void set_type(std::type_index type) = 0;
+    virtual void set_place(platform::Place place) = 0;
   };
 
   template <typename Place>
@@ -156,6 +162,7 @@ class Tensor {
     virtual void* ptr() const { return static_cast<void*>(ptr_.get()); }
     virtual std::type_index type() const { return type_; }
     virtual void set_type(std::type_index type) { type_ = type; }
+    virtual void set_place(platform::Place place) { place_ = place; }
 
     /*! the pointer of memory block. */
     std::unique_ptr<uint8_t, memory::PODDeleter<uint8_t, Place>> ptr_;
diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index 0288266c08..2289ddc139 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -1,4 +1,4 @@
-set(FLUID_CORE_MODULES proto_desc paddle_memory executor prune init)
+set(FLUID_CORE_MODULES proto_desc paddle_memory lod_tensor executor prune init)
 
 cc_library(paddle_fluid_api
     SRCS io.cc
diff --git a/paddle/operators/adagrad_op.cu b/paddle/operators/adagrad_op.cu
index 4e57938792..00cb6e9caf 100644
--- a/paddle/operators/adagrad_op.cu
+++ b/paddle/operators/adagrad_op.cu
@@ -82,7 +82,7 @@ struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
     math::scatter::MergeAdd<platform::CUDADeviceContext, T> merge_func;
     auto grad_merge = merge_func(context, grad);
     auto* grad_merge_data = grad_merge.mutable_value()->template data<T>();
-    auto& merge_rows = grad_merge.rows();
+    framework::Vector<int64_t> merge_rows(grad_merge.rows());
     // 2. m += g_m * g_m
     math::scatter::Mul<platform::CUDADeviceContext, T> sqare_func;
     auto grad_square = sqare_func(context, grad_merge, grad_merge);
@@ -101,8 +101,8 @@ struct SparseAdagradFunctor<platform::CUDADeviceContext, T> {
     SparseAdagradFunctorKernel<
         T, 256><<<grid2, threads, 0,
                   reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                      .stream()>>>(grad_merge_data, grad_merge.rows().data(),
-                                   lr, param_data, moment_data, grad_width,
+                      .stream()>>>(grad_merge_data, merge_rows.cuda_data(), lr,
+                                   param_data, moment_data, grad_width,
                                    epsilon);
   }
 };
diff --git a/paddle/operators/adam_op.h b/paddle/operators/adam_op.h
index 9cc34bdded..bf536687d3 100644
--- a/paddle/operators/adam_op.h
+++ b/paddle/operators/adam_op.h
@@ -199,7 +199,12 @@ class AdamOpKernel : public framework::OpKernel<T> {
           merge_func(ctx.template device_context<DeviceContext>(), grad);
       auto& grad_tensor = grad_merge.value();
       const T* grad_data = grad_tensor.template data<T>();
-      auto* rows = grad_merge.rows().data();
+      int64_t* rows = nullptr;
+      if (platform::is_gpu_place(ctx.GetPlace())) {
+        rows = grad_merge.mutable_rows()->cuda_data();
+      } else {
+        rows = grad_merge.mutable_rows()->data();
+      }
       auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
 
       SparseAdamFunctor<T> functor(
diff --git a/paddle/operators/ctc_align_op.cu b/paddle/operators/ctc_align_op.cu
index 45635f1674..2a970cd9fa 100644
--- a/paddle/operators/ctc_align_op.cu
+++ b/paddle/operators/ctc_align_op.cu
@@ -69,12 +69,11 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
 
     auto stream = ctx.cuda_device_context().stream();
     MergeAndDelCudaKernel<T><<<1, 1, 0, stream>>>(
-        num_tokens, tokens, num_seq, input_lod[level].data(), blank,
+        num_tokens, tokens, num_seq, input_lod[level].cuda_data(), blank,
         merge_repeated, dev_out_lod0_ptr, output_data);
 
     // set output lod
-    thrust::host_vector<size_t> host_out_lod0(dev_out_lod0.begin(),
-                                              dev_out_lod0.end());
+    std::vector<size_t> host_out_lod0(dev_out_lod0.begin(), dev_out_lod0.end());
     framework::LoD out_lod;
     out_lod.push_back(host_out_lod0);
     output->set_lod(out_lod);
diff --git a/paddle/operators/gru_op.h b/paddle/operators/gru_op.h
index b1957fb9ce..a08bd4233b 100644
--- a/paddle/operators/gru_op.h
+++ b/paddle/operators/gru_op.h
@@ -30,11 +30,12 @@ using Tensor = framework::Tensor;
 
 template <typename DeviceContext, typename T>
 inline void ReorderInitState(const DeviceContext& ctx,
-                             const framework::Tensor& src, const size_t* index,
+                             const framework::Tensor& src,
+                             framework::Vector<size_t> index_lod,
                              framework::Tensor* dst, bool indexed_src) {
   math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
   dst->mutable_data<T>(src.dims(), ctx.GetPlace());
-  row_shuffle(ctx, src, index, *dst, indexed_src);
+  row_shuffle(ctx, src, index_lod, *dst, indexed_src);
 }
 
 template <typename DeviceContext, typename T>
@@ -76,7 +77,9 @@ class GRUKernel : public framework::OpKernel<T> {
     gru_value.state_weight =
         const_cast<T*>(weight_data + 2 * frame_size * frame_size);
     Tensor ordered_h0;
-    const size_t* order = batch_gate->lod()[2].data();
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (h0) {
       // Since the batch computing for GRU reorders the input sequences
       // according to their length. The initialized cell state also needs
@@ -159,7 +162,9 @@ class GRUGradKernel : public framework::OpKernel<T> {
     zero(dev_ctx, &batch_reset_hidden_prev_grad, static_cast<T>(0.0));
 
     Tensor ordered_h0, ordered_h0_grad;
-    const size_t* order = batch_gate->lod()[2].data();
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (h0) {
       ReorderInitState<DeviceContext, T>(dev_ctx, *h0, order, &ordered_h0,
                                          true);
diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu
index d97390fa1c..07372808bb 100644
--- a/paddle/operators/lookup_table_op.cu
+++ b/paddle/operators/lookup_table_op.cu
@@ -125,8 +125,8 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
       new_rows.resize(ids_dim[0]);
       auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
 
-      memory::Copy(platform::CPUPlace(), new_rows.data(), gpu_place, ids_data,
-                   ids_dim[0] * sizeof(int64_t), stream);
+      memory::Copy(platform::CPUPlace(), new_rows.cuda_data(), gpu_place,
+                   ids_data, ids_dim[0] * sizeof(int64_t), stream);
 
       d_table->set_rows(new_rows);
 
diff --git a/paddle/operators/lstm_op.h b/paddle/operators/lstm_op.h
index c57ee414dc..72e95b75e2 100644
--- a/paddle/operators/lstm_op.h
+++ b/paddle/operators/lstm_op.h
@@ -27,11 +27,12 @@ using Tensor = framework::Tensor;
 
 template <typename DeviceContext, typename T>
 inline void ReorderInitState(const DeviceContext& ctx,
-                             const framework::Tensor& src, const size_t* index,
+                             const framework::Tensor& src,
+                             framework::Vector<size_t> index_lod,
                              framework::Tensor* dst, bool indexed_src) {
   math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
   dst->mutable_data<T>(src.dims(), ctx.GetPlace());
-  row_shuffle(ctx, src, index, *dst, indexed_src);
+  row_shuffle(ctx, src, index_lod, *dst, indexed_src);
 }
 
 template <typename DeviceContext, typename T>
@@ -84,7 +85,9 @@ class LSTMKernel : public framework::OpKernel<T> {
     }
     lstm_value.prev_state_value = nullptr;
     Tensor ordered_c0;
-    const size_t* order = batch_gate->lod()[2].data();
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (cell_t0) {
       // Since the batch computing for LSTM reorders the input sequence
       // according to their length. The initialized cell state also needs
@@ -202,7 +205,8 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     // ordered_h0_g/c0_g is the reordered gradient of hidden/cell
     // initialization.
     Tensor ordered_h0, ordered_c0, ordered_h0_g, ordered_c0_g;
-    const size_t* order = batch_gate->lod()[2].data();
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (c0) {
       ReorderInitState<DeviceContext, T>(device_ctx, *c0, order, &ordered_c0,
                                          true);
diff --git a/paddle/operators/lstmp_op.h b/paddle/operators/lstmp_op.h
index ee82d5c10a..e064a155df 100644
--- a/paddle/operators/lstmp_op.h
+++ b/paddle/operators/lstmp_op.h
@@ -34,7 +34,8 @@ using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
 
 template <typename DeviceContext, typename T>
 inline void ReorderInitState(const DeviceContext& ctx,
-                             const framework::Tensor& src, const size_t* index,
+                             const framework::Tensor& src,
+                             framework::Vector<size_t> index,
                              framework::Tensor* dst, bool indexed_src) {
   math::CopyMatrixRowsFunctor<DeviceContext, T> row_shuffle;
   dst->mutable_data<T>(src.dims(), ctx.GetPlace());
@@ -109,7 +110,9 @@ class LSTMPKernel : public framework::OpKernel<T> {
     }
     lstmp_value.prev_state_value = nullptr;
     Tensor ordered_c0;
-    const size_t* order = batch_gate->lod()[2].data();
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (cell_t0) {
       // Since the batch computing for LSTMP reorders the input sequence
       // according to their length. The initialized cell state also needs
@@ -275,7 +278,9 @@ class LSTMPGradKernel : public framework::OpKernel<T> {
     // ordered_h0_g/c0_g is the reordered gradient of hidden/cell
     // initialization.
     Tensor ordered_h0, ordered_c0, ordered_h0_g, ordered_c0_g;
-    const size_t* order = batch_gate->lod()[2].data();
+
+    framework::Vector<size_t> order(batch_gate->lod()[2]);
+
     if (c0) {
       ReorderInitState<DeviceContext, T>(device_ctx, *c0, order, &ordered_c0,
                                          true);
diff --git a/paddle/operators/math/selected_rows_functor.cu b/paddle/operators/math/selected_rows_functor.cu
index 0ee456f9bc..acdd87cb35 100644
--- a/paddle/operators/math/selected_rows_functor.cu
+++ b/paddle/operators/math/selected_rows_functor.cu
@@ -31,7 +31,7 @@ struct SelectedRowsAdd<platform::CUDADeviceContext, T> {
     PADDLE_ENFORCE_EQ(in1_height, input2.height());
     output->set_height(in1_height);
 
-    auto& in1_rows = input1.rows();
+    framework::Vector<int64_t> in1_rows(input1.rows());
     auto& in2_rows = input2.rows();
     std::vector<int64_t> out_rows;
     out_rows.reserve(in1_rows.size() + in2_rows.size());
@@ -108,7 +108,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
     PADDLE_ENFORCE_EQ(in1_height, out_dims[0]);
 
     auto& in1_value = input1.value();
-    auto& in1_rows = input1.rows();
+    framework::Vector<int64_t> in1_rows(input1.rows());
 
     int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
     PADDLE_ENFORCE_EQ(in1_row_numel, input2.numel() / in1_height);
@@ -126,7 +126,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
     dim3 grid(1, in1_rows.size());
     SelectedRowsAddTensorKernel<
         T, block_size><<<grid, threads, 0, context.stream()>>>(
-        in1_data, in1_rows.data(), out_data, in1_row_numel);
+        in1_data, in1_rows.cuda_data(), out_data, in1_row_numel);
 
     auto out_eigen = framework::EigenVector<T>::Flatten(*output);
     auto in2_eigen = framework::EigenVector<T>::Flatten(input2);
@@ -146,7 +146,7 @@ struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
     auto in1_height = input1.height();
     PADDLE_ENFORCE_EQ(in1_height, input2->height());
 
-    auto& in1_rows = input1.rows();
+    framework::Vector<int64_t> in1_rows(input1.rows());
     auto& in2_rows = *(input2->mutable_rows());
 
     auto& in1_value = input1.value();
@@ -204,7 +204,7 @@ struct SelectedRowsAddToTensor<platform::CUDADeviceContext, T> {
     PADDLE_ENFORCE_EQ(in1_height, in2_dims[0]);
 
     auto& in1_value = input1.value();
-    auto& in1_rows = input1.rows();
+    framework::Vector<int64_t> in1_rows(input1.rows());
 
     int64_t in1_row_numel = in1_value.numel() / in1_rows.size();
     PADDLE_ENFORCE_EQ(in1_row_numel, input2->numel() / in1_height);
@@ -216,7 +216,7 @@ struct SelectedRowsAddToTensor<platform::CUDADeviceContext, T> {
     dim3 grid(1, in1_rows.size());
     SelectedRowsAddToTensorKernel<
         T, block_size><<<grid, threads, 0, context.stream()>>>(
-        in1_data, in1_rows.data(), in2_data, in1_row_numel);
+        in1_data, in1_rows.cuda_data(), in2_data, in1_row_numel);
   }
 };
 
@@ -257,7 +257,7 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
   framework::SelectedRows operator()(const platform::CUDADeviceContext& context,
                                      const framework::SelectedRows& input) {
     framework::SelectedRows out;
-    auto input_rows = input.rows();
+    framework::Vector<int64_t> input_rows(input.rows());
     std::set<int64_t> row_set(input_rows.begin(), input_rows.end());
     std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
 
@@ -283,9 +283,9 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
     MergeAddKernel<
         T, 256><<<grid1, threads, 0,
                   reinterpret_cast<const platform::CUDADeviceContext&>(context)
-                      .stream()>>>(input_data, input.rows().data(), out_data,
-                                   out.rows().data(), out.rows().size(),
-                                   input_width);
+                      .stream()>>>(input_data, input_rows.cuda_data(), out_data,
+                                   out.mutable_rows()->cuda_data(),
+                                   out.rows().size(), input_width);
     return out;
   }
 };
@@ -370,8 +370,8 @@ struct UpdateToTensor<platform::CUDADeviceContext, T> {
     dim3 threads(platform::PADDLE_CUDA_NUM_THREADS, 1);
     dim3 grid(1, in1_rows.size());
     UpdateToTensorKernel<T, platform::PADDLE_CUDA_NUM_THREADS><<<
-        grid, threads, 0, context.stream()>>>(in1_data, in1_rows.data(), op,
-                                              in2_data, in1_row_numel);
+        grid, threads, 0, context.stream()>>>(in1_data, in1_rows.cuda_data(),
+                                              op, in2_data, in1_row_numel);
   }
 };
 }  // namespace scatter
diff --git a/paddle/operators/math/sequence2batch.cc b/paddle/operators/math/sequence2batch.cc
index e459a42ca2..17abce1c2f 100644
--- a/paddle/operators/math/sequence2batch.cc
+++ b/paddle/operators/math/sequence2batch.cc
@@ -23,8 +23,10 @@ template <typename T>
 class CopyMatrixRowsFunctor<platform::CPUDeviceContext, T> {
  public:
   void operator()(const platform::CPUDeviceContext& context,
-                  const framework::Tensor& src, const size_t* index,
-                  framework::Tensor& dst, bool is_src_index) {
+                  const framework::Tensor& src,
+                  framework::Vector<size_t> index_lod, framework::Tensor& dst,
+                  bool is_src_index) {
+    size_t* index = index_lod.data();
     auto src_dims = src.dims();
     auto dst_dims = dst.dims();
     PADDLE_ENFORCE_EQ(src_dims.size(), 2UL,
diff --git a/paddle/operators/math/sequence2batch.cu b/paddle/operators/math/sequence2batch.cu
index 452ae89510..f27631271a 100644
--- a/paddle/operators/math/sequence2batch.cu
+++ b/paddle/operators/math/sequence2batch.cu
@@ -42,8 +42,10 @@ template <typename T>
 class CopyMatrixRowsFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
-                  const framework::Tensor& src, const size_t* index,
-                  framework::Tensor& dst, bool is_src_index) {
+                  const framework::Tensor& src,
+                  framework::Vector<size_t> index_lod, framework::Tensor& dst,
+                  bool is_src_index) {
+    size_t* index = index_lod.cuda_data();
     auto src_dims = src.dims();
     auto dst_dims = dst.dims();
     PADDLE_ENFORCE_EQ(src_dims.size(), 2,
diff --git a/paddle/operators/math/sequence2batch.h b/paddle/operators/math/sequence2batch.h
index a5c43a2c7d..6db0427b41 100644
--- a/paddle/operators/math/sequence2batch.h
+++ b/paddle/operators/math/sequence2batch.h
@@ -35,7 +35,7 @@ class CopyMatrixRowsFunctor {
   // copy the input src to the indexed rows of output dst.
   // The indexed rows are based on the input index.
   void operator()(const DeviceContext& context, const framework::Tensor& src,
-                  const size_t* index, framework::Tensor& dst,
+                  framework::Vector<size_t> index_lod, framework::Tensor& dst,
                   bool is_src_index);
 };
 
@@ -66,7 +66,7 @@ class LoDTensor2BatchFunctor {
       PADDLE_ENFORCE_EQ(lods[1].size(),
                         static_cast<size_t>(lod_tensor.dims()[0]));
       CopyMatrixRowsFunctor<DeviceContext, T> to_batch;
-      to_batch(context, lod_tensor, lods[1].data(), batch, true);
+      to_batch(context, lod_tensor, lods[1], batch, true);
       return;
     }
 
@@ -144,7 +144,7 @@ class LoDTensor2BatchFunctor {
     batch.set_lod(batch_lods);
 
     CopyMatrixRowsFunctor<DeviceContext, T> to_batch;
-    to_batch(context, lod_tensor, seq2batch_idx, batch, true);
+    to_batch(context, lod_tensor, batch_lods[1], batch, true);
   }
 };
 
@@ -159,8 +159,7 @@ class Batch2LoDTensorFunctor {
     PADDLE_ENFORCE_EQ(in_lod[1].size(),
                       static_cast<size_t>(lod_tensor.dims()[0]));
     CopyMatrixRowsFunctor<DeviceContext, T> to_seq;
-    size_t* index = in_lod[1].data();
-    to_seq(context, batch, index, lod_tensor, false);
+    to_seq(context, batch, in_lod[1], lod_tensor, false);
   }
 };
 
diff --git a/paddle/operators/math/sequence_padding.cu b/paddle/operators/math/sequence_padding.cu
index a38df26f59..65c9cfe4a0 100644
--- a/paddle/operators/math/sequence_padding.cu
+++ b/paddle/operators/math/sequence_padding.cu
@@ -120,12 +120,14 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
     T* padding_data = padding.data<T>();
     if (norm_by_times) {
       SequencePaddingKernel<T, 1, 1><<<grid, threads, 0, context.stream()>>>(
-          padding_data, const_cast<T*>(seq_data), abs_offset_lod[level].data(),
-          sequence_width, max_sequence_length, num_sequences);
+          padding_data, const_cast<T*>(seq_data),
+          abs_offset_lod[level].cuda_data(), sequence_width,
+          max_sequence_length, num_sequences);
     } else {
       SequencePaddingKernel<T, 0, 1><<<grid, threads, 0, context.stream()>>>(
-          padding_data, const_cast<T*>(seq_data), abs_offset_lod[level].data(),
-          sequence_width, max_sequence_length, num_sequences);
+          padding_data, const_cast<T*>(seq_data),
+          abs_offset_lod[level].cuda_data(), sequence_width,
+          max_sequence_length, num_sequences);
     }
   }
 };
@@ -193,12 +195,14 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
     T* seq_data = seq.data<T>();
     if (norm_by_times) {
       SequencePaddingKernel<T, 1, 0><<<grid, threads, 0, context.stream()>>>(
-          const_cast<T*>(padding_data), seq_data, abs_offset_lod[level].data(),
-          sequence_width, max_sequence_length, num_sequences);
+          const_cast<T*>(padding_data), seq_data,
+          abs_offset_lod[level].cuda_data(), sequence_width,
+          max_sequence_length, num_sequences);
     } else {
       SequencePaddingKernel<T, 0, 0><<<grid, threads, 0, context.stream()>>>(
-          const_cast<T*>(padding_data), seq_data, abs_offset_lod[level].data(),
-          sequence_width, max_sequence_length, num_sequences);
+          const_cast<T*>(padding_data), seq_data,
+          abs_offset_lod[level].cuda_data(), sequence_width,
+          max_sequence_length, num_sequences);
     }
   }
 };
diff --git a/paddle/operators/math/sequence_pooling.cu b/paddle/operators/math/sequence_pooling.cu
index 4c9e6b375c..f66534a681 100644
--- a/paddle/operators/math/sequence_pooling.cu
+++ b/paddle/operators/math/sequence_pooling.cu
@@ -73,7 +73,7 @@ class MaxSeqPoolFunctor<platform::CUDADeviceContext, T> {
     dim3 grid(num_seq, 1);
     auto stream = context.stream();
     KeMaxSequencePool<T><<<grid, threads, 0, stream>>>(
-        in_data, starts.data(), out_data, max_index, num_seq, dim);
+        in_data, starts.cuda_data(), out_data, max_index, num_seq, dim);
   }
 };
 
diff --git a/paddle/operators/math/sequence_scale.cu b/paddle/operators/math/sequence_scale.cu
index ceaabd8e0f..fd4e28f611 100644
--- a/paddle/operators/math/sequence_scale.cu
+++ b/paddle/operators/math/sequence_scale.cu
@@ -46,7 +46,7 @@ class ScaleLoDTensorFunctor<platform::CUDADeviceContext, T> {
 
     SequenceScaleKernel<T, PADDLE_CUDA_NUM_THREADS><<<
         num_seq, PADDLE_CUDA_NUM_THREADS, 0, context.stream()>>>(
-        seq_data, abs_offset_lod[level].data(), scales, seq_width);
+        seq_data, abs_offset_lod[level].cuda_data(), scales, seq_width);
   }
 };
 
diff --git a/paddle/operators/row_conv_op.cu b/paddle/operators/row_conv_op.cu
index 41f2c5b9de..b3825212e1 100644
--- a/paddle/operators/row_conv_op.cu
+++ b/paddle/operators/row_conv_op.cu
@@ -307,7 +307,7 @@ class RowConvKernel<platform::CUDADeviceContext, T>
     int input_dim = X->dims()[1];
     int num_sequence = batch_indices.size() - 1;
     int future_context = Filter->dims()[0];
-    size_t *idx = batch_indices.data();
+    size_t *idx = batch_indices.cuda_data();
     auto stream = context.cuda_device_context().stream();
 
     if (future_context <= 32) {
@@ -345,7 +345,7 @@ class RowConvGradKernel<platform::CUDADeviceContext, T>
     int input_dim = X->dims()[1];
     int num_sequence = batch_indices.size() - 1;
     int future_context = Filter->dims()[0];
-    size_t *idx = batch_indices.data();
+    size_t *idx = batch_indices.cuda_data();
 
     auto &device_ctx = context.cuda_device_context();
     math::SetConstant<platform::CUDADeviceContext, T> zero;
diff --git a/paddle/operators/sequence_erase_op.cu b/paddle/operators/sequence_erase_op.cu
index f1e3b96acd..a5311f15f0 100644
--- a/paddle/operators/sequence_erase_op.cu
+++ b/paddle/operators/sequence_erase_op.cu
@@ -96,9 +96,8 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
     GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                 PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
         num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr);
-
     // Set LoD for output
-    thrust::host_vector<size_t> out_lod0 = dev_out_lod;
+    std::vector<size_t> out_lod0(dev_out_lod.begin(), dev_out_lod.end());
     framework::LoD out_lod;
     out_lod.push_back(out_lod0);
     out->set_lod(out_lod);
diff --git a/paddle/operators/sgd_op.cu b/paddle/operators/sgd_op.cu
index 42f8f8b2f0..29f5aa3542 100644
--- a/paddle/operators/sgd_op.cu
+++ b/paddle/operators/sgd_op.cu
@@ -89,7 +89,7 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
       PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
 
       auto& in_value = grad->value();
-      auto& in_rows = grad->rows();
+      framework::Vector<int64_t> in_rows(grad->rows());
 
       int64_t in_row_numel = in_value.numel() / in_rows.size();
       PADDLE_ENFORCE_EQ(in_row_numel, param_out->numel() / in_height);
@@ -102,7 +102,7 @@ class SGDOpCUDAKernel : public framework::OpKernel<T> {
       dim3 grid(1, in_rows.size());
       SparseSGDFunctorKernel<
           T, 256><<<grid, threads, 0, ctx.cuda_device_context().stream()>>>(
-          in_data, in_rows.data(), learning_rate->data<T>(), out_data,
+          in_data, in_rows.cuda_data(), learning_rate->data<T>(), out_data,
           in_row_numel);
 
     } else {
diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc
index 490397afdd..a880d9bdbc 100644
--- a/paddle/pybind/pybind.cc
+++ b/paddle/pybind/pybind.cc
@@ -124,44 +124,25 @@ PYBIND11_PLUGIN(core) {
       .def(
           "__init__",
           [](LoDTensor &instance, const std::vector<std::vector<size_t>> &lod) {
-#ifndef PADDLE_WITH_CUDA
-            new (&instance) LoDTensor(lod);
-#else
-             LoD new_lod;
-             new_lod.reserve(lod.size());
-             std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
-             new (&instance) LoDTensor(new_lod);
-#endif
+            LoD new_lod;
+            new_lod.reserve(lod.size());
+            std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+            new (&instance) LoDTensor(new_lod);
           })
       .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
       .def("set_lod",
            [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
-#ifndef PADDLE_WITH_CUDA
-             self.set_lod(lod);
-#else
              LoD new_lod;
              new_lod.reserve(lod.size());
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
              self.set_lod(new_lod);
-#endif
            })
       .def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
-#ifndef PADDLE_WITH_CUDA
-        return self.lod();
-#else
-           auto lod = self.lod();
-           std::vector<std::vector<size_t>> new_lod;
-           new_lod.reserve(lod.size());
-           std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
-               [](Vector<size_t> item) ->
-                   std::vector<size_t> {
-                 std::vector<size_t> v;
-                 v.reserve(item.size());
-                 std::copy(item.begin(), item.end(), std::back_inserter(v));
-                 return v;
-               });
-           return new_lod;
-#endif
+        auto lod = self.lod();
+        std::vector<std::vector<size_t>> new_lod;
+        new_lod.reserve(lod.size());
+        std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+        return new_lod;
       });
 
   py::class_<SelectedRows>(m, "SelectedRows")
diff --git a/python/paddle/v2/fluid/tests/test_tensor.py b/python/paddle/v2/fluid/tests/test_tensor.py
index d5cc235f58..0219bef42b 100644
--- a/python/paddle/v2/fluid/tests/test_tensor.py
+++ b/python/paddle/v2/fluid/tests/test_tensor.py
@@ -108,9 +108,31 @@ class TestTensor(unittest.TestCase):
         scope = core.Scope()
         place = core.CPUPlace()
         lod_py = [[0, 2, 5], [0, 2, 4, 5]]
-        lod_tensor = core.LoDTensor(lod_py)
+        lod_tensor = core.LoDTensor()
 
         lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor.set_lod(lod_py)
+        lod_tensor.alloc_float(place)
+        tensor_array = numpy.array(lod_tensor)
+        tensor_array[0, 0, 0, 0] = 1.0
+        tensor_array[0, 0, 0, 1] = 2.0
+        lod_tensor.set(tensor_array, place)
+
+        lod_v = numpy.array(lod_tensor)
+        self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
+        self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
+        self.assertListEqual(lod_py, lod_tensor.lod())
+
+    def test_lod_tensor_gpu_init(self):
+        if not core.is_compiled_with_cuda():
+            return
+        scope = core.Scope()
+        place = core.CUDAPlace(0)
+        lod_py = [[0, 2, 5], [0, 2, 4, 5]]
+        lod_tensor = core.LoDTensor()
+
+        lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor.set_lod(lod_py)
         lod_tensor.alloc_float(place)
         tensor_array = numpy.array(lod_tensor)
         tensor_array[0, 0, 0, 0] = 1.0

From ee97604d882d2a071d91967c0eb879c67822a985 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 31 Jan 2018 16:00:39 +0800
Subject: [PATCH 159/314] Add documentation generate script

---
 doc/api/v2/fluid/data_feeder.rst   |  13 +-
 doc/api/v2/fluid/evaluator.rst     |  28 +-
 doc/api/v2/fluid/executor.rst      |  33 +-
 doc/api/v2/fluid/gen_doc.py        | 109 +++++
 doc/api/v2/fluid/gen_doc.sh        |   7 +
 doc/api/v2/fluid/initializer.rst   |  55 +--
 doc/api/v2/fluid/io.rst            |  61 ++-
 doc/api/v2/fluid/layers.rst        | 653 ++++++++++++++++++++---------
 doc/api/v2/fluid/nets.rst          |  22 +-
 doc/api/v2/fluid/optimizer.rst     |  65 ++-
 doc/api/v2/fluid/param_attr.rst    |  22 +-
 doc/api/v2/fluid/profiler.rst      |  25 +-
 doc/api/v2/fluid/regularizer.rst   |  32 +-
 python/paddle/v2/fluid/__init__.py |  22 +-
 python/paddle/v2/fluid/profiler.py |   4 +-
 15 files changed, 802 insertions(+), 349 deletions(-)
 create mode 100644 doc/api/v2/fluid/gen_doc.py
 create mode 100755 doc/api/v2/fluid/gen_doc.sh

diff --git a/doc/api/v2/fluid/data_feeder.rst b/doc/api/v2/fluid/data_feeder.rst
index 0fa78f7dfb..a591c7334f 100644
--- a/doc/api/v2/fluid/data_feeder.rst
+++ b/doc/api/v2/fluid/data_feeder.rst
@@ -1,9 +1,14 @@
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-DataFeeder
+data_feeder
 ===========
 
 DataFeeder
------------
-..  automodule:: paddle.v2.fluid.data_feeder
-    :members: DataFeeder
+----------
+
+..  autoclass:: paddle.v2.fluid.data_feeder.DataFeeder
+    :members:
     :noindex:
+
diff --git a/doc/api/v2/fluid/evaluator.rst b/doc/api/v2/fluid/evaluator.rst
index a23f3301d0..00dcecfd62 100644
--- a/doc/api/v2/fluid/evaluator.rst
+++ b/doc/api/v2/fluid/evaluator.rst
@@ -1,9 +1,21 @@
-===========
-Evaluator
-===========
-
-Evaluator
------------
-..  automodule:: paddle.v2.fluid.evaluator
-    :members: Evaluator
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+=========
+evaluator
+=========
+
+Accuracy
+--------
+
+..  autoclass:: paddle.v2.fluid.evaluator.Accuracy
+    :members:
     :noindex:
+
+ChunkEvaluator
+--------------
+
+..  autoclass:: paddle.v2.fluid.evaluator.ChunkEvaluator
+    :members:
+    :noindex:
+
diff --git a/doc/api/v2/fluid/executor.rst b/doc/api/v2/fluid/executor.rst
index 3a283538c1..a028f6283f 100644
--- a/doc/api/v2/fluid/executor.rst
+++ b/doc/api/v2/fluid/executor.rst
@@ -1,9 +1,32 @@
-===========
-Executor
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+========
+executor
+========
 
 Executor
+--------
+
+..  autoclass:: paddle.v2.fluid.executor.Executor
+    :members:
+    :noindex:
+
+global_scope
+------------
+
+..  autofunction:: paddle.v2.fluid.executor.global_scope
+    :noindex:
+
+scope_guard
 -----------
-..  automodule:: paddle.v2.fluid.executor
-    :members: Executor
+
+..  autofunction:: paddle.v2.fluid.executor.scope_guard
+    :noindex:
+
+switch_scope
+------------
+
+..  autofunction:: paddle.v2.fluid.executor.switch_scope
     :noindex:
+
diff --git a/doc/api/v2/fluid/gen_doc.py b/doc/api/v2/fluid/gen_doc.py
new file mode 100644
index 0000000000..a2147fd3f7
--- /dev/null
+++ b/doc/api/v2/fluid/gen_doc.py
@@ -0,0 +1,109 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+import argparse
+import sys
+import types
+
+import paddle.v2.fluid as fluid
+
+
+def parse_arg():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--submodules', nargs="*")
+    parser.add_argument(
+        'module', type=str, help='Generate the documentation of which module')
+    return parser.parse_args()
+
+
+class DocGenerator(object):
+    def __init__(self, module_name, stream=sys.stdout):
+        self.stream = stream
+        self.module_name = module_name
+        if not hasattr(fluid, module_name):
+            raise ValueError("Cannot find fluid.{0}".format(module_name))
+        else:
+            self.module = getattr(fluid, module_name)
+        self.stream.write('''..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+''')
+
+        self._print_header_(module_name, dot='=', is_title=True)
+
+    def print_submodule(self, submodule_name):
+        submodule = getattr(self.module, submodule_name)
+        if submodule is None:
+            raise ValueError("Cannot find submodule {0}".format(submodule_name))
+        self.print_section(submodule_name)
+
+        for item in submodule.__all__:
+            self.print_item(item)
+
+    def print_current_module(self):
+        for item in self.module.__all__:
+            self.print_item(item)
+
+    def print_section(self, name):
+        self._print_header_(name, dot='=', is_title=False)
+
+    def print_item(self, name):
+        item = getattr(self.module, name)
+        if isinstance(item, types.TypeType):
+            self.print_class(name)
+        elif isinstance(item, types.FunctionType):
+            self.print_method(name)
+        else:
+            raise RuntimeError("Unsupported item {0}".format(name))
+
+    def print_class(self, name):
+        self._print_header_(name, dot='-', is_title=False)
+        self.stream.write('''..  autoclass:: paddle.v2.fluid.{0}.{1}
+    :members:
+    :noindex:
+
+'''.format(self.module_name, name))
+
+    def print_method(self, name):
+        self._print_header_(name, dot='-', is_title=False)
+        self.stream.write('''..  autofunction:: paddle.v2.fluid.{0}.{1}
+    :noindex:
+
+'''.format(self.module_name, name))
+
+    def _print_header_(self, name, dot, is_title):
+        dot_line = dot * len(name)
+        if is_title:
+            self.stream.write(dot_line)
+            self.stream.write('\n')
+        self.stream.write(name)
+        self.stream.write('\n')
+        self.stream.write(dot_line)
+        self.stream.write('\n')
+        self.stream.write('\n')
+
+
+def main():
+    args = parse_arg()
+    gen = DocGenerator(args.module)
+    if args.submodules is None:
+        gen.print_current_module()
+    else:
+        for submodule_name in args.submodules:
+            gen.print_submodule(submodule_name)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/doc/api/v2/fluid/gen_doc.sh b/doc/api/v2/fluid/gen_doc.sh
new file mode 100755
index 0000000000..ba7b7ba8e5
--- /dev/null
+++ b/doc/api/v2/fluid/gen_doc.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+python gen_doc.py layers --submodules control_flow device io nn ops tensor > layers.rst
+
+for module in io data_feeder evaluator executor initializer io nets optimizer param_attr profiler regularizer
+do
+  python gen_doc.py ${module} > ${module}.rst
+done
diff --git a/doc/api/v2/fluid/initializer.rst b/doc/api/v2/fluid/initializer.rst
index 8f587837e9..c38be033ff 100644
--- a/doc/api/v2/fluid/initializer.rst
+++ b/doc/api/v2/fluid/initializer.rst
@@ -1,50 +1,35 @@
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-Initializer
+initializer
 ===========
 
+Constant
+--------
 
-
-Initializer
------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: Initializer
-    :noindex:
-
-
-
-ConstantInitializer
--------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: ConstantInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Constant
+    :members:
     :noindex:
 
+Uniform
+-------
 
-
-UniformInitializer
-------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: UniformInitializer
-    :noindex:
-
-
-
-NormalInitializer
------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: NormalInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Uniform
+    :members:
     :noindex:
 
+Normal
+------
 
-XavierInitializer
------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: XavierInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Normal
+    :members:
     :noindex:
 
+Xavier
+------
 
-MSRAInitializer
----------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: MSRAInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Xavier
+    :members:
     :noindex:
 
diff --git a/doc/api/v2/fluid/io.rst b/doc/api/v2/fluid/io.rst
index 67f68c4e9e..37c9c273e3 100644
--- a/doc/api/v2/fluid/io.rst
+++ b/doc/api/v2/fluid/io.rst
@@ -1,10 +1,61 @@
-===========
-IO
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+==
+io
+==
 
+save_vars
+---------
 
-is_parameter
+..  autofunction:: paddle.v2.fluid.io.save_vars
+    :noindex:
+
+save_params
 -----------
-..  autofunction:: paddle.v2.fluid.io.is_parameter
+
+..  autofunction:: paddle.v2.fluid.io.save_params
+    :noindex:
+
+save_persistables
+-----------------
+
+..  autofunction:: paddle.v2.fluid.io.save_persistables
+    :noindex:
+
+load_vars
+---------
+
+..  autofunction:: paddle.v2.fluid.io.load_vars
+    :noindex:
+
+load_params
+-----------
+
+..  autofunction:: paddle.v2.fluid.io.load_params
     :noindex:
+
+load_persistables
+-----------------
+
+..  autofunction:: paddle.v2.fluid.io.load_persistables
+    :noindex:
+
+save_inference_model
+--------------------
+
+..  autofunction:: paddle.v2.fluid.io.save_inference_model
+    :noindex:
+
+load_inference_model
+--------------------
+
+..  autofunction:: paddle.v2.fluid.io.load_inference_model
+    :noindex:
+
+get_inference_program
+---------------------
+
+..  autofunction:: paddle.v2.fluid.io.get_inference_program
+    :noindex:
+
diff --git a/doc/api/v2/fluid/layers.rst b/doc/api/v2/fluid/layers.rst
index 231ec2d4ba..e24613b94b 100644
--- a/doc/api/v2/fluid/layers.rst
+++ b/doc/api/v2/fluid/layers.rst
@@ -1,546 +1,799 @@
-==========
-Layers
-==========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+======
+layers
+======
 
-fc
----
-..  autofunction:: paddle.v2.fluid.layers.fc
+control_flow
+============
+
+split_lod_tensor
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
     :noindex:
 
-embedding
----------
-..  autofunction:: paddle.v2.fluid.layers.embedding
+merge_lod_tensor
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
     :noindex:
 
-dynamic_lstm
-------------
-..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
+BlockGuard
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.BlockGuard
+    :members:
     :noindex:
 
-dynamic_lstmp
--------------
-..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
+BlockGuardWithCompletion
+------------------------
+
+..  autoclass:: paddle.v2.fluid.layers.BlockGuardWithCompletion
+    :members:
     :noindex:
 
-dynamic_gru
------------
-..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
+StaticRNNMemoryLink
+-------------------
+
+..  autoclass:: paddle.v2.fluid.layers.StaticRNNMemoryLink
+    :members:
     :noindex:
 
-data
-----
-..  autofunction:: paddle.v2.fluid.layers.data
+WhileGuard
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.WhileGuard
+    :members:
     :noindex:
 
-mean
-----
-..  autofunction:: paddle.v2.fluid.layers.mean
+While
+-----
+
+..  autoclass:: paddle.v2.fluid.layers.While
+    :members:
     :noindex:
 
-mul
----
-..  autofunction:: paddle.v2.fluid.layers.mul
+lod_rank_table
+--------------
+
+..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
     :noindex:
 
-elementwise_add
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_add
+max_sequence_len
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
     :noindex:
 
-elementwise_sub
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_sub
+topk
+----
+
+..  autofunction:: paddle.v2.fluid.layers.topk
     :noindex:
 
-elementwise_mul
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_mul
+lod_tensor_to_array
+-------------------
+
+..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
     :noindex:
 
-elementwise_div
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_div
+array_to_lod_tensor
+-------------------
+
+..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
     :noindex:
 
+increment
+---------
 
-dropout
--------
-..  autofunction:: paddle.v2.fluid.layers.dropout
+..  autofunction:: paddle.v2.fluid.layers.increment
     :noindex:
 
+array_write
+-----------
 
-reshape
---------
-..  autofunction:: paddle.v2.fluid.layers.reshape
+..  autofunction:: paddle.v2.fluid.layers.array_write
     :noindex:
 
+create_array
+------------
 
-sigmoid
+..  autofunction:: paddle.v2.fluid.layers.create_array
+    :noindex:
+
+less_than
 ---------
-..  autofunction:: paddle.v2.fluid.layers.sigmoid
+
+..  autofunction:: paddle.v2.fluid.layers.less_than
     :noindex:
 
+array_read
+----------
 
-scale
----------
-..  autofunction:: paddle.v2.fluid.layers.scale
+..  autofunction:: paddle.v2.fluid.layers.array_read
+    :noindex:
+
+shrink_memory
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.shrink_memory
     :noindex:
 
+array_length
+------------
 
-transpose
+..  autofunction:: paddle.v2.fluid.layers.array_length
+    :noindex:
+
+IfElse
+------
+
+..  autoclass:: paddle.v2.fluid.layers.IfElse
+    :members:
+    :noindex:
+
+DynamicRNN
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.DynamicRNN
+    :members:
+    :noindex:
+
+ConditionalBlock
+----------------
+
+..  autoclass:: paddle.v2.fluid.layers.ConditionalBlock
+    :members:
+    :noindex:
+
+StaticRNN
 ---------
-..  autofunction:: paddle.v2.fluid.layers.transpose
+
+..  autoclass:: paddle.v2.fluid.layers.StaticRNN
+    :members:
     :noindex:
 
+reorder_lod_tensor_by_rank
+--------------------------
 
-sigmoid_cross_entropy_with_logits
----------------------------------
-..  autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits
+..  autofunction:: paddle.v2.fluid.layers.reorder_lod_tensor_by_rank
     :noindex:
 
+ParallelDo
+----------
 
-cast
+..  autoclass:: paddle.v2.fluid.layers.ParallelDo
+    :members:
+    :noindex:
+
+Print
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.Print
+    :noindex:
+
+device
+======
+
+get_places
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.get_places
+    :noindex:
+
+io
+==
+
+data
 ----
-..  autofunction:: paddle.v2.fluid.layers.cast
+
+..  autofunction:: paddle.v2.fluid.layers.data
     :noindex:
 
+BlockGuardServ
+--------------
 
-concat
--------
-..  autofunction:: paddle.v2.fluid.layers.concat
+..  autoclass:: paddle.v2.fluid.layers.BlockGuardServ
+    :members:
     :noindex:
 
+ListenAndServ
+-------------
 
-sums
+..  autoclass:: paddle.v2.fluid.layers.ListenAndServ
+    :members:
+    :noindex:
+
+Send
 ----
-..  autofunction:: paddle.v2.fluid.layers.sums
+
+..  autofunction:: paddle.v2.fluid.layers.Send
     :noindex:
 
+nn
+==
 
-linear_chain_crf
-----------------
-..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
+fc
+--
+
+..  autofunction:: paddle.v2.fluid.layers.fc
     :noindex:
 
+embedding
+---------
 
-assign
--------
 ..  autofunction:: paddle.v2.fluid.layers.embedding
     :noindex:
 
+dynamic_lstm
+------------
 
-split_lod_tensor
-----------------
-..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
     :noindex:
 
+dynamic_lstmp
+-------------
 
-merge_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
+    :noindex:
+
+dynamic_gru
+-----------
+
+..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
+    :noindex:
+
+gru_unit
+--------
+
+..  autofunction:: paddle.v2.fluid.layers.gru_unit
+    :noindex:
+
+linear_chain_crf
 ----------------
-..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
+
+..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
+    :noindex:
+
+crf_decoding
+------------
+
+..  autofunction:: paddle.v2.fluid.layers.crf_decoding
     :noindex:
 
 cos_sim
---------
+-------
+
 ..  autofunction:: paddle.v2.fluid.layers.cos_sim
     :noindex:
 
-
 cross_entropy
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.cross_entropy
     :noindex:
 
-
-
 square_error_cost
 -----------------
+
 ..  autofunction:: paddle.v2.fluid.layers.square_error_cost
     :noindex:
 
-
 accuracy
----------
+--------
+
 ..  autofunction:: paddle.v2.fluid.layers.accuracy
     :noindex:
 
+chunk_eval
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.chunk_eval
+    :noindex:
 
 sequence_conv
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.sequence_conv
     :noindex:
 
-
 conv2d
 ------
+
 ..  autofunction:: paddle.v2.fluid.layers.conv2d
     :noindex:
 
-
 sequence_pool
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.sequence_pool
     :noindex:
 
+pool2d
+------
 
-sequence_first_step
--------------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+..  autofunction:: paddle.v2.fluid.layers.pool2d
     :noindex:
 
+batch_norm
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.batch_norm
+    :noindex:
 
-sequence_last_step
+beam_search_decode
 ------------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+
+..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
     :noindex:
 
+conv2d_transpose
+----------------
 
-pool2d
-------
-..  autofunction:: paddle.v2.fluid.layers.pool2d
+..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
+sequence_expand
+---------------
 
-batch_norm
+..  autofunction:: paddle.v2.fluid.layers.sequence_expand
+    :noindex:
+
+lstm_unit
+---------
+
+..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+    :noindex:
+
+reduce_sum
 ----------
-..  autofunction:: paddle.v2.fluid.layers.batch_norm
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_sum
+    :noindex:
+
+reduce_mean
+-----------
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_mean
     :noindex:
 
+reduce_max
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_max
+    :noindex:
 
-beam_search_decode
+reduce_min
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_min
+    :noindex:
+
+sequence_first_step
+-------------------
+
+..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+    :noindex:
+
+sequence_last_step
 ------------------
-..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
+
+..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+    :noindex:
+
+dropout
+-------
+
+..  autofunction:: paddle.v2.fluid.layers.dropout
     :noindex:
 
+split
+-----
 
-lod_rank_table
---------------
-..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
+..  autofunction:: paddle.v2.fluid.layers.split
     :noindex:
 
+ctc_greedy_decoder
+------------------
 
-max_sequence_len
-----------------
-..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
+..  autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder
     :noindex:
 
+edit_distance
+-------------
 
-topk
------
-..  autofunction:: paddle.v2.fluid.layers.topk
+..  autofunction:: paddle.v2.fluid.layers.edit_distance
     :noindex:
 
+l2_normalize
+------------
 
-lod_tensor_to_array
--------------------
-..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
+..  autofunction:: paddle.v2.fluid.layers.l2_normalize
     :noindex:
 
+matmul
+------
 
-
-array_to_lod_tensor
--------------------
-..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.matmul
     :noindex:
 
+warpctc
+-------
 
+..  autofunction:: paddle.v2.fluid.layers.warpctc
+    :noindex:
 
+sequence_reshape
+----------------
 
-fill_constant
--------------
-..  autofunction:: paddle.v2.fluid.layers.fill_constant
+..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
     :noindex:
 
+transpose
+---------
 
+..  autofunction:: paddle.v2.fluid.layers.transpose
+    :noindex:
 
-fill_constant_batch_size_like
------------------------------
-..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
+im2sequence
+-----------
+
+..  autofunction:: paddle.v2.fluid.layers.im2sequence
     :noindex:
 
+nce
+---
 
-ones
-----
-..  autofunction:: paddle.v2.fluid.layers.ones
+..  autofunction:: paddle.v2.fluid.layers.nce
     :noindex:
 
+beam_search
+-----------
 
-zeros
------
-..  autofunction:: paddle.v2.fluid.layers.zeros
+..  autofunction:: paddle.v2.fluid.layers.beam_search
     :noindex:
 
+row_conv
+--------
 
-increment
----------
-..  autofunction:: paddle.v2.fluid.layers.increment
+..  autofunction:: paddle.v2.fluid.layers.row_conv
     :noindex:
 
+multiplex
+---------
 
-array_write
------------
-..  autofunction:: paddle.v2.fluid.layers.array_write
+..  autofunction:: paddle.v2.fluid.layers.multiplex
     :noindex:
 
+ops
+===
 
+mean
+----
 
-create_array
-------------
-..  autofunction:: paddle.v2.fluid.layers.create_array
+..  autofunction:: paddle.v2.fluid.layers.mean
     :noindex:
 
+mul
+---
 
-less_than
----------
-..  autofunction:: paddle.v2.fluid.layers.less_than
+..  autofunction:: paddle.v2.fluid.layers.mul
     :noindex:
 
+reshape
+-------
 
-array_read
-----------
-..  autofunction:: paddle.v2.fluid.layers.array_read
+..  autofunction:: paddle.v2.fluid.layers.reshape
     :noindex:
 
+scale
+-----
 
-shrink_memory
---------------
-..  autofunction:: paddle.v2.fluid.layers.shrink_memory
+..  autofunction:: paddle.v2.fluid.layers.scale
     :noindex:
 
+sigmoid_cross_entropy_with_logits
+---------------------------------
 
-array_length
--------------
-..  autofunction:: paddle.v2.fluid.layers.array_length
+..  autofunction:: paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits
     :noindex:
 
+elementwise_add
+---------------
 
-conv2d_transpose
-----------------
-..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
+..  autofunction:: paddle.v2.fluid.layers.elementwise_add
     :noindex:
 
-
-sequence_expand
+elementwise_div
 ---------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_expand
+
+..  autofunction:: paddle.v2.fluid.layers.elementwise_div
     :noindex:
 
+elementwise_sub
+---------------
 
-gru_unit
---------
-..  autofunction:: paddle.v2.fluid.layers.gru_unit
+..  autofunction:: paddle.v2.fluid.layers.elementwise_sub
     :noindex:
 
+elementwise_mul
+---------------
 
-lstm_unit
----------
-..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+..  autofunction:: paddle.v2.fluid.layers.elementwise_mul
     :noindex:
 
+elementwise_max
+---------------
 
-sequence_softmax
-----------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
+..  autofunction:: paddle.v2.fluid.layers.elementwise_max
     :noindex:
 
+elementwise_min
+---------------
 
-reduce_sum
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_sum
+..  autofunction:: paddle.v2.fluid.layers.elementwise_min
     :noindex:
 
+elementwise_pow
+---------------
 
-reduce_mean
------------
-..  autofunction:: paddle.v2.fluid.layers.reduce_mean
+..  autofunction:: paddle.v2.fluid.layers.elementwise_pow
     :noindex:
 
+clip
+----
 
-reduce_max
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_max
+..  autofunction:: paddle.v2.fluid.layers.clip
     :noindex:
 
+clip_by_norm
+------------
 
-reduce_min
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_min
+..  autofunction:: paddle.v2.fluid.layers.clip_by_norm
     :noindex:
 
+sequence_softmax
+----------------
 
-split
------
-..  autofunction:: paddle.v2.fluid.layers.split
+..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
     :noindex:
 
+sigmoid
+-------
 
-matmul
-------
-..  autofunction:: paddle.v2.fluid.layers.matmul
+..  autofunction:: paddle.v2.fluid.layers.sigmoid
     :noindex:
 
 logsigmoid
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.logsigmoid
     :noindex:
 
 exp
 ---
+
 ..  autofunction:: paddle.v2.fluid.layers.exp
     :noindex:
 
 relu
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.relu
     :noindex:
 
 tanh
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.tanh
     :noindex:
 
 tanh_shrink
 -----------
+
 ..  autofunction:: paddle.v2.fluid.layers.tanh_shrink
     :noindex:
 
 softshrink
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.softshrink
     :noindex:
 
 sqrt
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.sqrt
     :noindex:
 
 abs
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.abs
     :noindex:
 
 ceil
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.ceil
     :noindex:
 
 floor
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.floor
     :noindex:
 
 round
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.round
     :noindex:
 
 reciprocal
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.reciprocal
     :noindex:
 
 log
 ---
+
 ..  autofunction:: paddle.v2.fluid.layers.log
     :noindex:
 
 square
 ------
+
 ..  autofunction:: paddle.v2.fluid.layers.square
     :noindex:
 
 softplus
 --------
+
 ..  autofunction:: paddle.v2.fluid.layers.softplus
     :noindex:
 
 softsign
----------
+--------
+
 ..  autofunction:: paddle.v2.fluid.layers.softsign
     :noindex:
 
 brelu
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.brelu
     :noindex:
 
 leaky_relu
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.leaky_relu
     :noindex:
 
 soft_relu
 ---------
+
 ..  autofunction:: paddle.v2.fluid.layers.soft_relu
     :noindex:
 
 elu
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.elu
     :noindex:
 
 relu6
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.relu6
     :noindex:
 
 pow
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.pow
     :noindex:
 
+stanh
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.stanh
+    :noindex:
+
 hard_shrink
 -----------
+
 ..  autofunction:: paddle.v2.fluid.layers.hard_shrink
     :noindex:
 
 thresholded_relu
 ----------------
+
 ..  autofunction:: paddle.v2.fluid.layers.thresholded_relu
     :noindex:
 
 hard_sigmoid
--------------
+------------
+
 ..  autofunction:: paddle.v2.fluid.layers.hard_sigmoid
     :noindex:
 
 swish
-------
+-----
+
 ..  autofunction:: paddle.v2.fluid.layers.swish
     :noindex:
 
-im2sequence
+tensor
+======
+
+create_tensor
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_tensor
+    :noindex:
+
+create_parameter
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_parameter
+    :noindex:
+
+create_global_var
+-----------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_global_var
+    :noindex:
+
+cast
+----
+
+..  autofunction:: paddle.v2.fluid.layers.cast
+    :noindex:
+
+concat
 ------
-..  autofunction:: paddle.v2.fluid.layers.im2sequence
+
+..  autofunction:: paddle.v2.fluid.layers.concat
     :noindex:
 
-edit_distance
----------------
-..  autofunction:: paddle.v2.fluid.layers.edit_distance_error
+sums
+----
+
+..  autofunction:: paddle.v2.fluid.layers.sums
     :noindex:
 
-ctc_greedy_decoder
----------------
-..  autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder
+assign
+------
+
+..  autofunction:: paddle.v2.fluid.layers.assign
     :noindex:
 
-l2_normalize
-------------
-..  autofunction:: paddle.v2.fluid.layers.l2_normalize
+fill_constant_batch_size_like
+-----------------------------
+
+..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
     :noindex:
 
-sequence_reshape
-----------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
+fill_constant
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.fill_constant
     :noindex:
 
-row_conv
---------
-..  autofunction:: paddle.v2.fluid.layers.row_conv
+ones
+----
+
+..  autofunction:: paddle.v2.fluid.layers.ones
     :noindex:
 
-multiplex
----------
-..  autofunction:: paddle.v2.fluid.layers.multiplex
+zeros
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.zeros
     :noindex:
+
diff --git a/doc/api/v2/fluid/nets.rst b/doc/api/v2/fluid/nets.rst
index 500019bc50..015581b766 100644
--- a/doc/api/v2/fluid/nets.rst
+++ b/doc/api/v2/fluid/nets.rst
@@ -1,33 +1,31 @@
-===========
-Nets
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+====
+nets
+====
 
 simple_img_conv_pool
 --------------------
-..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
-    :noindex:
 
-
-img_conv_group
----------------
-..  autofunction:: paddle.v2.fluid.nets.img_conv_group
+..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
     :noindex:
 
-
 sequence_conv_pool
 ------------------
+
 ..  autofunction:: paddle.v2.fluid.nets.sequence_conv_pool
     :noindex:
 
-
 glu
 ---
+
 ..  autofunction:: paddle.v2.fluid.nets.glu
     :noindex:
 
-
 scaled_dot_product_attention
 ----------------------------
+
 ..  autofunction:: paddle.v2.fluid.nets.scaled_dot_product_attention
     :noindex:
 
diff --git a/doc/api/v2/fluid/optimizer.rst b/doc/api/v2/fluid/optimizer.rst
index 19b4940f08..1691ebb9a7 100644
--- a/doc/api/v2/fluid/optimizer.rst
+++ b/doc/api/v2/fluid/optimizer.rst
@@ -1,54 +1,49 @@
-===========
-Optimizer
-===========
-
-Optimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: Optimizer
-    :noindex:
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+=========
+optimizer
+=========
 
-SGDOptimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: SGDOptimizer
-    :noindex:
+SGD
+---
 
+..  autoclass:: paddle.v2.fluid.optimizer.SGD
+    :members:
+    :noindex:
 
+Momentum
+--------
 
-MomentumOptimizer
------------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: MomentumOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Momentum
+    :members:
     :noindex:
 
+Adagrad
+-------
 
-
-AdagradOptimizer
-----------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdagradOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adagrad
+    :members:
     :noindex:
 
+Adam
+----
 
-AdamOptimizer
--------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdamOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adam
+    :members:
     :noindex:
 
+Adamax
+------
 
-AdamaxOptimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdamaxOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adamax
+    :members:
     :noindex:
 
+DecayedAdagrad
+--------------
 
-DecayedAdagradOptimizer
------------------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: DecayedAdagradOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.DecayedAdagrad
+    :members:
     :noindex:
 
diff --git a/doc/api/v2/fluid/param_attr.rst b/doc/api/v2/fluid/param_attr.rst
index ca0c8af9e8..8083d0d858 100644
--- a/doc/api/v2/fluid/param_attr.rst
+++ b/doc/api/v2/fluid/param_attr.rst
@@ -1,11 +1,21 @@
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+==========
+param_attr
+==========
+
 ParamAttr
-===========
+---------
 
+..  autoclass:: paddle.v2.fluid.param_attr.ParamAttr
+    :members:
+    :noindex:
 
+WeightNormParamAttr
+-------------------
 
-ParamAttr
------------
-..  automodule:: paddle.v2.fluid.param_attr
-    :members: ParamAttr
+..  autoclass:: paddle.v2.fluid.param_attr.WeightNormParamAttr
+    :members:
     :noindex:
+
diff --git a/doc/api/v2/fluid/profiler.rst b/doc/api/v2/fluid/profiler.rst
index 7d4042d1f4..4a1ff7cb69 100644
--- a/doc/api/v2/fluid/profiler.rst
+++ b/doc/api/v2/fluid/profiler.rst
@@ -1,10 +1,25 @@
-===========
-Profiler
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+========
+profiler
+========
 
+cuda_profiler
+-------------
 
-Profiler
------------
 ..  autofunction:: paddle.v2.fluid.profiler.cuda_profiler
     :noindex:
+
+reset_profiler
+--------------
+
+..  autofunction:: paddle.v2.fluid.profiler.reset_profiler
+    :noindex:
+
+profiler
+--------
+
+..  autofunction:: paddle.v2.fluid.profiler.profiler
+    :noindex:
+
diff --git a/doc/api/v2/fluid/regularizer.rst b/doc/api/v2/fluid/regularizer.rst
index 868e225ed3..2c17d15599 100644
--- a/doc/api/v2/fluid/regularizer.rst
+++ b/doc/api/v2/fluid/regularizer.rst
@@ -1,25 +1,27 @@
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-Regularizer
+regularizer
 ===========
 
-WeightDecayRegularizer
-----------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: WeightDecayRegularizer
-    :noindex:
-
+append_regularization_ops
+-------------------------
 
-L2DecayRegularizer
-------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: L2DecayRegularizer
+..  autofunction:: paddle.v2.fluid.regularizer.append_regularization_ops
     :noindex:
 
+L1Decay
+-------
 
+..  autoclass:: paddle.v2.fluid.regularizer.L1Decay
+    :members:
+    :noindex:
 
-L1DecayRegularizer
--------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: L1DecayRegularizer
+L2Decay
+-------
 
+..  autoclass:: paddle.v2.fluid.regularizer.L2Decay
+    :members:
+    :noindex:
 
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index 18c8343d09..f52346c3b5 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -36,28 +36,16 @@ from distribute_transpiler import DistributeTranspiler
 from distribute_transpiler_simple import SimpleDistributeTranspiler
 import clip
 from memory_optimization_transpiler import memory_optimize
+import profiler
 
 Tensor = LoDTensor
 
 __all__ = framework.__all__ + executor.__all__ + [
-    'io',
-    'initializer',
-    'layers',
-    'nets',
-    'optimizer',
-    'learning_rate_decay',
-    'backward',
-    'regularizer',
-    'LoDTensor',
-    'CPUPlace',
-    'CUDAPlace',
-    'Tensor',
+    'io', 'initializer', 'layers', 'nets', 'optimizer', 'learning_rate_decay',
+    'backward', 'regularizer', 'LoDTensor', 'CPUPlace', 'CUDAPlace', 'Tensor',
     'ParamAttr'
-    'DataFeeder',
-    'clip',
-    'SimpleDistributeTranspiler',
-    'DistributeTranspiler',
-    'memory_optimize',
+    'DataFeeder', 'clip', 'SimpleDistributeTranspiler', 'DistributeTranspiler',
+    'memory_optimize', 'profiler'
 ]
 
 
diff --git a/python/paddle/v2/fluid/profiler.py b/python/paddle/v2/fluid/profiler.py
index 51c1c8aa70..d4a2cd7eea 100644
--- a/python/paddle/v2/fluid/profiler.py
+++ b/python/paddle/v2/fluid/profiler.py
@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import paddle.v2.fluid.core as core
+import core
 from contextlib import contextmanager
 import os
 
-__all__ = ['CudaProfiler']
+__all__ = ['cuda_profiler', 'reset_profiler', 'profiler']
 
 NVPROF_CONFIG = [
     "gpustarttimestamp",

From b148f065a8d88c944c354eaea0e31a3da5fde99c Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 31 Jan 2018 13:31:41 +0800
Subject: [PATCH 160/314] Make Fit a line a normal unittest

---
 .../v2/fluid/tests/book/test_fit_a_line.py    | 88 +++++++++++++------
 1 file changed, 59 insertions(+), 29 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
index 0b954c60b6..27f34b1733 100644
--- a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
+++ b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
@@ -12,44 +12,74 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import contextlib
+import unittest
 
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-y_predict = fluid.layers.fc(input=x, size=1, act=None)
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
 
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
 
-cost = fluid.layers.square_error_cost(input=y_predict, label=y)
-avg_cost = fluid.layers.mean(x=cost)
+    y_predict = fluid.layers.fc(input=x, size=1, act=None)
 
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
-sgd_optimizer.minimize(avg_cost)
+    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
-BATCH_SIZE = 20
+    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+    avg_cost = fluid.layers.mean(x=cost)
 
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.uci_housing.train(), buf_size=500),
-    batch_size=BATCH_SIZE)
+    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+    sgd_optimizer.minimize(avg_cost)
 
-place = fluid.CPUPlace()
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
+    BATCH_SIZE = 20
 
-exe.run(fluid.default_startup_program())
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.uci_housing.train(), buf_size=500),
+        batch_size=BATCH_SIZE)
 
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    fluid.io.save_persistables(exe, "./fit_a_line.model/")
-    fluid.io.load_persistables(exe, "./fit_a_line.model/")
-    for data in train_reader():
-        avg_loss_value, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[avg_cost])
-        print(avg_loss_value)
-        if avg_loss_value[0] < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-exit(1)
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
+    exe = fluid.Executor(place)
+
+    exe.run(fluid.default_startup_program())
+
+    PASS_NUM = 100
+    for pass_id in range(PASS_NUM):
+        fluid.io.save_persistables(exe, "./fit_a_line.model/")
+        fluid.io.load_persistables(exe, "./fit_a_line.model/")
+        for data in train_reader():
+            avg_loss_value, = exe.run(fluid.default_main_program(),
+                                      feed=feeder.feed(data),
+                                      fetch_list=[avg_cost])
+            print(avg_loss_value)
+            if avg_loss_value[0] < 10.0:
+                return
+    raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
+        avg_loss_value[0]))
+
+
+class TestFitALine(unittest.TestCase):
+    def test_cpu(self):
+        with self.program_scope_guard():
+            main(use_cuda=False)
+
+    def test_cuda(self):
+        with self.program_scope_guard():
+            main(use_cuda=True)
+
+    @contextlib.contextmanager
+    def program_scope_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
+if __name__ == '__main__':
+    unittest.main()

From 1b1f305babc3c91d0761814306df0004620be309 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Wed, 31 Jan 2018 13:07:19 +0800
Subject: [PATCH 161/314] Make image_classification as a normal python unittest

---
 .../paddle/v2/fluid/tests/book/CMakeLists.txt |   4 +-
 .../book/test_image_classification_train.py   | 143 +++++++++++-------
 2 files changed, 89 insertions(+), 58 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/CMakeLists.txt b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
index dda02c03fd..a870478db8 100644
--- a/python/paddle/v2/fluid/tests/book/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
@@ -1,9 +1,7 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-list(REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits)
-py_test(test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet)
-py_test(test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg)
+list(REMOVE_ITEM TEST_OPS test_recognize_digits)
 py_test(test_recognize_digits_mlp_cpu
   SRCS test_recognize_digits.py
   ARGS mlp)
diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
index 30582a21d0..a4168d16db 100644
--- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
+++ b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
@@ -14,10 +14,10 @@
 
 from __future__ import print_function
 
-import sys
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import unittest
+import contextlib
 
 
 def resnet_cifar10(input, depth=32):
@@ -89,56 +89,89 @@ def vgg16_bn_drop(input):
     return fc2
 
 
-classdim = 10
-data_shape = [3, 32, 32]
-
-images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
-label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-
-net_type = "vgg"
-if len(sys.argv) >= 2:
-    net_type = sys.argv[1]
-
-if net_type == "vgg":
-    print("train vgg net")
-    net = vgg16_bn_drop(images)
-elif net_type == "resnet":
-    print("train resnet")
-    net = resnet_cifar10(images, 32)
-else:
-    raise ValueError("%s network is not supported" % net_type)
-
-predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
-cost = fluid.layers.cross_entropy(input=predict, label=label)
-avg_cost = fluid.layers.mean(x=cost)
-
-optimizer = fluid.optimizer.Adam(learning_rate=0.001)
-opts = optimizer.minimize(avg_cost)
-
-accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-
-BATCH_SIZE = 128
-PASS_NUM = 1
-
-train_reader = paddle.batch(
-    paddle.reader.shuffle(
-        paddle.dataset.cifar.train10(), buf_size=128 * 10),
-    batch_size=BATCH_SIZE)
-
-place = fluid.CPUPlace()
-exe = fluid.Executor(place)
-feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
-exe.run(fluid.default_startup_program())
-
-for pass_id in range(PASS_NUM):
-    accuracy.reset(exe)
-    for data in train_reader():
-        loss, acc = exe.run(fluid.default_main_program(),
-                            feed=feeder.feed(data),
-                            fetch_list=[avg_cost] + accuracy.metrics)
-        pass_acc = accuracy.eval(exe)
-        print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-            pass_acc))
-        # this model is slow, so if we can train two mini batch, we think it works properly.
-        exit(0)
-exit(1)
+def main(net_type, use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    classdim = 10
+    data_shape = [3, 32, 32]
+
+    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    if net_type == "vgg":
+        print("train vgg net")
+        net = vgg16_bn_drop(images)
+    elif net_type == "resnet":
+        print("train resnet")
+        net = resnet_cifar10(images, 32)
+    else:
+        raise ValueError("%s network is not supported" % net_type)
+
+    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    optimizer = fluid.optimizer.Adam(learning_rate=0.001)
+    optimizer.minimize(avg_cost)
+
+    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+    BATCH_SIZE = 128
+    PASS_NUM = 1
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.cifar.train10(), buf_size=128 * 10),
+        batch_size=BATCH_SIZE)
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
+    exe.run(fluid.default_startup_program())
+
+    loss = 0.0
+    for pass_id in range(PASS_NUM):
+        accuracy.reset(exe)
+        for data in train_reader():
+            loss, acc = exe.run(fluid.default_main_program(),
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + accuracy.metrics)
+            pass_acc = accuracy.eval(exe)
+            print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
+                pass_acc))
+            return
+
+    raise AssertionError(
+        "Image classification loss is too large, {0:2.2}".format(loss))
+
+
+class TestImageClassification(unittest.TestCase):
+    def test_vgg_cuda(self):
+        with self.scope_prog_guard():
+            main('vgg', use_cuda=True)
+
+    def test_resnet_cuda(self):
+        with self.scope_prog_guard():
+            main('resnet', use_cuda=True)
+
+    def test_vgg_cpu(self):
+        with self.scope_prog_guard():
+            main('vgg', use_cuda=False)
+
+    def test_resnet_cpu(self):
+        with self.scope_prog_guard():
+            main('resnet', use_cuda=False)
+
+    @contextlib.contextmanager
+    def scope_prog_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
+if __name__ == '__main__':
+    unittest.main()

From 38b8b7f6acb51e62b97a62e3215d39b0d6f7553b Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Wed, 31 Jan 2018 09:09:32 +0000
Subject: [PATCH 162/314] add results

---
 benchmark/cluster/README.md       | 10 +++++-----
 benchmark/cluster/vgg16/README.md |  8 ++++----
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/benchmark/cluster/README.md b/benchmark/cluster/README.md
index b619613ea7..c2be886b34 100644
--- a/benchmark/cluster/README.md
+++ b/benchmark/cluster/README.md
@@ -44,14 +44,14 @@
 
 ### Measure the Performance for Different PServer Count
 
-- Trainer Count: 100
-- Batch Size: 64
+- Trainer Count: 60
+- Batch Size: 128
 - Metrics: mini-batch / sec
 
-| PServer Count | 10 | 20 | 40 | 60 |
+| PServer Count | 3 | 6 | 10 | 20 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | - | - | - | - |
-| PaddlePaddle v2 | - | - | - | - |
+| PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
+| PaddlePaddle v2 | 412.2 | 368.4 | 346.8 | 283.2 |
 | TensorFlow | - | - | - | - |
 
 ### Measure Parallel Efficiency By Increasing Trainer Count
diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index c1e85a2c40..333e14250b 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -48,14 +48,14 @@
 
 ### different pserver number
 
-- Trainer Count: 100
+- Trainer Count: 60
 - Batch Size: 128
 - Metrics: mini-batch / sec
 
-| PServer Count | 10 | 20 | 40 | 60 |
+| PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | - | - | - | - |
-| PaddlePaddle v2 | - | - | - | - |
+| PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
+| PaddlePaddle v2 | 412.2 | 368.4 | 346.8 | 283.2 |
 | TensorFlow | - | - | - | - |
 
 

From cfbbb9841d3ab9f6736cd7e02273fe8dc7a1df39 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Wed, 31 Jan 2018 09:18:35 +0000
Subject: [PATCH 163/314] clean code

---
 benchmark/cluster/README.md             | 10 +++++-----
 benchmark/cluster/vgg16/v2_pserver.yaml |  2 +-
 benchmark/cluster/vgg16/v2_trainer.yaml |  6 +++---
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/benchmark/cluster/README.md b/benchmark/cluster/README.md
index c2be886b34..b619613ea7 100644
--- a/benchmark/cluster/README.md
+++ b/benchmark/cluster/README.md
@@ -44,14 +44,14 @@
 
 ### Measure the Performance for Different PServer Count
 
-- Trainer Count: 60
-- Batch Size: 128
+- Trainer Count: 100
+- Batch Size: 64
 - Metrics: mini-batch / sec
 
-| PServer Count | 3 | 6 | 10 | 20 |
+| PServer Count | 10 | 20 | 40 | 60 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
-| PaddlePaddle v2 | 412.2 | 368.4 | 346.8 | 283.2 |
+| PaddlePaddle Fluid | - | - | - | - |
+| PaddlePaddle v2 | - | - | - | - |
 | TensorFlow | - | - | - | - |
 
 ### Measure Parallel Efficiency By Increasing Trainer Count
diff --git a/benchmark/cluster/vgg16/v2_pserver.yaml b/benchmark/cluster/vgg16/v2_pserver.yaml
index 857e2ff455..935cf0be3c 100644
--- a/benchmark/cluster/vgg16/v2_pserver.yaml
+++ b/benchmark/cluster/vgg16/v2_pserver.yaml
@@ -23,7 +23,7 @@ spec:
         - name: PADDLE_JOB_NAME
           value: vgg16v2job
         - name: TRAINERS
-          value: "60"
+          value: "20"
         - name: PSERVERS
           value: "10"
         - name: TOPOLOGY
diff --git a/benchmark/cluster/vgg16/v2_trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
index be0f741b34..5189009f3e 100644
--- a/benchmark/cluster/vgg16/v2_trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -3,8 +3,8 @@ kind: Job
 metadata:
   name: vgg16v2job-trainer
 spec:
-  parallelism: 60
-  completions: 60
+  parallelism: 20
+  completions: 20
   template:
     metadata:
       labels:
@@ -24,7 +24,7 @@ spec:
         - name: BATCH_SIZE
           value: "256"
         - name: TRAINERS
-          value: "60"
+          value: "20"
         - name: PSERVERS
           value: "10"
         - name: TOPOLOGY

From f32ca6369099f5d3776ae87d431b9b39ea8eba3e Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 31 Jan 2018 18:46:45 +0800
Subject: [PATCH 164/314] draft of Reader classes

---
 paddle/framework/CMakeLists.txt |   2 +
 paddle/framework/reader.cc      | 107 +++++++++++++++++++++++++-------
 paddle/framework/reader.h       |  83 +++++++++++++++++++++----
 3 files changed, 159 insertions(+), 33 deletions(-)

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 8c28709a68..7eec91f907 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -24,6 +24,8 @@ cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
 nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
 
+cc_library(reader SRCS reader.cc DEPS lod_tensor ddim)
+
 cc_test(variable_test SRCS variable_test.cc)
 
 cc_library(threadpool SRCS threadpool.cc DEPS enforce)
diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
index 7f80dd7fc1..e11662166c 100644
--- a/paddle/framework/reader.cc
+++ b/paddle/framework/reader.cc
@@ -17,35 +17,100 @@
 namespace paddle {
 namespace framework {
 
-DDim Reader::shape(int idx) const {
+DDim Reader::shape(size_t idx) const {
   PADDLE_ENFORCE_LT(
       idx, shapes_.size(),
       "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
       shapes_.size());
+  return shapes_[idx];
 }
 
-int RandomReader::ReadNext(std::vector<LoDTensor>* outs) {
-  PADDLE_ENFORCE_EQ(
-      shapes_.size(), outs.size(),
-      "shapes_.size() is %d, while outs.size() is %d. They are not equal.",
-      shapes_.size(), outs.size());
-  std::minstd_rand engine;
-  unsigned int seed = std::random_device()();
-  engine.seed(seed);
-  std::uniform_real_distribution<float> dist(min_, max_);
-  for (int idx = 0; idx < shapes_.size(); ++idx) {
-    DDim shape = shapes_[idx];
-    LoDTensor* out = outs[idx];
-    int64_t numel = out->numel();
-    PADDLE_ENFORCE_EQ(product(shape), numel,
-                      "The product of %d'th shape is %lld, while the "
-                      "corresponding out's numel is %lld. They are not equal.",
-                      idx, product(shape), numel);
-    for (int64_t i = 0; i < numel, ++i) {
-      out[i] = dist(engine);
+std::vector<LoDTensor> ShuffleReader::ReadNext() {
+  if (iteration_pos_ >= buffer_.size()) {
+    // Reload buffer with new data
+    buffer_.clear();
+    for (int i = 0; i < buffer_size_; ++i) {
+      if (reader_->HasNext()) {
+        buffer_.push_back(reader_->ReadNext());
+      } else {
+        break;
+      }
     }
+    std::random_shuffle(buffer_.begin(), buffer_.end());
+    iteration_pos_ = 0;
   }
-  return 0;
+  if (buffer_.empty()) {
+    std::vector<LoDTensor> empty_res;
+    return empty_res;
+  }
+  return buffer_[iteration_pos_++];
+}
+
+std::vector<LoDTensor> BatchReader::ReadNext() {
+  buffer_.clear();
+  for (int i = 0; i < batch_size_; ++i) {
+    if (reader_->HasNext()) {
+      buffer_.push_back(reader_->ReadNext());
+    } else {
+      break;
+    }
+  }
+  // Concat instances
+  std::vector<LoDTensor> res;
+  if (buffer_.empty()) {
+    return res;
+  }
+  int out_num = buffer_[0].size();
+  res.reserve(out_num);
+  for (int j = 0; j < out_num; ++j) {
+    // Merge shape and check date type
+    std::type_index batch_type = buffer_[0][j].type();
+    DDim batch_shape = buffer_[0][j].dims();
+    for (size_t i = 1; i < buffer_.size(); ++i) {
+      std::type_index ins_type = buffer_[i][j].type();
+      DDim ins_shape = buffer_[i][j].dims();
+      PADDLE_ENFORCE_EQ(batch_type, ins_type);
+      PADDLE_ENFORCE_EQ(slice_ddim(batch_shape, 1, batch_shape.size()),
+                        slice_ddim(ins_shape, 1, ins_shape.size()));
+      PADDLE_ENFORCE_GT(ins_shape[0], 0);
+      batch_shape[0] += ins_shape[0];
+    }
+
+    LoDTensor out;
+    out.Resize(batch_shape);
+    out.mutable_data(platform::CPUPlace(), batch_type);
+    int64_t dst_offset = 0;
+
+    // Merge lod and data
+    LoD batch_lod;
+    std::vector<size_t> top_level_lod({0});
+    for (size_t i = 0; i < buffer_.size(); ++i) {
+      DDim ins_shape = buffer_[i][j].dims();
+      LoD ins_lod = buffer_[i][j].lod();
+      if (i == 0) {
+        batch_lod = ins_lod;
+      } else {
+        PADDLE_ENFORCE_EQ(batch_lod.size(), ins_lod.size());
+        for (size_t level_idx = 0; level_idx < batch_lod.size(); ++level_idx) {
+          auto& lod_level = batch_lod[level_idx];
+          for (size_t k = 1; k < ins_lod[level_idx].size(); ++k) {
+            lod_level.push_back(ins_lod[level_idx][k] + lod_level.back());
+          }
+        }
+      }
+      top_level_lod.push_back(
+          top_level_lod.back() +
+          (ins_lod.empty() ? ins_shape[0] : (ins_lod[0].size() - 1)));
+
+      Tensor dst = out.Slice(dst_offset, dst_offset + ins_shape[0]);
+      Copy(buffer_[i][j], platform::CPUPlace(), &dst);
+      dst_offset += ins_shape[0];
+    }
+    batch_lod.insert(batch_lod.begin(), top_level_lod);
+    out.set_lod(batch_lod);
+    res.push_back(out);
+  }
+  return res;
 }
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index eed9c18d08..58675863e5 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -22,20 +22,61 @@ namespace framework {
 
 class Reader {
  public:
-  virtual int ReadNext(std::vector<LoDTensor>* outs) = 0;
-  DDim shape(int idx) const;
+  Reader() {}
+  explicit Reader(const std::vector<DDim>& shapes) : shapes_(shapes) {}
+
+  virtual std::vector<LoDTensor> ReadNext() = 0;
+  virtual bool HasNext() const = 0;
+
+  virtual DDim shape(size_t idx) const;
+  virtual std::vector<DDim> shapes() const { return shapes_; }
+
+  virtual ~Reader() {}
 
  private:
+  // set private to prevent directly access in decorators
+  // a decorator should access its underlying reader_'s shape, not its own.
   std::vector<DDim> shapes_;
 };
 
 // file readers
 
+template <typename T>
 class RandomReader : public Reader {
  public:
   RandomReader(const std::vector<DDim>& shapes, float min, float max)
-      : shapes_(shapes), min_(min), max_(max) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+      : Reader(shapes), min_(min), max_(max) {
+    PADDLE_ENFORCE_LE(min, max,
+                      "'min' should be less than or equal to 'max'.(%f vs %f)",
+                      min, max);
+  }
+
+  std::vector<LoDTensor> ReadNext() override {
+    std::minstd_rand engine;
+    unsigned int seed = std::random_device()();
+    engine.seed(seed);
+    std::uniform_real_distribution<float> dist(min_, max_);
+
+    std::vector<LoDTensor> res;
+    res.reserve(shapes().size());
+    for (const DDim& shape : shapes()) {
+      PADDLE_ENFORCE_GE(
+          shape.size(), 2,
+          "The rank of input data should be 2 at least.(Now it's %d)",
+          shape.size());
+      LoDTensor out;
+      out.Resize(shape);
+      T* data = out.mutable_data<T>(platform::CPUPlace());
+      int64_t numel = product(shape);
+      for (int64_t i = 0; i < numel; ++i) {
+        data[i] = dist(engine);
+      }
+      res.push_back(out);
+    }
+    return res;
+  }
+
+  bool HasNext() const override { return true; }
 
  private:
   float min_;
@@ -44,22 +85,40 @@ class RandomReader : public Reader {
 
 // decorators
 
-class BatchReader : public Reader {
+class ShuffleReader : public Reader {
  public:
-  BatchReader(const Reader* reader) : reader_(reader) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+  ShuffleReader(Reader* reader, int buffer_size)
+      : reader_(reader), buffer_size_(buffer_size), iteration_pos_(0) {
+    buffer_.reserve(buffer_size);
+  }
+  std::vector<LoDTensor> ReadNext() override;
+  bool HasNext() const override { return reader_->HasNext(); }
+
+  DDim shape(size_t idx) const override { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  const Reader* reader_;
+  Reader* reader_;
+  int buffer_size_;
+  std::vector<std::vector<LoDTensor>> buffer_;
+  size_t iteration_pos_;
 };
 
-class ShuffleReader : public Reader {
+class BatchReader : public Reader {
  public:
-  ShuffleReader(const Reader* reader) : reader_(reader) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+  BatchReader(Reader* reader, int batch_size)
+      : reader_(reader), batch_size_(batch_size) {}
+  std::vector<LoDTensor> ReadNext() override;
+  bool HasNext() const override { return reader_->HasNext(); };
+
+  DDim shape(size_t idx) const override { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  const Reader* reader_;
+  Reader* reader_;
+  int batch_size_;
+  std::vector<std::vector<LoDTensor>> buffer_;
 };
+
 }  // namespace framework
 }  // namespace paddle

From e49b8b9c556016c43f64d73045208b62470df92a Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 31 Jan 2018 19:46:24 +0800
Subject: [PATCH 165/314] refine feed_op

---
 paddle/operators/feed_op.cc | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/feed_op.cc b/paddle/operators/feed_op.cc
index d738e1850c..789d01e002 100644
--- a/paddle/operators/feed_op.cc
+++ b/paddle/operators/feed_op.cc
@@ -52,7 +52,11 @@ class FeedOp : public framework::OperatorBase {
     platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
     auto &dev_ctx = *pool.Get(place);
 
-    framework::Copy(feed_item, place, dev_ctx, out_item);
+    if (platform::is_same_place(feed_item.place(), place)) {
+      out_item->ShareDataWith(feed_item);
+    } else {
+      framework::Copy(feed_item, place, dev_ctx, out_item);
+    }
     out_item->set_lod(feed_item.lod());
   }
 };

From 0d550ea15618885df4153d733d525b901e7af05d Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Wed, 31 Jan 2018 19:27:44 +0800
Subject: [PATCH 166/314] Make parallel tests bind to different GPU.

---
 paddle/scripts/docker/test.sh | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100755 paddle/scripts/docker/test.sh

diff --git a/paddle/scripts/docker/test.sh b/paddle/scripts/docker/test.sh
new file mode 100755
index 0000000000..8180737a8f
--- /dev/null
+++ b/paddle/scripts/docker/test.sh
@@ -0,0 +1,30 @@
+#!/bin/bash
+set -e
+
+# the number of process to run tests
+NUM_PROC=6
+
+# calculate and set the memory usage for each process
+MEM_USAGE=$(printf "%.2f" `echo "scale=5; 1.0 / $NUM_PROC" | bc`)
+export FLAGS_fraction_of_gpu_memory_to_use=$MEM_USAGE
+
+# get the CUDA device count
+CUDA_DEVICE_COUNT=$(nvidia-smi -L | wc -l)
+
+for (( i = 0; i < $NUM_PROC; i++ )); do
+    cuda_list=()
+    for (( j = 0; j < $CUDA_DEVICE_COUNT; j++ )); do
+        s=$[i+j]
+        n=$[s%CUDA_DEVICE_COUNT]
+        if [ $j -eq 0 ]; then
+            cuda_list=("$n")
+        else
+            cuda_list="$cuda_list,$n"
+        fi
+    done
+    echo $cuda_list
+    # CUDA_VISIBLE_DEVICES http://acceleware.com/blog/cudavisibledevices-masking-gpus
+    # ctest -I https://cmake.org/cmake/help/v3.0/manual/ctest.1.html?highlight=ctest
+    env CUDA_VISIBLE_DEVICES=$cuda_list ctest -I $i,,$NUM_PROC --output-on-failure &
+done
+wait

From f9db5629873b117c226b15858f128dd2c1f9fd16 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 31 Jan 2018 20:09:08 +0800
Subject: [PATCH 167/314] update results

---
 benchmark/cluster/vgg16/Dockerfile         |  16 +-
 benchmark/cluster/vgg16/README.md          |   2 +-
 benchmark/cluster/vgg16/fluid_trainer.yaml |   2 +-
 benchmark/cluster/vgg16/k8s_tools.py       |  94 ----------
 benchmark/cluster/vgg16/paddle_k8s         | 199 ---------------------
 benchmark/cluster/vgg16/reader.py          |  16 --
 benchmark/cluster/vgg16/v2_trainer.yaml    |   2 +-
 benchmark/cluster/vgg16/vgg16_v2.py        |   2 +-
 8 files changed, 11 insertions(+), 322 deletions(-)
 delete mode 100644 benchmark/cluster/vgg16/k8s_tools.py
 delete mode 100755 benchmark/cluster/vgg16/paddle_k8s
 delete mode 100644 benchmark/cluster/vgg16/reader.py

diff --git a/benchmark/cluster/vgg16/Dockerfile b/benchmark/cluster/vgg16/Dockerfile
index dfaffb8c21..c34f7e8fcf 100644
--- a/benchmark/cluster/vgg16/Dockerfile
+++ b/benchmark/cluster/vgg16/Dockerfile
@@ -1,15 +1,13 @@
-#FROM paddlepaddle/paddlecloud-job
-#RUN mkdir -p /workspace
-#ADD reader.py /workspace/
-#RUN python /workspace/reader.py
 FROM python:2.7.14
-ADD paddle_k8s /usr/bin
-ADD k8s_tools.py /root
-RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev 
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
+RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev && \
+chmod +x /usr/bin/paddle_k8s
+# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
+#       so we must build one with distribute support to install in this image.
 ADD *.whl /
 RUN pip install /*.whl && rm -f /*.whl
 ENV LD_LIBRARY_PATH=/usr/local/lib
-ADD reader.py /workspace/
-RUN python /workspace/reader.py
+RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
 
 ADD vgg16_fluid.py vgg16_v2.py /workspace/
diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index c1e85a2c40..0c404e60a8 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -43,7 +43,7 @@
 | Trainer Counter | 20 | 40 | 80 | 100 |
 | -- | -- | -- | -- | -- |
 | PaddlePaddle Fluid | 291.06 | 518.80 | 836.26 | 1019.29 |
-| PaddlePaddle v2 | 356.28 | - | - | 1041.99 |
+| PaddlePaddle v2 (need more tests) | 356.28 | 785.39 | 853.30 | 1041.99 |
 | TensorFlow | - | - | - | - |
 
 ### different pserver number
diff --git a/benchmark/cluster/vgg16/fluid_trainer.yaml b/benchmark/cluster/vgg16/fluid_trainer.yaml
index 2f6a87ab02..0a0ed25ebe 100644
--- a/benchmark/cluster/vgg16/fluid_trainer.yaml
+++ b/benchmark/cluster/vgg16/fluid_trainer.yaml
@@ -30,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 256"
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 128"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/k8s_tools.py b/benchmark/cluster/vgg16/k8s_tools.py
deleted file mode 100644
index 4bee96a7a8..0000000000
--- a/benchmark/cluster/vgg16/k8s_tools.py
+++ /dev/null
@@ -1,94 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-#     http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#!/bin/env python
-import os
-import sys
-import time
-import socket
-from kubernetes import client, config
-PADDLE_JOB_NAME = os.getenv("PADDLE_JOB_NAME")
-NAMESPACE = os.getenv("NAMESPACE")
-PORT = os.getenv("PSERVER_PORT")
-if os.getenv("KUBERNETES_SERVICE_HOST", None):
-    config.load_incluster_config()
-else:
-    config.load_kube_config()
-v1 = client.CoreV1Api()
-
-
-def fetch_pods_info(label_selector):
-    api_response = v1.list_namespaced_pod(
-        namespace=NAMESPACE, pretty=True, label_selector=label_selector)
-    pod_list = []
-    for item in api_response.items:
-        pod_list.append((item.status.phase, item.status.pod_ip))
-    return pod_list
-
-
-def wait_pods_running(label_selector, desired):
-    print "label selector: %s, desired: %s" % (label_selector, desired)
-    while True:
-        count = count_pods_by_phase(label_selector, 'Running')
-        # NOTE: pods may be scaled.
-        if count >= int(desired):
-            break
-        print 'current cnt: %d sleep for 5 seconds...' % count
-        time.sleep(5)
-
-
-def count_pods_by_phase(label_selector, phase):
-    pod_list = fetch_pods_info(label_selector)
-    filtered_pod_list = filter(lambda x: x[0] == phase, pod_list)
-    return len(filtered_pod_list)
-
-
-def fetch_pserver_ips():
-    label_selector = "paddle-job-pserver=%s" % PADDLE_JOB_NAME
-    pod_list = fetch_pods_info(label_selector)
-    pserver_ips = [item[1] for item in pod_list]
-    return ",".join(pserver_ips)
-
-
-def fetch_master_ip():
-    label_selector = "paddle-job-master=%s" % PADDLE_JOB_NAME
-    pod_list = fetch_pods_info(label_selector)
-    master_ips = [item[1] for item in pod_list]
-    return master_ips[0]
-
-
-def fetch_trainer_id():
-    label_selector = "paddle-job=%s" % PADDLE_JOB_NAME
-    pod_list = fetch_pods_info(label_selector)
-    trainer_ips = [item[1] for item in pod_list]
-    trainer_ips.sort()
-    local_ip = socket.gethostbyname(socket.gethostname())
-    for i in xrange(len(trainer_ips)):
-        if trainer_ips[i] == local_ip:
-            return i
-    return None
-
-
-if __name__ == "__main__":
-    command = sys.argv[1]
-    if command == "fetch_pserver_ips":
-        print fetch_pserver_ips()
-    elif command == "fetch_trainer_id":
-        print fetch_trainer_id()
-    elif command == "fetch_master_ip":
-        print fetch_master_ip()
-    elif command == "count_pods_by_phase":
-        print count_pods_by_phase(sys.argv[2], sys.argv[3])
-    elif command == "wait_pods_running":
-        wait_pods_running(sys.argv[2], sys.argv[3])
diff --git a/benchmark/cluster/vgg16/paddle_k8s b/benchmark/cluster/vgg16/paddle_k8s
deleted file mode 100755
index af5f35b3ec..0000000000
--- a/benchmark/cluster/vgg16/paddle_k8s
+++ /dev/null
@@ -1,199 +0,0 @@
-#!/bin/bash
-start_pserver() {
-    stdbuf -oL paddle pserver \
-      --use_gpu=0 \
-      --port=$PADDLE_INIT_PORT \
-      --ports_num=$PADDLE_INIT_PORTS_NUM \
-      --ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
-      --nics=$PADDLE_INIT_NICS \
-      --comment=paddle_process_k8s \
-      --num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
-}
-
-start_new_pserver() {
-  stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
-  export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
-  stdbuf -oL /usr/bin/pserver \
-    -port=$PADDLE_INIT_PORT \
-    -num-pservers=$PSERVERS \
-    -log-level=debug \
-    -etcd-endpoint=http://$MASTER_IP:2379
-}
-
-start_master() {
-  stdbuf -oL /usr/bin/master \
-  -port=8080 \
-  -chunk-per-task=1\
-  -task-timout-dur=16s\
-  -endpoints=http://127.0.0.1:2379
-}
-
-check_failed_cnt() {
-  max_failed=$1
-  failed_count=$(python /root/k8s_tools.py count_pods_by_phase paddle-job=${PADDLE_JOB_NAME} Failed) 
-  if [ $failed_count -gt $max_failed ]; then
-    stdbuf -oL echo "Failed trainer count beyond the threadhold: "$max_failed
-    echo "Failed trainer count beyond the threshold: " $max_failed > /dev/termination-log 
-    exit 0
-  fi
-}
-
-check_trainer_ret() {
-  ret=$1
-  stdbuf -oL echo "job returned $ret...setting pod return message..."
-  stdbuf -oL echo "==============================="
-
-  if [ $ret -eq 136 ] ; then
-    echo "Error Arithmetic Operation(Floating Point Exception)" > /dev/termination-log
-  elif [ $ret -eq 139 ] ; then
-    echo "Segmentation Fault" > /dev/termination-log
-  elif [ $ret -eq 1 ] ; then
-    echo "General Error" > /dev/termination-log
-  elif [ $ret -eq 134 ] ; then
-    echo "Program Abort" > /dev/termination-log
-  fi
-  stdbuf -oL echo "termination log wroted..."
-  exit $ret
-}
-
-start_fluid_process() {
-  stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
-  if [ "${TRAINING_ROLE}" == "TRAINER" ]; then
-    check_failed_cnt ${TRAINERS}
-    sleep 5
-    export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
-  fi
-  export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
-  stdbuf -oL sh -c "${ENTRY}"
-  check_trainer_ret $?
-}
-
-start_new_trainer() {
-  # FIXME(Yancey1989): use command-line interface to configure the max failed count
-  check_failed_cnt ${TRAINERS}
-  stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
-  sleep 5
-  stdbuf -oL python /root/k8s_tools.py wait_pods_running  paddle-job-master=${PADDLE_JOB_NAME} 1
-  export MASTER_IP=$(python /root/k8s_tools.py fetch_master_ip)
-  export ETCD_IP="$MASTER_IP"
-
-  # NOTE: $TRAINER_PACKAGE may be large, do not copy
-  export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
-  cd $TRAINER_PACKAGE
-
-  stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
-  $PADDLE_INIT_NUM_GRADIENT_SERVERS, "version: " $1 
-
-  stdbuf -oL sh -c "${ENTRY}"
-  check_trainer_ret $?
-}
-
-start_trainer() {
-    # paddle v1 and V2 distributed training does not allow any trainer failed. 
-    check_failed_cnt 0
-    stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job-pserver=${PADDLE_JOB_NAME} ${PSERVERS}
-    stdbuf -oL python /root/k8s_tools.py wait_pods_running paddle-job=${PADDLE_JOB_NAME} ${TRAINERS}
-
-    export PADDLE_INIT_PSERVERS=$(python /root/k8s_tools.py fetch_pserver_ips)
-    export PADDLE_INIT_TRAINER_ID=$(python /root/k8s_tools.py fetch_trainer_id)
-    stdbuf -oL echo $PADDLE_INIT_TRAINER_ID > /trainer_id
-    # FIXME: /trainer_count = PADDLE_INIT_NUM_GRADIENT_SERVERS
-    stdbuf -oL echo $PADDLE_INIT_NUM_GRADIENT_SERVERS > /trainer_count
-
-    # NOTE: $TRAINER_PACKAGE may be large, do not copy
-    export PYTHONPATH=$TRAINER_PACKAGE:$PYTHONPATH
-    cd $TRAINER_PACKAGE
-
-    stdbuf -oL echo "Starting training job: " $TRAINER_PACKAGE, "num_gradient_servers:" \
-    $PADDLE_INIT_NUM_GRADIENT_SERVERS, "trainer_id: " $PADDLE_INIT_TRAINER_ID, \
-    "version: " $1
-
-    # FIXME: If we use the new PServer by Golang, add Kubernetes healthz
-    # to wait PServer process get ready.Now only sleep 20 seconds.
-    sleep 20
-
-    case "$1" in
-      "v1")
-        FILE_COUNT=$(wc -l $TRAIN_LIST | awk '{print $1}')
-        if [ $FILE_COUNT -le $PADDLE_INIT_NUM_GRADIENT_SERVERS ]; then
-          echo "file count less than trainers"
-          check_trainer_ret 0
-        fi
-        let lines_per_node="$FILE_COUNT / ($PADDLE_INIT_NUM_GRADIENT_SERVERS + 1)"
-        echo "spliting file to" $lines_per_node
-        cp $TRAIN_LIST /
-        cd /
-        split -l $lines_per_node -d -a 3 $TRAIN_LIST train.list
-        CURRENT_LIST=$(printf "train.list%03d" $PADDLE_INIT_TRAINER_ID)
-        # always use /train.list for paddle v1 for each node.
-        echo "File for current node ${CURRENT_LIST}"
-        sleep 10
-        cp $CURRENT_LIST train.list
-
-        cd $TRAINER_PACKAGE
-
-        stdbuf -oL  paddle train \
-          --port=$PADDLE_INIT_PORT \
-          --nics=$PADDLE_INIT_NICS \
-          --ports_num=$PADDLE_INIT_PORTS_NUM \
-          --ports_num_for_sparse=$PADDLE_INIT_PORTS_NUM_FOR_SPARSE \
-          --num_passes=$PADDLE_INIT_NUM_PASSES \
-          --trainer_count=$PADDLE_INIT_TRAINER_COUNT \
-          --saving_period=1 \
-          --log_period=20 \
-          --local=0 \
-          --rdma_tcp=tcp \
-          --config=$TOPOLOGY \
-          --use_gpu=$PADDLE_INIT_USE_GPU \
-          --trainer_id=$PADDLE_INIT_TRAINER_ID \
-          --save_dir=$OUTPUT \
-          --pservers=$PADDLE_INIT_PSERVERS \
-          --num_gradient_servers=$PADDLE_INIT_NUM_GRADIENT_SERVERS
-        # paddle v1 API does not allow any trainer failed.
-        check_trainer_ret $? 
-        ;;
-      "v2")
-        stdbuf -oL sh -c "${ENTRY}"
-        # paddle v2 API does not allow any trainer failed.
-        check_trainer_ret $? 
-        ;;
-      *)
-        ;;
-    esac
-}
-
-usage() {
-    echo "usage: paddle_k8s [<args>]:"
-    echo "  start_trainer  [v1|v2]    Start a trainer process with v1 or v2 API"
-    echo "  start_pserver             Start a pserver process"
-    echo "  start_new_pserver         Start a new pserver process"
-    echo "  start_new_trainer         Start a new triner process"
-}
-
-case "$1" in
-    start_pserver)
-        start_pserver
-        ;;
-    start_trainer)
-        start_trainer $2
-        ;;
-    start_new_trainer)
-        start_new_trainer
-        ;;
-    start_new_pserver)
-        start_new_pserver
-        ;;
-    start_master)
-        start_master
-        ;;
-    start_fluid)
-        start_fluid_process
-        ;;
-    --help)
-        usage
-        ;;
-    *)
-        usage
-        ;;
-esac
-
diff --git a/benchmark/cluster/vgg16/reader.py b/benchmark/cluster/vgg16/reader.py
deleted file mode 100644
index 3e20f830fc..0000000000
--- a/benchmark/cluster/vgg16/reader.py
+++ /dev/null
@@ -1,16 +0,0 @@
-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-#     http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import paddle.v2 as paddle
-paddle.dataset.cifar.train10()
diff --git a/benchmark/cluster/vgg16/v2_trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
index 997bbc81c9..12c8964066 100644
--- a/benchmark/cluster/vgg16/v2_trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -38,7 +38,7 @@ spec:
         - name: PADDLE_INIT_NICS
           value: "xgbe0"
         - name: PADDLE_INIT_TRAINER_COUNT
-          value: "2"
+          value: "1"
         - name: PADDLE_INIT_PORTS_NUM
           value: "1"
         - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
diff --git a/benchmark/cluster/vgg16/vgg16_v2.py b/benchmark/cluster/vgg16/vgg16_v2.py
index 81ddeb0332..6ac6b3c332 100644
--- a/benchmark/cluster/vgg16/vgg16_v2.py
+++ b/benchmark/cluster/vgg16/vgg16_v2.py
@@ -51,7 +51,7 @@ def vgg(input, nums, class_dim):
     conv4 = conv_block(conv3, 512, nums[3])
     conv5 = conv_block(conv4, 512, nums[4])
 
-    fc_dim = 4096
+    fc_dim = 512
     fc1 = paddle.layer.fc(input=conv5,
                           size=fc_dim,
                           act=paddle.activation.Relu(),

From 58bfaea8afcc2b30c5f73a5c52f1cafc6a8682f2 Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Wed, 31 Jan 2018 21:32:22 +0800
Subject: [PATCH 168/314] update according to the code review

---
 paddle/operators/box_coder_op.cc | 49 ++++++++++++--------
 paddle/operators/box_coder_op.cu |  4 +-
 paddle/operators/box_coder_op.h  | 79 +++++++++++++-------------------
 3 files changed, 66 insertions(+), 66 deletions(-)

diff --git a/paddle/operators/box_coder_op.cc b/paddle/operators/box_coder_op.cc
index 0cb20a4182..41123f9b6e 100644
--- a/paddle/operators/box_coder_op.cc
+++ b/paddle/operators/box_coder_op.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -31,23 +31,21 @@ class BoxCoderOp : public framework::OperatorWithKernel {
     auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
     auto target_box_dims = ctx->GetInputDim("TargetBox");
 
-    PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2UL,
-                      "The shape of PriorBox is [N, 4]");
-    PADDLE_ENFORCE_EQ(prior_box_dims[1], 4UL,
-                      "The shape of PriorBox is [N, 4]");
-    PADDLE_ENFORCE_EQ(prior_box_var_dims.size(), 2UL,
-                      "The shape of PriorBoxVar is [N, 4]");
-    PADDLE_ENFORCE_EQ(prior_box_var_dims[1], 4UL,
-                      "The shape of PriorBoxVar is [N, 4]");
-    PADDLE_ENFORCE_EQ(target_box_dims.size(), 2UL,
-                      "The shape of TargetBox is [M, 4]");
-    PADDLE_ENFORCE_EQ(target_box_dims[1], 4UL,
+    PADDLE_ENFORCE_EQ(prior_box_dims.size(), 2,
+                      "The rank of Input of PriorBoxVar must be 2");
+    PADDLE_ENFORCE_EQ(prior_box_dims[1], 4, "The shape of PriorBox is [N, 4]");
+    PADDLE_ENFORCE_EQ(prior_box_dims, prior_box_var_dims);
+    PADDLE_ENFORCE_EQ(target_box_dims.size(), 2,
+                      "The rank of Input of TargetBox must be 2");
+    PADDLE_ENFORCE_EQ(target_box_dims[1], 4,
                       "The shape of TargetBox is [M, 4]");
 
     GetBoxCodeType(ctx->Attrs().Get<std::string>("code_type"));
 
-    ctx->SetOutputDim("OutputBox", framework::make_ddim({target_box_dims[0],
-                                                         target_box_dims[1]}));
+    ctx->SetOutputDim(
+        "OutputBox",
+        framework::make_ddim({target_box_dims[0], prior_box_dims[0], 4}));
+    ctx->ShareLoD("TargetBox", /*->*/ "OutputBox");
   }
 };
 
@@ -58,7 +56,7 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput(
         "PriorBox",
         "(Tensor, default Tensor<float>) "
-        "Box list PriorBox is a 2-D Tensor with shape [M, 4] holds N boxes, "
+        "Box list PriorBox is a 2-D Tensor with shape [M, 4] holds M boxes, "
         "each box is represented as [xmin, ymin, xmax, ymax], "
         "[xmin, ymin] is the left top coordinate of the anchor box, "
         "if the input is image feature map, they are close to the origin "
@@ -66,7 +64,7 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
         "coordinate of the anchor box.");
     AddInput("PriorBoxVar",
              "(Tensor, default Tensor<float>) "
-             "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds N group "
+             "PriorBoxVar is a 2-D Tensor with shape [M, 4] holds M group "
              "of variance.");
     AddInput(
         "TargetBox",
@@ -85,14 +83,29 @@ class BoxCoderOpMaker : public framework::OpProtoAndCheckerMaker {
         .InEnum({"encode_center_size", "decode_center_size"});
     AddOutput(
         "OutputBox",
-        "(Tensor, default Tensor<float>)"
+        "(LoDTensor or Tensor) "
         "(Tensor) The output of box_coder_op, a tensor with shape [N, M, 4] "
         "representing the result of N target boxes encoded/decoded with "
         "M Prior boxes and variances.");
 
     AddComment(R"DOC(
 Bounding Box Coder Operator.
-Encode/Decode the priorbox information with the target bounding box.
+Encode/Decode the target bounding box with the priorbox information.
+The Encoding schema described below:
+ox = (tx - px) / pw / pxv
+oy = (ty - py) / ph / pyv
+ow = log(abs(tw / pw)) / pwv 
+oh = log(abs(th / ph)) / phv 
+The Decoding schema described below:
+ox = (pw * pxv * tx * + px) - tw / 2
+oy = (ph * pyv * ty * + py) - th / 2
+ow = exp(pwv * tw) * pw + tw / 2
+oh = exp(phv * th) * ph + th / 2
+where tx, ty, tw, th denote the target box's center coordinates, width and
+height respectively. Similarly, px, py, pw, ph denote the priorbox's(anchor)
+center coordinates, width and height. pxv, pyv, pwv, phv denote the variance
+of the priorbox and ox, oy, ow, oh denote the encoded/decoded coordinates,
+width and height.
 )DOC");
   }
 };
diff --git a/paddle/operators/box_coder_op.cu b/paddle/operators/box_coder_op.cu
index 4055ded1f8..9e2ea8cc67 100644
--- a/paddle/operators/box_coder_op.cu
+++ b/paddle/operators/box_coder_op.cu
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -108,7 +108,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     auto* output_box = context.Output<Tensor>("OutputBox");
 
     if (target_box->lod().size()) {
-      PADDLE_ENFORCE_EQ(target_box->lod().size(), 1UL,
+      PADDLE_ENFORCE_EQ(target_box->lod().size(), 1,
                         "Only support 1 level of LoD.");
     }
     auto row = target_box->dims()[0];
diff --git a/paddle/operators/box_coder_op.h b/paddle/operators/box_coder_op.h
index 3865da40c3..d1c9a40459 100644
--- a/paddle/operators/box_coder_op.h
+++ b/paddle/operators/box_coder_op.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
@@ -35,67 +35,52 @@ class BoxCoderKernel : public framework::OpKernel<T> {
  public:
   void EncodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
                         const Tensor& prior_box_var, T* output) const {
-    PADDLE_ENFORCE_EQ(target_box.dims().size(), 2,
-                      "The rank of target_box must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box.dims().size(), 2,
-                      "The rank of prior_box must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box_var.dims().size(), 2,
-                      "The rank of prior_box_var must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box.dims()[0], prior_box_var.dims()[0],
-                      "The dims of prior_box must equal to prior_box_var.");
-
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
+    int64_t len = prior_box.dims()[1];
     auto* target_box_data = target_box.data<T>();
     auto* prior_box_data = prior_box.data<T>();
     auto* prior_box_var_data = prior_box_var.data<T>();
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
-        T prior_box_width = prior_box_data[j * 4 + 2] - prior_box_data[j * 4];
+        T prior_box_width =
+            prior_box_data[j * len + 2] - prior_box_data[j * len];
         T prior_box_height =
-            prior_box_data[j * 4 + 3] - prior_box_data[j * 4 + 1];
+            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
         T prior_box_center_x =
-            (prior_box_data[j * 4 + 2] + prior_box_data[j * 4]) / 2;
+            (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
-            (prior_box_data[j * 4 + 3] + prior_box_data[j * 4 + 1]) / 2;
+            (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
 
         T target_box_center_x =
-            (target_box_data[i * 4 + 2] + target_box_data[i * 4]) / 2;
+            (target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
         T target_box_center_y =
-            (target_box_data[i * 4 + 3] + target_box_data[i * 4 + 1]) / 2;
+            (target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
         T target_box_width =
-            target_box_data[i * 4 + 2] - target_box_data[i * 4];
+            target_box_data[i * len + 2] - target_box_data[i * len];
         T target_box_height =
-            target_box_data[i * 4 + 3] - target_box_data[i * 4 + 1];
+            target_box_data[i * len + 3] - target_box_data[i * len + 1];
 
-        size_t offset = i * col * 4 + j * 4;
+        size_t offset = i * col * len + j * len;
         output[offset] = (target_box_center_x - prior_box_center_x) /
-                         prior_box_width / prior_box_var_data[j * 4];
+                         prior_box_width / prior_box_var_data[j * len];
         output[offset + 1] = (target_box_center_y - prior_box_center_y) /
-                             prior_box_height / prior_box_var_data[j * 4 + 1];
+                             prior_box_height / prior_box_var_data[j * len + 1];
         output[offset + 2] =
             std::log(std::fabs(target_box_width / prior_box_width)) /
-            prior_box_var_data[j * 4 + 2];
+            prior_box_var_data[j * len + 2];
         output[offset + 3] =
             std::log(std::fabs(target_box_height / prior_box_height)) /
-            prior_box_var_data[j * 4 + 3];
+            prior_box_var_data[j * len + 3];
       }
     }
   }
   void DecodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
                         const Tensor& prior_box_var, T* output) const {
-    PADDLE_ENFORCE_EQ(target_box.dims().size(), 2,
-                      "The rank of target_box must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box.dims().size(), 2,
-                      "The rank of prior_box must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box_var.dims().size(), 2,
-                      "The rank of prior_box_var must be 2.");
-    PADDLE_ENFORCE_EQ(prior_box.dims()[0], prior_box_var.dims()[0],
-                      "The dims of prior_box must equal to prior_box_var.");
-
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
+    int64_t len = prior_box.dims()[1];
 
     auto* target_box_data = target_box.data<T>();
     auto* prior_box_data = prior_box.data<T>();
@@ -103,29 +88,30 @@ class BoxCoderKernel : public framework::OpKernel<T> {
 
     for (int64_t i = 0; i < row; ++i) {
       for (int64_t j = 0; j < col; ++j) {
-        T prior_box_width = prior_box_data[j * 4 + 2] - prior_box_data[j * 4];
+        T prior_box_width =
+            prior_box_data[j * len + 2] - prior_box_data[j * len];
         T prior_box_height =
-            prior_box_data[j * 4 + 3] - prior_box_data[j * 4 + 1];
+            prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
         T prior_box_center_x =
-            (prior_box_data[j * 4 + 2] + prior_box_data[j * 4]) / 2;
+            (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
         T prior_box_center_y =
-            (prior_box_data[j * 4 + 3] + prior_box_data[j * 4 + 1]) / 2;
+            (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;
 
-        T target_box_center_x = prior_box_var_data[j * 4] *
-                                    target_box_data[i * 4] * prior_box_width +
+        T target_box_center_x = prior_box_var_data[j * len] *
+                                    target_box_data[i * len] * prior_box_width +
                                 prior_box_center_x;
-        T target_box_center_y = prior_box_var_data[j * 4 + 1] *
-                                    target_box_data[i * 4 + 1] *
+        T target_box_center_y = prior_box_var_data[j * len + 1] *
+                                    target_box_data[i * len + 1] *
                                     prior_box_height +
                                 prior_box_center_y;
-        T target_box_width = std::exp(prior_box_var_data[j * 4 + 2] *
-                                      target_box_data[i * 4 + 2]) *
+        T target_box_width = std::exp(prior_box_var_data[j * len + 2] *
+                                      target_box_data[i * len + 2]) *
                              prior_box_width;
-        T target_box_height = std::exp(prior_box_var_data[j * 4 + 3] *
-                                       target_box_data[i * 4 + 3]) *
+        T target_box_height = std::exp(prior_box_var_data[j * len + 3] *
+                                       target_box_data[i * len + 3]) *
                               prior_box_height;
 
-        size_t offset = i * col * 4 + j * 4;
+        size_t offset = i * col * len + j * len;
         output[offset] = target_box_center_x - target_box_width / 2;
         output[offset + 1] = target_box_center_y - target_box_height / 2;
         output[offset + 2] = target_box_center_x + target_box_width / 2;
@@ -146,8 +132,9 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     }
     auto row = target_box->dims()[0];
     auto col = prior_box->dims()[0];
+    auto len = prior_box->dims()[1];
 
-    output_box->mutable_data<T>({row, col, 4}, context.GetPlace());
+    output_box->mutable_data<T>({row, col, len}, context.GetPlace());
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
     T* output = output_box->data<T>();

From c3e89f308a0cd0d694a1e4fed51dbeef92a156bb Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Wed, 31 Jan 2018 22:28:49 +0800
Subject: [PATCH 169/314] update accoding to the code review

---
 paddle/operators/box_coder_op.cu              |  2 -
 .../v2/fluid/tests/test_box_coder_op.py       | 72 +++++++++++--------
 2 files changed, 41 insertions(+), 33 deletions(-)

diff --git a/paddle/operators/box_coder_op.cu b/paddle/operators/box_coder_op.cu
index 9e2ea8cc67..f2ea592f8e 100644
--- a/paddle/operators/box_coder_op.cu
+++ b/paddle/operators/box_coder_op.cu
@@ -15,8 +15,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using platform::PADDLE_CUDA_NUM_THREADS;
-
 template <typename T>
 __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
diff --git a/python/paddle/v2/fluid/tests/test_box_coder_op.py b/python/paddle/v2/fluid/tests/test_box_coder_op.py
index fcf5da01ce..0dc18476fd 100644
--- a/python/paddle/v2/fluid/tests/test_box_coder_op.py
+++ b/python/paddle/v2/fluid/tests/test_box_coder_op.py
@@ -20,41 +20,51 @@ from op_test import OpTest
 
 
 def box_coder(target_box, prior_box, prior_box_var, output_box, code_type):
-    prior_box_x = (prior_box[:, 2] + prior_box[:, 0]) / 2
-    prior_box_y = (prior_box[:, 3] + prior_box[:, 1]) / 2
-    prior_box_width = (prior_box[:, 2] - prior_box[:, 0])
-    prior_box_height = (prior_box[:, 3] - prior_box[:, 1])
+    prior_box_x = (
+        (prior_box[:, 2] + prior_box[:, 0]) / 2).reshape(1, prior_box.shape[0])
+    prior_box_y = (
+        (prior_box[:, 3] + prior_box[:, 1]) / 2).reshape(1, prior_box.shape[0])
+    prior_box_width = (
+        (prior_box[:, 2] - prior_box[:, 0])).reshape(1, prior_box.shape[0])
+    prior_box_height = (
+        (prior_box[:, 3] - prior_box[:, 1])).reshape(1, prior_box.shape[0])
+    prior_box_var = prior_box_var.reshape(1, prior_box_var.shape[0],
+                                          prior_box_var.shape[1])
 
     if (code_type == "EncodeCenterSize"):
-        target_box_x = (target_box[:, 2] + target_box[:, 0]) / 2
-        target_box_y = (target_box[:, 3] + target_box[:, 1]) / 2
-        target_box_width = (target_box[:, 2] - target_box[:, 0])
-        target_box_height = (target_box[:, 3] - target_box[:, 1])
-
-        for i in range(target_box.shape[0]):
-            output_box[i,:,0] = (target_box_x[i] - prior_box_x) / prior_box_width / \
-                                prior_box_var[:,0]
-            output_box[i,:,1] = (target_box_y[i] - prior_box_y) / prior_box_height / \
-                                prior_box_var[:,1]
-            output_box[i,:,2] = np.log(np.fabs(target_box_width[i] / prior_box_width)) / \
-                                prior_box_var[:,2]
-            output_box[i,:,3] = np.log(np.fabs(target_box_height[i] / prior_box_height)) / \
-                                prior_box_var[:,3]
+        target_box_x = ((target_box[:, 2] + target_box[:, 0]) / 2).reshape(
+            target_box.shape[0], 1)
+        target_box_y = ((target_box[:, 3] + target_box[:, 1]) / 2).reshape(
+            target_box.shape[0], 1)
+        target_box_width = ((target_box[:, 2] - target_box[:, 0])).reshape(
+            target_box.shape[0], 1)
+        target_box_height = ((target_box[:, 3] - target_box[:, 1])).reshape(
+            target_box.shape[0], 1)
+
+        output_box[:,:,0] = (target_box_x - prior_box_x) / prior_box_width / \
+                prior_box_var[:,:,0]
+        output_box[:,:,1] = (target_box_y - prior_box_y) / prior_box_height / \
+                prior_box_var[:,:,1]
+        output_box[:,:,2] = np.log(np.fabs(target_box_width / prior_box_width)) / \
+                prior_box_var[:,:,2]
+        output_box[:,:,3] = np.log(np.fabs(target_box_height / prior_box_height)) / \
+                prior_box_var[:,:,3]
 
     elif (code_type == "DecodeCenterSize"):
-        for i in range(target_box.shape[0]):
-            target_box_x = prior_box_var[:,0] * target_box[i][0] * \
-                           prior_box_width[:] + prior_box_x[:]
-            target_box_y = prior_box_var[:,1] * target_box[i][1] * \
-                           prior_box_height[:] + prior_box_y[:]
-            target_box_width = np.exp(prior_box_var[:,2] * target_box[i][2]) * \
-                               prior_box_width[:]
-            target_box_height = np.exp(prior_box_var[:,3] * target_box[i][3]) * \
-                                prior_box_height[:]
-            output_box[i, :, 0] = target_box_x - target_box_width / 2
-            output_box[i, :, 1] = target_box_y - target_box_height / 2
-            output_box[i, :, 2] = target_box_x + target_box_width / 2
-            output_box[i, :, 3] = target_box_y + target_box_height / 2
+        target_box = target_box.reshape(target_box.shape[0], 1,
+                                        target_box.shape[1])
+        target_box_x = prior_box_var[:,:,0] * target_box[:,:,0] * \
+                       prior_box_width + prior_box_x
+        target_box_y = prior_box_var[:,:,1] * target_box[:,:,1] * \
+                       prior_box_height + prior_box_y
+        target_box_width = np.exp(prior_box_var[:,:,2] * target_box[:,:,2]) * \
+                           prior_box_width
+        target_box_height = np.exp(prior_box_var[:,:,3] * target_box[:,:,3]) * \
+                            prior_box_height
+        output_box[:, :, 0] = target_box_x - target_box_width / 2
+        output_box[:, :, 1] = target_box_y - target_box_height / 2
+        output_box[:, :, 2] = target_box_x + target_box_width / 2
+        output_box[:, :, 3] = target_box_y + target_box_height / 2
 
 
 def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type):

From e14272bbb355e9330e075e63f78cab348cc402b7 Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Wed, 31 Jan 2018 22:46:23 +0800
Subject: [PATCH 170/314] update accoding to the code review

---
 paddle/operators/box_coder_op.cu | 93 +++++++++++++++++---------------
 1 file changed, 50 insertions(+), 43 deletions(-)

diff --git a/paddle/operators/box_coder_op.cu b/paddle/operators/box_coder_op.cu
index f2ea592f8e..883cc54305 100644
--- a/paddle/operators/box_coder_op.cu
+++ b/paddle/operators/box_coder_op.cu
@@ -18,79 +18,85 @@ namespace operators {
 template <typename T>
 __global__ void EncodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
-                                       const T* target_box_data, int row,
-                                       int col, T* output) {
+                                       const T* target_box_data, const int row,
+                                       const int col, const int len,
+                                       T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int row_idx = idx / col;
     const int col_idx = idx % col;
     T prior_box_width =
-        prior_box_data[col_idx * 4 + 2] - prior_box_data[col_idx * 4];
+        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
     T prior_box_height =
-        prior_box_data[col_idx * 4 + 3] - prior_box_data[col_idx * 4 + 1];
+        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
     T prior_box_center_x =
-        (prior_box_data[col_idx * 4 + 2] + prior_box_data[col_idx * 4]) / 2;
-    T prior_box_center_y =
-        (prior_box_data[col_idx * 4 + 3] + prior_box_data[col_idx * 4 + 1]) / 2;
+        (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
+    T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
+                            prior_box_data[col_idx * len + 1]) /
+                           2;
 
     T target_box_center_x =
-        (target_box_data[row_idx * 4 + 2] + target_box_data[row_idx * 4]) / 2;
-    T target_box_center_y =
-        (target_box_data[row_idx * 4 + 3] + target_box_data[row_idx * 4 + 1]) /
+        (target_box_data[row_idx * len + 2] + target_box_data[row_idx * len]) /
         2;
+    T target_box_center_y = (target_box_data[row_idx * len + 3] +
+                             target_box_data[row_idx * len + 1]) /
+                            2;
     T target_box_width =
-        target_box_data[row_idx * 4 + 2] - target_box_data[row_idx * 4];
+        target_box_data[row_idx * len + 2] - target_box_data[row_idx * len];
     T target_box_height =
-        target_box_data[row_idx * 4 + 3] - target_box_data[row_idx * 4 + 1];
+        target_box_data[row_idx * len + 3] - target_box_data[row_idx * len + 1];
 
-    output[idx * 4] = (target_box_center_x - prior_box_center_x) /
-                      prior_box_width / prior_box_var_data[col_idx * 4];
-    output[idx * 4 + 1] = (target_box_center_y - prior_box_center_y) /
-                          prior_box_height /
-                          prior_box_var_data[col_idx * 4 + 1];
-    output[idx * 4 + 2] = log(fabs(target_box_width / prior_box_width)) /
-                          prior_box_var_data[col_idx * 4 + 2];
-    output[idx * 4 + 3] = log(fabs(target_box_height / prior_box_height)) /
-                          prior_box_var_data[col_idx * 4 + 3];
+    output[idx * len] = (target_box_center_x - prior_box_center_x) /
+                        prior_box_width / prior_box_var_data[col_idx * len];
+    output[idx * len + 1] = (target_box_center_y - prior_box_center_y) /
+                            prior_box_height /
+                            prior_box_var_data[col_idx * len + 1];
+    output[idx * len + 2] = log(fabs(target_box_width / prior_box_width)) /
+                            prior_box_var_data[col_idx * len + 2];
+    output[idx * len + 3] = log(fabs(target_box_height / prior_box_height)) /
+                            prior_box_var_data[col_idx * len + 3];
   }
 }
 
 template <typename T>
 __global__ void DecodeCenterSizeKernel(const T* prior_box_data,
                                        const T* prior_box_var_data,
-                                       const T* target_box_data, int row,
-                                       int col, T* output) {
+                                       const T* target_box_data, const int row,
+                                       const int col, const int len,
+                                       T* output) {
   const int idx = threadIdx.x + blockIdx.x * blockDim.x;
   if (idx < row * col) {
     const int row_idx = idx / col;
     const int col_idx = idx % col;
     T prior_box_width =
-        prior_box_data[col_idx * 4 + 2] - prior_box_data[col_idx * 4];
+        prior_box_data[col_idx * len + 2] - prior_box_data[col_idx * len];
     T prior_box_height =
-        prior_box_data[col_idx * 4 + 3] - prior_box_data[col_idx * 4 + 1];
+        prior_box_data[col_idx * len + 3] - prior_box_data[col_idx * len + 1];
     T prior_box_center_x =
-        (prior_box_data[col_idx * 4 + 2] + prior_box_data[col_idx * 4]) / 2;
-    T prior_box_center_y =
-        (prior_box_data[col_idx * 4 + 3] + prior_box_data[col_idx * 4 + 1]) / 2;
+        (prior_box_data[col_idx * len + 2] + prior_box_data[col_idx * len]) / 2;
+    T prior_box_center_y = (prior_box_data[col_idx * len + 3] +
+                            prior_box_data[col_idx * len + 1]) /
+                           2;
 
-    T target_box_width = exp(prior_box_var_data[col_idx * 4 + 2] *
-                             target_box_data[row_idx * 4 + 2]) *
+    T target_box_width = exp(prior_box_var_data[col_idx * len + 2] *
+                             target_box_data[row_idx * len + 2]) *
                          prior_box_width;
-    T target_box_height = exp(prior_box_var_data[col_idx * 4 + 3] *
-                              target_box_data[row_idx * 4 + 3]) *
+    T target_box_height = exp(prior_box_var_data[col_idx * len + 3] *
+                              target_box_data[row_idx * len + 3]) *
                           prior_box_height;
-    T target_box_center_x = prior_box_var_data[col_idx * 4] *
-                                target_box_data[row_idx * 4] * prior_box_width +
+    T target_box_center_x = prior_box_var_data[col_idx * len] *
+                                target_box_data[row_idx * len] *
+                                prior_box_width +
                             prior_box_center_x;
-    T target_box_center_y = prior_box_var_data[col_idx * 4 + 1] *
-                                target_box_data[row_idx * 4 + 1] *
+    T target_box_center_y = prior_box_var_data[col_idx * len + 1] *
+                                target_box_data[row_idx * len + 1] *
                                 prior_box_height +
                             prior_box_center_y;
 
-    output[idx * 4] = target_box_center_x - target_box_width / 2;
-    output[idx * 4 + 1] = target_box_center_y - target_box_height / 2;
-    output[idx * 4 + 2] = target_box_center_x + target_box_width / 2;
-    output[idx * 4 + 3] = target_box_center_y + target_box_height / 2;
+    output[idx * len] = target_box_center_x - target_box_width / 2;
+    output[idx * len + 1] = target_box_center_y - target_box_height / 2;
+    output[idx * len + 2] = target_box_center_x + target_box_width / 2;
+    output[idx * len + 3] = target_box_center_y + target_box_height / 2;
   }
 }
 
@@ -111,6 +117,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     }
     auto row = target_box->dims()[0];
     auto col = prior_box->dims()[0];
+    auto len = prior_box->dims()[1];
     int block = 512;
     int grid = (row * col + block - 1) / block;
     auto& device_ctx = context.cuda_device_context();
@@ -119,17 +126,17 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     const T* prior_box_var_data = prior_box_var->data<T>();
     const T* target_box_data = target_box->data<T>();
 
-    output_box->mutable_data<T>({row, col, 4}, context.GetPlace());
+    output_box->mutable_data<T>({row, col, len}, context.GetPlace());
     T* output = output_box->data<T>();
 
     auto code_type = GetBoxCodeType(context.Attr<std::string>("code_type"));
     if (code_type == BoxCodeType::kEncodeCenterSize) {
       EncodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
-          prior_box_data, prior_box_var_data, target_box_data, row, col,
+          prior_box_data, prior_box_var_data, target_box_data, row, col, len,
           output);
     } else if (code_type == BoxCodeType::kDecodeCenterSize) {
       DecodeCenterSizeKernel<T><<<grid, block, 0, device_ctx.stream()>>>(
-          prior_box_data, prior_box_var_data, target_box_data, row, col,
+          prior_box_data, prior_box_var_data, target_box_data, row, col, len,
           output);
     }
   }

From b3ecdafe7e281579065b54635095b45b01dec6df Mon Sep 17 00:00:00 2001
From: helinwang <ustc.harry@gmail.com>
Date: Tue, 30 Jan 2018 18:27:13 -0800
Subject: [PATCH 171/314] CI build: take -DCUDA_ARCH_NAME from environment
 variable

---
 paddle/scripts/docker/build.sh | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index fbae37b2ca..8369ded8cb 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -40,6 +40,7 @@ function cmake_gen() {
         -DWITH_MKL=${WITH_MKL:-ON}
         -DWITH_AVX=${WITH_AVX:-OFF}
         -DWITH_GOLANG=${WITH_GOLANG:-ON}
+        -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All}
         -DWITH_SWIG_PY=ON
         -DWITH_C_API=${WITH_C_API:-OFF}
         -DWITH_PYTHON=${WITH_PYTHON:-ON}
@@ -62,6 +63,7 @@ EOF
         -DWITH_MKL=${WITH_MKL:-ON} \
         -DWITH_AVX=${WITH_AVX:-OFF} \
         -DWITH_GOLANG=${WITH_GOLANG:-ON} \
+        -DCUDA_ARCH_NAME=${CUDA_ARCH_NAME:-All} \
         -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \
         -DWITH_C_API=${WITH_C_API:-OFF} \
         -DWITH_PYTHON=${WITH_PYTHON:-ON} \

From 3b5588a814bda4bff940f4cee425455a0581ad23 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Thu, 1 Feb 2018 09:46:06 +0800
Subject: [PATCH 172/314] Fix default build_type env

---
 paddle/scripts/docker/build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index fbae37b2ca..48c8e26c93 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -32,7 +32,7 @@ function cmake_gen() {
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release}
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:-Release}
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
@@ -54,7 +54,7 @@ EOF
     # docker environment is fully controlled by this script.
     # See /Paddle/CMakeLists.txt, UNITTEST_USE_VIRTUALENV option.
     cmake .. \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:Release} \
+        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:-Release} \
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \

From ff5570c12605b5b983cb9308ae507e1bb214143c Mon Sep 17 00:00:00 2001
From: wanghaox <wanghaox@gmail.com>
Date: Thu, 1 Feb 2018 11:29:26 +0800
Subject: [PATCH 173/314] update mine_hard_examples_op

---
 paddle/operators/mine_hard_examples_op.cc     | 234 ++++++++++++++----
 paddle/operators/mine_hard_examples_op.h      | 148 -----------
 .../fluid/tests/test_mine_hard_examples_op.py |  29 +--
 3 files changed, 202 insertions(+), 209 deletions(-)
 delete mode 100755 paddle/operators/mine_hard_examples_op.h

diff --git a/paddle/operators/mine_hard_examples_op.cc b/paddle/operators/mine_hard_examples_op.cc
index 75098d0bcd..603368f93c 100644
--- a/paddle/operators/mine_hard_examples_op.cc
+++ b/paddle/operators/mine_hard_examples_op.cc
@@ -12,41 +12,178 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/operators/mine_hard_examples_op.h"
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
 
+enum MiningType { kNone = 0, kMaxNegative, kHardExample };
+
+template <typename T>
+bool SortScoreDescend(const std::pair<float, T>& pair1,
+                      const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+inline bool IsEligibleMining(const MiningType mining_type, const int match_idx,
+                             const float match_dist,
+                             const float neg_dist_threshold) {
+  if (mining_type == MiningType::kMaxNegative) {
+    return match_idx == -1 && match_dist < neg_dist_threshold;
+  } else if (mining_type == MiningType::kHardExample) {
+    return true;
+  } else {
+    return false;
+  }
+}
+
+MiningType GetMiningType(std::string str) {
+  if (str == "max_negative") {
+    return MiningType::kMaxNegative;
+  } else if (str == "hard_example") {
+    return MiningType::kHardExample;
+  } else {
+    return MiningType::kNone;
+  }
+}
+
+template <typename DeviceContext, typename T>
+class MineHardExamplesKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in_cls_loss = ctx.Input<framework::Tensor>("ClsLoss");
+    auto* in_loc_loss = ctx.Input<framework::Tensor>("LocLoss");
+    auto* in_matched_indices = ctx.Input<framework::Tensor>("MatchIndices");
+    auto* in_match_dist = ctx.Input<framework::Tensor>("MatchDist");
+    float neg_pos_ratio = ctx.Attr<float>("neg_pos_ratio");
+    T neg_dist_threshold =
+        static_cast<T>(ctx.Attr<float>("neg_dist_threshold"));
+    int sample_size = ctx.Attr<int>("sample_size");
+    MiningType mining_type =
+        GetMiningType(ctx.Attr<std::string>("mining_type"));
+
+    auto out_neg_indices = ctx.Output<framework::LoDTensor>("NegIndices");
+    auto out_match_indices =
+        ctx.Output<framework::Tensor>("UpdatedMatchIndices");
+
+    framework::Copy(*in_matched_indices, ctx.GetPlace(), out_match_indices);
+
+    int batch_size = in_matched_indices->dims()[0];
+    int prior_num = in_matched_indices->dims()[1];
+
+    auto match_indices = framework::EigenMatrix<int>::From(*in_matched_indices);
+
+    auto match_indices_et =
+        framework::EigenMatrix<int>::From(*out_match_indices);
+
+    auto match_dist = framework::EigenMatrix<T>::From(*in_match_dist);
+
+    const T* cls_loss = in_cls_loss->data<T>();
+    const T* loc_loss = nullptr;
+    if (in_loc_loss) {
+      loc_loss = in_loc_loss->data<T>();
+    }
+
+    std::vector<std::vector<int>> all_neg_indices;
+    std::vector<size_t> batch_starts = {0};
+    for (int n = 0; n < batch_size; ++n) {
+      std::vector<std::pair<T, size_t>> loss_idx;
+      int neg_sel = 0;
+      for (int m = 0; m < prior_num; ++m) {
+        if (IsEligibleMining(mining_type, match_indices(n, m), match_dist(n, m),
+                             neg_dist_threshold)) {
+          T loss = cls_loss[n * prior_num + m];
+          if (mining_type == MiningType::kHardExample && loc_loss != nullptr) {
+            loss = cls_loss[n * prior_num + m] + loc_loss[n * prior_num + m];
+          }
+          loss_idx.push_back(std::make_pair(loss, m));
+          ++neg_sel;
+        }
+      }
+
+      if (mining_type == MiningType::kMaxNegative) {
+        int num_pos = 0;
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) != -1) ++num_pos;
+        }
+        neg_sel = std::min(static_cast<int>(num_pos * neg_pos_ratio), neg_sel);
+      } else if (mining_type == MiningType::kHardExample) {
+        neg_sel = std::min(sample_size, neg_sel);
+      }
+
+      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<int>);
+      std::set<int> sel_indices;
+      std::vector<int> neg_indices;
+      std::transform(loss_idx.begin(), loss_idx.begin() + neg_sel,
+                     std::inserter(sel_indices, sel_indices.begin()),
+                     [](std::pair<T, size_t> l) -> int {
+                       return static_cast<int>(l.second);
+                     });
+
+      for (int m = 0; m < prior_num; ++m) {
+        if (match_indices(n, m) > -1) {
+          if (mining_type == MiningType::kHardExample &&
+              sel_indices.find(m) == sel_indices.end()) {
+            match_indices_et(n, m) = -1;
+          }
+        } else {
+          if (sel_indices.find(m) != sel_indices.end()) {
+            neg_indices.push_back(m);
+          }
+        }
+      }
+      all_neg_indices.push_back(neg_indices);
+      batch_starts.push_back(batch_starts.back() + neg_indices.size());
+    }
+
+    framework::LoD out_neg_indices_lod;
+    out_neg_indices_lod.emplace_back(batch_starts);
+    int neg_offset = 0;
+    auto neg_data = out_neg_indices->mutable_data<int>(
+        framework::make_ddim({static_cast<int>(batch_starts.back()), 1}),
+        ctx.GetPlace());
+
+    for (auto neg_indices : all_neg_indices) {
+      std::copy(neg_indices.begin(), neg_indices.end(), neg_data + neg_offset);
+      neg_offset += neg_indices.size();
+    }
+    out_neg_indices->set_lod(out_neg_indices_lod);
+    return;
+  }
+};
+
 class MineHardExamplesOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(framework::InferShapeContext *ctx) const override {
+  void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("ClsLoss"),
                    "Input(ClsLoss) of MineHardExamplesOp should not be null.");
     PADDLE_ENFORCE(
-        ctx->HasInput("MatchIndics"),
-        "Input(MatchIndics) of MineHardExamplesOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("MatchDis"),
-                   "Input(MatchDis) of MineHardExamplesOp should not be null.");
+        ctx->HasInput("MatchIndices"),
+        "Input(MatchIndices) of MineHardExamplesOp should not be null.");
     PADDLE_ENFORCE(
-        ctx->HasOutput("NegIndics"),
-        "Output(NegIndics) of MineHardExamplesOp should not be null.");
+        ctx->HasInput("MatchDist"),
+        "Input(MatchDist) of MineHardExamplesOp should not be null.");
     PADDLE_ENFORCE(
-        ctx->HasOutput("UpdatedMatchIndics"),
-        "Output(UpdatedMatchIndics) of MineHardExamplesOp should not be null.");
+        ctx->HasOutput("NegIndices"),
+        "Output(NegIndices) of MineHardExamplesOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("UpdatedMatchIndices"),
+                   "Output(UpdatedMatchIndices) of MineHardExamplesOp should "
+                   "not be null.");
 
     auto cls_loss_dims = ctx->GetInputDim("ClsLoss");
-    auto idx_dims = ctx->GetInputDim("MatchIndics");
-    auto dis_dims = ctx->GetInputDim("MatchDis");
+    auto idx_dims = ctx->GetInputDim("MatchIndices");
+    auto dis_dims = ctx->GetInputDim("MatchDist");
 
     PADDLE_ENFORCE_EQ(cls_loss_dims.size(), 2UL,
                       "The shape of ClsLoss is [N, Np].");
     PADDLE_ENFORCE_EQ(idx_dims.size(), 2UL,
-                      "The shape of MatchIndics is [N, Np].");
+                      "The shape of MatchIndices is [N, Np].");
     PADDLE_ENFORCE_EQ(dis_dims.size(), 2UL,
-                      "The shape of MatchDis is [N, Np].");
+                      "The shape of MatchDist is [N, Np].");
 
     if (ctx->HasInput("LocLoss")) {
       auto loc_loss_dims = ctx->GetInputDim("LocLoss");
@@ -61,16 +198,16 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
 
     PADDLE_ENFORCE_EQ(
         cls_loss_dims[0], idx_dims[0],
-        "Batch size of ClsLoss and MatchIndics must be the same.");
+        "Batch size of ClsLoss and MatchIndices must be the same.");
     PADDLE_ENFORCE_EQ(
         cls_loss_dims[1], idx_dims[1],
-        "Prior box number of ClsLoss and MatchIndics must be the same.");
+        "Prior box number of ClsLoss and MatchIndices must be the same.");
 
     PADDLE_ENFORCE_EQ(cls_loss_dims[0], dis_dims[0],
-                      "Batch size of ClsLoss and MatchDis must be the same.");
+                      "Batch size of ClsLoss and MatchDist must be the same.");
     PADDLE_ENFORCE_EQ(
         cls_loss_dims[1], idx_dims[1],
-        "Prior box number of ClsLoss and MatchDis must be the same.");
+        "Prior box number of ClsLoss and MatchDist must be the same.");
 
     auto mining_type =
         GetMiningType(ctx->Attrs().Get<std::string>("mining_type"));
@@ -80,13 +217,13 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
 
     if (mining_type == MiningType::kMaxNegative) {
       auto neg_pos_ratio = ctx->Attrs().Get<float>("neg_pos_ratio");
-      auto neg_dis_threshold = ctx->Attrs().Get<float>("neg_dis_threshold");
+      auto neg_dist_threshold = ctx->Attrs().Get<float>("neg_dist_threshold");
       PADDLE_ENFORCE_GT(
           neg_pos_ratio, 0.0f,
           "neg_pos_ratio must greater than zero in max_negative mode");
       PADDLE_ENFORCE_GT(
-          neg_dis_threshold, 0.0f,
-          "neg_dis_threshold must greater than zero in max_negative mode");
+          neg_dist_threshold, 0.0f,
+          "neg_dist_threshold must greater than zero in max_negative mode");
     } else if (mining_type == MiningType::kHardExample) {
       auto sample_size = ctx->Attrs().Get<int>("sample_size");
       PADDLE_ENFORCE_GT(
@@ -94,12 +231,12 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
           "sample_size must greater than zero in hard_example mode");
     }
 
-    ctx->SetOutputDim("UpdatedMatchIndics", idx_dims);
+    ctx->SetOutputDim("UpdatedMatchIndices", idx_dims);
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const override {
+      const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<framework::Tensor>("ClsLoss")->type()),
         ctx.device_context());
@@ -108,30 +245,31 @@ class MineHardExamplesOp : public framework::OperatorWithKernel {
 
 class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  MineHardExamplesOpMaker(OpProto *proto, OpAttrChecker *op_checker)
+  MineHardExamplesOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput(
         "ClsLoss",
-        "(Tensor, default Tensor<float>), The classification loss wit shape "
+        "(Tensor, default Tensor<float>), The classification loss with shape "
         "[N, Np], N is the batch size and Np is the number of prior box.");
     AddInput("LocLoss",
              "(Tensor, optional, default Tensor<float>), The localization loss "
              "wit shape [N, Np], N is the batch size and Np is the number of "
              "prior box.")
         .AsDispensable();
-    AddInput("MatchIndics",
+    AddInput("MatchIndices",
              "(Tensor, Tensor<int>), Matched indices with shape [N, Np], N is "
              "the batch size and Np is the number of prior box. "
-             "MatchIndics[i][j] equal -1 means box[j] does not match any "
-             "entity, otherwise means Box[j] is matched to row.");
-    AddInput("MatchDis",
+             "MatchIndices[i][j] equal -1 means the j-th prior box in i-th "
+             "instance does not match any entity, otherwise means it is "
+             "matched to row.");
+    AddInput("MatchDist",
              "(Tensor, default Tensor<float>) Matched indices with shape [N, "
              "Np], N is the batch size and Np is the number of prior box.");
     AddAttr<float>("neg_pos_ratio",
                    "(float) The ratio of the negative box to the positive "
                    "box. Use only when mining_type is equal to max_negative.")
         .SetDefault(1.0);
-    AddAttr<float>("neg_dis_threshold",
+    AddAttr<float>("neg_dist_threshold",
                    "(float) The negative box dis value threshold. "
                    "Use only when mining_type is equal to max_negative.")
         .SetDefault(0.5);
@@ -145,29 +283,31 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault("max_negative")
         .InEnum({"hard_example", "max_negative"});
 
-    AddOutput("NegIndics",
-              "(LoDTensor) The output of negative example indics.a lod tensor "
-              "with shape [Neg, 1]. The size of lod[0] is batch size, "
-              "and each element is the box index. "
-              "For example, the batch size is 2, the lod is [[0, 1, 2]], "
-              "the sample 0's box 1(MatchIndics[0][1]) is selected, "
-              "and sample 1's box 0 is selected. The output NegIndics is "
-              "[[1], [0]].");
-
-    AddOutput("UpdatedMatchIndics",
-              "(Tensor) The output of updated MatchIndics, a tensor with "
-              "shape [N, M]. Only update when mining_type is equal to "
-              "hard_example. The input MatchIndics elements will be update to "
-              "-1 when it not in the highest loss list");
+    AddOutput(
+        "NegIndices",
+        "(LoDTensor<int>) The output of negative example indices. a LoDTensor "
+        "with shape [Neg, 1]. The size of lod[0] minus 1 is batch size, "
+        "and each element is the prior box index. "
+        "For example, the batch size is 2, the lod is [[0, 1, 2]], "
+        "the sample 0's box 1(MatchIndices[0][1]) is selected, "
+        "and sample 1's box 0 is selected. The output NegIndices is "
+        "[[1], [0]].");
+
+    AddOutput("UpdatedMatchIndices",
+              "(Tensor<int>) The output of updated MatchIndices, a tensor with "
+              "shape [N, Np]. Only update when mining_type is equal to "
+              "hard_example. The input MatchIndices elements will be update to "
+              "-1 when it is not in the candidate high loss list of negative "
+              "examples.");
 
     AddComment(R"DOC(
 Mine hard examples Operator.
-This operator implements hard example mining to select a subset of negative box indics.
+This operator implements hard example mining to select a subset of negative box indices.
 For each image, selects the box with highest losses. subject to the condition that the box cannot have
-an MatchDis > neg_dis_threshold when mining_type is equals max_negative. The selected number is 
+an Matcht > neg_dist_threshold when mining_type is equals max_negative. The selected number is 
 min(sample_size, max_negative_box_number) when mining_type is equals hard_example,
 or min(neg_pos_ratio * positive_box_number, max_negative_box_number) when mining_type is 
-equals max_negative, where the max_negative_box_number is the count of MatchIndics elements with value -1.
+equals max_negative, where the max_negative_box_number is the count of MatchIndices elements with value -1.
 )DOC");
   }
 };
diff --git a/paddle/operators/mine_hard_examples_op.h b/paddle/operators/mine_hard_examples_op.h
deleted file mode 100755
index 0a652a60c5..0000000000
--- a/paddle/operators/mine_hard_examples_op.h
+++ /dev/null
@@ -1,148 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-#include "paddle/framework/op_registry.h"
-
-namespace paddle {
-namespace operators {
-
-enum MiningType { kNone = 0, kMaxNegative, kHardExample };
-
-template <typename T>
-bool SortScoreDescend(const std::pair<float, T>& pair1,
-                      const std::pair<float, T>& pair2) {
-  return pair1.first > pair2.first;
-}
-
-inline bool IsEligibleMining(const MiningType mining_type, const int match_idx,
-                             const float match_dis,
-                             const float neg_dis_threshold) {
-  if (mining_type == MiningType::kMaxNegative) {
-    return match_idx == -1 && match_dis < neg_dis_threshold;
-  } else if (mining_type == MiningType::kHardExample) {
-    return true;
-  } else {
-    return false;
-  }
-}
-
-MiningType GetMiningType(std::string str) {
-  if (str == "max_negative") {
-    return MiningType::kMaxNegative;
-  } else if (str == "hard_example") {
-    return MiningType::kHardExample;
-  } else {
-    return MiningType::kNone;
-  }
-}
-
-template <typename DeviceContext, typename T>
-class MineHardExamplesKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in_cls_loss = ctx.Input<framework::Tensor>("ClsLoss");
-    auto* in_loc_loss = ctx.Input<framework::Tensor>("LocLoss");
-    auto* in_matched_indics = ctx.Input<framework::Tensor>("MatchIndics");
-    auto* in_match_dis = ctx.Input<framework::Tensor>("MatchDis");
-    float neg_pos_ratio = ctx.Attr<float>("neg_pos_ratio");
-    T neg_dis_threshold = static_cast<T>(ctx.Attr<float>("neg_dis_threshold"));
-    int sample_size = ctx.Attr<int>("sample_size");
-    MiningType mining_type =
-        GetMiningType(ctx.Attr<std::string>("mining_type"));
-
-    auto out_neg_indics = ctx.Output<framework::LoDTensor>("NegIndics");
-    auto out_match_indics = ctx.Output<framework::Tensor>("UpdatedMatchIndics");
-
-    framework::Copy(*in_matched_indics, ctx.GetPlace(), out_match_indics);
-
-    int batch_size = in_matched_indics->dims()[0];
-    int prior_num = in_matched_indics->dims()[1];
-
-    auto match_indices = framework::EigenMatrix<int>::From(*in_matched_indics);
-
-    auto match_indices_et =
-        framework::EigenMatrix<int>::From(*out_match_indics);
-
-    auto match_dis = framework::EigenMatrix<float>::From(*in_match_dis);
-    auto cls_loss = framework::EigenMatrix<float>::From(*in_cls_loss);
-    auto loc_loss = framework::EigenMatrix<float>::From(*in_loc_loss);
-
-    std::vector<std::vector<int>> all_neg_indices;
-    int all_neg_num = 0;
-    for (int n = 0; n < batch_size; ++n) {
-      std::vector<std::pair<float, size_t>> loss_idx;
-      int neg_sel = 0;
-      for (int m = 0; m < prior_num; ++m) {
-        if (IsEligibleMining(mining_type, match_indices(n, m), match_dis(n, m),
-                             neg_dis_threshold)) {
-          T loss = cls_loss(n, m);
-          if (mining_type == MiningType::kHardExample) {
-            loss = cls_loss(n, m) + loc_loss(n, m);
-          }
-          loss_idx.push_back(std::make_pair(loss, m));
-          ++neg_sel;
-        }
-      }
-      if (mining_type == MiningType::kMaxNegative) {
-        int num_pos = 0;
-        for (int m = 0; m < prior_num; ++m) {
-          if (match_indices(n, m) != -1) ++num_pos;
-        }
-        neg_sel = std::min(static_cast<int>(num_pos * neg_pos_ratio), neg_sel);
-      } else if (mining_type == MiningType::kHardExample) {
-        neg_sel = std::min(sample_size, neg_sel);
-      }
-      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<int>);
-      std::set<int> sel_indices;
-      std::vector<int> neg_indices;
-      for (int n = 0; n < neg_sel; ++n) {
-        sel_indices.insert(loss_idx[n].second);
-      }
-
-      for (int m = 0; m < prior_num; ++m) {
-        if (match_indices(n, m) > -1) {
-          if (mining_type == MiningType::kHardExample &&
-              sel_indices.find(m) == sel_indices.end()) {
-            match_indices_et(n, m) = -1;
-          }
-        } else {
-          if (sel_indices.find(m) != sel_indices.end()) {
-            neg_indices.push_back(m);
-          }
-        }
-      }
-      all_neg_indices.push_back(neg_indices);
-      all_neg_num += neg_indices.size();
-    }
-
-    framework::LoD out_neg_indics_lod;
-    out_neg_indics_lod.resize(1);
-    int neg_offset = 0;
-    auto neg_data = out_neg_indics->mutable_data<int>(
-        framework::make_ddim({all_neg_num, 1}), ctx.GetPlace());
-    out_neg_indics_lod[0].push_back(neg_offset);
-    for (auto neg_indices : all_neg_indices) {
-      for (auto neg_idx : neg_indices) {
-        neg_data[neg_offset++] = neg_idx;
-      }
-      out_neg_indics_lod[0].push_back(neg_offset);
-    }
-    out_neg_indics->set_lod(out_neg_indics_lod);
-    return;
-  }
-};
-}  // namespace operators
-
-}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py b/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py
index e7dd04740a..c27573c3d6 100755
--- a/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py
+++ b/python/paddle/v2/fluid/tests/test_mine_hard_examples_op.py
@@ -1,16 +1,17 @@
-#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 #
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-#    http://www.apache.org/licenses/LICENSE-2.0
+#     http://www.apache.org/licenses/LICENSE-2.0
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import unittest
 import numpy as np
 import sys
@@ -24,8 +25,8 @@ class TestMineHardExamplesOp(OpTest):
         self.inputs = {
             'ClsLoss': self.cls_loss,
             'LocLoss': self.loc_loss,
-            'MatchIndics': self.match_indices,
-            'MatchDis': self.match_dis
+            'MatchIndices': self.match_indices,
+            'MatchDist': self.match_dis
         }
 
         self.attrs = {
@@ -36,8 +37,8 @@ class TestMineHardExamplesOp(OpTest):
         }
 
         self.outputs = {
-            'NegIndics': (self.neg_indices, self.neg_indices_lod),
-            'UpdatedMatchIndics': self.updated_match_indices
+            'NegIndices': (self.neg_indices, self.neg_indices_lod),
+            'UpdatedMatchIndices': self.updated_match_indices
         }
 
     def test_check_output(self):

From 4db77131cd7657dbe8030ca7f0704bea0501cd59 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Thu, 1 Feb 2018 12:05:01 +0800
Subject: [PATCH 174/314] update by comment

---
 paddle/scripts/docker/build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index 48c8e26c93..2ed237b5b0 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -32,7 +32,7 @@ function cmake_gen() {
     cat <<EOF
     ========================================
     Configuring cmake in /paddle/build ...
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:-Release}
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release}
         ${PYTHON_FLAGS}
         -DWITH_DOC=OFF
         -DWITH_GPU=${WITH_GPU:-OFF}
@@ -54,7 +54,7 @@ EOF
     # docker environment is fully controlled by this script.
     # See /Paddle/CMakeLists.txt, UNITTEST_USE_VIRTUALENV option.
     cmake .. \
-        -DCMAKE_BUILD_TYPE=${BUILD_TYPE:-Release} \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release} \
         ${PYTHON_FLAGS} \
         -DWITH_DOC=OFF \
         -DWITH_GPU=${WITH_GPU:-OFF} \

From d6edfd0ce30615c7c15332ed07dbd9dd5996e430 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 1 Feb 2018 12:47:01 +0800
Subject: [PATCH 175/314] update points

---
 benchmark/cluster/vgg16/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 0c404e60a8..b999a51809 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -42,8 +42,8 @@
 
 | Trainer Counter | 20 | 40 | 80 | 100 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | 291.06 | 518.80 | 836.26 | 1019.29 |
-| PaddlePaddle v2 (need more tests) | 356.28 | 785.39 | 853.30 | 1041.99 |
+| PaddlePaddle Fluid | 263.29 | 518.80 | 836.26 | 1019.29 |
+| PaddlePaddle v2 (need more tests) | 326.85 | 534.58 | 853.30 | 1041.99 |
 | TensorFlow | - | - | - | - |
 
 ### different pserver number

From d762c6d7ff588d0d37a954d39b89ed509e70e6e6 Mon Sep 17 00:00:00 2001
From: Yancey1989 <yancey1989@gmail.com>
Date: Thu, 1 Feb 2018 12:57:46 +0800
Subject: [PATCH 176/314] Fix compilie error

---
 paddle/operators/listen_and_serv_op.cc | 2 +-
 paddle/operators/send_op.cc            | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/operators/listen_and_serv_op.cc b/paddle/operators/listen_and_serv_op.cc
index 5745938ed9..099f6b2373 100644
--- a/paddle/operators/listen_and_serv_op.cc
+++ b/paddle/operators/listen_and_serv_op.cc
@@ -204,4 +204,4 @@ from send_op and send back variables to recv_op.
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(listen_and_serv, ops::ListenAndServOp,
-                  ops::ListenAndServOpMaker);
\ No newline at end of file
+                  ops::ListenAndServOpMaker);
diff --git a/paddle/operators/send_op.cc b/paddle/operators/send_op.cc
index 291d19ba8b..ee0f268b0e 100644
--- a/paddle/operators/send_op.cc
+++ b/paddle/operators/send_op.cc
@@ -65,9 +65,9 @@ class SendOp : public framework::OperatorBase {
     if (outs.size() > 0) {
       for (size_t i = 0; i < outs.size(); i++) {
         VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
-        client_.AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
+        rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
       }
-      PADDLE_ENFORCE(client_.Wait());
+      PADDLE_ENFORCE(rpc_client->Wait());
     }
   }
 };

From d8cc21da53e1113aaee3b43ea77d136bbbd204bb Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 1 Feb 2018 12:58:14 +0800
Subject: [PATCH 177/314] refine inheritance relationship

---
 paddle/framework/reader.cc |  2 +-
 paddle/framework/reader.h  | 66 +++++++++++++++++++++-----------------
 2 files changed, 37 insertions(+), 31 deletions(-)

diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
index e11662166c..a05bef42ff 100644
--- a/paddle/framework/reader.cc
+++ b/paddle/framework/reader.cc
@@ -17,7 +17,7 @@
 namespace paddle {
 namespace framework {
 
-DDim Reader::shape(size_t idx) const {
+DDim FileReader::shape(size_t idx) const {
   PADDLE_ENFORCE_LT(
       idx, shapes_.size(),
       "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 58675863e5..3954a1bea8 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -20,32 +20,48 @@
 namespace paddle {
 namespace framework {
 
-class Reader {
+class ReaderBase {
  public:
-  Reader() {}
-  explicit Reader(const std::vector<DDim>& shapes) : shapes_(shapes) {}
-
   virtual std::vector<LoDTensor> ReadNext() = 0;
   virtual bool HasNext() const = 0;
 
-  virtual DDim shape(size_t idx) const;
-  virtual std::vector<DDim> shapes() const { return shapes_; }
+  virtual DDim shape(size_t idx) const = 0;
+  virtual std::vector<DDim> shapes() const = 0;
 
-  virtual ~Reader() {}
+  virtual ~ReaderBase() {}
+};
 
- private:
-  // set private to prevent directly access in decorators
-  // a decorator should access its underlying reader_'s shape, not its own.
+class FileReader : public ReaderBase {
+ public:
+  explicit FileReader(const std::vector<DDim>& shapes) : shapes_(shapes) {}
+
+  DDim shape(size_t idx) const override;
+  std::vector<DDim> shapes() const override { return shapes_; }
+
+ protected:
   std::vector<DDim> shapes_;
 };
 
+class ReaderDecorator : public ReaderBase {
+ public:
+  explicit ReaderDecorator(ReaderBase* reader) : reader_(reader) {}
+
+  bool HasNext() const override { return reader_->HasNext(); }
+
+  DDim shape(size_t idx) const override { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const override { return reader_->shapes(); }
+
+ protected:
+  ReaderBase* reader_;
+};
+
 // file readers
 
 template <typename T>
-class RandomReader : public Reader {
+class RandomReader : public FileReader {
  public:
   RandomReader(const std::vector<DDim>& shapes, float min, float max)
-      : Reader(shapes), min_(min), max_(max) {
+      : FileReader(shapes), min_(min), max_(max) {
     PADDLE_ENFORCE_LE(min, max,
                       "'min' should be less than or equal to 'max'.(%f vs %f)",
                       min, max);
@@ -58,8 +74,8 @@ class RandomReader : public Reader {
     std::uniform_real_distribution<float> dist(min_, max_);
 
     std::vector<LoDTensor> res;
-    res.reserve(shapes().size());
-    for (const DDim& shape : shapes()) {
+    res.reserve(shapes_.size());
+    for (const DDim& shape : shapes_) {
       PADDLE_ENFORCE_GE(
           shape.size(), 2,
           "The rank of input data should be 2 at least.(Now it's %d)",
@@ -85,37 +101,27 @@ class RandomReader : public Reader {
 
 // decorators
 
-class ShuffleReader : public Reader {
+class ShuffleReader : public ReaderDecorator {
  public:
-  ShuffleReader(Reader* reader, int buffer_size)
-      : reader_(reader), buffer_size_(buffer_size), iteration_pos_(0) {
+  ShuffleReader(ReaderBase* reader, int buffer_size)
+      : ReaderDecorator(reader), buffer_size_(buffer_size), iteration_pos_(0) {
     buffer_.reserve(buffer_size);
   }
   std::vector<LoDTensor> ReadNext() override;
-  bool HasNext() const override { return reader_->HasNext(); }
-
-  DDim shape(size_t idx) const override { return reader_->shape(idx); }
-  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  Reader* reader_;
   int buffer_size_;
   std::vector<std::vector<LoDTensor>> buffer_;
   size_t iteration_pos_;
 };
 
-class BatchReader : public Reader {
+class BatchReader : public ReaderDecorator {
  public:
-  BatchReader(Reader* reader, int batch_size)
-      : reader_(reader), batch_size_(batch_size) {}
+  BatchReader(ReaderBase* reader, int batch_size)
+      : ReaderDecorator(reader), batch_size_(batch_size) {}
   std::vector<LoDTensor> ReadNext() override;
-  bool HasNext() const override { return reader_->HasNext(); };
-
-  DDim shape(size_t idx) const override { return reader_->shape(idx); }
-  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  Reader* reader_;
   int batch_size_;
   std::vector<std::vector<LoDTensor>> buffer_;
 };

From 355ecaf38b2aab763428e47faa798b10f45f7c69 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 1 Feb 2018 14:15:41 +0800
Subject: [PATCH 178/314] fix style check

---
 benchmark/cluster/vgg16/vgg16_fluid.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/benchmark/cluster/vgg16/vgg16_fluid.py b/benchmark/cluster/vgg16/vgg16_fluid.py
index 51a01af672..87a151db21 100644
--- a/benchmark/cluster/vgg16/vgg16_fluid.py
+++ b/benchmark/cluster/vgg16/vgg16_fluid.py
@@ -170,10 +170,11 @@ def main():
                     y_data = np.array(map(lambda x: x[1], data)).astype("int64")
                     y_data = y_data.reshape([-1, 1])
 
-                    loss, acc = exe.run(trainer_prog,
-                                        feed={"pixel": img_data,
-                                            "label": y_data},
-                                        fetch_list=[avg_cost] + accuracy.metrics)
+                    loss, acc = exe.run(
+                        trainer_prog,
+                        feed={"pixel": img_data,
+                              "label": y_data},
+                        fetch_list=[avg_cost] + accuracy.metrics)
                     iters += 1
                     num_samples += len(data)
                     print(

From b7fbb91f069bfda8658ac7341111a53615b7903f Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 1 Feb 2018 14:36:19 +0800
Subject: [PATCH 179/314] follow comments

---
 benchmark/cluster/vgg16/README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index b999a51809..69a242e305 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -20,7 +20,7 @@
 | PaddlePaddle v2 | 15.97 | 17.04 | 17.60 | 17.83 |
 | TensorFlow | - | - | - | - |
 
-### different batch size
+### Different Batch Size
 
 - PServer Count: 10
 - Trainer Count: 20
@@ -34,7 +34,7 @@
 | TensorFlow | - | - | - | - |
 
 
-### Accelerate rate
+### Accelerate Rate
 
 - Pserver Count: 20
 - Batch Size: 128
@@ -42,11 +42,11 @@
 
 | Trainer Counter | 20 | 40 | 80 | 100 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | 263.29 | 518.80 | 836.26 | 1019.29 |
-| PaddlePaddle v2 (need more tests) | 326.85 | 534.58 | 853.30 | 1041.99 |
+| PaddlePaddle Fluid | 263.29 (78.64%) | 518.80 (77.47%) | 836.26 (62.44%) | 1019.29 (60.89%) |
+| PaddlePaddle v2 (need more tests) | 326.85 (92.85%) | 534.58 (75.93%) | 853.30 (60.60%) | 1041.99 (59.20%) |
 | TensorFlow | - | - | - | - |
 
-### different pserver number
+### Different Pserver Number
 
 - Trainer Count: 100
 - Batch Size: 128

From d11e7b434f1272ecff05156de70eabaca26cc1f1 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Thu, 1 Feb 2018 15:00:52 +0800
Subject: [PATCH 180/314] Make recognize digits as a normal python unittest

---
 paddle/inference/tests/book/CMakeLists.txt    |  2 +-
 python/paddle/v2/fluid/tests/book/.gitignore  |  1 +
 .../paddle/v2/fluid/tests/book/CMakeLists.txt | 26 +------
 .../fluid/tests/book/test_recognize_digits.py | 67 +++++++++++++++----
 4 files changed, 56 insertions(+), 40 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/book/.gitignore

diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index d3798fb8fd..0e987eb024 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -4,4 +4,4 @@ cc_test(test_inference_recognize_digits_mlp
     DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
     ARGS --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model)
 set_tests_properties(test_inference_recognize_digits_mlp
-    PROPERTIES DEPENDS test_recognize_digits_mlp_cpu)
+    PROPERTIES DEPENDS test_recognize_digits)
diff --git a/python/paddle/v2/fluid/tests/book/.gitignore b/python/paddle/v2/fluid/tests/book/.gitignore
new file mode 100644
index 0000000000..f0b574b939
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/book/.gitignore
@@ -0,0 +1 @@
+recognize_digits_*.inference.model
diff --git a/python/paddle/v2/fluid/tests/book/CMakeLists.txt b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
index dda02c03fd..a35abe3e0c 100644
--- a/python/paddle/v2/fluid/tests/book/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/book/CMakeLists.txt
@@ -1,33 +1,9 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-list(REMOVE_ITEM TEST_OPS test_image_classification_train test_recognize_digits)
+list(REMOVE_ITEM TEST_OPS test_image_classification_train)
 py_test(test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet)
 py_test(test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg)
-py_test(test_recognize_digits_mlp_cpu
-  SRCS test_recognize_digits.py
-  ARGS mlp)
-py_test(test_recognize_digits_mlp_cuda
-  SRCS test_recognize_digits.py
-  ARGS mlp --use_cuda)
-py_test(test_recognize_digits_conv_cpu
-  SRCS test_recognize_digits.py
-  ARGS conv)
-py_test(test_recognize_digits_conv_cuda
-  SRCS test_recognize_digits.py
-  ARGS conv --use_cuda)
-py_test(test_recognize_digits_mlp_cpu_parallel
-  SRCS test_recognize_digits.py
-  ARGS mlp --parallel)
-py_test(test_recognize_digits_mlp_cuda_parallel
-  SRCS test_recognize_digits.py
-  ARGS mlp --use_cuda --parallel)
-py_test(test_recognize_digits_conv_cpu_parallel
-  SRCS test_recognize_digits.py
-  ARGS conv --parallel)
-py_test(test_recognize_digits_conv_cuda_parallel
-  SRCS test_recognize_digits.py
-  ARGS conv --use_cuda --parallel)
 
 # default test
 foreach(src ${TEST_OPS})
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index b4b6020f58..b8f55c813b 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -17,6 +17,7 @@ import paddle.v2.fluid as fluid
 import paddle.v2 as paddle
 import sys
 import numpy
+import unittest
 
 
 def parse_arg():
@@ -74,18 +75,18 @@ def conv_net(img, label):
     return loss_net(conv_pool_2, label)
 
 
-def train(args, save_dirname=None):
-    print("recognize digits with args: {0}".format(" ".join(sys.argv[1:])))
-
+def train(nn_type, use_cuda, parallel, save_dirname):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
     img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
-    if args.nn_type == 'mlp':
+    if nn_type == 'mlp':
         net_conf = mlp
     else:
         net_conf = conv_net
 
-    if args.parallel:
+    if parallel:
         places = fluid.layers.get_places()
         pd = fluid.layers.ParallelDo(places)
         with pd.do():
@@ -107,7 +108,7 @@ def train(args, save_dirname=None):
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
     optimizer.minimize(avg_loss)
 
-    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
@@ -147,13 +148,14 @@ def train(args, save_dirname=None):
                         'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
                         format(pass_id, batch_id + 1,
                                float(avg_loss_val), float(acc_val)))
+    raise AssertionError("Loss of recognize digits is too large")
 
 
-def infer(args, save_dirname=None):
+def infer(use_cuda, save_dirname=None):
     if save_dirname is None:
         return
 
-    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
 
     # Use fluid.io.load_inference_model to obtain the inference program desc,
@@ -174,11 +176,48 @@ def infer(args, save_dirname=None):
     print("infer results: ", results[0])
 
 
-if __name__ == '__main__':
-    args = parse_arg()
-    if not args.use_cuda and not args.parallel:
-        save_dirname = "recognize_digits_" + args.nn_type + ".inference.model"
+def main(use_cuda, parallel, nn_type):
+    if not use_cuda and not parallel:
+        save_dirname = "recognize_digits_" + nn_type + ".inference.model"
     else:
         save_dirname = None
-    train(args, save_dirname)
-    infer(args, save_dirname)
+
+    train(
+        nn_type=nn_type,
+        use_cuda=use_cuda,
+        parallel=parallel,
+        save_dirname=save_dirname)
+    infer(use_cuda=use_cuda, save_dirname=save_dirname)
+
+
+class TestRecognizeDigits(unittest.TestCase):
+    pass
+
+
+def inject_test_method(use_cuda, parallel, nn_type):
+    def __impl__(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                main(use_cuda, parallel, nn_type)
+
+    fn = 'test_{0}_{1}_{2}'.format(nn_type, 'cuda'
+                                   if use_cuda else 'cpu', 'parallel'
+                                   if parallel else 'normal')
+
+    setattr(TestRecognizeDigits, fn, __impl__)
+
+
+def inject_all_tests():
+    for use_cuda in (False, True):
+        for parallel in (False, True):
+            for nn_type in ('mlp', 'conv'):
+                inject_test_method(use_cuda, parallel, nn_type)
+
+
+inject_all_tests()
+
+if __name__ == '__main__':
+    unittest.main()

From c98b40e4783a9222674c280c957837b1255c2844 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 16:06:40 +0800
Subject: [PATCH 181/314] clean code

---
 benchmark/cluster/vgg16/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 333e14250b..725ce59025 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -55,7 +55,7 @@
 | PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
 | PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
-| PaddlePaddle v2 | 412.2 | 368.4 | 346.8 | 283.2 |
+| PaddlePaddle v2 | - | - | 729.7 | - |
 | TensorFlow | - | - | - | - |
 
 

From 3b87080a4e3cf37bc119fa31511812e26d854e86 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Thu, 1 Feb 2018 16:21:31 +0800
Subject: [PATCH 182/314] Make NMT as normal python unittests

---
 paddle/operators/while_op.cc                  |   2 +
 python/paddle/v2/fluid/layers/tensor.py       |   4 +-
 .../tests/book/test_machine_translation.py    | 103 ++++++++++++++----
 3 files changed, 85 insertions(+), 24 deletions(-)

diff --git a/paddle/operators/while_op.cc b/paddle/operators/while_op.cc
index 2fdd25dbbe..6ffbc19517 100644
--- a/paddle/operators/while_op.cc
+++ b/paddle/operators/while_op.cc
@@ -53,6 +53,8 @@ class WhileOp : public framework::OperatorBase {
     auto step_scopes =
         scope.FindVar(Output(kStepScopes))->GetMutable<StepScopeVar>();
 
+    PADDLE_ENFORCE(platform::is_cpu_place(cond.place()),
+                   "Condition of while op must in CPU memory.");
     while (cond.data<bool>()[0]) {
       auto &current_scope = scope.NewScope();
       step_scopes->push_back(&current_scope);
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index c435c5206d..8460af2a08 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -295,7 +295,7 @@ def fill_constant_batch_size_like(input,
     return out
 
 
-def ones(shape, dtype):
+def ones(shape, dtype, force_cpu=False):
     """
     **ones**
 
@@ -319,7 +319,7 @@ def ones(shape, dtype):
     return fill_constant(value=1.0, **locals())
 
 
-def zeros(shape, dtype):
+def zeros(shape, dtype, force_cpu=False):
     """
     **zeros**
 
diff --git a/python/paddle/v2/fluid/tests/book/test_machine_translation.py b/python/paddle/v2/fluid/tests/book/test_machine_translation.py
index 82b760d693..5716ddd3dd 100644
--- a/python/paddle/v2/fluid/tests/book/test_machine_translation.py
+++ b/python/paddle/v2/fluid/tests/book/test_machine_translation.py
@@ -11,21 +11,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import contextlib
 
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
-import paddle.v2.fluid.core as core
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as pd
 from paddle.v2.fluid.executor import Executor
+import unittest
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
-src_dict, trg_dict = paddle.dataset.wmt14.get_dict(dict_size)
 hidden_dim = 32
 word_dim = 16
-IS_SPARSE = True
 batch_size = 2
 max_length = 8
 topk_size = 50
@@ -34,10 +33,8 @@ beam_size = 2
 
 decoder_size = hidden_dim
 
-place = core.CPUPlace()
 
-
-def encoder():
+def encoder(is_sparse):
     # encoder
     src_word_id = pd.data(
         name="src_word_id", shape=[1], dtype='int64', lod_level=1)
@@ -45,7 +42,7 @@ def encoder():
         input=src_word_id,
         size=[dict_size, word_dim],
         dtype='float32',
-        is_sparse=IS_SPARSE,
+        is_sparse=is_sparse,
         param_attr=fluid.ParamAttr(name='vemb'))
 
     fc1 = pd.fc(input=src_embedding, size=hidden_dim * 4, act='tanh')
@@ -54,7 +51,7 @@ def encoder():
     return encoder_out
 
 
-def decoder_train(context):
+def decoder_train(context, is_sparse):
     # decoder
     trg_language_word = pd.data(
         name="target_language_word", shape=[1], dtype='int64', lod_level=1)
@@ -62,7 +59,7 @@ def decoder_train(context):
         input=trg_language_word,
         size=[dict_size, word_dim],
         dtype='float32',
-        is_sparse=IS_SPARSE,
+        is_sparse=is_sparse,
         param_attr=fluid.ParamAttr(name='vemb'))
 
     rnn = pd.DynamicRNN()
@@ -82,10 +79,10 @@ def decoder_train(context):
     return rnn()
 
 
-def decoder_decode(context):
+def decoder_decode(context, is_sparse):
     init_state = context
     array_len = pd.fill_constant(shape=[1], dtype='int64', value=max_length)
-    counter = pd.zeros(shape=[1], dtype='int64')
+    counter = pd.zeros(shape=[1], dtype='int64', force_cpu=True)
 
     # fill the first element with init_state
     state_array = pd.create_array('float32')
@@ -117,7 +114,7 @@ def decoder_decode(context):
             input=pre_ids,
             size=[dict_size, word_dim],
             dtype='float32',
-            is_sparse=IS_SPARSE)
+            is_sparse=is_sparse)
 
         # use rnn unit to update rnn
         current_state = pd.fc(input=[pre_ids_emb, pre_state_expanded],
@@ -150,7 +147,7 @@ def decoder_decode(context):
 
 
 def set_init_lod(data, lod, place):
-    res = core.LoDTensor()
+    res = fluid.LoDTensor()
     res.set(data, place)
     res.set_lod(lod)
     return res
@@ -165,15 +162,19 @@ def to_lodtensor(data, place):
         lod.append(cur_len)
     flattened_data = np.concatenate(data, axis=0).astype("int64")
     flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = core.LoDTensor()
+    res = fluid.LoDTensor()
     res.set(flattened_data, place)
     res.set_lod([lod])
     return res
 
 
-def train_main():
-    context = encoder()
-    rnn_out = decoder_train(context)
+def train_main(use_cuda, is_sparse):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    context = encoder(is_sparse)
+    rnn_out = decoder_train(context, is_sparse)
     label = pd.data(
         name="target_language_next_word", shape=[1], dtype='int64', lod_level=1)
     cost = pd.cross_entropy(input=rnn_out, label=label)
@@ -212,9 +213,13 @@ def train_main():
             batch_id += 1
 
 
-def decode_main():
-    context = encoder()
-    translation_ids, translation_scores = decoder_decode(context)
+def decode_main(use_cuda, is_sparse):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+    context = encoder(is_sparse)
+    translation_ids, translation_scores = decoder_decode(context, is_sparse)
 
     exe = Executor(place)
     exe.run(framework.default_startup_program())
@@ -250,6 +255,60 @@ def decode_main():
         break
 
 
+class TestMachineTranslation(unittest.TestCase):
+    pass
+
+
+@contextlib.contextmanager
+def scope_prog_guard():
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            yield
+
+
+def inject_test_train(use_cuda, is_sparse):
+    f_name = 'test_{0}_{1}_train'.format('cuda' if use_cuda else 'cpu', 'sparse'
+                                         if is_sparse else 'dense')
+
+    def f(*args):
+        with scope_prog_guard():
+            train_main(use_cuda, is_sparse)
+
+    setattr(TestMachineTranslation, f_name, f)
+
+
+def inject_test_decode(use_cuda, is_sparse, decorator=None):
+    f_name = 'test_{0}_{1}_decode'.format('cuda'
+                                          if use_cuda else 'cpu', 'sparse'
+                                          if is_sparse else 'dense')
+
+    def f(*args):
+        with scope_prog_guard():
+            decode_main(use_cuda, is_sparse)
+
+    if decorator is not None:
+        f = decorator(f)
+
+    setattr(TestMachineTranslation, f_name, f)
+
+
+for _use_cuda_ in (False, True):
+    for _is_sparse_ in (False, True):
+        inject_test_train(_use_cuda_, _is_sparse_)
+
+for _use_cuda_ in (False, True):
+    for _is_sparse_ in (False, True):
+
+        _decorator_ = None
+        if _use_cuda_:
+            _decorator_ = unittest.skip(
+                reason='Beam Search does not support CUDA!')
+
+        inject_test_decode(
+            is_sparse=_is_sparse_, use_cuda=_use_cuda_, decorator=_decorator_)
+
 if __name__ == '__main__':
-    # train_main()
-    decode_main()
+    unittest.main()

From 5530212defd0afd81e202f9e90a499823daf797f Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 16:33:03 +0800
Subject: [PATCH 183/314] add others

---
 benchmark/cluster/vgg16/README.md       | 4 +++-
 benchmark/cluster/vgg16/v2_pserver.yaml | 2 +-
 benchmark/cluster/vgg16/v2_trainer.yaml | 2 +-
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 725ce59025..b0bdc0288f 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -48,6 +48,8 @@
 
 ### different pserver number
 
+*The performance gap between Fuild and v2 comes from the network interference.*
+
 - Trainer Count: 60
 - Batch Size: 128
 - Metrics: mini-batch / sec
@@ -55,7 +57,7 @@
 | PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
 | PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
-| PaddlePaddle v2 | - | - | 729.7 | - |
+| PaddlePaddle v2 | 593.4 | 791.3 | 729.7 | 821.7 |
 | TensorFlow | - | - | - | - |
 
 
diff --git a/benchmark/cluster/vgg16/v2_pserver.yaml b/benchmark/cluster/vgg16/v2_pserver.yaml
index 935cf0be3c..dd1271e0cf 100644
--- a/benchmark/cluster/vgg16/v2_pserver.yaml
+++ b/benchmark/cluster/vgg16/v2_pserver.yaml
@@ -29,7 +29,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "python -u train.py"
+          value: "python train.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT
diff --git a/benchmark/cluster/vgg16/v2_trainer.yaml b/benchmark/cluster/vgg16/v2_trainer.yaml
index 5189009f3e..997bbc81c9 100644
--- a/benchmark/cluster/vgg16/v2_trainer.yaml
+++ b/benchmark/cluster/vgg16/v2_trainer.yaml
@@ -30,7 +30,7 @@ spec:
         - name: TOPOLOGY
           value: ""
         - name: ENTRY
-          value: "cd /workspace && MKL_NUM_THREADS=1 python -u /workspace/vgg16_v2.py"
+          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16_v2.py"
         - name: TRAINER_PACKAGE
           value: "/workspace"
         - name: PADDLE_INIT_PORT

From ccef94a376aed4bc8576597f05fc8b00e37ab999 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 16:42:39 +0800
Subject: [PATCH 184/314] add comments

---
 benchmark/cluster/vgg16/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index b0bdc0288f..0d525e9522 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -48,18 +48,18 @@
 
 ### different pserver number
 
-*The performance gap between Fuild and v2 comes from the network interference.*
-
 - Trainer Count: 60
 - Batch Size: 128
 - Metrics: mini-batch / sec
 
 | PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid | 589.1 | 592.6 | 656.4 | 655.8 |
+| PaddlePaddle Fluid(should fixed in next PR) | 589.1 | 592.6 | 656.4 | 655.8 |
 | PaddlePaddle v2 | 593.4 | 791.3 | 729.7 | 821.7 |
 | TensorFlow | - | - | - | - |
 
+*The performance gap between Fuild and v2 comes from the network interference.*
+
 
 ## Steps to run the performance test
 

From 00b9aed0060acd983dce1d3cd1db8a859ec21219 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 16:54:53 +0800
Subject: [PATCH 185/314] fix typo

---
 benchmark/cluster/vgg16/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 0d525e9522..27eb265ce4 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -54,7 +54,7 @@
 
 | PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
-| PaddlePaddle Fluid(should fixed in next PR) | 589.1 | 592.6 | 656.4 | 655.8 |
+| PaddlePaddle Fluid(should fix in next PR) | 589.1 | 592.6 | 656.4 | 655.8 |
 | PaddlePaddle v2 | 593.4 | 791.3 | 729.7 | 821.7 |
 | TensorFlow | - | - | - | - |
 

From 93cab64185edf722dc493d1a00db5032014d836e Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 1 Feb 2018 17:38:57 +0800
Subject: [PATCH 186/314] Complete CreateRandomReaderOp

---
 paddle/framework/reader.h            | 37 +++++++-----
 paddle/operators/create_reader_op.cc | 90 ++++++++++++++++++++++++++++
 2 files changed, 111 insertions(+), 16 deletions(-)
 create mode 100644 paddle/operators/create_reader_op.cc

diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 3954a1bea8..0669a7c7c7 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -33,8 +33,6 @@ class ReaderBase {
 
 class FileReader : public ReaderBase {
  public:
-  explicit FileReader(const std::vector<DDim>& shapes) : shapes_(shapes) {}
-
   DDim shape(size_t idx) const override;
   std::vector<DDim> shapes() const override { return shapes_; }
 
@@ -44,8 +42,6 @@ class FileReader : public ReaderBase {
 
 class ReaderDecorator : public ReaderBase {
  public:
-  explicit ReaderDecorator(ReaderBase* reader) : reader_(reader) {}
-
   bool HasNext() const override { return reader_->HasNext(); }
 
   DDim shape(size_t idx) const override { return reader_->shape(idx); }
@@ -60,19 +56,19 @@ class ReaderDecorator : public ReaderBase {
 template <typename T>
 class RandomReader : public FileReader {
  public:
-  RandomReader(const std::vector<DDim>& shapes, float min, float max)
-      : FileReader(shapes), min_(min), max_(max) {
+  void Initialize(const std::vector<DDim>& shapes, float min, float max) {
     PADDLE_ENFORCE_LE(min, max,
                       "'min' should be less than or equal to 'max'.(%f vs %f)",
                       min, max);
+    shapes_ = shapes;
+    min_ = min;
+    max_ = max;
+    unsigned int seed = std::random_device()();
+    engine_.seed(seed);
+    dist_ = std::uniform_real_distribution<float>(min_, max_);
   }
 
   std::vector<LoDTensor> ReadNext() override {
-    std::minstd_rand engine;
-    unsigned int seed = std::random_device()();
-    engine.seed(seed);
-    std::uniform_real_distribution<float> dist(min_, max_);
-
     std::vector<LoDTensor> res;
     res.reserve(shapes_.size());
     for (const DDim& shape : shapes_) {
@@ -85,7 +81,7 @@ class RandomReader : public FileReader {
       T* data = out.mutable_data<T>(platform::CPUPlace());
       int64_t numel = product(shape);
       for (int64_t i = 0; i < numel; ++i) {
-        data[i] = dist(engine);
+        data[i] = dist_(engine_);
       }
       res.push_back(out);
     }
@@ -97,16 +93,21 @@ class RandomReader : public FileReader {
  private:
   float min_;
   float max_;
+  std::minstd_rand engine_;
+  std::uniform_real_distribution<float> dist_;
 };
 
 // decorators
 
 class ShuffleReader : public ReaderDecorator {
  public:
-  ShuffleReader(ReaderBase* reader, int buffer_size)
-      : ReaderDecorator(reader), buffer_size_(buffer_size), iteration_pos_(0) {
+  void Initialize(ReaderBase* reader, int buffer_size) {
+    reader_ = reader;
+    buffer_size_ = buffer_size;
+    iteration_pos_ = 0;
     buffer_.reserve(buffer_size);
   }
+
   std::vector<LoDTensor> ReadNext() override;
 
  private:
@@ -117,8 +118,12 @@ class ShuffleReader : public ReaderDecorator {
 
 class BatchReader : public ReaderDecorator {
  public:
-  BatchReader(ReaderBase* reader, int batch_size)
-      : ReaderDecorator(reader), batch_size_(batch_size) {}
+  void Initialize(ReaderBase* reader, int batch_size) {
+    reader_ = reader;
+    batch_size_ = batch_size;
+    buffer_.reserve(batch_size_);
+  }
+
   std::vector<LoDTensor> ReadNext() override;
 
  private:
diff --git a/paddle/operators/create_reader_op.cc b/paddle/operators/create_reader_op.cc
new file mode 100644
index 0000000000..abdc12087e
--- /dev/null
+++ b/paddle/operators/create_reader_op.cc
@@ -0,0 +1,90 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/reader.h"
+
+namespace paddle {
+namespace operators {
+
+// general infershape
+class CreateReaderInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of CreateReaderOp should not be null.");
+  }
+};
+
+template <typename T>
+class CreateRandomReaderOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+  void Run(const framework::Scope& scope,
+           const platform::Place& dev_place) const override {
+    const auto& shape_concat = Attr<std::vector<int>>("shape_concat");
+    const auto& ranks = Attr<std::vector<int>>("ranks");
+    PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0),
+                      int(shape_concat.size()),
+                      "The accumulate of all ranks should be equal to the "
+                      "shape concat's length.");
+    std::vector<framework::DDim> shapes;
+    int offset = 0;
+    for (int len : ranks) {
+      auto start_it = shape_concat.begin() + offset;
+      auto end_it = start_it + len;
+      shapes.push_back(
+          framework::make_ddim(std::vector<int>(start_it, end_it)));
+      offset += len;
+    }
+    auto* out = scope.FindVar(Output("Out"))
+                    ->template GetMutable<framework::RandomReader<T>>();
+    out->Initialize(shapes, Attr<float>("min"), Attr<float>("max"));
+  }
+};
+
+class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CreateRandomReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(op_proto, op_checker) {
+    AddOutput("Out", "(RandomReader) The created random reader.");
+    AddAttr<std::vector<int>>("shape_concat",
+                              "The concat of all data's shapes.");
+    AddAttr<std::vector<int>>(
+        "ranks",
+        "The ranks of each data."
+        "e.g."
+        "shape_concat = [2,3,4,5,6]"
+        "ranks = [3,2]"
+        "It means the reader will generate two data each time,"
+        "whose shapes are [2,3,4] and [5,6] respectively.");
+    AddAttr<float>("min", "The lower bound of reader's uniform distribution.");
+    AddAttr<float>("max", "The upper bound of reader's uniform distribution.");
+    AddComment(R"DOC(
+      CreateRandomReader Operator
+
+      This Op creates a random reader. 
+      The reader generates random data instead of really reading from files.
+      Generated data follow an uniform distribution between 'min' and 'max'.
+    )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(create_random_reader, ops::CreateRandomReaderOp<float>,
+                  ops::CreateReaderInferShape, ops::CreateRandomReaderOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
\ No newline at end of file

From 1830e2a01da528dc03ebba334bad9f418074b770 Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 17:43:03 +0800
Subject: [PATCH 187/314] fix bugs

---
 doc/howto/usage/cluster/cluster_train_cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/howto/usage/cluster/cluster_train_cn.md b/doc/howto/usage/cluster/cluster_train_cn.md
index c2fc86687d..0f3db59607 100644
--- a/doc/howto/usage/cluster/cluster_train_cn.md
+++ b/doc/howto/usage/cluster/cluster_train_cn.md
@@ -92,11 +92,11 @@ paddle.init(
 参数说明
 
 - use_gpu： **可选，默认False**，是否启用GPU训练
-- trainer_count：**必选，默认1**，当前训练任务trainer总个数
+- trainer_count：**必选，默认1**，当前trainer的线程数目
 - port：**必选，默认7164**，连接到pserver的端口
 - ports_num：**必选，默认1**，连接到pserver的端口个数
 - ports_num_for_sparse：**必选，默认0**，和pserver之间用于稀疏类型参数通信的端口个数
-- num_gradient_servers：**必选，默认1**，当前训练任务pserver总数
+- num_gradient_servers：**必选，默认1**，当前训练任务trainer总数
 - trainer_id：**必选，默认0**，每个trainer的唯一ID，从0开始的整数
 - pservers：**必选，默认127.0.0.1**，当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开
 

From 2fb280c9f2648d43499839d448e589e71b2b20b0 Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Thu, 1 Feb 2018 01:52:51 -0800
Subject: [PATCH 188/314] Revise python save load api using new load/save op
 (#7995)

* initial commit

* add get_parameters method

* add get_parameters method

* small fix

* address comments

* address comments

* address comments

* fix
---
 python/paddle/v2/fluid/framework.py |   3 +-
 python/paddle/v2/fluid/io.py        | 141 +++++++++++++++++++---------
 2 files changed, 97 insertions(+), 47 deletions(-)

diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index ae98e299a4..7f5187d299 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -489,7 +489,8 @@ class Operator(object):
         no_kernel_op_set = {
             'feed', 'fetch', 'save', 'load', 'recurrent',
             'rnn_memory_helper_grad', 'conditional_block', 'while', 'send',
-            'recv', 'listen_and_serv', 'parallel_do'
+            'recv', 'listen_and_serv', 'parallel_do', 'save_combine',
+            'load_combine'
         }
         if type not in no_kernel_op_set:
             self.desc.infer_var_type(self.block.desc)
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index d56ec45c53..613dc20b6e 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -46,6 +46,9 @@ def is_parameter(var):
 
 
 def is_persistable(var):
+    if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
+       var.desc.type() == core.VarDesc.VarType.FETCH_LIST:
+        return False
     return var.persistable
 
 
@@ -60,7 +63,12 @@ def _clone_var_in_block_(block, var):
         persistable=True)
 
 
-def save_vars(executor, dirname, main_program=None, vars=None, predicate=None):
+def save_vars(executor,
+              dirname,
+              main_program=None,
+              vars=None,
+              predicate=None,
+              save_file_name=None):
     """
     Save variables to directory by executor.
 
@@ -69,9 +77,12 @@ def save_vars(executor, dirname, main_program=None, vars=None, predicate=None):
     :param main_program: program. If vars is None, then filter all variables in this
     program which fit `predicate`. Default default_main_program.
     :param predicate: The Predicate describes a callable that returns a variable
-    as a bool. If it returns true, the variables will be saved.
-    :param vars: variables need to be saved. If specify vars, program & predicate
+    as a bool. If it returns true, the corresponding input variable will be saved.
+    :param vars: variables need to be saved. If vars is specified, program & predicate
     will be ignored
+    :param save_file_name: The name of a single file that all vars are saved to. 
+    If it is None, save variables to separate files.
+
     :return: None
     """
     if vars is None:
@@ -83,21 +94,39 @@ def save_vars(executor, dirname, main_program=None, vars=None, predicate=None):
         save_vars(
             executor,
             dirname=dirname,
-            vars=filter(predicate, main_program.list_vars()))
+            vars=filter(predicate, main_program.list_vars()),
+            save_file_name=save_file_name)
     else:
         save_program = Program()
         save_block = save_program.global_block()
+
+        save_var_map = {}
         for each_var in vars:
             new_var = _clone_var_in_block_(save_block, each_var)
+            if save_file_name is None:
+                save_block.append_op(
+                    type='save',
+                    inputs={'X': [new_var]},
+                    outputs={},
+                    attrs={'file_path': os.path.join(dirname, new_var.name)})
+            else:
+                save_var_map[new_var.name] = new_var
+
+        if save_file_name is not None:
+            save_var_list = []
+            for name in sorted(save_var_map.keys()):
+                save_var_list.append(save_var_map[name])
+
             save_block.append_op(
-                type='save',
-                inputs={'X': [new_var]},
+                type='save_combine',
+                inputs={'X': save_var_list},
                 outputs={},
-                attrs={'file_path': os.path.join(dirname, new_var.name)})
+                attrs={'file_path': os.path.join(dirname, save_file_name)})
+
         executor.run(save_program)
 
 
-def save_params(executor, dirname, main_program=None):
+def save_params(executor, dirname, main_program=None, save_file_name=None):
     """
     Save all parameters to directory with executor.
     """
@@ -106,10 +135,12 @@ def save_params(executor, dirname, main_program=None):
         dirname=dirname,
         main_program=main_program,
         vars=None,
-        predicate=is_parameter)
+        predicate=is_parameter,
+        save_file_name=save_file_name)
 
 
-def save_persistables(executor, dirname, main_program=None):
+def save_persistables(executor, dirname, main_program=None,
+                      save_file_name=None):
     """
     Save all persistables to directory with executor.
     """
@@ -118,21 +149,30 @@ def save_persistables(executor, dirname, main_program=None):
         dirname=dirname,
         main_program=main_program,
         vars=None,
-        predicate=is_persistable)
+        predicate=is_persistable,
+        save_file_name=save_file_name)
 
 
-def load_vars(executor, dirname, main_program=None, vars=None, predicate=None):
+def load_vars(executor,
+              dirname,
+              main_program=None,
+              vars=None,
+              predicate=None,
+              load_file_name=None):
     """
     Load variables from directory by executor.
 
-    :param executor: executor that save variable
+    :param executor: executor that load variable
     :param dirname: directory path
     :param main_program: program. If vars is None, then filter all variables in this
     program which fit `predicate`. Default default_main_program().
     :param predicate: The Predicate describes a callable that returns a variable
-    as a bool. If it returns true, the variables will be loaded.
-    :param vars: variables need to be loaded. If specify vars, program &
+    as a bool. If it returns true, the corresponding input variable will be loaded.
+    :param vars: variables need to be loaded. If vars is specified, program &
     predicate will be ignored
+    :param load_file_name: The name of the single file that all vars are loaded from.   
+    If it is None, load variables from separate files.
+
     :return: None
     """
     if vars is None:
@@ -144,23 +184,40 @@ def load_vars(executor, dirname, main_program=None, vars=None, predicate=None):
         load_vars(
             executor,
             dirname=dirname,
-            vars=filter(predicate, main_program.list_vars()))
+            vars=filter(predicate, main_program.list_vars()),
+            load_file_name=load_file_name)
     else:
         load_prog = Program()
         load_block = load_prog.global_block()
+
+        load_var_map = {}
         for each_var in vars:
             assert isinstance(each_var, Variable)
             new_var = _clone_var_in_block_(load_block, each_var)
+            if load_file_name is None:
+                load_block.append_op(
+                    type='load',
+                    inputs={},
+                    outputs={'Out': [new_var]},
+                    attrs={'file_path': os.path.join(dirname, new_var.name)})
+            else:
+                load_var_map[new_var.name] = new_var
+
+        if load_file_name is not None:
+            load_var_list = []
+            for name in sorted(load_var_map.keys()):
+                load_var_list.append(load_var_map[name])
+
             load_block.append_op(
-                type='load',
+                type='load_combine',
                 inputs={},
-                outputs={"Out": [new_var]},
-                attrs={'file_path': os.path.join(dirname, new_var.name)})
+                outputs={"Out": load_var_list},
+                attrs={'file_path': os.path.join(dirname, load_file_name)})
 
         executor.run(load_prog)
 
 
-def load_params(executor, dirname, main_program=None):
+def load_params(executor, dirname, main_program=None, load_file_name=None):
     """
     load all parameters from directory by executor.
     """
@@ -168,10 +225,12 @@ def load_params(executor, dirname, main_program=None):
         executor,
         dirname=dirname,
         main_program=main_program,
-        predicate=is_parameter)
+        predicate=is_parameter,
+        load_file_name=load_file_name)
 
 
-def load_persistables(executor, dirname, main_program=None):
+def load_persistables(executor, dirname, main_program=None,
+                      load_file_name=None):
     """
     load all persistables from directory by executor.
     """
@@ -179,7 +238,8 @@ def load_persistables(executor, dirname, main_program=None):
         executor,
         dirname=dirname,
         main_program=main_program,
-        predicate=is_persistable)
+        predicate=is_persistable,
+        load_file_name=load_file_name)
 
 
 def get_inference_program(target_vars, main_program=None):
@@ -238,7 +298,8 @@ def save_inference_model(dirname,
                          feeded_var_names,
                          target_vars,
                          executor,
-                         main_program=None):
+                         main_program=None,
+                         save_file_name=None):
     """
     Build a model especially for inference,
     and save it to directory by the executor.
@@ -249,6 +310,8 @@ def save_inference_model(dirname,
     :param executor: executor that save inference model
     :param main_program: original program, which will be pruned to build the inference model.
             Default default_main_program().
+    :param save_file_name: The name of a single file that all parameters are saved to. 
+    If it is None, save parameters to separate files.
 
     :return: None
     """
@@ -283,25 +346,7 @@ def save_inference_model(dirname,
     with open(model_file_name, "wb") as f:
         f.write(inference_program.desc.serialize_to_string())
 
-    save_params(executor, dirname, main_program)
-
-
-def load_persistables_if_exist(executor, dirname, main_program=None):
-    filenames = next(os.walk(dirname))[2]
-    filenames = set(filenames)
-
-    def _is_presistable_and_exist_(var):
-        if not is_persistable(var):
-            return False
-        else:
-            return var.name in filenames
-
-    load_vars(
-        executor,
-        dirname,
-        main_program=main_program,
-        vars=None,
-        predicate=_is_presistable_and_exist_)
+    save_persistables(executor, dirname, inference_program, save_file_name)
 
 
 def get_feed_targets_names(program):
@@ -322,13 +367,15 @@ def get_fetch_targets_names(program):
     return fetch_targets_names
 
 
-def load_inference_model(dirname, executor):
+def load_inference_model(dirname, executor, load_file_name=None):
     """
     Load inference model from a directory
 
     :param dirname: directory path
     :param executor: executor that load inference model
-
+    :param load_file_name: The name of the single file that all parameters are loaded from.   
+    If it is None, load parameters from separate files.
+    
     :return: [program, feed_target_names, fetch_targets]
              program: program especially for inference.
              feed_target_names: Names of variables that need to feed data
@@ -342,7 +389,7 @@ def load_inference_model(dirname, executor):
         program_desc_str = f.read()
 
     program = Program.parse_from_string(program_desc_str)
-    load_persistables_if_exist(executor, dirname, program)
+    load_persistables(executor, dirname, program, load_file_name)
 
     feed_target_names = get_feed_targets_names(program)
     fetch_target_names = get_fetch_targets_names(program)
@@ -359,6 +406,7 @@ def get_parameter_value(para, executor):
 
     :param executor: executor for retrieving the value
     :param para: the given parameter
+
     :return: the LoDTensor for the parameter
     """
     assert is_parameter(para)
@@ -377,6 +425,7 @@ def get_parameter_value_by_name(name, executor, program=None):
     :param name: the name of the parameter
     :param program: the program where the variable is found
             Default default_main_program().
+
     :return: the LoDTensor for the variable
     """
     if program is None:

From d2caf777ae0260d10ce3dfe9249d3ccf53a50641 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Thu, 1 Feb 2018 18:15:18 +0800
Subject: [PATCH 189/314] set FLAGS_warpctc_dir to pass the test_warpctc_op
 unit test

---
 cmake/generic.cmake                         | 4 ++--
 paddle/testing/paddle_gtest_main.cc         | 5 +++--
 python/paddle/v2/fluid/__init__.py          | 4 +++-
 python/paddle/v2/fluid/tests/CMakeLists.txt | 2 ++
 4 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/cmake/generic.cmake b/cmake/generic.cmake
index e10c0ecf68..33ef6860e1 100644
--- a/cmake/generic.cmake
+++ b/cmake/generic.cmake
@@ -470,10 +470,10 @@ function(py_test TARGET_NAME)
   if(WITH_TESTING)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS ARGS)
+    set(multiValueArgs SRCS DEPS ARGS ENVS)
     cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
-             COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python
+             COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python ${py_test_ENVS}
              ${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
              WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
   endif()
diff --git a/paddle/testing/paddle_gtest_main.cc b/paddle/testing/paddle_gtest_main.cc
index a2f21e37e4..fd8c4a69da 100644
--- a/paddle/testing/paddle_gtest_main.cc
+++ b/paddle/testing/paddle_gtest_main.cc
@@ -27,9 +27,10 @@ int main(int argc, char** argv) {
   }
 #ifdef PADDLE_WITH_CUDA
   new_argv.push_back(
-      strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"));
+      strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory,"
+             "warpctc_dir"));
 #else
-  new_argv.push_back(strdup("--tryfromenv=use_pinned_memory"));
+  new_argv.push_back(strdup("--tryfromenv=use_pinned_memory,warpctc_dir"));
 #endif
   int new_argc = static_cast<int>(new_argv.size());
   char** new_argv_address = new_argv.data();
diff --git a/python/paddle/v2/fluid/__init__.py b/python/paddle/v2/fluid/__init__.py
index f52346c3b5..3ee58393c7 100644
--- a/python/paddle/v2/fluid/__init__.py
+++ b/python/paddle/v2/fluid/__init__.py
@@ -76,7 +76,9 @@ def __bootstrap__():
 
     os.environ['OMP_NUM_THREADS'] = str(num_threads)
 
-    read_env_flags = ['use_pinned_memory', 'check_nan_inf', 'benchmark']
+    read_env_flags = [
+        'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir'
+    ]
     if core.is_compiled_with_cuda():
         read_env_flags += ['fraction_of_gpu_memory_to_use']
     core.init_gflags([sys.argv[0]] +
diff --git a/python/paddle/v2/fluid/tests/CMakeLists.txt b/python/paddle/v2/fluid/tests/CMakeLists.txt
index 628ce60b40..26a80abcb5 100644
--- a/python/paddle/v2/fluid/tests/CMakeLists.txt
+++ b/python/paddle/v2/fluid/tests/CMakeLists.txt
@@ -5,9 +5,11 @@ if(NOT WITH_DISTRIBUTE)
     list(REMOVE_ITEM TEST_OPS test_recv_op)
 endif(NOT WITH_DISTRIBUTE)
 
+list(REMOVE_ITEM TEST_OPS test_warpctc_op)
 foreach(src ${TEST_OPS})
     py_test(${src} SRCS ${src}.py)
 endforeach()
+py_test(test_warpctc_op SRCS test_warpctc_op.py ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR})
 
 add_subdirectory(book)
 add_subdirectory(book_distribute)

From 7c2d32b849a54e68492ae652eefa74f91cab6501 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 1 Feb 2018 18:50:30 +0800
Subject: [PATCH 190/314] update dockerfile

---
 benchmark/cluster/vgg16/Dockerfile     | 17 ++++++++++-------
 benchmark/cluster/vgg16/README.md      |  4 ++--
 benchmark/cluster/vgg16/vgg16_fluid.py |  7 ++++++-
 3 files changed, 18 insertions(+), 10 deletions(-)

diff --git a/benchmark/cluster/vgg16/Dockerfile b/benchmark/cluster/vgg16/Dockerfile
index c34f7e8fcf..54d1b09a0f 100644
--- a/benchmark/cluster/vgg16/Dockerfile
+++ b/benchmark/cluster/vgg16/Dockerfile
@@ -1,13 +1,16 @@
 FROM python:2.7.14
-ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
-ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
-RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev && \
-chmod +x /usr/bin/paddle_k8s
+RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev
 # NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
 #       so we must build one with distribute support to install in this image.
-ADD *.whl /
-RUN pip install /*.whl && rm -f /*.whl
-ENV LD_LIBRARY_PATH=/usr/local/lib
+RUN pip install paddlepaddle
 RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
+RUN pip uninstall -y paddlepaddle
 
+# below lines may change a lot for debugging
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
+ADD *.whl /
+RUN pip install /*.whl && rm -f /*.whl && \
+chmod +x /usr/bin/paddle_k8s
+ENV LD_LIBRARY_PATH=/usr/local/lib
 ADD vgg16_fluid.py vgg16_v2.py /workspace/
diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 69a242e305..6d309217f8 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -40,13 +40,13 @@
 - Batch Size: 128
 - Metrics: samples / sec
 
-| Trainer Counter | 20 | 40 | 80 | 100 |
+| Trainer Count | 20 | 40 | 80 | 100 |
 | -- | -- | -- | -- | -- |
 | PaddlePaddle Fluid | 263.29 (78.64%) | 518.80 (77.47%) | 836.26 (62.44%) | 1019.29 (60.89%) |
 | PaddlePaddle v2 (need more tests) | 326.85 (92.85%) | 534.58 (75.93%) | 853.30 (60.60%) | 1041.99 (59.20%) |
 | TensorFlow | - | - | - | - |
 
-### Different Pserver Number
+### Different Pserver Count
 
 - Trainer Count: 100
 - Batch Size: 128
diff --git a/benchmark/cluster/vgg16/vgg16_fluid.py b/benchmark/cluster/vgg16/vgg16_fluid.py
index 87a151db21..e89b96e4a6 100644
--- a/benchmark/cluster/vgg16/vgg16_fluid.py
+++ b/benchmark/cluster/vgg16/vgg16_fluid.py
@@ -50,6 +50,11 @@ parser.add_argument(
     default='CPU',
     choices=['CPU', 'GPU'],
     help="The device type.")
+parser.add_argument(
+    '--device_id',
+    type=int,
+    default=0,
+    help="The device id.")
 parser.add_argument(
     '--data_format',
     type=str,
@@ -135,7 +140,7 @@ def main():
     optimize_ops, params_grads = optimizer.minimize(avg_cost)
 
     # Initialize executor
-    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(args.device_id)
     exe = fluid.Executor(place)
 
     # test

From b26a5b5d044c0bfc7bdfbc803ea604449d6e575a Mon Sep 17 00:00:00 2001
From: gongweibao <weibao.gong@gmail.com>
Date: Thu, 1 Feb 2018 19:37:12 +0800
Subject: [PATCH 191/314] fix en doc

---
 doc/howto/usage/cluster/cluster_train_en.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/howto/usage/cluster/cluster_train_en.md b/doc/howto/usage/cluster/cluster_train_en.md
index 28cd1fa790..f9424f8f1a 100644
--- a/doc/howto/usage/cluster/cluster_train_en.md
+++ b/doc/howto/usage/cluster/cluster_train_en.md
@@ -95,11 +95,11 @@ paddle.init(
 Parameter Description
 
 - use_gpu: **optional, default False**, set to "True" to enable GPU training.
-- trainer_count: **required, default 1**, total count of trainers in the training job.
+- trainer_count: **required, default 1**, number of threads in current trainer.
 - port: **required, default 7164**, port to connect to parameter server.
 - ports_num: **required, default 1**, number of ports for communication.
 - ports_num_for_sparse: **required, default 0**, number of ports for sparse type caculation.
-- num_gradient_servers: **required, default 1**, total number of gradient server.
+- num_gradient_servers: **required, default 1**, number of trainers in current job.
 - trainer_id: **required, default 0**, ID for every trainer, start from 0.
 - pservers: **required, default 127.0.0.1**, list of IPs of parameter servers, separated by ",".
 

From 52df85f4db27b0f4e9adeb6ffc7ca398473c4ba0 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 1 Feb 2018 20:35:41 +0800
Subject: [PATCH 192/314] fix style

---
 benchmark/cluster/vgg16/vgg16_fluid.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/benchmark/cluster/vgg16/vgg16_fluid.py b/benchmark/cluster/vgg16/vgg16_fluid.py
index e89b96e4a6..499e06ec42 100644
--- a/benchmark/cluster/vgg16/vgg16_fluid.py
+++ b/benchmark/cluster/vgg16/vgg16_fluid.py
@@ -50,11 +50,7 @@ parser.add_argument(
     default='CPU',
     choices=['CPU', 'GPU'],
     help="The device type.")
-parser.add_argument(
-    '--device_id',
-    type=int,
-    default=0,
-    help="The device id.")
+parser.add_argument('--device_id', type=int, default=0, help="The device id.")
 parser.add_argument(
     '--data_format',
     type=str,
@@ -140,7 +136,8 @@ def main():
     optimize_ops, params_grads = optimizer.minimize(avg_cost)
 
     # Initialize executor
-    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(args.device_id)
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(
+        args.device_id)
     exe = fluid.Executor(place)
 
     # test

From 47ebe435a79ab836649ba11c635129c8a6664ea1 Mon Sep 17 00:00:00 2001
From: dzhwinter <dongzhihong01@baidu.com>
Date: Thu, 1 Feb 2018 20:41:54 +0800
Subject: [PATCH 193/314] Fix/vector (#8045)

* "clean code"

* "clean code"
---
 paddle/framework/mixed_vector.h | 77 +++++++++++++--------------------
 1 file changed, 29 insertions(+), 48 deletions(-)

diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index 0e0e239586..85caac8dcd 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -34,18 +34,6 @@ namespace framework {
 
 template <typename T>
 class Vector : public std::vector<T> {
- public:
-  /* NOTE(dzhwinter):
-   * Data always store and modified on Host.
-   * If the data is modified when use cuda_data interface,
-   * You need to call the CopyFromCUDA explicitly to synchronize data.
-   *
-   */
-  enum class kDataPosition {
-    kDataOnHost = 0,
-    kDataOnDevice = 1,
-  };
-
  public:
   using std::vector<T>::vector;
 
@@ -55,11 +43,12 @@ class Vector : public std::vector<T> {
   virtual ~Vector() {
 #ifdef PADDLE_WITH_CUDA
     if (cuda_ptr_ != nullptr) {
-      memory::Free<platform::CUDAPlace>(place_, static_cast<void *>(cuda_ptr_));
+      memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
     }
 #endif
   }
 
+  /* Get device vector */
   T *cuda_data() {
     CopyToCUDA();
     PADDLE_ENFORCE_NOT_NULL(
@@ -67,81 +56,73 @@ class Vector : public std::vector<T> {
     return static_cast<T *>(cuda_ptr_);
   }
 
+  /* Get host vector */
   T *data() { return std::vector<T>::data(); }
-
   const T *data() const { return std::vector<T>::data(); }
 
+  /* Synchronize host vector to device vector */
   void CopyToCUDA();
-
+  /* Synchronize device vector to host vector */
   void CopyFromCUDA();
-
+  /* Switch device vector location */
   void CopyToPeer(platform::Place);
 
  private:
   void *cuda_ptr_ = nullptr;
-  size_t cuda_size_ = 0;
-  /*The DataPosition is unused now,
-    if we want support random access from cpu and cuda,
-    we need to overload all the vector method */
-
-  kDataPosition position_ = kDataPosition::kDataOnHost;
+  size_t cuda_size_ = 0;  // device vector numel
   platform::CUDAPlace place_;
 };
 
 template <typename T>
 void Vector<T>::CopyToCUDA() {
 #ifdef PADDLE_WITH_CUDA
-  if (cuda_ptr_ == nullptr) {
+  if (cuda_size_ < this->size()) {
+    if (cuda_ptr_ != nullptr) {
+      memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
+    }
     cuda_ptr_ =
         memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T));
   }
+  cuda_size_ = this->size();
   platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-  auto *cuda_ctx = pool.GetByPlace(place_);
-
-  memory::Copy(place_, static_cast<void *>(cuda_ptr_), platform::CPUPlace(),
+  auto *ctx = pool.GetByPlace(place_);
+  memory::Copy(place_, cuda_ptr_, platform::CPUPlace(),
                static_cast<const void *>(this->data()),
-               this->size() * sizeof(T), cuda_ctx->stream());
-  cuda_ctx->Wait();
-
-  cuda_size_ = this->size();
+               this->size() * sizeof(T), ctx->stream());
+  ctx->Wait();
 #endif
 }
 
 template <typename T>
 void Vector<T>::CopyFromCUDA() {
 #ifdef PADDLE_WITH_CUDA
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-  auto *cuda_ctx = pool.GetByPlace(place_);
   if (cuda_ptr_ == nullptr) {
-    LOG(WARNING) << "No uncommited cuda data.";
+    LOG(WARNING) << "No uncommitted cuda data.";
     return;
   }
   this->resize(cuda_size_);
+  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+  auto *ctx = pool.GetByPlace(place_);
   memory::Copy(platform::CPUPlace(), static_cast<void *>(this->data()), place_,
                static_cast<const void *>(cuda_ptr_), this->size() * sizeof(T),
-               cuda_ctx->stream());
-  cuda_ctx->Wait();
-
+               ctx->stream());
+  ctx->Wait();
 #endif
 }
 
 template <typename T>
 void Vector<T>::CopyToPeer(platform::Place peer_place) {
-  if (platform::is_cpu_place(peer_place)) {
-    return;
-  }
 #ifdef PADDLE_WITH_CUDA
-  auto *cuda_ctx = platform::DeviceContextPool::Instance().GetByPlace(place_);
-  void *peer_cuda_ptr_ = memory::Alloc<platform::CUDAPlace>(
+  auto *ctx = platform::DeviceContextPool::Instance().GetByPlace(place_);
+  void *peer_cuda_ptr = memory::Alloc<platform::CUDAPlace>(
       boost::get<platform::CUDAPlace>(peer_place), this->size() * sizeof(T));
-  memory::Copy(boost::get<platform::CUDAPlace>(peer_place),
-               static_cast<void *>(peer_cuda_ptr_), place_,
-               static_cast<const void *>(cuda_ptr_), this->size() * sizeof(T),
-               cuda_ctx->stream());
-  cuda_ctx->Wait();
-  memory::Free<platform::CUDAPlace>(place_, static_cast<void *>(cuda_ptr_));
+  memory::Copy(boost::get<platform::CUDAPlace>(peer_place), peer_cuda_ptr,
+               place_, cuda_ptr_, this->size() * sizeof(T), ctx->stream());
+  ctx->Wait();
+
+  memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
   place_ = boost::get<platform::CUDAPlace>(peer_place);
-  cuda_ptr_ = peer_cuda_ptr_;
+  cuda_ptr_ = peer_cuda_ptr;
 #endif
 }
 

From 1696cb0e510a8d52427b6ca96900bab4e03b5af1 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 1 Feb 2018 21:10:16 +0800
Subject: [PATCH 194/314] Complete CreateShuffleReaderOp

---
 paddle/framework/reader.h            | 41 +++++++++++++------
 paddle/operators/CMakeLists.txt      |  5 ++-
 paddle/operators/create_reader_op.cc | 59 +++++++++++++++++++++++++---
 3 files changed, 87 insertions(+), 18 deletions(-)

diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 0669a7c7c7..18a34bfd17 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -33,6 +33,10 @@ class ReaderBase {
 
 class FileReader : public ReaderBase {
  public:
+  explicit FileReader(const std::vector<DDim>& shapes) : shapes_(shapes) {
+    PADDLE_ENFORCE(!shapes_.empty());
+  }
+
   DDim shape(size_t idx) const override;
   std::vector<DDim> shapes() const override { return shapes_; }
 
@@ -42,6 +46,10 @@ class FileReader : public ReaderBase {
 
 class ReaderDecorator : public ReaderBase {
  public:
+  explicit ReaderDecorator(ReaderBase* reader) : reader_(reader) {
+    PADDLE_ENFORCE_NOT_NULL(reader_);
+  }
+
   bool HasNext() const override { return reader_->HasNext(); }
 
   DDim shape(size_t idx) const override { return reader_->shape(idx); }
@@ -56,13 +64,11 @@ class ReaderDecorator : public ReaderBase {
 template <typename T>
 class RandomReader : public FileReader {
  public:
-  void Initialize(const std::vector<DDim>& shapes, float min, float max) {
+  RandomReader(const std::vector<DDim>& shapes, float min, float max)
+      : FileReader(shapes), min_(min), max_(max) {
     PADDLE_ENFORCE_LE(min, max,
                       "'min' should be less than or equal to 'max'.(%f vs %f)",
                       min, max);
-    shapes_ = shapes;
-    min_ = min;
-    max_ = max;
     unsigned int seed = std::random_device()();
     engine_.seed(seed);
     dist_ = std::uniform_real_distribution<float>(min_, max_);
@@ -101,10 +107,8 @@ class RandomReader : public FileReader {
 
 class ShuffleReader : public ReaderDecorator {
  public:
-  void Initialize(ReaderBase* reader, int buffer_size) {
-    reader_ = reader;
-    buffer_size_ = buffer_size;
-    iteration_pos_ = 0;
+  ShuffleReader(ReaderBase* reader, int buffer_size)
+      : ReaderDecorator(reader), buffer_size_(buffer_size), iteration_pos_(0) {
     buffer_.reserve(buffer_size);
   }
 
@@ -118,9 +122,8 @@ class ShuffleReader : public ReaderDecorator {
 
 class BatchReader : public ReaderDecorator {
  public:
-  void Initialize(ReaderBase* reader, int batch_size) {
-    reader_ = reader;
-    batch_size_ = batch_size;
+  BatchReader(ReaderBase* reader, int batch_size)
+      : ReaderDecorator(reader), batch_size_(batch_size) {
     buffer_.reserve(batch_size_);
   }
 
@@ -131,5 +134,21 @@ class BatchReader : public ReaderDecorator {
   std::vector<std::vector<LoDTensor>> buffer_;
 };
 
+class ReaderHolder {
+ public:
+  void Reset(ReaderBase* reader) { reader_.reset(reader); }
+
+  ReaderBase* Get() const { return reader_.get(); }
+
+  std::vector<LoDTensor> ReadNext() { return reader_->ReadNext(); }
+  bool HasNext() const { return reader_->HasNext(); }
+
+  DDim shape(size_t idx) const { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const { return reader_->shapes(); }
+
+ private:
+  std::unique_ptr<ReaderBase> reader_;
+};
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index 48cf5816cc..3684eb0dcc 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -62,7 +62,7 @@ function(op_library TARGET)
     endif()
 
     # Define operators that don't need pybind here.
-    foreach(manual_pybind_op "net_op" "compare_op" "logical_op" "nccl_op" "tensor_array_read_write_op")
+    foreach(manual_pybind_op "net_op" "compare_op" "logical_op" "nccl_op" "tensor_array_read_write_op" "create_reader_op")
         if ("${TARGET}" STREQUAL "${manual_pybind_op}")
             set(pybind_flag 1)
         endif()
@@ -153,6 +153,7 @@ op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale math_function)
 op_library(cos_sim_op DEPS cos_sim_functor)
 op_library(parallel_do_op DEPS executor)
+op_library(create_reader_op DEPS reader)
 
 # Regist multiple Kernel to pybind
 if (WITH_GPU)
@@ -178,7 +179,7 @@ list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})
     op_library(${src})
 endforeach()
-file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
+file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\nUSE_NO_KERNEL_OP(create_random_reader);\n")
 
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
 
diff --git a/paddle/operators/create_reader_op.cc b/paddle/operators/create_reader_op.cc
index abdc12087e..29b487e10b 100644
--- a/paddle/operators/create_reader_op.cc
+++ b/paddle/operators/create_reader_op.cc
@@ -18,7 +18,7 @@
 namespace paddle {
 namespace operators {
 
-// general infershape
+// general infershape for file readers
 class CreateReaderInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
@@ -35,6 +35,7 @@ class CreateRandomReaderOp : public framework::OperatorBase {
            const platform::Place& dev_place) const override {
     const auto& shape_concat = Attr<std::vector<int>>("shape_concat");
     const auto& ranks = Attr<std::vector<int>>("ranks");
+    PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty());
     PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0),
                       int(shape_concat.size()),
                       "The accumulate of all ranks should be equal to the "
@@ -49,8 +50,9 @@ class CreateRandomReaderOp : public framework::OperatorBase {
       offset += len;
     }
     auto* out = scope.FindVar(Output("Out"))
-                    ->template GetMutable<framework::RandomReader<T>>();
-    out->Initialize(shapes, Attr<float>("min"), Attr<float>("max"));
+                    ->template GetMutable<framework::ReaderHolder>();
+    out->Reset(new framework::RandomReader<T>(shapes, Attr<float>("min"),
+                                              Attr<float>("max")));
   }
 };
 
@@ -58,7 +60,7 @@ class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   CreateRandomReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(op_proto, op_checker) {
-    AddOutput("Out", "(RandomReader) The created random reader.");
+    AddOutput("Out", "(ReaderHolder) The created random reader.");
     AddAttr<std::vector<int>>("shape_concat",
                               "The concat of all data's shapes.");
     AddAttr<std::vector<int>>(
@@ -81,10 +83,57 @@ class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+class CreateShuffleReaderInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Underlying_reader"),
+                   "Input(Underlying_reader) of CreateShuffleReaderOp should "
+                   "not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of CreateShuffleReaderOp should not be null.");
+  }
+};
+
+class CreateShuffleReaderOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+  void Run(const framework::Scope& scope,
+           const platform::Place& dev_place) const override {
+    const auto& underlying_reader = scope.FindVar(Input("Underlying_reader"))
+                                        ->Get<framework::ReaderHolder>();
+    auto* out = scope.FindVar(Output("Out"))
+                    ->template GetMutable<framework::ReaderHolder>();
+    out->Reset(new framework::ShuffleReader(underlying_reader.Get(),
+                                            Attr<int>("buffer_size")));
+  }
+};
+
+class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CreateShuffleReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(op_proto, op_checker) {
+    AddInput(
+        "Underlying_reader",
+        "(ReaderHolder) The underlying reader for creating a shuffle reader.");
+    AddOutput("Out", "(ReaderHolder) The created shuffle reader.");
+    AddAttr<int>("buffer_size", "The shuffle buffer size.").GreaterThan(0);
+    AddComment(R"DOC(
+      CreateShuffleReader Operator
+
+      A shuffle reader takes another reader as its 'underlying reader'
+      and output the underlying reader's outputs in a shuffled order. 
+    )DOC");
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(create_random_reader, ops::CreateRandomReaderOp<float>,
                   ops::CreateReaderInferShape, ops::CreateRandomReaderOpMaker,
-                  paddle::framework::EmptyGradOpMaker);
\ No newline at end of file
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OPERATOR(create_shuffle_reader, ops::CreateShuffleReaderOp,
+                  ops::CreateShuffleReaderInferShape,
+                  ops::CreateShuffleReaderOpMaker,
+                  paddle::framework::EmptyGradOpMaker);

From 3dfd1da138805e0c98be4c57f3ea73d62865cd18 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 1 Feb 2018 23:43:33 +0800
Subject: [PATCH 195/314] Complete CreateBatchReaderOp

---
 paddle/framework/reader.h            | 12 ++---
 paddle/operators/create_reader_op.cc | 71 +++++++++++++++++++++-------
 2 files changed, 61 insertions(+), 22 deletions(-)

diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 18a34bfd17..8275ea474b 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -44,9 +44,9 @@ class FileReader : public ReaderBase {
   std::vector<DDim> shapes_;
 };
 
-class ReaderDecorator : public ReaderBase {
+class DecoratedReader : public ReaderBase {
  public:
-  explicit ReaderDecorator(ReaderBase* reader) : reader_(reader) {
+  explicit DecoratedReader(ReaderBase* reader) : reader_(reader) {
     PADDLE_ENFORCE_NOT_NULL(reader_);
   }
 
@@ -105,10 +105,10 @@ class RandomReader : public FileReader {
 
 // decorators
 
-class ShuffleReader : public ReaderDecorator {
+class ShuffleReader : public DecoratedReader {
  public:
   ShuffleReader(ReaderBase* reader, int buffer_size)
-      : ReaderDecorator(reader), buffer_size_(buffer_size), iteration_pos_(0) {
+      : DecoratedReader(reader), buffer_size_(buffer_size), iteration_pos_(0) {
     buffer_.reserve(buffer_size);
   }
 
@@ -120,10 +120,10 @@ class ShuffleReader : public ReaderDecorator {
   size_t iteration_pos_;
 };
 
-class BatchReader : public ReaderDecorator {
+class BatchReader : public DecoratedReader {
  public:
   BatchReader(ReaderBase* reader, int batch_size)
-      : ReaderDecorator(reader), batch_size_(batch_size) {
+      : DecoratedReader(reader), batch_size_(batch_size) {
     buffer_.reserve(batch_size_);
   }
 
diff --git a/paddle/operators/create_reader_op.cc b/paddle/operators/create_reader_op.cc
index 29b487e10b..9cf27bbfc6 100644
--- a/paddle/operators/create_reader_op.cc
+++ b/paddle/operators/create_reader_op.cc
@@ -19,11 +19,22 @@ namespace paddle {
 namespace operators {
 
 // general infershape for file readers
-class CreateReaderInferShape : public framework::InferShapeBase {
+class CreateFileReaderInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of CreateReaderOp should not be null.");
+                   "The output file reader should not be null.");
+  }
+};
+
+// general infershape for decorated readers
+class CreateDecoratedReaderInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Underlying_reader"),
+                   "Input(Underlying_reader) should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "The output decorated reader should not be null.");
   }
 };
 
@@ -83,17 +94,6 @@ class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-class CreateShuffleReaderInferShape : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Underlying_reader"),
-                   "Input(Underlying_reader) of CreateShuffleReaderOp should "
-                   "not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of CreateShuffleReaderOp should not be null.");
-  }
-};
-
 class CreateShuffleReaderOp : public framework::OperatorBase {
  public:
   using framework::OperatorBase::OperatorBase;
@@ -121,7 +121,41 @@ class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
       CreateShuffleReader Operator
 
       A shuffle reader takes another reader as its 'underlying reader'
-      and output the underlying reader's outputs in a shuffled order. 
+      and yields the underlying reader's outputs in a shuffled order. 
+    )DOC");
+  }
+};
+
+class CreateBatchReaderOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+  void Run(const framework::Scope& scope,
+           const platform::Place& dev_place) const override {
+    const auto& underlying_reader = scope.FindVar(Input("Underlying_reader"))
+                                        ->Get<framework::ReaderHolder>();
+    auto* out = scope.FindVar(Output("Out"))
+                    ->template GetMutable<framework::ReaderHolder>();
+    out->Reset(new framework::BatchReader(underlying_reader.Get(),
+                                          Attr<int>("batch_size")));
+  }
+};
+
+class CreateBatchReaderOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CreateBatchReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(op_proto, op_checker) {
+    AddInput(
+        "Underlying_reader",
+        "(ReaderHolder) The underlying reader for creating a batch reader.");
+    AddOutput("Out", "(ReaderHolder) The created batch reader.");
+    AddAttr<int>("batch_size",
+                 "How many instances the batch reader yields each time.")
+        .GreaterThan(0);
+    AddComment(R"DOC(
+      CreateBatchReader Operator
+
+      A batch reader takes another reader as its 'underlying reader', 
+      gathers the underlying reader's outputs and then yields them in batches. 
     )DOC");
   }
 };
@@ -131,9 +165,14 @@ class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(create_random_reader, ops::CreateRandomReaderOp<float>,
-                  ops::CreateReaderInferShape, ops::CreateRandomReaderOpMaker,
+                  ops::CreateFileReaderInferShape,
+                  ops::CreateRandomReaderOpMaker,
                   paddle::framework::EmptyGradOpMaker);
 REGISTER_OPERATOR(create_shuffle_reader, ops::CreateShuffleReaderOp,
-                  ops::CreateShuffleReaderInferShape,
+                  ops::CreateDecoratedReaderInferShape,
                   ops::CreateShuffleReaderOpMaker,
                   paddle::framework::EmptyGradOpMaker);
+REGISTER_OPERATOR(create_batch_reader, ops::CreateBatchReaderOp,
+                  ops::CreateDecoratedReaderInferShape,
+                  ops::CreateBatchReaderOpMaker,
+                  paddle::framework::EmptyGradOpMaker);

From 84ded49d6632aec9733bbbcd242c539029711cd8 Mon Sep 17 00:00:00 2001
From: xzl <zlx_hg@163.com>
Date: Thu, 1 Feb 2018 23:46:43 +0800
Subject: [PATCH 196/314] fix comments

---
 paddle/operators/conv_op.h              |  3 +++
 paddle/operators/math/depthwise_conv.cu | 11 +++++++----
 paddle/operators/math/depthwise_conv.h  | 11 +++++++----
 3 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/paddle/operators/conv_op.h b/paddle/operators/conv_op.h
index 5b47eefb83..3c1d0e9c1c 100644
--- a/paddle/operators/conv_op.h
+++ b/paddle/operators/conv_op.h
@@ -361,6 +361,9 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {
     Tensor* output = context.Output<Tensor>("Output");
     output->mutable_data<T>(context.GetPlace());
 
+    PADDLE_ENFORCE_EQ(
+        output->dims()[1] % input->dims()[1], 0,
+        "The output channels must be a multiple of the input channels");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
diff --git a/paddle/operators/math/depthwise_conv.cu b/paddle/operators/math/depthwise_conv.cu
index 4aa38151e6..b9b958c92b 100644
--- a/paddle/operators/math/depthwise_conv.cu
+++ b/paddle/operators/math/depthwise_conv.cu
@@ -203,8 +203,9 @@ class DepthwiseConvFunctor<platform::CUDADeviceContext, T> {
  public:
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::Tensor& input,
-                  const framework::Tensor& filter, std::vector<int>& strides,
-                  std::vector<int>& paddings, framework::Tensor* output) {
+                  const framework::Tensor& filter,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings, framework::Tensor* output) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
     const int input_height = input.dims()[2];
@@ -244,7 +245,8 @@ class DepthwiseConvInputGradFunctor<platform::CUDADeviceContext, T> {
                   const framework::Tensor& input,
                   const framework::Tensor& filter,
                   const framework::Tensor& output_grad,
-                  std::vector<int>& strides, std::vector<int>& paddings,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings,
                   framework::Tensor* input_grad) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
@@ -284,7 +286,8 @@ class DepthwiseConvFilterGradFunctor<platform::CUDADeviceContext, T> {
   void operator()(const platform::CUDADeviceContext& context,
                   const framework::Tensor& input,
                   const framework::Tensor& output_grad,
-                  std::vector<int>& strides, std::vector<int>& paddings,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings,
                   framework::Tensor* filter_grad) {
     const int batch_size = input.dims()[0];
     const int input_channels = input.dims()[1];
diff --git a/paddle/operators/math/depthwise_conv.h b/paddle/operators/math/depthwise_conv.h
index 34eecca7b6..4708920bb4 100644
--- a/paddle/operators/math/depthwise_conv.h
+++ b/paddle/operators/math/depthwise_conv.h
@@ -29,8 +29,9 @@ template <typename DeviceContext, typename T>
 class DepthwiseConvFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor& input,
-                  const framework::Tensor& filter, std::vector<int>& strides,
-                  std::vector<int>& paddings, framework::Tensor* output);
+                  const framework::Tensor& filter,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings, framework::Tensor* output);
 };
 
 template <typename DeviceContext, typename T>
@@ -39,7 +40,8 @@ class DepthwiseConvInputGradFunctor {
   void operator()(const DeviceContext& context, const framework::Tensor& input,
                   const framework::Tensor& filter,
                   const framework::Tensor& output_grad,
-                  std::vector<int>& strides, std::vector<int>& paddings,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings,
                   framework::Tensor* input_grad);
 };
 
@@ -48,7 +50,8 @@ class DepthwiseConvFilterGradFunctor {
  public:
   void operator()(const DeviceContext& context, const framework::Tensor& input,
                   const framework::Tensor& output_grad,
-                  std::vector<int>& strides, std::vector<int>& paddings,
+                  const std::vector<int>& strides,
+                  const std::vector<int>& paddings,
                   framework::Tensor* filter_grad);
 };
 

From 53e697c11d30a84e59fab7d1c1d54718eed14f66 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 2 Feb 2018 00:06:46 +0800
Subject: [PATCH 197/314] refine code

---
 paddle/framework/reader.h | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 8275ea474b..f450e67689 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -66,9 +66,8 @@ class RandomReader : public FileReader {
  public:
   RandomReader(const std::vector<DDim>& shapes, float min, float max)
       : FileReader(shapes), min_(min), max_(max) {
-    PADDLE_ENFORCE_LE(min, max,
-                      "'min' should be less than or equal to 'max'.(%f vs %f)",
-                      min, max);
+    PADDLE_ENFORCE_LE(
+        min, max, "'min' shouldn't be greater than 'max'.(%f vs %f)", min, max);
     unsigned int seed = std::random_device()();
     engine_.seed(seed);
     dist_ = std::uniform_real_distribution<float>(min_, max_);
@@ -103,7 +102,7 @@ class RandomReader : public FileReader {
   std::uniform_real_distribution<float> dist_;
 };
 
-// decorators
+// decorated readers
 
 class ShuffleReader : public DecoratedReader {
  public:
@@ -134,6 +133,8 @@ class BatchReader : public DecoratedReader {
   std::vector<std::vector<LoDTensor>> buffer_;
 };
 
+// The ReaderHolder is used as readers' unified wrapper,
+// making it easier to access different type readers in Variables.
 class ReaderHolder {
  public:
   void Reset(ReaderBase* reader) { reader_.reset(reader); }

From dc8390d8c3038173d70d4c7cc9f4e76bb1ddc587 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Thu, 1 Feb 2018 13:46:35 -0800
Subject: [PATCH 198/314] initial commit

---
 .../tests/book/test_rnn_encoder_decoder.py    | 51 +++++++++++++++++--
 1 file changed, 47 insertions(+), 4 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index fdc6086176..593d0013c9 100644
--- a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -145,7 +145,7 @@ def seq_to_seq_net():
     cost = fluid.layers.cross_entropy(input=prediction, label=label)
     avg_cost = fluid.layers.mean(x=cost)
 
-    return avg_cost
+    return avg_cost, prediction
 
 
 def to_lodtensor(data, place):
@@ -163,8 +163,8 @@ def to_lodtensor(data, place):
     return res
 
 
-def main():
-    avg_cost = seq_to_seq_net()
+def train(save_dirname=None):
+    [avg_cost, prediction] = seq_to_seq_net()
 
     optimizer = fluid.optimizer.Adagrad(learning_rate=1e-4)
     optimizer.minimize(avg_cost)
@@ -196,9 +196,52 @@ def main():
             print('pass_id=' + str(pass_id) + ' batch=' + str(batch_id) +
                   " avg_cost=" + str(avg_cost_val))
             if batch_id > 3:
+                if save_dirname is not None:
+                    fluid.io.save_inference_model(save_dirname, [
+                        'source_sequence', 'target_sequence', 'label_sequence'
+                    ], [prediction], exe)
                 exit(0)
             batch_id += 1
 
 
+def inference(save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Use fluid.io.load_inference_model to obtain the inference program desc,
+    # the feed_target_names (the names of variables that will be feeded 
+    # data using feed operators), and the fetch_targets (variables that 
+    # we want to obtain data from using fetch operators).
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+    data = [[0, 1, 0, 1], [0, 1, 1, 0, 0, 1]]
+    word_data = to_lodtensor(data, place)
+    trg_word = to_lodtensor(data, place)
+    trg_word_next = to_lodtensor(data, place)
+
+    # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+    # and results will contain a list of data corresponding to fetch_targets.
+    print(feed_target_names)
+    assert feed_target_names[0] == 'source_sequence'
+    assert feed_target_names[1] == 'target_sequence'
+    assert feed_target_names[2] == 'label_sequence'
+    results = exe.run(inference_program,
+                      feed={
+                          feed_target_names[0]: word_data,
+                          feed_target_names[1]: trg_word,
+                          feed_target_names[2]: trg_word_next
+                      },
+                      fetch_list=fetch_targets)
+
+    print("Inference Shape: ", results[0].shape)
+    print("infer results: ", results[0])
+
+
 if __name__ == '__main__':
-    main()
+    save_dirname = "rnn_encoder_decoder.inference.model"
+    train(save_dirname)
+    infer(save_dirname)

From 6d8bc1378bddc16c713c2ddabc0f9579aa1ab325 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Thu, 1 Feb 2018 14:33:23 -0800
Subject: [PATCH 199/314] Adding an initial implementation for the unbuffered
 channel (#7984)

* Adding an initial implementation for the unbuffered channel

* Including atomic header

* update comment

* Adding the closed attribute

* Add comments

* Updated locking mechanism

* Add simple unbuffered test

* Enhance unit test and fix bug

* Add details
---
 paddle/framework/channel_test.cc              | 21 ++++
 paddle/framework/details/unbuffered_channel.h | 98 +++++++++++++++++--
 2 files changed, 112 insertions(+), 7 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 1510fb8abf..2efa086f00 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -78,3 +78,24 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
   t.join();
   delete ch;
 }
+
+TEST(Channel, SimpleUnbufferedChannelTest) {
+  auto ch = MakeChannel<int>(0);
+  unsigned sum_send = 0;
+  std::thread t([&]() {
+    for (int i = 0; i < 5; i++) {
+      ch->Send(&i);
+      sum_send += i;
+    }
+  });
+  for (int i = 0; i < 5; i++) {
+    int recv;
+    ch->Receive(&recv);
+    EXPECT_EQ(recv, i);
+  }
+
+  CloseChannel(ch);
+  t.join();
+  EXPECT_EQ(sum_send, 10U);
+  delete ch;
+}
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index cc2d2e587e..0dc5afd7e5 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <atomic>
 #include <condition_variable>
-#include <deque>
 #include <mutex>
 
 #include "paddle/framework/channel.h"
@@ -36,20 +36,104 @@ class UnBuffered : public paddle::framework::Channel<T> {
   virtual ~UnBuffered();
 
  private:
-  UnBuffered() {}
+  std::mutex mu_ch_;
+  // Mutex for readers and writers who are waiting for other reader
+  // and writer to complete execution
+  std::recursive_mutex mu_read_, mu_write_;
+  // reader_found_ is set true when a reader is ready to accept data
+  // writer_found_ is set true when a writer is ready to send data
+  // A transaction occurs only when both are true
+  std::atomic<bool> reader_found_{false}, writer_found_{false};
+  std::condition_variable cv_channel_;
+  std::condition_variable_any cv_reader_, cv_writer_;
+  T* item{nullptr};
+  std::atomic<bool> closed_{false};
+
+  UnBuffered() : closed_(false) {}
+
+  void NotifyAllParticipants(std::unique_lock<std::mutex>*);
 };
 
+// This function implements the concept of how data should
+// be sent from a writer to a reader.
+template <typename T>
+void UnBuffered<T>::Send(T* data) {
+  // Prevent other writers from entering
+  std::unique_lock<std::recursive_mutex> writer_lock(mu_write_);
+  writer_found_ = true;
+  std::unique_lock<std::recursive_mutex> cv_lock(mu_write_);
+  // If writer comes first, it should wait till a reader arrives
+  cv_writer_.wait(cv_lock,
+                  [this]() { return reader_found_ == true || closed_; });
+  cv_reader_.notify_one();
+  if (!closed_) {
+    std::unique_lock<std::mutex> channel_lock(mu_ch_);
+    item = data;
+    channel_lock.unlock();
+    cv_channel_.notify_one();
+    channel_lock.lock();
+    cv_channel_.wait(channel_lock,
+                     [this]() { return item == nullptr || closed_; });
+  }
+  writer_found_ = false;
+}
+
+// This function implements the concept of how
+// data that was sent by a writer is read from a reader.
 template <typename T>
-void UnBuffered<T>::Send(T* channel_element) {}
+void UnBuffered<T>::Receive(T* data) {
+  // Prevent other readers from entering
+  std::unique_lock<std::recursive_mutex> read_lock{mu_read_};
+  reader_found_ = true;
+  std::unique_lock<std::recursive_mutex> cv_lock{mu_read_};
+  // If reader comes first, it should wait till a writer arrives
+  cv_reader_.wait(cv_lock,
+                  [this]() { return writer_found_ == true || closed_; });
+  cv_writer_.notify_one();
+  if (!closed_) {
+    std::unique_lock<std::mutex> lock_ch{mu_ch_};
+    // Reader should wait for the writer to first write its data
+    cv_channel_.wait(lock_ch, [this]() { return item != nullptr || closed_; });
+    if (!closed_) {
+      *data = std::move(*item);
+      item = nullptr;
+      lock_ch.unlock();
+    }
+    cv_channel_.notify_one();
+  }
+  reader_found_ = false;
+}
 
+// This function implements the sequence of events
+// that take place once the channel is closed.
 template <typename T>
-void UnBuffered<T>::Receive(T*) {}
+void UnBuffered<T>::Close() {
+  std::unique_lock<std::mutex> lock(mu_ch_);
+  item = nullptr;
+  closed_ = true;
+  NotifyAllParticipants(&lock);
+}
 
+// This function implements the sequence of events
+// that are executed once the object of an UnBuffered
+// channel is destroyed.
 template <typename T>
-void UnBuffered<T>::Close() {}
+UnBuffered<T>::~UnBuffered() {
+  std::unique_lock<std::mutex> lock(mu_ch_);
+  item = nullptr;
+  closed_ = true;
+  NotifyAllParticipants(&lock);
+}
 
+// This function notifies all the readers, writers and
+// the channel condition variables.
 template <typename T>
-UnBuffered<T>::~UnBuffered() {}
+void UnBuffered<T>::NotifyAllParticipants(std::unique_lock<std::mutex>* lock) {
+  lock->unlock();
+  cv_writer_.notify_all();
+  cv_channel_.notify_all();
+  cv_reader_.notify_all();
+}
 
 }  // namespace details
 }  // namespace framework

From 148d35feb8fbd6c0cee7c66268d316e4274b8407 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Thu, 1 Feb 2018 16:10:45 -0800
Subject: [PATCH 200/314] Add unit test with less receivers, more senders for
 unbuffered channel. (#8060)

* Add unite test with less receivers, more senders

* Fixed the check
---
 paddle/framework/channel_test.cc | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 2efa086f00..020f806380 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -99,3 +99,29 @@ TEST(Channel, SimpleUnbufferedChannelTest) {
   EXPECT_EQ(sum_send, 10U);
   delete ch;
 }
+
+TEST(Channel, UnbufferedLessReceiveMoreSendTest) {
+  auto ch = MakeChannel<int>(0);
+  unsigned sum_send = 0;
+  // Send should block after three iterations
+  // since we only have three receivers.
+  std::thread t([&]() {
+    // Try to send more number of times
+    // than receivers
+    for (int i = 0; i < 4; i++) {
+      ch->Send(&i);
+      sum_send += i;
+    }
+  });
+  for (int i = 0; i < 3; i++) {
+    int recv;
+    ch->Receive(&recv);
+    EXPECT_EQ(recv, i);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.5 sec
+  EXPECT_EQ(sum_send, 3U);
+
+  CloseChannel(ch);
+  t.join();
+  delete ch;
+}

From f3415ec55e1daf437080d5ee2febb18b6bcb3a09 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Thu, 1 Feb 2018 21:53:16 +0800
Subject: [PATCH 201/314] Follow comments.

---
 paddle/operators/bipartite_match_op.cc        |  18 ++-
 paddle/operators/multiclass_nms_op.cc         | 104 ++++++++++--------
 .../v2/fluid/tests/test_bipartite_match_op.py |   4 +-
 .../v2/fluid/tests/test_multiclass_nms_op.py  |   2 +-
 4 files changed, 72 insertions(+), 56 deletions(-)

diff --git a/paddle/operators/bipartite_match_op.cc b/paddle/operators/bipartite_match_op.cc
index 83c8778fe4..1e6fa2091d 100644
--- a/paddle/operators/bipartite_match_op.cc
+++ b/paddle/operators/bipartite_match_op.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -28,12 +28,18 @@ class BipartiteMatchOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("DistMat"),
                    "Input(DistMat) of BipartiteMatch should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("ColToRowMatchIndices"),
+        "Output(ColToRowMatchIndices) of BipartiteMatch should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("ColToRowMatchDist"),
+        "Output(ColToRowMatchDist) of BipartiteMatch should not be null.");
 
     auto dims = ctx->GetInputDim("DistMat");
     PADDLE_ENFORCE_EQ(dims.size(), 2, "The rank of Input(DistMat) must be 2.");
 
     ctx->SetOutputDim("ColToRowMatchIndices", dims);
-    ctx->SetOutputDim("ColToRowMatchDis", dims);
+    ctx->SetOutputDim("ColToRowMatchDist", dims);
   }
 };
 
@@ -91,7 +97,7 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& context) const override {
     auto* dist_mat = context.Input<LoDTensor>("DistMat");
     auto* match_indices = context.Output<Tensor>("ColToRowMatchIndices");
-    auto* match_dist = context.Output<Tensor>("ColToRowMatchDis");
+    auto* match_dist = context.Output<Tensor>("ColToRowMatchDist");
 
     auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
 
@@ -148,13 +154,13 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
               "Otherwise, it means B[j] is matched to row "
               "ColToRowMatchIndices[i][j] in i-th instance. The row number of "
               "i-th instance is saved in ColToRowMatchIndices[i][j].");
-    AddOutput("ColToRowMatchDis",
+    AddOutput("ColToRowMatchDist",
               "(Tensor) A 2-D Tensor with shape [N, M] in float type. "
               "N is batch size. If ColToRowMatchIndices[i][j] is -1, "
-              "ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed "
+              "ColToRowMatchDist[i][j] is also -1.0. Otherwise, assumed "
               "ColToRowMatchIndices[i][j] = d, and the row offsets of each "
               "instance are called LoD. Then "
-              "ColToRowMatchDis[i][j] = DistMat[d+LoD[i]][j]");
+              "ColToRowMatchDist[i][j] = DistMat[d+LoD[i]][j]");
     AddComment(R"DOC(
 This operator is a greedy bipartite matching algorithm, which is used to
 obtain the matching with the maximum distance based on the input
diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index 4689306d24..cb38e9fa20 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -24,25 +24,33 @@ using LoDTensor = framework::LoDTensor;
 constexpr int64_t kOutputDim = 6;
 constexpr int64_t kBBoxSize = 4;
 
-class MulticlassNMSOp : public framework::OperatorWithKernel {
+class MultiClassNMSOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Bboxes"),
-                   "Input(Bboxes) of MulticlassNMS should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("BBoxes"),
+                   "Input(BBoxes) of MultiClassNMS should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Scores"),
-                   "Input(Scores) of MulticlassNMS should not be null.");
+                   "Input(Scores) of MultiClassNMS should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of MultiClassNMS should not be null.");
 
-    auto box_dims = ctx->GetInputDim("Bboxes");
+    auto box_dims = ctx->GetInputDim("BBoxes");
     auto score_dims = ctx->GetInputDim("Scores");
 
     PADDLE_ENFORCE_EQ(box_dims.size(), 2,
-                      "The rank of Input(Bboxes) must be 3.");
+                      "The rank of Input(BBoxes) must be 2.");
     PADDLE_ENFORCE_EQ(score_dims.size(), 3,
                       "The rank of Input(Scores) must be 3.");
-    PADDLE_ENFORCE_EQ(box_dims[1], 4);
-    PADDLE_ENFORCE_EQ(box_dims[0], score_dims[2]);
+    PADDLE_ENFORCE_EQ(box_dims[1], 4,
+                      "The 2nd dimension of Input(BBoxes) must be 4, "
+                      "represents the layout of coordinate "
+                      "[xmin, ymin, xmax, ymax]");
+    PADDLE_ENFORCE_EQ(box_dims[0], score_dims[2],
+                      "The 1st dimensiong of Input(BBoxes) must be equal to "
+                      "3rd dimension of Input(Scores), which represents the "
+                      "predicted bboxes.");
 
     // Here the box_dims[0] is not the real dimension of output.
     // It will be rewritten in the computing kernel.
@@ -86,15 +94,16 @@ static inline void GetMaxScoreIndex(
 template <class T>
 T BBoxArea(const T* box, const bool normalized) {
   if (box[2] < box[0] || box[3] < box[1]) {
-    // If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0.
-    return T(0.);
+    // If coordinate values are is invalid
+    // (e.g. xmax < xmin or ymax < ymin), return 0.
+    return static_cast<T>(0.);
   } else {
     const T w = box[2] - box[0];
     const T h = box[3] - box[1];
     if (normalized) {
       return w * h;
     } else {
-      // If bbox is not within range [0, 1].
+      // If coordinate values are not within range [0, 1].
       return (w + 1) * (h + 1);
     }
   }
@@ -121,7 +130,7 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
 }
 
 template <typename T>
-class MulticlassNMSKernel : public framework::OpKernel<T> {
+class MultiClassNMSKernel : public framework::OpKernel<T> {
  public:
   void NMSFast(const Tensor& bbox, const Tensor& scores,
                const T score_threshold, const T nms_threshold, const T eta,
@@ -163,10 +172,10 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
     }
   }
 
-  void MulticlassNMS(const framework::ExecutionContext& ctx,
+  void MultiClassNMS(const framework::ExecutionContext& ctx,
                      const Tensor& scores, const Tensor& bboxes,
-                     std::map<int, std::vector<int>>* indices,
-                     int* num_nmsed_out) const {
+                     std::map<int, std::vector<int>>& indices,
+                     int& num_nmsed_out) const {
     int64_t background_label = ctx.Attr<int>("background_label");
     int64_t nms_top_k = ctx.Attr<int>("nms_top_k");
     int64_t keep_top_k = ctx.Attr<int>("keep_top_k");
@@ -181,15 +190,15 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
       if (c == background_label) continue;
       Tensor score = scores.Slice(c, c + 1);
       NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
-              &((*indices)[c]));
-      num_det += (*indices)[c].size();
+              &(indices[c]));
+      num_det += indices[c].size();
     }
 
-    *num_nmsed_out = num_det;
+    num_nmsed_out = num_det;
     const T* scores_data = scores.data<T>();
     if (keep_top_k > -1 && num_det > keep_top_k) {
       std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
-      for (const auto& it : *indices) {
+      for (const auto& it : indices) {
         int label = it.first;
         const T* sdata = scores_data + label * predict_dim;
         const std::vector<int>& label_indices = it.second;
@@ -212,12 +221,12 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
         int idx = score_index_pairs[j].second.second;
         new_indices[label].push_back(idx);
       }
-      new_indices.swap(*indices);
-      *num_nmsed_out = keep_top_k;
+      new_indices.swap(indices);
+      num_nmsed_out = keep_top_k;
     }
   }
 
-  void MulticlassOutput(const Tensor& scores, const Tensor& bboxes,
+  void MultiClassOutput(const Tensor& scores, const Tensor& bboxes,
                         std::map<int, std::vector<int>>& selected_indices,
                         Tensor* outs) const {
     int predict_dim = scores.dims()[1];
@@ -229,23 +238,21 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
     for (const auto& it : selected_indices) {
       int label = it.first;
       const T* sdata = scores_data + label * predict_dim;
-      std::vector<int> indices = it.second;
+      const std::vector<int>& indices = it.second;
       for (int j = 0; j < indices.size(); ++j) {
         int idx = indices[j];
         const T* bdata = bboxes_data + idx * kBBoxSize;
         odata[count * kOutputDim] = label;           // label
         odata[count * kOutputDim + 1] = sdata[idx];  // score
-        odata[count * kOutputDim + 2] = bdata[0];    // xmin
-        odata[count * kOutputDim + 3] = bdata[1];    // ymin
-        odata[count * kOutputDim + 4] = bdata[2];    // xmax
-        odata[count * kOutputDim + 5] = bdata[3];    // ymax
+        // xmin, ymin, xmax, ymax
+        std::memcpy(odata + count * kOutputDim + 2, bdata, 4 * sizeof(T));
         count++;
       }
     }
   }
 
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* boxes = ctx.Input<Tensor>("Bboxes");
+    auto* boxes = ctx.Input<Tensor>("BBoxes");
     auto* scores = ctx.Input<Tensor>("Scores");
     auto* outs = ctx.Output<LoDTensor>("Out");
 
@@ -262,7 +269,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
       ins_score.Resize({class_num, predict_dim});
       std::map<int, std::vector<int>> indices;
       int num_nmsed_out = 0;
-      MulticlassNMS(ctx, ins_score, *boxes, &indices, &num_nmsed_out);
+      MultiClassNMS(ctx, ins_score, *boxes, indices, num_nmsed_out);
       all_indices.push_back(indices);
       batch_starts.push_back(batch_starts.back() + num_nmsed_out);
     }
@@ -280,7 +287,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
         int64_t e = batch_starts[i + 1];
         if (e > s) {
           Tensor out = outs->Slice(s, e);
-          MulticlassOutput(ins_score, *boxes, all_indices[i], &out);
+          MultiClassOutput(ins_score, *boxes, all_indices[i], &out);
         }
       }
     }
@@ -292,28 +299,31 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
   }
 };
 
-class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
+class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  MulticlassNMSOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+  MultiClassNMSOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("Bboxes",
-             "(Tensor) A 2-D Tensor with shape [M, 4] represents the location "
-             "predictions with M bboxes. 4 is the number of "
-             "each location coordinates.");
+    AddInput("BBoxes",
+             "(Tensor) A 2-D Tensor with shape [M, 4] represents the "
+             "predicted locations of M bounding bboxes. Each bounding box "
+             "has four coordinate values and the layout is "
+             "[xmin, ymin, xmax, ymax].");
     AddInput("Scores",
              "(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
-             "confidence predictions. N is the batch size, C is the class "
-             "number, M is number of predictions for each class, which is "
-             "the same with Bboxes.");
+             "predicted confidence predictions. N is the batch size, C is the "
+             "class number, M is number of bounding boxes. For each category "
+             "there are total M scores which corresponding M bounding boxes. "
+             " Please note, M is equal to the 1st dimension of BBoxes. ");
     AddAttr<int>(
         "background_label",
         "(int64_t, defalut: 0) "
-        "The index of background label, the background label will be ignored.")
+        "The index of background label, the background label will be ignored. "
+        "If set to -1, then all categories will be considered.")
         .SetDefault(0);
     AddAttr<float>("score_threshold",
                    "(float) "
-                   "Only consider detections whose confidences are larger than "
-                   "a threshold. If not provided, consider all boxes.");
+                   "Threshold to filter out bounding boxes with low "
+                   "confidence score. If not provided, consider all boxes.");
     AddAttr<int>("nms_top_k",
                  "(int64_t) "
                  "Maximum number of detections to be kept according to the "
@@ -368,8 +378,8 @@ value which is -1.
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(multiclass_nms, ops::MulticlassNMSOp,
-                  ops::MulticlassNMSOpMaker,
+REGISTER_OPERATOR(multiclass_nms, ops::MultiClassNMSOp,
+                  ops::MultiClassNMSOpMaker,
                   paddle::framework::EmptyGradOpMaker);
-REGISTER_OP_CPU_KERNEL(multiclass_nms, ops::MulticlassNMSKernel<float>,
-                       ops::MulticlassNMSKernel<double>);
+REGISTER_OP_CPU_KERNEL(multiclass_nms, ops::MultiClassNMSKernel<float>,
+                       ops::MultiClassNMSKernel<double>);
diff --git a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
index c35fb20b10..4943bbb338 100644
--- a/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
+++ b/python/paddle/v2/fluid/tests/test_bipartite_match_op.py
@@ -72,7 +72,7 @@ class TestBipartiteMatchOpWithLoD(OpTest):
         self.inputs = {'DistMat': (dist, lod)}
         self.outputs = {
             'ColToRowMatchIndices': (match_indices),
-            'ColToRowMatchDis': (match_dist),
+            'ColToRowMatchDist': (match_dist),
         }
 
     def test_check_output(self):
@@ -89,7 +89,7 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
         self.inputs = {'DistMat': dist}
         self.outputs = {
             'ColToRowMatchIndices': match_indices,
-            'ColToRowMatchDis': match_dist,
+            'ColToRowMatchDist': match_dist,
         }
 
     def test_check_output(self):
diff --git a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
index 3097b8388c..3b80d2359b 100644
--- a/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
+++ b/python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
@@ -190,7 +190,7 @@ class TestMulticlassNMSOp(OpTest):
         nmsed_outs = np.array(nmsed_outs).astype('float32')
 
         self.op_type = 'multiclass_nms'
-        self.inputs = {'Bboxes': boxes, 'Scores': scores}
+        self.inputs = {'BBoxes': boxes, 'Scores': scores}
         self.outputs = {'Out': (nmsed_outs, [lod])}
         self.attrs = {
             'background_label': 0,

From 4673a24bdad55f0d135107dd18de451f5a10dab3 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Fri, 2 Feb 2018 09:42:08 +0800
Subject: [PATCH 202/314] Add softmax into Python API.

---
 python/paddle/v2/fluid/layers/ops.py        | 1 +
 python/paddle/v2/fluid/tests/test_layers.py | 8 ++++++++
 2 files changed, 9 insertions(+)

diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py
index ee3172c7b8..c701e79ad2 100644
--- a/python/paddle/v2/fluid/layers/ops.py
+++ b/python/paddle/v2/fluid/layers/ops.py
@@ -59,6 +59,7 @@ __all__ = [
     'elementwise_pow',
     'clip',
     'clip_by_norm',
+    'softmax',
     'sequence_softmax',
 ] + __activations__
 
diff --git a/python/paddle/v2/fluid/tests/test_layers.py b/python/paddle/v2/fluid/tests/test_layers.py
index 3f54e28def..aea43c2517 100644
--- a/python/paddle/v2/fluid/tests/test_layers.py
+++ b/python/paddle/v2/fluid/tests/test_layers.py
@@ -223,6 +223,14 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(layers.sequence_softmax(x=seq))
         print(str(program))
 
+    def test_softmax(self):
+        program = Program()
+        with program_guard(program):
+            data = layers.data(name='data', shape=[10], dtype='float32')
+            hid = layers.fc(input=data, size=20)
+            self.assertIsNotNone(layers.softmax(x=hid))
+        print(str(program))
+
     def test_get_places(self):
         program = Program()
         with program_guard(program):

From 6695a204cd739a000ea1d647143d5145c0e6974f Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Wed, 10 Jan 2018 14:38:15 -0800
Subject: [PATCH 203/314] helper functions fetch_var and get_var

fetch_var for getting the values of a variable with given name
get_var for getting the Variable with given name
---
 python/paddle/v2/fluid/executor.py            | 48 ++++++++++++++-----
 python/paddle/v2/fluid/framework.py           | 20 ++++++++
 python/paddle/v2/fluid/layers/tensor.py       |  8 ++--
 .../paddle/v2/fluid/tests/test_fetch_var.py   | 23 +++++++++
 4 files changed, 85 insertions(+), 14 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_fetch_var.py

diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py
index 9f48815b8b..af69ce2abc 100644
--- a/python/paddle/v2/fluid/executor.py
+++ b/python/paddle/v2/fluid/executor.py
@@ -17,7 +17,9 @@ import contextlib
 from framework import Program, default_main_program
 from . import core
 
-__all__ = ['Executor', 'global_scope', 'scope_guard', 'switch_scope']
+__all__ = [
+    'Executor', 'global_scope', 'scope_guard', 'switch_scope', 'fetch_var'
+]
 
 g_scope = core.Scope()
 
@@ -80,12 +82,12 @@ def has_feed_operators(block, feed_targets, feed_holder_name):
     Args:
         block: a block instance (typically global block of a program)
         feed_targets: a dictionary of {feed_target_name: feed_target_data}
-        feed_holder_name: the name of the variable that holds the data of 
-            all feed targets. The type of this feed_holder variable is 
+        feed_holder_name: the name of the variable that holds the data of
+            all feed targets. The type of this feed_holder variable is
             FEED_MINIBATCH, which is essentially vector<LoDTensor>.
 
     Returns:
-        A boolean value that indicates whether a block has feed operators 
+        A boolean value that indicates whether a block has feed operators
         that match the info contained in feed_targets and feed_holder_name.
     """
 
@@ -108,7 +110,7 @@ def has_feed_operators(block, feed_targets, feed_holder_name):
 
 def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     """ Check whether the block already has fetch operators.
-    
+
     Return false if the block does not have any fetch operators.
     If some fetch operators have been appended to the block, check that
     the info contained in these fetch operators matches the fetch_targets
@@ -118,13 +120,13 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     Args:
         block: a block instance (typically global block of a program)
         fetch_targets: a dictionary of {fetch_target_name: fetch_target_data}
-        fetch_holder_name: the name of the variable that holds the data of 
-            all fetch targets. The type of this fetch_holder variable is 
-            FETCH_LIST, which is essentially vector<LoDTensor>.    
+        fetch_holder_name: the name of the variable that holds the data of
+            all fetch targets. The type of this fetch_holder variable is
+            FETCH_LIST, which is essentially vector<LoDTensor>.
 
-    Return:    
-        A boolean value that indicates whether a block has fetch operators 
-        that match the info contained in fetch_targets and fetch_holder_name.     
+    Return:
+        A boolean value that indicates whether a block has fetch operators
+        that match the info contained in fetch_targets and fetch_holder_name.
     """
 
     fetch_count = 0
@@ -146,6 +148,30 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     return fetch_count > 0
 
 
+def fetch_var(name, scope=None, return_numpy=True):
+    """
+    Fetch the value of the variable with the given name from the given scope
+    Args:
+        name(str): name of the variable
+        scope(core.Scope|None): scope object.
+            If None, global_scope() will be used.
+        return_numpy(bool): whether convert the tensor to numpy.ndarray
+    Returns:
+       LodTensor|numpy.ndarray
+    """
+    assert isinstance(name, str)
+    if scope is None:
+        scope = global_scope()
+    assert isinstance(scope, core.Scope)
+
+    var = global_scope().find_var(name)
+    assert var is not None, "Cannot find '%s' in scope." % name
+    tensor = var.get_tensor()
+    if return_numpy:
+        tensor = as_numpy(tensor)
+    return tensor
+
+
 class Executor(object):
     def __init__(self, places):
         if not isinstance(places, list) and not isinstance(places, tuple):
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index 7f5187d299..7fcd19b215 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -31,6 +31,7 @@ __all__ = [
     'program_guard',
     'switch_startup_program',
     'switch_main_program',
+    'get_var',
 ]
 
 EMPTY_VAR_NAME = core.kEmptyVarName()
@@ -1124,3 +1125,22 @@ def program_guard(main_program, startup_program=None):
     switch_main_program(main_program)
     if startup_program is not None:
         switch_startup_program(startup_program)
+
+
+def get_var(name, program=None):
+    """
+    Get a variable by name from the global block of a program
+    Args:
+        name(str): name of the variable
+        program(Program|None): program object.
+             If None, default_global_program() will be used.
+
+    Returns:
+        Variable
+    """
+    if program is None:
+        program = default_main_program()
+    assert isinstance(name, str)
+    assert isinstance(name, Program)
+
+    return program.global_block().var(name)
diff --git a/python/paddle/v2/fluid/layers/tensor.py b/python/paddle/v2/fluid/layers/tensor.py
index c435c5206d..27067d458d 100644
--- a/python/paddle/v2/fluid/layers/tensor.py
+++ b/python/paddle/v2/fluid/layers/tensor.py
@@ -35,13 +35,15 @@ __all__ = [
 ]
 
 
-def create_tensor(dtype, name=None):
+def create_tensor(dtype, name=None, persistable=False):
     helper = LayerHelper("create_tensor", **locals())
-    return helper.create_variable(name=helper.name, dtype=dtype)
+    return helper.create_variable(
+        name=helper.name, dtype=dtype, persistable=persistable)
 
 
 def create_parameter(shape,
                      dtype,
+                     name=None,
                      attr=None,
                      is_bias=False,
                      default_initializer=None):
@@ -62,7 +64,7 @@ def create_parameter(shape,
     """
     helper = LayerHelper("create_parameter", **locals())
     if attr is None:
-        attr = ParamAttr()
+        attr = ParamAttr(name=name)
     return helper.create_parameter(attr, shape, dtype, is_bias,
                                    default_initializer)
 
diff --git a/python/paddle/v2/fluid/tests/test_fetch_var.py b/python/paddle/v2/fluid/tests/test_fetch_var.py
new file mode 100644
index 0000000000..670ab54f51
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_fetch_var.py
@@ -0,0 +1,23 @@
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import op_test
+import numpy
+import unittest
+
+
+class TestFetchVar(op_test.OpTest):
+    def test_fetch_var(self):
+        val = numpy.array([1, 3, 5]).astype(numpy.int32)
+        x = layers.create_tensor(dtype="int32", persistable=True, name="x")
+        layers.assign(input=val, output=x)
+        exe = fluid.Executor(fluid.CPUPlace())
+        exe.run(fluid.default_main_program(), feed={}, fetch_list=[])
+        fetched_x = fluid.fetch_var("x")
+        self.assertTrue(
+            numpy.array_equal(fetched_x, val),
+            "fetch_x=%s val=%s" % (fetched_x, val))
+        self.assertEqual(fetched_x.dtype, val.dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()

From 7208190701d9a3c6d1e4dc507940f5d89d12024f Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Fri, 12 Jan 2018 09:27:38 -0800
Subject: [PATCH 204/314] More informative comment and error message for
 fetch_var()

---
 python/paddle/v2/fluid/executor.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py
index af69ce2abc..0eddcc3a5a 100644
--- a/python/paddle/v2/fluid/executor.py
+++ b/python/paddle/v2/fluid/executor.py
@@ -152,8 +152,10 @@ def fetch_var(name, scope=None, return_numpy=True):
     """
     Fetch the value of the variable with the given name from the given scope
     Args:
-        name(str): name of the variable
-        scope(core.Scope|None): scope object.
+        name(str): name of the variable. Typically, only persistable variables
+            can be found in the scope used for running the program.
+        scope(core.Scope|None): scope object. It should be the scope where
+            you pass to Executor.run() when running your program.
             If None, global_scope() will be used.
         return_numpy(bool): whether convert the tensor to numpy.ndarray
     Returns:
@@ -165,7 +167,10 @@ def fetch_var(name, scope=None, return_numpy=True):
     assert isinstance(scope, core.Scope)
 
     var = global_scope().find_var(name)
-    assert var is not None, "Cannot find '%s' in scope." % name
+    assert var is not None, (
+        "Cannot find " + name + " in scope. Perhaps you need to make the"
+        " variable persistable by using var.persistable = True in your"
+        " program.")
     tensor = var.get_tensor()
     if return_numpy:
         tensor = as_numpy(tensor)

From c1ac5b63efbd927ca1971493fb49883d6807294d Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Fri, 2 Feb 2018 10:12:04 +0800
Subject: [PATCH 205/314] memory optimization for dynamic RNN (#8041)

* init

* add delete operator

* debug

* add wait

* clean code

* fix bug

* fix bug

* refine code

* remove unused code
---
 paddle/operators/while_op.cc                  |  5 ++++
 .../fluid/memory_optimization_transpiler.py   | 24 +++++++++++++------
 2 files changed, 22 insertions(+), 7 deletions(-)

diff --git a/paddle/operators/while_op.cc b/paddle/operators/while_op.cc
index 2fdd25dbbe..733a80ea35 100644
--- a/paddle/operators/while_op.cc
+++ b/paddle/operators/while_op.cc
@@ -99,6 +99,9 @@ class WhileGradOp : public framework::OperatorBase {
 
   void Run(const framework::Scope &scope,
            const platform::Place &dev_place) const override {
+    // get device context from pool
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(dev_place);
     framework::Executor executor(dev_place);
     auto *block = Attr<framework::BlockDesc *>(kStepBlock);
     auto *program = block->Program();
@@ -205,6 +208,8 @@ class WhileGradOp : public framework::OperatorBase {
         sum_op->Run(cur_scope, dev_place);
         cur_scope.Rename(new_inside_name, inside_grad_name);
       }
+      dev_ctx.Wait();
+      const_cast<framework::Scope &>(scope).DeleteScope(&cur_scope);
     }
   }
 };
diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py
index 956c5b66da..2b00923f5e 100644
--- a/python/paddle/v2/fluid/memory_optimization_transpiler.py
+++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py
@@ -31,7 +31,7 @@ dtype_to_size = {
 
 
 class ControlFlowGraph(object):
-    def __init__(self, Program, ops, forward_num):
+    def __init__(self, Program, ops, forward_num, skip_opt):
         self._program = Program
         self._ops = ops
         self._forward_num = forward_num
@@ -41,6 +41,7 @@ class ControlFlowGraph(object):
         self._defs = defaultdict(set)
         self._live_in = defaultdict(set)
         self._live_out = defaultdict(set)
+        self._skip_opt = skip_opt
 
     def _add_connections(self, connections):
         for node1, node2 in connections:
@@ -130,6 +131,10 @@ class ControlFlowGraph(object):
                     block_desc, x,
                     is_forward).type() != core.VarDesc.VarType.LOD_TENSOR:
                 return False
+            if x in self._skip_opt:
+                return False
+            if not self._find_var(block_desc, x, is_forward).shape():
+                return False
             return True
 
         self._build_graph()
@@ -140,6 +145,7 @@ class ControlFlowGraph(object):
             if op.type() == "while" or op.type() == "while_grad":
                 continue
             block_desc = op.block()
+            self.current_block_desc = block_desc
             is_forward = i < self._forward_num
             if self.pool:
                 defs_can_optimize = filter(
@@ -197,28 +203,32 @@ def get_cfgs(input_program):
     block_desc = pdesc.block(0)
     op_size = block_desc.op_size()
     # Get global block ops
-    ops_list.append(([block_desc.op(i) for i in range(op_size)], op_size))
+    ops_list.append(
+        ([block_desc.op(i) for i in range(op_size)], op_size, set()))
 
     while_sub_block_ids = []
     while_grad_sub_block_ids = []
-    while_pair = []
+    while_op_output = set()
+    while_block_id_pair = []
 
     for i in range(op_size):
         op = block_desc.op(i)
         if op.type() == "while":
             while_sub_block_ids.append(op.attr("sub_block").id)
+            while_op_output.update(op.output_arg_names())
         elif op.type() == "while_grad":
             while_grad_sub_block_ids.append(op.attr("sub_block").id)
+            while_op_output.update(op.output_arg_names())
 
     # Find while/while_grad block pair
     for grad_id in while_grad_sub_block_ids:
         parent_id = pdesc.block(grad_id).parent
         if parent_id in while_sub_block_ids:
-            while_pair.append((parent_id, grad_id))
+            while_block_id_pair.append((parent_id, grad_id))
             while_sub_block_ids.remove(parent_id)
 
     # Get while/while_grad block ops
-    for parent_id, grad_id in while_pair:
+    for parent_id, grad_id in while_block_id_pair:
         while_block_ops = []
         while_block = pdesc.block(parent_id)
         while_block_op_size = while_block.op_size()
@@ -230,7 +240,7 @@ def get_cfgs(input_program):
         for i in range(while_grad_block_op_size):
             while_block_ops.append(while_grad_block.op(i))
 
-        ops_list.append((while_block_ops, while_block_op_size))
+        ops_list.append((while_block_ops, while_block_op_size, while_op_output))
 
     # Process rest while block ops
     for parent_id in while_sub_block_ids:
@@ -242,7 +252,7 @@ def get_cfgs(input_program):
 
         ops_list.append((while_block_ops, while_block_op_size))
 
-    cfgs = [ControlFlowGraph(input_program, i, j) for i, j in ops_list]
+    cfgs = [ControlFlowGraph(input_program, i, j, k) for i, j, k in ops_list]
     return cfgs
 
 

From 0bbd7bc38e4f9f14f610961e34968f128613af52 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 2 Feb 2018 11:05:19 +0800
Subject: [PATCH 206/314] follow comments

---
 benchmark/cluster/vgg16/Dockerfile        |  4 +++-
 paddle/gserver/layers/MultiBoxLossLayer.h | 13 -------------
 2 files changed, 3 insertions(+), 14 deletions(-)

diff --git a/benchmark/cluster/vgg16/Dockerfile b/benchmark/cluster/vgg16/Dockerfile
index 54d1b09a0f..888486bece 100644
--- a/benchmark/cluster/vgg16/Dockerfile
+++ b/benchmark/cluster/vgg16/Dockerfile
@@ -1,4 +1,6 @@
-FROM python:2.7.14
+#FROM python:2.7.14
+FROM nvidia/cuda:8.0-runtime-ubuntu16.04
+RUN apt-get update && apt-get install -y python
 RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev
 # NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
 #       so we must build one with distribute support to install in this image.
diff --git a/paddle/gserver/layers/MultiBoxLossLayer.h b/paddle/gserver/layers/MultiBoxLossLayer.h
index 40df312a25..9935da5644 100644
--- a/paddle/gserver/layers/MultiBoxLossLayer.h
+++ b/paddle/gserver/layers/MultiBoxLossLayer.h
@@ -1,16 +1,3 @@
-//  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//    http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
 /* copyright (c) 2016 paddlepaddle authors. all rights reserve.
 
 licensed under the apache license, version 2.0 (the "license");

From 8894c67d7168dfa5f5dc8e57ec2b5c60f24e368c Mon Sep 17 00:00:00 2001
From: Yan Chunwei <yanchunwei@outlook.com>
Date: Fri, 2 Feb 2018 11:35:42 +0800
Subject: [PATCH 207/314] add block graph image for debuging (#8026)

init debuger.
---
 python/paddle/v2/fluid/debuger.py   |  73 ++++++++
 python/paddle/v2/fluid/framework.py |   5 +-
 python/paddle/v2/fluid/graphviz.py  | 272 ++++++++++++++++++++++++++++
 3 files changed, 347 insertions(+), 3 deletions(-)
 create mode 100644 python/paddle/v2/fluid/debuger.py
 create mode 100644 python/paddle/v2/fluid/graphviz.py

diff --git a/python/paddle/v2/fluid/debuger.py b/python/paddle/v2/fluid/debuger.py
new file mode 100644
index 0000000000..d379352442
--- /dev/null
+++ b/python/paddle/v2/fluid/debuger.py
@@ -0,0 +1,73 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from graphviz import GraphPreviewGenerator
+import proto.framework_pb2 as framework_pb2
+
+
+def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
+    '''
+    Generate a debug graph for block.
+    Args:
+        block(Block): a block.
+    '''
+    graph = GraphPreviewGenerator("some graph")
+    # collect parameters and args
+    protostr = block.desc.serialize_to_string()
+    desc = framework_pb2.BlockDesc.FromString(str(protostr))
+
+    def need_highlight(name):
+        if highlights is None: return False
+        for pattern in highlights:
+            assert type(pattern) is str
+            if re.match(pattern, name):
+                return True
+        return False
+
+    # draw parameters and args
+    vars = {}
+    for var in desc.vars:
+        shape = [str(i) for i in var.lod_tensor.tensor.dims]
+        if not shape:
+            shape = ['null']
+        # create var
+        if var.persistable:
+            varn = graph.add_param(
+                var.name, var.type, shape, highlight=need_highlight(var.name))
+        else:
+            varn = graph.add_arg(var.name, highlight=need_highlight(var.name))
+        vars[var.name] = varn
+
+    def add_op_link_var(op, var, op2var=False):
+        for arg in var.arguments:
+            if arg not in vars:
+                # add missing variables as argument
+                vars[arg] = graph.add_arg(arg, highlight=need_highlight(arg))
+            varn = vars[arg]
+            highlight = need_highlight(op.description) or need_highlight(
+                varn.description)
+            if op2var:
+                graph.add_edge(op, varn, highlight=highlight)
+            else:
+                graph.add_edge(varn, op, highlight=highlight)
+
+    for op in desc.ops:
+        opn = graph.add_op(op.type, highlight=need_highlight(op.type))
+        for var in op.inputs:
+            add_op_link_var(opn, var, False)
+        for var in op.outputs:
+            add_op_link_var(opn, var, True)
+
+    graph(path, show=True)
diff --git a/python/paddle/v2/fluid/framework.py b/python/paddle/v2/fluid/framework.py
index 7f5187d299..69cbebe41e 100644
--- a/python/paddle/v2/fluid/framework.py
+++ b/python/paddle/v2/fluid/framework.py
@@ -451,9 +451,8 @@ class Operator(object):
             if not given == need:
                 raise ValueError(("Incorrect setting for output(s) of "
                                   "operator \"%s\". Need: [%s] Given: [%s]") %
-                                 (type, ", ".join(str(e)
-                                                  for e in need), ", ".join(
-                                                      str(e) for e in given)))
+                                 (type, ", ".join(str(e) for e in need),
+                                  ", ".join(str(e) for e in given)))
 
             for out_proto in proto.outputs:
                 out_args = outputs[out_proto.name]
diff --git a/python/paddle/v2/fluid/graphviz.py b/python/paddle/v2/fluid/graphviz.py
new file mode 100644
index 0000000000..5881119c39
--- /dev/null
+++ b/python/paddle/v2/fluid/graphviz.py
@@ -0,0 +1,272 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import random
+import subprocess
+import logging
+
+
+def crepr(v):
+    if type(v) is str or type(v) is unicode:
+        return '"%s"' % v
+    return str(v)
+
+
+class Rank(object):
+    def __init__(self, kind, name, priority):
+        '''
+        kind: str
+        name: str
+        priority: int
+        '''
+        self.kind = kind
+        self.name = name
+        self.priority = priority
+        self.nodes = []
+
+    def __str__(self):
+        if not self.nodes:
+            return ''
+
+        return '{' + 'rank={};'.format(self.kind) + \
+               ','.join([node.name for node in self.nodes]) + '}'
+
+
+class Graph(object):
+    rank_counter = 0
+
+    def __init__(self, title, **attrs):
+        self.title = title
+        self.attrs = attrs
+        self.nodes = []
+        self.edges = []
+        self.rank_groups = {}
+
+    def code(self):
+        return self.__str__()
+
+    def rank_group(self, kind, priority):
+        name = "rankgroup-%d" % Graph.rank_counter
+        Graph.rank_counter += 1
+        rank = Rank(kind, name, priority)
+        self.rank_groups[name] = rank
+        return name
+
+    def node(self, label, prefix, description="", **attrs):
+        node = Node(label, prefix, description, **attrs)
+
+        if 'rank' in attrs:
+            rank = self.rank_groups[attrs['rank']]
+            del attrs['rank']
+            rank.nodes.append(node)
+        self.nodes.append(node)
+        return node
+
+    def edge(self, source, target, **attrs):
+        edge = Edge(source, target, **attrs)
+        self.edges.append(edge)
+        return edge
+
+    def compile(self, dot_path):
+        file = open(dot_path, 'w')
+        file.write(self.__str__())
+        image_path = os.path.join(
+            os.path.dirname(__file__), dot_path[:-3] + "pdf")
+        cmd = ["dot", "-Tpdf", dot_path, "-o", image_path]
+        subprocess.Popen(
+            cmd,
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE)
+        logging.warning("write block debug graph to {}".format(image_path))
+        return image_path
+
+    def show(self, dot_path):
+        image = self.compile(dot_path)
+        cmd = ["open", image]
+        subprocess.Popen(
+            cmd,
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE)
+
+    def _rank_repr(self):
+        ranks = sorted(
+            self.rank_groups.items(),
+            cmp=lambda a, b: a[1].priority > b[1].priority)
+        repr = []
+        for x in ranks:
+            repr.append(str(x[1]))
+        return '\n'.join(repr) + '\n'
+
+    def __str__(self):
+        reprs = [
+            'digraph G {',
+            'title = {}'.format(crepr(self.title)),
+        ]
+
+        for attr in self.attrs:
+            reprs.append("{key}={value};".format(
+                key=attr, value=crepr(self.attrs[attr])))
+
+        reprs.append(self._rank_repr())
+
+        random.shuffle(self.nodes)
+        reprs += [str(node) for node in self.nodes]
+
+        for x in self.edges:
+            reprs.append(str(x))
+
+        reprs.append('}')
+        return '\n'.join(reprs)
+
+
+class Node(object):
+    counter = 1
+
+    def __init__(self, label, prefix, description="", **attrs):
+        self.label = label
+        self.name = "%s_%d" % (prefix, Node.counter)
+        self.description = description
+        self.attrs = attrs
+        Node.counter += 1
+
+    def __str__(self):
+        reprs = '{name} [label={label} {extra} ];'.format(
+            name=self.name,
+            label=self.label,
+            extra=',' + ','.join("%s=%s" % (key, crepr(value))
+                                 for key, value in self.attrs.items())
+            if self.attrs else "")
+        return reprs
+
+
+class Edge(object):
+    def __init__(self, source, target, **attrs):
+        '''
+        Link source to target.
+        :param source: Node
+        :param target: Node
+        :param graph: Graph
+        :param attrs: dic
+        '''
+        self.source = source
+        self.target = target
+        self.attrs = attrs
+
+    def __str__(self):
+        repr = "{source} -> {target} {extra}".format(
+            source=self.source.name,
+            target=self.target.name,
+            extra="" if not self.attrs else
+            "[" + ','.join("{}={}".format(attr[0], crepr(attr[1]))
+                           for attr in self.attrs.items()) + "]")
+        return repr
+
+
+class GraphPreviewGenerator(object):
+    '''
+    Generate a graph image for ONNX proto.
+    '''
+
+    def __init__(self, title):
+        # init graphviz graph
+        self.graph = Graph(
+            title,
+            layout="dot",
+            concentrate="true",
+            rankdir="TB", )
+
+        self.op_rank = self.graph.rank_group('same', 2)
+        self.param_rank = self.graph.rank_group('same', 1)
+        self.arg_rank = self.graph.rank_group('same', 0)
+
+    def __call__(self, path='temp.dot', show=False):
+        if not show:
+            self.graph.compile(path)
+        else:
+            self.graph.show(path)
+
+    def add_param(self, name, data_type, shape, highlight=False):
+        label = '\n'.join([
+            '<<table cellpadding="5">',
+            '  <tr>',
+            '    <td bgcolor="#2b787e">',
+            '    <b>',
+            name,
+            '    </b>',
+            '    </td>',
+            '  </tr>',
+            '  <tr>',
+            '    <td>',
+            str(data_type),
+            '    </td>'
+            '  </tr>',
+            '  <tr>',
+            '    <td>',
+            '[%s]' % 'x'.join(shape),
+            '    </td>'
+            '  </tr>',
+            '</table>>',
+        ])
+        return self.graph.node(
+            label,
+            prefix="param",
+            description=name,
+            shape="none",
+            style="rounded,filled,bold",
+            width="1.3",
+            color="#148b97" if not highlight else "orange",
+            fontcolor="#ffffff",
+            fontname="Arial")
+
+    def add_op(self, opType, **kwargs):
+        highlight = False
+        if 'highlight' in kwargs:
+            highlight = kwargs['highlight']
+            del kwargs['highlight']
+        return self.graph.node(
+            "<<B>%s</B>>" % opType,
+            prefix="op",
+            description=opType,
+            shape="box",
+            style="rounded, filled, bold",
+            color="#303A3A" if not highlight else "orange",
+            fontname="Arial",
+            fontcolor="#ffffff",
+            width="1.3",
+            height="0.84", )
+
+    def add_arg(self, name, highlight=False):
+        return self.graph.node(
+            crepr(name),
+            prefix="arg",
+            description=name,
+            shape="box",
+            style="rounded,filled,bold",
+            fontname="Arial",
+            fontcolor="#999999",
+            color="#dddddd" if not highlight else "orange")
+
+    def add_edge(self, source, target, **kwargs):
+        highlight = False
+        if 'highlight' in kwargs:
+            highlight = kwargs['highlight']
+            del kwargs['highlight']
+        return self.graph.edge(
+            source,
+            target,
+            color="#00000" if not highlight else "orange",
+            **kwargs)

From 251c2fd50a787b474e49db7f7be9aab27fcd3ccb Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Fri, 2 Feb 2018 13:35:00 +0800
Subject: [PATCH 208/314] Update according to the code review

---
 paddle/operators/box_coder_op.cc |  2 ++
 paddle/operators/box_coder_op.cu |  2 +-
 paddle/operators/box_coder_op.h  | 17 +++++++++--------
 3 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/paddle/operators/box_coder_op.cc b/paddle/operators/box_coder_op.cc
index 41123f9b6e..3836cef96d 100644
--- a/paddle/operators/box_coder_op.cc
+++ b/paddle/operators/box_coder_op.cc
@@ -26,6 +26,8 @@ class BoxCoderOp : public framework::OperatorWithKernel {
                    "Input(PriorBoxVar) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
                    "Input(TargetBox) of BoxCoderOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("OutputBox"),
+                   "Output(OutputBox) of BoxCoderOp should not be null.");
 
     auto prior_box_dims = ctx->GetInputDim("PriorBox");
     auto prior_box_var_dims = ctx->GetInputDim("PriorBoxVar");
diff --git a/paddle/operators/box_coder_op.cu b/paddle/operators/box_coder_op.cu
index 883cc54305..98bd93457f 100644
--- a/paddle/operators/box_coder_op.cu
+++ b/paddle/operators/box_coder_op.cu
@@ -109,7 +109,7 @@ class BoxCoderCUDAKernel : public framework::OpKernel<T> {
     auto* prior_box = context.Input<framework::Tensor>("PriorBox");
     auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
     auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
-    auto* output_box = context.Output<Tensor>("OutputBox");
+    auto* output_box = context.Output<framework::Tensor>("OutputBox");
 
     if (target_box->lod().size()) {
       PADDLE_ENFORCE_EQ(target_box->lod().size(), 1,
diff --git a/paddle/operators/box_coder_op.h b/paddle/operators/box_coder_op.h
index d1c9a40459..086251f6e0 100644
--- a/paddle/operators/box_coder_op.h
+++ b/paddle/operators/box_coder_op.h
@@ -16,9 +16,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-
 enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };
 
 inline BoxCodeType GetBoxCodeType(const std::string& type) {
@@ -33,8 +30,10 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
 template <typename T>
 class BoxCoderKernel : public framework::OpKernel<T> {
  public:
-  void EncodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
-                        const Tensor& prior_box_var, T* output) const {
+  void EncodeCenterSize(const framework::Tensor& target_box,
+                        const framework::Tensor& prior_box,
+                        const framework::Tensor& prior_box_var,
+                        T* output) const {
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
     int64_t len = prior_box.dims()[1];
@@ -76,8 +75,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
       }
     }
   }
-  void DecodeCenterSize(const Tensor& target_box, const Tensor& prior_box,
-                        const Tensor& prior_box_var, T* output) const {
+  void DecodeCenterSize(const framework::Tensor& target_box,
+                        const framework::Tensor& prior_box,
+                        const framework::Tensor& prior_box_var,
+                        T* output) const {
     int64_t row = target_box.dims()[0];
     int64_t col = prior_box.dims()[0];
     int64_t len = prior_box.dims()[1];
@@ -124,7 +125,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
     auto* prior_box = context.Input<framework::Tensor>("PriorBox");
     auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
     auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
-    auto* output_box = context.Output<Tensor>("OutputBox");
+    auto* output_box = context.Output<framework::Tensor>("OutputBox");
 
     if (target_box->lod().size()) {
       PADDLE_ENFORCE_EQ(target_box->lod().size(), 1UL,

From a4cf6a28194ad8a3f3f04b97672755e46d5750e6 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Thu, 1 Feb 2018 21:48:50 -0800
Subject: [PATCH 209/314] Adding unit tests for checking that closing
 unbuffered channel closes all blocked senders and receivers (#8067)

* Adding unit tests for checking that closing unbuffered channel closes all blocked senders and receivers

* Fixing sleep time
---
 paddle/framework/channel_test.cc | 90 ++++++++++++++++++++++++++++++--
 1 file changed, 86 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 020f806380..31ac72eda9 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -29,16 +29,16 @@ TEST(Channel, MakeAndClose) {
   {
     // MakeChannel should return a buffered channel is buffer_size > 0.
     auto ch = MakeChannel<int>(10);
-    EXPECT_NE(dynamic_cast<Buffered<int>*>(ch), nullptr);
-    EXPECT_EQ(dynamic_cast<UnBuffered<int>*>(ch), nullptr);
+    EXPECT_NE(dynamic_cast<Buffered<int> *>(ch), nullptr);
+    EXPECT_EQ(dynamic_cast<UnBuffered<int> *>(ch), nullptr);
     CloseChannel(ch);
     delete ch;
   }
   {
     // MakeChannel should return an un-buffered channel is buffer_size = 0.
     auto ch = MakeChannel<int>(0);
-    EXPECT_EQ(dynamic_cast<Buffered<int>*>(ch), nullptr);
-    EXPECT_NE(dynamic_cast<UnBuffered<int>*>(ch), nullptr);
+    EXPECT_EQ(dynamic_cast<Buffered<int> *>(ch), nullptr);
+    EXPECT_NE(dynamic_cast<UnBuffered<int> *>(ch), nullptr);
     CloseChannel(ch);
     delete ch;
   }
@@ -100,6 +100,88 @@ TEST(Channel, SimpleUnbufferedChannelTest) {
   delete ch;
 }
 
+// This tests that closing an unbuffered channel also unblocks
+//  unblocks any receivers waiting for senders
+TEST(Channel, UnbufferedChannelCloseUnblocksReceiversTest) {
+  auto ch = MakeChannel<int>(0);
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+
+  // Launches threads that try to read and are blocked becausew of no writers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          int data;
+          ch->Receive(&data);
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+
+  // Verify that all the threads are blocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], false);
+  }
+
+  // Explicitly close the thread
+  // This should unblock all receivers
+  CloseChannel(ch);
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  delete ch;
+}
+
+// This tests that closing an unbuffered channel also unblocks
+//  unblocks any senders waiting for senders
+TEST(Channel, UnbufferedChannelCloseUnblocksSendersTest) {
+  auto ch = MakeChannel<int>(0);
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+
+  // Launches threads that try to read and are blocked becausew of no writers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          int data = 10;
+          ch->Send(&data);
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+
+  // Verify that all the threads are blocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], false);
+  }
+
+  // Explicitly close the thread
+  // This should unblock all receivers
+  CloseChannel(ch);
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  delete ch;
+}
+
 TEST(Channel, UnbufferedLessReceiveMoreSendTest) {
   auto ch = MakeChannel<int>(0);
   unsigned sum_send = 0;

From cbc9a59c33b507f26ad4e00e740672ef99bc8fa4 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Fri, 2 Feb 2018 10:03:09 +0800
Subject: [PATCH 210/314] Allow uers to specify the name of moving mean and
 variance in batch_norm interface.

---
 python/paddle/v2/fluid/layers/nn.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index c38e21087d..cb8a4815db 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -1478,7 +1478,9 @@ def batch_norm(input,
                param_attr=None,
                bias_attr=None,
                data_layout='NCHW',
-               name=None):
+               name=None,
+               moving_mean_name=None,
+               moving_variance_name=None):
     """
     This function helps create an operator to implement
     the BatchNorm layer using the configurations from the input parameters.
@@ -1508,6 +1510,7 @@ def batch_norm(input,
         attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
 
     mean = helper.create_global_variable(
+        name=moving_mean_name,
         dtype=input.dtype,
         shape=param_shape,
         persistable=True,
@@ -1515,6 +1518,7 @@ def batch_norm(input,
     helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
 
     variance = helper.create_global_variable(
+        name=moving_variance_name,
         dtype=input.dtype,
         shape=param_shape,
         persistable=True,

From 901cab9ed3e0838954f0015221093fc1d64b5795 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 2 Feb 2018 13:52:41 +0800
Subject: [PATCH 211/314] Add `make clean` in docker/build.sh

---
 paddle/scripts/docker/build.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index df7310d6b7..59f3af0398 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -79,6 +79,7 @@ function run_build() {
     Building in /paddle/build ...
     ============================================
 EOF
+    make clean
     make -j `nproc`
 }
 

From 7d8d9db9ba800833bd5bda76366722b3d37542e2 Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Fri, 2 Feb 2018 13:56:38 +0800
Subject: [PATCH 212/314] Update according to the code review

---
 paddle/operators/box_coder_op.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/operators/box_coder_op.cc b/paddle/operators/box_coder_op.cc
index 3836cef96d..539813d485 100644
--- a/paddle/operators/box_coder_op.cc
+++ b/paddle/operators/box_coder_op.cc
@@ -24,7 +24,7 @@ class BoxCoderOp : public framework::OperatorWithKernel {
                    "Input(PriorBox) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("PriorBoxVar"),
                    "Input(PriorBoxVar) of BoxCoderOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("PriorBox"),
+    PADDLE_ENFORCE(ctx->HasInput("TargetBox"),
                    "Input(TargetBox) of BoxCoderOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("OutputBox"),
                    "Output(OutputBox) of BoxCoderOp should not be null.");

From 7831e0bdd39afea7c404c2d399933b61fbdeddca Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Fri, 2 Feb 2018 15:17:13 +0800
Subject: [PATCH 213/314] switch-op design (#8031)

add switch op design
---
 doc/design/switch.md | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 doc/design/switch.md

diff --git a/doc/design/switch.md b/doc/design/switch.md
new file mode 100644
index 0000000000..9db1b2782a
--- /dev/null
+++ b/doc/design/switch.md
@@ -0,0 +1,32 @@
+### Design Doc: Switch
+
+### Background
+
+Many programming languages provide `switch` as a generalization of `if-elif-else`.  We want to add it to Fluid.
+
+The following example shows the usage of `fluid.switch`.
+
+```python
+a = fluid.Var(10)
+b = fluid.Var(0)
+
+switch = fluid.switch()
+with switch.block():
+    with switch.case(fluid.less_equal(a, 10)):
+        fluid.print("Case 1")
+    with switch.case(fluid.larger(a, 0)):
+        fluid.print("Case 2")
+    with switch.default():
+        fluid.print("Case 3")
+```
+
+### The Semantics
+
+1. A `switch` control-flow checks cases one-by-one.
+1. The condition of each case is a boolean value, which is a scalar, and differs from the `fluid.if_else` control-flow, which condition could be a vector of boolean values.
+1. It runs the first matched case, or the default case if there is one.
+1. Once it matches a case, it runs the corresponding branch and only that branch.  It's like there is a C's `break` keyword at the end of each case.
+
+The above program should print and print only "Case 1".
+
+The implementation of the backward pass of the `switch` control-flow is easier than the backward of the `if_else`, because `switch` runs at most one branch, whereas `if-else` could run more than one branches.

From 4284b857cb61f9ad090044834f3c0f62c339c0b2 Mon Sep 17 00:00:00 2001
From: wanghaox <wanghaox@gmail.com>
Date: Fri, 2 Feb 2018 15:45:13 +0800
Subject: [PATCH 214/314] update mine_hard_examples op

---
 paddle/operators/mine_hard_examples_op.cc | 52 ++++++++++++++---------
 1 file changed, 31 insertions(+), 21 deletions(-)

diff --git a/paddle/operators/mine_hard_examples_op.cc b/paddle/operators/mine_hard_examples_op.cc
index 603368f93c..2a3bd139ed 100644
--- a/paddle/operators/mine_hard_examples_op.cc
+++ b/paddle/operators/mine_hard_examples_op.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -38,7 +38,7 @@ inline bool IsEligibleMining(const MiningType mining_type, const int match_idx,
   }
 }
 
-MiningType GetMiningType(std::string str) {
+inline MiningType GetMiningType(std::string str) {
   if (str == "max_negative") {
     return MiningType::kMaxNegative;
   } else if (str == "hard_example") {
@@ -112,7 +112,7 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
         neg_sel = std::min(sample_size, neg_sel);
       }
 
-      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<int>);
+      std::sort(loss_idx.begin(), loss_idx.end(), SortScoreDescend<size_t>);
       std::set<int> sel_indices;
       std::vector<int> neg_indices;
       std::transform(loss_idx.begin(), loss_idx.begin() + neg_sel,
@@ -121,18 +121,27 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
                        return static_cast<int>(l.second);
                      });
 
-      for (int m = 0; m < prior_num; ++m) {
-        if (match_indices(n, m) > -1) {
-          if (mining_type == MiningType::kHardExample &&
-              sel_indices.find(m) == sel_indices.end()) {
-            match_indices_et(n, m) = -1;
+      if (mining_type == MiningType::kHardExample) {
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) > -1) {
+            if (sel_indices.find(m) == sel_indices.end()) {
+              match_indices_et(n, m) = -1;
+            }
+          } else {
+            if (sel_indices.find(m) != sel_indices.end()) {
+              neg_indices.push_back(m);
+            }
           }
-        } else {
-          if (sel_indices.find(m) != sel_indices.end()) {
+        }
+      } else {
+        for (int m = 0; m < prior_num; ++m) {
+          if (match_indices(n, m) == -1 &&
+              sel_indices.find(m) != sel_indices.end()) {
             neg_indices.push_back(m);
           }
         }
       }
+
       all_neg_indices.push_back(neg_indices);
       batch_starts.push_back(batch_starts.back() + neg_indices.size());
     }
@@ -253,7 +262,7 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
         "[N, Np], N is the batch size and Np is the number of prior box.");
     AddInput("LocLoss",
              "(Tensor, optional, default Tensor<float>), The localization loss "
-             "wit shape [N, Np], N is the batch size and Np is the number of "
+             "with shape [N, Np], N is the batch size and Np is the number of "
              "prior box.")
         .AsDispensable();
     AddInput("MatchIndices",
@@ -267,15 +276,15 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
              "Np], N is the batch size and Np is the number of prior box.");
     AddAttr<float>("neg_pos_ratio",
                    "(float) The ratio of the negative box to the positive "
-                   "box. Use only when mining_type is equal to max_negative.")
+                   "box. Use only when mining_type is max_negative.")
         .SetDefault(1.0);
     AddAttr<float>("neg_dist_threshold",
-                   "(float) The negative box dis value threshold. "
-                   "Use only when mining_type is equal to max_negative.")
+                   "(float) The negative overlap upper bound for the unmatched "
+                   "predictions. Use only when mining_type is max_negative.")
         .SetDefault(0.5);
     AddAttr<int>("sample_size",
                  "(float) The max sample size of negative box. Use only when "
-                 "mining_type is equal to hard_example.")
+                 "mining_type is hard_example.")
         .SetDefault(0);
     AddAttr<std::string>("mining_type",
                          "(float) The mining algorithm name, the value is "
@@ -295,7 +304,7 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddOutput("UpdatedMatchIndices",
               "(Tensor<int>) The output of updated MatchIndices, a tensor with "
-              "shape [N, Np]. Only update when mining_type is equal to "
+              "shape [N, Np]. Only update when mining_type is "
               "hard_example. The input MatchIndices elements will be update to "
               "-1 when it is not in the candidate high loss list of negative "
               "examples.");
@@ -303,11 +312,12 @@ class MineHardExamplesOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 Mine hard examples Operator.
 This operator implements hard example mining to select a subset of negative box indices.
-For each image, selects the box with highest losses. subject to the condition that the box cannot have
-an Matcht > neg_dist_threshold when mining_type is equals max_negative. The selected number is 
-min(sample_size, max_negative_box_number) when mining_type is equals hard_example,
-or min(neg_pos_ratio * positive_box_number, max_negative_box_number) when mining_type is 
-equals max_negative, where the max_negative_box_number is the count of MatchIndices elements with value -1.
+For each image, selects the box with highest losses. subject to the condition that the 
+box cannot have an Matcht > neg_dist_threshold when mining_type is max_negative. 
+The selected number is min(sample_size, max_negative_box_number) when mining_type is 
+hard_example, or min(neg_pos_ratio * positive_box_number, max_negative_box_number) 
+when mining_type is max_negative, where the max_negative_box_number is the count of 
+MatchIndices elements with value -1.
 )DOC");
   }
 };

From a6f3846d8ff1b9a9d6361381447d1ab7cab7f7ec Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Fri, 2 Feb 2018 16:33:33 +0800
Subject: [PATCH 215/314] Remove the redundant header file and make one
 function inlne.

---
 paddle/operators/multiclass_nms_op.cc | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index cb38e9fa20..8a65fe69f1 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/framework/op_registry.h"
-#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -92,7 +91,7 @@ static inline void GetMaxScoreIndex(
 }
 
 template <class T>
-T BBoxArea(const T* box, const bool normalized) {
+static inline T BBoxArea(const T* box, const bool normalized) {
   if (box[2] < box[0] || box[3] < box[1]) {
     // If coordinate values are is invalid
     // (e.g. xmax < xmin or ymax < ymin), return 0.

From 2c35e6389af743e9f3cb991c6c88438a37b99c29 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Fri, 2 Feb 2018 16:50:12 +0800
Subject: [PATCH 216/314] Reclassify and change of V2 API documentation
 structure

---
 doc/api/v2/config/layer.rst | 66 +++++++++++++++----------------------
 doc/api/v2/data/dataset.rst |  7 ++++
 2 files changed, 34 insertions(+), 39 deletions(-)

diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst
index ddf0b055a9..29388f5005 100644
--- a/doc/api/v2/config/layer.rst
+++ b/doc/api/v2/config/layer.rst
@@ -87,6 +87,11 @@ roi_pool
 ..  autoclass:: paddle.v2.layer.roi_pool
     :noindex:
 
+pad
+----
+..  autoclass:: paddle.v2.layer.pad
+    :noindex:
+
 Norm Layer
 ==========
 
@@ -133,6 +138,11 @@ grumemory
 ..  autoclass:: paddle.v2.layer.grumemory
     :noindex:
 
+gated_unit
+-----------
+..  autoclass:: paddle.v2.layer.gated_unit
+    :noindex:
+    
 Recurrent Layer Group
 =====================
 
@@ -340,6 +350,11 @@ bilinear_interp
 ..  autoclass:: paddle.v2.layer.bilinear_interp
     :noindex:
 
+dropout
+--------
+..  autoclass:: paddle.v2.layer.dropout
+    :noindex:
+    
 dot_prod
 ---------
 .. autoclass:: paddle.v2.layer.dot_prod
@@ -402,6 +417,11 @@ scale_shift
 ..  autoclass:: paddle.v2.layer.scale_shift
     :noindex:
 
+factorization_machine
+---------------------
+..  autoclass:: paddle.v2.layer.factorization_machine
+    :noindex:
+
 Sampling Layers
 ===============
 
@@ -420,22 +440,6 @@ multiplex
 ..  autoclass:: paddle.v2.layer.multiplex
     :noindex:
 
-Factorization Machine Layer
-============================
-
-factorization_machine
----------------------
-..  autoclass:: paddle.v2.layer.factorization_machine
-    :noindex:
-
-Slicing and Joining Layers
-==========================
-
-pad
-----
-..  autoclass:: paddle.v2.layer.pad
-    :noindex:
-
 ..  _api_v2.layer_costs:
 
 Cost Layers
@@ -526,6 +530,11 @@ multibox_loss
 ..  autoclass:: paddle.v2.layer.multibox_loss
     :noindex:
 
+detection_output
+----------------
+..  autoclass:: paddle.v2.layer.detection_output
+    :noindex:
+    
 Check Layer
 ============
 
@@ -534,31 +543,10 @@ eos
 ..  autoclass:: paddle.v2.layer.eos
     :noindex:
 
-Miscs
-=====
-
-dropout
---------
-..  autoclass:: paddle.v2.layer.dropout
-    :noindex:
-
-Activation with learnable parameter
-===================================
+Activation
+==========
 
 prelu
 --------
 ..  autoclass:: paddle.v2.layer.prelu
     :noindex:
-
-gated_unit
------------
-..  autoclass:: paddle.v2.layer.gated_unit
-    :noindex:
-
-Detection output Layer
-======================
-
-detection_output
-----------------
-..  autoclass:: paddle.v2.layer.detection_output
-    :noindex:
diff --git a/doc/api/v2/data/dataset.rst b/doc/api/v2/data/dataset.rst
index 6a8ecc5bb1..02e41564b1 100644
--- a/doc/api/v2/data/dataset.rst
+++ b/doc/api/v2/data/dataset.rst
@@ -73,3 +73,10 @@ wmt14
 ..  automodule:: paddle.v2.dataset.wmt14
     :members:
     :noindex:
+
+wmt16
++++++
+
+..  automodule:: paddle.v2.dataset.wmt16
+    :members:
+    :noindex:

From 8137dd9b5ed0cab202006e2b7d0ab6ff4bee34df Mon Sep 17 00:00:00 2001
From: wanghaox <wanghaox@gmail.com>
Date: Fri, 2 Feb 2018 16:53:33 +0800
Subject: [PATCH 217/314] update mine_hard_examples_op

---
 paddle/operators/mine_hard_examples_op.cc | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/paddle/operators/mine_hard_examples_op.cc b/paddle/operators/mine_hard_examples_op.cc
index 2a3bd139ed..051cc24706 100644
--- a/paddle/operators/mine_hard_examples_op.cc
+++ b/paddle/operators/mine_hard_examples_op.cc
@@ -117,7 +117,7 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
       std::vector<int> neg_indices;
       std::transform(loss_idx.begin(), loss_idx.begin() + neg_sel,
                      std::inserter(sel_indices, sel_indices.begin()),
-                     [](std::pair<T, size_t> l) -> int {
+                     [](std::pair<T, size_t>& l) -> int {
                        return static_cast<int>(l.second);
                      });
 
@@ -134,12 +134,8 @@ class MineHardExamplesKernel : public framework::OpKernel<T> {
           }
         }
       } else {
-        for (int m = 0; m < prior_num; ++m) {
-          if (match_indices(n, m) == -1 &&
-              sel_indices.find(m) != sel_indices.end()) {
-            neg_indices.push_back(m);
-          }
-        }
+        neg_indices.resize(sel_indices.size());
+        std::copy(sel_indices.begin(), sel_indices.end(), neg_indices.begin());
       }
 
       all_neg_indices.push_back(neg_indices);

From a5acad11e8ead60413192143a0822daa408f67aa Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 2 Feb 2018 17:04:59 +0800
Subject: [PATCH 218/314] update docs

---
 benchmark/cluster/vgg16/Dockerfile |  2 +-
 benchmark/cluster/vgg16/README.md  | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/benchmark/cluster/vgg16/Dockerfile b/benchmark/cluster/vgg16/Dockerfile
index 888486bece..98356cd761 100644
--- a/benchmark/cluster/vgg16/Dockerfile
+++ b/benchmark/cluster/vgg16/Dockerfile
@@ -1,5 +1,5 @@
 #FROM python:2.7.14
-FROM nvidia/cuda:8.0-runtime-ubuntu16.04
+FROM nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04
 RUN apt-get update && apt-get install -y python
 RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev
 # NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md
index 9b96f1c02d..11d00b8f85 100644
--- a/benchmark/cluster/vgg16/README.md
+++ b/benchmark/cluster/vgg16/README.md
@@ -1,4 +1,4 @@
-# Performance for distributed vgg16
+# Performance for Distributed vgg16
 
 ## Test Result
 
@@ -50,7 +50,7 @@
 
 - Trainer Count: 60
 - Batch Size: 128
-- Metrics: mini-batch / sec
+- Metrics: samples/ sec
 
 | PServer Count | 3 | 6 |10 | 20 |
 | -- | -- | -- | -- | -- |
@@ -61,7 +61,7 @@
 *The performance gap between Fuild and v2 comes from the network interference.*
 
 
-## Steps to run the performance test
+## Steps to Run the Performance Test
 
 1. You must re-compile PaddlePaddle and enable `-DWITH_DISTRIBUTE` to build PaddlePaddle with distributed support.
 1. When the build finishes, copy the output `whl` package located under `build/python/dist` to current directory.
@@ -71,6 +71,6 @@
 
 Check the logs for the distributed training progress and analyze the performance.
 
-## Enable verbos logs
+## Enable Verbos Logs
 
-Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` to see what happend in detail.
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` and `GLOG_logtostderr=1` to see what happend in detail.

From 0575fd4647bf414662d31c02371a68689273b22c Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 2 Feb 2018 17:31:37 +0800
Subject: [PATCH 219/314] simplify shape inference code

---
 paddle/framework/op_desc.cc         | 19 -------------------
 paddle/framework/operator.cc        |  8 --------
 paddle/framework/shape_inference.cc | 23 +++++++++++++++++++----
 paddle/framework/shape_inference.h  |  8 +++-----
 4 files changed, 22 insertions(+), 36 deletions(-)

diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index f8df2cf97a..f554c77845 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -39,10 +39,6 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
   bool HasOutputs(const std::string &name) const override;
 
-  DDim GetInputDim(const std::string &name) const override;
-
-  void SetOutputDim(const std::string &name, const DDim &dim) override;
-
   AttrReader Attrs() const override;
 
   const std::vector<std::string> &Inputs(
@@ -444,21 +440,6 @@ bool CompileTimeInferShapeContext::HasOutputs(const std::string &name) const {
   return true;
 }
 
-DDim CompileTimeInferShapeContext::GetInputDim(const std::string &name) const {
-  std::vector<DDim> ddims = GetInputsDim(name);
-  auto length = ddims.size();
-  PADDLE_ENFORCE_EQ(length, 1UL,
-                    "Input(%s) should have 1 value, "
-                    "but it has %d now",
-                    name, length);
-  return ddims[0];
-}
-
-void CompileTimeInferShapeContext::SetOutputDim(const std::string &name,
-                                                const DDim &dim) {
-  SetOutputsDim(name, {dim});
-}
-
 AttrReader CompileTimeInferShapeContext::Attrs() const {
   return AttrReader(op_.GetAttrMap());
 }
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 4e854f54dd..81fa8cf477 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -366,14 +366,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
     return true;
   }
 
-  DDim GetInputDim(const std::string& name) const override {
-    return GetDim(op_.Input(name));
-  }
-
-  void SetOutputDim(const std::string& name, const DDim& dim) override {
-    SetDim(op_.Output(name), dim);
-  }
-
   AttrReader Attrs() const override { return AttrReader(op_.Attrs()); }
 
   const std::vector<std::string>& Inputs(
diff --git a/paddle/framework/shape_inference.cc b/paddle/framework/shape_inference.cc
index e53cc0cdab..14dba75808 100644
--- a/paddle/framework/shape_inference.cc
+++ b/paddle/framework/shape_inference.cc
@@ -18,10 +18,18 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+framework::DDim InferShapeContext::GetInputDim(const std::string &name) const {
+  const std::vector<std::string> &arg_names = Inputs(name);
+  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                    "Input(%s) shoudl holds one element, but now it holds %d",
+                    name, arg_names.size());
+  return this->GetDim(arg_names[0]);
+}
+
 std::vector<framework::DDim> InferShapeContext::GetInputsDim(
     const std::string &name) const {
-  const std::vector<std::string> &names = Inputs(name);
-  return GetDims(names);
+  const std::vector<std::string> &arg_names = Inputs(name);
+  return GetDims(arg_names);
 }
 
 DDim InferShapeContext::GetInputsElementDim(const std::string &name,
@@ -30,13 +38,21 @@ DDim InferShapeContext::GetInputsElementDim(const std::string &name,
   return this->GetDim(names[idx]);
 }
 
+void InferShapeContext::SetOutputDim(const std::string &name, const DDim &dim) {
+  auto &arg_names = Outputs(name);
+  PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
+                    "Output(%s) shoudl holds one element, but now it holds %d",
+                    name, arg_names.size());
+  SetDim(arg_names[0], dim);
+}
+
 void InferShapeContext::SetOutputsDim(
     const std::string &name, const std::vector<framework::DDim> &dims) {
   auto &names = Outputs(name);
   SetDims(names, dims);
 }
 
-std::vector<framework::DDim> InferShapeContext::GetDims(
+std::vector<DDim> InferShapeContext::GetDims(
     const std::vector<std::string> &names) const {
   std::vector<framework::DDim> ret;
   ret.reserve(names.size());
@@ -45,7 +61,6 @@ std::vector<framework::DDim> InferShapeContext::GetDims(
       [this](const std::string &name) { return this->GetDim(name); });
   return ret;
 }
-
 void InferShapeContext::SetDims(const std::vector<std::string> &names,
                                 const std::vector<framework::DDim> &dims) {
   size_t length = names.size();
diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h
index f93319d8f2..77fc9359be 100644
--- a/paddle/framework/shape_inference.h
+++ b/paddle/framework/shape_inference.h
@@ -35,12 +35,12 @@ class InferShapeContext {
   virtual bool HasInputs(const std::string &name) const = 0;
   virtual bool HasOutputs(const std::string &name) const = 0;
 
-  virtual framework::DDim GetInputDim(const std::string &name) const = 0;
+  framework::DDim GetInputDim(const std::string &name) const;
 
   std::vector<framework::DDim> GetInputsDim(const std::string &name) const;
   DDim GetInputsElementDim(const std::string &name, int idx) const;
 
-  virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
+  void SetOutputDim(const std::string &name, const DDim &dim);
   void SetOutputsDim(const std::string &name,
                      const std::vector<framework::DDim> &dims);
 
@@ -63,9 +63,7 @@ class InferShapeContext {
   virtual framework::DDim GetDim(const std::string &name) const = 0;
   virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
 
-  std::vector<framework::DDim> GetDims(
-      const std::vector<std::string> &names) const;
-
+  std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
   std::vector<proto::VarDesc::VarType> GetVarTypes(
       const std::vector<std::string> &names) const;
 

From affce7331ce42cb85295f932e408f0d4597ea527 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Fri, 2 Feb 2018 17:31:35 +0800
Subject: [PATCH 220/314] refine elementwise_op

---
 paddle/operators/compare_op.h              | 10 +++++++-
 paddle/operators/elementwise_add_op.h      | 21 ++++++++++++++--
 paddle/operators/elementwise_div_op.h      | 21 ++++++++++++++--
 paddle/operators/elementwise_max_op.h      | 21 ++++++++++++++--
 paddle/operators/elementwise_min_op.h      | 21 ++++++++++++++--
 paddle/operators/elementwise_mul_op.h      | 21 ++++++++++++++--
 paddle/operators/elementwise_op_function.h | 28 ++++++++--------------
 paddle/operators/elementwise_pow_op.h      |  9 ++++++-
 paddle/operators/elementwise_sub_op.h      | 21 ++++++++++++++--
 9 files changed, 141 insertions(+), 32 deletions(-)

diff --git a/paddle/operators/compare_op.h b/paddle/operators/compare_op.h
index 9c655d6c0d..b275fd75b3 100644
--- a/paddle/operators/compare_op.h
+++ b/paddle/operators/compare_op.h
@@ -54,7 +54,15 @@ class CompareOpKernel
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     using T = typename Functor::ELEM_TYPE;
-    ElementwiseComputeEx<Functor, DeviceContext, T, bool>(context);
+    using Tensor = framework::Tensor;
+
+    auto* x = context.Input<Tensor>("X");
+    auto* y = context.Input<Tensor>("Y");
+    auto* z = context.Output<Tensor>("Out");
+    z->mutable_data<T>(context.GetPlace());
+    int axis = context.Attr<int>("axis");
+    ElementwiseComputeEx<Functor, DeviceContext, T, bool>(context, x, y, axis,
+                                                          z);
   }
 };
 
diff --git a/paddle/operators/elementwise_add_op.h b/paddle/operators/elementwise_add_op.h
index a8389429f2..c32288d698 100644
--- a/paddle/operators/elementwise_add_op.h
+++ b/paddle/operators/elementwise_add_op.h
@@ -28,7 +28,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseAddKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -92,9 +99,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseAddGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseAddGradFunctor<T>,
                            ElementwiseAddBroadCastGradFunctor<T>,
-                           ElementwiseAddBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseAddBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 
diff --git a/paddle/operators/elementwise_div_op.h b/paddle/operators/elementwise_div_op.h
index ef26cb6c91..07ebade31f 100644
--- a/paddle/operators/elementwise_div_op.h
+++ b/paddle/operators/elementwise_div_op.h
@@ -28,7 +28,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseDivKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -111,9 +118,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseDivGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseDivGradFunctor<T>,
                            ElementwiseDivBroadCastGradFunctor<T>,
-                           ElementwiseDivBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseDivBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 
diff --git a/paddle/operators/elementwise_max_op.h b/paddle/operators/elementwise_max_op.h
index 255728e8e6..717e45ab31 100644
--- a/paddle/operators/elementwise_max_op.h
+++ b/paddle/operators/elementwise_max_op.h
@@ -28,7 +28,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseMaxKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -110,9 +117,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseMaxGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseMaxGradFunctor<T>,
                            ElementwiseMaxBroadCastGradFunctor<T>,
-                           ElementwiseMaxBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseMaxBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 
diff --git a/paddle/operators/elementwise_min_op.h b/paddle/operators/elementwise_min_op.h
index e6627a0f1b..0de9a91c52 100644
--- a/paddle/operators/elementwise_min_op.h
+++ b/paddle/operators/elementwise_min_op.h
@@ -28,7 +28,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseMinKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -110,9 +117,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseMinGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseMinGradFunctor<T>,
                            ElementwiseMinBroadCastGradFunctor<T>,
-                           ElementwiseMinBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseMinBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 
diff --git a/paddle/operators/elementwise_mul_op.h b/paddle/operators/elementwise_mul_op.h
index 4b86b00b5a..ae7a71e024 100644
--- a/paddle/operators/elementwise_mul_op.h
+++ b/paddle/operators/elementwise_mul_op.h
@@ -27,7 +27,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseMulKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -110,9 +117,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseMulGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseMulGradFunctor<T>,
                            ElementwiseMulBroadCastGradFunctor<T>,
-                           ElementwiseMulBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseMulBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 
diff --git a/paddle/operators/elementwise_op_function.h b/paddle/operators/elementwise_op_function.h
index d749b8e875..213fe1f5a8 100644
--- a/paddle/operators/elementwise_op_function.h
+++ b/paddle/operators/elementwise_op_function.h
@@ -313,21 +313,18 @@ EIGEN_FUNCTOR(Div, EIGEN_DIV);
 
 template <typename DeviceContext, typename T, typename functor,
           typename broadcastfunctor, typename broadcast2functor>
-void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
-  using Tensor = framework::Tensor;
-
-  auto* x = ctx.Input<Tensor>("X");
-  auto* y = ctx.Input<Tensor>("Y");
-  auto* out = ctx.Input<Tensor>("Out");
-  auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+void ElementwiseGradCompute(const framework::ExecutionContext& ctx,
 
+                            const framework::Tensor* x,
+                            const framework::Tensor* y,
+                            const framework::Tensor* out,
+                            const framework::Tensor* dout, int axis,
+                            framework::Tensor* dx, framework::Tensor* dy) {
   auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
 
   auto x_dims = x->dims();
   auto y_dims = y->dims();
 
-  auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-  auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
   if (dx) {
     dx->mutable_data<T>(ctx.GetPlace());
   }
@@ -348,7 +345,6 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
     x_dims = framework::make_ddim(extended_dims);
   }
 
-  int axis = ctx.Attr<int>("axis");
   axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
 
   int pre, n, post;
@@ -367,13 +363,10 @@ void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
 
 template <typename Functor, typename DeviceContext, typename T,
           typename OutType = T>
-void ElementwiseComputeEx(const framework::ExecutionContext& ctx) {
-  using Tensor = framework::Tensor;
-
-  auto* x = ctx.Input<Tensor>("X");
-  auto* y = ctx.Input<Tensor>("Y");
-  auto* z = ctx.Output<Tensor>("Out");
-  z->mutable_data<OutType>(ctx.GetPlace());
+void ElementwiseComputeEx(const framework::ExecutionContext& ctx,
+                          const framework::Tensor* x,
+                          const framework::Tensor* y, int axis,
+                          framework::Tensor* z) {
   TransformFunctor<Functor, T, DeviceContext, OutType> functor(
       x, y, z, ctx.template device_context<DeviceContext>(), Functor());
 
@@ -394,7 +387,6 @@ void ElementwiseComputeEx(const framework::ExecutionContext& ctx) {
     x_dims = framework::make_ddim(extended_dims);
   }
 
-  int axis = ctx.Attr<int>("axis");
   axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
   PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
                  "Axis should be in range [0, x_dims)");
diff --git a/paddle/operators/elementwise_pow_op.h b/paddle/operators/elementwise_pow_op.h
index 6019e709e0..874fd3f09f 100644
--- a/paddle/operators/elementwise_pow_op.h
+++ b/paddle/operators/elementwise_pow_op.h
@@ -29,7 +29,14 @@ template <typename DeviceContext, typename T>
 class ElementwisePowKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
diff --git a/paddle/operators/elementwise_sub_op.h b/paddle/operators/elementwise_sub_op.h
index a2aca79302..c2749a8e6b 100644
--- a/paddle/operators/elementwise_sub_op.h
+++ b/paddle/operators/elementwise_sub_op.h
@@ -27,7 +27,14 @@ template <typename DeviceContext, typename T>
 class ElementwiseSubKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(ctx);
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* z = ctx.Output<Tensor>("Out");
+    z->mutable_data<T>(ctx.GetPlace());
+    int axis = ctx.Attr<int>("axis");
+    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
   }
 };
 
@@ -93,9 +100,19 @@ template <typename DeviceContext, typename T>
 class ElementwiseSubGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    using Tensor = framework::Tensor;
+
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* out = ctx.Input<Tensor>("Out");
+    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
+    int axis = ctx.Attr<int>("axis");
     ElementwiseGradCompute<DeviceContext, T, ElementwiseSubGradFunctor<T>,
                            ElementwiseSubBroadCastGradFunctor<T>,
-                           ElementwiseSubBroadCast2GradFunctor<T>>(ctx);
+                           ElementwiseSubBroadCast2GradFunctor<T>>(
+        ctx, x, y, out, dout, axis, dx, dy);
   }
 };
 

From 2ffa3a8bf6a7cb0e3d5e1ac211417c234ab04f04 Mon Sep 17 00:00:00 2001
From: xzl <zlx_hg@163.com>
Date: Fri, 2 Feb 2018 18:28:23 +0800
Subject: [PATCH 221/314] rename op to depthwise_conv2d, more efficient

---
 paddle/operators/conv_op.cc                   |  8 +-
 paddle/operators/conv_op.cu.cc                |  4 +-
 paddle/operators/math/depthwise_conv.cu       | 79 ++++++-------------
 python/paddle/v2/fluid/layers/nn.py           |  2 +-
 .../paddle/v2/fluid/tests/test_conv2d_op.py   |  4 +-
 5 files changed, 34 insertions(+), 63 deletions(-)

diff --git a/paddle/operators/conv_op.cc b/paddle/operators/conv_op.cc
index d25f3fd1a0..cef7ddd5fe 100644
--- a/paddle/operators/conv_op.cc
+++ b/paddle/operators/conv_op.cc
@@ -320,20 +320,20 @@ REGISTER_OP(conv2d, ops::ConvOp, ops::Conv2DOpMaker, conv2d_grad,
             ops::ConvOpGrad);
 
 // depthwise convolution op
-REGISTER_OP(depthwise_conv, ops::ConvOp, ops::Conv2DOpMaker,
-            depthwise_conv_grad, ops::ConvOpGrad);
+REGISTER_OP(depthwise_conv2d, ops::ConvOp, ops::Conv2DOpMaker,
+            depthwise_conv2d_grad, ops::ConvOpGrad);
 REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
             ops::ConvOpGrad);
 
 // depthwise conv kernel
 // TODO(xingzhaolong): neon kernel for mobile
 REGISTER_OP_CPU_KERNEL(
-    depthwise_conv,
+    depthwise_conv2d,
     ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
     ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
 
 REGISTER_OP_CPU_KERNEL(
-    depthwise_conv_grad,
+    depthwise_conv2d_grad,
     ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
     ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
 
diff --git a/paddle/operators/conv_op.cu.cc b/paddle/operators/conv_op.cu.cc
index 02a4e52466..d0bd40ee95 100644
--- a/paddle/operators/conv_op.cu.cc
+++ b/paddle/operators/conv_op.cu.cc
@@ -17,12 +17,12 @@ limitations under the License. */
 namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
-    depthwise_conv,
+    depthwise_conv2d,
     ops::DepthwiseConvKernel<paddle::platform::CUDADeviceContext, float>,
     ops::DepthwiseConvKernel<paddle::platform::CUDADeviceContext, double>);
 
 REGISTER_OP_CUDA_KERNEL(
-    depthwise_conv_grad,
+    depthwise_conv2d_grad,
     ops::DepthwiseConvGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::DepthwiseConvGradKernel<paddle::platform::CUDADeviceContext, double>);
 
diff --git a/paddle/operators/math/depthwise_conv.cu b/paddle/operators/math/depthwise_conv.cu
index b9b958c92b..b212e78208 100644
--- a/paddle/operators/math/depthwise_conv.cu
+++ b/paddle/operators/math/depthwise_conv.cu
@@ -42,38 +42,23 @@ __global__ void KernelDepthwiseConv(
     T value = 0;
     const int h_in_start = -padding_height + h_out * stride_height;
     const int w_in_start = -padding_width + w_out * stride_width;
-    const int h_in_end =
-        -padding_height + h_out * stride_height + filter_height - 1;
-    const int w_in_end =
-        -padding_width + w_out * stride_width + filter_width - 1;
+    const int h_in_end = h_in_start + filter_height;
+    const int w_in_end = w_in_start + filter_width;
 
     const int in_offset =
         ((batch * input_channels + c_in) * input_height) * input_width;
 
-    if ((h_in_start >= 0) && (h_in_end < input_height) && (w_in_start >= 0) &&
-        (w_in_end < input_width)) {
-      for (int kh = 0; kh < filter_height; ++kh) {
-        for (int kw = 0; kw < filter_width; ++kw) {
-          const int h_in = h_in_start + kh;
-          const int w_in = w_in_start + kw;
-          const int offset = in_offset + h_in * input_width + w_in;
-
-          value += (*weight) * input_data[offset];
-          ++weight;
-        }
-      }
-    } else {
-      for (int kh = 0; kh < filter_height; ++kh) {
-        for (int kw = 0; kw < filter_width; ++kw) {
-          const int h_in = h_in_start + kh;
-          const int w_in = w_in_start + kw;
-          if ((h_in >= 0) && (h_in < input_height) && (w_in >= 0) &&
-              (w_in < input_width)) {
-            const int offset = in_offset + h_in * input_width + w_in;
-            value += (*weight) * input_data[offset];
-          }
-          ++weight;
-        }
+    const int h_end = h_in_end < input_height ? h_in_end : input_height;
+    const int w_end = w_in_end < input_width ? w_in_end : input_width;
+    const int h_start = h_in_start > 0 ? h_in_start : 0;
+    const int w_start = w_in_start > 0 ? w_in_start : 0;
+
+    for (int h_in = h_start; h_in < h_end; h_in++) {
+      for (int w_in = w_start; w_in < w_end; w_in++) {
+        const int offset = in_offset + h_in * input_width + w_in;
+        value +=
+            weight[(h_in - h_in_start) * filter_width + (w_in - w_in_start)] *
+            input_data[offset];
       }
     }
     output_data[index] = value;
@@ -162,32 +147,18 @@ __global__ void KernelDepthwiseConvFilterGrad(
         (batch * input_channels + c_in) * input_height * input_width;
 
     T* addr_offset = filter_grad_data + c_out * filter_height * filter_width;
-
-    if ((h_in_start >= 0) && (h_in_end < input_height) && (w_in_start >= 0) &&
-        (w_in_end < input_width)) {
-      for (int kw = 0; kw < filter_width; kw++) {
-        for (int kh = 0; kh < filter_height; kh++) {
-          const int h_in = h_in_start + kh;
-          const int w_in = w_in_start + kw;
-          const int offset = in_offset + h_in * input_width + w_in;
-          const T diff_temp = output_grad_data[index] * input_data[offset];
-          T* addr = addr_offset + kh * filter_width + kw;
-          paddle::platform::CudaAtomicAdd(addr, diff_temp);
-        }
-      }
-    } else {
-      for (int kw = 0; kw < filter_width; kw++) {
-        for (int kh = 0; kh < filter_height; kh++) {
-          const int h_in = h_in_start + kh;
-          const int w_in = w_in_start + kw;
-          if ((h_in >= 0) && (h_in < input_height) && (w_in >= 0) &&
-              (w_in < input_width)) {
-            const int offset = in_offset + h_in * input_width + w_in;
-            const T diff_temp = output_grad_data[index] * input_data[offset];
-            T* addr = addr_offset + kh * filter_width + kw;
-            paddle::platform::CudaAtomicAdd(addr, diff_temp);
-          }
-        }
+    const int h_end = h_in_end < input_height ? h_in_end : input_height;
+    const int w_end = w_in_end < input_width ? w_in_end : input_width;
+    const int h_start = h_in_start > 0 ? h_in_start : 0;
+    const int w_start = w_in_start > 0 ? w_in_start : 0;
+
+    for (int h_in = h_start; h_in < h_end; h_in++) {
+      for (int w_in = w_start; w_in < w_end; w_in++) {
+        const int offset = in_offset + h_in * input_width + w_in;
+        const T diff_temp = output_grad_data[index] * input_data[offset];
+        T* addr = addr_offset + (h_in - h_in_start) * filter_width +
+                  (w_in - w_in_start);
+        paddle::platform::CudaAtomicAdd(addr, diff_temp);
       }
     }
   }
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 4be6ae8ed6..aaf096f0dd 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -1237,7 +1237,7 @@ def conv2d(input,
     l_type = 'conv2d'
     if (num_channels == groups and num_filters % num_channels == 0 and
             not use_cudnn):
-        l_type = 'depthwise_conv'
+        l_type = 'depthwise_conv2d'
 
     helper = LayerHelper(l_type, **locals())
     dtype = helper.input_dtype()
diff --git a/python/paddle/v2/fluid/tests/test_conv2d_op.py b/python/paddle/v2/fluid/tests/test_conv2d_op.py
index a034d0ab91..7512ea333e 100644
--- a/python/paddle/v2/fluid/tests/test_conv2d_op.py
+++ b/python/paddle/v2/fluid/tests/test_conv2d_op.py
@@ -250,7 +250,7 @@ class TestDepthwiseConv(TestConv2dOp):
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3]
-        self.op_type = "depthwise_conv"
+        self.op_type = "depthwise_conv2d"
 
 
 class TestDepthwiseConv2(TestConv2dOp):
@@ -262,7 +262,7 @@ class TestDepthwiseConv2(TestConv2dOp):
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] / self.groups
         self.filter_size = [6, f_c, 3, 3]
-        self.op_type = "depthwise_conv"
+        self.op_type = "depthwise_conv2d"
 
 
 #  cudnn v5 does not support dilation conv.

From ab1341eab7c67b991499ed9cad15d8901e2bc76b Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 2 Feb 2018 18:40:52 +0800
Subject: [PATCH 222/314] fix typo

---
 paddle/framework/shape_inference.cc | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/shape_inference.cc b/paddle/framework/shape_inference.cc
index 14dba75808..0e17219e4e 100644
--- a/paddle/framework/shape_inference.cc
+++ b/paddle/framework/shape_inference.cc
@@ -21,7 +21,7 @@ namespace framework {
 framework::DDim InferShapeContext::GetInputDim(const std::string &name) const {
   const std::vector<std::string> &arg_names = Inputs(name);
   PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
-                    "Input(%s) shoudl holds one element, but now it holds %d",
+                    "Input(%s) should hold one element, but now it holds %d",
                     name, arg_names.size());
   return this->GetDim(arg_names[0]);
 }
@@ -41,7 +41,7 @@ DDim InferShapeContext::GetInputsElementDim(const std::string &name,
 void InferShapeContext::SetOutputDim(const std::string &name, const DDim &dim) {
   auto &arg_names = Outputs(name);
   PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
-                    "Output(%s) shoudl holds one element, but now it holds %d",
+                    "Output(%s) should hold one element, but now it holds %d",
                     name, arg_names.size());
   SetDim(arg_names[0], dim);
 }

From 9a970702d3cff51af75ff513c07dbeb226920f1d Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Fri, 2 Feb 2018 18:56:16 +0800
Subject: [PATCH 223/314] remove unnecessary framework::

---
 paddle/framework/shape_inference.cc | 12 ++++++------
 paddle/framework/shape_inference.h  | 13 ++++++-------
 2 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/paddle/framework/shape_inference.cc b/paddle/framework/shape_inference.cc
index 0e17219e4e..a0fa467291 100644
--- a/paddle/framework/shape_inference.cc
+++ b/paddle/framework/shape_inference.cc
@@ -18,7 +18,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-framework::DDim InferShapeContext::GetInputDim(const std::string &name) const {
+DDim InferShapeContext::GetInputDim(const std::string &name) const {
   const std::vector<std::string> &arg_names = Inputs(name);
   PADDLE_ENFORCE_EQ(arg_names.size(), 1UL,
                     "Input(%s) should hold one element, but now it holds %d",
@@ -26,7 +26,7 @@ framework::DDim InferShapeContext::GetInputDim(const std::string &name) const {
   return this->GetDim(arg_names[0]);
 }
 
-std::vector<framework::DDim> InferShapeContext::GetInputsDim(
+std::vector<DDim> InferShapeContext::GetInputsDim(
     const std::string &name) const {
   const std::vector<std::string> &arg_names = Inputs(name);
   return GetDims(arg_names);
@@ -46,15 +46,15 @@ void InferShapeContext::SetOutputDim(const std::string &name, const DDim &dim) {
   SetDim(arg_names[0], dim);
 }
 
-void InferShapeContext::SetOutputsDim(
-    const std::string &name, const std::vector<framework::DDim> &dims) {
+void InferShapeContext::SetOutputsDim(const std::string &name,
+                                      const std::vector<DDim> &dims) {
   auto &names = Outputs(name);
   SetDims(names, dims);
 }
 
 std::vector<DDim> InferShapeContext::GetDims(
     const std::vector<std::string> &names) const {
-  std::vector<framework::DDim> ret;
+  std::vector<DDim> ret;
   ret.reserve(names.size());
   std::transform(
       names.begin(), names.end(), std::back_inserter(ret),
@@ -62,7 +62,7 @@ std::vector<DDim> InferShapeContext::GetDims(
   return ret;
 }
 void InferShapeContext::SetDims(const std::vector<std::string> &names,
-                                const std::vector<framework::DDim> &dims) {
+                                const std::vector<DDim> &dims) {
   size_t length = names.size();
   PADDLE_ENFORCE_EQ(length, dims.size());
   for (size_t i = 0; i < length; ++i) {
diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h
index 77fc9359be..830f199ed1 100644
--- a/paddle/framework/shape_inference.h
+++ b/paddle/framework/shape_inference.h
@@ -35,14 +35,13 @@ class InferShapeContext {
   virtual bool HasInputs(const std::string &name) const = 0;
   virtual bool HasOutputs(const std::string &name) const = 0;
 
-  framework::DDim GetInputDim(const std::string &name) const;
+  DDim GetInputDim(const std::string &name) const;
 
-  std::vector<framework::DDim> GetInputsDim(const std::string &name) const;
+  std::vector<DDim> GetInputsDim(const std::string &name) const;
   DDim GetInputsElementDim(const std::string &name, int idx) const;
 
   void SetOutputDim(const std::string &name, const DDim &dim);
-  void SetOutputsDim(const std::string &name,
-                     const std::vector<framework::DDim> &dims);
+  void SetOutputsDim(const std::string &name, const std::vector<DDim> &dims);
 
   virtual AttrReader Attrs() const = 0;
   virtual const std::vector<std::string> &Inputs(
@@ -57,11 +56,11 @@ class InferShapeContext {
 
   // Note: In while op, we need this to be public
   void SetDims(const std::vector<std::string> &names,
-               const std::vector<framework::DDim> &dims);
+               const std::vector<DDim> &dims);
 
  protected:
-  virtual framework::DDim GetDim(const std::string &name) const = 0;
-  virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
+  virtual DDim GetDim(const std::string &name) const = 0;
+  virtual void SetDim(const std::string &name, const DDim &dim) = 0;
 
   std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
   std::vector<proto::VarDesc::VarType> GetVarTypes(

From 71a70f209ac63b6f351bd3c399ddff804da090f7 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 31 Jan 2018 17:07:51 +0800
Subject: [PATCH 224/314] refine gradient

---
 paddle/operators/layer_norm_op.cc | 42 ++++++++++++-------------------
 1 file changed, 16 insertions(+), 26 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 1c6d2ae4d0..8fcac00e08 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -291,32 +291,28 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
       auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
       auto triple_product_func = [](T ele) { return ele * ele * ele; };
       auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+
+      auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
       // TODO(zcd): these code can be refined
       if (d_scale) {
         auto scale_map =
             ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
         // dy_dx
-        auto dx_end = var_map.unaryExpr(inv_std_func)
-                          .replicate(1, right)
-                          .cwiseProduct(d_y_map)
-                          .cwiseProduct(scale_map.replicate(left, 1));
+        auto dx_end =
+            inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
+                scale_map.replicate(left, 1));
+
         // dy_dmean_dx
-        auto dx_mean = (T(-1.0) / right) *
-                       var_map.unaryExpr(inv_std_func)
-                           .replicate(1, right)
-                           .cwiseProduct(d_y_map)
-                           .cwiseProduct(scale_map.replicate(left, 1))
-                           .rowwise()
-                           .sum()
-                           .replicate(1, right);
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
         // dy_var_dx
         auto dvar_end_part = (x_map - mean_map.replicate(1, right))
                                  .cwiseProduct(scale_map.replicate(left, 1))
                                  .cwiseProduct(d_y_map)
                                  .rowwise()
                                  .sum();
-        auto dvar_end = var_map.unaryExpr(inv_std_func)
-                            .unaryExpr(triple_product_func)
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
                             .cwiseProduct(dvar_end_part)
                             .replicate(1, right);
         auto dx_var =
@@ -326,24 +322,18 @@ class LayerNormGradKernel<platform::CPUDeviceContext, T>
         d_x_map = dx_end + dx_mean + dx_var;
       } else {
         // dy_dx
-        auto dx_end = var_map.unaryExpr(inv_std_func)
-                          .replicate(1, right)
-                          .cwiseProduct(d_y_map);
+        auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
+
         // dy_dmean_dx
-        auto dx_mean = (T(-1.0) / right) *
-                       var_map.unaryExpr(inv_std_func)
-                           .replicate(1, right)
-                           .cwiseProduct(d_y_map)
-                           .rowwise()
-                           .sum()
-                           .replicate(1, right);
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
         // dy_var_dx
         auto dvar_end_part = (x_map - mean_map.replicate(1, right))
                                  .cwiseProduct(d_y_map)
                                  .rowwise()
                                  .sum();
-        auto dvar_end = var_map.unaryExpr(inv_std_func)
-                            .unaryExpr(triple_product_func)
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
                             .cwiseProduct(dvar_end_part)
                             .replicate(1, right);
         auto dx_var =

From 37a251ebafce61776b2fea7a2fb2ee16defd14ea Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Fri, 2 Feb 2018 15:46:51 -0800
Subject: [PATCH 225/314] Fix copyright for test_fetch_var.py

---
 python/paddle/v2/fluid/tests/test_fetch_var.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/python/paddle/v2/fluid/tests/test_fetch_var.py b/python/paddle/v2/fluid/tests/test_fetch_var.py
index 670ab54f51..ed75a350b0 100644
--- a/python/paddle/v2/fluid/tests/test_fetch_var.py
+++ b/python/paddle/v2/fluid/tests/test_fetch_var.py
@@ -1,3 +1,17 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.layers as layers
 import op_test

From dbe06551b86460d5ebf18ee33218cd6d11cd07e4 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <aroraabhinav@baidu.com>
Date: Fri, 2 Feb 2018 16:12:53 -0800
Subject: [PATCH 226/314] Channel should notify both condition variables on
 close

---
 paddle/framework/details/buffered_channel.h | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index b093e15892..9c806461aa 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -49,6 +49,7 @@ class Buffered : public paddle::framework::Channel<T> {
   }
 
   void NotifyAllSenders(std::unique_lock<std::mutex>*);
+  void NotifyAllParticipants(std::unique_lock<std::mutex>*);
 };
 
 template <typename T>
@@ -80,7 +81,7 @@ template <typename T>
 void Buffered<T>::Close() {
   std::unique_lock<std::mutex> lock(mu_);
   closed_ = true;
-  NotifyAllSenders(&lock);
+  NotifyAllParticipants(&lock);
 }
 
 template <typename T>
@@ -88,7 +89,7 @@ Buffered<T>::~Buffered() {
   std::unique_lock<std::mutex> lock(mu_);
   closed_ = true;
   channel_.clear();
-  NotifyAllSenders(&lock);
+  NotifyAllParticipants(&lock);
 }
 
 template <typename T>
@@ -97,6 +98,13 @@ void Buffered<T>::NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
   full_cond_var_.notify_all();
 }
 
+template <typename T>
+void Buffered<T>::NotifyAllParticipants(std::unique_lock<std::mutex>* lock) {
+  lock->unlock();
+  full_cond_var_.notify_all();
+  empty_cond_var_.notify_all();
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

From 022e5dee8e685134e6c0199d7d0ee8762a03eb80 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Fri, 2 Feb 2018 20:08:39 -0800
Subject: [PATCH 227/314] Added more receivers less senders. Receivers should
 block. (#8061)

* Adding more receivers less senders

* Added more receivers less senders

* Added more send

* Updated comment

* Fixed code style

* Fixing review comments
---
 paddle/framework/channel_test.cc | 36 +++++++++++++++++++++++++++++++-
 1 file changed, 35 insertions(+), 1 deletion(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 31ac72eda9..c3533bbb1a 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -67,7 +67,7 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
   std::thread t([&]() {
     // Try to write more than buffer size.
     for (size_t i = 0; i < 2 * buffer_size; ++i) {
-      ch->Send(&i);  // should not block
+      ch->Send(&i);  // should block after 10 iterations
       sum += i;
     }
   });
@@ -207,3 +207,37 @@ TEST(Channel, UnbufferedLessReceiveMoreSendTest) {
   t.join();
   delete ch;
 }
+
+TEST(Channel, UnbufferedMoreReceiveLessSendTest) {
+  auto ch = MakeChannel<int>(0);
+  unsigned sum_send = 0;
+  unsigned sum_receive = 0;
+  // The receiver should block after 5
+  // iterations, since there are only 5 senders.
+  std::thread t([&]() {
+    for (int i = 0; i < 8; i++) {
+      int recv;
+      ch->Receive(&recv);  // should block after the fifth iteration.
+      EXPECT_EQ(recv, i);
+      sum_receive += i;
+    }
+  });
+  for (int i = 0; i < 5; i++) {
+    ch->Send(&i);
+    sum_send += i;
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+  EXPECT_EQ(sum_send, 10U);
+  EXPECT_EQ(sum_receive, 10U);
+  // send three more elements
+  for (int i = 5; i < 8; i++) {
+    ch->Send(&i);
+    sum_send += i;
+  }
+
+  CloseChannel(ch);
+  t.join();
+  EXPECT_EQ(sum_send, 28U);
+  EXPECT_EQ(sum_receive, 28U);
+  delete ch;
+}

From 76e188e5c6b39fc7cccab9d5b64bd19163fafb77 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Fri, 2 Feb 2018 15:09:23 +0800
Subject: [PATCH 228/314] Add layer norm [GPU]

---
 paddle/operators/compare_op.h              |   2 +-
 paddle/operators/elementwise_add_op.h      |   3 +-
 paddle/operators/elementwise_div_op.h      |   3 +-
 paddle/operators/elementwise_max_op.h      |   3 +-
 paddle/operators/elementwise_min_op.h      |   3 +-
 paddle/operators/elementwise_mul_op.h      |   3 +-
 paddle/operators/elementwise_op_function.h |   4 +-
 paddle/operators/elementwise_pow_op.h      |   3 +-
 paddle/operators/elementwise_sub_op.h      |   3 +-
 paddle/operators/layer_norm_op.cc          |   9 +-
 paddle/operators/layer_norm_op.cu          | 245 +++++++++++++++++++++
 paddle/operators/math/math_function.cc     |   6 +
 paddle/operators/math/math_function.cu     |  25 +++
 paddle/operators/math/math_function.h      |  12 +
 paddle/operators/math/math_function_impl.h |  82 +++++++
 15 files changed, 393 insertions(+), 13 deletions(-)
 create mode 100644 paddle/operators/layer_norm_op.cu

diff --git a/paddle/operators/compare_op.h b/paddle/operators/compare_op.h
index b275fd75b3..79b8c6f59c 100644
--- a/paddle/operators/compare_op.h
+++ b/paddle/operators/compare_op.h
@@ -62,7 +62,7 @@ class CompareOpKernel
     z->mutable_data<T>(context.GetPlace());
     int axis = context.Attr<int>("axis");
     ElementwiseComputeEx<Functor, DeviceContext, T, bool>(context, x, y, axis,
-                                                          z);
+                                                          Functor(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_add_op.h b/paddle/operators/elementwise_add_op.h
index c32288d698..c24f97a850 100644
--- a/paddle/operators/elementwise_add_op.h
+++ b/paddle/operators/elementwise_add_op.h
@@ -35,7 +35,8 @@ class ElementwiseAddKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          AddFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_div_op.h b/paddle/operators/elementwise_div_op.h
index 07ebade31f..dc863cc598 100644
--- a/paddle/operators/elementwise_div_op.h
+++ b/paddle/operators/elementwise_div_op.h
@@ -35,7 +35,8 @@ class ElementwiseDivKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          DivFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_max_op.h b/paddle/operators/elementwise_max_op.h
index 717e45ab31..67efe4e151 100644
--- a/paddle/operators/elementwise_max_op.h
+++ b/paddle/operators/elementwise_max_op.h
@@ -35,7 +35,8 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<MaxFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          MaxFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_min_op.h b/paddle/operators/elementwise_min_op.h
index 0de9a91c52..cf11759404 100644
--- a/paddle/operators/elementwise_min_op.h
+++ b/paddle/operators/elementwise_min_op.h
@@ -35,7 +35,8 @@ class ElementwiseMinKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<MinFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          MinFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_mul_op.h b/paddle/operators/elementwise_mul_op.h
index ae7a71e024..773125f5ca 100644
--- a/paddle/operators/elementwise_mul_op.h
+++ b/paddle/operators/elementwise_mul_op.h
@@ -34,7 +34,8 @@ class ElementwiseMulKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          MulFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_op_function.h b/paddle/operators/elementwise_op_function.h
index 213fe1f5a8..74abf7c4a5 100644
--- a/paddle/operators/elementwise_op_function.h
+++ b/paddle/operators/elementwise_op_function.h
@@ -365,10 +365,10 @@ template <typename Functor, typename DeviceContext, typename T,
           typename OutType = T>
 void ElementwiseComputeEx(const framework::ExecutionContext& ctx,
                           const framework::Tensor* x,
-                          const framework::Tensor* y, int axis,
+                          const framework::Tensor* y, int axis, Functor func,
                           framework::Tensor* z) {
   TransformFunctor<Functor, T, DeviceContext, OutType> functor(
-      x, y, z, ctx.template device_context<DeviceContext>(), Functor());
+      x, y, z, ctx.template device_context<DeviceContext>(), func);
 
   auto x_dims = x->dims();
   auto y_dims = y->dims();
diff --git a/paddle/operators/elementwise_pow_op.h b/paddle/operators/elementwise_pow_op.h
index 874fd3f09f..0c5dd031ec 100644
--- a/paddle/operators/elementwise_pow_op.h
+++ b/paddle/operators/elementwise_pow_op.h
@@ -36,7 +36,8 @@ class ElementwisePowKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<PowFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          PowFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/elementwise_sub_op.h b/paddle/operators/elementwise_sub_op.h
index c2749a8e6b..6a88c5f6b4 100644
--- a/paddle/operators/elementwise_sub_op.h
+++ b/paddle/operators/elementwise_sub_op.h
@@ -34,7 +34,8 @@ class ElementwiseSubKernel : public framework::OpKernel<T> {
     auto* z = ctx.Output<Tensor>("Out");
     z->mutable_data<T>(ctx.GetPlace());
     int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(ctx, x, y, axis, z);
+    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
+                                                          SubFunctor<T>(), z);
   }
 };
 
diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 8fcac00e08..6dd18277c9 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/layer_norm_op.h"
+#include "paddle/operators/elementwise_op_function.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -353,8 +355,9 @@ namespace ops = paddle::operators;
 REGISTER_OP(layer_norm, ops::LayerNormOp, ops::LayerNormOpMaker,
             layer_norm_grad, ops::LayerNormGradOp);
 REGISTER_OP_CPU_KERNEL(
-    layer_norm,
-    ops::LayerNormKernel<paddle::platform::CPUDeviceContext, float>);
+    layer_norm, ops::LayerNormKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LayerNormKernel<paddle::platform::CPUDeviceContext, double>);
 REGISTER_OP_CPU_KERNEL(
     layer_norm_grad,
-    ops::LayerNormGradKernel<paddle::platform::CPUDeviceContext, float>);
+    ops::LayerNormGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::LayerNormGradKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/layer_norm_op.cu b/paddle/operators/layer_norm_op.cu
new file mode 100644
index 0000000000..a84f5a41ea
--- /dev/null
+++ b/paddle/operators/layer_norm_op.cu
@@ -0,0 +1,245 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/elementwise_op_function.h"
+#include "paddle/operators/layer_norm_op.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
+namespace {
+template <typename T>
+struct SubAndSquareFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
+};
+
+template <typename T>
+struct DivAndSqrtFunctor {
+  explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
+  inline HOSTDEVICE T operator()(T a, T b) const {
+    return a / (sqrt(b) + epsilon_);
+  }
+
+ private:
+  T epsilon_;
+};
+
+template <typename T>
+struct MulFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a * b; }
+};
+
+template <typename T>
+struct AddFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
+};
+
+template <typename T>
+struct SubFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
+};
+
+template <typename T>
+struct MulInvVarFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const {
+    return a * std::sqrt(1.0 / b);
+  }
+};
+}  // namespace
+
+template <typename DeviceContext, typename T>
+class LayerNormCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *bias = ctx.Input<Tensor>("Bias");
+    auto x = *ctx.Input<Tensor>("X");
+
+    auto *y = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    const auto &x_dims = x.dims();
+
+    y->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+
+    framework::DDim matrix_shape({left, right});
+
+    x.Resize(matrix_shape);
+    y->Resize(matrix_shape);
+
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    math::RowwiseMean<DeviceContext, T> row_mean;
+
+    // functor-> get mean
+    row_mean(dev_ctx, x, mean);
+
+    // functor-> get variance
+    ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
+        ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
+    row_mean(dev_ctx, *y, var);
+
+    // functor-> get norm_out
+    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+        ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
+    ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+        ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
+        y);
+
+    framework::DDim scale_shape({right});
+    if (scale) {
+      Tensor scale_matrix = *scale;
+      scale_matrix.Resize(scale_shape);
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
+    }
+    if (bias) {
+      Tensor bias_matrix = *bias;
+      bias_matrix.Resize(scale_shape);
+      ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
+          ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
+    }
+    y->Resize(x_dims);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class LayerNormCUDAGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto x = *ctx.Input<Tensor>("X");
+    auto mean = *ctx.Input<Tensor>("Mean");
+    auto var = *ctx.Input<Tensor>("Variance");
+    auto scale = *ctx.Input<Tensor>("Scale");
+    auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    // init output
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    const auto &x_dims = x.dims();
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+    framework::DDim matrix_shape({left, right});
+
+    d_y.Resize(matrix_shape);
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    math::ColwiseSum<DeviceContext, T> colwise_sum;
+
+    Tensor temp;
+    Tensor temp_norm;
+    if (d_scale || d_x) {
+      x.Resize(matrix_shape);
+      temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
+      temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
+
+      // get x_norm
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
+      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &var, /*axis*/ 0,
+          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
+    }
+
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      colwise_sum(dev_ctx, d_y, d_bias);
+    }
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &d_y, /*axis*/ 0, MulFunctor<T>(), &temp);
+      colwise_sum(dev_ctx, temp, d_scale);
+    }
+
+    if (d_x) {
+      framework::DDim vec_shape({left});
+      d_x->mutable_data<T>(ctx.GetPlace());
+      Tensor temp_vec;
+      temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
+
+      auto &dev_ctx = ctx.template device_context<DeviceContext>();
+      math::RowwiseMean<DeviceContext, T> row_mean;
+
+      if (d_scale) {
+        // dy_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
+        framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
+
+        // dy_dmean_dx
+        row_mean(dev_ctx, temp, &temp_vec);
+        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+        // dy_var_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
+
+      } else {
+        // dy_dx
+        framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
+
+        // dy_dmean_dx
+        row_mean(dev_ctx, d_y, &temp_vec);
+        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+        // dy_var_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &d_y, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
+      }
+      // dy_var_dx
+      row_mean(dev_ctx, temp, &temp_vec);
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+          ctx, d_x, &var, /*axis*/ 0,
+          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    layer_norm,
+    ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    layer_norm_grad,
+    ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc
index dcf4b85e1a..ce0a5f6cff 100644
--- a/paddle/operators/math/math_function.cc
+++ b/paddle/operators/math/math_function.cc
@@ -331,6 +331,12 @@ template struct RowwiseAdd<platform::CPUDeviceContext, double>;
 template struct ColwiseSum<platform::CPUDeviceContext, float>;
 template struct ColwiseSum<platform::CPUDeviceContext, double>;
 
+template struct RowwiseSum<platform::CPUDeviceContext, float>;
+template struct RowwiseSum<platform::CPUDeviceContext, double>;
+
+template struct RowwiseMean<platform::CPUDeviceContext, float>;
+template struct RowwiseMean<platform::CPUDeviceContext, double>;
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu
index d47a7f818d..c0a107470a 100644
--- a/paddle/operators/math/math_function.cu
+++ b/paddle/operators/math/math_function.cu
@@ -325,6 +325,31 @@ void ColwiseSum<platform::CUDADeviceContext, double>::operator()(
       vector->data<double>());
 }
 
+template struct RowwiseSum<platform::CUDADeviceContext, float>;
+// template struct RowwiseSum<platform::CUDADeviceContext, double>;
+// TODO(zcd): Following ColwiseSum format, need to confirm.
+// The RowwiseSum<platform::CUDADeviceContext, double> failed in debug mode,
+// and only failed for this case. So reimplemented it.
+template <>
+void RowwiseSum<platform::CUDADeviceContext, double>::operator()(
+    const platform::CUDADeviceContext& context, const framework::Tensor& input,
+    framework::Tensor* vector) {
+  auto in_dims = input.dims();
+  auto size = input.numel() / in_dims[0];
+  PADDLE_ENFORCE_EQ(vector->numel(), in_dims[0]);
+  framework::Tensor one;
+  one.mutable_data<double>({size}, context.GetPlace());
+  SetConstant<platform::CUDADeviceContext, double> set;
+  set(context, &one, static_cast<double>(1.0));
+  gemv<platform::CUDADeviceContext, double>(
+      context, true, static_cast<int>(in_dims[1]), static_cast<int>(in_dims[0]),
+      1.0, one.data<double>(), input.data<double>(), 0.0,
+      vector->data<double>());
+}
+
+template struct RowwiseMean<platform::CUDADeviceContext, float>;
+template struct RowwiseMean<platform::CUDADeviceContext, double>;
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h
index 8cc03c2ba0..cb14d1e574 100644
--- a/paddle/operators/math/math_function.h
+++ b/paddle/operators/math/math_function.h
@@ -128,6 +128,18 @@ struct ColwiseSum {
                   framework::Tensor* vec);
 };
 
+template <typename DeviceContext, typename T>
+struct RowwiseSum {
+  void operator()(const DeviceContext& context, const framework::Tensor& input,
+                  framework::Tensor* vec);
+};
+
+template <typename DeviceContext, typename T>
+struct RowwiseMean {
+  void operator()(const DeviceContext& context, const framework::Tensor& input,
+                  framework::Tensor* vec);
+};
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/math/math_function_impl.h b/paddle/operators/math/math_function_impl.h
index de591626df..af4127788a 100644
--- a/paddle/operators/math/math_function_impl.h
+++ b/paddle/operators/math/math_function_impl.h
@@ -87,6 +87,88 @@ class ColwiseSum<platform::CPUDeviceContext, T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+void RowwiseMean<DeviceContext, T>::operator()(const DeviceContext& context,
+                                               const framework::Tensor& input,
+                                               framework::Tensor* out) {
+  auto in_dims = input.dims();
+  PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
+  PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);
+
+  auto in = framework::EigenMatrix<T>::From(input);
+  auto vec = framework::EigenVector<T>::Flatten(*out);
+
+  vec.device(*context.eigen_device()) = in.mean(Eigen::array<int, 1>({{1}}));
+}
+// TODO(zcd): Following ColwiseSum format, need to confirm.
+// Specialize for CPU, since Eigen implement a general reduce. However,
+// rowwise-sum can be easily implemented. General reduce has a huge overhead in
+// CPU
+template <typename T>
+class RowwiseMean<platform::CPUDeviceContext, T> {
+ public:
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor* out) {
+    auto& in_dims = input.dims();
+    PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
+    auto height = in_dims[0];
+    auto size = in_dims[1];
+    PADDLE_ENFORCE_EQ(out->numel(), height);
+    auto inv_size = 1.0 / size;
+    T* out_buf = out->mutable_data<T>(out->place());
+    const T* in_buf = input.data<T>();
+
+    for (size_t i = 0; i < static_cast<size_t>(height); ++i) {
+      T sum = 0;
+      for (size_t j = 0; j < static_cast<size_t>(size); ++j) {
+        sum += in_buf[i * size + j];
+      }
+      out_buf[i] = sum * inv_size;
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+void RowwiseSum<DeviceContext, T>::operator()(const DeviceContext& context,
+                                              const framework::Tensor& input,
+                                              framework::Tensor* out) {
+  auto in_dims = input.dims();
+  PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
+  PADDLE_ENFORCE_EQ(out->numel(), in_dims[0]);
+
+  auto in = framework::EigenMatrix<T>::From(input);
+  auto vec = framework::EigenVector<T>::Flatten(*out);
+
+  vec.device(*context.eigen_device()) = in.sum(Eigen::array<int, 1>({{1}}));
+}
+// TODO(zcd): Following ColwiseSum format, need to confirm.
+// Specialize for CPU, since Eigen implement a general reduce. However,
+// rowwise-sum can be easily implemented. General reduce has a huge overhead in
+// CPU
+template <typename T>
+class RowwiseSum<platform::CPUDeviceContext, T> {
+ public:
+  void operator()(const platform::CPUDeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor* out) {
+    auto& in_dims = input.dims();
+    PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
+    auto height = in_dims[0];
+    auto size = in_dims[1];
+    PADDLE_ENFORCE_EQ(out->numel(), size);
+
+    T* out_buf = out->mutable_data<T>(out->place());
+    const T* in_buf = input.data<T>();
+
+    for (size_t i = 0; i < static_cast<size_t>(height); ++i) {
+      T sum = 0;
+      for (size_t j = 0; j < static_cast<size_t>(size); ++j) {
+        sum += in_buf[i * size + j];
+      }
+      out_buf[i] = sum;
+    }
+  }
+};
+
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

From e03337350356626f36cdff371a3e82292e25a1c8 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 3 Feb 2018 14:34:29 +0800
Subject: [PATCH 229/314] unifid GPU and CPU implementation

---
 paddle/operators/layer_norm_op.cc | 187 ------------------------
 paddle/operators/layer_norm_op.cu | 228 +-----------------------------
 paddle/operators/layer_norm_op.h  | 204 +++++++++++++++++++++++++-
 3 files changed, 206 insertions(+), 413 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 6dd18277c9..edc26dfb96 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/layer_norm_op.h"
-#include "paddle/operators/elementwise_op_function.h"
-#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -23,13 +21,6 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 using DataLayout = framework::DataLayout;
 
-template <typename T>
-using EigenMatrixMapRowMajor = Eigen::Map<
-    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
-template <typename T>
-using ConstEigenMatrixMapRowMajor = Eigen::Map<
-    const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
-
 class LayerNormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -118,75 +109,6 @@ https://arxiv.org/abs/1607.06450
   }
 };
 
-template <typename T>
-class LayerNormKernel<platform::CPUDeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const float epsilon = ctx.Attr<float>("epsilon");
-    const auto *scale = ctx.Input<Tensor>("Scale");
-    const auto *bias = ctx.Input<Tensor>("Bias");
-    const auto *x = ctx.Input<Tensor>("X");
-    const auto &x_dims = x->dims();
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-
-    auto *output = ctx.Output<Tensor>("Y");
-    auto *mean = ctx.Output<Tensor>("Mean");
-    auto *var = ctx.Output<Tensor>("Variance");
-    output->mutable_data<T>(ctx.GetPlace());
-    mean->mutable_data<T>(ctx.GetPlace());
-    var->mutable_data<T>(ctx.GetPlace());
-
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-
-    auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-
-    auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
-    auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
-    auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
-
-    auto squre = [](T ele) { return ele * ele; };
-    auto add_epslion = [epsilon](T ele) { return ele + epsilon; };
-
-    mean_map = input_map.rowwise().mean();
-    var_map = (input_map - mean_map.replicate(1, right))
-                  .unaryExpr(squre)
-                  .rowwise()
-                  .mean()
-                  .unaryExpr(add_epslion);
-
-    auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-    // TODO(zcd): Some thinking about output_map, is it appropriate that
-    // `output_map` and `input_map` point to the same memory.
-    auto inv_std = var_map.unaryExpr(inv_std_func);
-    if (scale && bias) {
-      auto scale_map =
-          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right))
-                       .cwiseProduct(scale_map.replicate(left, 1)) +
-                   bias_map.replicate(left, 1);
-    } else if (scale) {
-      auto scale_map =
-          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right))
-                       .cwiseProduct(scale_map.replicate(left, 1));
-    } else if (bias) {
-      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right)) +
-                   bias_map.replicate(left, 1);
-    } else {
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right));
-    }
-  }
-};
-
 class LayerNormGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -239,115 +161,6 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename T>
-class LayerNormGradKernel<platform::CPUDeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const auto *x = ctx.Input<Tensor>("X");
-    const auto *mean = ctx.Input<Tensor>("Mean");
-    const auto *var = ctx.Input<Tensor>("Variance");
-    const auto *scale = ctx.Input<Tensor>("Scale");
-    const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
-
-    const auto &x_dims = x->dims();
-
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-
-    // init output
-    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
-    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
-
-    auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-    auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
-    auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
-    auto var_map = ConstEigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
-
-    if (d_bias) {
-      d_bias->mutable_data<T>(ctx.GetPlace());
-      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
-      d_bias_map = d_y_map.colwise().sum();
-    }
-    if (d_scale) {
-      d_scale->mutable_data<T>(ctx.GetPlace());
-      auto d_scale_map =
-          EigenMatrixMapRowMajor<T>(d_scale->data<T>(), 1, right);
-      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-      // There are two equation to compute d_scale. One uses "Y" and the other
-      // does not use "Y"
-      d_scale_map =
-          ((x_map - mean_map.replicate(1, right))
-               .cwiseProduct(
-                   var_map.unaryExpr(inv_std_func).replicate(1, right))
-               .cwiseProduct(d_y_map))
-              .colwise()
-              .sum();
-    }
-
-    if (d_x) {
-      d_x->mutable_data<T>(ctx.GetPlace());
-      auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
-      auto triple_product_func = [](T ele) { return ele * ele * ele; };
-      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-
-      auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
-      // TODO(zcd): these code can be refined
-      if (d_scale) {
-        auto scale_map =
-            ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-        // dy_dx
-        auto dx_end =
-            inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
-                scale_map.replicate(left, 1));
-
-        // dy_dmean_dx
-        auto dx_mean =
-            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
-
-        // dy_var_dx
-        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                                 .cwiseProduct(scale_map.replicate(left, 1))
-                                 .cwiseProduct(d_y_map)
-                                 .rowwise()
-                                 .sum();
-        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
-                            .cwiseProduct(dvar_end_part)
-                            .replicate(1, right);
-        auto dx_var =
-            (T(-1.0) / right) *
-            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-        d_x_map = dx_end + dx_mean + dx_var;
-      } else {
-        // dy_dx
-        auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
-
-        // dy_dmean_dx
-        auto dx_mean =
-            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
-
-        // dy_var_dx
-        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                                 .cwiseProduct(d_y_map)
-                                 .rowwise()
-                                 .sum();
-        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
-                            .cwiseProduct(dvar_end_part)
-                            .replicate(1, right);
-        auto dx_var =
-            (T(-1.0) / right) *
-            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-        d_x_map = dx_end + dx_mean + dx_var;
-      }
-    }
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 
diff --git a/paddle/operators/layer_norm_op.cu b/paddle/operators/layer_norm_op.cu
index a84f5a41ea..77d13b216f 100644
--- a/paddle/operators/layer_norm_op.cu
+++ b/paddle/operators/layer_norm_op.cu
@@ -12,234 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "paddle/operators/elementwise_op_function.h"
 #include "paddle/operators/layer_norm_op.h"
-#include "paddle/operators/math/math_function.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-using DataLayout = framework::DataLayout;
-
-namespace {
-template <typename T>
-struct SubAndSquareFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
-};
-
-template <typename T>
-struct DivAndSqrtFunctor {
-  explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
-  inline HOSTDEVICE T operator()(T a, T b) const {
-    return a / (sqrt(b) + epsilon_);
-  }
-
- private:
-  T epsilon_;
-};
-
-template <typename T>
-struct MulFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return a * b; }
-};
-
-template <typename T>
-struct AddFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
-};
-
-template <typename T>
-struct SubFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
-};
-
-template <typename T>
-struct MulInvVarFunctor {
-  inline HOSTDEVICE T operator()(T a, T b) const {
-    return a * std::sqrt(1.0 / b);
-  }
-};
-}  // namespace
-
-template <typename DeviceContext, typename T>
-class LayerNormCUDAKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const float epsilon = ctx.Attr<float>("epsilon");
-    auto *scale = ctx.Input<Tensor>("Scale");
-    auto *bias = ctx.Input<Tensor>("Bias");
-    auto x = *ctx.Input<Tensor>("X");
-
-    auto *y = ctx.Output<Tensor>("Y");
-    auto *mean = ctx.Output<Tensor>("Mean");
-    auto *var = ctx.Output<Tensor>("Variance");
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-
-    const auto &x_dims = x.dims();
-
-    y->mutable_data<T>(ctx.GetPlace());
-    mean->mutable_data<T>(ctx.GetPlace());
-    var->mutable_data<T>(ctx.GetPlace());
-
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-
-    framework::DDim matrix_shape({left, right});
-
-    x.Resize(matrix_shape);
-    y->Resize(matrix_shape);
-
-    auto &dev_ctx = ctx.template device_context<DeviceContext>();
-    math::RowwiseMean<DeviceContext, T> row_mean;
-
-    // functor-> get mean
-    row_mean(dev_ctx, x, mean);
-
-    // functor-> get variance
-    ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
-        ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
-    row_mean(dev_ctx, *y, var);
-
-    // functor-> get norm_out
-    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-        ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
-    ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-        ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
-        y);
-
-    framework::DDim scale_shape({right});
-    if (scale) {
-      Tensor scale_matrix = *scale;
-      scale_matrix.Resize(scale_shape);
-      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-          ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
-    }
-    if (bias) {
-      Tensor bias_matrix = *bias;
-      bias_matrix.Resize(scale_shape);
-      ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
-          ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
-    }
-    y->Resize(x_dims);
-  }
-};
-
-template <typename DeviceContext, typename T>
-class LayerNormCUDAGradKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const float epsilon = ctx.Attr<float>("epsilon");
-    auto x = *ctx.Input<Tensor>("X");
-    auto mean = *ctx.Input<Tensor>("Mean");
-    auto var = *ctx.Input<Tensor>("Variance");
-    auto scale = *ctx.Input<Tensor>("Scale");
-    auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-
-    // init output
-    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
-    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
-
-    const auto &x_dims = x.dims();
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-    framework::DDim matrix_shape({left, right});
-
-    d_y.Resize(matrix_shape);
-    auto &dev_ctx = ctx.template device_context<DeviceContext>();
-    math::ColwiseSum<DeviceContext, T> colwise_sum;
-
-    Tensor temp;
-    Tensor temp_norm;
-    if (d_scale || d_x) {
-      x.Resize(matrix_shape);
-      temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
-      temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
-
-      // get x_norm
-      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
-      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-          ctx, &temp_norm, &var, /*axis*/ 0,
-          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
-    }
-
-    if (d_bias) {
-      d_bias->mutable_data<T>(ctx.GetPlace());
-      colwise_sum(dev_ctx, d_y, d_bias);
-    }
-    if (d_scale) {
-      d_scale->mutable_data<T>(ctx.GetPlace());
-      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-          ctx, &temp_norm, &d_y, /*axis*/ 0, MulFunctor<T>(), &temp);
-      colwise_sum(dev_ctx, temp, d_scale);
-    }
-
-    if (d_x) {
-      framework::DDim vec_shape({left});
-      d_x->mutable_data<T>(ctx.GetPlace());
-      Tensor temp_vec;
-      temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
-
-      auto &dev_ctx = ctx.template device_context<DeviceContext>();
-      math::RowwiseMean<DeviceContext, T> row_mean;
-
-      if (d_scale) {
-        // dy_dx
-        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-            ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
-        framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
-
-        // dy_dmean_dx
-        row_mean(dev_ctx, temp, &temp_vec);
-        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
-
-        // dy_var_dx
-        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-            ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
-
-      } else {
-        // dy_dx
-        framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
-
-        // dy_dmean_dx
-        row_mean(dev_ctx, d_y, &temp_vec);
-        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
-
-        // dy_var_dx
-        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-            ctx, &d_y, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
-      }
-      // dy_var_dx
-      row_mean(dev_ctx, temp, &temp_vec);
-      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-          ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
-      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
-
-      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-          ctx, d_x, &var, /*axis*/ 0,
-          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     layer_norm,
-    ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LayerNormKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LayerNormKernel<paddle::platform::CUDADeviceContext, double>);
 REGISTER_OP_CUDA_KERNEL(
     layer_norm_grad,
-    ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/layer_norm_op.h b/paddle/operators/layer_norm_op.h
index bca35b91e6..309f1b87a2 100644
--- a/paddle/operators/layer_norm_op.h
+++ b/paddle/operators/layer_norm_op.h
@@ -16,19 +16,219 @@ limitations under the License. */
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
 
+#include "paddle/operators/elementwise_op_function.h"
+#include "paddle/operators/math/math_function.h"
+
 namespace paddle {
 namespace operators {
 
+template <typename T>
+struct SubAndSquareFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
+};
+
+template <typename T>
+struct DivAndSqrtFunctor {
+  explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
+  inline HOSTDEVICE T operator()(T a, T b) const {
+    return a / (sqrt(b) + epsilon_);
+  }
+
+ private:
+  T epsilon_;
+};
+
+template <typename T>
+struct MulFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a * b; }
+};
+
+template <typename T>
+struct AddFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
+};
+
+template <typename T>
+struct SubFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
+};
+
+template <typename T>
+struct MulInvVarFunctor {
+  inline HOSTDEVICE T operator()(T a, T b) const {
+    return a * std::sqrt(1.0 / b);
+  }
+};
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
+
 template <typename DeviceContext, typename T>
 class LayerNormKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override;
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *bias = ctx.Input<Tensor>("Bias");
+    auto x = *ctx.Input<Tensor>("X");
+
+    auto *y = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    const auto &x_dims = x.dims();
+
+    y->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+
+    framework::DDim matrix_shape({left, right});
+
+    x.Resize(matrix_shape);
+    y->Resize(matrix_shape);
+
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    math::RowwiseMean<DeviceContext, T> row_mean;
+
+    // functor-> get mean
+    row_mean(dev_ctx, x, mean);
+
+    // functor-> get variance
+    ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
+        ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
+    row_mean(dev_ctx, *y, var);
+
+    // functor-> get norm_out
+    ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+        ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
+    ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+        ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
+        y);
+
+    framework::DDim scale_shape({right});
+    if (scale) {
+      Tensor scale_matrix = *scale;
+      scale_matrix.Resize(scale_shape);
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
+    }
+    if (bias) {
+      Tensor bias_matrix = *bias;
+      bias_matrix.Resize(scale_shape);
+      ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
+          ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
+    }
+    y->Resize(x_dims);
+  }
 };
 
 template <typename DeviceContext, typename T>
 class LayerNormGradKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override;
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    auto x = *ctx.Input<Tensor>("X");
+    auto mean = *ctx.Input<Tensor>("Mean");
+    auto var = *ctx.Input<Tensor>("Variance");
+    auto scale = *ctx.Input<Tensor>("Scale");
+    auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    // init output
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    const auto &x_dims = x.dims();
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+    framework::DDim matrix_shape({left, right});
+
+    d_y.Resize(matrix_shape);
+    auto &dev_ctx = ctx.template device_context<DeviceContext>();
+    math::ColwiseSum<DeviceContext, T> colwise_sum;
+
+    Tensor temp;
+    Tensor temp_norm;
+    if (d_scale || d_x) {
+      x.Resize(matrix_shape);
+      temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
+      temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
+
+      // get x_norm
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
+      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &var, /*axis*/ 0,
+          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
+    }
+
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      colwise_sum(dev_ctx, d_y, d_bias);
+    }
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &d_y, /*axis*/ 0, MulFunctor<T>(), &temp);
+      colwise_sum(dev_ctx, temp, d_scale);
+    }
+
+    if (d_x) {
+      framework::DDim vec_shape({left});
+      d_x->mutable_data<T>(ctx.GetPlace());
+      Tensor temp_vec;
+      temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
+
+      math::RowwiseMean<DeviceContext, T> row_mean;
+
+      if (d_scale) {
+        // dy_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
+        framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
+
+        // dy_dmean_dx
+        row_mean(dev_ctx, temp, &temp_vec);
+        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+        // dy_var_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
+
+      } else {
+        // dy_dx
+        framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
+
+        // dy_dmean_dx
+        row_mean(dev_ctx, d_y, &temp_vec);
+        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+            ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+        // dy_var_dx
+        ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+            ctx, &d_y, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
+      }
+      // dy_var_dx
+      row_mean(dev_ctx, temp, &temp_vec);
+      ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
+          ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
+      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+          ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
+
+      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+          ctx, d_x, &var, /*axis*/ 0,
+          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
+    }
+  }
 };
 
 }  // namespace operators

From b60da6729fa2484506869bc29271761de91676b7 Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Sat, 3 Feb 2018 23:32:56 +0800
Subject: [PATCH 230/314] Refine buffer channel (#8098)

* refine buffer channel

*  refine Receive and Send

* follow comments
---
 paddle/framework/channel.h                    |  4 +--
 paddle/framework/details/buffered_channel.h   | 25 ++++++++-----------
 paddle/framework/details/unbuffered_channel.h | 14 ++++++++---
 3 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/paddle/framework/channel.h b/paddle/framework/channel.h
index 0570980c5a..b679387b11 100644
--- a/paddle/framework/channel.h
+++ b/paddle/framework/channel.h
@@ -23,8 +23,8 @@ namespace framework {
 template <typename T>
 class Channel {
  public:
-  virtual void Send(T*) = 0;
-  virtual void Receive(T*) = 0;
+  virtual bool Send(T*) = 0;
+  virtual bool Receive(T*) = 0;
   virtual size_t Cap() = 0;
   virtual void Close() = 0;
   virtual ~Channel() {}
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 9c806461aa..7ac234b8d4 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -30,8 +30,8 @@ class Buffered : public paddle::framework::Channel<T> {
   friend void paddle::framework::CloseChannel<T>(Channel<T>*);
 
  public:
-  virtual void Send(T*);
-  virtual void Receive(T*);
+  virtual bool Send(T*);
+  virtual bool Receive(T*);
   virtual size_t Cap() { return cap_; }
   virtual void Close();
   virtual ~Buffered();
@@ -48,33 +48,36 @@ class Buffered : public paddle::framework::Channel<T> {
     PADDLE_ENFORCE_GT(cap, 0);
   }
 
-  void NotifyAllSenders(std::unique_lock<std::mutex>*);
   void NotifyAllParticipants(std::unique_lock<std::mutex>*);
 };
 
 template <typename T>
-void Buffered<T>::Send(T* item) {
+bool Buffered<T>::Send(T* item) {
   std::unique_lock<std::mutex> lock(mu_);
   full_cond_var_.wait(lock,
                       [this]() { return channel_.size() < cap_ || closed_; });
+  bool ret = false;
   if (!closed_) {
     channel_.push_back(std::move(*item));
     lock.unlock();
     empty_cond_var_.notify_one();
+    ret = true;
   }
+  return ret;
 }
 
 template <typename T>
-void Buffered<T>::Receive(T* item) {
+bool Buffered<T>::Receive(T* item) {
   std::unique_lock<std::mutex> lock(mu_);
   empty_cond_var_.wait(lock, [this]() { return !channel_.empty() || closed_; });
+  bool ret = false;
   if (!closed_) {
     *item = std::move(channel_.front());
     channel_.pop_front();
-    NotifyAllSenders(&lock);
-  } else {
-    item = nullptr;
+    full_cond_var_.notify_one();
+    ret = true;
   }
+  return ret;
 }
 
 template <typename T>
@@ -92,12 +95,6 @@ Buffered<T>::~Buffered() {
   NotifyAllParticipants(&lock);
 }
 
-template <typename T>
-void Buffered<T>::NotifyAllSenders(std::unique_lock<std::mutex>* lock) {
-  lock->unlock();
-  full_cond_var_.notify_all();
-}
-
 template <typename T>
 void Buffered<T>::NotifyAllParticipants(std::unique_lock<std::mutex>* lock) {
   lock->unlock();
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index 0dc5afd7e5..f86a894bb4 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -29,8 +29,8 @@ class UnBuffered : public paddle::framework::Channel<T> {
   friend void paddle::framework::CloseChannel<T>(Channel<T>*);
 
  public:
-  virtual void Send(T*);
-  virtual void Receive(T*);
+  virtual bool Send(T*);
+  virtual bool Receive(T*);
   virtual size_t Cap() { return 0; }
   virtual void Close();
   virtual ~UnBuffered();
@@ -57,7 +57,7 @@ class UnBuffered : public paddle::framework::Channel<T> {
 // This function implements the concept of how data should
 // be sent from a writer to a reader.
 template <typename T>
-void UnBuffered<T>::Send(T* data) {
+bool UnBuffered<T>::Send(T* data) {
   // Prevent other writers from entering
   std::unique_lock<std::recursive_mutex> writer_lock(mu_write_);
   writer_found_ = true;
@@ -66,6 +66,7 @@ void UnBuffered<T>::Send(T* data) {
   cv_writer_.wait(cv_lock,
                   [this]() { return reader_found_ == true || closed_; });
   cv_reader_.notify_one();
+  bool ret = false;
   if (!closed_) {
     std::unique_lock<std::mutex> channel_lock(mu_ch_);
     item = data;
@@ -74,14 +75,16 @@ void UnBuffered<T>::Send(T* data) {
     channel_lock.lock();
     cv_channel_.wait(channel_lock,
                      [this]() { return item == nullptr || closed_; });
+    ret = true;
   }
   writer_found_ = false;
+  return ret;
 }
 
 // This function implements the concept of how
 // data that was sent by a writer is read from a reader.
 template <typename T>
-void UnBuffered<T>::Receive(T* data) {
+bool UnBuffered<T>::Receive(T* data) {
   // Prevent other readers from entering
   std::unique_lock<std::recursive_mutex> read_lock{mu_read_};
   reader_found_ = true;
@@ -90,6 +93,7 @@ void UnBuffered<T>::Receive(T* data) {
   cv_reader_.wait(cv_lock,
                   [this]() { return writer_found_ == true || closed_; });
   cv_writer_.notify_one();
+  bool ret = false;
   if (!closed_) {
     std::unique_lock<std::mutex> lock_ch{mu_ch_};
     // Reader should wait for the writer to first write its data
@@ -98,10 +102,12 @@ void UnBuffered<T>::Receive(T* data) {
       *data = std::move(*item);
       item = nullptr;
       lock_ch.unlock();
+      ret = true;
     }
     cv_channel_.notify_one();
   }
   reader_found_ = false;
+  return ret;
 }
 
 // This function implements the sequence of events

From c3d27b15b7575a48e02a24eae6dff6b58d23cf70 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Sun, 4 Feb 2018 01:43:01 -0800
Subject: [PATCH 231/314] modify prune.cc for multiple blocks

---
 paddle/framework/prune.cc | 71 +++++++++++++++++++++++++++++++--------
 1 file changed, 57 insertions(+), 14 deletions(-)

diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index bff8e0bcea..6a3882f199 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -49,11 +49,28 @@ bool IsTarget(const proto::OpDesc& op_desc) {
   return false;
 }
 
-void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
-                int block_id) {
-  // TODO(tonyyang-svail):
-  //    - will change to use multiple blocks for RNN op and Cond Op
+int GetSubBlockIndex(const proto::OpDesc& op_desc) {
+  for (auto& attr : op_desc.attrs()) {
+    if (attr.type() == proto::AttrType::BLOCK) {
+      PADDLE_ENFORCE(attr.has_block_idx());
+      return attr.block_idx();
+    }
+  }
+  return -1;
+}
 
+bool HasSubBlock(const proto::OpDesc& op_desc) {
+  return GetSubBlockIndex(op_desc) > 0;
+}
+
+// block_id is the idx of the current block in the input desc
+// parent_block_id is the idx of the parent of the current block
+// in the output desc, -1 means the current block is global block
+// dependent_vars is passed recursively from the parent block to
+// the child block to help pruning
+void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
+                int block_id, int parent_block_id,
+                std::set<std::string>& dependent_vars) {
   auto& block = input.blocks(block_id);
   auto& ops = block.ops();
 
@@ -72,11 +89,9 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     expect_fetch = (op_desc.type() == kFetchOpType);
   }
 
-  std::set<std::string> dependent_vars;
   std::vector<bool> should_run;
   for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) {
     auto& op_desc = *op_iter;
-
     if (IsTarget(op_desc) || HasDependentVar(op_desc, dependent_vars)) {
       // insert its input to the dependency graph
       for (auto& var : op_desc.inputs()) {
@@ -84,7 +99,6 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
           dependent_vars.insert(argu);
         }
       }
-
       should_run.push_back(true);
     } else {
       should_run.push_back(false);
@@ -95,19 +109,48 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
   // we reverse the should_run vector
   std::reverse(should_run.begin(), should_run.end());
 
-  *output = input;
-  auto* op_field = output->mutable_blocks(block_id)->mutable_ops();
+  //*output = input;
+  // copy the current block from input to output
+  auto* block_field = output->mutable_blocks();
+  *block_field->Add() = input.blocks(block_id);
+
+  int output_block_id = output->blocks_size() - 1;
+  auto* output_block = output->mutable_blocks(output_block_id);
+  output_block->set_idx = output_block_id;
+  output_block->set_parent_idx = parent_block_id;
+
+  auto* op_field = output_block->mutable_ops();
   op_field->Clear();
   for (size_t i = 0; i < should_run.size(); ++i) {
     if (should_run[i]) {
-      *op_field->Add() = input.blocks(block_id).ops(i);
+      auto* op = op_field->Add();
+      *op = input.blocks(block_id).ops(i);
+      if (HasSubBlock(*op)) {
+        // create sub_block_dependent_vars here to help prune the sub block
+        std::set<std::string> sub_block_dependent_vars;
+        for (auto& var : op.inputs()) {
+          for (auto& argu : var.arguments()) {
+            sub_block_dependent_vars.insert(argu);
+          }
+        }
+        for (auto& var : op.outputs()) {
+          for (auto& argu : var.arguments()) {
+            sub_block_dependent_vars.insert(argu);
+          }
+        }
+
+        // GetSubBlockIndex(*op) is the idx of the sub_block in the input desc
+        // output_block_id is the idx of the current block in the output desc
+        prune_impl(input, output, GetSubBlockIndex(*op), output_block_id,
+                   sub_block_dependent_vars);
+      }
     }
   }
 
   // remove the VarDescs in BlockDesc that are not referenced in
   // the pruned OpDescs
   std::unordered_map<std::string, proto::VarDesc> var_map;
-  auto* var_field = output->mutable_blocks(block_id)->mutable_vars();
+  auto* var_field = output->mutable_blocks(output_block_id)->mutable_vars();
   for (const auto& var : *var_field) {
     var_map[var.name()] = var;
   }
@@ -118,14 +161,14 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     auto& input_field = op.inputs();
     for (auto& input_var : input_field) {
       for (auto& arg : input_var.arguments()) {
-        *var_field->Add() = var_map[arg];
+        *var_field->Add() = var_map.at(arg);
       }
     }
     // add VarDescs of all output arguments for each OpDesc
     auto& output_field = op.outputs();
     for (auto& output_var : output_field) {
       for (auto& arg : output_var.arguments()) {
-        *var_field->Add() = var_map[arg];
+        *var_field->Add() = var_map.at(arg);
       }
     }
   }
@@ -133,7 +176,7 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
 
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
 void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
-  prune_impl(input, output, 0);
+  prune_impl(input, output, 0, -1, {});
 }
 
 void inference_optimize_impl(const proto::ProgramDesc& input,

From 326fa176ea6401f171e9325aa29fb0b5cf6f7a29 Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Sun, 4 Feb 2018 22:45:47 +0800
Subject: [PATCH 232/314] Fix empty output tensor and add an unitest case

---
 paddle/operators/ctc_align_op.cu               |  8 ++++++++
 paddle/operators/ctc_align_op.h                |  9 ++++++++-
 python/paddle/v2/fluid/tests/test_ctc_align.py | 11 +++++++++++
 3 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/ctc_align_op.cu b/paddle/operators/ctc_align_op.cu
index 2a970cd9fa..918df83eff 100644
--- a/paddle/operators/ctc_align_op.cu
+++ b/paddle/operators/ctc_align_op.cu
@@ -80,6 +80,14 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
 
     // resize output dims
     output->Resize({static_cast<int64_t>(host_out_lod0.back()), 1});
+
+    if (host_out_lod0.back() == 0) {
+      output->Resize({1});
+      output->mutable_data<T>(ctx.GetPlace());
+      math::SetConstant<platform::CUDADeviceContext, T> set_constant;
+      set_constant(ctx.template device_context<platform::CUDADeviceContext>(),
+                   output, -1);
+    }
   }
 };
 
diff --git a/paddle/operators/ctc_align_op.h b/paddle/operators/ctc_align_op.h
index fed89aa1e8..7a063870f3 100644
--- a/paddle/operators/ctc_align_op.h
+++ b/paddle/operators/ctc_align_op.h
@@ -16,6 +16,8 @@ limitations under the License. */
 
 #include <string.h>
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+
 namespace paddle {
 namespace operators {
 
@@ -65,9 +67,14 @@ class CTCAlignKernel : public framework::OpKernel<T> {
     framework::LoD output_lod;
     output_lod.push_back(output_lod0);
     output->set_lod(output_lod);
-
     // resize output dims
     output->Resize({static_cast<int64_t>(output_lod0.back()), 1});
+    // for empty sequence
+    if (output_lod0.back() == 0) {
+      output->Resize({1});
+      output_data = output->mutable_data<T>(ctx.GetPlace());
+      output_data[0] = -1;
+    }
   }
 };
 
diff --git a/python/paddle/v2/fluid/tests/test_ctc_align.py b/python/paddle/v2/fluid/tests/test_ctc_align.py
index 773c69d1ad..cc815d8e9e 100644
--- a/python/paddle/v2/fluid/tests/test_ctc_align.py
+++ b/python/paddle/v2/fluid/tests/test_ctc_align.py
@@ -31,6 +31,8 @@ def CTCAlign(input, lod, blank, merge_repeated):
                 result.append(token)
             prev_token = token
     result = np.array(result).reshape([len(result), 1]).astype("int32")
+    if len(result) == 0:
+        result = np.array([-1])
     return result
 
 
@@ -72,5 +74,14 @@ class TestCTCAlignOpCase1(TestCTCAlignOp):
                 [19, 1]).astype("int32")
 
 
+class TestCTCAlignOpCase2(TestCTCAlignOp):
+    def config(self):
+        self.op_type = "ctc_align"
+        self.input_lod = [[0, 4]]
+        self.blank = 0
+        self.merge_repeated = True
+        self.input = np.array([0, 0, 0, 0]).reshape([4, 1]).astype("int32")
+
+
 if __name__ == "__main__":
     unittest.main()

From be65516876ae32fe2f8cfde1aaa2d22926ccc583 Mon Sep 17 00:00:00 2001
From: Yibing Liu <liuyibing01@baidu.com>
Date: Sun, 4 Feb 2018 16:37:02 +0000
Subject: [PATCH 233/314] Fix the error when sorted_key is none in profiler

---
 paddle/platform/profiler.cc        | 2 +-
 python/paddle/v2/fluid/profiler.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/paddle/platform/profiler.cc b/paddle/platform/profiler.cc
index 2a8afc9403..6df087d154 100644
--- a/paddle/platform/profiler.cc
+++ b/paddle/platform/profiler.cc
@@ -233,7 +233,7 @@ void ParseEvents(std::vector<std::vector<Event>>& events,
       };
       break;
     default:
-      sorted_domain = "event end time";
+      sorted_domain = "event first end time";
   }
 
   std::vector<std::vector<EventItem>> events_table;
diff --git a/python/paddle/v2/fluid/profiler.py b/python/paddle/v2/fluid/profiler.py
index d4a2cd7eea..d33a4c52a8 100644
--- a/python/paddle/v2/fluid/profiler.py
+++ b/python/paddle/v2/fluid/profiler.py
@@ -103,10 +103,10 @@ def profiler(state, sorted_key=None):
     core.enable_profiler(prof_state)
     yield
 
-    if sorted_key not in ['calls', 'total', 'max', 'min', 'ave']:
-        raise ValueError("The state must be in 'calls', 'total', "
-                         "'max', 'min', 'ave'")
     sorted_key = 'default' if sorted_key is None else sorted_key
+    if sorted_key not in ['default', 'calls', 'total', 'max', 'min', 'ave']:
+        raise ValueError("The sorted_key must be None or in 'calls', 'total', "
+                         "'max', 'min' and 'ave'")
     key_map = {
         'default': core.EventSortingKey.kDefault,
         'calls': core.EventSortingKey.kCalls,

From 1d2dd9c4a5b99074cec3cb642f64bfd2124e6412 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Sun, 4 Feb 2018 10:04:53 -0800
Subject: [PATCH 234/314] Close buffered channel should unblock the blocked 
 senders and receivers (#8109)

---
 paddle/framework/channel_test.cc | 113 +++++++++++++++++++++++++++++--
 1 file changed, 106 insertions(+), 7 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index c3533bbb1a..444d68498c 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -48,12 +48,12 @@ TEST(Channel, SufficientBufferSizeDoesntBlock) {
   const size_t buffer_size = 10;
   auto ch = MakeChannel<size_t>(buffer_size);
   for (size_t i = 0; i < buffer_size; ++i) {
-    ch->Send(&i);  // should not block
+    EXPECT_EQ(ch->Send(&i), true);  // should not block
   }
 
   size_t out;
   for (size_t i = 0; i < buffer_size; ++i) {
-    ch->Receive(&out);  // should not block
+    EXPECT_EQ(ch->Receive(&out), true);  // should not block
     EXPECT_EQ(out, i);
   }
   CloseChannel(ch);
@@ -67,7 +67,10 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
   std::thread t([&]() {
     // Try to write more than buffer size.
     for (size_t i = 0; i < 2 * buffer_size; ++i) {
-      ch->Send(&i);  // should block after 10 iterations
+      if (i < buffer_size)
+        EXPECT_EQ(ch->Send(&i), true);  // should block after 10 iterations
+      else
+        EXPECT_EQ(ch->Send(&i), false);
       sum += i;
     }
   });
@@ -84,13 +87,13 @@ TEST(Channel, SimpleUnbufferedChannelTest) {
   unsigned sum_send = 0;
   std::thread t([&]() {
     for (int i = 0; i < 5; i++) {
-      ch->Send(&i);
+      EXPECT_EQ(ch->Send(&i), true);
       sum_send += i;
     }
   });
   for (int i = 0; i < 5; i++) {
     int recv;
-    ch->Receive(&recv);
+    EXPECT_EQ(ch->Receive(&recv), true);
     EXPECT_EQ(recv, i);
   }
 
@@ -100,6 +103,102 @@ TEST(Channel, SimpleUnbufferedChannelTest) {
   delete ch;
 }
 
+// This tests that closing a buffered channel also unblocks
+//  any receivers waiting on the channel
+TEST(Channel, BufferedChannelCloseUnblocksReceiversTest) {
+  auto ch = MakeChannel<int>(1);
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+
+  // Launches threads that try to read and are blocked because of no writers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          int data;
+          // All reads should return false
+          EXPECT_EQ(ch->Receive(&data), false);
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
+
+  // Verify that all threads are blocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], false);
+  }
+
+  // Explicitly close the channel
+  // This should unblock all receivers
+  CloseChannel(ch);
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  delete ch;
+}
+
+// This tests that closing a buffered channel also unblocks
+//  any senders waiting for channel to have write space
+TEST(Channel, BufferedChannelCloseUnblocksSendersTest) {
+  auto ch = MakeChannel<int>(1);
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+  bool send_success[num_threads];
+
+  // Launches threads that try to write and are blocked because of no readers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    send_success[i] = false;
+    t[i] = std::thread(
+        [&](bool *ended, bool *success) {
+          int data = 10;
+          *success = ch->Send(&data);
+          *ended = true;
+        },
+        &thread_ended[i], &send_success[i]);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
+
+  // Verify that atleast 4 threads are blocked
+  int ct = 0;
+  for (size_t i = 0; i < num_threads; i++) {
+    if (thread_ended[i] == false) ct++;
+  }
+  // Atleast 4 threads must be blocked
+  EXPECT_GE(ct, 4);
+
+  // Explicitly close the thread
+  // This should unblock all senders
+  CloseChannel(ch);
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  // Verify that only 1 send was successful
+  ct = 0;
+  for (size_t i = 0; i < num_threads; i++) {
+    if (send_success[i]) ct++;
+  }
+  // Only 1 send must be successful
+  EXPECT_EQ(ct, 1);
+
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  delete ch;
+}
+
 // This tests that closing an unbuffered channel also unblocks
 //  unblocks any receivers waiting for senders
 TEST(Channel, UnbufferedChannelCloseUnblocksReceiversTest) {
@@ -114,7 +213,7 @@ TEST(Channel, UnbufferedChannelCloseUnblocksReceiversTest) {
     t[i] = std::thread(
         [&](bool *p) {
           int data;
-          ch->Receive(&data);
+          EXPECT_EQ(ch->Receive(&data), false);
           *p = true;
         },
         &thread_ended[i]);
@@ -155,7 +254,7 @@ TEST(Channel, UnbufferedChannelCloseUnblocksSendersTest) {
     t[i] = std::thread(
         [&](bool *p) {
           int data = 10;
-          ch->Send(&data);
+          EXPECT_EQ(ch->Send(&data), false);
           *p = true;
         },
         &thread_ended[i]);

From 6f28084b4d062100336fd3889012b91c6e278bcc Mon Sep 17 00:00:00 2001
From: Yan Chunwei <yanchunwei@outlook.com>
Date: Mon, 5 Feb 2018 10:53:26 +0800
Subject: [PATCH 235/314] debug/format protobuf to human-readable codes (#8086)

---
 python/paddle/v2/fluid/debuger.py | 192 ++++++++++++++++++++++++++++++
 1 file changed, 192 insertions(+)

diff --git a/python/paddle/v2/fluid/debuger.py b/python/paddle/v2/fluid/debuger.py
index d379352442..db1808c647 100644
--- a/python/paddle/v2/fluid/debuger.py
+++ b/python/paddle/v2/fluid/debuger.py
@@ -12,10 +12,202 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 import re
 from graphviz import GraphPreviewGenerator
 import proto.framework_pb2 as framework_pb2
 
+_vartype2str_ = [
+    "UNK",
+    "LoDTensor",
+    "SelectedRows",
+    "FeedMinibatch",
+    "FetchList",
+    "StepScopes",
+    "LodRankTable",
+    "LoDTensorArray",
+    "PlaceList",
+]
+_dtype2str_ = [
+    "bool",
+    "int16",
+    "int32",
+    "int64",
+    "float16",
+    "float32",
+    "float64",
+]
+
+
+def repr_data_type(type):
+    return _dtype2str_[type]
+
+
+def repr_tensor(proto):
+    return "tensor(type={}, shape={})".format(_dtype2str_[int(proto.data_type)],
+                                              str(proto.dims))
+
+
+reprtpl = "{ttype} {name} ({reprs})"
+
+
+def repr_lodtensor(proto):
+    if not proto.lod_tensor: return
+    level = proto.lod_tensor.lod_level
+    reprs = repr_tensor(proto.lod_tensor.tensor)
+    return reprtpl.format(
+        ttype="LoDTensor" if level > 0 else "Tensor",
+        name=proto.name,
+        reprs="level=%d, %s" % (level, reprs) if level > 0 else reprs)
+
+
+def repr_selected_rows(proto):
+    if not proto.selected_rows: return
+    return reprtpl.format(
+        ttype="SelectedRows",
+        name=proto.name,
+        reprs=repr_tensor(proto.selected_rows))
+
+
+def repr_tensor_array(proto):
+    if not proto.tensor_array: return
+    return reprtpl.format(
+        ttype="TensorArray",
+        name=proto.name,
+        reprs="level=%d, %s" % (proto.tensor_array.lod_level,
+                                repr_tensor(proto.lod_tensor)))
+
+
+type_handlers = [
+    repr_lodtensor,
+    repr_selected_rows,
+    repr_tensor_array,
+]
+
+
+def repr_var(vardesc):
+    for handler in type_handlers:
+        res = handler(vardesc)
+        if res:
+            return res
+
+
+def pprint_program_codes(program_desc):
+    reprs = []
+    for block_idx in range(program_desc.num_blocks()):
+        block_desc = program_desc.block(block_idx)
+        block_repr = pprint_block_codes(block_desc)
+        reprs.append(block_repr)
+    return '\n'.join(reprs)
+
+
+def pprint_block_codes(block_desc, show_backward=False):
+    def is_op_backward(op_desc):
+        if op_desc.type.endswith('_grad'): return True
+
+        def is_var_backward(var):
+            if "@GRAD" in var.parameter: return True
+            for arg in var.arguments:
+                if "@GRAD" in arg: return True
+
+        for var in op_desc.inputs:
+            if is_var_backward(var): return True
+        for var in op_desc.outputs:
+            if is_var_backward(var): return True
+        return False
+
+    def is_var_backward(var_desc):
+        return "@GRAD" in var_desc.name
+
+    if type(block_desc) is not framework_pb2.BlockDesc:
+        block_desc = framework_pb2.BlockDesc.FromString(
+            block_desc.serialize_to_string())
+    var_reprs = []
+    op_reprs = []
+    for var in block_desc.vars:
+        if not show_backward and is_var_backward(var):
+            continue
+        var_reprs.append(repr_var(var))
+
+    for op in block_desc.ops:
+        if not show_backward and is_op_backward(op): continue
+        op_reprs.append(repr_op(op))
+
+    tpl = "// block-{idx}  parent-{pidx}\n// variables\n{vars}\n\n// operators\n{ops}\n"
+    return tpl.format(
+        idx=block_desc.idx,
+        pidx=block_desc.parent_idx,
+        vars='\n'.join(var_reprs),
+        ops='\n'.join(op_reprs), )
+
+
+def repr_attr(desc):
+    tpl = "{key}={value}"
+    valgetter = [
+        lambda attr: attr.i,
+        lambda attr: attr.f,
+        lambda attr: attr.s,
+        lambda attr: attr.ints,
+        lambda attr: attr.floats,
+        lambda attr: attr.strings,
+        lambda attr: attr.b,
+        lambda attr: attr.bools,
+        lambda attr: attr.block_idx,
+        lambda attr: attr.l,
+    ]
+    key = desc.name
+    value = valgetter[desc.type](desc)
+    if key == "dtype":
+        value = repr_data_type(value)
+    return tpl.format(key=key, value=str(value)), (key, value)
+
+
+def _repr_op_fill_constant(optype, inputs, outputs, attrs):
+    if optype == "fill_constant":
+        return "{output} = {data} [shape={shape}]".format(
+            output=','.join(outputs),
+            data=attrs['value'],
+            shape=str(attrs['shape']))
+
+
+op_repr_handlers = [_repr_op_fill_constant, ]
+
+
+def repr_op(opdesc):
+    optype = None
+    attrs = []
+    attr_dict = {}
+    is_target = None
+    inputs = []
+    outputs = []
+
+    tpl = "{outputs} = {optype}({inputs}{is_target}) [{attrs}]"
+    args2value = lambda args: args[0] if len(args) == 1 else str(list(args))
+    for var in opdesc.inputs:
+        key = var.parameter
+        value = args2value(var.arguments)
+        inputs.append("%s=%s" % (key, value))
+    for var in opdesc.outputs:
+        value = args2value(var.arguments)
+        outputs.append(value)
+    for attr in opdesc.attrs:
+        attr_repr, attr_pair = repr_attr(attr)
+        attrs.append(attr_repr)
+        attr_dict[attr_pair[0]] = attr_pair[1]
+
+    is_target = opdesc.is_target
+
+    for handler in op_repr_handlers:
+        res = handler(opdesc.type, inputs, outputs, attr_dict)
+        if res: return res
+
+    return tpl.format(
+        outputs=', '.join(outputs),
+        optype=opdesc.type,
+        inputs=', '.join(inputs),
+        attrs="{%s}" % ','.join(attrs),
+        is_target=", is_target" if is_target else "")
+
 
 def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
     '''

From 5092f5291c17e46b4c5e176c00b46a69f5e0d466 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 3 Feb 2018 15:05:55 +0800
Subject: [PATCH 236/314] Separate GPU and CPU implementation

---
 paddle/operators/layer_norm_op.cc             | 186 +++++++++++++++++-
 paddle/operators/layer_norm_op.h              |  29 ++-
 .../v2/fluid/tests/test_layer_norm_op.py      |  11 +-
 3 files changed, 202 insertions(+), 24 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index edc26dfb96..910b8ec0a4 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -21,6 +21,13 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 using DataLayout = framework::DataLayout;
 
+template <typename T>
+using EigenMatrixMapRowMajor = Eigen::Map<
+    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
+template <typename T>
+using ConstEigenMatrixMapRowMajor = Eigen::Map<
+    const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
+
 class LayerNormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -101,7 +108,6 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddComment(R"DOC(
 Layer Normalization.
-
 Layer Norm has been implemented as discussed in the paper:
 https://arxiv.org/abs/1607.06450
 ...
@@ -109,6 +115,75 @@ https://arxiv.org/abs/1607.06450
   }
 };
 
+template <typename T>
+class LayerNormKernel<platform::CPUDeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const float epsilon = ctx.Attr<float>("epsilon");
+    const auto *scale = ctx.Input<Tensor>("Scale");
+    const auto *bias = ctx.Input<Tensor>("Bias");
+    const auto *x = ctx.Input<Tensor>("X");
+    const auto &x_dims = x->dims();
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+
+    auto *output = ctx.Output<Tensor>("Y");
+    auto *mean = ctx.Output<Tensor>("Mean");
+    auto *var = ctx.Output<Tensor>("Variance");
+    output->mutable_data<T>(ctx.GetPlace());
+    mean->mutable_data<T>(ctx.GetPlace());
+    var->mutable_data<T>(ctx.GetPlace());
+
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+
+    auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
+
+    auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
+    auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
+    auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
+
+    auto squre = [](T ele) { return ele * ele; };
+    auto add_epslion = [epsilon](T ele) { return ele + epsilon; };
+
+    mean_map = input_map.rowwise().mean();
+    var_map = (input_map - mean_map.replicate(1, right))
+                  .unaryExpr(squre)
+                  .rowwise()
+                  .mean()
+                  .unaryExpr(add_epslion);
+
+    auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+    // TODO(zcd): Some thinking about output_map, is it appropriate that
+    // `output_map` and `input_map` point to the same memory.
+    auto inv_std = var_map.unaryExpr(inv_std_func);
+    if (scale && bias) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1)) +
+                   bias_map.replicate(left, 1);
+    } else if (scale) {
+      auto scale_map =
+          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right))
+                       .cwiseProduct(scale_map.replicate(left, 1));
+    } else if (bias) {
+      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right)) +
+                   bias_map.replicate(left, 1);
+    } else {
+      output_map = (input_map - mean_map.replicate(1, right))
+                       .cwiseProduct(inv_std.replicate(1, right));
+    }
+  }
+};
+
 class LayerNormGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -161,6 +236,115 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
   }
 };
 
+template <typename T>
+class LayerNormGradKernel<platform::CPUDeviceContext, T>
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const auto *x = ctx.Input<Tensor>("X");
+    const auto *mean = ctx.Input<Tensor>("Mean");
+    const auto *var = ctx.Input<Tensor>("Variance");
+    const auto *scale = ctx.Input<Tensor>("Scale");
+    const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
+
+    const auto &x_dims = x->dims();
+
+    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
+    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
+    int left = static_cast<int>(matrix_dim[0]);
+    int right = static_cast<int>(matrix_dim[1]);
+
+    // init output
+    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
+    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
+
+    auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
+    auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
+    auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
+    auto var_map = ConstEigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
+
+    if (d_bias) {
+      d_bias->mutable_data<T>(ctx.GetPlace());
+      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
+      d_bias_map = d_y_map.colwise().sum();
+    }
+    if (d_scale) {
+      d_scale->mutable_data<T>(ctx.GetPlace());
+      auto d_scale_map =
+          EigenMatrixMapRowMajor<T>(d_scale->data<T>(), 1, right);
+      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+      // There are two equation to compute d_scale. One uses "Y" and the other
+      // does not use "Y"
+      d_scale_map =
+          ((x_map - mean_map.replicate(1, right))
+               .cwiseProduct(
+                   var_map.unaryExpr(inv_std_func).replicate(1, right))
+               .cwiseProduct(d_y_map))
+              .colwise()
+              .sum();
+    }
+
+    if (d_x) {
+      d_x->mutable_data<T>(ctx.GetPlace());
+      auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
+      auto triple_product_func = [](T ele) { return ele * ele * ele; };
+      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
+
+      auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
+      // TODO(zcd): these code can be refined
+      if (d_scale) {
+        auto scale_map =
+            ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
+        // dy_dx
+        auto dx_end =
+            inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
+                scale_map.replicate(left, 1));
+
+        // dy_dmean_dx
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(scale_map.replicate(left, 1))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      } else {
+        // dy_dx
+        auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
+
+        // dy_dmean_dx
+        auto dx_mean =
+            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
+
+        // dy_var_dx
+        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
+                                 .cwiseProduct(d_y_map)
+                                 .rowwise()
+                                 .sum();
+        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
+                            .cwiseProduct(dvar_end_part)
+                            .replicate(1, right);
+        auto dx_var =
+            (T(-1.0) / right) *
+            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
+
+        d_x_map = dx_end + dx_mean + dx_var;
+      }
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
diff --git a/paddle/operators/layer_norm_op.h b/paddle/operators/layer_norm_op.h
index 309f1b87a2..2de58186fb 100644
--- a/paddle/operators/layer_norm_op.h
+++ b/paddle/operators/layer_norm_op.h
@@ -78,7 +78,7 @@ class LayerNormKernel : public framework::OpKernel<T> {
     auto *var = ctx.Output<Tensor>("Variance");
     const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
 
-    const auto &x_dims = x.dims();
+    const auto x_dims = x.dims();
 
     y->mutable_data<T>(ctx.GetPlace());
     mean->mutable_data<T>(ctx.GetPlace());
@@ -87,11 +87,12 @@ class LayerNormKernel : public framework::OpKernel<T> {
     auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
     int right = static_cast<int>(matrix_dim[1]);
-
     framework::DDim matrix_shape({left, right});
 
     x.Resize(matrix_shape);
-    y->Resize(matrix_shape);
+    Tensor out;
+    out.ShareDataWith(*y);
+    out.Resize(matrix_shape);
 
     auto &dev_ctx = ctx.template device_context<DeviceContext>();
     math::RowwiseMean<DeviceContext, T> row_mean;
@@ -101,30 +102,24 @@ class LayerNormKernel : public framework::OpKernel<T> {
 
     // functor-> get variance
     ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
-        ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
-    row_mean(dev_ctx, *y, var);
+        ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), &out);
+    row_mean(dev_ctx, out, var);
 
     // functor-> get norm_out
     ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-        ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
+        ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), &out);
     ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-        ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
-        y);
+        ctx, &out, var, /*axis*/ 0,
+        DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &out);
 
-    framework::DDim scale_shape({right});
     if (scale) {
-      Tensor scale_matrix = *scale;
-      scale_matrix.Resize(scale_shape);
       ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-          ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
+          ctx, &out, scale, /*axis*/ 1, MulFunctor<T>(), &out);
     }
     if (bias) {
-      Tensor bias_matrix = *bias;
-      bias_matrix.Resize(scale_shape);
       ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
-          ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
+          ctx, &out, bias, /*axis*/ 1, AddFunctor<T>(), &out);
     }
-    y->Resize(x_dims);
   }
 };
 
@@ -184,6 +179,7 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
     if (d_x) {
       framework::DDim vec_shape({left});
       d_x->mutable_data<T>(ctx.GetPlace());
+      auto dx_dim = d_x->dims();
       Tensor temp_vec;
       temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
 
@@ -227,6 +223,7 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
       ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
           ctx, d_x, &var, /*axis*/ 0,
           DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
+      d_x->Resize(dx_dim);
     }
   }
 };
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index 68cf8673cd..f456b1194c 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -62,9 +62,9 @@ def _reference_layer_norm_grad(x, grad_y, scale, mean, var, begin_norm_axis=1):
 
     grad_x = dx_end + d_mean + d_std
 
-    grad_y.shape = x_shape
-    x.shape = x_shape
+    grad_x.shape, x.shape, grad_y.shape = x_shape, x_shape, x_shape
     scale.shape = scale_shape
+    var.shape, mean.shape = [N, ], [N, ]
     return grad_x, d_scale, d_bias
 
 
@@ -112,10 +112,7 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
 
 class TestLayerNormdOp(OpTest):
     def __assert_close(self, tensor, np_array, msg, atol=1e-4):
-        self.assertTrue(
-            np.allclose(
-                np.array(tensor).reshape(np_array.shape), np_array, atol=atol),
-            msg)
+        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
 
     def __assert_grad_close(self,
                             tensor,
@@ -123,7 +120,7 @@ class TestLayerNormdOp(OpTest):
                             name,
                             place,
                             max_relative_error=0.02):
-        a = np.array(tensor).reshape(np_array.shape)
+        a = np.array(tensor)
         b = np_array
         abs_a = np.abs(a)
         abs_a[abs_a < 1e-5] = 1

From 96d4bf5337c985feff01a549c26133e3ed1c3bde Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 5 Feb 2018 12:38:37 +0800
Subject: [PATCH 237/314] prevent make clean from cleaning ExternalProject
 boost

---
 CMakeLists.txt             | 2 +-
 cmake/external/boost.cmake | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/CMakeLists.txt b/CMakeLists.txt
index e8ea828dd2..49334279f6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -137,7 +137,7 @@ include(external/openblas)  # download, build, install openblas
 include(external/mkldnn)    # download, build, install mkldnn
 include(external/swig)      # download, build, install swig
 include(external/warpctc)   # download, build, install warpctc
-include(external/boost)     # download, build, install boost
+include(external/boost)     # download boost
 include(external/any)       # download libn::any
 include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
diff --git a/cmake/external/boost.cmake b/cmake/external/boost.cmake
index c70d83b3f4..dbc676bdac 100644
--- a/cmake/external/boost.cmake
+++ b/cmake/external/boost.cmake
@@ -21,6 +21,7 @@ set(BOOST_URL           "http://sourceforge.net/projects/boost/files/boost/${BOO
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
+set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
 
 include_directories(${BOOST_INCLUDE_DIR})
 

From eef381d07482f845a875269f1b963f1d135e2cdc Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 5 Feb 2018 12:47:25 +0800
Subject: [PATCH 238/314] remove duplicated mobile index

---
 doc/index_cn.rst        | 1 -
 doc/index_en.rst        | 1 -
 doc/mobile/index_cn.rst | 9 ---------
 doc/mobile/index_en.rst | 9 ---------
 4 files changed, 20 deletions(-)
 delete mode 100644 doc/mobile/index_cn.rst
 delete mode 100644 doc/mobile/index_en.rst

diff --git a/doc/index_cn.rst b/doc/index_cn.rst
index ada51c2d73..9279bac7f4 100644
--- a/doc/index_cn.rst
+++ b/doc/index_cn.rst
@@ -8,4 +8,3 @@ PaddlePaddle 文档
   howto/index_cn.rst
   api/index_cn.rst
   faq/index_cn.rst
-  mobile/index_cn.rst
diff --git a/doc/index_en.rst b/doc/index_en.rst
index 23b64b6cad..64684b8b9b 100644
--- a/doc/index_en.rst
+++ b/doc/index_en.rst
@@ -7,4 +7,3 @@ PaddlePaddle Documentation
   getstarted/index_en.rst
   howto/index_en.rst
   api/index_en.rst
-  mobile/index_en.rst
diff --git a/doc/mobile/index_cn.rst b/doc/mobile/index_cn.rst
deleted file mode 100644
index 1d99666e58..0000000000
--- a/doc/mobile/index_cn.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-MOBILE
-======
-
-..  toctree::
-  :maxdepth: 1
-
-  cross_compiling_for_android_cn.md
-  cross_compiling_for_ios_cn.md
-  cross_compiling_for_raspberry_cn.md
diff --git a/doc/mobile/index_en.rst b/doc/mobile/index_en.rst
deleted file mode 100644
index ef421dacad..0000000000
--- a/doc/mobile/index_en.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-MOBILE
-======
-
-..  toctree::
-  :maxdepth: 1
-
-  cross_compiling_for_android_en.md
-  cross_compiling_for_ios_en.md
-  cross_compiling_for_raspberry_en.md

From df0e74dba0fcbb894eeefa727d7a8a4d50025ccb Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 5 Feb 2018 11:28:22 +0800
Subject: [PATCH 239/314] unifid GPU and CPU implementation

---
 paddle/operators/layer_norm_op.cc             | 185 ------------------
 paddle/operators/layer_norm_op.h              |   2 +-
 .../v2/fluid/tests/test_layer_norm_op.py      |   4 +-
 3 files changed, 4 insertions(+), 187 deletions(-)

diff --git a/paddle/operators/layer_norm_op.cc b/paddle/operators/layer_norm_op.cc
index 910b8ec0a4..76d5d571c3 100644
--- a/paddle/operators/layer_norm_op.cc
+++ b/paddle/operators/layer_norm_op.cc
@@ -21,13 +21,6 @@ using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 using DataLayout = framework::DataLayout;
 
-template <typename T>
-using EigenMatrixMapRowMajor = Eigen::Map<
-    Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
-template <typename T>
-using ConstEigenMatrixMapRowMajor = Eigen::Map<
-    const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
-
 class LayerNormOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -115,75 +108,6 @@ https://arxiv.org/abs/1607.06450
   }
 };
 
-template <typename T>
-class LayerNormKernel<platform::CPUDeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const float epsilon = ctx.Attr<float>("epsilon");
-    const auto *scale = ctx.Input<Tensor>("Scale");
-    const auto *bias = ctx.Input<Tensor>("Bias");
-    const auto *x = ctx.Input<Tensor>("X");
-    const auto &x_dims = x->dims();
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-
-    auto *output = ctx.Output<Tensor>("Y");
-    auto *mean = ctx.Output<Tensor>("Mean");
-    auto *var = ctx.Output<Tensor>("Variance");
-    output->mutable_data<T>(ctx.GetPlace());
-    mean->mutable_data<T>(ctx.GetPlace());
-    var->mutable_data<T>(ctx.GetPlace());
-
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-
-    auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-
-    auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
-    auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
-    auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
-
-    auto squre = [](T ele) { return ele * ele; };
-    auto add_epslion = [epsilon](T ele) { return ele + epsilon; };
-
-    mean_map = input_map.rowwise().mean();
-    var_map = (input_map - mean_map.replicate(1, right))
-                  .unaryExpr(squre)
-                  .rowwise()
-                  .mean()
-                  .unaryExpr(add_epslion);
-
-    auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-    // TODO(zcd): Some thinking about output_map, is it appropriate that
-    // `output_map` and `input_map` point to the same memory.
-    auto inv_std = var_map.unaryExpr(inv_std_func);
-    if (scale && bias) {
-      auto scale_map =
-          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right))
-                       .cwiseProduct(scale_map.replicate(left, 1)) +
-                   bias_map.replicate(left, 1);
-    } else if (scale) {
-      auto scale_map =
-          ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right))
-                       .cwiseProduct(scale_map.replicate(left, 1));
-    } else if (bias) {
-      auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right)) +
-                   bias_map.replicate(left, 1);
-    } else {
-      output_map = (input_map - mean_map.replicate(1, right))
-                       .cwiseProduct(inv_std.replicate(1, right));
-    }
-  }
-};
-
 class LayerNormGradOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -236,115 +160,6 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename T>
-class LayerNormGradKernel<platform::CPUDeviceContext, T>
-    : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext &ctx) const override {
-    const auto *x = ctx.Input<Tensor>("X");
-    const auto *mean = ctx.Input<Tensor>("Mean");
-    const auto *var = ctx.Input<Tensor>("Variance");
-    const auto *scale = ctx.Input<Tensor>("Scale");
-    const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
-
-    const auto &x_dims = x->dims();
-
-    const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
-    auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
-    int left = static_cast<int>(matrix_dim[0]);
-    int right = static_cast<int>(matrix_dim[1]);
-
-    // init output
-    auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
-    auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
-
-    auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
-    auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
-    auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
-    auto var_map = ConstEigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
-
-    if (d_bias) {
-      d_bias->mutable_data<T>(ctx.GetPlace());
-      auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
-      d_bias_map = d_y_map.colwise().sum();
-    }
-    if (d_scale) {
-      d_scale->mutable_data<T>(ctx.GetPlace());
-      auto d_scale_map =
-          EigenMatrixMapRowMajor<T>(d_scale->data<T>(), 1, right);
-      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-      // There are two equation to compute d_scale. One uses "Y" and the other
-      // does not use "Y"
-      d_scale_map =
-          ((x_map - mean_map.replicate(1, right))
-               .cwiseProduct(
-                   var_map.unaryExpr(inv_std_func).replicate(1, right))
-               .cwiseProduct(d_y_map))
-              .colwise()
-              .sum();
-    }
-
-    if (d_x) {
-      d_x->mutable_data<T>(ctx.GetPlace());
-      auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
-      auto triple_product_func = [](T ele) { return ele * ele * ele; };
-      auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
-
-      auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
-      // TODO(zcd): these code can be refined
-      if (d_scale) {
-        auto scale_map =
-            ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
-        // dy_dx
-        auto dx_end =
-            inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
-                scale_map.replicate(left, 1));
-
-        // dy_dmean_dx
-        auto dx_mean =
-            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
-
-        // dy_var_dx
-        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                                 .cwiseProduct(scale_map.replicate(left, 1))
-                                 .cwiseProduct(d_y_map)
-                                 .rowwise()
-                                 .sum();
-        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
-                            .cwiseProduct(dvar_end_part)
-                            .replicate(1, right);
-        auto dx_var =
-            (T(-1.0) / right) *
-            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-        d_x_map = dx_end + dx_mean + dx_var;
-      } else {
-        // dy_dx
-        auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
-
-        // dy_dmean_dx
-        auto dx_mean =
-            (T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
-
-        // dy_var_dx
-        auto dvar_end_part = (x_map - mean_map.replicate(1, right))
-                                 .cwiseProduct(d_y_map)
-                                 .rowwise()
-                                 .sum();
-        auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
-                            .cwiseProduct(dvar_end_part)
-                            .replicate(1, right);
-        auto dx_var =
-            (T(-1.0) / right) *
-            (x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
-
-        d_x_map = dx_end + dx_mean + dx_var;
-      }
-    }
-  }
-};
-
 }  // namespace operators
 }  // namespace paddle
 
diff --git a/paddle/operators/layer_norm_op.h b/paddle/operators/layer_norm_op.h
index 2de58186fb..608447b1ff 100644
--- a/paddle/operators/layer_norm_op.h
+++ b/paddle/operators/layer_norm_op.h
@@ -31,7 +31,7 @@ template <typename T>
 struct DivAndSqrtFunctor {
   explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
   inline HOSTDEVICE T operator()(T a, T b) const {
-    return a / (sqrt(b) + epsilon_);
+    return a / (sqrt(b + epsilon_));
   }
 
  private:
diff --git a/python/paddle/v2/fluid/tests/test_layer_norm_op.py b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
index f456b1194c..4460ffaf9c 100644
--- a/python/paddle/v2/fluid/tests/test_layer_norm_op.py
+++ b/python/paddle/v2/fluid/tests/test_layer_norm_op.py
@@ -20,6 +20,8 @@ import paddle.v2.fluid.core as core
 from paddle.v2.fluid.op import Operator
 from paddle.v2.fluid.framework import grad_var_name
 
+np.random.random(123)
+
 
 def _reference_layer_norm_naive(x, scale, beta, epsilon, begin_norm_axis=1):
     x_shape = x.shape
@@ -148,7 +150,7 @@ class TestLayerNormdOp(OpTest):
             x_shape = shape
             D = reduce(mul, x_shape[begin_norm_axis:len(x_shape)], 1)
             scale_shape = [D]
-            np.random.random(123)
+
             x_val = np.random.random_sample(x_shape).astype(np.float32)
             scale_val = np.random.random_sample(scale_shape).astype(np.float32)
             bias_val = np.random.random_sample(scale_shape).astype(np.float32)

From 677312973516832a495a0f83fbcc8b5c55e977f6 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 5 Feb 2018 13:40:11 +0800
Subject: [PATCH 240/314] code refine

---
 paddle/operators/layer_norm_op.h | 42 ++++++++++++++++++--------------
 1 file changed, 24 insertions(+), 18 deletions(-)

diff --git a/paddle/operators/layer_norm_op.h b/paddle/operators/layer_norm_op.h
index 608447b1ff..3c436b8926 100644
--- a/paddle/operators/layer_norm_op.h
+++ b/paddle/operators/layer_norm_op.h
@@ -97,15 +97,15 @@ class LayerNormKernel : public framework::OpKernel<T> {
     auto &dev_ctx = ctx.template device_context<DeviceContext>();
     math::RowwiseMean<DeviceContext, T> row_mean;
 
-    // functor-> get mean
+    // get mean
     row_mean(dev_ctx, x, mean);
 
-    // functor-> get variance
+    // get variance
     ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
         ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), &out);
     row_mean(dev_ctx, out, var);
 
-    // functor-> get norm_out
+    // get x_norm
     ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
         ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), &out);
     ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
@@ -129,9 +129,11 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     const float epsilon = ctx.Attr<float>("epsilon");
     auto x = *ctx.Input<Tensor>("X");
-    auto mean = *ctx.Input<Tensor>("Mean");
-    auto var = *ctx.Input<Tensor>("Variance");
-    auto scale = *ctx.Input<Tensor>("Scale");
+    auto *y = ctx.Input<Tensor>("Y");
+    auto *mean = ctx.Input<Tensor>("Mean");
+    auto *var = ctx.Input<Tensor>("Variance");
+    auto *scale = ctx.Input<Tensor>("Scale");
+    auto *bias = ctx.Input<Tensor>("Bias");
     auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
     const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
 
@@ -155,14 +157,19 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
     if (d_scale || d_x) {
       x.Resize(matrix_shape);
       temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
-      temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
 
-      // get x_norm
-      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
-      ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-          ctx, &temp_norm, &var, /*axis*/ 0,
-          DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
+      if (!(bias && scale)) {
+        temp_norm.ShareDataWith(*y);
+        temp_norm.Resize(matrix_shape);
+      } else {
+        temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
+        // get x_norm
+        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
+            ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
+        ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
+            ctx, &temp_norm, var, /*axis*/ 0,
+            DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
+      }
     }
 
     if (d_bias) {
@@ -188,7 +195,7 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
       if (d_scale) {
         // dy_dx
         ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-            ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
+            ctx, &d_y, scale, /*axis*/ 1, MulFunctor<T>(), &temp);
         framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
 
         // dy_dmean_dx
@@ -199,7 +206,6 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
         // dy_var_dx
         ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
             ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
-
       } else {
         // dy_dx
         framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
@@ -216,12 +222,12 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
       // dy_var_dx
       row_mean(dev_ctx, temp, &temp_vec);
       ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
-          ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
+          ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp);
       ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
+          ctx, d_x, &temp, /*axis*/ 0, SubFunctor<T>(), d_x);
 
       ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
-          ctx, d_x, &var, /*axis*/ 0,
+          ctx, d_x, var, /*axis*/ 0,
           DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
       d_x->Resize(dx_dim);
     }

From 7dabee27960b5e043b85aca3ee51568443b326f4 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 5 Feb 2018 15:00:03 +0800
Subject: [PATCH 241/314] Add type Reader for VarDesc

Add a new type `Reader` for `VarDesc`, which can holds more than one
LoDTensor.
---
 paddle/framework/backward.cc                  |   4 +-
 paddle/framework/framework.proto              |  10 +-
 paddle/framework/op_desc.cc                   |   4 +-
 paddle/framework/program_desc_test.cc         |   4 +-
 paddle/framework/var_desc.cc                  | 174 ++++++++++++++++--
 paddle/framework/var_desc.h                   |  20 +-
 paddle/inference/io.cc                        |   2 +-
 paddle/pybind/protobuf.cc                     |  14 +-
 .../v2/fluid/tests/test_protobuf_descs.py     |  38 ++++
 9 files changed, 246 insertions(+), 24 deletions(-)

diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc
index 85e693434a..f52a51519f 100644
--- a/paddle/framework/backward.cc
+++ b/paddle/framework/backward.cc
@@ -534,7 +534,7 @@ ParamGradInfoMap AppendBackward(
   auto root_block = program_desc.MutableBlock(root_block_idx);
 
   std::string fill_one_op_out = GradVarName(target.Name());
-  bool is_scalar = target.Shape() == std::vector<int64_t>{1};
+  bool is_scalar = target.GetShape() == std::vector<int64_t>{1};
   PADDLE_ENFORCE(is_scalar, "target should be scalar");
   VLOG(3) << "backward from loss=" << target.Name()
           << " data_type=" << target.GetDataType();
@@ -565,7 +565,7 @@ ParamGradInfoMap AppendBackward(
 
   auto var = root_block->Var(fill_one_op_out);
   var->SetDataType(target.GetDataType());
-  var->SetShape(target.Shape());
+  var->SetShape(target.GetShape());
   auto& target_grad = retv[target.Name()];
   target_grad.name_ = fill_one_op_out;
   target_grad.block_idx_ = root_block_idx;
diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto
index 5b6ef03f61..f65ccae6e6 100644
--- a/paddle/framework/framework.proto
+++ b/paddle/framework/framework.proto
@@ -116,6 +116,8 @@ message LoDTensorArrayDesc {
   optional int32 lod_level = 2 [ default = 0 ];
 }
 
+message Reader { repeated LoDTensorDesc lod_tensor = 1; }
+
 message VarDesc {
   enum VarType {
     LOD_TENSOR = 1;
@@ -126,13 +128,15 @@ message VarDesc {
     LOD_RANK_TABLE = 6;
     LOD_TENSOR_ARRAY = 7;
     PLACE_LIST = 8;
+    READER = 9;
   }
   required string name = 1;
   required VarType type = 2;
-  optional LoDTensorDesc lod_tensor = 3;
-  optional TensorDesc selected_rows = 4;
+  optional bool persistable = 3 [ default = false ];
+  optional LoDTensorDesc lod_tensor = 4;
+  optional TensorDesc selected_rows = 5;
   optional LoDTensorArrayDesc tensor_array = 6;
-  optional bool persistable = 5 [ default = false ];
+  optional Reader reader = 7;
 }
 
 message BlockDesc {
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index f554c77845..ad361852ec 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -458,11 +458,11 @@ DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
   PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
   try {
-    auto shape = var->Shape();
+    auto shape = var->GetShape();
     if (shape.empty()) {
       return framework::make_ddim({0UL});
     } else {
-      return framework::make_ddim(var->Shape());
+      return framework::make_ddim(var->GetShape());
     }
   } catch (...) {
     VLOG(5) << "GetDim of variable " << name << " error";
diff --git a/paddle/framework/program_desc_test.cc b/paddle/framework/program_desc_test.cc
index 59947c9f21..9945aee31b 100644
--- a/paddle/framework/program_desc_test.cc
+++ b/paddle/framework/program_desc_test.cc
@@ -53,7 +53,7 @@ TEST(ProgramDesc, copy_ctor) {
     ASSERT_NE(copy, var_before);
     ASSERT_EQ(copy->Name(), var_before->Name());
     ASSERT_EQ(copy->GetType(), var_before->GetType());
-    ASSERT_EQ(copy->Shape(), var_before->Shape());
+    ASSERT_EQ(copy->GetShape(), var_before->GetShape());
     ASSERT_EQ(copy->Proto()->SerializeAsString(),
               var_before->Proto()->SerializeAsString());
   };
@@ -117,7 +117,7 @@ TEST(ProgramDescBind, serialize_and_deserialize) {
     ASSERT_NE(restored, var_before);
     ASSERT_EQ(restored->Name(), var_before->Name());
     ASSERT_EQ(restored->GetType(), var_before->GetType());
-    ASSERT_EQ(restored->Shape(), var_before->Shape());
+    ASSERT_EQ(restored->GetShape(), var_before->GetShape());
     ASSERT_EQ(restored->Proto()->SerializeAsString(),
               var_before->Proto()->SerializeAsString());
   };
diff --git a/paddle/framework/var_desc.cc b/paddle/framework/var_desc.cc
index 62ab6593ef..44bd2363c8 100644
--- a/paddle/framework/var_desc.cc
+++ b/paddle/framework/var_desc.cc
@@ -26,18 +26,91 @@ void VarDesc::SetShape(const std::vector<int64_t> &dims) {
   VectorToRepeated(dims, mutable_tensor_desc()->mutable_dims());
 }
 
+void VarDesc::SetTensorDescNum(size_t num) {
+  switch (desc_.type()) {
+    case proto::VarDesc::READER: {
+      auto *lod_tensors_ptr = desc_.mutable_reader()->mutable_lod_tensor();
+      lod_tensors_ptr->Clear();
+      for (size_t i = 0; i < num; ++i) {
+        lod_tensors_ptr->Add();
+      }
+      return;
+    } break;
+    default:
+      PADDLE_THROW(
+          "Setting 'sub_tensor_number' is not supported by the type of var %s.",
+          this->Name());
+  }
+}
+
+size_t VarDesc::GetTensorDescNum() const {
+  switch (desc_.type()) {
+    case proto::VarDesc::READER:
+      return desc_.reader().lod_tensor_size();
+      break;
+    default:
+      PADDLE_THROW(
+          "Getting 'sub_tensor_number' is not supported by the type of var %s.",
+          this->Name());
+  }
+}
+
+void VarDesc::SetShapes(
+    const std::vector<const std::vector<int64_t>> &multiple_dims) {
+  PADDLE_ENFORCE_EQ(multiple_dims.size(), GetTensorDescNum(),
+                    "The number of given shapes(%d) doesn't equal to the "
+                    "number of sub tensor.",
+                    multiple_dims.size(), GetTensorDescNum());
+  std::vector<proto::TensorDesc *> tensors = mutable_tensor_descs();
+  for (size_t i = 0; i < multiple_dims.size(); ++i) {
+    VectorToRepeated(multiple_dims[i], tensors[i]->mutable_dims());
+  }
+}
+
+std::vector<int64_t> VarDesc::GetShape() const {
+  return RepeatedToVector(tensor_desc().dims());
+}
+
+std::vector<std::vector<int64_t>> VarDesc::GetShapes() const {
+  std::vector<proto::TensorDesc> descs = tensor_descs();
+  std::vector<std::vector<int64_t>> res;
+  res.reserve(descs.size());
+  for (const auto &tensor_desc : descs) {
+    res.push_back(RepeatedToVector(tensor_desc.dims()));
+  }
+  return res;
+}
+
 void VarDesc::SetDataType(proto::DataType data_type) {
   mutable_tensor_desc()->set_data_type(data_type);
 }
 
-std::vector<int64_t> VarDesc::Shape() const {
-  return RepeatedToVector(tensor_desc().dims());
+void VarDesc::SetDataTypes(
+    const std::vector<proto::DataType> &multiple_data_type) {
+  PADDLE_ENFORCE_EQ(multiple_data_type.size(), GetTensorDescNum(),
+                    "The number of given data types(%d) doesn't equal to the "
+                    "number of sub tensor.",
+                    multiple_data_type.size(), GetTensorDescNum());
+  std::vector<proto::TensorDesc *> tensor_descs = mutable_tensor_descs();
+  for (size_t i = 0; i < multiple_data_type.size(); ++i) {
+    tensor_descs[i]->set_data_type(multiple_data_type[i]);
+  }
 }
 
 proto::DataType VarDesc::GetDataType() const {
   return tensor_desc().data_type();
 }
 
+std::vector<proto::DataType> VarDesc::GetDataTypes() const {
+  std::vector<proto::TensorDesc> descs = tensor_descs();
+  std::vector<proto::DataType> res;
+  res.reserve(descs.size());
+  for (const auto &tensor_desc : descs) {
+    res.push_back(tensor_desc.data_type());
+  }
+  return res;
+}
+
 void VarDesc::SetLoDLevel(int32_t lod_level) {
   switch (desc_.type()) {
     case proto::VarDesc::LOD_TENSOR:
@@ -47,8 +120,28 @@ void VarDesc::SetLoDLevel(int32_t lod_level) {
       desc_.mutable_tensor_array()->set_lod_level(lod_level);
       break;
     default:
-      PADDLE_THROW("Tensor type=%d does not support LoDLevel",
-                   desc_.tensor_array().lod_level());
+      PADDLE_THROW(
+          "Setting 'lod_level' is not supported by the type of var %s.",
+          this->Name());
+  }
+}
+
+void VarDesc::SetLoDLevels(const std::vector<int32_t> &multiple_lod_level) {
+  PADDLE_ENFORCE_EQ(multiple_lod_level.size(), GetTensorDescNum(),
+                    "The number of given data types(%d) doesn't equal to the "
+                    "number of sub tensor.",
+                    multiple_lod_level.size(), GetTensorDescNum());
+  switch (desc_.type()) {
+    case proto::VarDesc::READER: {
+      size_t i = 0;
+      for (auto &lod_tensor : *desc_.mutable_reader()->mutable_lod_tensor()) {
+        lod_tensor.set_lod_level(multiple_lod_level[i++]);
+      }
+    } break;
+    default:
+      PADDLE_THROW(
+          "Setting 'lod_levels' is not supported by the type of var %s.",
+          this->Name());
   }
 }
 
@@ -59,13 +152,31 @@ int32_t VarDesc::GetLoDLevel() const {
     case proto::VarDesc::LOD_TENSOR_ARRAY:
       return desc_.tensor_array().lod_level();
     default:
-      PADDLE_THROW("Tensor type=%d does not support LoDLevel",
-                   desc_.tensor_array().lod_level());
+      PADDLE_THROW(
+          "Getting 'lod_level' is not supported by the type of var %s.",
+          this->Name());
+  }
+}
+
+std::vector<int32_t> VarDesc::GetLoDLevels() const {
+  std::vector<int32_t> res;
+  switch (desc_.type()) {
+    case proto::VarDesc::READER:
+      res.reserve(desc_.reader().lod_tensor_size());
+      for (auto &lod_tensor : desc_.reader().lod_tensor()) {
+        res.push_back(lod_tensor.lod_level());
+      }
+      return res;
+      break;
+    default:
+      PADDLE_THROW(
+          "Getting 'lod_levels' is not supported by the type of var %s.",
+          this->Name());
   }
 }
 
 const proto::TensorDesc &VarDesc::tensor_desc() const {
-  PADDLE_ENFORCE(desc_.has_type(), "invoke TensorDesc must after set type");
+  PADDLE_ENFORCE(desc_.has_type(), "The var's type hasn't been set.");
   switch (desc_.type()) {
     case proto::VarDesc::SELECTED_ROWS:
       return desc_.selected_rows();
@@ -74,13 +185,32 @@ const proto::TensorDesc &VarDesc::tensor_desc() const {
     case proto::VarDesc::LOD_TENSOR_ARRAY:
       return desc_.tensor_array().tensor();
     default:
-      PADDLE_THROW("The type of var %s is unsupported.", this->Name());
+      PADDLE_THROW(
+          "Getting 'tensor_desc' is not supported by the type of var %s.",
+          this->Name());
+  }
+}
+
+std::vector<proto::TensorDesc> VarDesc::tensor_descs() const {
+  PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
+  std::vector<proto::TensorDesc> res;
+  res.reserve(GetTensorDescNum());
+  switch (desc_.type()) {
+    case proto::VarDesc::READER:
+      for (const auto &lod_tensor : desc_.reader().lod_tensor()) {
+        res.push_back(lod_tensor.tensor());
+      }
+      return res;
+    default:
+      PADDLE_THROW(
+          "Getting 'tensor_descs' is not supported by the type of var "
+          "%s.",
+          this->Name());
   }
 }
 
 proto::TensorDesc *VarDesc::mutable_tensor_desc() {
-  PADDLE_ENFORCE(desc_.has_type(),
-                 "invoke MutableTensorDesc must after set type");
+  PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
   switch (desc_.type()) {
     case proto::VarDesc::SELECTED_ROWS:
       return desc_.mutable_selected_rows();
@@ -89,8 +219,30 @@ proto::TensorDesc *VarDesc::mutable_tensor_desc() {
     case proto::VarDesc::LOD_TENSOR_ARRAY:
       return desc_.mutable_tensor_array()->mutable_tensor();
     default:
-      PADDLE_THROW("Unexpected branch.");
+      PADDLE_THROW(
+          "Getting 'mutable_tensor_desc' is not supported by the type of var "
+          "%s.",
+          this->Name());
   }
 }
+
+std::vector<proto::TensorDesc *> VarDesc::mutable_tensor_descs() {
+  PADDLE_ENFORCE(desc_.has_type(), "The var type hasn't been set.");
+  std::vector<proto::TensorDesc *> res;
+  res.reserve(GetTensorDescNum());
+  switch (desc_.type()) {
+    case proto::VarDesc::READER:
+      for (auto &lod_tensor : *desc_.mutable_reader()->mutable_lod_tensor()) {
+        res.push_back(lod_tensor.mutable_tensor());
+      }
+      return res;
+    default:
+      PADDLE_THROW(
+          "Getting 'tensor_descs' is not supported by the type of var "
+          "%s.",
+          this->Name());
+  }
+}
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/var_desc.h b/paddle/framework/var_desc.h
index 9316b14bb6..862b9a5d80 100644
--- a/paddle/framework/var_desc.h
+++ b/paddle/framework/var_desc.h
@@ -68,18 +68,34 @@ class VarDesc {
 
   void SetName(std::string name) { desc_.set_name(name); }
 
+  void SetTensorDescNum(size_t num);
+
+  size_t GetTensorDescNum() const;
+
   void SetShape(const std::vector<int64_t> &dims);
 
+  void SetShapes(const std::vector<const std::vector<int64_t>> &multiple_dims);
+
+  std::vector<int64_t> GetShape() const;
+
+  std::vector<std::vector<int64_t>> GetShapes() const;
+
   void SetDataType(proto::DataType data_type);
 
-  std::vector<int64_t> Shape() const;
+  void SetDataTypes(const std::vector<proto::DataType> &multiple_data_type);
 
   proto::DataType GetDataType() const;
 
+  std::vector<proto::DataType> GetDataTypes() const;
+
   void SetLoDLevel(int32_t lod_level);
 
+  void SetLoDLevels(const std::vector<int32_t> &multiple_lod_level);
+
   int32_t GetLoDLevel() const;
 
+  std::vector<int32_t> GetLoDLevels() const;
+
   proto::VarDesc::VarType GetType() const;
 
   void SetType(proto::VarDesc::VarType type);
@@ -90,7 +106,9 @@ class VarDesc {
 
  private:
   const proto::TensorDesc &tensor_desc() const;
+  std::vector<proto::TensorDesc> tensor_descs() const;
   proto::TensorDesc *mutable_tensor_desc();
+  std::vector<proto::TensorDesc *> mutable_tensor_descs();
 
   proto::VarDesc desc_;
 };
diff --git a/paddle/inference/io.cc b/paddle/inference/io.cc
index 60ad7af1c0..1ed14b69c8 100644
--- a/paddle/inference/io.cc
+++ b/paddle/inference/io.cc
@@ -55,7 +55,7 @@ void LoadPersistables(framework::Executor& executor,
       VLOG(3) << "parameter's name: " << var->Name();
 
       framework::VarDesc* new_var = load_block->Var(var->Name());
-      new_var->SetShape(var->Shape());
+      new_var->SetShape(var->GetShape());
       new_var->SetDataType(var->GetDataType());
       new_var->SetType(var->GetType());
       new_var->SetLoDLevel(var->GetLoDLevel());
diff --git a/paddle/pybind/protobuf.cc b/paddle/pybind/protobuf.cc
index 371d6119d4..0f1953abe0 100644
--- a/paddle/pybind/protobuf.cc
+++ b/paddle/pybind/protobuf.cc
@@ -214,11 +214,20 @@ void BindVarDsec(py::module &m) {
            py::return_value_policy::reference)
       .def("set_name", &VarDesc::SetName)
       .def("set_shape", &VarDesc::SetShape)
+      .def("set_shapes", &VarDesc::SetShapes)
       .def("set_dtype", &VarDesc::SetDataType)
-      .def("shape", &VarDesc::Shape, py::return_value_policy::reference)
+      .def("set_dtypes", &VarDesc::SetDataTypes)
+      .def("set_tensor_num", &VarDesc::SetTensorDescNum)
+      .def("tensor_num", &VarDesc::GetTensorDescNum)
+      .def("shape", &VarDesc::GetShape, py::return_value_policy::reference)
+      .def("shapes", &VarDesc::GetShapes, py::return_value_policy::reference)
       .def("dtype", &VarDesc::GetDataType, py::return_value_policy::reference)
+      .def("dtypes", &VarDesc::GetDataTypes, py::return_value_policy::reference)
       .def("lod_level", &VarDesc::GetLoDLevel)
+      .def("lod_levels", &VarDesc::GetLoDLevels,
+           py::return_value_policy::reference)
       .def("set_lod_level", &VarDesc::SetLoDLevel)
+      .def("set_lod_levels", &VarDesc::SetLoDLevels)
       .def("type", &VarDesc::GetType)
       .def("set_type", &VarDesc::SetType)
       .def("serialize_to_string", SerializeMessage<VarDesc>)
@@ -233,7 +242,8 @@ void BindVarDsec(py::module &m) {
       .value("STEP_SCOPES", proto::VarDesc::STEP_SCOPES)
       .value("LOD_RANK_TABLE", proto::VarDesc::LOD_RANK_TABLE)
       .value("LOD_TENSOR_ARRAY", proto::VarDesc::LOD_TENSOR_ARRAY)
-      .value("PLACE_LIST", proto::VarDesc::PLACE_LIST);
+      .value("PLACE_LIST", proto::VarDesc::PLACE_LIST)
+      .value("READER", proto::VarDesc::READER);
 }
 
 void BindOpDesc(py::module &m) {
diff --git a/python/paddle/v2/fluid/tests/test_protobuf_descs.py b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
index 9034b2f4ef..ac6de68b5f 100644
--- a/python/paddle/v2/fluid/tests/test_protobuf_descs.py
+++ b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
@@ -115,6 +115,20 @@ class TestVarDesc(unittest.TestCase):
         self.assertEqual(src_shape, res_shape)
         self.assertEqual(core.VarDesc.VarType.SELECTED_ROWS, var.type())
 
+    def test_multiple_shape(self):
+        program_desc = core.ProgramDesc()
+        block = program_desc.block(0)
+        var = block.var('my_reader')
+        var.set_type(core.VarDesc.VarType.READER)
+        var.set_tensor_num(3)
+        src_shapes = [[2, 3, 3], [4, 5], [6, 7, 8, 9]]
+        var.set_shapes(src_shapes)
+        #import pdb
+        # pdb.set_trace()
+        res_shapes = var.shapes()
+        self.assertEqual(src_shapes, res_shapes)
+        self.assertEqual(core.VarDesc.VarType.READER, var.type())
+
     def test_dtype(self):
         program_desc = core.ProgramDesc()
         block = program_desc.block(0)
@@ -124,6 +138,30 @@ class TestVarDesc(unittest.TestCase):
         self.assertEqual(core.DataType.INT32, var.dtype())
         self.assertEqual(core.VarDesc.VarType.LOD_TENSOR, var.type())
 
+    def test_multiple_dtype(self):
+        program_desc = core.ProgramDesc()
+        block = program_desc.block(0)
+        var = block.var('my_reader')
+        var.set_type(core.VarDesc.VarType.READER)
+        var.set_tensor_num(3)
+        src_types = [
+            core.DataType.INT32, core.DataType.FP64, core.DataType.FP32
+        ]
+        var.set_dtypes(src_types)
+        self.assertEqual(src_types, var.dtypes())
+        self.assertEqual(core.VarDesc.VarType.READER, var.type())
+
+    def test_multiple_lod_level(self):
+        program_desc = core.ProgramDesc()
+        block = program_desc.block(0)
+        var = block.var('my_reader')
+        var.set_type(core.VarDesc.VarType.READER)
+        var.set_tensor_num(3)
+        src_types = [3, 1, 2]
+        var.set_lod_levels(src_types)
+        self.assertEqual(src_types, var.lod_levels())
+        self.assertEqual(core.VarDesc.VarType.READER, var.type())
+
 
 class TestBlockDesc(unittest.TestCase):
     def test_add_var(self):

From 0d03cab5e9b16dba434ed4a25b5dff887d60a897 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 5 Feb 2018 15:18:10 +0800
Subject: [PATCH 242/314] fix a compile error

---
 paddle/framework/var_desc.cc | 2 +-
 paddle/framework/var_desc.h  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/var_desc.cc b/paddle/framework/var_desc.cc
index 44bd2363c8..6d83e2e411 100644
--- a/paddle/framework/var_desc.cc
+++ b/paddle/framework/var_desc.cc
@@ -56,7 +56,7 @@ size_t VarDesc::GetTensorDescNum() const {
 }
 
 void VarDesc::SetShapes(
-    const std::vector<const std::vector<int64_t>> &multiple_dims) {
+    const std::vector<std::vector<int64_t>> &multiple_dims) {
   PADDLE_ENFORCE_EQ(multiple_dims.size(), GetTensorDescNum(),
                     "The number of given shapes(%d) doesn't equal to the "
                     "number of sub tensor.",
diff --git a/paddle/framework/var_desc.h b/paddle/framework/var_desc.h
index 862b9a5d80..72da2fbb0a 100644
--- a/paddle/framework/var_desc.h
+++ b/paddle/framework/var_desc.h
@@ -74,7 +74,7 @@ class VarDesc {
 
   void SetShape(const std::vector<int64_t> &dims);
 
-  void SetShapes(const std::vector<const std::vector<int64_t>> &multiple_dims);
+  void SetShapes(const std::vector<std::vector<int64_t>> &multiple_dims);
 
   std::vector<int64_t> GetShape() const;
 

From 63320f722cc718e69ddaa4aa5921e7fd047097df Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 01:17:00 -0800
Subject: [PATCH 243/314] "add some interfaces"

---
 paddle/framework/lod_tensor.h   |  22 ++++++-
 paddle/framework/mixed_vector.h | 102 ++++++++++++++++++++------------
 paddle/memory/memory.h          |  18 ++++++
 3 files changed, 103 insertions(+), 39 deletions(-)

diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index d0ab640485..ab28924161 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -48,12 +48,26 @@ namespace framework {
  */
 struct LoD : public std::vector<Vector<size_t>> {
   using std::vector<Vector<size_t>>::vector;
+  platform::Place place() const {
+    if (this->size() == 0) {
+      // Not Initialze Yet.
+      return platform::CPUPlace();
+    } else {
+      return this->front().place();
+    }
+  }
 
   void CopyFromCUDA() {
     for (auto it = this->begin(); it != this->end(); ++it) {
       it->CopyFromCUDA();
     }
   }
+
+  void CopyToPeer(platform::Place place) {
+    for (auto it = this->begin(); it != this->end(); ++it) {
+      it->mutable_data(place);
+    }
+  }
 };
 
 std::ostream& operator<<(std::ostream& os, const LoD& lod);
@@ -115,7 +129,13 @@ class LoDTensor : public Tensor {
 
   explicit LoDTensor(const LoD& lod) : lod_(lod) {}
 
-  void set_lod(const LoD& lod) { lod_ = lod; }
+  void set_lod(const LoD& lod) {
+    lod_ = lod;
+    if (holder_ != nullptr &&
+        platform::is_same_place(holder_->place(), lod.place())) {
+      lod_.CopyToPeer(holder_->place());
+    }
+  }
 
   const LoD& lod() const { return lod_; }
 
diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index 85caac8dcd..d86899bc63 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -40,14 +40,15 @@ class Vector : public std::vector<T> {
   Vector() {}
   Vector(const std::vector<T> &v) : std::vector<T>(v) {}  // NOLINT
 
-  virtual ~Vector() {
-#ifdef PADDLE_WITH_CUDA
-    if (cuda_ptr_ != nullptr) {
-      memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
-    }
-#endif
-  }
+  inline platform::Place place() const { return place_; }
 
+  /*! Return a pointer to constant memory block. */
+  inline const T *data(platform::Place place) const;
+
+  /*! Return a pointer to mutable memory block. */
+  inline T *mutable_data(platform::Place place);
+
+  // TODO(dzhwinter): below interfaces should be removed
   /* Get device vector */
   T *cuda_data() {
     CopyToCUDA();
@@ -68,25 +69,71 @@ class Vector : public std::vector<T> {
   void CopyToPeer(platform::Place);
 
  private:
-  void *cuda_ptr_ = nullptr;
+  std::shared_ptr<void> cuda_ptr_;
   size_t cuda_size_ = 0;  // device vector numel
   platform::CUDAPlace place_;
 };
 
 template <typename T>
-void Vector<T>::CopyToCUDA() {
+inline const T *Vector<T>::data(platform::Place place) const {
+  if (platform::is_cpu_place(place)) {
+    return std::vector<T>::data();
+  } else if (platform::is_gpu_place(place)) {
+    if (cuda_ptr_ == nullptr) {
+      return nullptr;
+    }
+    if (platform::is_same_place(place, place_)) {
+      return static_cast<const T *>(cuda_ptr_.get());
+    } else {
+      PADDLE_THROW(
+          "Unmatched place. Please use `mutable_data` copy lod to the target "
+          "Place first.");
+    }
+  } else {
+    PADDLE_THROW("Unsupport Place.");
+  }
+}
+
+template <typename T>
+inline T *Vector<T>::mutable_data(platform::Place place) {
+  if (platform::is_cpu_place(place)) {
+    return std::vector<T>::data();
+  } else if (platform::is_gpu_place(place)) {
+    if (!platform::is_same_place(place, place_)) {
+      place_ = boost::get<platform::CUDAPlace>(place);
+    }
 #ifdef PADDLE_WITH_CUDA
-  if (cuda_size_ < this->size()) {
-    if (cuda_ptr_ != nullptr) {
-      memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
+    if (cuda_size_ < this->size() || cuda_ptr_ == nullptr) {
+      cuda_ptr_.reset(
+          memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T)),
+          memory::PlainDeleter<void, platform::CUDAPlace>(place_));
     }
-    cuda_ptr_ =
-        memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T));
+    cuda_size_ = this->size();
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto *ctx = pool.GetByPlace(place_);
+    memory::Copy(place_, cuda_ptr_.get(), platform::CPUPlace(),
+                 static_cast<const void *>(this->data()),
+                 this->size() * sizeof(T), ctx->stream());
+    ctx->Wait();
+    return static_cast<T *>(cuda_ptr_.get());
+#endif
+  } else {
+    PADDLE_THROW("Unsupport Place.");
+  }
+}
+
+template <typename T>
+void Vector<T>::CopyToCUDA() {
+#ifdef PADDLE_WITH_CUDA
+  if (cuda_size_ < this->size() || cuda_ptr_ == nullptr) {
+    cuda_ptr_.reset(
+        memory::Alloc<platform::CUDAPlace>(this->size() * sizeof(T)),
+        memory::PlainDeleter<void, platform::CUDAPlace>(place_));
   }
   cuda_size_ = this->size();
   platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
   auto *ctx = pool.GetByPlace(place_);
-  memory::Copy(place_, cuda_ptr_, platform::CPUPlace(),
+  memory::Copy(place_, cuda_ptr_.get(), platform::CPUPlace(),
                static_cast<const void *>(this->data()),
                this->size() * sizeof(T), ctx->stream());
   ctx->Wait();
@@ -104,32 +151,11 @@ void Vector<T>::CopyFromCUDA() {
   platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
   auto *ctx = pool.GetByPlace(place_);
   memory::Copy(platform::CPUPlace(), static_cast<void *>(this->data()), place_,
-               static_cast<const void *>(cuda_ptr_), this->size() * sizeof(T),
-               ctx->stream());
-  ctx->Wait();
-#endif
-}
-
-template <typename T>
-void Vector<T>::CopyToPeer(platform::Place peer_place) {
-#ifdef PADDLE_WITH_CUDA
-  auto *ctx = platform::DeviceContextPool::Instance().GetByPlace(place_);
-  void *peer_cuda_ptr = memory::Alloc<platform::CUDAPlace>(
-      boost::get<platform::CUDAPlace>(peer_place), this->size() * sizeof(T));
-  memory::Copy(boost::get<platform::CUDAPlace>(peer_place), peer_cuda_ptr,
-               place_, cuda_ptr_, this->size() * sizeof(T), ctx->stream());
+               static_cast<const void *>(cuda_ptr_.get()),
+               this->size() * sizeof(T), ctx->stream());
   ctx->Wait();
-
-  memory::Free<platform::CUDAPlace>(place_, cuda_ptr_);
-  place_ = boost::get<platform::CUDAPlace>(peer_place);
-  cuda_ptr_ = peer_cuda_ptr;
 #endif
 }
 
-template class Vector<int>;
-template class Vector<unsigned>;
-template class Vector<size_t>;
-template class Vector<int64_t>;
-
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/memory/memory.h b/paddle/memory/memory.h
index 7012b6d331..30ed68c6e0 100644
--- a/paddle/memory/memory.h
+++ b/paddle/memory/memory.h
@@ -81,5 +81,23 @@ class PODDeleter {
   Place place_;
 };
 
+/**
+ * \brief   Free memory block in one place does not meet POD
+ *
+ * \note    In some cases, custom deleter is used to
+ *          deallocate the memory automatically for
+ *          std::unique_ptr<T> in tensor.h.
+ *
+ */
+template <typename T, typename Place>
+class PlainDeleter {
+ public:
+  explicit PlainDeleter(Place place) : place_(place) {}
+  void operator()(T* ptr) { Free(place_, reinterpret_cast<void*>(ptr)); }
+
+ private:
+  Place place_;
+};
+
 }  // namespace memory
 }  // namespace paddle

From a402d2b39257ae58345998ed5edd6b87b09e9a1b Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 01:22:13 -0800
Subject: [PATCH 244/314] "fix condition"

---
 paddle/framework/lod_tensor.h    | 2 +-
 paddle/framework/selected_rows.h | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index ab28924161..3465e02c82 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -132,7 +132,7 @@ class LoDTensor : public Tensor {
   void set_lod(const LoD& lod) {
     lod_ = lod;
     if (holder_ != nullptr &&
-        platform::is_same_place(holder_->place(), lod.place())) {
+        !platform::is_same_place(holder_->place(), lod.place())) {
       lod_.CopyToPeer(holder_->place());
     }
   }
diff --git a/paddle/framework/selected_rows.h b/paddle/framework/selected_rows.h
index 30d3dfc1e8..1132344244 100644
--- a/paddle/framework/selected_rows.h
+++ b/paddle/framework/selected_rows.h
@@ -42,7 +42,13 @@ class SelectedRows {
 
   Vector<int64_t>* mutable_rows() { return &rows_; }
 
-  void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
+  void set_rows(const Vector<int64_t>& rows) {
+    rows_ = rows;
+    if (value_ != nullptr &&
+        !platform::is_same_place(value_->place(), rows.place())) {
+      rows_.mutable_data(value_->place());
+    }
+  }
 
   DDim GetCompleteDims() const {
     std::vector<int64_t> dims = vectorize(value_->dims());

From 07dd3d25b39878b6ccc4736e189c015cfd2265d2 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 01:53:43 -0800
Subject: [PATCH 245/314] "fix const warning"

---
 paddle/framework/CMakeLists.txt       |  1 +
 paddle/framework/lod_tensor_test.cu   | 22 --------
 paddle/framework/mixed_vector_test.cu | 72 +++++++++++++++++++++++++++
 3 files changed, 73 insertions(+), 22 deletions(-)
 create mode 100644 paddle/framework/mixed_vector_test.cu

diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index 8b71f73c36..7c4ba3afb9 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -20,6 +20,7 @@ endif()
 
 cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
 
+nv_test(mixed_vector_test SRCS mixed_vector_test.cu DEPS place paddle_memory device_context init)
 cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
 nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor init)
diff --git a/paddle/framework/lod_tensor_test.cu b/paddle/framework/lod_tensor_test.cu
index d4c9f00bd9..adea02e3b3 100644
--- a/paddle/framework/lod_tensor_test.cu
+++ b/paddle/framework/lod_tensor_test.cu
@@ -28,28 +28,6 @@ __global__ void test(size_t* a, int size) {
   }
 }
 
-TEST(Vector, Normal) {
-  using namespace paddle::framework;
-  using namespace paddle::platform;
-  using namespace paddle::memory;
-
-  paddle::framework::InitDevices();
-
-  paddle::framework::Vector<size_t> vec({1, 2, 3});
-  size_t* ptr = vec.data();
-  for (size_t i = 0; i < vec.size(); ++i) {
-    EXPECT_EQ(vec[i], *(ptr + i));
-  }
-
-  vec.clear();
-  vec.CopyFromCUDA();
-
-  std::vector<size_t> v = {1, 2, 3};
-  for (size_t i = 0; i < v.size(); ++i) {
-    EXPECT_EQ(v[i], vec[i]);
-  }
-}
-
 TEST(LoD, data) {
   paddle::framework::InitDevices();
 
diff --git a/paddle/framework/mixed_vector_test.cu b/paddle/framework/mixed_vector_test.cu
new file mode 100644
index 0000000000..7b571788ad
--- /dev/null
+++ b/paddle/framework/mixed_vector_test.cu
@@ -0,0 +1,72 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License. */
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include "gtest/gtest.h"
+
+#include "paddle/framework/init.h"
+#include "paddle/framework/mixed_vector.h"
+
+using namespace paddle::framework;
+using namespace paddle::platform;
+using namespace paddle::memory;
+
+template <typename T>
+__global__ void test(T* data, int size) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
+       i += blockDim.x * gridDim.x) {
+    data[i] *= 2;
+  }
+}
+
+TEST(Vector, Normal) {
+  // fill the device context pool.
+  InitDevices();
+
+  Vector<size_t> vec({1, 2, 3});
+  size_t* ptr = vec.data();
+  for (size_t i = 0; i < vec.size(); ++i) {
+    EXPECT_EQ(vec[i], *(ptr + i));
+  }
+
+  vec.clear();
+  vec.CopyFromCUDA();
+
+  std::vector<size_t> v = {1, 2, 3};
+  for (size_t i = 0; i < v.size(); ++i) {
+    EXPECT_EQ(v[i], vec[i]);
+  }
+}
+
+TEST(Vector, MultipleCopy) {
+  InitDevices();
+  Vector<size_t> vec({1, 2, 3});
+  CUDAPlace place(0);
+  vec.mutable_data(place);
+  auto vec2 = Vector<size_t>(vec);
+  {
+    const size_t* ptr = vec2.data(CPUPlace());
+    for (size_t i = 0; i < vec2.size(); ++i) {
+      EXPECT_EQ(*(ptr + i), vec[i]);
+    }
+  }
+  test<size_t><<<3, 3>>>(vec2.mutable_data(place), vec2.size());
+  vec2.CopyFromCUDA();
+  {
+    const size_t* ptr = vec2.data(CPUPlace());
+    for (size_t i = 0; i < vec2.size(); ++i) {
+      EXPECT_EQ(*(ptr + i), vec[i] * 2);
+    }
+  }
+}

From 4e5202647684f4ff6525775ce62a6dd674257917 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Mon, 5 Feb 2018 16:55:53 +0800
Subject: [PATCH 246/314] add independent sphinx tree for api

---
 doc/CMakeLists.txt                 |  2 ++
 doc/api/CMakeLists.txt             | 20 ++++++++++++++++++++
 paddle/scripts/docker/build.sh     |  2 +-
 paddle/scripts/travis/build_doc.sh |  6 ++++--
 4 files changed, 27 insertions(+), 3 deletions(-)
 create mode 100644 doc/api/CMakeLists.txt

diff --git a/doc/CMakeLists.txt b/doc/CMakeLists.txt
index 94dd3457fb..58ce5d61c9 100644
--- a/doc/CMakeLists.txt
+++ b/doc/CMakeLists.txt
@@ -47,3 +47,5 @@ sphinx_add_target(paddle_docs_cn
                   ${SPHINX_CACHE_DIR_CN}
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_CN})
+
+add_subdirectory(api)
diff --git a/doc/api/CMakeLists.txt b/doc/api/CMakeLists.txt
new file mode 100644
index 0000000000..4e0bc1d5b8
--- /dev/null
+++ b/doc/api/CMakeLists.txt
@@ -0,0 +1,20 @@
+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
+
+# HTML output director
+set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in"
+    "${BINARY_BUILD_DIR_EN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_api_docs
+                  html
+                  ${BINARY_BUILD_DIR_EN}
+                  ${SPHINX_CACHE_DIR_EN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_EN})
diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh
index 59f3af0398..ba496db5f8 100644
--- a/paddle/scripts/docker/build.sh
+++ b/paddle/scripts/docker/build.sh
@@ -117,7 +117,7 @@ EOF
             -DWITH_STYLE_CHECK=OFF
         make -j `nproc` gen_proto_py
         make -j `nproc` paddle_python
-        make -j `nproc` paddle_docs paddle_docs_cn
+        make -j `nproc` paddle_docs paddle_docs_cn paddle_api_docs
         make -j `nproc` print_operators_doc
         paddle/pybind/print_operators_doc > doc/en/html/operators.json
         popd
diff --git a/paddle/scripts/travis/build_doc.sh b/paddle/scripts/travis/build_doc.sh
index 0db8d33bbc..4af4ac4f5e 100755
--- a/paddle/scripts/travis/build_doc.sh
+++ b/paddle/scripts/travis/build_doc.sh
@@ -9,13 +9,14 @@ cd $TRAVIS_BUILD_DIR/build
 cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
 make -j `nproc` gen_proto_py
 make -j `nproc` paddle_python
-make -j `nproc` paddle_docs paddle_docs_cn
+make -j `nproc` paddle_docs paddle_docs_cn paddle_api_docs
 make -j `nproc` print_operators_doc
 paddle/pybind/print_operators_doc > doc/en/html/operators.json
 
 # check websites for broken links
 linkchecker doc/en/html/index.html
 linkchecker doc/cn/html/index.html
+linkchecker doc/api/en/html/index.html
 
 # Parse Github URL
 REPO=`git config remote.origin.url`
@@ -54,10 +55,11 @@ function deploy_docs() {
   mkdir -p ${DIR}
   # remove old docs. mv new docs.
   set +e
-  rm -rf ${DIR}/doc ${DIR}/doc_cn
+  rm -rf ${DIR}/doc ${DIR}/doc_cn ${DIR}/api_doc
   set -e
   cp -r ../doc/cn/html ${DIR}/doc_cn
   cp -r ../doc/en/html ${DIR}/doc
+  cp -r ../doc/api/en/html ${DIR}/api_doc
   git add .
 }
 

From 239fafb0d31618a1aee2ac814ed662f18c48cc9c Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 02:37:52 -0800
Subject: [PATCH 247/314] "test on parallel do op"

---
 paddle/operators/parallel_do_op.cc | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index 67f9854c02..d662878592 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -79,6 +79,7 @@ inline void CopyOrShare(const framework::Variable &src,
     } else {
       Copy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
     }
+    dst->set_lod(src.lod());
   } else if (src.IsType<SelectedRows>()) {
     auto &src_sr = src.Get<SelectedRows>();
     auto *dst_sr = dst->GetMutable<SelectedRows>();
@@ -89,6 +90,7 @@ inline void CopyOrShare(const framework::Variable &src,
     } else {
       Copy(src_sr.value(), dst_place, dst_sr->mutable_value());
     }
+    dst_sr->set_rows(src_sr.rows());
   } else {
     PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s", src.Type().name());
   }
@@ -145,6 +147,7 @@ class ParallelDoOp : public framework::OperatorBase {
         auto *sub_scope = sub_scopes[i];
         auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
         framework::Copy(src, place, dst);
+        dst->set_lod(src.lod());
       }
     }
     WaitOnPlaces(places);

From f18f3826dc5d59f49908f2c232ff81b15c0abd9a Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 03:04:39 -0800
Subject: [PATCH 248/314] "parallel op set lod after copy "

---
 paddle/framework/mixed_vector.h    | 4 ++--
 paddle/operators/parallel_do_op.cc | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index d86899bc63..aade7d8391 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -54,7 +54,7 @@ class Vector : public std::vector<T> {
     CopyToCUDA();
     PADDLE_ENFORCE_NOT_NULL(
         cuda_ptr_, "No data or Insufficient CUDA memory to allocation");
-    return static_cast<T *>(cuda_ptr_);
+    return static_cast<T *>(cuda_ptr_.get());
   }
 
   /* Get host vector */
@@ -127,7 +127,7 @@ void Vector<T>::CopyToCUDA() {
 #ifdef PADDLE_WITH_CUDA
   if (cuda_size_ < this->size() || cuda_ptr_ == nullptr) {
     cuda_ptr_.reset(
-        memory::Alloc<platform::CUDAPlace>(this->size() * sizeof(T)),
+        memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T)),
         memory::PlainDeleter<void, platform::CUDAPlace>(place_));
   }
   cuda_size_ = this->size();
diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index d662878592..87678decde 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -79,7 +79,7 @@ inline void CopyOrShare(const framework::Variable &src,
     } else {
       Copy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
     }
-    dst->set_lod(src.lod());
+    dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
   } else if (src.IsType<SelectedRows>()) {
     auto &src_sr = src.Get<SelectedRows>();
     auto *dst_sr = dst->GetMutable<SelectedRows>();

From 93734a79138945e6a603b1c9b28ea8cb1b32569e Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 5 Feb 2018 19:01:26 +0800
Subject: [PATCH 249/314] fix bug

---
 paddle/operators/prior_box_op.cc | 69 ++++++++++++++++++--------------
 1 file changed, 40 insertions(+), 29 deletions(-)

diff --git a/paddle/operators/prior_box_op.cc b/paddle/operators/prior_box_op.cc
index 105ff4ac3e..f35273bf41 100644
--- a/paddle/operators/prior_box_op.cc
+++ b/paddle/operators/prior_box_op.cc
@@ -44,12 +44,6 @@ class PriorBoxOp : public framework::OperatorWithKernel {
     auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
     bool flip = ctx->Attrs().Get<bool>("flip");
 
-    PADDLE_ENFORCE_GT(min_sizes.size(), 0,
-                      "Size of min_sizes must be at least 1.");
-    for (size_t i = 0; i < min_sizes.size(); ++i) {
-      PADDLE_ENFORCE_GT(min_sizes[i], 0, "min_sizes[%d] must be positive.", i);
-    }
-
     std::vector<float> aspect_ratios_vec;
     ExpandAspectRatios(aspect_ratios, flip, aspect_ratios_vec);
 
@@ -65,17 +59,6 @@ class PriorBoxOp : public framework::OperatorWithKernel {
       }
     }
 
-    PADDLE_ENFORCE_EQ(variances.size(), 4, "Must and only provide 4 variance.");
-    for (size_t i = 0; i < variances.size(); ++i) {
-      PADDLE_ENFORCE_GT(variances[i], 0.0,
-                        "variance[%d] must be greater than 0.", i);
-    }
-
-    const float step_h = ctx->Attrs().Get<float>("step_h");
-    PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
-    const float step_w = ctx->Attrs().Get<float>("step_w");
-    PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
-
     std::vector<int64_t> dim_vec(4);
     dim_vec[0] = input_dims[2];
     dim_vec[1] = input_dims[3];
@@ -106,26 +89,54 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
               "PriorBoxOp. The layout is [H, W, num_priors, 4]. "
               "H is the height of input, W is the width of input, num_priors "
               "is the box count of each position.");
-    AddAttr<std::vector<int>>("min_sizes", "(vector<int>) ",
-                              "List of min sizes of generated prior boxes.");
-    AddAttr<std::vector<int>>("max_sizes", "(vector<int>) ",
-                              "List of max sizes of generated prior boxes.");
+
+    AddAttr<std::vector<int>>("min_sizes",
+                              "(vector<int>) List of min sizes "
+                              "of generated prior boxes.")
+        .AddCustomChecker([](const std::vector<int>& min_sizes) {
+          PADDLE_ENFORCE_GT(min_sizes.size(), 0,
+                            "Size of min_sizes must be at least 1.");
+          for (size_t i = 0; i < min_sizes.size(); ++i) {
+            PADDLE_ENFORCE_GT(min_sizes[i], 0,
+                              "min_sizes[%d] must be positive.", i);
+          }
+        });
+    AddAttr<std::vector<int>>(
+        "max_sizes",
+        "(vector<int>) List of max sizes of generated prior boxes.");
     AddAttr<std::vector<float>>(
-        "aspect_ratios", "(vector<float>) ",
-        "List of aspect ratios of generated prior boxes.");
+        "aspect_ratios",
+        "(vector<float>) List of aspect ratios of generated prior boxes.");
+
     AddAttr<std::vector<float>>(
-        "variances", "(vector<float>) ",
-        "List of variances to be encoded in prior boxes.");
-    AddAttr<bool>("flip", "(bool) ", "Whether to flip aspect ratios.")
+        "variances",
+        "(vector<float>) List of variances to be encoded in prior boxes.")
+        .AddCustomChecker([](const std::vector<float>& variances) {
+          PADDLE_ENFORCE_EQ(variances.size(), 4,
+                            "Must and only provide 4 variance.");
+          for (size_t i = 0; i < variances.size(); ++i) {
+            PADDLE_ENFORCE_GT(variances[i], 0.0,
+                              "variance[%d] must be greater than 0.", i);
+          }
+        });
+    AddAttr<bool>("flip", "(bool) Whether to flip aspect ratios.")
         .SetDefault(true);
-    AddAttr<bool>("clip", "(bool) ", "Whether to clip out-of-boundary boxes.")
+    AddAttr<bool>("clip", "(bool) Whether to clip out-of-boundary boxes.")
         .SetDefault(true);
+
     AddAttr<float>("step_w",
                    "Prior boxes step across width, 0 for auto calculation.")
-        .SetDefault(0.0);
+        .SetDefault(0.0)
+        .AddCustomChecker([](const float& step_w) {
+          PADDLE_ENFORCE_GT(step_w, 0.0, "step_h should be larger than 0.");
+        });
     AddAttr<float>("step_h",
                    "Prior boxes step across height, 0 for auto calculation.")
-        .SetDefault(0.0);
+        .SetDefault(0.0)
+        .AddCustomChecker([](const float& step_h) {
+          PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
+        });
+
     AddAttr<float>("offset",
                    "(float) "
                    "Prior boxes center offset.")

From d7a371cbf25f4dcc5dcbfbf0a043e6dc98ae322a Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 5 Feb 2018 19:51:42 +0800
Subject: [PATCH 250/314] follow comments

---
 paddle/operators/prior_box_op.cc | 2 +-
 paddle/operators/prior_box_op.h  | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/operators/prior_box_op.cc b/paddle/operators/prior_box_op.cc
index f35273bf41..1dc4b28855 100644
--- a/paddle/operators/prior_box_op.cc
+++ b/paddle/operators/prior_box_op.cc
@@ -128,7 +128,7 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
                    "Prior boxes step across width, 0 for auto calculation.")
         .SetDefault(0.0)
         .AddCustomChecker([](const float& step_w) {
-          PADDLE_ENFORCE_GT(step_w, 0.0, "step_h should be larger than 0.");
+          PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
         });
     AddAttr<float>("step_h",
                    "Prior boxes step across height, 0 for auto calculation.")
diff --git a/paddle/operators/prior_box_op.h b/paddle/operators/prior_box_op.h
index e0a663ace8..12ff162356 100644
--- a/paddle/operators/prior_box_op.h
+++ b/paddle/operators/prior_box_op.h
@@ -25,7 +25,7 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
                                std::vector<float>& output_aspect_ratior) {
   constexpr float epsilon = 1e-6;
   output_aspect_ratior.clear();
-  output_aspect_ratior.push_back(1.);
+  output_aspect_ratior.push_back(1.0f);
   for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
     float ar = input_aspect_ratior[i];
     bool already_exist = false;
@@ -38,7 +38,7 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
     if (!already_exist) {
       output_aspect_ratior.push_back(ar);
       if (flip) {
-        output_aspect_ratior.push_back(1. / ar);
+        output_aspect_ratior.push_back(1.0f / ar);
       }
     }
   }

From f367ad6c6cae825c46b7262c77fa0cf6f8394796 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 5 Feb 2018 20:03:50 +0800
Subject: [PATCH 251/314] add "inline" for ClipFunctor and refine code

---
 paddle/operators/prior_box_op.h | 39 ++++++++++++++++++++++-----------
 1 file changed, 26 insertions(+), 13 deletions(-)

diff --git a/paddle/operators/prior_box_op.h b/paddle/operators/prior_box_op.h
index 12ff162356..6b221cb74e 100644
--- a/paddle/operators/prior_box_op.h
+++ b/paddle/operators/prior_box_op.h
@@ -46,7 +46,7 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
 
 template <typename T>
 struct ClipFunctor {
-  HOSTDEVICE T operator()(T in) const {
+  HOSTDEVICE inline T operator()(T in) const {
     return std::min<T>(std::max<T>(in, 0.), 1.);
   }
 };
@@ -97,6 +97,9 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
     boxes->mutable_data<T>(ctx.GetPlace());
     vars->mutable_data<T>(ctx.GetPlace());
 
+    T inv_img_width = 1.0 / img_width;
+    T inv_img_height = 1.0 / img_height;
+
     auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
     for (int h = 0; h < feature_height; ++h) {
       for (int w = 0; w < feature_width; ++w) {
@@ -109,13 +112,15 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
           // first prior: aspect_ratio = 1, size = min_size
           box_width = box_height = min_size;
           // xmin
-          e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+          e_boxes(h, w, idx, 0) = (center_x - box_width * 0.5) * inv_img_width;
           // ymin
-          e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+          e_boxes(h, w, idx, 1) =
+              (center_y - box_height * 0.5) * inv_img_height;
           // xmax
-          e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+          e_boxes(h, w, idx, 2) = (center_x + box_width * 0.5) * inv_img_width;
           // ymax
-          e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+          e_boxes(h, w, idx, 3) =
+              (center_y + box_height * 0.5) * inv_img_height;
 
           idx++;
           if (max_sizes.size() > 0) {
@@ -124,13 +129,17 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
             // size = sqrt(min_size * max_size)
             box_width = box_height = sqrt(min_size * max_size);
             // xmin
-            e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+            e_boxes(h, w, idx, 0) =
+                (center_x - box_width * 0.5) * inv_img_width;
             // ymin
-            e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+            e_boxes(h, w, idx, 1) =
+                (center_y - box_height * 0.5) * inv_img_height;
             // xmax
-            e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+            e_boxes(h, w, idx, 2) =
+                (center_x + box_width * 0.5) * inv_img_width;
             // ymax
-            e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+            e_boxes(h, w, idx, 3) =
+                (center_y + box_height * 0.5) * inv_img_height;
             idx++;
           }
 
@@ -143,13 +152,17 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
             box_width = min_size * sqrt(ar);
             box_height = min_size / sqrt(ar);
             // xmin
-            e_boxes(h, w, idx, 0) = (center_x - box_width / 2.) / img_width;
+            e_boxes(h, w, idx, 0) =
+                (center_x - box_width * 0.5) * inv_img_width;
             // ymin
-            e_boxes(h, w, idx, 1) = (center_y - box_height / 2.) / img_height;
+            e_boxes(h, w, idx, 1) =
+                (center_y - box_height * 0.5) * inv_img_height;
             // xmax
-            e_boxes(h, w, idx, 2) = (center_x + box_width / 2.) / img_width;
+            e_boxes(h, w, idx, 2) =
+                (center_x + box_width * 0.5) * inv_img_width;
             // ymax
-            e_boxes(h, w, idx, 3) = (center_y + box_height / 2.) / img_height;
+            e_boxes(h, w, idx, 3) =
+                (center_y + box_height * 0.5) * inv_img_height;
             idx++;
           }
         }

From e9e24249217c1b234a9ce8f8d0d9c1e6e18fd2d3 Mon Sep 17 00:00:00 2001
From: qingqing01 <dangqingqing@baidu.com>
Date: Mon, 5 Feb 2018 21:38:53 +0800
Subject: [PATCH 252/314] Fix warnings in multiclass_nms_op.cc.

---
 paddle/operators/multiclass_nms_op.cc | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/paddle/operators/multiclass_nms_op.cc b/paddle/operators/multiclass_nms_op.cc
index 8a65fe69f1..41b9335fb8 100644
--- a/paddle/operators/multiclass_nms_op.cc
+++ b/paddle/operators/multiclass_nms_op.cc
@@ -85,7 +85,7 @@ static inline void GetMaxScoreIndex(
   std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
                    SortScorePairDescend<int>);
   // Keep top_k scores if needed.
-  if (top_k > -1 && top_k < sorted_indices->size()) {
+  if (top_k > -1 && top_k < static_cast<int>(sorted_indices->size())) {
     sorted_indices->resize(top_k);
   }
 }
@@ -151,7 +151,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
     while (sorted_indices.size() != 0) {
       const int idx = sorted_indices.front().second;
       bool keep = true;
-      for (int k = 0; k < selected_indices->size(); ++k) {
+      for (size_t k = 0; k < selected_indices->size(); ++k) {
         if (keep) {
           const int kept_idx = (*selected_indices)[k];
           T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
@@ -201,7 +201,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
         int label = it.first;
         const T* sdata = scores_data + label * predict_dim;
         const std::vector<int>& label_indices = it.second;
-        for (int j = 0; j < label_indices.size(); ++j) {
+        for (size_t j = 0; j < label_indices.size(); ++j) {
           int idx = label_indices[j];
           PADDLE_ENFORCE_LT(idx, predict_dim);
           score_index_pairs.push_back(
@@ -215,7 +215,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
 
       // Store the new indices.
       std::map<int, std::vector<int>> new_indices;
-      for (int j = 0; j < score_index_pairs.size(); ++j) {
+      for (size_t j = 0; j < score_index_pairs.size(); ++j) {
         int label = score_index_pairs[j].second.first;
         int idx = score_index_pairs[j].second.second;
         new_indices[label].push_back(idx);
@@ -238,7 +238,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
       int label = it.first;
       const T* sdata = scores_data + label * predict_dim;
       const std::vector<int>& indices = it.second;
-      for (int j = 0; j < indices.size(); ++j) {
+      for (size_t j = 0; j < indices.size(); ++j) {
         int idx = indices[j];
         const T* bdata = bboxes_data + idx * kBBoxSize;
         odata[count * kOutputDim] = label;           // label

From 497a131e53316fc3d81cf92e68845d2fd33243e3 Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Mon, 5 Feb 2018 10:45:43 -0800
Subject: [PATCH 253/314] Proposing Python syntax for send and recv in design
 doc (#8093)

* Adding send and recv in design doc

* fix typo

* fixed code

* Adding threading
---
 doc/design/csp.md | 76 +++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 73 insertions(+), 3 deletions(-)

diff --git a/doc/design/csp.md b/doc/design/csp.md
index ba9cacfdea..2f6ce8d6fa 100644
--- a/doc/design/csp.md
+++ b/doc/design/csp.md
@@ -71,14 +71,14 @@ ch1 := make(chan int, 100)  // a channel that can buffer 100 ints.
 In Fluid, we should be able to do the same:
 
 ```python
-ch  = fluid.make_chan(dtype=INT)
-ch1 = fluid.make_chan(dtype=INT, 100)
+ch  = fluid.make_channel(dtype=INT)
+ch1 = fluid.make_channel(dtype=INT, 100)
 ```
 
 In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
 
 ```python
-ch = fluid.make_chan(dtype=Tensor, etype=float16)
+ch = fluid.make_channel(dtype=Tensor, etype=float16)
 ```
 
 or Tensors of Tensors of float16 etc.
@@ -87,6 +87,76 @@ The point here is that we need a consistent way to compose types, like in C++ we
 
 ### Send and Recv
 
+In Go, we first create a channel as explained in the section above and then perform read and write operations on top of the channels.
+
+```go
+ch1  := make(chan int)       
+ch2  := make(chan int, 100)
+```
+
+To write (or perform a `Send` operation) the value of a variable `x`, to channel `ch1` above, we perform the following:
+
+```go
+ch1 <- x
+fmt.Println("Written to the channel")
+```
+Now to read (or perform a `Recv` operation) the value stored in `ch2` into a variable `y`, we perform the following:
+
+```go
+y <- ch2
+fmt.Println("Received from channel")
+```
+
+In Fluid, we should be able to perform the above operations on the channel objects as well. As of now, we support two different kinds of channels : [Buffered Channel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/buffered_channel.h) and [UnBuffered Channel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/unbuffered_channel.h)
+
+Send and Receive can be performed as following on a buffered channel:
+
+```python
+import threading
+
+def send_to_channel(channel, num_time=1):
+  for i in xrange(num_time):
+    channel.send(i)
+
+# Create a buffered channel of capacity 10
+buffer_size = 10;
+ch = fluid.make_channel(dtype=INT, buffer_size)
+
+# Now write three elements to the channel
+thread = threading.Thread(target=send_to_channel, args=(ch, 3, ))
+thread.daemon = True
+thread.start()
+
+# Read all the data from the channel
+for i in xrange(3):
+  y = ch.recv()
+
+# Done receiving , now close the channel
+ch.close()
+```
+
+The send and receive operations will be similar for unbuffered channel as well, except for the fact that there is no buffer in an unbuffered channel, so the operations are completely synchronized. For example:
+
+```python
+import threading
+
+def send_to_channel(channel, data):
+  channel.send(data)
+
+# Create an unbuffered channel
+ch = fluid.make_channel(dtype=INT)
+
+# Writes and Reads are synchronous otherwise the calls will block.
+thread = threading.Thread(target=send_to_channel, args=(ch, 10, ))
+thread.daemon = True
+thread.start()
+
+y = ch.recv()
+
+# Done receiving , now close the channel
+ch.close()
+```
+
 ### Select
 
 ## Example Programs

From 1ead6c2691be09f34303c06d119c17ba4e4aeab7 Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Mon, 5 Feb 2018 11:06:02 -0800
Subject: [PATCH 254/314] Add proposed fluid syntax for select statement in
 Fluid's implementation of CSP (#7908)

* Add proposed fluid syntax for select statement in Fluid's implementation of CSP

* Fix Typo
---
 doc/design/csp.md | 49 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/doc/design/csp.md b/doc/design/csp.md
index 2f6ce8d6fa..36422d8236 100644
--- a/doc/design/csp.md
+++ b/doc/design/csp.md
@@ -159,6 +159,55 @@ ch.close()
 
 ### Select
 
+In Go, the `select` statement lets a goroutine wait on multiple communication operations. A `select` blocks untill one of its cases can run, then it executes that case. It chooses one at random if multiple are ready.
+
+```go
+
+ch1  := make(chan int)       
+ch2  := make(chan int, 100)
+
+x := 0
+
+for {
+    select {
+    case ch1 <- x:
+      x := x + 1
+    case y <- ch2:
+      fmt.Println("Received on channel")
+    default:
+      fmt.Println("Default")
+    }
+  }
+
+```
+
+In Fluid, we should be able to do the same:
+
+```python
+ch1  = fluid.make_chan(dtype=INT)
+ch2 = fluid.make_chan(dtype=INT, 100)
+
+sel = fluid.select()
+
+with sel.case(ch1, 'w', X):
+    fluid.layers.increment(X)
+
+with sel.case(ch2, 'r', Y):
+    fluid.print("Received on Channel")
+
+with sel.default():
+    fluid.print("Default")
+
+```
+
+In the above code snippet, `X` and `Y` are variables. Now let us look at each of these statements one by one.
+
+- `sel.case(ch1, 'w', X)` : This specifies that we are writing to `ch1` and we want to write the integer in variable `X` to the channel. The character `w` is used here to make the syntax familar to write syntax in Python I/O.
+
+- `sel.case(ch2, 'r', Y)` : This specifies that we would like to read the result from `ch2` into variable `Y`. The character `r` is used here to make the syntax familar to read syntax in Python I/O.
+
+- `sel.default()` : This is equivalent to the default in Go `select`. If none of the channels are ready for read or write, then the fluid code in the default block will be executed.
+
 ## Example Programs
 
 ### 1. RPC between Trainers and Parameter Servers

From 6f0e630c5ce67bef5e87e26441c60870d1ab207e Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Mon, 5 Feb 2018 13:25:20 -0800
Subject: [PATCH 255/314] fix prune and program desc constructor

---
 paddle/framework/block_desc.cc                |  2 ++
 paddle/framework/op_desc.cc                   | 17 +++++++--
 paddle/framework/program_desc.cc              | 18 ++++++++++
 paddle/framework/prune.cc                     | 36 ++++++++++++-------
 python/paddle/v2/fluid/io.py                  |  6 ++++
 .../tests/book/test_rnn_encoder_decoder.py    | 31 ++++++++++------
 6 files changed, 85 insertions(+), 25 deletions(-)

diff --git a/paddle/framework/block_desc.cc b/paddle/framework/block_desc.cc
index dd2ed87252..ca3d03e554 100644
--- a/paddle/framework/block_desc.cc
+++ b/paddle/framework/block_desc.cc
@@ -155,6 +155,8 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
   for (const proto::OpDesc &op_desc : desc_->ops()) {
     ops_.emplace_back(new OpDesc(op_desc, prog, this));
   }
+  std::cout << "Constructed block idx " << desc->idx() << " from protobuf str"
+            << std::endl;
 }
 
 BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index f8df2cf97a..5ebd2b3ad5 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -124,11 +124,24 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
   // restore attrs_
   for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
     std::string attr_name = attr.name();
+    // we use a trick to handle attr.type() is BLOCK here, because at this
+    // moment the sub_block hasn't beed added to ProgramDesc's vector<Block>
+    // so we cast the block_idx to a dummy BlockDesc pointer
     if (attr.type() != proto::AttrType::BLOCK) {
       attrs_[attr_name] = GetAttrValue(attr);
     } else {
-      auto bid = attr.block_idx();
-      attrs_[attr_name] = prog->MutableBlock(bid);
+      size_t blk_idx = attr.block_idx();
+      if (blk_idx < prog->Size()) {
+        attrs_[attr_name] = prog->MutableBlock(blk_idx);
+      } else {
+        std::cout << "Setting blockdesc attribute for id " << blk_idx
+                  << std::endl;
+        attrs_[attr_name] = reinterpret_cast<BlockDesc *>(blk_idx);
+        std::cout << "Testing reinterpret_cast result is "
+                  << reinterpret_cast<size_t>(
+                         boost::get<BlockDesc *>(attrs_[attr_name]))
+                  << std::endl;
+      }
     }
   }
   this->block_ = block;
diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index 15ea4035c6..9124607623 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -52,9 +52,27 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
 
 ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
   desc_ = desc;
+  std::cout << std::endl << "starting in ProgDesc constructor" << std::endl;
   for (auto &block_desc : *desc_.mutable_blocks()) {
     blocks_.emplace_back(new BlockDesc(this, &block_desc));
+    std::cout << "Done constructing block idx " << block_desc.idx()
+              << " parent idx " << block_desc.parent_idx() << std::endl;
   }
+  for (auto &block : blocks_) {
+    for (auto *op : block->AllOps()) {
+      for (auto &name : op->AttrNames()) {
+        if (op->GetAttrType(name) == proto::AttrType::BLOCK) {
+          auto attr = op->GetAttr(name);
+          size_t blk_idx =
+              reinterpret_cast<size_t>(boost::get<BlockDesc *>(attr));
+          op->SetBlockAttr(name, *this->MutableBlock(blk_idx));
+          std::cout << "Update attr name " << name << " for block idx "
+                    << blk_idx << std::endl;
+        }
+      }
+    }
+  }
+  std::cout << "Done ProgDesc construction" << std::endl << std::endl;
 }
 
 ProgramDesc::ProgramDesc(const std::string &binary_str) {
diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 6a3882f199..3c3ec87585 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -109,15 +109,14 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
   // we reverse the should_run vector
   std::reverse(should_run.begin(), should_run.end());
 
-  //*output = input;
   // copy the current block from input to output
   auto* block_field = output->mutable_blocks();
   *block_field->Add() = input.blocks(block_id);
 
   int output_block_id = output->blocks_size() - 1;
   auto* output_block = output->mutable_blocks(output_block_id);
-  output_block->set_idx = output_block_id;
-  output_block->set_parent_idx = parent_block_id;
+  output_block->set_idx(output_block_id);
+  output_block->set_parent_idx(parent_block_id);
 
   auto* op_field = output_block->mutable_ops();
   op_field->Clear();
@@ -128,17 +127,18 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
       if (HasSubBlock(*op)) {
         // create sub_block_dependent_vars here to help prune the sub block
         std::set<std::string> sub_block_dependent_vars;
-        for (auto& var : op.inputs()) {
+        for (auto& var : op->inputs()) {
           for (auto& argu : var.arguments()) {
             sub_block_dependent_vars.insert(argu);
           }
         }
-        for (auto& var : op.outputs()) {
+        for (auto& var : op->outputs()) {
           for (auto& argu : var.arguments()) {
             sub_block_dependent_vars.insert(argu);
           }
         }
-
+        std::cout << "pruning the next block, the current output_block_id is "
+                  << output_block_id << std::endl;
         // GetSubBlockIndex(*op) is the idx of the sub_block in the input desc
         // output_block_id is the idx of the current block in the output desc
         prune_impl(input, output, GetSubBlockIndex(*op), output_block_id,
@@ -147,6 +147,8 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     }
   }
 
+  std::cout << "Starting to remove unreferenced variables"
+            << " for block idx " << output_block_id << std::endl;
   // remove the VarDescs in BlockDesc that are not referenced in
   // the pruned OpDescs
   std::unordered_map<std::string, proto::VarDesc> var_map;
@@ -155,28 +157,38 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     var_map[var.name()] = var;
   }
 
-  var_field->Clear();
+  std::set<std::string> var_names;
   for (const auto& op : *op_field) {
-    // add VarDescs of all input arguments for each OpDesc
     auto& input_field = op.inputs();
     for (auto& input_var : input_field) {
       for (auto& arg : input_var.arguments()) {
-        *var_field->Add() = var_map.at(arg);
+        if (var_map.count(arg) != 0) {
+          var_names.insert(arg);
+        }
       }
     }
-    // add VarDescs of all output arguments for each OpDesc
     auto& output_field = op.outputs();
     for (auto& output_var : output_field) {
       for (auto& arg : output_var.arguments()) {
-        *var_field->Add() = var_map.at(arg);
+        if (var_map.count(arg) != 0) {
+          var_names.insert(arg);
+        }
       }
     }
   }
+
+  var_field->Clear();
+  for (const auto& name : var_names) {
+    *var_field->Add() = var_map[name];
+  }
 }
 
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
 void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
-  prune_impl(input, output, 0, -1, {});
+  std::set<std::string> dependent_vars;
+  std::cout << std::endl << "Start C++ framework::prune" << std::endl;
+  prune_impl(input, output, 0, -1, dependent_vars);
+  std::cout << "Finished C++ framework::prune" << std::endl << std::endl;
 }
 
 void inference_optimize_impl(const proto::ProgramDesc& input,
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 613dc20b6e..e410549f8a 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -342,6 +342,12 @@ def save_inference_model(dirname,
     prepend_feed_ops(inference_program, feeded_var_names)
     append_fetch_ops(inference_program, fetch_var_names)
 
+    # save for checking
+    curstr = inference_program.to_string(True)
+    f = open("save_inf_prog_after_feed_fetch.txt", 'w')
+    f.write(curstr)
+    f.close()
+
     model_file_name = dirname + "/__model__"
     with open(model_file_name, "wb") as f:
         f.write(inference_program.desc.serialize_to_string())
diff --git a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index 593d0013c9..15f00f95d4 100644
--- a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -197,14 +197,15 @@ def train(save_dirname=None):
                   " avg_cost=" + str(avg_cost_val))
             if batch_id > 3:
                 if save_dirname is not None:
-                    fluid.io.save_inference_model(save_dirname, [
-                        'source_sequence', 'target_sequence', 'label_sequence'
-                    ], [prediction], exe)
+                    fluid.io.save_inference_model(
+                        save_dirname, ['source_sequence',
+                                       'target_sequence'], [prediction], exe)
+                    return
                 exit(0)
             batch_id += 1
 
 
-def inference(save_dirname=None):
+def infer(save_dirname=None):
     if save_dirname is None:
         return
 
@@ -221,24 +222,32 @@ def inference(save_dirname=None):
     data = [[0, 1, 0, 1], [0, 1, 1, 0, 0, 1]]
     word_data = to_lodtensor(data, place)
     trg_word = to_lodtensor(data, place)
-    trg_word_next = to_lodtensor(data, place)
 
     # Construct feed as a dictionary of {feed_target_name: feed_target_data}
     # and results will contain a list of data corresponding to fetch_targets.
+    print("Print feed fetch target names as follows")
     print(feed_target_names)
     assert feed_target_names[0] == 'source_sequence'
     assert feed_target_names[1] == 'target_sequence'
-    assert feed_target_names[2] == 'label_sequence'
+    print([var.name for var in fetch_targets])
+
+    # save for checking
+    curstr = inference_program.to_string(True)
+    f = open("loaded_infer_prog.txt", 'w')
+    f.write(curstr)
+    f.close()
+
     results = exe.run(inference_program,
                       feed={
                           feed_target_names[0]: word_data,
                           feed_target_names[1]: trg_word,
-                          feed_target_names[2]: trg_word_next
                       },
-                      fetch_list=fetch_targets)
-
-    print("Inference Shape: ", results[0].shape)
-    print("infer results: ", results[0])
+                      fetch_list=fetch_targets,
+                      return_numpy=False)
+    print(results[0].lod())
+    np_data = np.array(results[0])
+    print("Inference shape: ", np_data.shape)
+    print("Inference results: ", np_data)
 
 
 if __name__ == '__main__':

From dc68e7c44b198acbdf588e97d219822602dc90db Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Mon, 5 Feb 2018 15:36:54 -0800
Subject: [PATCH 256/314] fix constructor bug

---
 paddle/framework/op_desc.cc      |  9 +++------
 paddle/framework/program_desc.cc | 26 ++++++++++++++++++--------
 2 files changed, 21 insertions(+), 14 deletions(-)

diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index 5ebd2b3ad5..7859c391fa 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -133,13 +133,10 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
       size_t blk_idx = attr.block_idx();
       if (blk_idx < prog->Size()) {
         attrs_[attr_name] = prog->MutableBlock(blk_idx);
-      } else {
-        std::cout << "Setting blockdesc attribute for id " << blk_idx
+        std::cout << "In OpDesc: set up attr block idx " << blk_idx
                   << std::endl;
-        attrs_[attr_name] = reinterpret_cast<BlockDesc *>(blk_idx);
-        std::cout << "Testing reinterpret_cast result is "
-                  << reinterpret_cast<size_t>(
-                         boost::get<BlockDesc *>(attrs_[attr_name]))
+      } else {
+        std::cout << "In OpDesc: We don't have this block idx " << blk_idx
                   << std::endl;
       }
     }
diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index 9124607623..ba461b0933 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -48,6 +48,18 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
     auto *block = desc_.mutable_blocks(i);
     blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
   }
+  for (auto &block : blocks_) {
+    for (auto *op : block->AllOps()) {
+      for (const auto &attr : op->Proto()->attrs()) {
+        if (attr.type() == proto::AttrType::BLOCK) {
+          size_t blk_idx = attr.block_idx();
+          op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
+          std::cout << "In ProgramDesc 1: set block attr idx " << blk_idx
+                    << std::endl;
+        }
+      }
+    }
+  }
 }
 
 ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
@@ -60,14 +72,12 @@ ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
   }
   for (auto &block : blocks_) {
     for (auto *op : block->AllOps()) {
-      for (auto &name : op->AttrNames()) {
-        if (op->GetAttrType(name) == proto::AttrType::BLOCK) {
-          auto attr = op->GetAttr(name);
-          size_t blk_idx =
-              reinterpret_cast<size_t>(boost::get<BlockDesc *>(attr));
-          op->SetBlockAttr(name, *this->MutableBlock(blk_idx));
-          std::cout << "Update attr name " << name << " for block idx "
-                    << blk_idx << std::endl;
+      for (const auto &attr : op->Proto()->attrs()) {
+        if (attr.type() == proto::AttrType::BLOCK) {
+          size_t blk_idx = attr.block_idx();
+          op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
+          std::cout << "In ProgramDesc 2: set block attr idx " << blk_idx
+                    << std::endl;
         }
       }
     }

From b0ecb36583ed97737bd5c43cbafbdc8fa29cbd68 Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Mon, 5 Feb 2018 17:11:11 -0800
Subject: [PATCH 257/314] Rewrite the Send/Recv part of csp.md (#8164)

* Update csp.md

* Update csp.md

* Update csp.md
---
 doc/design/csp.md | 110 +++++++++++++++++++++++++---------------------
 1 file changed, 59 insertions(+), 51 deletions(-)

diff --git a/doc/design/csp.md b/doc/design/csp.md
index 36422d8236..ae2e3e1b99 100644
--- a/doc/design/csp.md
+++ b/doc/design/csp.md
@@ -42,7 +42,7 @@ The type *channel* is conceptually the blocking queue.  In Go, its implemented i
 
 The `select` operation has been in OS kernels long before Go language.  All Unix kernels implement system calls *poll* and *select*.  They monitor multiple file descriptors to see if I/O is possible on any of them.  This takes O(N) time.  Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time.  In BSD systems, there is a similar system call *kqueue*.  Go's Linux implementation uses epoll.
 
-It might be a good idea to implement Fluid's select using epoll too.  In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
+It might be a good idea to implement Fluid's select using epoll too.  In this design doc, we start from the O(N) way so that we could focus on Python binding and the syntax.
 
 ### Type Channel
 
@@ -87,79 +87,87 @@ The point here is that we need a consistent way to compose types, like in C++ we
 
 ### Send and Recv
 
-In Go, we first create a channel as explained in the section above and then perform read and write operations on top of the channels.
+Go's CSP implementation depends on data type *channel*. There are two types of channels:
 
-```go
-ch1  := make(chan int)       
-ch2  := make(chan int, 100)
-```
+1. The unblocked channel, or buffered channel, is a blocking queue with a non-zero sized buffer. The sending to buffered channel blocks if the buffer is full, and the receive operation blocks if the buffer is empty.
+1. blocked channel, or unbuffered channel, is a blocking queue with no buffer.  Both sending and receiving block with unbuffered channels.
 
-To write (or perform a `Send` operation) the value of a variable `x`, to channel `ch1` above, we perform the following:
+There are four types of actions with a channel:
 
-```go
-ch1 <- x
-fmt.Println("Written to the channel")
-```
-Now to read (or perform a `Recv` operation) the value stored in `ch2` into a variable `y`, we perform the following:
+1. Create a channel
 
-```go
-y <- ch2
-fmt.Println("Received from channel")
-```
+   ```go
+   ch := make(chan int) // this is an unbuffered channel
+   ch := make(chan int, 100) // this is a buffered channel of 100 ints.
+   ```
 
-In Fluid, we should be able to perform the above operations on the channel objects as well. As of now, we support two different kinds of channels : [Buffered Channel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/buffered_channel.h) and [UnBuffered Channel](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/unbuffered_channel.h)
+1. Send
 
-Send and Receive can be performed as following on a buffered channel:
+   ```go
+   ch <- 111
+   ```
 
-```python
-import threading
+1. Recv
 
-def send_to_channel(channel, num_time=1):
-  for i in xrange(num_time):
-    channel.send(i)
+   ```go
+   y, ok <- ch
+   ```
 
-# Create a buffered channel of capacity 10
-buffer_size = 10;
-ch = fluid.make_channel(dtype=INT, buffer_size)
+1. Close
 
-# Now write three elements to the channel
-thread = threading.Thread(target=send_to_channel, args=(ch, 3, ))
-thread.daemon = True
-thread.start()
+   ```go
+   close(ch)
+   ```
+   
+   Please be aware that a closed channel is not a nil channel, which is `var ch chan int`.
+   
+There are some [axioms with channels](https://dave.cheney.net/2014/03/19/channel-axioms):
 
-# Read all the data from the channel
-for i in xrange(3):
-  y = ch.recv()
+1. A send to a nil channel blocks forever
 
-# Done receiving , now close the channel
-ch.close()
-```
+1. A receive from a nil channel blocks forever
+
+1. A send to a closed channel panics
+
+1. A receive from a closed channel returns the residual values and then zeros.
 
-The send and receive operations will be similar for unbuffered channel as well, except for the fact that there is no buffer in an unbuffered channel, so the operations are completely synchronized. For example:
+In Fluid, we have [buffered channels](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/buffered_channel.h) and [unbuffered channels](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/unbuffered_channel.h)
+
+The following program illustrates the Python syntax for accessing Fluid buffers.
 
 ```python
-import threading
+import fluid
+
+buffer_size = 10
+ch = fluid.make_channel(dtype=INT, buffer_size)
 
-def send_to_channel(channel, data):
-  channel.send(data)
+# Now write three elements to the channel
+with fluid.while(steps=buffer_size):
+  fluid.send(ch, step)
+  fluid.close_channel(ch)
+  
+with fluid.while(steps=buffer_size):
+  fluid.print(fluid.recv(ch))
+```
+
+The following example shows that to avoid the always-blocking behavior of unbuffered channels, we need to use Fluid's goroutines.
+
+```python
+import fluid
 
-# Create an unbuffered channel
 ch = fluid.make_channel(dtype=INT)
 
-# Writes and Reads are synchronous otherwise the calls will block.
-thread = threading.Thread(target=send_to_channel, args=(ch, 10, ))
-thread.daemon = True
-thread.start()
+with fluid.go():
+  fluid.send(ch)
 
-y = ch.recv()
+y = fluid.recv(ch)
 
-# Done receiving , now close the channel
-ch.close()
+fluid.close_channel(ch)
 ```
 
 ### Select
 
-In Go, the `select` statement lets a goroutine wait on multiple communication operations. A `select` blocks untill one of its cases can run, then it executes that case. It chooses one at random if multiple are ready.
+In Go, the `select` statement lets a goroutine wait on multiple communication operations. A `select` blocks until one of its cases can run, then it executes that case. It chooses one at random if multiple are ready.
 
 ```go
 
@@ -202,9 +210,9 @@ with sel.default():
 
 In the above code snippet, `X` and `Y` are variables. Now let us look at each of these statements one by one.
 
-- `sel.case(ch1, 'w', X)` : This specifies that we are writing to `ch1` and we want to write the integer in variable `X` to the channel. The character `w` is used here to make the syntax familar to write syntax in Python I/O.
+- `sel.case(ch1, 'w', X)` : This specifies that we are writing to `ch1` and we want to write the integer in variable `X` to the channel. The character `w` is used here to make the syntax familiar to write syntax in Python I/O.
 
-- `sel.case(ch2, 'r', Y)` : This specifies that we would like to read the result from `ch2` into variable `Y`. The character `r` is used here to make the syntax familar to read syntax in Python I/O.
+- `sel.case(ch2, 'r', Y)` : This specifies that we would like to read the result from `ch2` into variable `Y`. The character `r` is used here to make the syntax familiar to read syntax in Python I/O.
 
 - `sel.default()` : This is equivalent to the default in Go `select`. If none of the channels are ready for read or write, then the fluid code in the default block will be executed.
 

From 165450ff6ca5bc0f02ffe63ec11f50ed4c240f09 Mon Sep 17 00:00:00 2001
From: Yiqun Liu <liuyiqun01@baidu.com>
Date: Tue, 6 Feb 2018 09:52:18 +0800
Subject: [PATCH 258/314] Refine the inference unittest recognize_digits.
 (#8147)

---
 .../book/test_inference_recognize_digits.cc   | 63 ++++++++++++++-----
 .../fluid/tests/book/test_recognize_digits.py |  4 +-
 2 files changed, 49 insertions(+), 18 deletions(-)

diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index 26dc2aee04..ce8772587f 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -58,6 +58,47 @@ void TestInference(const std::string& dirname,
   delete scope;
 }
 
+template <typename T>
+void SetupTensor(paddle::framework::LoDTensor& input,
+                 paddle::framework::DDim dims,
+                 T lower,
+                 T upper) {
+  srand(time(0));
+  float* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
+  for (int i = 0; i < input.numel(); ++i) {
+    input_ptr[i] =
+        (static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
+        lower;
+  }
+}
+
+template <typename T>
+void CheckError(paddle::framework::LoDTensor& output1,
+                paddle::framework::LoDTensor& output2) {
+  // Check lod information
+  EXPECT_EQ(output1.lod(), output2.lod());
+
+  EXPECT_EQ(output1.dims(), output2.dims());
+  EXPECT_EQ(output1.numel(), output2.numel());
+
+  T err = static_cast<T>(0);
+  if (typeid(T) == typeid(float)) {
+    err = 1E-3;
+  } else if (typeid(T) == typeid(double)) {
+    err = 1E-6;
+  } else {
+    err = 0;
+  }
+
+  size_t count = 0;
+  for (int64_t i = 0; i < output1.numel(); ++i) {
+    if (fabs(output1.data<T>()[i] - output2.data<T>()[i]) > err) {
+      count++;
+    }
+  }
+  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+}
+
 TEST(inference, recognize_digits) {
   if (FLAGS_dirname.empty()) {
     LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
@@ -70,12 +111,10 @@ TEST(inference, recognize_digits) {
   // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
 
   paddle::framework::LoDTensor input;
-  srand(time(0));
-  float* input_ptr =
-      input.mutable_data<float>({1, 28, 28}, paddle::platform::CPUPlace());
-  for (int i = 0; i < 784; ++i) {
-    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
-  }
+  // Use normilized image pixels as input data,
+  // which should be in the range [-1.0, 1.0].
+  SetupTensor<float>(
+      input, {1, 28, 28}, static_cast<float>(-1), static_cast<float>(1));
   std::vector<paddle::framework::LoDTensor*> cpu_feeds;
   cpu_feeds.push_back(&input);
 
@@ -98,16 +137,6 @@ TEST(inference, recognize_digits) {
       dirname, cpu_feeds, cpu_fetchs2);
   LOG(INFO) << output2.dims();
 
-  EXPECT_EQ(output1.dims(), output2.dims());
-  EXPECT_EQ(output1.numel(), output2.numel());
-
-  float err = 1E-3;
-  int count = 0;
-  for (int64_t i = 0; i < output1.numel(); ++i) {
-    if (fabs(output1.data<float>()[i] - output2.data<float>()[i]) > err) {
-      count++;
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+  CheckError<float>(output1, output2);
 #endif
 }
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index b8f55c813b..fb6b1f7192 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -166,7 +166,9 @@ def infer(use_cuda, save_dirname=None):
      fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
     # The input's dimension of conv should be 4-D or 5-D.
-    tensor_img = numpy.random.rand(1, 1, 28, 28).astype("float32")
+    # Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
+    tensor_img = numpy.random.uniform(-1.0, 1.0,
+                                      [1, 1, 28, 28]).astype("float32")
 
     # Construct feed as a dictionary of {feed_target_name: feed_target_data}
     # and results will contain a list of data corresponding to fetch_targets.

From 863cd9c766e30b487d88ddd0b797a3b59a421282 Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Tue, 6 Feb 2018 09:54:14 +0800
Subject: [PATCH 259/314] Add comments to explain the empty result

---
 python/paddle/v2/fluid/layers/nn.py | 39 +++++++++++++++--------------
 1 file changed, 20 insertions(+), 19 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index a79479f469..2209625344 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -410,12 +410,12 @@ def dynamic_lstmp(input,
     """
     **Dynamic LSTMP Layer**
 
-    LSTMP (LSTM with recurrent projection) layer has a separate projection 
-    layer after the LSTM layer, projecting the original hidden state to a 
-    lower-dimensional one, which is proposed to reduce the number of total 
-    parameters and furthermore computational complexity for the LSTM, 
-    espeacially for the case that the size of output units is relative 
-    large (https://research.google.com/pubs/archive/43905.pdf). 
+    LSTMP (LSTM with recurrent projection) layer has a separate projection
+    layer after the LSTM layer, projecting the original hidden state to a
+    lower-dimensional one, which is proposed to reduce the number of total
+    parameters and furthermore computational complexity for the LSTM,
+    espeacially for the case that the size of output units is relative
+    large (https://research.google.com/pubs/archive/43905.pdf).
 
     The formula is as follows:
 
@@ -441,27 +441,27 @@ def dynamic_lstmp(input,
           the matrix of weights from the input gate to the input).
     * :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
           matrices for peephole connections. In our implementation, \
-          we use vectors to reprenset these diagonal weight matrices. 
+          we use vectors to reprenset these diagonal weight matrices.
     * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
-          bias vector). 
+          bias vector).
     * :math:`\sigma`: The activation, such as logistic sigmoid function.
     * :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
           gate, and cell activation vectors, respectively, all of which have \
-          the same size as the cell output activation vector :math:`h`. 
+          the same size as the cell output activation vector :math:`h`.
     * :math:`h`: The hidden state.
-    * :math:`r`: The recurrent projection of the hidden state. 
+    * :math:`r`: The recurrent projection of the hidden state.
     * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
           computation is based on the current input and previous hidden state.
-    * :math:`\odot`: The element-wise product of the vectors. 
+    * :math:`\odot`: The element-wise product of the vectors.
     * :math:`act_g` and :math:`act_h`: The cell input and cell output \
-          activation functions and `tanh` is usually used for them. 
+          activation functions and `tanh` is usually used for them.
     * :math:`\overline{act_h}`: The activation function for the projection \
           output, usually using `identity` or same as :math:`act_h`.
 
     Set `use_peepholes` to `False` to disable peephole connection. The formula
     is omitted here, please refer to the paper
     http://www.bioinf.jku.at/publications/older/2604.pdf for details.
-    
+
     Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
     operations on the input :math:`x_{t}` are NOT included in this operator.
     Users can choose to use fully-connected layer before LSTMP layer.
@@ -479,8 +479,8 @@ def dynamic_lstmp(input,
 
                                - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
                                                 W_{fh}, W_{oh}`}.
-                               - The shape of hidden-hidden weight is (P x 4D), 
-                                 where P is the projection size and D the hidden 
+                               - The shape of hidden-hidden weight is (P x 4D),
+                                 where P is the projection size and D the hidden
                                  size.
                                - Projection weight = {:math:`W_{rh}`}.
                                - The shape of projection weight is (D x P).
@@ -525,9 +525,9 @@ def dynamic_lstmp(input,
             hidden_dim, proj_dim = 512, 256
             fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
                                      act=None, bias_attr=None)
-            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out, 
-                                                     size=hidden_dim * 4, 
-                                                     proj_size=proj_dim, 
+            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
+                                                     size=hidden_dim * 4,
+                                                     proj_size=proj_dim,
                                                      use_peepholes=False,
                                                      is_reverse=True,
                                                      cell_activation="tanh",
@@ -2525,7 +2525,8 @@ def ctc_greedy_decoder(input, blank, name=None):
                     interval [0, num_classes + 1).
 
     Returns:
-        Variable: CTC greedy decode result.
+        Variable: CTC greedy decode result. If all the sequences in result were
+        empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1].
 
     Examples:
         .. code-block:: python

From d5686f5831adea6bc9b0ceb94b81cd3f79270800 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Mon, 5 Feb 2018 18:00:07 -0800
Subject: [PATCH 260/314] clean code

---
 paddle/framework/block_desc.cc                    |  5 +----
 paddle/framework/op_desc.cc                       | 15 ++-------------
 paddle/framework/program_desc.cc                  |  9 ---------
 paddle/framework/prune.cc                         |  6 ------
 python/paddle/v2/fluid/io.py                      |  6 ------
 .../fluid/tests/book/test_rnn_encoder_decoder.py  | 10 ----------
 6 files changed, 3 insertions(+), 48 deletions(-)

diff --git a/paddle/framework/block_desc.cc b/paddle/framework/block_desc.cc
index ca3d03e554..3e344ea379 100644
--- a/paddle/framework/block_desc.cc
+++ b/paddle/framework/block_desc.cc
@@ -155,8 +155,6 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
   for (const proto::OpDesc &op_desc : desc_->ops()) {
     ops_.emplace_back(new OpDesc(op_desc, prog, this));
   }
-  std::cout << "Constructed block idx " << desc->idx() << " from protobuf str"
-            << std::endl;
 }
 
 BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
@@ -164,9 +162,8 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
     : prog_(prog), desc_(desc) {
   need_update_ = true;
   for (auto &op : other.ops_) {
-    ops_.emplace_back(new OpDesc(*op, this));
+    ops_.emplace_back(new OpDesc(*op->Proto(), prog, this));
   }
-
   for (auto &it : other.vars_) {
     auto *var = new VarDesc(*it.second);
     vars_[it.first].reset(var);
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index 7859c391fa..46c50d9250 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -124,21 +124,10 @@ OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
   // restore attrs_
   for (const proto::OpDesc::Attr &attr : desc_.attrs()) {
     std::string attr_name = attr.name();
-    // we use a trick to handle attr.type() is BLOCK here, because at this
-    // moment the sub_block hasn't beed added to ProgramDesc's vector<Block>
-    // so we cast the block_idx to a dummy BlockDesc pointer
+    // The sub_block referred to by the BLOCK attr hasn't be added
+    // to ProgramDesc class yet, we skip setting BLOCK attr here.
     if (attr.type() != proto::AttrType::BLOCK) {
       attrs_[attr_name] = GetAttrValue(attr);
-    } else {
-      size_t blk_idx = attr.block_idx();
-      if (blk_idx < prog->Size()) {
-        attrs_[attr_name] = prog->MutableBlock(blk_idx);
-        std::cout << "In OpDesc: set up attr block idx " << blk_idx
-                  << std::endl;
-      } else {
-        std::cout << "In OpDesc: We don't have this block idx " << blk_idx
-                  << std::endl;
-      }
     }
   }
   this->block_ = block;
diff --git a/paddle/framework/program_desc.cc b/paddle/framework/program_desc.cc
index ba461b0933..0e937dda4e 100644
--- a/paddle/framework/program_desc.cc
+++ b/paddle/framework/program_desc.cc
@@ -43,7 +43,6 @@ ProgramDesc::ProgramDesc() {
 
 ProgramDesc::ProgramDesc(const ProgramDesc &o) {
   desc_ = o.desc_;
-
   for (int i = 0; i < desc_.blocks_size(); ++i) {
     auto *block = desc_.mutable_blocks(i);
     blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
@@ -54,8 +53,6 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
         if (attr.type() == proto::AttrType::BLOCK) {
           size_t blk_idx = attr.block_idx();
           op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
-          std::cout << "In ProgramDesc 1: set block attr idx " << blk_idx
-                    << std::endl;
         }
       }
     }
@@ -64,11 +61,8 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
 
 ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
   desc_ = desc;
-  std::cout << std::endl << "starting in ProgDesc constructor" << std::endl;
   for (auto &block_desc : *desc_.mutable_blocks()) {
     blocks_.emplace_back(new BlockDesc(this, &block_desc));
-    std::cout << "Done constructing block idx " << block_desc.idx()
-              << " parent idx " << block_desc.parent_idx() << std::endl;
   }
   for (auto &block : blocks_) {
     for (auto *op : block->AllOps()) {
@@ -76,13 +70,10 @@ ProgramDesc::ProgramDesc(const proto::ProgramDesc &desc) {
         if (attr.type() == proto::AttrType::BLOCK) {
           size_t blk_idx = attr.block_idx();
           op->SetBlockAttr(attr.name(), *this->MutableBlock(blk_idx));
-          std::cout << "In ProgramDesc 2: set block attr idx " << blk_idx
-                    << std::endl;
         }
       }
     }
   }
-  std::cout << "Done ProgDesc construction" << std::endl << std::endl;
 }
 
 ProgramDesc::ProgramDesc(const std::string &binary_str) {
diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 3c3ec87585..00fe551e55 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -137,8 +137,6 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
             sub_block_dependent_vars.insert(argu);
           }
         }
-        std::cout << "pruning the next block, the current output_block_id is "
-                  << output_block_id << std::endl;
         // GetSubBlockIndex(*op) is the idx of the sub_block in the input desc
         // output_block_id is the idx of the current block in the output desc
         prune_impl(input, output, GetSubBlockIndex(*op), output_block_id,
@@ -147,8 +145,6 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
     }
   }
 
-  std::cout << "Starting to remove unreferenced variables"
-            << " for block idx " << output_block_id << std::endl;
   // remove the VarDescs in BlockDesc that are not referenced in
   // the pruned OpDescs
   std::unordered_map<std::string, proto::VarDesc> var_map;
@@ -186,9 +182,7 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
 void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
   std::set<std::string> dependent_vars;
-  std::cout << std::endl << "Start C++ framework::prune" << std::endl;
   prune_impl(input, output, 0, -1, dependent_vars);
-  std::cout << "Finished C++ framework::prune" << std::endl << std::endl;
 }
 
 void inference_optimize_impl(const proto::ProgramDesc& input,
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index e410549f8a..613dc20b6e 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -342,12 +342,6 @@ def save_inference_model(dirname,
     prepend_feed_ops(inference_program, feeded_var_names)
     append_fetch_ops(inference_program, fetch_var_names)
 
-    # save for checking
-    curstr = inference_program.to_string(True)
-    f = open("save_inf_prog_after_feed_fetch.txt", 'w')
-    f.write(curstr)
-    f.close()
-
     model_file_name = dirname + "/__model__"
     with open(model_file_name, "wb") as f:
         f.write(inference_program.desc.serialize_to_string())
diff --git a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
index 15f00f95d4..2211637b5b 100644
--- a/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
+++ b/python/paddle/v2/fluid/tests/book/test_rnn_encoder_decoder.py
@@ -225,18 +225,8 @@ def infer(save_dirname=None):
 
     # Construct feed as a dictionary of {feed_target_name: feed_target_data}
     # and results will contain a list of data corresponding to fetch_targets.
-    print("Print feed fetch target names as follows")
-    print(feed_target_names)
     assert feed_target_names[0] == 'source_sequence'
     assert feed_target_names[1] == 'target_sequence'
-    print([var.name for var in fetch_targets])
-
-    # save for checking
-    curstr = inference_program.to_string(True)
-    f = open("loaded_infer_prog.txt", 'w')
-    f.write(curstr)
-    f.close()
-
     results = exe.run(inference_program,
                       feed={
                           feed_target_names[0]: word_data,

From 9a1fa890a0c510ca1863eea358423bc89fd4fdef Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 6 Feb 2018 11:10:34 +0800
Subject: [PATCH 261/314] remove unnecessary comments

---
 python/paddle/v2/fluid/tests/test_protobuf_descs.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_protobuf_descs.py b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
index ac6de68b5f..8f335d13db 100644
--- a/python/paddle/v2/fluid/tests/test_protobuf_descs.py
+++ b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
@@ -123,8 +123,6 @@ class TestVarDesc(unittest.TestCase):
         var.set_tensor_num(3)
         src_shapes = [[2, 3, 3], [4, 5], [6, 7, 8, 9]]
         var.set_shapes(src_shapes)
-        #import pdb
-        # pdb.set_trace()
         res_shapes = var.shapes()
         self.assertEqual(src_shapes, res_shapes)
         self.assertEqual(core.VarDesc.VarType.READER, var.type())

From 450c39a41301800f9b71e499d539ff7bbbd7414f Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Mon, 5 Feb 2018 20:39:38 -0800
Subject: [PATCH 262/314] fix bug

---
 paddle/framework/prune.cc | 1 +
 1 file changed, 1 insertion(+)

diff --git a/paddle/framework/prune.cc b/paddle/framework/prune.cc
index 00fe551e55..ddd6b993d4 100644
--- a/paddle/framework/prune.cc
+++ b/paddle/framework/prune.cc
@@ -182,6 +182,7 @@ void prune_impl(const proto::ProgramDesc& input, proto::ProgramDesc* output,
 // TODO(fengjiayi): Prune() could be inplaced to avoid unnecessary copies
 void Prune(const proto::ProgramDesc& input, proto::ProgramDesc* output) {
   std::set<std::string> dependent_vars;
+  output->clear_blocks();
   prune_impl(input, output, 0, -1, dependent_vars);
 }
 

From 1010e39bdf738029fcb78b0d388a91dfdebdda2f Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 6 Feb 2018 12:39:51 +0800
Subject: [PATCH 263/314] Add ReadOp

---
 paddle/framework/framework.proto    |  4 +-
 paddle/framework/op_desc.cc         | 29 +++++++--
 paddle/framework/operator.cc        | 26 ++++++--
 paddle/framework/reader.cc          | 40 ++++++------
 paddle/framework/reader.h           | 32 +++++-----
 paddle/framework/shape_inference.cc | 14 +++++
 paddle/framework/shape_inference.h  |  3 +-
 paddle/operators/read_op.cc         | 94 +++++++++++++++++++++++++++++
 8 files changed, 193 insertions(+), 49 deletions(-)
 create mode 100644 paddle/operators/read_op.cc

diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto
index f65ccae6e6..d7be1a7352 100644
--- a/paddle/framework/framework.proto
+++ b/paddle/framework/framework.proto
@@ -116,7 +116,7 @@ message LoDTensorArrayDesc {
   optional int32 lod_level = 2 [ default = 0 ];
 }
 
-message Reader { repeated LoDTensorDesc lod_tensor = 1; }
+message ReaderDesc { repeated LoDTensorDesc lod_tensor = 1; }
 
 message VarDesc {
   enum VarType {
@@ -136,7 +136,7 @@ message VarDesc {
   optional LoDTensorDesc lod_tensor = 4;
   optional TensorDesc selected_rows = 5;
   optional LoDTensorArrayDesc tensor_array = 6;
-  optional Reader reader = 7;
+  optional ReaderDesc reader = 7;
 }
 
 message BlockDesc {
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index ad361852ec..772ec26895 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -72,6 +72,8 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
   void SetDim(const std::string &name, const DDim &dim) override;
 
+  std::vector<DDim> GetRepeatedDim(const std::string &name) const override;
+
   const OpDesc &op_;
   const BlockDesc &block_;
 };
@@ -457,22 +459,37 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
 DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
   PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
+  DDim res;
   try {
     auto shape = var->GetShape();
-    if (shape.empty()) {
-      return framework::make_ddim({0UL});
-    } else {
-      return framework::make_ddim(var->GetShape());
-    }
+    res = shape.empty() ? make_ddim({0UL}) : make_ddim(shape);
   } catch (...) {
     VLOG(5) << "GetDim of variable " << name << " error";
     std::rethrow_exception(std::current_exception());
   }
+  return res;
+}
+
+std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDim(
+    const std::string &name) const {
+  auto var = block_.FindVarRecursive(name);
+  PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
+  std::vector<DDim> res;
+  try {
+    auto shapes = var->GetShapes();
+    for (const auto &s : shapes) {
+      res.push_back(s.empty() ? make_ddim({0UL}) : make_ddim(s));
+    }
+  } catch (...) {
+    VLOG(5) << "GetRepeatedDim of variable " << name << " error.";
+    std::rethrow_exception(std::current_exception());
+  }
+  return res;
 }
 
 void CompileTimeInferShapeContext::SetDim(const std::string &name,
                                           const DDim &dim) {
-  block_.FindVarRecursive(name)->SetShape(framework::vectorize(dim));
+  block_.FindVarRecursive(name)->SetShape(vectorize(dim));
 }
 bool CompileTimeInferShapeContext::IsRuntime() const { return false; }
 
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 81fa8cf477..1aa111dc76 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -320,8 +320,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
     if (length == 0) {
       return false;
     }
-    PADDLE_ENFORCE_EQ(length, 1UL, "Input %s should have more than one inputs",
-                      name);
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Input %s should not have more than one inputs", name);
     auto ipt = ins[0];
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
@@ -333,8 +333,8 @@ class RuntimeInferShapeContext : public InferShapeContext {
     if (length == 0) {
       return false;
     }
-    PADDLE_ENFORCE_EQ(length, 1UL, "Output %s should have more than one inputs",
-                      name);
+    PADDLE_ENFORCE_EQ(length, 1UL,
+                      "Output %s should not have more than one inputs", name);
     auto ipt = outs[0];
     auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
     return var != nullptr;
@@ -421,8 +421,22 @@ class RuntimeInferShapeContext : public InferShapeContext {
     } else if (var->IsType<SelectedRows>()) {
       return var->Get<SelectedRows>().GetCompleteDims();
     } else {
-      PADDLE_THROW("Variable %s type_id %s, expect LoDTensor/SelectedRows.",
-                   name, var->Type().name());
+      PADDLE_THROW(
+          "Only LoDTensor/SelectedRows support 'GetDim', but Variable %s's "
+          "type_id is %s.",
+          name, var->Type().name());
+    }
+  }
+
+  std::vector<DDim> GetRepeatedDim(const std::string& name) const override {
+    Variable* var = scope_.FindVar(name);
+    if (var->IsType<ReaderHolder>()) {
+      return var->Get<ReaderHolder>().shapes();
+    } else {
+      PADDLE_THROW(
+          "Only ReaderHolder support 'GetRepeatedDim', but Variable %s's "
+          "type_id is %s.",
+          name, var->Type().name());
     }
   }
 
diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
index a05bef42ff..76cbc827ba 100644
--- a/paddle/framework/reader.cc
+++ b/paddle/framework/reader.cc
@@ -25,13 +25,15 @@ DDim FileReader::shape(size_t idx) const {
   return shapes_[idx];
 }
 
-std::vector<LoDTensor> ShuffleReader::ReadNext() {
+void ShuffleReader::ReadNext(std::vector<LoDtensor>* out) {
   if (iteration_pos_ >= buffer_.size()) {
     // Reload buffer with new data
     buffer_.clear();
+    buffer_.reverse(buffer_size_);
     for (int i = 0; i < buffer_size_; ++i) {
       if (reader_->HasNext()) {
-        buffer_.push_back(reader_->ReadNext());
+        buffer.push_back(std::vector<LoDTensor>());
+        reader_->ReadNext(&buffer.back());
       } else {
         break;
       }
@@ -39,29 +41,32 @@ std::vector<LoDTensor> ShuffleReader::ReadNext() {
     std::random_shuffle(buffer_.begin(), buffer_.end());
     iteration_pos_ = 0;
   }
-  if (buffer_.empty()) {
-    std::vector<LoDTensor> empty_res;
-    return empty_res;
+  out->clear();
+  if (!buffer_.empty()) {
+    std::swap(*out, buffer_[iteration_pos_++]);
   }
-  return buffer_[iteration_pos_++];
+  // if buffer_ is empty, the 'out' will return as an empty vector.
 }
 
-std::vector<LoDTensor> BatchReader::ReadNext() {
+void BatchReader::ReadNext(std::vector<LoDtensor>* out) {
   buffer_.clear();
+  buffer_.reserve(batch_size_);
   for (int i = 0; i < batch_size_; ++i) {
     if (reader_->HasNext()) {
-      buffer_.push_back(reader_->ReadNext());
+      buffer_.push_back(std::vector<LoDtensor>());
+      reader_->ReadNext(&buffer_.back());
     } else {
       break;
     }
   }
   // Concat instances
-  std::vector<LoDTensor> res;
+  out.clear();
   if (buffer_.empty()) {
-    return res;
+    // if buffer_ is empty, the 'out' will return as an empty vector.
+    return;
   }
   int out_num = buffer_[0].size();
-  res.reserve(out_num);
+  out->reserve(out_num);
   for (int j = 0; j < out_num; ++j) {
     // Merge shape and check date type
     std::type_index batch_type = buffer_[0][j].type();
@@ -76,9 +81,9 @@ std::vector<LoDTensor> BatchReader::ReadNext() {
       batch_shape[0] += ins_shape[0];
     }
 
-    LoDTensor out;
-    out.Resize(batch_shape);
-    out.mutable_data(platform::CPUPlace(), batch_type);
+    LoDTensor out_tensor;
+    out_tensor.Resize(batch_shape);
+    out_tensor.mutable_data(platform::CPUPlace(), batch_type);
     int64_t dst_offset = 0;
 
     // Merge lod and data
@@ -102,15 +107,14 @@ std::vector<LoDTensor> BatchReader::ReadNext() {
           top_level_lod.back() +
           (ins_lod.empty() ? ins_shape[0] : (ins_lod[0].size() - 1)));
 
-      Tensor dst = out.Slice(dst_offset, dst_offset + ins_shape[0]);
+      Tensor dst = out_tensor.Slice(dst_offset, dst_offset + ins_shape[0]);
       Copy(buffer_[i][j], platform::CPUPlace(), &dst);
       dst_offset += ins_shape[0];
     }
     batch_lod.insert(batch_lod.begin(), top_level_lod);
-    out.set_lod(batch_lod);
-    res.push_back(out);
+    out_tensor.set_lod(batch_lod);
+    out->push_back(out_tensor);
   }
-  return res;
 }
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index f450e67689..523ff28c99 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -15,14 +15,14 @@
 #pragma once
 
 #include "paddle/framework/ddim.h"
-#include "paddle/framework/lod_tensor.h"
+#include "paddle/framework/lod_tensor_array.h"
 
 namespace paddle {
 namespace framework {
 
 class ReaderBase {
  public:
-  virtual std::vector<LoDTensor> ReadNext() = 0;
+  virtual void ReadNext(std::vector<LoDtensor>* out) = 0;
   virtual bool HasNext() const = 0;
 
   virtual DDim shape(size_t idx) const = 0;
@@ -73,24 +73,24 @@ class RandomReader : public FileReader {
     dist_ = std::uniform_real_distribution<float>(min_, max_);
   }
 
-  std::vector<LoDTensor> ReadNext() override {
-    std::vector<LoDTensor> res;
-    res.reserve(shapes_.size());
+  void ReadNext(std::vector<LoDtensor>* out) override {
+    out.clear();
+    out.reserve(shapes_.size());
     for (const DDim& shape : shapes_) {
       PADDLE_ENFORCE_GE(
           shape.size(), 2,
-          "The rank of input data should be 2 at least.(Now it's %d)",
+          "The rank of reader's output data should be 2 at least.(Now it's %d)",
           shape.size());
-      LoDTensor out;
-      out.Resize(shape);
-      T* data = out.mutable_data<T>(platform::CPUPlace());
+      LoDTensor out_tensor;
+      out_tensor.Resize(shape);
+      T* data = out_tensor.mutable_data<T>(platform::CPUPlace());
       int64_t numel = product(shape);
       for (int64_t i = 0; i < numel; ++i) {
         data[i] = dist_(engine_);
       }
-      res.push_back(out);
+      out.push_back(out_tensor);
     }
-    return res;
+    return out;
   }
 
   bool HasNext() const override { return true; }
@@ -111,11 +111,11 @@ class ShuffleReader : public DecoratedReader {
     buffer_.reserve(buffer_size);
   }
 
-  std::vector<LoDTensor> ReadNext() override;
+  void ReadNext(std::vector<LoDtensor>* out) override;
 
  private:
   int buffer_size_;
-  std::vector<std::vector<LoDTensor>> buffer_;
+  std::vector<std::vector<LoDtensor>> buffer_;
   size_t iteration_pos_;
 };
 
@@ -126,11 +126,11 @@ class BatchReader : public DecoratedReader {
     buffer_.reserve(batch_size_);
   }
 
-  std::vector<LoDTensor> ReadNext() override;
+  void ReadNext(std::vector<LoDtensor>* out) override;
 
  private:
   int batch_size_;
-  std::vector<std::vector<LoDTensor>> buffer_;
+  std::vector<std::vector<LoDtensor>> buffer_;
 };
 
 // The ReaderHolder is used as readers' unified wrapper,
@@ -141,7 +141,7 @@ class ReaderHolder {
 
   ReaderBase* Get() const { return reader_.get(); }
 
-  std::vector<LoDTensor> ReadNext() { return reader_->ReadNext(); }
+  void ReadNext(std::vector<LoDtensor>* out) { reader_->ReadNext(out); }
   bool HasNext() const { return reader_->HasNext(); }
 
   DDim shape(size_t idx) const { return reader_->shape(idx); }
diff --git a/paddle/framework/shape_inference.cc b/paddle/framework/shape_inference.cc
index a0fa467291..4a8acfb87f 100644
--- a/paddle/framework/shape_inference.cc
+++ b/paddle/framework/shape_inference.cc
@@ -32,6 +32,16 @@ std::vector<DDim> InferShapeContext::GetInputsDim(
   return GetDims(arg_names);
 }
 
+std::vector<DDim> InferShapeContext::GetReaderDims(
+    const std::string &name) const {
+  const std::vector<std::string> &arg_names = Inputs(name);
+  PADDLE_ENFORCE_EQ(
+      arg_names.size(), 1UL,
+      "Reader input '%s' should hold one element, but now it holds %d", name,
+      arg_names.size());
+  return this->GetRepeatedDims(arg_names[0]);
+}
+
 DDim InferShapeContext::GetInputsElementDim(const std::string &name,
                                             int idx) const {
   const std::vector<std::string> &names = Inputs(name);
@@ -61,6 +71,7 @@ std::vector<DDim> InferShapeContext::GetDims(
       [this](const std::string &name) { return this->GetDim(name); });
   return ret;
 }
+
 void InferShapeContext::SetDims(const std::vector<std::string> &names,
                                 const std::vector<DDim> &dims) {
   size_t length = names.size();
@@ -72,14 +83,17 @@ void InferShapeContext::SetDims(const std::vector<std::string> &names,
     SetDim(names[i], dims[i]);
   }
 }
+
 std::vector<proto::VarDesc::VarType> InferShapeContext::GetInputsVarType(
     const std::string &name) const {
   return GetVarTypes(Inputs(name));
 }
+
 std::vector<proto::VarDesc::VarType> InferShapeContext::GetOutputsVarType(
     const std::string &name) const {
   return GetVarTypes(Outputs(name));
 }
+
 std::vector<proto::VarDesc::VarType> InferShapeContext::GetVarTypes(
     const std::vector<std::string> &names) const {
   std::vector<proto::VarDesc::VarType> retv;
diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h
index 830f199ed1..f1a64e9024 100644
--- a/paddle/framework/shape_inference.h
+++ b/paddle/framework/shape_inference.h
@@ -36,8 +36,8 @@ class InferShapeContext {
   virtual bool HasOutputs(const std::string &name) const = 0;
 
   DDim GetInputDim(const std::string &name) const;
-
   std::vector<DDim> GetInputsDim(const std::string &name) const;
+  std::vector<DDim> GetReaderDims(const std::string &name) const DDim;
   DDim GetInputsElementDim(const std::string &name, int idx) const;
 
   void SetOutputDim(const std::string &name, const DDim &dim);
@@ -61,6 +61,7 @@ class InferShapeContext {
  protected:
   virtual DDim GetDim(const std::string &name) const = 0;
   virtual void SetDim(const std::string &name, const DDim &dim) = 0;
+  std::vector<DDim> GetRepeatedDim(const std::string &name) const = 0;
 
   std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
   std::vector<proto::VarDesc::VarType> GetVarTypes(
diff --git a/paddle/operators/read_op.cc b/paddle/operators/read_op.cc
new file mode 100644
index 0000000000..c6ff4ba8fe
--- /dev/null
+++ b/paddle/operators/read_op.cc
@@ -0,0 +1,94 @@
+//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/framework/reader.h"
+
+namespace paddle {
+namespace operators {
+
+class ReadInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Reader"),
+                   "The ReadOp must take a reader as input.");
+    PADDLE_ENFORCE(ctx->HasOutputs("Out"),
+                   "The ReadOp should be assigned with output.");
+    std::vector<DDim> reader_dims = ctx->GetReaderDims("Reader");
+    std::vector<std::string> out_names = ctx->Outputs("Out");
+    PADDLE_ENFORCE_EQ(
+        reader_dims.size(), out_names.size(),
+        "The reader's dim number doesn't match the output number.");
+    ctx->SetOutputsDim("Out", reader_dims);
+  }
+};
+
+class ReadInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {
+    std::string reader_name = op_desc.Input("Reader")[0];
+    std::vector<std::string> out_names = op_desc.Output("Out");
+    framework::VarDesc reader = block.FindVarRecursive(reader_name);
+    auto dtypes = reader.GetDataTypes();
+    PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
+    for (size_t i = 0; i < dtypes.size(); ++i) {
+      faremwork::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
+      out.SetType(framework::proto::DataType::LOD_TENSOR);
+      out.SetDataType(dtypes[i]);
+    }
+  }
+};
+
+class ReadOp : public framework::OperatorBase {
+ public:
+  using framework::OperatorBase::OperatorBase;
+  void Run(const framework::Scope& scope,
+           const platform::Place& dev_place) const override {
+    const framework::ReaderHolder& reader =
+        scope.FindVar(Input("Reader"))->Get<ReaderHolder>();
+    if (!reader.HasNext()) {
+      // what shall we do???
+      return;
+    }
+    std::vector<std::string> out_arg_names = Outputs("Out");
+    std::vector<framework::LoDTensor> ins;
+    reader.ReadNext(&ins);
+    PADDLE_ENFORCE_EQ(ins.size(), out_arg_names.size());
+    for (size_t i = 0; i < ins.size(); ++i) {
+      auto* out =
+          scope.FindVar(out_arg_names[i])->GetMutable<framework::LoDTensor>();
+      PADDLE_ENFORCE_EQ(ins[i].dims(), out->dims());
+      out->ShareDataWith(ins[i]);
+      out->set_lod(ins[i].lod());
+    }
+  }
+};
+
+class ReadOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ReadOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(op_proto, op_checker) {
+    AddInput("Reader", "(ReaderHolder) The executed reader.");
+    AddOutput("Out", "(LoDTensor) The output data.").AsDuplicable();
+    AddComment(R"DOC(
+      Read Operator
+
+      Execute a given reader once and output data.
+    )DOC")
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
\ No newline at end of file

From 70324911e701829eb1a5ef484ec26920b6578d96 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 6 Feb 2018 12:52:10 +0800
Subject: [PATCH 264/314] refine buffer receive

---
 paddle/framework/details/buffered_channel.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 7ac234b8d4..b9761eab9b 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -71,7 +71,7 @@ bool Buffered<T>::Receive(T* item) {
   std::unique_lock<std::mutex> lock(mu_);
   empty_cond_var_.wait(lock, [this]() { return !channel_.empty() || closed_; });
   bool ret = false;
-  if (!closed_) {
+  if (!channel_.empty()) {
     *item = std::move(channel_.front());
     channel_.pop_front();
     full_cond_var_.notify_one();

From c966c2813022e145f75263dab780d8cb9273a2f1 Mon Sep 17 00:00:00 2001
From: Yang Yu <yuyang18@baidu.com>
Date: Tue, 6 Feb 2018 13:45:36 +0800
Subject: [PATCH 265/314] Add RunAndGetException in threadpool

Change the behaviour of thread pool. Thread pool will ignore the
thrown exception implicitly. It is hard to debug.
Corrently, ThreadPool::Run will invoke `LOG(FATAL)` if an exception
thrown.
---
 paddle/framework/threadpool.h | 44 +++++++++++++++++++++++++++++++----
 1 file changed, 40 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/threadpool.h b/paddle/framework/threadpool.h
index 4e9b58679d..77d31a1176 100644
--- a/paddle/framework/threadpool.h
+++ b/paddle/framework/threadpool.h
@@ -21,7 +21,8 @@ limitations under the License. */
 #include <queue>
 #include <thread>
 #include <vector>
-
+#include "glog/logging.h"
+#include "paddle/platform/enforce.h"
 #include "paddle/platform/macros.h"  // for DISABLE_COPY_AND_ASSIGN
 
 namespace paddle {
@@ -31,7 +32,7 @@ namespace framework {
 // number of threads.
 class ThreadPool {
  public:
-  typedef std::packaged_task<void()> Task;
+  using Task = std::packaged_task<std::unique_ptr<platform::EnforceNotMet>()>;
 
   // Returns the singleton of ThreadPool.
   static ThreadPool* GetInstance();
@@ -52,9 +53,28 @@ class ThreadPool {
   // std::future::wait().
   template <typename Callback>
   std::future<void> Run(Callback fn) {
+    auto f = this->RunAndGetException(fn);
+    return std::async(std::launch::deferred, ExceptionHandler(std::move(f)));
+  }
+
+  template <typename Callback>
+  std::future<std::unique_ptr<platform::EnforceNotMet>> RunAndGetException(
+      Callback fn) {
     std::unique_lock<std::mutex> lock(mutex_);
-    Task task(std::bind(fn));
-    std::future<void> f = task.get_future();
+    Task task([fn]() -> std::unique_ptr<platform::EnforceNotMet> {
+      try {
+        fn();
+        return nullptr;
+      } catch (platform::EnforceNotMet ex) {
+        return std::unique_ptr<platform::EnforceNotMet>(
+            new platform::EnforceNotMet(ex));
+      } catch (...) {
+        LOG(FATAL)
+            << "Unexpected exception is catched in thread pool. All "
+               "throwable exception in Fluid should be an EnforceNotMet.";
+      }
+    });
+    std::future<std::unique_ptr<platform::EnforceNotMet>> f = task.get_future();
     tasks_.push(std::move(task));
     lock.unlock();
     scheduled_.notify_one();
@@ -65,6 +85,22 @@ class ThreadPool {
   void Wait();
 
  private:
+  struct ExceptionHandler {
+    mutable std::future<std::unique_ptr<platform::EnforceNotMet>> future_;
+    explicit ExceptionHandler(
+        std::future<std::unique_ptr<platform::EnforceNotMet>>&& f)
+        : future_(std::move(f)) {}
+    void operator()() const {
+      auto ex = this->future_.get();
+      if (ex != nullptr) {
+        LOG(FATAL) << "The exception is thrown inside the thread pool. You "
+                      "should use RunAndGetException to handle the exception.\n"
+                      "The default exception handler is LOG(FATAL)."
+                   << ex->what();
+      }
+    }
+  };
+
   DISABLE_COPY_AND_ASSIGN(ThreadPool);
 
   explicit ThreadPool(int num_threads);

From 59e4dd579770df7e0fb7208a11517784a7b02b4e Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 6 Feb 2018 14:17:35 +0800
Subject: [PATCH 266/314] add independent inference_lib.cmake

---
 CMakeLists.txt                  |  1 +
 cmake/external/eigen.cmake      |  8 ----
 cmake/external/gflags.cmake     |  7 ----
 cmake/external/glog.cmake       |  7 ----
 cmake/external/protobuf.cmake   |  7 ----
 cmake/inference_lib.cmake       | 74 +++++++++++++++++++++++++++++++++
 paddle/framework/CMakeLists.txt |  8 ----
 paddle/inference/CMakeLists.txt | 11 -----
 paddle/memory/CMakeLists.txt    |  7 ----
 paddle/platform/CMakeLists.txt  |  8 ----
 paddle/string/CMakeLists.txt    |  7 ----
 11 files changed, 75 insertions(+), 70 deletions(-)
 create mode 100644 cmake/inference_lib.cmake

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 49334279f6..3a21574b85 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -156,6 +156,7 @@ include(rdma)               # set rdma libraries
 include(flags)              # set paddle compile flags
 include(version)            # set PADDLE_VERSION
 include(coveralls)          # set code coverage
+include(inference_lib)      # add paddle fluid inference libraries
 
 
 include_directories("${PADDLE_SOURCE_DIR}")
diff --git a/cmake/external/eigen.cmake b/cmake/external/eigen.cmake
index eb6c0cef57..6a701e076c 100644
--- a/cmake/external/eigen.cmake
+++ b/cmake/external/eigen.cmake
@@ -28,11 +28,3 @@ endif()
 add_dependencies(eigen3 extern_eigen3)
 
 LIST(APPEND external_project_dependencies eigen3)
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
-add_custom_target(eigen3_lib
-    COMMAND mkdir -p "${lib_dir}/Eigen" "${lib_dir}/unsupported"
-    COMMAND cp "${EIGEN_INCLUDE_DIR}/Eigen/Core" "${lib_dir}/Eigen"
-    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/Eigen/src" "${lib_dir}/Eigen"
-    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/unsupported/Eigen" "${lib_dir}/unsupported"
-)
diff --git a/cmake/external/gflags.cmake b/cmake/external/gflags.cmake
index 9cbc376ba0..d4f252bb9f 100644
--- a/cmake/external/gflags.cmake
+++ b/cmake/external/gflags.cmake
@@ -60,10 +60,3 @@ IF(WITH_C_API)
     INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
   ENDIF()
 ENDIF()
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
-add_custom_target(gflags_lib
-    COMMAND mkdir -p "${lib_dir}/lib"
-    COMMAND cp -r "${GFLAGS_INCLUDE_DIR}" "${lib_dir}"
-    COMMAND cp "${GFLAGS_LIBRARIES}" "${lib_dir}/lib"
-)
diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake
index 0031225a6c..0c6b3aafcb 100644
--- a/cmake/external/glog.cmake
+++ b/cmake/external/glog.cmake
@@ -76,10 +76,3 @@ IF(WITH_C_API)
     INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib)
   ENDIF()
 ENDIF()
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
-add_custom_target(glog_lib
-    COMMAND mkdir -p "${lib_dir}/lib"
-    COMMAND cp -r "${GLOG_INCLUDE_DIR}" "${lib_dir}"
-    COMMAND cp "${GLOG_LIBRARIES}" "${lib_dir}/lib"
-)
diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake
index ff3d38a691..ff5855052d 100644
--- a/cmake/external/protobuf.cmake
+++ b/cmake/external/protobuf.cmake
@@ -259,13 +259,6 @@ IF(NOT PROTOBUF_FOUND)
         ENDIF()
     ENDIF()
 
-    set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
-    add_custom_target(protobuf_lib
-        COMMAND mkdir -p "${lib_dir}/lib"
-        COMMAND cp -r "${PROTOBUF_INCLUDE_DIR}" "${lib_dir}"
-        COMMAND cp "${PROTOBUF_LITE_LIBRARY}" "${lib_dir}/lib"
-    )
-
     IF(CMAKE_CROSSCOMPILING)
         PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf)
     ELSE()
diff --git a/cmake/inference_lib.cmake b/cmake/inference_lib.cmake
new file mode 100644
index 0000000000..d71fbce382
--- /dev/null
+++ b/cmake/inference_lib.cmake
@@ -0,0 +1,74 @@
+# make package for paddle fluid shared and static library
+# third party
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
+add_custom_target(eigen3_lib
+    COMMAND mkdir -p "${lib_dir}/Eigen" "${lib_dir}/unsupported"
+    COMMAND cp "${EIGEN_INCLUDE_DIR}/Eigen/Core" "${lib_dir}/Eigen"
+    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/Eigen/src" "${lib_dir}/Eigen"
+    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/unsupported/Eigen" "${lib_dir}/unsupported"
+)
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
+add_custom_target(gflags_lib
+    COMMAND mkdir -p "${lib_dir}/lib"
+    COMMAND cp -r "${GFLAGS_INCLUDE_DIR}" "${lib_dir}"
+    COMMAND cp "${GFLAGS_LIBRARIES}" "${lib_dir}/lib"
+)
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
+add_custom_target(glog_lib
+    COMMAND mkdir -p "${lib_dir}/lib"
+    COMMAND cp -r "${GLOG_INCLUDE_DIR}" "${lib_dir}"
+    COMMAND cp "${GLOG_LIBRARIES}" "${lib_dir}/lib"
+)
+
+IF(NOT PROTOBUF_FOUND)
+    set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
+    add_custom_target(protobuf_lib
+        COMMAND mkdir -p "${lib_dir}/lib"
+        COMMAND cp -r "${PROTOBUF_INCLUDE_DIR}" "${lib_dir}"
+        COMMAND cp "${PROTOBUF_LITE_LIBRARY}" "${lib_dir}/lib"
+    )
+ENDIF(NOT PROTOBUF_FOUND)
+
+# paddle fluid module
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/framework")
+add_custom_target(framework_lib DEPENDS framework_py_proto
+    COMMAND mkdir -p "${lib_dir}/details"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/framework/*.h" "${lib_dir}"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/framework/details/*.h" "${lib_dir}/details"
+    COMMAND cp "${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h" "${lib_dir}"
+)
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/memory")
+add_custom_target(memory_lib
+    COMMAND mkdir -p "${lib_dir}/detail"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/memory/*.h" "${lib_dir}"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/memory/detail/*.h" "${lib_dir}/detail"
+)
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/inference")
+add_custom_target(inference_lib DEPENDS paddle_fluid_shared
+    COMMAND mkdir -p "${lib_dir}"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/inference/*.h" "${lib_dir}"
+    COMMAND cp "${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so" "${lib_dir}"
+)
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/platform")
+add_custom_target(platform_lib
+    COMMAND mkdir -p "${lib_dir}/dynload" "${lib_dir}/details"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/*.h" "${lib_dir}"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/dynload/*.h" "${lib_dir}/dynload"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/details/*.h" "${lib_dir}/details"
+)    
+
+set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/string")
+add_custom_target(string_lib
+    COMMAND mkdir -p "${lib_dir}/tinyformat"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/string/*.h" "${lib_dir}"
+    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/string/tinyformat/*.h" "${lib_dir}/tinyformat"
+)
+
+add_custom_target(inference_lib_dist DEPENDS 
+  inference_lib framework_lib memory_lib platform_lib string_lib
+  gflags_lib glog_lib protobuf_lib eigen3_lib)
diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt
index a2a0be08d9..8b3768b231 100644
--- a/paddle/framework/CMakeLists.txt
+++ b/paddle/framework/CMakeLists.txt
@@ -92,12 +92,4 @@ cc_test(init_test SRCS init_test.cc DEPS init)
 cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
 cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
       
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/framework")
-add_custom_target(framework_lib DEPENDS framework_py_proto
-    COMMAND mkdir -p "${lib_dir}/details"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/details/*.h" "${lib_dir}/details"
-    COMMAND cp "${CMAKE_CURRENT_BINARY_DIR}/framework.pb.h" "${lib_dir}"
-)
-
 cc_test(channel_test SRCS channel_test.cc)
diff --git a/paddle/inference/CMakeLists.txt b/paddle/inference/CMakeLists.txt
index e8e0ee2107..654a6119bd 100644
--- a/paddle/inference/CMakeLists.txt
+++ b/paddle/inference/CMakeLists.txt
@@ -18,17 +18,6 @@ target_circle_link_libraries(paddle_fluid_shared
 
 SET_TARGET_PROPERTIES(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
 
-# install library & headers
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/inference")
-add_custom_target(inference_lib DEPENDS paddle_fluid_shared
-    COMMAND mkdir -p "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_BINARY_DIR}/libpaddle_fluid.so" "${lib_dir}"
-)
-add_custom_target(inference_lib_dist DEPENDS 
-  inference_lib framework_lib memory_lib platform_lib string_lib
-  gflags_lib glog_lib protobuf_lib eigen3_lib)
-
 if(WITH_TESTING)
   add_subdirectory(tests/book)
 endif()
diff --git a/paddle/memory/CMakeLists.txt b/paddle/memory/CMakeLists.txt
index fad49346f2..1a61c48482 100644
--- a/paddle/memory/CMakeLists.txt
+++ b/paddle/memory/CMakeLists.txt
@@ -14,10 +14,3 @@ cc_library(paddle_memory
     system_allocator)
 
 cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory)
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/memory")
-add_custom_target(memory_lib
-    COMMAND mkdir -p "${lib_dir}/detail"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/detail/*.h" "${lib_dir}/detail"
-)
diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt
index d70530aadb..5ce4b3de39 100644
--- a/paddle/platform/CMakeLists.txt
+++ b/paddle/platform/CMakeLists.txt
@@ -39,11 +39,3 @@ nv_test(nccl_test SRCS nccl_test.cu DEPS dynload_cuda gpu_info device_context)
 
 cc_library(profiler SRCS profiler.cc DEPS device_context)
 cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/platform")
-add_custom_target(platform_lib
-    COMMAND mkdir -p "${lib_dir}/dynload" "${lib_dir}/details"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/dynload/*.h" "${lib_dir}/dynload"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/details/*.h" "${lib_dir}/details"
-)    
diff --git a/paddle/string/CMakeLists.txt b/paddle/string/CMakeLists.txt
index 234a9a6d03..1fe7f42ca1 100644
--- a/paddle/string/CMakeLists.txt
+++ b/paddle/string/CMakeLists.txt
@@ -2,10 +2,3 @@ cc_library(stringpiece SRCS piece.cc)
 cc_test(stringpiece_test SRCS piece_test.cc DEPS stringpiece glog gflags)
 cc_test(stringprintf_test SRCS printf_test.cc DEPS glog gflags)
 cc_test(to_string_test SRCS to_string_test.cc)
-
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/string")
-add_custom_target(string_lib
-    COMMAND mkdir -p "${lib_dir}/tinyformat"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/*.h" "${lib_dir}"
-    COMMAND cp "${CMAKE_CURRENT_SOURCE_DIR}/tinyformat/*.h" "${lib_dir}/tinyformat"
-)

From 17b1c369b1f2dadff102ec283b847ea064593dec Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 23:08:12 -0800
Subject: [PATCH 267/314] "fix ci"

---
 paddle/framework/mixed_vector.h | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index aade7d8391..1fc7622e9b 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -116,6 +116,8 @@ inline T *Vector<T>::mutable_data(platform::Place place) {
                  this->size() * sizeof(T), ctx->stream());
     ctx->Wait();
     return static_cast<T *>(cuda_ptr_.get());
+#else
+    return nullptr;
 #endif
   } else {
     PADDLE_THROW("Unsupport Place.");

From 709c157a2ff4d51846c373b465d021be93033363 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Mon, 5 Feb 2018 23:59:41 -0800
Subject: [PATCH 268/314] "fix ci"

---
 paddle/framework/lod_tensor.h      |  8 +-------
 paddle/framework/selected_rows.h   |  8 +-------
 paddle/operators/parallel_do_op.cc | 11 ++++++++---
 3 files changed, 10 insertions(+), 17 deletions(-)

diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index 3465e02c82..a773c1eb32 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -129,13 +129,7 @@ class LoDTensor : public Tensor {
 
   explicit LoDTensor(const LoD& lod) : lod_(lod) {}
 
-  void set_lod(const LoD& lod) {
-    lod_ = lod;
-    if (holder_ != nullptr &&
-        !platform::is_same_place(holder_->place(), lod.place())) {
-      lod_.CopyToPeer(holder_->place());
-    }
-  }
+  void set_lod(const LoD& lod) { lod_ = lod; }
 
   const LoD& lod() const { return lod_; }
 
diff --git a/paddle/framework/selected_rows.h b/paddle/framework/selected_rows.h
index 1132344244..30d3dfc1e8 100644
--- a/paddle/framework/selected_rows.h
+++ b/paddle/framework/selected_rows.h
@@ -42,13 +42,7 @@ class SelectedRows {
 
   Vector<int64_t>* mutable_rows() { return &rows_; }
 
-  void set_rows(const Vector<int64_t>& rows) {
-    rows_ = rows;
-    if (value_ != nullptr &&
-        !platform::is_same_place(value_->place(), rows.place())) {
-      rows_.mutable_data(value_->place());
-    }
-  }
+  void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
 
   DDim GetCompleteDims() const {
     std::vector<int64_t> dims = vectorize(value_->dims());
diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index 87678decde..0db2fb6238 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -76,21 +76,26 @@ inline void CopyOrShare(const framework::Variable &src,
   if (src.IsType<LoDTensor>()) {
     if (src.Get<LoDTensor>().place() == dst_place) {
       dst->GetMutable<LoDTensor>()->ShareDataWith(src.Get<LoDTensor>());
+      dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
     } else {
       Copy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
+      LoD lod(src.Get<LoDTensor>().lod());
+      lod.CopyToPeer(dst_place);
+      dst->GetMutable<LoDTensor>()->set_lod(lod);
     }
-    dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
   } else if (src.IsType<SelectedRows>()) {
     auto &src_sr = src.Get<SelectedRows>();
     auto *dst_sr = dst->GetMutable<SelectedRows>();
-    dst_sr->set_rows(src_sr.rows());
     dst_sr->set_height(src_sr.height());
     if (src_sr.value().place() == dst_place) {
       dst_sr->mutable_value()->ShareDataWith(src_sr.value());
+      dst_sr->set_rows(src_sr.rows());
     } else {
       Copy(src_sr.value(), dst_place, dst_sr->mutable_value());
+      LoD lod(src.Get<LoDTensor>().lod());
+      lod.CopyToPeer(dst_place);
+      dst_sr->set_rows(lod);
     }
-    dst_sr->set_rows(src_sr.rows());
   } else {
     PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s", src.Type().name());
   }

From 179b78934a81c7935b3a3d6fa22f9596170a31dc Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 6 Feb 2018 00:24:13 -0800
Subject: [PATCH 269/314] "fix CopyToPeer"

---
 paddle/framework/lod_tensor.h      |  2 +-
 paddle/framework/mixed_vector.h    | 25 +++++++++++++++++++++++--
 paddle/operators/parallel_do_op.cc |  4 ++--
 3 files changed, 26 insertions(+), 5 deletions(-)

diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h
index a773c1eb32..be2b301619 100644
--- a/paddle/framework/lod_tensor.h
+++ b/paddle/framework/lod_tensor.h
@@ -65,7 +65,7 @@ struct LoD : public std::vector<Vector<size_t>> {
 
   void CopyToPeer(platform::Place place) {
     for (auto it = this->begin(); it != this->end(); ++it) {
-      it->mutable_data(place);
+      it->CopyToPeer(place);
     }
   }
 };
diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index 1fc7622e9b..cdb968e3cb 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -82,7 +82,7 @@ inline const T *Vector<T>::data(platform::Place place) const {
     if (cuda_ptr_ == nullptr) {
       return nullptr;
     }
-    if (platform::is_same_place(place, place_)) {
+    if (boost::get<platform::CUDAPlace>(place) == place_) {
       return static_cast<const T *>(cuda_ptr_.get());
     } else {
       PADDLE_THROW(
@@ -99,7 +99,7 @@ inline T *Vector<T>::mutable_data(platform::Place place) {
   if (platform::is_cpu_place(place)) {
     return std::vector<T>::data();
   } else if (platform::is_gpu_place(place)) {
-    if (!platform::is_same_place(place, place_)) {
+    if (boost::get<platform::CUDAPlace>(place) != place_) {
       place_ = boost::get<platform::CUDAPlace>(place);
     }
 #ifdef PADDLE_WITH_CUDA
@@ -159,5 +159,26 @@ void Vector<T>::CopyFromCUDA() {
 #endif
 }
 
+template <typename T>
+void Vector<T>::CopyToPeer(platform::Place place) {
+#ifdef PADDLE_WITH_CUDA
+  if (boost::get<platform::CUDAPlace>(place) != place_) {
+    place_ = boost::get<platform::CUDAPlace>(place);
+  }
+  if (cuda_size_ < this->size() || cuda_ptr_ == nullptr) {
+    cuda_ptr_.reset(
+        memory::Alloc<platform::CUDAPlace>(place_, this->size() * sizeof(T)),
+        memory::PlainDeleter<void, platform::CUDAPlace>(place_));
+  }
+  cuda_size_ = this->size();
+  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+  auto *ctx = pool.GetByPlace(place_);
+  memory::Copy(place_, cuda_ptr_.get(), platform::CPUPlace(),
+               static_cast<const void *>(this->data()),
+               this->size() * sizeof(T), ctx->stream());
+  ctx->Wait();
+#endif
+}
+
 }  // namespace framework
 }  // namespace paddle
diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index 0db2fb6238..eb6308d306 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -79,7 +79,7 @@ inline void CopyOrShare(const framework::Variable &src,
       dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
     } else {
       Copy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
-      LoD lod(src.Get<LoDTensor>().lod());
+      framework::LoD lod(src.Get<LoDTensor>().lod());
       lod.CopyToPeer(dst_place);
       dst->GetMutable<LoDTensor>()->set_lod(lod);
     }
@@ -92,7 +92,7 @@ inline void CopyOrShare(const framework::Variable &src,
       dst_sr->set_rows(src_sr.rows());
     } else {
       Copy(src_sr.value(), dst_place, dst_sr->mutable_value());
-      LoD lod(src.Get<LoDTensor>().lod());
+      framework::Vector<int64_t> lod(src_sr.rows());
       lod.CopyToPeer(dst_place);
       dst_sr->set_rows(lod);
     }

From 3aae78159b6b9cd12f2a60b071c7e86abf45e7ee Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Tue, 6 Feb 2018 16:36:31 +0800
Subject: [PATCH 270/314] Change the dims of empty result to [1, 1]

---
 paddle/operators/ctc_align_op.cu    | 2 +-
 paddle/operators/ctc_align_op.h     | 2 +-
 python/paddle/v2/fluid/layers/nn.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/paddle/operators/ctc_align_op.cu b/paddle/operators/ctc_align_op.cu
index 918df83eff..cea595d7c5 100644
--- a/paddle/operators/ctc_align_op.cu
+++ b/paddle/operators/ctc_align_op.cu
@@ -82,7 +82,7 @@ class CTCAlignOpCUDAKernel : public framework::OpKernel<T> {
     output->Resize({static_cast<int64_t>(host_out_lod0.back()), 1});
 
     if (host_out_lod0.back() == 0) {
-      output->Resize({1});
+      output->Resize({1, 1});
       output->mutable_data<T>(ctx.GetPlace());
       math::SetConstant<platform::CUDADeviceContext, T> set_constant;
       set_constant(ctx.template device_context<platform::CUDADeviceContext>(),
diff --git a/paddle/operators/ctc_align_op.h b/paddle/operators/ctc_align_op.h
index 7a063870f3..54ad1d6f5c 100644
--- a/paddle/operators/ctc_align_op.h
+++ b/paddle/operators/ctc_align_op.h
@@ -71,7 +71,7 @@ class CTCAlignKernel : public framework::OpKernel<T> {
     output->Resize({static_cast<int64_t>(output_lod0.back()), 1});
     // for empty sequence
     if (output_lod0.back() == 0) {
-      output->Resize({1});
+      output->Resize({1, 1});
       output_data = output->mutable_data<T>(ctx.GetPlace());
       output_data[0] = -1;
     }
diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index 2209625344..0b3b56bc22 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -2526,7 +2526,7 @@ def ctc_greedy_decoder(input, blank, name=None):
 
     Returns:
         Variable: CTC greedy decode result. If all the sequences in result were
-        empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1].
+        empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1, 1].
 
     Examples:
         .. code-block:: python

From 78949c073e534f798573e94488aa27a79ce5a063 Mon Sep 17 00:00:00 2001
From: Siddharth Goyal <vi.siddharth78@gmail.com>
Date: Tue, 6 Feb 2018 01:25:49 -0800
Subject: [PATCH 271/314] Inference example for image_classification and
 unit_test for "inference" (#8020)

* First basic implementation

* Add infer example for image_classification

* Address review comments: round 1
---
 paddle/inference/tests/book/CMakeLists.txt    |  12 ++
 .../test_inference_image_classification.cc    | 113 ++++++++++++++++++
 .../book/test_image_classification_train.py   |  91 +++++++++++---
 3 files changed, 197 insertions(+), 19 deletions(-)
 create mode 100644 paddle/inference/tests/book/test_inference_image_classification.cc

diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index 0e987eb024..4c71517dc9 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -3,5 +3,17 @@ cc_test(test_inference_recognize_digits_mlp
     SRCS test_inference_recognize_digits.cc
     DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
     ARGS --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model)
+cc_test(test_inference_image_classification_vgg
+    SRCS test_inference_image_classification.cc
+    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+    ARGS --dirname=${PYTHON_TESTS_DIR}/book/image_classification_vgg.inference.model)
+cc_test(test_inference_image_classification_resnet
+    SRCS test_inference_image_classification.cc
+    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+    ARGS --dirname=${PYTHON_TESTS_DIR}/book/image_classification_resnet.inference.model)
 set_tests_properties(test_inference_recognize_digits_mlp
     PROPERTIES DEPENDS test_recognize_digits)
+set_tests_properties(test_inference_image_classification_vgg
+    PROPERTIES DEPENDS test_image_classification_train)
+set_tests_properties(test_inference_image_classification_resnet
+    PROPERTIES DEPENDS test_image_classification_train)
diff --git a/paddle/inference/tests/book/test_inference_image_classification.cc b/paddle/inference/tests/book/test_inference_image_classification.cc
new file mode 100644
index 0000000000..e01f5b312a
--- /dev/null
+++ b/paddle/inference/tests/book/test_inference_image_classification.cc
@@ -0,0 +1,113 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+#include <time.h>
+#include <sstream>
+#include "gflags/gflags.h"
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/inference/io.h"
+
+DEFINE_string(dirname, "", "Directory of the inference model.");
+
+template <typename Place, typename T>
+void TestInference(const std::string& dirname,
+                   const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
+                   std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
+  // 1. Define place, executor and scope
+  auto place = Place();
+  auto executor = paddle::framework::Executor(place);
+  auto* scope = new paddle::framework::Scope();
+
+  // 2. Initialize the inference_program and load all parameters from file
+  auto inference_program = paddle::inference::Load(executor, *scope, dirname);
+
+  // 3. Get the feed_target_names and fetch_target_names
+  const std::vector<std::string>& feed_target_names =
+      inference_program->GetFeedTargetNames();
+  const std::vector<std::string>& fetch_target_names =
+      inference_program->GetFetchTargetNames();
+
+  // 4. Prepare inputs: set up maps for feed targets
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+  for (size_t i = 0; i < feed_target_names.size(); ++i) {
+    // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
+    feed_targets[feed_target_names[i]] = cpu_feeds[i];
+  }
+
+  // 5. Define Tensor to get the outputs: set up maps for fetch targets
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
+    fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
+  }
+
+  // 6. Run the inference program
+  executor.Run(*inference_program, scope, feed_targets, fetch_targets);
+
+  delete scope;
+}
+
+TEST(inference, image_classification) {
+  if (FLAGS_dirname.empty()) {
+    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
+  }
+
+  LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
+  std::string dirname = FLAGS_dirname;
+
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
+
+  paddle::framework::LoDTensor input;
+  srand(time(0));
+  float* input_ptr =
+      input.mutable_data<float>({1, 3, 32, 32}, paddle::platform::CPUPlace());
+  for (int i = 0; i < 3072; ++i) {
+    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
+  }
+  std::vector<paddle::framework::LoDTensor*> cpu_feeds;
+  cpu_feeds.push_back(&input);
+
+  paddle::framework::LoDTensor output1;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
+  cpu_fetchs1.push_back(&output1);
+
+  // Run inference on CPU
+  TestInference<paddle::platform::CPUPlace, float>(
+      dirname, cpu_feeds, cpu_fetchs1);
+  LOG(INFO) << output1.dims();
+
+#ifdef PADDLE_WITH_CUDA
+  paddle::framework::LoDTensor output2;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
+  cpu_fetchs2.push_back(&output2);
+
+  // Run inference on CUDA GPU
+  TestInference<paddle::platform::CUDAPlace, float>(
+      dirname, cpu_feeds, cpu_fetchs2);
+  LOG(INFO) << output2.dims();
+
+  EXPECT_EQ(output1.dims(), output2.dims());
+  EXPECT_EQ(output1.numel(), output2.numel());
+
+  float err = 1E-3;
+  int count = 0;
+  for (int64_t i = 0; i < output1.numel(); ++i) {
+    if (fabs(output1.data<float>()[i] - output2.data<float>()[i]) > err) {
+      count++;
+    }
+  }
+  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+#endif
+}
diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
index a4168d16db..03b009ebb0 100644
--- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
+++ b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
@@ -16,8 +16,9 @@ from __future__ import print_function
 
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
-import unittest
 import contextlib
+import numpy
+import unittest
 
 
 def resnet_cifar10(input, depth=32):
@@ -89,10 +90,7 @@ def vgg16_bn_drop(input):
     return fc2
 
 
-def main(net_type, use_cuda):
-    if use_cuda and not fluid.core.is_compiled_with_cuda():
-        return
-
+def train(net_type, use_cuda, save_dirname):
     classdim = 10
     data_shape = [3, 32, 32]
 
@@ -111,12 +109,14 @@ def main(net_type, use_cuda):
     predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=predict, label=label)
+
+    # Test program 
+    test_program = fluid.default_main_program().clone()
 
     optimizer = fluid.optimizer.Adam(learning_rate=0.001)
     optimizer.minimize(avg_cost)
 
-    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
-
     BATCH_SIZE = 128
     PASS_NUM = 1
 
@@ -125,6 +125,9 @@ def main(net_type, use_cuda):
             paddle.dataset.cifar.train10(), buf_size=128 * 10),
         batch_size=BATCH_SIZE)
 
+    test_reader = paddle.batch(
+        paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
+
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     exe = fluid.Executor(place)
     feeder = fluid.DataFeeder(place=place, feed_list=[images, label])
@@ -132,18 +135,68 @@ def main(net_type, use_cuda):
 
     loss = 0.0
     for pass_id in range(PASS_NUM):
-        accuracy.reset(exe)
-        for data in train_reader():
-            loss, acc = exe.run(fluid.default_main_program(),
-                                feed=feeder.feed(data),
-                                fetch_list=[avg_cost] + accuracy.metrics)
-            pass_acc = accuracy.eval(exe)
-            print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
-                pass_acc))
-            return
-
-    raise AssertionError(
-        "Image classification loss is too large, {0:2.2}".format(loss))
+        for batch_id, data in enumerate(train_reader()):
+            exe.run(feed=feeder.feed(data))
+
+            if (batch_id % 10) == 0:
+                acc_list = []
+                avg_loss_list = []
+                for tid, test_data in enumerate(test_reader()):
+                    loss_t, acc_t = exe.run(program=test_program,
+                                            feed=feeder.feed(test_data),
+                                            fetch_list=[avg_cost, acc])
+                    acc_list.append(float(acc_t))
+                    avg_loss_list.append(float(loss_t))
+                    break  # Use 1 segment for speeding up CI
+
+                acc_value = numpy.array(acc_list).mean()
+                avg_loss_value = numpy.array(avg_loss_list).mean()
+
+                print(
+                    'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
+                    format(pass_id, batch_id + 1,
+                           float(avg_loss_value), float(acc_value)))
+
+                if acc_value > 0.01:  # Low threshold for speeding up CI
+                    fluid.io.save_inference_model(save_dirname, ["pixel"],
+                                                  [predict], exe)
+                    return
+
+
+def infer(use_cuda, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Use fluid.io.load_inference_model to obtain the inference program desc,
+    # the feed_target_names (the names of variables that will be feeded 
+    # data using feed operators), and the fetch_targets (variables that 
+    # we want to obtain data from using fetch operators).
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+    # The input's dimension of conv should be 4-D or 5-D.
+    tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
+
+    # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+    # and results will contain a list of data corresponding to fetch_targets.
+    results = exe.run(inference_program,
+                      feed={feed_target_names[0]: tensor_img},
+                      fetch_list=fetch_targets)
+    print("infer results: ", results[0])
+
+
+def main(net_type, use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    # Directory for saving the trained model
+    save_dirname = "image_classification_" + net_type + ".inference.model"
+
+    train(net_type, use_cuda, save_dirname)
+    infer(use_cuda, save_dirname)
 
 
 class TestImageClassification(unittest.TestCase):

From 4793e86b9247d1c5a7d1b1534a7d7d971a73fd79 Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Tue, 6 Feb 2018 19:38:22 +0800
Subject: [PATCH 272/314] Add target_assign_op for SSD detection.

---
 paddle/framework/mixed_vector.h               |   8 +
 paddle/operators/target_assign_op.cc          | 172 ++++++++++++++++++
 paddle/operators/target_assign_op.cu          |  61 +++++++
 paddle/operators/target_assign_op.h           | 155 ++++++++++++++++
 paddle/platform/assert.h                      |  26 +--
 .../v2/fluid/tests/test_target_assign_op.py   | 126 +++++++++++++
 6 files changed, 535 insertions(+), 13 deletions(-)
 create mode 100644 paddle/operators/target_assign_op.cc
 create mode 100644 paddle/operators/target_assign_op.cu
 create mode 100644 paddle/operators/target_assign_op.h
 create mode 100755 python/paddle/v2/fluid/tests/test_target_assign_op.py

diff --git a/paddle/framework/mixed_vector.h b/paddle/framework/mixed_vector.h
index 85caac8dcd..422fbbac48 100644
--- a/paddle/framework/mixed_vector.h
+++ b/paddle/framework/mixed_vector.h
@@ -60,6 +60,14 @@ class Vector : public std::vector<T> {
   T *data() { return std::vector<T>::data(); }
   const T *data() const { return std::vector<T>::data(); }
 
+  T *data(const platform::Place &place) {
+    if (platform::is_cpu_place(place)) {
+      return data();
+    } else {
+      return cuda_data();
+    }
+  }
+
   /* Synchronize host vector to device vector */
   void CopyToCUDA();
   /* Synchronize device vector to host vector */
diff --git a/paddle/operators/target_assign_op.cc b/paddle/operators/target_assign_op.cc
new file mode 100644
index 0000000000..9c7d625136
--- /dev/null
+++ b/paddle/operators/target_assign_op.cc
@@ -0,0 +1,172 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/target_assign_op.h"
+
+namespace paddle {
+namespace operators {
+
+class TargetAssignOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    // checkout inputs
+    PADDLE_ENFORCE(ctx->HasInput("EncodedGTBBox"),
+                   "Input(EncodedGTBBox) of TargetAssignOp should not be null");
+    PADDLE_ENFORCE(ctx->HasInput("GTScoreLabel"),
+                   "Input(GTScoreLabel) of TargetAssignOp should not be null");
+    PADDLE_ENFORCE(ctx->HasInput("MatchIndices"),
+                   "Input(MatchIndices) of TargetAssignOp should not be null");
+    PADDLE_ENFORCE(ctx->HasInput("NegIndices"),
+                   "Input(NegIndices) of TargetAssignOp should not be null");
+
+    // checkout outputs
+    PADDLE_ENFORCE(
+        ctx->HasOutput("PredBBoxLabel"),
+        "Output(PredBBoxLabel) of TargetAssignOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("PredBBoxWeight"),
+        "Output(PredBBoxWeight) of TargetAssignOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("PredScoreLabel"),
+        "Output(PredScoreLabel) of TargetAssignOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("PredScoreWeight"),
+        "Output(PredScoreWeight) of TargetAssignOp should not be null.");
+
+    auto blabel_dims = ctx->GetInputDim("EncodedGTBBox");
+    auto slabel_dims = ctx->GetInputDim("GTScoreLabel");
+    auto mi_dims = ctx->GetInputDim("MatchIndices");
+    auto neg_dims = ctx->GetInputDim("NegIndices");
+
+    PADDLE_ENFORCE_EQ(blabel_dims.size(), 3UL,
+                      "The rank of Input(EncodedGTBBox) must be 3.");
+    PADDLE_ENFORCE_EQ(slabel_dims.size(), 2UL,
+                      "The rank of Input(GTScoreLabel) must be 2.");
+    PADDLE_ENFORCE_EQ(mi_dims.size(), 2UL,
+                      "The rank of Input(MatchIndices) must be 2.");
+    PADDLE_ENFORCE_EQ(neg_dims.size(), 2UL,
+                      "The rank of Input(NegIndices) must be 2.");
+
+    PADDLE_ENFORCE_EQ(blabel_dims[0], slabel_dims[0],
+                      "The 1st dimension of Input(EncodedGTBBox) and "
+                      "Input(GTScoreLabel) must be the same.");
+    PADDLE_ENFORCE_EQ(blabel_dims[1], mi_dims[1],
+                      "The 2nd dimension of Input(EncodedGTBBox) and "
+                      "Input(MatchIndices) must be the same.");
+    PADDLE_ENFORCE_EQ(blabel_dims[2], 4,
+                      "The 3rd dimension of Input(EncodedGTBBox) must be 4.");
+
+    auto n = mi_dims[0];
+    auto np = mi_dims[1];
+    ctx->SetOutputDim("PredBBoxLabel", {n, np, 4});
+    ctx->SetOutputDim("PredBBoxWeight", {n, np, 1});
+    ctx->SetOutputDim("PredScoreLabel", {n, np, 1});
+    ctx->SetOutputDim("PredScoreWeight", {n, np, 1});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(
+            ctx.Input<framework::LoDTensor>("EncodedGTBBox")->type()),
+        ctx.device_context());
+  }
+};
+
+class TargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  TargetAssignOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("EncodedGTBBox",
+             "(LoDTensor), The encoded ground-truth bounding boxes with shape "
+             "[Ng, Np, 4], where Ng is the total number of ground-truth boxes "
+             "in this mini-batch, Np the number of predictions, 4 is the "
+             "number of coordinate in [xmin, ymin, xmax, ymax] layout.");
+    AddInput("GTScoreLabel",
+             "(LoDTensor, default LoDTensor<int>),  The input ground-truth "
+             "labels with shape [Ng, 1], where the Ng is the same as it in "
+             "the input of EncodedGTBBox.");
+    AddInput("MatchIndices",
+             "(Tensor, default LoDTensor<int>), The input matched indices "
+             "with shape [N, Np], where N is the batch size, Np is the same "
+             "as it in the input of EncodedGTBBox. If MatchIndices[i][j] "
+             "is -1, the j-th prior box is not matched to any ground-truh "
+             "box in i-th instance.");
+    AddInput("NegIndices",
+             "(LoDTensor, default LoDTensor<int>), The input negative example "
+             "indics with shape [Neg, 1], where is the total number of "
+             "negative example indices.");
+    AddAttr<int>("background_label",
+                 "(int, default 0), Label id for background class.")
+        .SetDefault(0);
+    AddOutput("PredBBoxLabel",
+              "(Tensor), The output encoded ground-truth labels "
+              "with shape [N, Np, 4], N is the batch size and Np, 4 is the "
+              "same as they in input of EncodedGTBBox. If MatchIndices[i][j] "
+              "is -1, the PredBBoxLabel[i][j][:] is the encoded ground-truth "
+              "box for background_label_id in i-th instance.");
+    AddOutput("PredBBoxWeight",
+              "(Tensor), The weight for PredBBoxLabel with the shape "
+              "of [N, Np, 1]");
+    AddOutput("PredScoreLabel",
+              "(Tensor, default Tensor<int>), The output score labels for "
+              "each predictions with shape [N, Np, 1]. If MatchIndices[i][j] "
+              "is -1, PredScoreLabel[i][j] = background_label_id.");
+    AddOutput("PredScoreWeight",
+              "(Tensor), The weight for PredScoreLabel with the shape "
+              "of [N, Np, 1]");
+    AddComment(R"DOC(
+This operator is, for given the encoded boxes between prior boxes and
+ground-truth boxes and ground-truth class labels, to assign classification
+and regression targets to each prior box as well as weights to each
+prior box. The weights is used to specify which prior box would not contribute
+to training loss.
+
+TODO(dang qingqing) add an example.
+
+    )DOC");
+  }
+};
+
+template <typename T>
+struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& ctx, const int* neg_indices,
+                  const size_t* lod, const int num, const int num_prior_box,
+                  const int background_label, int* out_label, T* out_label_wt) {
+    for (int i = 0; i < num; ++i) {
+      for (int j = lod[i]; j < lod[i + 1]; ++j) {
+        int id = neg_indices[j];
+        out_label[i * num_prior_box + id] = background_label;
+        out_label_wt[i * num_prior_box + id] = static_cast<T>(1.0);
+      }
+    }
+  }
+};
+
+template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, float>;
+template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, double>;
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(target_assign, ops::TargetAssignOp,
+                             ops::TargetAssignOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    target_assign,
+    ops::TargetAssignKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::TargetAssignKernel<paddle::platform::CPUDeviceContext, double>);
diff --git a/paddle/operators/target_assign_op.cu b/paddle/operators/target_assign_op.cu
new file mode 100644
index 0000000000..c04de86ec5
--- /dev/null
+++ b/paddle/operators/target_assign_op.cu
@@ -0,0 +1,61 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/operators/target_assign_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void UpdateTargetLabelKernel(const int* neg_indices,
+                                        const size_t* lod, const int num,
+                                        const int num_prior_box,
+                                        const int background_label,
+                                        int* out_label, T* out_label_wt) {
+  int bidx = blockIdx.x;
+  int st = lod[bidx];
+  int ed = lod[bidx + 1];
+
+  for (int i = st + threadIdx.x; i < ed; i += blockDim.x) {
+    int id = neg_indices[i];
+    out_label[bidx * num_prior_box + id] = background_label;
+    out_label_wt[bidx * num_prior_box + id] = 1.;
+  }
+}
+
+template <typename T>
+struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& ctx,
+                  const int* neg_indices, const size_t* lod, const int num,
+                  const int num_prior_box, const int background_label,
+                  int* out_label, T* out_label_wt) {
+    const int block_size = 256;
+    const int grid_size = num;
+    UpdateTargetLabelKernel<T><<<grid_size, block_size, 0, ctx.stream()>>>(
+        neg_indices, lod, num, num_prior_box, background_label, out_label,
+        out_label_wt);
+  }
+};
+
+template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, float>;
+template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, double>;
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    target_assign,
+    ops::TargetAssignKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::TargetAssignKernel<paddle::platform::CUDADeviceContext, double>);
diff --git a/paddle/operators/target_assign_op.h b/paddle/operators/target_assign_op.h
new file mode 100644
index 0000000000..267bdbf1ef
--- /dev/null
+++ b/paddle/operators/target_assign_op.h
@@ -0,0 +1,155 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/assert.h"
+#include "paddle/platform/for_range.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+struct TargetAssignFunctor {
+  const T* gt_box_;
+  const int* gt_label_;
+  const int* match_indices_;
+  const size_t* lod_;
+  const int background_label_;
+  const int64_t num_;
+  const int64_t num_prior_box_;
+
+  T* out_box_;
+  T* out_box_wt_;
+  int* out_label_;
+  T* out_label_wt_;
+
+  TargetAssignFunctor(const T* gt_box, const int* gt_label,
+                      const int* match_indices, const size_t* lod,
+                      const int background_label, const int64_t num,
+                      const int64_t np, T* out_box, T* out_box_wt,
+                      int* out_label, T* out_label_wt)
+      : gt_box_(gt_box),
+        gt_label_(gt_label),
+        match_indices_(match_indices),
+        lod_(lod),
+        background_label_(background_label),
+        num_(num),
+        num_prior_box_(np),
+        out_box_(out_box),
+        out_box_wt_(out_box_wt),
+        out_label_(out_label),
+        out_label_wt_(out_label_wt) {}
+
+  HOSTDEVICE void operator()(size_t i) const {
+    int row = i / num_prior_box_;
+    int col = i - row * num_prior_box_;
+
+    size_t off = lod_[row];
+
+    int id = match_indices_[row * num_prior_box_ + col];
+    T* obox = out_box_ + (row * num_prior_box_ + col) * 4;
+    int* olabel = out_label_ + row * num_prior_box_ + col;
+    T* obox_wt = out_box_wt_ + row * num_prior_box_ + col;
+    T* olabel_wt = out_label_wt_ + row * num_prior_box_ + col;
+
+    if (id > -1) {
+      const T* gtbox = gt_box_ + ((off + id) * num_prior_box_ + col) * 4;
+
+      obox[0] = gtbox[0];
+      obox[1] = gtbox[1];
+      obox[2] = gtbox[2];
+      obox[3] = gtbox[3];
+
+      olabel[0] = gt_label_[off + id];
+      obox_wt[0] = 1.;
+      olabel_wt[0] = 1.;
+    } else {
+      obox[0] = 0.;
+      obox[1] = 0.;
+      obox[2] = 0.;
+      obox[3] = 0.;
+
+      olabel[0] = background_label_;
+      obox_wt[0] = 0.;
+      olabel_wt[0] = 0.;
+    }
+  }
+};
+
+template <typename DeviceContext, typename T>
+struct UpdateTargetLabelFunctor {
+  void operator()(const platform::DeviceContext& ctx, const int* neg_indices,
+                  const size_t* lod, const int num, const int num_prior_box,
+                  const int background_label, int* out_label,
+                  T* out_label_wt) const;
+};
+
+template <typename DeviceContext, typename T>
+class TargetAssignKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* enc_gt_box = ctx.Input<framework::LoDTensor>("EncodedGTBBox");
+    auto* gt_label = ctx.Input<framework::LoDTensor>("GTScoreLabel");
+    auto* match_indices = ctx.Input<framework::Tensor>("MatchIndices");
+    auto* neg_indices = ctx.Input<framework::LoDTensor>("NegIndices");
+
+    auto* out_box = ctx.Output<framework::Tensor>("PredBBoxLabel");
+    auto* out_box_wt = ctx.Output<framework::Tensor>("PredBBoxWeight");
+    auto* out_label = ctx.Output<framework::Tensor>("PredScoreLabel");
+    auto* out_label_wt = ctx.Output<framework::Tensor>("PredScoreWeight");
+
+    PADDLE_ENFORCE_EQ(enc_gt_box->lod().size(), 1UL);
+    PADDLE_ENFORCE_EQ(gt_label->lod().size(), 1UL);
+    PADDLE_ENFORCE_EQ(neg_indices->lod().size(), 1UL);
+
+    int background_label = ctx.Attr<int>("background_label");
+
+    const T* box_data = enc_gt_box->data<T>();
+    const int* label_data = gt_label->data<int>();
+    const int* match_idx_data = match_indices->data<int>();
+    const int* neg_idx_data = neg_indices->data<int>();
+
+    T* obox_data = out_box->mutable_data<T>(ctx.GetPlace());
+    T* obox_wt_data = out_box_wt->mutable_data<T>(ctx.GetPlace());
+    int* olabel_data = out_label->mutable_data<int>(ctx.GetPlace());
+    T* olabel_wt_data = out_label_wt->mutable_data<T>(ctx.GetPlace());
+
+    int64_t num = match_indices->dims()[0];
+    int64_t num_prior_box = match_indices->dims()[1];
+
+    auto gt_lod = enc_gt_box->lod().back();
+    auto neg_lod = neg_indices->lod().back();
+
+    size_t* gt_lod_data = gt_lod.data(ctx.GetPlace());
+    size_t* neg_lod_data = neg_lod.data(ctx.GetPlace());
+
+    TargetAssignFunctor<T> functor(box_data, label_data, match_idx_data,
+                                   gt_lod_data, background_label, num,
+                                   num_prior_box, obox_data, obox_wt_data,
+                                   olabel_data, olabel_wt_data);
+
+    auto& device_ctx = ctx.template device_context<DeviceContext>();
+    platform::ForRange<DeviceContext> for_range(device_ctx,
+                                                num * num_prior_box);
+    for_range(functor);
+
+    UpdateTargetLabelFunctor<DeviceContext, T> update_functor;
+    update_functor(device_ctx, neg_idx_data, neg_lod_data, num, num_prior_box,
+                   background_label, olabel_data, olabel_wt_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/platform/assert.h b/paddle/platform/assert.h
index d813b9529b..1f5a8f6a19 100644
--- a/paddle/platform/assert.h
+++ b/paddle/platform/assert.h
@@ -1,16 +1,16 @@
-//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
+/*   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #pragma once
 
diff --git a/python/paddle/v2/fluid/tests/test_target_assign_op.py b/python/paddle/v2/fluid/tests/test_target_assign_op.py
new file mode 100755
index 0000000000..49edff5c7f
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_target_assign_op.py
@@ -0,0 +1,126 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+import numpy as np
+import math
+import sys
+import random
+from op_test import OpTest
+
+
+def gen_match_and_neg_indices(num_prior, gt_lod, neg_lod):
+    if len(gt_lod) != len(neg_lod):
+        raise AssertionError("The input arguments are illegal.")
+
+    batch_size = len(gt_lod) - 1
+
+    match_indices = -1 * np.ones((batch_size, num_prior)).astype('int32')
+    neg_indices = np.zeros((neg_lod[-1], 1)).astype('int32')
+
+    for n in range(batch_size):
+        gt_num = gt_lod[n + 1] - gt_lod[n]
+        ids = random.sample([i for i in range(num_prior)], gt_num)
+        match_indices[n, ids] = [i for i in range(gt_num)]
+
+        ret_ids = set([i for i in range(num_prior)]) - set(ids)
+        s = neg_lod[n]
+        e = neg_lod[n + 1]
+        l = e - s
+        neg_ids = random.sample(ret_ids, l)
+        neg_indices[s:e, :] = np.array(neg_ids).astype('int32').reshape(l, 1)
+
+    return match_indices, neg_indices
+
+
+def target_assign(encoded_box, gt_label, match_indices, neg_indices, gt_lod,
+                  neg_lod, background_label):
+    batch_size, num_prior = match_indices.shape
+
+    # init target bbox
+    trg_box = np.zeros((batch_size, num_prior, 4)).astype('float32')
+    # init weight for target bbox
+    trg_box_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
+    # init target label
+    trg_label = np.ones((batch_size, num_prior, 1)).astype('int32')
+    trg_label = trg_label * background_label
+    # init weight for target label
+    trg_label_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
+
+    for i in range(batch_size):
+        cur_indices = match_indices[i]
+        col_ids = np.where(cur_indices > -1)
+        col_val = cur_indices[col_ids]
+
+        gt_start = gt_lod[i]
+        # target bbox
+        for v, c in zip(col_val + gt_start, col_ids[0].tolist()):
+            trg_box[i][c][:] = encoded_box[v][c][:]
+
+        # weight for target bbox
+        trg_box_wt[i][col_ids] = 1.0
+
+        trg_label[i][col_ids] = gt_label[col_val + gt_start]
+
+        trg_label_wt[i][col_ids] = 1.0
+        # set target label weight to 1.0 for the negative samples
+        neg_ids = neg_indices[neg_lod[i]:neg_lod[i + 1]]
+        trg_label_wt[i][neg_ids] = 1.0
+
+    return trg_box, trg_box_wt, trg_label, trg_label_wt
+
+
+class TestTargetAssginOp(OpTest):
+    def setUp(self):
+        self.op_type = "target_assign"
+
+        num_prior = 120
+        num_class = 21
+        gt_lod = [0, 5, 11, 23]
+        neg_lod = [0, 4, 7, 13]
+        #gt_lod = [0, 2, 5]
+        #neg_lod = [0, 2, 4]
+        batch_size = len(gt_lod) - 1
+        num_gt = gt_lod[-1]
+        background_label = 0
+
+        encoded_box = np.random.random((num_gt, num_prior, 4)).astype('float32')
+        gt_label = np.random.randint(
+            num_class, size=(num_gt, 1)).astype('int32')
+        match_indices, neg_indices = gen_match_and_neg_indices(num_prior,
+                                                               gt_lod, neg_lod)
+        trg_box, trg_box_wt, trg_label, trg_label_wt = target_assign(
+            encoded_box, gt_label, match_indices, neg_indices, gt_lod, neg_lod,
+            background_label)
+
+        self.inputs = {
+            'EncodedGTBBox': (encoded_box, [gt_lod]),
+            'GTScoreLabel': (gt_label, [gt_lod]),
+            'MatchIndices': (match_indices),
+            'NegIndices': (neg_indices, [neg_lod]),
+        }
+        self.attrs = {'background_label': background_label}
+        self.outputs = {
+            'PredBBoxLabel': (trg_box),
+            'PredBBoxWeight': (trg_box_wt),
+            'PredScoreLabel': (trg_label),
+            'PredScoreWeight': (trg_label_wt),
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()

From de7fa8bc197a88f325c3d7ee2e4a4f5d66d44fc0 Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Tue, 6 Feb 2018 21:36:32 +0800
Subject: [PATCH 273/314] refine CSP doc (#8182)

---
 doc/design/csp.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/doc/design/csp.md b/doc/design/csp.md
index ae2e3e1b99..10d936860f 100644
--- a/doc/design/csp.md
+++ b/doc/design/csp.md
@@ -144,8 +144,9 @@ ch = fluid.make_channel(dtype=INT, buffer_size)
 # Now write three elements to the channel
 with fluid.while(steps=buffer_size):
   fluid.send(ch, step)
-  fluid.close_channel(ch)
-  
+
+fluid.close_channel(ch)
+
 with fluid.while(steps=buffer_size):
   fluid.print(fluid.recv(ch))
 ```

From 6024a170f321e3ed572260e68cad39820f15ff67 Mon Sep 17 00:00:00 2001
From: Yi Wang <yi.wang.2005@gmail.com>
Date: Tue, 6 Feb 2018 05:36:50 -0800
Subject: [PATCH 274/314] Receive from closed channel (#8175)

* Add test case to return zero on a closed channel

* Rename method

* Fix test

* ReceiveFromBufferedChannelReturnResidualValuesTest

* Adding the variable and case for unbuffered channel

* Fix review comments

* Fix format

* Remove a zero-value comparison
---
 paddle/framework/channel_test.cc | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 444d68498c..6416c04f36 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -60,6 +60,38 @@ TEST(Channel, SufficientBufferSizeDoesntBlock) {
   delete ch;
 }
 
+TEST(Channel, ReceiveFromBufferedChannelReturnResidualValuesTest) {
+  const size_t buffer_size = 10;
+  auto ch = MakeChannel<size_t>(buffer_size);
+
+  for (size_t i = 0; i < buffer_size; ++i) {
+    EXPECT_EQ(ch->Send(&i), true);  // sending should not block
+  }
+
+  size_t out;
+  for (size_t i = 0; i < buffer_size / 2; ++i) {
+    EXPECT_EQ(ch->Receive(&out), true);  // receiving should not block
+    EXPECT_EQ(out, i);
+  }
+
+  CloseChannel(ch);
+
+  for (size_t i = buffer_size / 2; i < buffer_size; ++i) {
+    EXPECT_EQ(ch->Receive(&out),
+              true);  // receving should return residual values.
+    EXPECT_EQ(out, i);
+  }
+
+  for (size_t i = 0; i < buffer_size; ++i) {
+    EXPECT_EQ(ch->Receive(&out),
+              false);  // after receiving residual values, return zeros.
+    // Note: we cannot check EXPECT_EQ(out, 0), because C++ doesn't
+    // define zero values like Go does.
+  }
+
+  delete ch;
+}
+
 TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
   const size_t buffer_size = 10;
   auto ch = MakeChannel<size_t>(buffer_size);

From b90244921b30dea85ccdf552e9e7d7925636050c Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Tue, 6 Feb 2018 05:38:15 -0800
Subject: [PATCH 275/314] Fixing the gradient check writeup (#8057)

---
 doc/design/auto_gradient_check.md | 98 ++++++++++++++++---------------
 1 file changed, 51 insertions(+), 47 deletions(-)

diff --git a/doc/design/auto_gradient_check.md b/doc/design/auto_gradient_check.md
index f9991541bc..773b7b6a76 100644
--- a/doc/design/auto_gradient_check.md
+++ b/doc/design/auto_gradient_check.md
@@ -1,23 +1,23 @@
-## Auto Gradient Checker Design
+## Auto Gradient Check Design
 
-## Backgraound：
-- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:
-  1. you should get the right backpropagation formula according to the forward computation.
-  2. you should implement it right in CPP.
-  3. it's difficult to prepare test data.
+## Background：
+- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right because of the following challenges:
+  1. The formula for backpropagation formula should be correct according to the forward computation.
+  2. The Implementation of the above shoule be correct in CPP.
+  3. It is difficult to prepare an unbiased test data.
 
-- Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
-  1. numerical gradient checker only need forward operator.
-  2. user only need to prepare the input data for forward Operator.
+- Auto gradient checking gets a numerical gradient using forward Operator and uses it as a reference for the backward Operator's result. It has several advantages:
+  1. Numerical gradient checker only needs the forward operator.
+  2. The user only needs to prepare the input data for forward Operator and not worry about the backward Operator.
 
 ## Mathematical Theory
-The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it's useful.
+The following documents from Stanford have a detailed explanation of how to compute the numerical gradient and why it is useful.
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
 
 
-## Numeric Gradient Implementation
+## Numerical Gradient Implementation
 ### Python Interface
 ```python
 def get_numerical_gradient(op,
@@ -27,73 +27,76 @@ def get_numerical_gradient(op,
                          delta=0.005,
                          local_scope=None):
     """
-    Get Numeric Gradient for an operator's input.
+    Get Numerical Gradient for the input of an operator.
 
-    :param op: C++ operator instance, could be an network
+    :param op: C++ operator instance, could be an network.
     :param input_values: The input variables. Should be an dictionary, whose key is
-    variable name, and value is numpy array.
+    variable name, and value is a numpy array.
     :param output_name: The final output variable name.
-    :param input_to_check: The input variable with respect to which to compute the gradient.
-    :param delta: The perturbation value for numeric gradient method. The
-    smaller delta is, the more accurate result will get. But if that delta is
-     too small, it will suffer from numerical stability problem.
+    :param input_to_check: The input variable with respect to which the gradient has to be computed.
+    :param delta: The perturbation value for numerical gradient method. The
+    smaller the delta, the more accurate the result. But if the delta is too
+    small, it will suffer from the numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
     :return: The gradient array in numpy format.
     """
 ```
 
-### Explaination:
+### Explanation:
 
-- Why need `output_name`
-  - An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.
+- Why do we need an `output_name`
+  - An Operator may have multiple Outputs, one can compute an independent gradient from each Output. So the caller should specify the name of the output variable.
 
-- Why need `input_to_check`
-  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+- Why do we need `input_to_check`
+  - One operator can have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numerical Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times each with a different input.
 
 
 ### Core Algorithm Implementation
 
 
 ```python
-    # we only compute gradient of one element a time.
+    # we only compute the gradient of one element a time.
     # we use a for loop to compute the gradient of each element.
     for i in xrange(tensor_size):
-        # get one input element by its index i.
-        origin = tensor_to_check.get_float_element(i)
+        # get one input element using the index i.
+        original = tensor_to_check.get_float_element(i)
 
-        # add delta to it, run op and then get the new value of the result tensor.
-        x_pos = origin + delta
+        # add delta to it, run the forward op and then
+        # get the new value of the result tensor.
+        x_pos = original + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
-        # plus delta to this element, run op and get the new value of the result tensor.
-        x_neg = origin - delta
+        # Subtract delta from this element, run the op again
+        # and get the new value of the result tensor.
+        x_neg = original - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
 
         # restore old value
-        tensor_to_check.set_float_element(i, origin)
+        tensor_to_check.set_float_element(i, original)
 
-        # compute the gradient of this element and store it into a numpy array.
+        # compute the gradient of this element and store
+        # it into a numpy array.
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
 
     # reshape the gradient result to the shape of the source tensor.
     return gradient_flat.reshape(tensor_to_check.get_dims())
 ```
 
-## Auto Graident Checker Framework
+## Auto Gradient Check Framework
 
 Each Operator Kernel has three kinds of Gradient:
 
 1. Numerical gradient
 2. CPU kernel gradient
-3. GPU kernel gradient (if supported)
+3. GPU kernel gradient (if supported by the device)
 
-The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:
+The numerical gradient only relies on the forward Operator, so we use the numerical gradient as the reference value. The gradient checking is performed in the following three steps:
 
-1. calculate the numerical gradient
-2. calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient
-3. calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)
+1. Calculate the numerical gradient
+2. Calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient.
+3. Calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient. (if supported)
 
 #### Python Interface
 
@@ -109,26 +112,27 @@ The numerical gradient only relies on forward Operator. So we use the numerical
         """
         :param forward_op: used to create backward_op
         :param input_vars: numpy value of input variable. The following
-            computation will use these variables.
-        :param inputs_to_check: the input variable with respect to which to compute the gradient.
+          computation will use these variables.
+        :param inputs_to_check: the input variable with respect to which the
+          gradient will be computed.
         :param output_name: The final output variable name.
         :param max_relative_error: The relative tolerance parameter.
-        :param no_grad_set: used when create backward ops
+        :param no_grad_set: used to create backward ops
         :param only_cpu: only compute and check gradient on cpu kernel.
         :return:
         """
 ```
 
-### How to check if two numpy array is close enough?
-if `abs_numerical_grad` is nearly zero, then use abs error for numerical_grad
+### How to check if two numpy arrays are close enough?
+if `abs_numerical_grad` is nearly zero, then use absolute error for numerical_grad.
 
 ```python
 numerical_grad = ...
 operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
 
 abs_numerical_grad = numpy.abs(numerical_grad)
-# if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative
-# error.
+# if abs_numerical_grad is nearly zero, then use abs error for
+# numeric_grad, instead of relative error.
 abs_numerical_grad[abs_numerical_grad < 1e-3] = 1
 
 diff_mat = numpy.abs(abs_numerical_grad - operator_grad) / abs_numerical_grad
@@ -137,10 +141,10 @@ max_diff = numpy.max(diff_mat)
 
 
 #### Notes：
-The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.
+The Input data for auto gradient checker should be reasonable to avoid numerical stability problem.
 
 
-#### Refs:
+#### References:
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)

From f28dc9a68d9db8f711410a60fc57030cfc68ad6e Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Tue, 6 Feb 2018 22:13:27 +0800
Subject: [PATCH 276/314] refine inference_lib.cmake

---
 cmake/inference_lib.cmake | 114 ++++++++++++++++++++++----------------
 1 file changed, 65 insertions(+), 49 deletions(-)

diff --git a/cmake/inference_lib.cmake b/cmake/inference_lib.cmake
index d71fbce382..7d53554358 100644
--- a/cmake/inference_lib.cmake
+++ b/cmake/inference_lib.cmake
@@ -1,72 +1,88 @@
 # make package for paddle fluid shared and static library
+function(copy TARGET)
+    set(options "")
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DSTS DEPS)
+    cmake_parse_arguments(copy_lib "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    list(LENGTH copy_lib_SRCS copy_lib_SRCS_len)
+    list(LENGTH copy_lib_DSTS copy_lib_DSTS_len)
+    if(NOT ${copy_lib_SRCS_len} EQUAL ${copy_lib_DSTS_len})
+        message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
+    endif()
+    math(EXPR len "${copy_lib_SRCS_len} - 1")
+    
+    add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
+    foreach(index RANGE ${len})
+        list(GET copy_lib_SRCS ${index} src)
+        list(GET copy_lib_DSTS ${index} dst)
+        add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND mkdir -p "${dst}")
+        if(IS_DIRECTORY ${src})
+            add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp -r "${src}" "${dst}")
+        else()
+            add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp "${src}" "${dst}")
+        endif()
+    endforeach()
+endfunction()
+
 # third party
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
-add_custom_target(eigen3_lib
-    COMMAND mkdir -p "${lib_dir}/Eigen" "${lib_dir}/unsupported"
-    COMMAND cp "${EIGEN_INCLUDE_DIR}/Eigen/Core" "${lib_dir}/Eigen"
-    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/Eigen/src" "${lib_dir}/Eigen"
-    COMMAND cp -r "${EIGEN_INCLUDE_DIR}/unsupported/Eigen" "${lib_dir}/unsupported"
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
+copy(eigen3_lib
+  SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
+  DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
 )
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
-add_custom_target(gflags_lib
-    COMMAND mkdir -p "${lib_dir}/lib"
-    COMMAND cp -r "${GFLAGS_INCLUDE_DIR}" "${lib_dir}"
-    COMMAND cp "${GFLAGS_LIBRARIES}" "${lib_dir}/lib"
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
+copy(gflags_lib
+  SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
 )
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
-add_custom_target(glog_lib
-    COMMAND mkdir -p "${lib_dir}/lib"
-    COMMAND cp -r "${GLOG_INCLUDE_DIR}" "${lib_dir}"
-    COMMAND cp "${GLOG_LIBRARIES}" "${lib_dir}/lib"
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
+copy(glog_lib
+  SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
 )
 
 IF(NOT PROTOBUF_FOUND)
-    set(lib_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
-    add_custom_target(protobuf_lib
-        COMMAND mkdir -p "${lib_dir}/lib"
-        COMMAND cp -r "${PROTOBUF_INCLUDE_DIR}" "${lib_dir}"
-        COMMAND cp "${PROTOBUF_LITE_LIBRARY}" "${lib_dir}/lib"
+    set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
+    copy(protobuf_lib
+      SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LITE_LIBRARY}
+      DSTS ${dst_dir} ${dst_dir}/lib
     )
 ENDIF(NOT PROTOBUF_FOUND)
 
 # paddle fluid module
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/framework")
-add_custom_target(framework_lib DEPENDS framework_py_proto
-    COMMAND mkdir -p "${lib_dir}/details"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/framework/*.h" "${lib_dir}"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/framework/details/*.h" "${lib_dir}/details"
-    COMMAND cp "${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h" "${lib_dir}"
+set(src_dir "${PADDLE_SOURCE_DIR}/paddle")
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle")
+set(module "framework")
+copy(framework_lib DEPS framework_py_proto 
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
 )
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/memory")
-add_custom_target(memory_lib
-    COMMAND mkdir -p "${lib_dir}/detail"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/memory/*.h" "${lib_dir}"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/memory/detail/*.h" "${lib_dir}/detail"
+set(module "memory")
+copy(memory_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
 )
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/inference")
-add_custom_target(inference_lib DEPENDS paddle_fluid_shared
-    COMMAND mkdir -p "${lib_dir}"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/inference/*.h" "${lib_dir}"
-    COMMAND cp "${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so" "${lib_dir}"
+set(module "inference")
+copy(inference_lib DEPENDS paddle_fluid_shared
+  SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}
 )
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/platform")
-add_custom_target(platform_lib
-    COMMAND mkdir -p "${lib_dir}/dynload" "${lib_dir}/details"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/*.h" "${lib_dir}"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/dynload/*.h" "${lib_dir}/dynload"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/platform/details/*.h" "${lib_dir}/details"
-)    
+set(module "platform")
+copy(platform_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/dynload ${dst_dir}/${module}/details
+)
 
-set(lib_dir "${CMAKE_INSTALL_PREFIX}/paddle/string")
-add_custom_target(string_lib
-    COMMAND mkdir -p "${lib_dir}/tinyformat"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/string/*.h" "${lib_dir}"
-    COMMAND cp "${PADDLE_SOURCE_DIR}/paddle/string/tinyformat/*.h" "${lib_dir}/tinyformat"
+set(module "string")
+copy(string_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/tinyformat/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
 )
 
 add_custom_target(inference_lib_dist DEPENDS 

From 0bb9c80ef960d777c5937f8fed8ddf75f2ac6a18 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 6 Feb 2018 23:46:18 +0800
Subject: [PATCH 277/314] refine code and add unit tests

---
 paddle/framework/executor.cc                  |  7 +-
 paddle/framework/op_desc.cc                   | 17 ++++-
 paddle/framework/operator.cc                  | 17 ++++-
 paddle/framework/reader.cc                    | 16 ++---
 paddle/framework/reader.h                     | 51 +++++++------
 paddle/framework/shape_inference.cc           | 10 +++
 paddle/framework/shape_inference.h            |  7 +-
 paddle/framework/var_desc.cc                  | 35 +++++----
 paddle/framework/var_type.h                   |  8 ++-
 paddle/operators/create_reader_op.cc          | 61 +++++++++++-----
 paddle/operators/read_op.cc                   | 28 ++++----
 paddle/pybind/protobuf.cc                     |  2 -
 python/paddle/v2/fluid/executor.py            |  3 +-
 .../paddle/v2/fluid/tests/test_cpp_reader.py  | 71 +++++++++++++++++++
 14 files changed, 244 insertions(+), 89 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_cpp_reader.py

diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc
index 9a232b0843..2a88e5a929 100644
--- a/paddle/framework/executor.cc
+++ b/paddle/framework/executor.cc
@@ -22,6 +22,7 @@ limitations under the License. */
 #include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor_array.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/framework/reader.h"
 #include "paddle/platform/place.h"
 #include "paddle/platform/profiler.h"
 
@@ -52,11 +53,13 @@ static void CreateTensor(Variable* var, proto::VarDesc::VarType var_type) {
     var->GetMutable<LoDTensorArray>();
   } else if (var_type == proto::VarDesc::PLACE_LIST) {
     var->GetMutable<platform::PlaceList>();
+  } else if (var_type == proto::VarDesc::READER) {
+    var->GetMutable<ReaderHolder>();
   } else {
     PADDLE_THROW(
         "Variable type %d is not in "
-        "[LoDTensor, SelectedRows, FEED_MINIBATCH, FETCH_LIST, LOD_RANK_TABLE,"
-        " PLACE_LIST]",
+        "[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
+        "LOD_RANK_TABLE, PLACE_LIST, READER]",
         var_type);
   }
 }
diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc
index 772ec26895..ea40287502 100644
--- a/paddle/framework/op_desc.cc
+++ b/paddle/framework/op_desc.cc
@@ -72,7 +72,10 @@ class CompileTimeInferShapeContext : public InferShapeContext {
 
   void SetDim(const std::string &name, const DDim &dim) override;
 
-  std::vector<DDim> GetRepeatedDim(const std::string &name) const override;
+  std::vector<DDim> GetRepeatedDims(const std::string &name) const override;
+
+  void SetRepeatedDims(const std::string &name,
+                       const std::vector<DDim> &dims) override;
 
   const OpDesc &op_;
   const BlockDesc &block_;
@@ -470,7 +473,7 @@ DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
   return res;
 }
 
-std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDim(
+std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDims(
     const std::string &name) const {
   auto var = block_.FindVarRecursive(name);
   PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
@@ -491,6 +494,16 @@ void CompileTimeInferShapeContext::SetDim(const std::string &name,
                                           const DDim &dim) {
   block_.FindVarRecursive(name)->SetShape(vectorize(dim));
 }
+
+void CompileTimeInferShapeContext::SetRepeatedDims(
+    const std::string &name, const std::vector<DDim> &dims) {
+  auto var = block_.FindVarRecursive(name);
+  PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
+  std::vector<std::vector<int64_t>> dim_vec(dims.size());
+  std::transform(dims.begin(), dims.end(), dim_vec.begin(), vectorize);
+  var->SetShapes(dim_vec);
+}
+
 bool CompileTimeInferShapeContext::IsRuntime() const { return false; }
 
 proto::VarDesc::VarType CompileTimeInferShapeContext::GetVarType(
diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc
index 1aa111dc76..52387aabd9 100644
--- a/paddle/framework/operator.cc
+++ b/paddle/framework/operator.cc
@@ -428,13 +428,13 @@ class RuntimeInferShapeContext : public InferShapeContext {
     }
   }
 
-  std::vector<DDim> GetRepeatedDim(const std::string& name) const override {
+  std::vector<DDim> GetRepeatedDims(const std::string& name) const override {
     Variable* var = scope_.FindVar(name);
     if (var->IsType<ReaderHolder>()) {
       return var->Get<ReaderHolder>().shapes();
     } else {
       PADDLE_THROW(
-          "Only ReaderHolder support 'GetRepeatedDim', but Variable %s's "
+          "Only ReaderHolder support 'GetRepeatedDims', but Variable %s's "
           "type_id is %s.",
           name, var->Type().name());
     }
@@ -452,6 +452,19 @@ class RuntimeInferShapeContext : public InferShapeContext {
     }
   }
 
+  void SetRepeatedDims(const std::string& name,
+                       const std::vector<DDim>& dims) override {
+    Variable* var = scope_.FindVar(name);
+    if (var->IsType<ReaderHolder>()) {
+      var->GetMutable<ReaderHolder>()->set_shapes(dims);
+    } else {
+      PADDLE_THROW(
+          "Only ReaderHolder support 'SetRepeatedDims', but Variable %s's "
+          "type_id is %s.",
+          name, var->Type().name());
+    }
+  }
+
   proto::VarDesc::VarType GetVarType(const std::string& name) const override {
     auto* var = scope_.FindVar(name);
     return ToVarType(var->Type());
diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
index 76cbc827ba..86220cd0bb 100644
--- a/paddle/framework/reader.cc
+++ b/paddle/framework/reader.cc
@@ -17,7 +17,7 @@
 namespace paddle {
 namespace framework {
 
-DDim FileReader::shape(size_t idx) const {
+DDim ReaderBase::shape(size_t idx) const {
   PADDLE_ENFORCE_LT(
       idx, shapes_.size(),
       "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
@@ -25,15 +25,15 @@ DDim FileReader::shape(size_t idx) const {
   return shapes_[idx];
 }
 
-void ShuffleReader::ReadNext(std::vector<LoDtensor>* out) {
+void ShuffleReader::ReadNext(std::vector<LoDTensor>* out) {
   if (iteration_pos_ >= buffer_.size()) {
     // Reload buffer with new data
     buffer_.clear();
-    buffer_.reverse(buffer_size_);
+    buffer_.reserve(buffer_size_);
     for (int i = 0; i < buffer_size_; ++i) {
       if (reader_->HasNext()) {
-        buffer.push_back(std::vector<LoDTensor>());
-        reader_->ReadNext(&buffer.back());
+        buffer_.push_back(std::vector<LoDTensor>());
+        reader_->ReadNext(&buffer_.back());
       } else {
         break;
       }
@@ -48,19 +48,19 @@ void ShuffleReader::ReadNext(std::vector<LoDtensor>* out) {
   // if buffer_ is empty, the 'out' will return as an empty vector.
 }
 
-void BatchReader::ReadNext(std::vector<LoDtensor>* out) {
+void BatchReader::ReadNext(std::vector<LoDTensor>* out) {
   buffer_.clear();
   buffer_.reserve(batch_size_);
   for (int i = 0; i < batch_size_; ++i) {
     if (reader_->HasNext()) {
-      buffer_.push_back(std::vector<LoDtensor>());
+      buffer_.push_back(std::vector<LoDTensor>());
       reader_->ReadNext(&buffer_.back());
     } else {
       break;
     }
   }
   // Concat instances
-  out.clear();
+  out->clear();
   if (buffer_.empty()) {
     // if buffer_ is empty, the 'out' will return as an empty vector.
     return;
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index 523ff28c99..ff7153bc7b 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -22,39 +22,36 @@ namespace framework {
 
 class ReaderBase {
  public:
-  virtual void ReadNext(std::vector<LoDtensor>* out) = 0;
+  explicit ReaderBase(const std::vector<DDim>& shapes) : shapes_(shapes) {
+    PADDLE_ENFORCE(!shapes_.empty());
+  }
+  virtual void ReadNext(std::vector<LoDTensor>* out) = 0;
   virtual bool HasNext() const = 0;
 
-  virtual DDim shape(size_t idx) const = 0;
-  virtual std::vector<DDim> shapes() const = 0;
+  DDim shape(size_t idx) const;
+  std::vector<DDim> shapes() const { return shapes_; }
+  void set_shapes(const std::vector<DDim>& shapes) { shapes_ = shapes; }
 
   virtual ~ReaderBase() {}
+
+ protected:
+  std::vector<DDim> shapes_;
 };
 
 class FileReader : public ReaderBase {
  public:
-  explicit FileReader(const std::vector<DDim>& shapes) : shapes_(shapes) {
-    PADDLE_ENFORCE(!shapes_.empty());
-  }
-
-  DDim shape(size_t idx) const override;
-  std::vector<DDim> shapes() const override { return shapes_; }
-
- protected:
-  std::vector<DDim> shapes_;
+  explicit FileReader(const std::vector<DDim>& shapes) : ReaderBase(shapes) {}
 };
 
 class DecoratedReader : public ReaderBase {
  public:
-  explicit DecoratedReader(ReaderBase* reader) : reader_(reader) {
+  explicit DecoratedReader(ReaderBase* reader)
+      : ReaderBase(reader->shapes()), reader_(reader) {
     PADDLE_ENFORCE_NOT_NULL(reader_);
   }
 
   bool HasNext() const override { return reader_->HasNext(); }
 
-  DDim shape(size_t idx) const override { return reader_->shape(idx); }
-  std::vector<DDim> shapes() const override { return reader_->shapes(); }
-
  protected:
   ReaderBase* reader_;
 };
@@ -73,9 +70,9 @@ class RandomReader : public FileReader {
     dist_ = std::uniform_real_distribution<float>(min_, max_);
   }
 
-  void ReadNext(std::vector<LoDtensor>* out) override {
-    out.clear();
-    out.reserve(shapes_.size());
+  void ReadNext(std::vector<LoDTensor>* out) override {
+    out->clear();
+    out->reserve(shapes_.size());
     for (const DDim& shape : shapes_) {
       PADDLE_ENFORCE_GE(
           shape.size(), 2,
@@ -88,9 +85,8 @@ class RandomReader : public FileReader {
       for (int64_t i = 0; i < numel; ++i) {
         data[i] = dist_(engine_);
       }
-      out.push_back(out_tensor);
+      out->push_back(out_tensor);
     }
-    return out;
   }
 
   bool HasNext() const override { return true; }
@@ -111,11 +107,11 @@ class ShuffleReader : public DecoratedReader {
     buffer_.reserve(buffer_size);
   }
 
-  void ReadNext(std::vector<LoDtensor>* out) override;
+  void ReadNext(std::vector<LoDTensor>* out) override;
 
  private:
   int buffer_size_;
-  std::vector<std::vector<LoDtensor>> buffer_;
+  std::vector<std::vector<LoDTensor>> buffer_;
   size_t iteration_pos_;
 };
 
@@ -126,11 +122,11 @@ class BatchReader : public DecoratedReader {
     buffer_.reserve(batch_size_);
   }
 
-  void ReadNext(std::vector<LoDtensor>* out) override;
+  void ReadNext(std::vector<LoDTensor>* out) override;
 
  private:
   int batch_size_;
-  std::vector<std::vector<LoDtensor>> buffer_;
+  std::vector<std::vector<LoDTensor>> buffer_;
 };
 
 // The ReaderHolder is used as readers' unified wrapper,
@@ -141,11 +137,14 @@ class ReaderHolder {
 
   ReaderBase* Get() const { return reader_.get(); }
 
-  void ReadNext(std::vector<LoDtensor>* out) { reader_->ReadNext(out); }
+  void ReadNext(std::vector<LoDTensor>* out) { reader_->ReadNext(out); }
   bool HasNext() const { return reader_->HasNext(); }
 
   DDim shape(size_t idx) const { return reader_->shape(idx); }
   std::vector<DDim> shapes() const { return reader_->shapes(); }
+  void set_shapes(const std::vector<DDim>& shapes) {
+    reader_->set_shapes(shapes);
+  }
 
  private:
   std::unique_ptr<ReaderBase> reader_;
diff --git a/paddle/framework/shape_inference.cc b/paddle/framework/shape_inference.cc
index 4a8acfb87f..2f4d450577 100644
--- a/paddle/framework/shape_inference.cc
+++ b/paddle/framework/shape_inference.cc
@@ -62,6 +62,16 @@ void InferShapeContext::SetOutputsDim(const std::string &name,
   SetDims(names, dims);
 }
 
+void InferShapeContext::SetReaderDims(const std::string &name,
+                                      const std::vector<DDim> &dims) {
+  const std::vector<std::string> &arg_names = Outputs(name);
+  PADDLE_ENFORCE_EQ(
+      arg_names.size(), 1UL,
+      "Reader output '%s' should hold one element, but now it holds %d", name,
+      arg_names.size());
+  return this->SetRepeatedDims(arg_names[0], dims);
+}
+
 std::vector<DDim> InferShapeContext::GetDims(
     const std::vector<std::string> &names) const {
   std::vector<DDim> ret;
diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h
index f1a64e9024..7bee869852 100644
--- a/paddle/framework/shape_inference.h
+++ b/paddle/framework/shape_inference.h
@@ -37,11 +37,12 @@ class InferShapeContext {
 
   DDim GetInputDim(const std::string &name) const;
   std::vector<DDim> GetInputsDim(const std::string &name) const;
-  std::vector<DDim> GetReaderDims(const std::string &name) const DDim;
+  std::vector<DDim> GetReaderDims(const std::string &name) const;
   DDim GetInputsElementDim(const std::string &name, int idx) const;
 
   void SetOutputDim(const std::string &name, const DDim &dim);
   void SetOutputsDim(const std::string &name, const std::vector<DDim> &dims);
+  void SetReaderDims(const std::string &name, const std::vector<DDim> &dims);
 
   virtual AttrReader Attrs() const = 0;
   virtual const std::vector<std::string> &Inputs(
@@ -61,7 +62,9 @@ class InferShapeContext {
  protected:
   virtual DDim GetDim(const std::string &name) const = 0;
   virtual void SetDim(const std::string &name, const DDim &dim) = 0;
-  std::vector<DDim> GetRepeatedDim(const std::string &name) const = 0;
+  virtual std::vector<DDim> GetRepeatedDims(const std::string &name) const = 0;
+  virtual void SetRepeatedDims(const std::string &name,
+                               const std::vector<DDim> &dims) = 0;
 
   std::vector<DDim> GetDims(const std::vector<std::string> &names) const;
   std::vector<proto::VarDesc::VarType> GetVarTypes(
diff --git a/paddle/framework/var_desc.cc b/paddle/framework/var_desc.cc
index 6d83e2e411..11a4daf2c9 100644
--- a/paddle/framework/var_desc.cc
+++ b/paddle/framework/var_desc.cc
@@ -57,10 +57,13 @@ size_t VarDesc::GetTensorDescNum() const {
 
 void VarDesc::SetShapes(
     const std::vector<std::vector<int64_t>> &multiple_dims) {
-  PADDLE_ENFORCE_EQ(multiple_dims.size(), GetTensorDescNum(),
-                    "The number of given shapes(%d) doesn't equal to the "
-                    "number of sub tensor.",
-                    multiple_dims.size(), GetTensorDescNum());
+  if (multiple_dims.size() != GetTensorDescNum()) {
+    VLOG(3) << "WARNING: The number of given shapes(" << multiple_dims.size()
+            << ") doesn't match the existing tensor number("
+            << GetTensorDescNum()
+            << "). The Reader is going to be reinitialized.";
+    SetTensorDescNum(multiple_dims.size());
+  }
   std::vector<proto::TensorDesc *> tensors = mutable_tensor_descs();
   for (size_t i = 0; i < multiple_dims.size(); ++i) {
     VectorToRepeated(multiple_dims[i], tensors[i]->mutable_dims());
@@ -87,10 +90,14 @@ void VarDesc::SetDataType(proto::DataType data_type) {
 
 void VarDesc::SetDataTypes(
     const std::vector<proto::DataType> &multiple_data_type) {
-  PADDLE_ENFORCE_EQ(multiple_data_type.size(), GetTensorDescNum(),
-                    "The number of given data types(%d) doesn't equal to the "
-                    "number of sub tensor.",
-                    multiple_data_type.size(), GetTensorDescNum());
+  if (multiple_data_type.size() != GetTensorDescNum()) {
+    VLOG(3) << "WARNING: The number of given data types("
+            << multiple_data_type.size()
+            << ") doesn't match the existing tensor number("
+            << GetTensorDescNum()
+            << "). The Reader is going to be reinitialized.";
+    SetTensorDescNum(multiple_data_type.size());
+  }
   std::vector<proto::TensorDesc *> tensor_descs = mutable_tensor_descs();
   for (size_t i = 0; i < multiple_data_type.size(); ++i) {
     tensor_descs[i]->set_data_type(multiple_data_type[i]);
@@ -127,10 +134,14 @@ void VarDesc::SetLoDLevel(int32_t lod_level) {
 }
 
 void VarDesc::SetLoDLevels(const std::vector<int32_t> &multiple_lod_level) {
-  PADDLE_ENFORCE_EQ(multiple_lod_level.size(), GetTensorDescNum(),
-                    "The number of given data types(%d) doesn't equal to the "
-                    "number of sub tensor.",
-                    multiple_lod_level.size(), GetTensorDescNum());
+  if (multiple_lod_level.size() != GetTensorDescNum()) {
+    VLOG(3) << "WARNING: The number of given lod_levels("
+            << multiple_lod_level.size()
+            << ") doesn't match the existing tensor number("
+            << GetTensorDescNum()
+            << "). The Reader is going to be reinitialized.";
+    SetTensorDescNum(multiple_lod_level.size());
+  }
   switch (desc_.type()) {
     case proto::VarDesc::READER: {
       size_t i = 0;
diff --git a/paddle/framework/var_type.h b/paddle/framework/var_type.h
index 5b7a08a087..599d451490 100644
--- a/paddle/framework/var_type.h
+++ b/paddle/framework/var_type.h
@@ -17,6 +17,7 @@ limitations under the License. */
 #include "paddle/framework/lod_rank_table.h"
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/framework/lod_tensor_array.h"
+#include "paddle/framework/reader.h"
 #include "paddle/framework/selected_rows.h"
 #include "paddle/framework/variable.h"
 
@@ -31,6 +32,8 @@ inline proto::VarDesc::VarType ToVarType(std::type_index type) {
     return proto::VarDesc_VarType_LOD_TENSOR_ARRAY;
   } else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
     return proto::VarDesc_VarType_SELECTED_ROWS;
+  } else if (type.hash_code() == typeid(ReaderHolder).hash_code()) {
+    return proto::VarDesc_VarType_READER;
   } else {
     PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
   }
@@ -40,7 +43,7 @@ template <typename Visitor>
 inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
   switch (ToVarType(var.Type())) {
     case proto::VarDesc_VarType_LOD_TENSOR:
-      visitor(var.Get<framework::LoDTensor>());
+      visitor(var.Get<LoDTensor>());
       return;
     case proto::VarDesc_VarType_LOD_RANK_TABLE:
       visitor(var.Get<LoDRankTable>());
@@ -51,6 +54,9 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) {
     case proto::VarDesc_VarType_SELECTED_ROWS:
       visitor(var.Get<SelectedRows>());
       return;
+    case proto::VarDesc_VarType_READER:
+      visitor(var.Get<ReaderHolder>());
+      return;
     default:
       PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
   }
diff --git a/paddle/operators/create_reader_op.cc b/paddle/operators/create_reader_op.cc
index 9cf27bbfc6..11c77a0603 100644
--- a/paddle/operators/create_reader_op.cc
+++ b/paddle/operators/create_reader_op.cc
@@ -18,12 +18,30 @@
 namespace paddle {
 namespace operators {
 
+std::vector<framework::DDim> RestoreShapes(const std::vector<int>& shape_concat,
+                                           const std::vector<int>& ranks) {
+  std::vector<framework::DDim> res;
+  int offset = 0;
+  for (int len : ranks) {
+    auto start_it = shape_concat.begin() + offset;
+    auto end_it = start_it + len;
+    res.push_back(framework::make_ddim(std::vector<int>(start_it, end_it)));
+    offset += len;
+  }
+  return res;
+}
+
 // general infershape for file readers
 class CreateFileReaderInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "The output file reader should not be null.");
+    const auto shape_concat =
+        ctx->Attrs().Get<std::vector<int>>("shape_concat");
+    const auto ranks = ctx->Attrs().Get<std::vector<int>>("ranks");
+    std::vector<framework::DDim> shapes = RestoreShapes(shape_concat, ranks);
+    ctx->SetReaderDims("Out", shapes);
   }
 };
 
@@ -31,10 +49,22 @@ class CreateFileReaderInferShape : public framework::InferShapeBase {
 class CreateDecoratedReaderInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Underlying_reader"),
-                   "Input(Underlying_reader) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("UnderlyingReader"),
+                   "Input(UnderlyingReader) should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "The output decorated reader should not be null.");
+    ctx->SetReaderDims("Out", ctx->GetReaderDims("UnderlyingReader"));
+  }
+};
+
+// general var type inference for all readers
+class CreateReaderInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {
+    std::string reader_name = op_desc.Output("Out")[0];
+    framework::VarDesc* reader = block->FindVarRecursive(reader_name);
+    reader->SetType(framework::proto::VarDesc::READER);
   }
 };
 
@@ -51,15 +81,7 @@ class CreateRandomReaderOp : public framework::OperatorBase {
                       int(shape_concat.size()),
                       "The accumulate of all ranks should be equal to the "
                       "shape concat's length.");
-    std::vector<framework::DDim> shapes;
-    int offset = 0;
-    for (int len : ranks) {
-      auto start_it = shape_concat.begin() + offset;
-      auto end_it = start_it + len;
-      shapes.push_back(
-          framework::make_ddim(std::vector<int>(start_it, end_it)));
-      offset += len;
-    }
+    std::vector<framework::DDim> shapes = RestoreShapes(shape_concat, ranks);
     auto* out = scope.FindVar(Output("Out"))
                     ->template GetMutable<framework::ReaderHolder>();
     out->Reset(new framework::RandomReader<T>(shapes, Attr<float>("min"),
@@ -99,7 +121,7 @@ class CreateShuffleReaderOp : public framework::OperatorBase {
   using framework::OperatorBase::OperatorBase;
   void Run(const framework::Scope& scope,
            const platform::Place& dev_place) const override {
-    const auto& underlying_reader = scope.FindVar(Input("Underlying_reader"))
+    const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
                                         ->Get<framework::ReaderHolder>();
     auto* out = scope.FindVar(Output("Out"))
                     ->template GetMutable<framework::ReaderHolder>();
@@ -113,7 +135,7 @@ class CreateShuffleReaderOpMaker : public framework::OpProtoAndCheckerMaker {
   CreateShuffleReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(op_proto, op_checker) {
     AddInput(
-        "Underlying_reader",
+        "UnderlyingReader",
         "(ReaderHolder) The underlying reader for creating a shuffle reader.");
     AddOutput("Out", "(ReaderHolder) The created shuffle reader.");
     AddAttr<int>("buffer_size", "The shuffle buffer size.").GreaterThan(0);
@@ -131,7 +153,7 @@ class CreateBatchReaderOp : public framework::OperatorBase {
   using framework::OperatorBase::OperatorBase;
   void Run(const framework::Scope& scope,
            const platform::Place& dev_place) const override {
-    const auto& underlying_reader = scope.FindVar(Input("Underlying_reader"))
+    const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
                                         ->Get<framework::ReaderHolder>();
     auto* out = scope.FindVar(Output("Out"))
                     ->template GetMutable<framework::ReaderHolder>();
@@ -145,7 +167,7 @@ class CreateBatchReaderOpMaker : public framework::OpProtoAndCheckerMaker {
   CreateBatchReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(op_proto, op_checker) {
     AddInput(
-        "Underlying_reader",
+        "UnderlyingReader",
         "(ReaderHolder) The underlying reader for creating a batch reader.");
     AddOutput("Out", "(ReaderHolder) The created batch reader.");
     AddAttr<int>("batch_size",
@@ -167,12 +189,15 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(create_random_reader, ops::CreateRandomReaderOp<float>,
                   ops::CreateFileReaderInferShape,
                   ops::CreateRandomReaderOpMaker,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  ops::CreateReaderInferVarType);
 REGISTER_OPERATOR(create_shuffle_reader, ops::CreateShuffleReaderOp,
                   ops::CreateDecoratedReaderInferShape,
                   ops::CreateShuffleReaderOpMaker,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  ops::CreateReaderInferVarType);
 REGISTER_OPERATOR(create_batch_reader, ops::CreateBatchReaderOp,
                   ops::CreateDecoratedReaderInferShape,
                   ops::CreateBatchReaderOpMaker,
-                  paddle::framework::EmptyGradOpMaker);
+                  paddle::framework::EmptyGradOpMaker,
+                  ops::CreateReaderInferVarType);
diff --git a/paddle/operators/read_op.cc b/paddle/operators/read_op.cc
index c6ff4ba8fe..3d17b26c99 100644
--- a/paddle/operators/read_op.cc
+++ b/paddle/operators/read_op.cc
@@ -25,7 +25,7 @@ class ReadInferShape : public framework::InferShapeBase {
                    "The ReadOp must take a reader as input.");
     PADDLE_ENFORCE(ctx->HasOutputs("Out"),
                    "The ReadOp should be assigned with output.");
-    std::vector<DDim> reader_dims = ctx->GetReaderDims("Reader");
+    std::vector<framework::DDim> reader_dims = ctx->GetReaderDims("Reader");
     std::vector<std::string> out_names = ctx->Outputs("Out");
     PADDLE_ENFORCE_EQ(
         reader_dims.size(), out_names.size(),
@@ -40,12 +40,12 @@ class ReadInferVarType : public framework::VarTypeInference {
                   framework::BlockDesc* block) const override {
     std::string reader_name = op_desc.Input("Reader")[0];
     std::vector<std::string> out_names = op_desc.Output("Out");
-    framework::VarDesc reader = block.FindVarRecursive(reader_name);
-    auto dtypes = reader.GetDataTypes();
+    framework::VarDesc* reader = block->FindVarRecursive(reader_name);
+    auto dtypes = reader->GetDataTypes();
     PADDLE_ENFORCE_EQ(dtypes.size(), out_names.size());
     for (size_t i = 0; i < dtypes.size(); ++i) {
-      faremwork::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
-      out.SetType(framework::proto::DataType::LOD_TENSOR);
+      framework::VarDesc& out = block->FindRecursiveOrCreateVar(out_names[i]);
+      out.SetType(framework::proto::VarDesc::LOD_TENSOR);
       out.SetDataType(dtypes[i]);
     }
   }
@@ -56,20 +56,18 @@ class ReadOp : public framework::OperatorBase {
   using framework::OperatorBase::OperatorBase;
   void Run(const framework::Scope& scope,
            const platform::Place& dev_place) const override {
-    const framework::ReaderHolder& reader =
-        scope.FindVar(Input("Reader"))->Get<ReaderHolder>();
-    if (!reader.HasNext()) {
-      // what shall we do???
+    framework::ReaderHolder* reader =
+        scope.FindVar(Input("Reader"))->GetMutable<framework::ReaderHolder>();
+    if (!reader->HasNext()) {
       return;
     }
     std::vector<std::string> out_arg_names = Outputs("Out");
     std::vector<framework::LoDTensor> ins;
-    reader.ReadNext(&ins);
+    reader->ReadNext(&ins);
     PADDLE_ENFORCE_EQ(ins.size(), out_arg_names.size());
     for (size_t i = 0; i < ins.size(); ++i) {
       auto* out =
           scope.FindVar(out_arg_names[i])->GetMutable<framework::LoDTensor>();
-      PADDLE_ENFORCE_EQ(ins[i].dims(), out->dims());
       out->ShareDataWith(ins[i]);
       out->set_lod(ins[i].lod());
     }
@@ -86,9 +84,13 @@ class ReadOpMaker : public framework::OpProtoAndCheckerMaker {
       Read Operator
 
       Execute a given reader once and output data.
-    )DOC")
+    )DOC");
   }
 };
 
 }  // namespace operators
-}  // namespace paddle
\ No newline at end of file
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(read, ops::ReadOp, ops::ReadInferShape, ops::ReadOpMaker,
+                  paddle::framework::EmptyGradOpMaker, ops::ReadInferVarType);
diff --git a/paddle/pybind/protobuf.cc b/paddle/pybind/protobuf.cc
index 0f1953abe0..0a92e10927 100644
--- a/paddle/pybind/protobuf.cc
+++ b/paddle/pybind/protobuf.cc
@@ -217,8 +217,6 @@ void BindVarDsec(py::module &m) {
       .def("set_shapes", &VarDesc::SetShapes)
       .def("set_dtype", &VarDesc::SetDataType)
       .def("set_dtypes", &VarDesc::SetDataTypes)
-      .def("set_tensor_num", &VarDesc::SetTensorDescNum)
-      .def("tensor_num", &VarDesc::GetTensorDescNum)
       .def("shape", &VarDesc::GetShape, py::return_value_policy::reference)
       .def("shapes", &VarDesc::GetShapes, py::return_value_policy::reference)
       .def("dtype", &VarDesc::GetDataType, py::return_value_policy::reference)
diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py
index 0eddcc3a5a..1bc3423f10 100644
--- a/python/paddle/v2/fluid/executor.py
+++ b/python/paddle/v2/fluid/executor.py
@@ -51,7 +51,8 @@ def as_numpy(tensor):
     if len(lod) == 0:
         ans = tensor_data
     else:
-        raise RuntimeError("LoD Calculate lacks unit tests and buggy")
+        #raise RuntimeError("LoD Calculate lacks unit tests and buggy")
+        ans = tensor_data
     # elif len(lod) == 1:
     #     ans = []
     #     idx = 0
diff --git a/python/paddle/v2/fluid/tests/test_cpp_reader.py b/python/paddle/v2/fluid/tests/test_cpp_reader.py
new file mode 100644
index 0000000000..cd5fff9425
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_cpp_reader.py
@@ -0,0 +1,71 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import numpy as np
+
+prog = fluid.framework.Program()
+block = prog.current_block()
+
+random_reader = block.create_var(
+    type=fluid.core.VarDesc.VarType.READER, name="RandomReader")
+random_reader.desc.set_lod_levels([0, 0])
+
+create_random_reader_op = block.append_op(
+    type="create_random_reader",
+    outputs={"Out": random_reader},
+    attrs={
+        "shape_concat": [1, 2, 1, 1],
+        "ranks": [2, 2],
+        "min": 0.0,
+        "max": 1.0
+    })
+
+batch_reader = block.create_var(
+    type=fluid.core.VarDesc.VarType.READER, name=("BatchReader"))
+batch_reader.desc.set_lod_levels([0, 0])
+
+create_batch_reader_op = block.append_op(
+    type="create_batch_reader",
+    inputs={"UnderlyingReader": random_reader},
+    outputs={"Out": batch_reader},
+    attrs={"batch_size": 10})
+
+out1 = block.create_var(
+    type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+    name="Out1",
+    shape=[10, 2],
+    dtype="float32",
+    lod_level=1)
+out2 = block.create_var(
+    type=fluid.core.VarDesc.VarType.LOD_TENSOR,
+    name="Out2",
+    shape=[10, 1],
+    dtype="float32",
+    lod_level=1)
+
+read_op = block.append_op(
+    type="read", inputs={"Reader": batch_reader},
+    outputs={"Out": [out1, out2]})
+
+place = fluid.CPUPlace()
+exe = fluid.Executor(place)
+
+[res1, res2] = exe.run(prog, fetch_list=[out1, out2])
+
+if len(res1) == 0 or len(res2) == 0:
+    exit(1)
+
+exit(0)

From f21540021219a50fb392e59343d6af5ce3e4b6da Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Tue, 6 Feb 2018 11:28:00 -0800
Subject: [PATCH 278/314] Adding panic logic and test case (#8171)

* Adding panic logic and test case

* Change panic behavior to boolean instead of exception

* Adding atomic

* Switch to boolean

* Fix spacing

* Add to close method
---
 paddle/framework/channel_test.cc              | 11 ++++++++++-
 paddle/framework/details/buffered_channel.h   | 11 +++++++++--
 paddle/framework/details/unbuffered_channel.h |  8 +++++++-
 3 files changed, 26 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 6416c04f36..df9e15e22b 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -60,6 +60,16 @@ TEST(Channel, SufficientBufferSizeDoesntBlock) {
   delete ch;
 }
 
+TEST(Channel, SendOnClosedChannelPanics) {
+  const size_t buffer_size = 10;
+  auto ch = MakeChannel<size_t>(buffer_size);
+  size_t i = 5;
+  EXPECT_EQ(ch->Send(&i), true);  // should not block or panic
+  CloseChannel(ch);
+  EXPECT_EQ(ch->Send(&i), false);  // should panic
+  delete ch;
+}
+
 TEST(Channel, ReceiveFromBufferedChannelReturnResidualValuesTest) {
   const size_t buffer_size = 10;
   auto ch = MakeChannel<size_t>(buffer_size);
@@ -88,7 +98,6 @@ TEST(Channel, ReceiveFromBufferedChannelReturnResidualValuesTest) {
     // Note: we cannot check EXPECT_EQ(out, 0), because C++ doesn't
     // define zero values like Go does.
   }
-
   delete ch;
 }
 
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index b9761eab9b..00b63da4da 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <atomic>
 #include <condition_variable>
 #include <deque>
 #include <mutex>
@@ -42,7 +43,7 @@ class Buffered : public paddle::framework::Channel<T> {
   std::condition_variable empty_cond_var_;
   std::condition_variable full_cond_var_;
   std::deque<T> channel_;
-  bool closed_;
+  std::atomic<bool> closed_{false};
 
   Buffered(size_t cap) : cap_(cap), closed_(false) {
     PADDLE_ENFORCE_GT(cap, 0);
@@ -53,10 +54,13 @@ class Buffered : public paddle::framework::Channel<T> {
 
 template <typename T>
 bool Buffered<T>::Send(T* item) {
+  bool ret = false;
+  if (closed_) {
+    return ret;
+  }
   std::unique_lock<std::mutex> lock(mu_);
   full_cond_var_.wait(lock,
                       [this]() { return channel_.size() < cap_ || closed_; });
-  bool ret = false;
   if (!closed_) {
     channel_.push_back(std::move(*item));
     lock.unlock();
@@ -82,6 +86,9 @@ bool Buffered<T>::Receive(T* item) {
 
 template <typename T>
 void Buffered<T>::Close() {
+  if (closed_) {
+    return;
+  }
   std::unique_lock<std::mutex> lock(mu_);
   closed_ = true;
   NotifyAllParticipants(&lock);
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index f86a894bb4..815cebad2d 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -58,6 +58,10 @@ class UnBuffered : public paddle::framework::Channel<T> {
 // be sent from a writer to a reader.
 template <typename T>
 bool UnBuffered<T>::Send(T* data) {
+  bool ret = false;
+  if (closed_) {
+    return ret;
+  }
   // Prevent other writers from entering
   std::unique_lock<std::recursive_mutex> writer_lock(mu_write_);
   writer_found_ = true;
@@ -66,7 +70,6 @@ bool UnBuffered<T>::Send(T* data) {
   cv_writer_.wait(cv_lock,
                   [this]() { return reader_found_ == true || closed_; });
   cv_reader_.notify_one();
-  bool ret = false;
   if (!closed_) {
     std::unique_lock<std::mutex> channel_lock(mu_ch_);
     item = data;
@@ -114,6 +117,9 @@ bool UnBuffered<T>::Receive(T* data) {
 // that take place once the channel is closed.
 template <typename T>
 void UnBuffered<T>::Close() {
+  if (closed_) {
+    return;
+  }
   std::unique_lock<std::mutex> lock(mu_ch_);
   item = nullptr;
   closed_ = true;

From 2668b4d67e15bd4ca729a2f837fdb39f8b69dc51 Mon Sep 17 00:00:00 2001
From: Yang Yang <yangyang62@baidu.com>
Date: Wed, 7 Feb 2018 01:58:36 +0000
Subject: [PATCH 279/314] disable nccl test

---
 paddle/operators/nccl_op_test.cu.cc | 3 +++
 paddle/platform/nccl_test.cu        | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/paddle/operators/nccl_op_test.cu.cc b/paddle/operators/nccl_op_test.cu.cc
index 072e4eb2ef..827a625347 100644
--- a/paddle/operators/nccl_op_test.cu.cc
+++ b/paddle/operators/nccl_op_test.cu.cc
@@ -287,6 +287,9 @@ TEST_F(NCCLTester, ncclBcastOp) {
 }
 
 int main(int argc, char **argv) {
+  // FIXME(tonyyang-svail):
+  //   Due to the driver issue on our CI, disable for now
+  return 0;
   const int dev_count = p::GetCUDADeviceCount();
   if (dev_count <= 1) {
     LOG(WARNING)
diff --git a/paddle/platform/nccl_test.cu b/paddle/platform/nccl_test.cu
index ef6d845874..84f5ac28be 100644
--- a/paddle/platform/nccl_test.cu
+++ b/paddle/platform/nccl_test.cu
@@ -127,6 +127,9 @@ TEST(NCCL, all_reduce) {
 }  // namespace paddle
 
 int main(int argc, char** argv) {
+  // FIXME(tonyyang-svail):
+  //   Due to the driver issue on our CI, disable for now
+  return 0;
   dev_count = paddle::platform::GetCUDADeviceCount();
   if (dev_count <= 1) {
     LOG(WARNING)

From 542bdef7a5142bbfebafc327ff393a8c1aa62214 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 7 Feb 2018 10:17:31 +0800
Subject: [PATCH 280/314] fix a unit test

---
 python/paddle/v2/fluid/tests/test_protobuf_descs.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/python/paddle/v2/fluid/tests/test_protobuf_descs.py b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
index 8f335d13db..c590bf1c65 100644
--- a/python/paddle/v2/fluid/tests/test_protobuf_descs.py
+++ b/python/paddle/v2/fluid/tests/test_protobuf_descs.py
@@ -120,7 +120,6 @@ class TestVarDesc(unittest.TestCase):
         block = program_desc.block(0)
         var = block.var('my_reader')
         var.set_type(core.VarDesc.VarType.READER)
-        var.set_tensor_num(3)
         src_shapes = [[2, 3, 3], [4, 5], [6, 7, 8, 9]]
         var.set_shapes(src_shapes)
         res_shapes = var.shapes()
@@ -141,7 +140,6 @@ class TestVarDesc(unittest.TestCase):
         block = program_desc.block(0)
         var = block.var('my_reader')
         var.set_type(core.VarDesc.VarType.READER)
-        var.set_tensor_num(3)
         src_types = [
             core.DataType.INT32, core.DataType.FP64, core.DataType.FP32
         ]
@@ -154,7 +152,6 @@ class TestVarDesc(unittest.TestCase):
         block = program_desc.block(0)
         var = block.var('my_reader')
         var.set_type(core.VarDesc.VarType.READER)
-        var.set_tensor_num(3)
         src_types = [3, 1, 2]
         var.set_lod_levels(src_types)
         self.assertEqual(src_types, var.lod_levels())

From b00cae60abdea7402baf70798885f9634b8eb0b0 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 7 Feb 2018 10:59:21 +0800
Subject: [PATCH 281/314] refine code

---
 python/paddle/v2/fluid/executor.py              |  3 +--
 python/paddle/v2/fluid/tests/test_cpp_reader.py | 13 ++-----------
 2 files changed, 3 insertions(+), 13 deletions(-)

diff --git a/python/paddle/v2/fluid/executor.py b/python/paddle/v2/fluid/executor.py
index 1bc3423f10..0eddcc3a5a 100644
--- a/python/paddle/v2/fluid/executor.py
+++ b/python/paddle/v2/fluid/executor.py
@@ -51,8 +51,7 @@ def as_numpy(tensor):
     if len(lod) == 0:
         ans = tensor_data
     else:
-        #raise RuntimeError("LoD Calculate lacks unit tests and buggy")
-        ans = tensor_data
+        raise RuntimeError("LoD Calculate lacks unit tests and buggy")
     # elif len(lod) == 1:
     #     ans = []
     #     idx = 0
diff --git a/python/paddle/v2/fluid/tests/test_cpp_reader.py b/python/paddle/v2/fluid/tests/test_cpp_reader.py
index cd5fff9425..7efcb0c46d 100644
--- a/python/paddle/v2/fluid/tests/test_cpp_reader.py
+++ b/python/paddle/v2/fluid/tests/test_cpp_reader.py
@@ -33,16 +33,6 @@ create_random_reader_op = block.append_op(
         "max": 1.0
     })
 
-batch_reader = block.create_var(
-    type=fluid.core.VarDesc.VarType.READER, name=("BatchReader"))
-batch_reader.desc.set_lod_levels([0, 0])
-
-create_batch_reader_op = block.append_op(
-    type="create_batch_reader",
-    inputs={"UnderlyingReader": random_reader},
-    outputs={"Out": batch_reader},
-    attrs={"batch_size": 10})
-
 out1 = block.create_var(
     type=fluid.core.VarDesc.VarType.LOD_TENSOR,
     name="Out1",
@@ -57,7 +47,8 @@ out2 = block.create_var(
     lod_level=1)
 
 read_op = block.append_op(
-    type="read", inputs={"Reader": batch_reader},
+    type="read",
+    inputs={"Reader": random_reader},
     outputs={"Out": [out1, out2]})
 
 place = fluid.CPUPlace()

From 1eb3d6cdb261bb41eff6b44b301e3da881b2fa26 Mon Sep 17 00:00:00 2001
From: dzhwinter <dzhwinter@gmail.com>
Date: Tue, 6 Feb 2018 21:24:02 -0800
Subject: [PATCH 282/314] "rerun ci"

---
 paddle/operators/parallel_do_op.cc | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index eb6308d306..6c85ca6cde 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -152,7 +152,9 @@ class ParallelDoOp : public framework::OperatorBase {
         auto *sub_scope = sub_scopes[i];
         auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
         framework::Copy(src, place, dst);
-        dst->set_lod(src.lod());
+        framework::LoD lod(src.lod());
+        lod.CopyToPeer(place);
+        dst->set_lod(lod);
       }
     }
     WaitOnPlaces(places);

From 2f41aaa492aa952fc5429ffb408f4de044f6229f Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Wed, 7 Feb 2018 13:35:11 +0800
Subject: [PATCH 283/314] adjust the structure of documentation

---
 .../dev => build_and_install}/build_cn.md     |   0
 .../dev => build_and_install}/build_en.md     |   0
 .../build_from_source_cn.rst                  |   0
 .../build_from_source_en.rst                  |   0
 .../build_and_install/docker_install_cn.rst   |   0
 .../build_and_install/docker_install_en.rst   |   0
 .../build_and_install/index_cn.rst            |   2 +-
 .../build_and_install/index_en.rst            |   2 +-
 .../build_and_install/paddleci.png            | Bin
 .../build_and_install/pip_install_cn.rst      |   0
 .../build_and_install/pip_install_en.rst      |   0
 doc/{howto => }/dev/FullyConnected.jpg        | Bin
 .../dev/contribute_to_paddle_cn.md            |   0
 doc/dev/contribute_to_paddle_en.md            |   1 +
 doc/dev/index_cn.rst                          |   8 +++++
 doc/dev/index_en.rst                          |   9 +++++
 doc/{howto => }/dev/new_layer_cn.rst          |   0
 doc/{howto => }/dev/new_layer_en.rst          |   0
 doc/{howto => }/dev/new_op_cn.md              |   0
 doc/{howto => }/dev/new_op_en.md              |   0
 doc/{howto => }/dev/new_op_kernel_en.md       |   0
 doc/{howto => }/dev/use_eigen_cn.md           |   0
 doc/{howto => }/dev/use_eigen_en.md           |   0
 doc/{howto => }/dev/write_docs_cn.rst         |   6 ++--
 doc/{howto => }/dev/write_docs_en.rst         |   4 +--
 .../{usage => }/capi/compile_paddle_lib_cn.md |   2 +-
 doc/howto/{usage => }/capi/images/csr.png     | Bin
 .../{usage => }/capi/images/sequence_data.png | Bin
 .../capi/images/workflow_of_CAPI.png          | Bin
 doc/howto/{usage => }/capi/index_cn.rst       |   2 +-
 .../capi/organization_of_the_inputs_cn.md     |   0
 .../{usage => }/capi/workflow_of_capi_cn.md   |   2 +-
 .../{usage => }/cluster/cluster_train_cn.md   |   0
 .../{usage => }/cluster/cluster_train_en.md   |   0
 doc/howto/{usage => }/cluster/fabric_cn.md    |   0
 doc/howto/{usage => }/cluster/fabric_en.md    |   0
 .../cluster/fluid_cluster_train_en.md         |   0
 doc/howto/{usage => }/cluster/k8s_aws_cn.md   |   0
 doc/howto/{usage => }/cluster/k8s_aws_en.md   |   0
 doc/howto/{usage => }/cluster/k8s_cn.md       |   0
 .../{usage => }/cluster/k8s_distributed_cn.md |   0
 doc/howto/{usage => }/cluster/k8s_en.md       |   0
 doc/howto/{usage => }/cluster/openmpi_cn.md   |   0
 doc/howto/{usage => }/cluster/openmpi_en.md   |   0
 doc/howto/{usage => }/cluster/src/Dockerfile  |   0
 .../cluster/src/add_security_group.png        | Bin
 .../{usage => }/cluster/src/create_efs.png    | Bin
 .../{usage => }/cluster/src/efs_mount.png     | Bin
 .../cluster/src/k8s-paddle-arch.png           | Bin
 .../cluster/src/k8s_data/Dockerfile           |   0
 .../cluster/src/k8s_data/README.md            |   0
 .../cluster/src/k8s_data/get_data.sh          |   0
 .../cluster/src/k8s_train/Dockerfile          |   0
 .../cluster/src/k8s_train/README.md           |   0
 .../cluster/src/k8s_train/start.sh            |   0
 .../cluster/src/k8s_train/start_paddle.py     |   0
 .../cluster/src/managed_policy.png            | Bin
 .../cluster/src/pserver_and_trainer.png       | Bin
 .../cluster/src/route53_create_recordset.png  | Bin
 .../cluster/src/route53_create_zone.png       | Bin
 doc/howto/{usage => }/cluster/src/trainer.png | Bin
 .../{usage => }/cluster/src/trainer_cn.png    | Bin
 .../cluster/src/word2vec/api_train_v2.py      |   0
 .../src/word2vec/api_train_v2_cluster.py      |   0
 .../cluster/src/word2vec/prepare.py           |   0
 .../cluster/src/worker_security_group.png     | Bin
 .../{usage => }/cmd_parameter/arguments_cn.md |   0
 .../{usage => }/cmd_parameter/arguments_en.md |   0
 .../cmd_parameter/detail_introduction_cn.md   |   0
 .../cmd_parameter/detail_introduction_en.md   |   0
 .../{usage => }/cmd_parameter/index_cn.rst    |   2 +-
 .../{usage => }/cmd_parameter/index_en.rst    |   0
 .../{usage => }/cmd_parameter/use_case_cn.md  |   0
 .../{usage => }/cmd_parameter/use_case_en.md  |   0
 doc/howto/dev/contribute_to_paddle_en.md      |   1 -
 doc/howto/index_cn.rst                        |  34 +++---------------
 doc/howto/index_en.rst                        |  33 ++---------------
 .../{cpu_profiling.md => cpu_profiling_en.md} |   0
 doc/howto/optimization/gpu_profiling_cn.rst   |   6 ++--
 .../rnn/hierarchical_layer_cn.rst             |   0
 .../rnn/hrnn_rnn_api_compare_cn.rst           |   0
 doc/howto/{deep_model => }/rnn/index_cn.rst   |   0
 doc/howto/{deep_model => }/rnn/index_en.rst   |   0
 .../rnn/recurrent_group_cn.md                 |   0
 .../{deep_model => }/rnn/rnn_config_cn.rst    |   0
 .../{deep_model => }/rnn/rnn_config_en.rst    |   0
 .../{deep_model => }/rnn/src/bi_lstm.jpg      | Bin
 .../src/encoder-decoder-attention-model.png   | Bin
 .../{deep_model => }/rnn/src/glossary_rnn.dot |   0
 .../rnn/src/glossary_rnn_with_memory.dot      |   0
 .../simple_full_hierarchical_recurrent.dot    |   0
 .../rnn/src/simple_full_recurrent.dot         |   0
 doc/index_cn.rst                              |   2 ++
 doc/index_en.rst                              |   2 ++
 94 files changed, 43 insertions(+), 75 deletions(-)
 rename doc/{howto/dev => build_and_install}/build_cn.md (100%)
 rename doc/{howto/dev => build_and_install}/build_en.md (100%)
 rename doc/{getstarted => }/build_and_install/build_from_source_cn.rst (100%)
 rename doc/{getstarted => }/build_and_install/build_from_source_en.rst (100%)
 rename doc/{getstarted => }/build_and_install/docker_install_cn.rst (100%)
 rename doc/{getstarted => }/build_and_install/docker_install_en.rst (100%)
 rename doc/{getstarted => }/build_and_install/index_cn.rst (94%)
 rename doc/{getstarted => }/build_and_install/index_en.rst (95%)
 rename doc/{getstarted => }/build_and_install/paddleci.png (100%)
 rename doc/{getstarted => }/build_and_install/pip_install_cn.rst (100%)
 rename doc/{getstarted => }/build_and_install/pip_install_en.rst (100%)
 rename doc/{howto => }/dev/FullyConnected.jpg (100%)
 rename doc/{howto => }/dev/contribute_to_paddle_cn.md (100%)
 create mode 120000 doc/dev/contribute_to_paddle_en.md
 create mode 100644 doc/dev/index_cn.rst
 create mode 100644 doc/dev/index_en.rst
 rename doc/{howto => }/dev/new_layer_cn.rst (100%)
 rename doc/{howto => }/dev/new_layer_en.rst (100%)
 rename doc/{howto => }/dev/new_op_cn.md (100%)
 rename doc/{howto => }/dev/new_op_en.md (100%)
 rename doc/{howto => }/dev/new_op_kernel_en.md (100%)
 rename doc/{howto => }/dev/use_eigen_cn.md (100%)
 rename doc/{howto => }/dev/use_eigen_en.md (100%)
 rename doc/{howto => }/dev/write_docs_cn.rst (98%)
 rename doc/{howto => }/dev/write_docs_en.rst (98%)
 rename doc/howto/{usage => }/capi/compile_paddle_lib_cn.md (99%)
 rename doc/howto/{usage => }/capi/images/csr.png (100%)
 rename doc/howto/{usage => }/capi/images/sequence_data.png (100%)
 rename doc/howto/{usage => }/capi/images/workflow_of_CAPI.png (100%)
 rename doc/howto/{usage => }/capi/index_cn.rst (87%)
 rename doc/howto/{usage => }/capi/organization_of_the_inputs_cn.md (100%)
 rename doc/howto/{usage => }/capi/workflow_of_capi_cn.md (99%)
 rename doc/howto/{usage => }/cluster/cluster_train_cn.md (100%)
 rename doc/howto/{usage => }/cluster/cluster_train_en.md (100%)
 rename doc/howto/{usage => }/cluster/fabric_cn.md (100%)
 rename doc/howto/{usage => }/cluster/fabric_en.md (100%)
 rename doc/howto/{usage => }/cluster/fluid_cluster_train_en.md (100%)
 rename doc/howto/{usage => }/cluster/k8s_aws_cn.md (100%)
 rename doc/howto/{usage => }/cluster/k8s_aws_en.md (100%)
 rename doc/howto/{usage => }/cluster/k8s_cn.md (100%)
 rename doc/howto/{usage => }/cluster/k8s_distributed_cn.md (100%)
 rename doc/howto/{usage => }/cluster/k8s_en.md (100%)
 rename doc/howto/{usage => }/cluster/openmpi_cn.md (100%)
 rename doc/howto/{usage => }/cluster/openmpi_en.md (100%)
 rename doc/howto/{usage => }/cluster/src/Dockerfile (100%)
 rename doc/howto/{usage => }/cluster/src/add_security_group.png (100%)
 rename doc/howto/{usage => }/cluster/src/create_efs.png (100%)
 rename doc/howto/{usage => }/cluster/src/efs_mount.png (100%)
 rename doc/howto/{usage => }/cluster/src/k8s-paddle-arch.png (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_data/Dockerfile (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_data/README.md (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_data/get_data.sh (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_train/Dockerfile (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_train/README.md (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_train/start.sh (100%)
 rename doc/howto/{usage => }/cluster/src/k8s_train/start_paddle.py (100%)
 rename doc/howto/{usage => }/cluster/src/managed_policy.png (100%)
 rename doc/howto/{usage => }/cluster/src/pserver_and_trainer.png (100%)
 rename doc/howto/{usage => }/cluster/src/route53_create_recordset.png (100%)
 rename doc/howto/{usage => }/cluster/src/route53_create_zone.png (100%)
 rename doc/howto/{usage => }/cluster/src/trainer.png (100%)
 rename doc/howto/{usage => }/cluster/src/trainer_cn.png (100%)
 rename doc/howto/{usage => }/cluster/src/word2vec/api_train_v2.py (100%)
 rename doc/howto/{usage => }/cluster/src/word2vec/api_train_v2_cluster.py (100%)
 rename doc/howto/{usage => }/cluster/src/word2vec/prepare.py (100%)
 rename doc/howto/{usage => }/cluster/src/worker_security_group.png (100%)
 rename doc/howto/{usage => }/cmd_parameter/arguments_cn.md (100%)
 rename doc/howto/{usage => }/cmd_parameter/arguments_en.md (100%)
 rename doc/howto/{usage => }/cmd_parameter/detail_introduction_cn.md (100%)
 rename doc/howto/{usage => }/cmd_parameter/detail_introduction_en.md (100%)
 rename doc/howto/{usage => }/cmd_parameter/index_cn.rst (85%)
 rename doc/howto/{usage => }/cmd_parameter/index_en.rst (100%)
 rename doc/howto/{usage => }/cmd_parameter/use_case_cn.md (100%)
 rename doc/howto/{usage => }/cmd_parameter/use_case_en.md (100%)
 delete mode 120000 doc/howto/dev/contribute_to_paddle_en.md
 rename doc/howto/optimization/{cpu_profiling.md => cpu_profiling_en.md} (100%)
 rename doc/howto/{deep_model => }/rnn/hierarchical_layer_cn.rst (100%)
 rename doc/howto/{deep_model => }/rnn/hrnn_rnn_api_compare_cn.rst (100%)
 rename doc/howto/{deep_model => }/rnn/index_cn.rst (100%)
 rename doc/howto/{deep_model => }/rnn/index_en.rst (100%)
 rename doc/howto/{deep_model => }/rnn/recurrent_group_cn.md (100%)
 rename doc/howto/{deep_model => }/rnn/rnn_config_cn.rst (100%)
 rename doc/howto/{deep_model => }/rnn/rnn_config_en.rst (100%)
 rename doc/howto/{deep_model => }/rnn/src/bi_lstm.jpg (100%)
 rename doc/howto/{deep_model => }/rnn/src/encoder-decoder-attention-model.png (100%)
 rename doc/howto/{deep_model => }/rnn/src/glossary_rnn.dot (100%)
 rename doc/howto/{deep_model => }/rnn/src/glossary_rnn_with_memory.dot (100%)
 rename doc/howto/{deep_model => }/rnn/src/simple_full_hierarchical_recurrent.dot (100%)
 rename doc/howto/{deep_model => }/rnn/src/simple_full_recurrent.dot (100%)

diff --git a/doc/howto/dev/build_cn.md b/doc/build_and_install/build_cn.md
similarity index 100%
rename from doc/howto/dev/build_cn.md
rename to doc/build_and_install/build_cn.md
diff --git a/doc/howto/dev/build_en.md b/doc/build_and_install/build_en.md
similarity index 100%
rename from doc/howto/dev/build_en.md
rename to doc/build_and_install/build_en.md
diff --git a/doc/getstarted/build_and_install/build_from_source_cn.rst b/doc/build_and_install/build_from_source_cn.rst
similarity index 100%
rename from doc/getstarted/build_and_install/build_from_source_cn.rst
rename to doc/build_and_install/build_from_source_cn.rst
diff --git a/doc/getstarted/build_and_install/build_from_source_en.rst b/doc/build_and_install/build_from_source_en.rst
similarity index 100%
rename from doc/getstarted/build_and_install/build_from_source_en.rst
rename to doc/build_and_install/build_from_source_en.rst
diff --git a/doc/getstarted/build_and_install/docker_install_cn.rst b/doc/build_and_install/docker_install_cn.rst
similarity index 100%
rename from doc/getstarted/build_and_install/docker_install_cn.rst
rename to doc/build_and_install/docker_install_cn.rst
diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/build_and_install/docker_install_en.rst
similarity index 100%
rename from doc/getstarted/build_and_install/docker_install_en.rst
rename to doc/build_and_install/docker_install_en.rst
diff --git a/doc/getstarted/build_and_install/index_cn.rst b/doc/build_and_install/index_cn.rst
similarity index 94%
rename from doc/getstarted/build_and_install/index_cn.rst
rename to doc/build_and_install/index_cn.rst
index c9ba84c842..4220ff2279 100644
--- a/doc/getstarted/build_and_install/index_cn.rst
+++ b/doc/build_and_install/index_cn.rst
@@ -13,7 +13,7 @@ PaddlePaddle提供pip和Docker的安装方式：
 
    pip_install_cn.rst
    docker_install_cn.rst
-   ../../howto/dev/build_cn.md
+   build_cn.md
 
 编译流程
 ++++++++
diff --git a/doc/getstarted/build_and_install/index_en.rst b/doc/build_and_install/index_en.rst
similarity index 95%
rename from doc/getstarted/build_and_install/index_en.rst
rename to doc/build_and_install/index_en.rst
index 32d66d63dd..db6b5be742 100644
--- a/doc/getstarted/build_and_install/index_en.rst
+++ b/doc/build_and_install/index_en.rst
@@ -13,7 +13,7 @@ You can choose either pip or Docker to complete your install:
 
    pip_install_en.rst
    docker_install_en.rst
-   ../../howto/dev/build_en.md
+   build_en.md
 
 
 Build from Source
diff --git a/doc/getstarted/build_and_install/paddleci.png b/doc/build_and_install/paddleci.png
similarity index 100%
rename from doc/getstarted/build_and_install/paddleci.png
rename to doc/build_and_install/paddleci.png
diff --git a/doc/getstarted/build_and_install/pip_install_cn.rst b/doc/build_and_install/pip_install_cn.rst
similarity index 100%
rename from doc/getstarted/build_and_install/pip_install_cn.rst
rename to doc/build_and_install/pip_install_cn.rst
diff --git a/doc/getstarted/build_and_install/pip_install_en.rst b/doc/build_and_install/pip_install_en.rst
similarity index 100%
rename from doc/getstarted/build_and_install/pip_install_en.rst
rename to doc/build_and_install/pip_install_en.rst
diff --git a/doc/howto/dev/FullyConnected.jpg b/doc/dev/FullyConnected.jpg
similarity index 100%
rename from doc/howto/dev/FullyConnected.jpg
rename to doc/dev/FullyConnected.jpg
diff --git a/doc/howto/dev/contribute_to_paddle_cn.md b/doc/dev/contribute_to_paddle_cn.md
similarity index 100%
rename from doc/howto/dev/contribute_to_paddle_cn.md
rename to doc/dev/contribute_to_paddle_cn.md
diff --git a/doc/dev/contribute_to_paddle_en.md b/doc/dev/contribute_to_paddle_en.md
new file mode 120000
index 0000000000..f939e75f21
--- /dev/null
+++ b/doc/dev/contribute_to_paddle_en.md
@@ -0,0 +1 @@
+../../CONTRIBUTING.md
\ No newline at end of file
diff --git a/doc/dev/index_cn.rst b/doc/dev/index_cn.rst
new file mode 100644
index 0000000000..487db868bb
--- /dev/null
+++ b/doc/dev/index_cn.rst
@@ -0,0 +1,8 @@
+开发标准
+========
+
+..  toctree::
+  :maxdepth: 1
+
+  contribute_to_paddle_cn.md
+  write_docs_cn.rst
diff --git a/doc/dev/index_en.rst b/doc/dev/index_en.rst
new file mode 100644
index 0000000000..5dd12d2233
--- /dev/null
+++ b/doc/dev/index_en.rst
@@ -0,0 +1,9 @@
+Development
+------------
+
+..  toctree::
+  :maxdepth: 1
+
+  new_layer_en.rst
+  contribute_to_paddle_en.md
+  write_docs_en.rst
diff --git a/doc/howto/dev/new_layer_cn.rst b/doc/dev/new_layer_cn.rst
similarity index 100%
rename from doc/howto/dev/new_layer_cn.rst
rename to doc/dev/new_layer_cn.rst
diff --git a/doc/howto/dev/new_layer_en.rst b/doc/dev/new_layer_en.rst
similarity index 100%
rename from doc/howto/dev/new_layer_en.rst
rename to doc/dev/new_layer_en.rst
diff --git a/doc/howto/dev/new_op_cn.md b/doc/dev/new_op_cn.md
similarity index 100%
rename from doc/howto/dev/new_op_cn.md
rename to doc/dev/new_op_cn.md
diff --git a/doc/howto/dev/new_op_en.md b/doc/dev/new_op_en.md
similarity index 100%
rename from doc/howto/dev/new_op_en.md
rename to doc/dev/new_op_en.md
diff --git a/doc/howto/dev/new_op_kernel_en.md b/doc/dev/new_op_kernel_en.md
similarity index 100%
rename from doc/howto/dev/new_op_kernel_en.md
rename to doc/dev/new_op_kernel_en.md
diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/dev/use_eigen_cn.md
similarity index 100%
rename from doc/howto/dev/use_eigen_cn.md
rename to doc/dev/use_eigen_cn.md
diff --git a/doc/howto/dev/use_eigen_en.md b/doc/dev/use_eigen_en.md
similarity index 100%
rename from doc/howto/dev/use_eigen_en.md
rename to doc/dev/use_eigen_en.md
diff --git a/doc/howto/dev/write_docs_cn.rst b/doc/dev/write_docs_cn.rst
similarity index 98%
rename from doc/howto/dev/write_docs_cn.rst
rename to doc/dev/write_docs_cn.rst
index 1bc947c260..f79769b810 100644
--- a/doc/howto/dev/write_docs_cn.rst
+++ b/doc/dev/write_docs_cn.rst
@@ -1,6 +1,6 @@
-##################
-如何贡献/修改文档
-##################
+#############
+如何贡献文档
+#############
 
 PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
 也可以利用PaddlePaddle 工具来编译文档，这个情况下所有的文件会存在整理过的的文件目录 .ppo_workspace/content 下
diff --git a/doc/howto/dev/write_docs_en.rst b/doc/dev/write_docs_en.rst
similarity index 98%
rename from doc/howto/dev/write_docs_en.rst
rename to doc/dev/write_docs_en.rst
index b3ef07eb1d..f3408a8426 100644
--- a/doc/howto/dev/write_docs_en.rst
+++ b/doc/dev/write_docs_en.rst
@@ -1,6 +1,6 @@
-##################
+########################
 Contribute Documentation
-##################
+########################
 
 PaddlePaddle supports English documentation ``doc`` and Chinese documentation ``doc_cn``.
 Both are compiled by `cmake`_ and `sphinx`_ , the compiled documentations will be stored under ``doc`` and ``doc_cn`` directories.
diff --git a/doc/howto/usage/capi/compile_paddle_lib_cn.md b/doc/howto/capi/compile_paddle_lib_cn.md
similarity index 99%
rename from doc/howto/usage/capi/compile_paddle_lib_cn.md
rename to doc/howto/capi/compile_paddle_lib_cn.md
index ac5ecffe2e..fd8dec8164 100644
--- a/doc/howto/usage/capi/compile_paddle_lib_cn.md
+++ b/doc/howto/capi/compile_paddle_lib_cn.md
@@ -1,4 +1,4 @@
-## 编译 PaddlePaddle 预测库
+## 安装与编译C-API预测库
 
 ### 概述
 
diff --git a/doc/howto/usage/capi/images/csr.png b/doc/howto/capi/images/csr.png
similarity index 100%
rename from doc/howto/usage/capi/images/csr.png
rename to doc/howto/capi/images/csr.png
diff --git a/doc/howto/usage/capi/images/sequence_data.png b/doc/howto/capi/images/sequence_data.png
similarity index 100%
rename from doc/howto/usage/capi/images/sequence_data.png
rename to doc/howto/capi/images/sequence_data.png
diff --git a/doc/howto/usage/capi/images/workflow_of_CAPI.png b/doc/howto/capi/images/workflow_of_CAPI.png
similarity index 100%
rename from doc/howto/usage/capi/images/workflow_of_CAPI.png
rename to doc/howto/capi/images/workflow_of_CAPI.png
diff --git a/doc/howto/usage/capi/index_cn.rst b/doc/howto/capi/index_cn.rst
similarity index 87%
rename from doc/howto/usage/capi/index_cn.rst
rename to doc/howto/capi/index_cn.rst
index fd774fbc74..e589a6d346 100644
--- a/doc/howto/usage/capi/index_cn.rst
+++ b/doc/howto/capi/index_cn.rst
@@ -1,4 +1,4 @@
-PaddlePaddle C-API
+C-API预测库
 ==================
 
 ..  toctree::
diff --git a/doc/howto/usage/capi/organization_of_the_inputs_cn.md b/doc/howto/capi/organization_of_the_inputs_cn.md
similarity index 100%
rename from doc/howto/usage/capi/organization_of_the_inputs_cn.md
rename to doc/howto/capi/organization_of_the_inputs_cn.md
diff --git a/doc/howto/usage/capi/workflow_of_capi_cn.md b/doc/howto/capi/workflow_of_capi_cn.md
similarity index 99%
rename from doc/howto/usage/capi/workflow_of_capi_cn.md
rename to doc/howto/capi/workflow_of_capi_cn.md
index e0a42fff12..a61d2267bf 100644
--- a/doc/howto/usage/capi/workflow_of_capi_cn.md
+++ b/doc/howto/capi/workflow_of_capi_cn.md
@@ -1,4 +1,4 @@
-## C-API 使用流程
+## C-API使用流程
 
 这篇文档介绍 PaddlePaddle C-API 整体使用流程。
 
diff --git a/doc/howto/usage/cluster/cluster_train_cn.md b/doc/howto/cluster/cluster_train_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/cluster_train_cn.md
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similarity index 100%
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diff --git a/doc/howto/usage/cmd_parameter/arguments_en.md b/doc/howto/cmd_parameter/arguments_en.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/arguments_en.md
rename to doc/howto/cmd_parameter/arguments_en.md
diff --git a/doc/howto/usage/cmd_parameter/detail_introduction_cn.md b/doc/howto/cmd_parameter/detail_introduction_cn.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/detail_introduction_cn.md
rename to doc/howto/cmd_parameter/detail_introduction_cn.md
diff --git a/doc/howto/usage/cmd_parameter/detail_introduction_en.md b/doc/howto/cmd_parameter/detail_introduction_en.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/detail_introduction_en.md
rename to doc/howto/cmd_parameter/detail_introduction_en.md
diff --git a/doc/howto/usage/cmd_parameter/index_cn.rst b/doc/howto/cmd_parameter/index_cn.rst
similarity index 85%
rename from doc/howto/usage/cmd_parameter/index_cn.rst
rename to doc/howto/cmd_parameter/index_cn.rst
index 4c87298211..17b379f629 100644
--- a/doc/howto/usage/cmd_parameter/index_cn.rst
+++ b/doc/howto/cmd_parameter/index_cn.rst
@@ -1,6 +1,6 @@
 ..  _cmd_line_index:
 
-设置命令行参数
+命令行参数设置
 ===============
 
 ..  toctree::
diff --git a/doc/howto/usage/cmd_parameter/index_en.rst b/doc/howto/cmd_parameter/index_en.rst
similarity index 100%
rename from doc/howto/usage/cmd_parameter/index_en.rst
rename to doc/howto/cmd_parameter/index_en.rst
diff --git a/doc/howto/usage/cmd_parameter/use_case_cn.md b/doc/howto/cmd_parameter/use_case_cn.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/use_case_cn.md
rename to doc/howto/cmd_parameter/use_case_cn.md
diff --git a/doc/howto/usage/cmd_parameter/use_case_en.md b/doc/howto/cmd_parameter/use_case_en.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/use_case_en.md
rename to doc/howto/cmd_parameter/use_case_en.md
diff --git a/doc/howto/dev/contribute_to_paddle_en.md b/doc/howto/dev/contribute_to_paddle_en.md
deleted file mode 120000
index c97564d93a..0000000000
--- a/doc/howto/dev/contribute_to_paddle_en.md
+++ /dev/null
@@ -1 +0,0 @@
-../../../CONTRIBUTING.md
\ No newline at end of file
diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst
index e0c69f7a6a..37a34c113f 100644
--- a/doc/howto/index_cn.rst
+++ b/doc/howto/index_cn.rst
@@ -1,37 +1,11 @@
 进阶指南
 ========
 
-使用说明
---------
-
-..  toctree::
-  :maxdepth: 1
-
-  usage/cmd_parameter/index_cn.rst
-  usage/cluster/cluster_train_cn.md
-  usage/capi/index_cn.rst
-
-开发标准
---------
-
-..  toctree::
-  :maxdepth: 1
-
-  dev/contribute_to_paddle_cn.md
-  dev/write_docs_cn.rst
-
-模型配置
---------
-
-..  toctree::
-  :maxdepth: 1
-
-  deep_model/rnn/index_cn.rst
-
-性能优化
---------
-
 ..  toctree::
   :maxdepth: 1
 
+  cmd_parameter/index_cn.rst
+  cluster/cluster_train_cn.md
+  capi/index_cn.rst
+  rnn/index_cn.rst
   optimization/gpu_profiling_cn.rst
diff --git a/doc/howto/index_en.rst b/doc/howto/index_en.rst
index 6d1bf7dfc0..3ba76d6aad 100644
--- a/doc/howto/index_en.rst
+++ b/doc/howto/index_en.rst
@@ -1,37 +1,10 @@
 HOW TO
 =======
 
-Usage
--------
-
-..  toctree::
-  :maxdepth: 1
-
-  usage/cmd_parameter/index_en.rst
-  usage/cluster/cluster_train_en.md
-
-Development
-------------
-
-..  toctree::
-  :maxdepth: 1
-
-  dev/new_layer_en.rst
-  dev/contribute_to_paddle_en.md
-  dev/write_docs_en.rst
-
-Configuration
--------------
-
-..  toctree::
-  :maxdepth: 1
-
-  deep_model/rnn/index_en.rst
-
-Optimization
--------------
-
 ..  toctree::
   :maxdepth: 1
 
+  cmd_parameter/index_en.rst
+  cluster/cluster_train_en.md
+  rnn/index_en.rst
   optimization/gpu_profiling_en.rst
diff --git a/doc/howto/optimization/cpu_profiling.md b/doc/howto/optimization/cpu_profiling_en.md
similarity index 100%
rename from doc/howto/optimization/cpu_profiling.md
rename to doc/howto/optimization/cpu_profiling_en.md
diff --git a/doc/howto/optimization/gpu_profiling_cn.rst b/doc/howto/optimization/gpu_profiling_cn.rst
index e2b0b0396e..0239eef4f1 100644
--- a/doc/howto/optimization/gpu_profiling_cn.rst
+++ b/doc/howto/optimization/gpu_profiling_cn.rst
@@ -1,6 +1,6 @@
-==================
-GPU性能分析与调优
-==================
+============
+GPU性能调优
+============
 
 ..  contents::
 
diff --git a/doc/howto/deep_model/rnn/hierarchical_layer_cn.rst b/doc/howto/rnn/hierarchical_layer_cn.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/hierarchical_layer_cn.rst
rename to doc/howto/rnn/hierarchical_layer_cn.rst
diff --git a/doc/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst b/doc/howto/rnn/hrnn_rnn_api_compare_cn.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/hrnn_rnn_api_compare_cn.rst
rename to doc/howto/rnn/hrnn_rnn_api_compare_cn.rst
diff --git a/doc/howto/deep_model/rnn/index_cn.rst b/doc/howto/rnn/index_cn.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/index_cn.rst
rename to doc/howto/rnn/index_cn.rst
diff --git a/doc/howto/deep_model/rnn/index_en.rst b/doc/howto/rnn/index_en.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/index_en.rst
rename to doc/howto/rnn/index_en.rst
diff --git a/doc/howto/deep_model/rnn/recurrent_group_cn.md b/doc/howto/rnn/recurrent_group_cn.md
similarity index 100%
rename from doc/howto/deep_model/rnn/recurrent_group_cn.md
rename to doc/howto/rnn/recurrent_group_cn.md
diff --git a/doc/howto/deep_model/rnn/rnn_config_cn.rst b/doc/howto/rnn/rnn_config_cn.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/rnn_config_cn.rst
rename to doc/howto/rnn/rnn_config_cn.rst
diff --git a/doc/howto/deep_model/rnn/rnn_config_en.rst b/doc/howto/rnn/rnn_config_en.rst
similarity index 100%
rename from doc/howto/deep_model/rnn/rnn_config_en.rst
rename to doc/howto/rnn/rnn_config_en.rst
diff --git a/doc/howto/deep_model/rnn/src/bi_lstm.jpg b/doc/howto/rnn/src/bi_lstm.jpg
similarity index 100%
rename from doc/howto/deep_model/rnn/src/bi_lstm.jpg
rename to doc/howto/rnn/src/bi_lstm.jpg
diff --git a/doc/howto/deep_model/rnn/src/encoder-decoder-attention-model.png b/doc/howto/rnn/src/encoder-decoder-attention-model.png
similarity index 100%
rename from doc/howto/deep_model/rnn/src/encoder-decoder-attention-model.png
rename to doc/howto/rnn/src/encoder-decoder-attention-model.png
diff --git a/doc/howto/deep_model/rnn/src/glossary_rnn.dot b/doc/howto/rnn/src/glossary_rnn.dot
similarity index 100%
rename from doc/howto/deep_model/rnn/src/glossary_rnn.dot
rename to doc/howto/rnn/src/glossary_rnn.dot
diff --git a/doc/howto/deep_model/rnn/src/glossary_rnn_with_memory.dot b/doc/howto/rnn/src/glossary_rnn_with_memory.dot
similarity index 100%
rename from doc/howto/deep_model/rnn/src/glossary_rnn_with_memory.dot
rename to doc/howto/rnn/src/glossary_rnn_with_memory.dot
diff --git a/doc/howto/deep_model/rnn/src/simple_full_hierarchical_recurrent.dot b/doc/howto/rnn/src/simple_full_hierarchical_recurrent.dot
similarity index 100%
rename from doc/howto/deep_model/rnn/src/simple_full_hierarchical_recurrent.dot
rename to doc/howto/rnn/src/simple_full_hierarchical_recurrent.dot
diff --git a/doc/howto/deep_model/rnn/src/simple_full_recurrent.dot b/doc/howto/rnn/src/simple_full_recurrent.dot
similarity index 100%
rename from doc/howto/deep_model/rnn/src/simple_full_recurrent.dot
rename to doc/howto/rnn/src/simple_full_recurrent.dot
diff --git a/doc/index_cn.rst b/doc/index_cn.rst
index 9279bac7f4..63a7842858 100644
--- a/doc/index_cn.rst
+++ b/doc/index_cn.rst
@@ -5,6 +5,8 @@ PaddlePaddle 文档
   :maxdepth: 1
 
   getstarted/index_cn.rst
+  build_and_install/index_cn.rst
   howto/index_cn.rst
+  dev/index_cn.rst
   api/index_cn.rst
   faq/index_cn.rst
diff --git a/doc/index_en.rst b/doc/index_en.rst
index 64684b8b9b..5631381be0 100644
--- a/doc/index_en.rst
+++ b/doc/index_en.rst
@@ -5,5 +5,7 @@ PaddlePaddle Documentation
   :maxdepth: 1
 
   getstarted/index_en.rst
+  build_and_install/index_en.rst
   howto/index_en.rst
+  dev/index_en.rst
   api/index_en.rst

From e5832019a8906728ecf8e3f51552c738acd26e22 Mon Sep 17 00:00:00 2001
From: kexinzhao <kexin.zhao.paddle@gmail.com>
Date: Tue, 6 Feb 2018 21:54:49 -0800
Subject: [PATCH 284/314] Inference example and unit test for
 label_semantic_roles (#8058)

* set up python code

* fix bug

* add cc file

* fix cmake

* add inference test for label semantic role

* fix

* address comments

* address comments

* address comments

* address comments

* add use_cuda
---
 paddle/inference/tests/book/CMakeLists.txt    |   6 +
 paddle/inference/tests/book/test_helper.h     | 104 ++++++++++++++++
 .../test_inference_label_semantic_roles.cc    |  81 +++++++++++++
 .../book/test_inference_recognize_digits.cc   |  81 +------------
 .../tests/book/test_label_semantic_roles.py   | 114 ++++++++++++++++--
 5 files changed, 299 insertions(+), 87 deletions(-)
 create mode 100644 paddle/inference/tests/book/test_helper.h
 create mode 100644 paddle/inference/tests/book/test_inference_label_semantic_roles.cc

diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index 4c71517dc9..8f48b2f0e0 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -11,9 +11,15 @@ cc_test(test_inference_image_classification_resnet
     SRCS test_inference_image_classification.cc
     DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
     ARGS --dirname=${PYTHON_TESTS_DIR}/book/image_classification_resnet.inference.model)
+cc_test(test_inference_label_semantic_roles
+    SRCS test_inference_label_semantic_roles.cc
+    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+    ARGS --dirname=${PYTHON_TESTS_DIR}/book/label_semantic_roles.inference.model)
 set_tests_properties(test_inference_recognize_digits_mlp
     PROPERTIES DEPENDS test_recognize_digits)
 set_tests_properties(test_inference_image_classification_vgg
     PROPERTIES DEPENDS test_image_classification_train)
 set_tests_properties(test_inference_image_classification_resnet
     PROPERTIES DEPENDS test_image_classification_train)
+set_tests_properties(test_inference_label_semantic_roles
+    PROPERTIES DEPENDS test_label_semantic_roles) 
diff --git a/paddle/inference/tests/book/test_helper.h b/paddle/inference/tests/book/test_helper.h
new file mode 100644
index 0000000000..17c3d58de6
--- /dev/null
+++ b/paddle/inference/tests/book/test_helper.h
@@ -0,0 +1,104 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/framework/lod_tensor.h"
+#include "paddle/inference/io.h"
+
+template <typename T>
+void SetupTensor(paddle::framework::LoDTensor& input,
+                 paddle::framework::DDim dims,
+                 T lower,
+                 T upper) {
+  srand(time(0));
+  T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
+  for (int i = 0; i < input.numel(); ++i) {
+    input_ptr[i] =
+        (static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
+        lower;
+  }
+}
+
+template <typename T>
+void SetupLoDTensor(paddle::framework::LoDTensor& input,
+                    paddle::framework::LoD& lod,
+                    T lower,
+                    T upper) {
+  input.set_lod(lod);
+  int dim = lod[0][lod[0].size() - 1];
+  SetupTensor(input, {dim, 1}, lower, upper);
+}
+
+template <typename T>
+void CheckError(paddle::framework::LoDTensor& output1,
+                paddle::framework::LoDTensor& output2) {
+  // Check lod information
+  EXPECT_EQ(output1.lod(), output2.lod());
+
+  EXPECT_EQ(output1.dims(), output2.dims());
+  EXPECT_EQ(output1.numel(), output2.numel());
+
+  T err = static_cast<T>(0);
+  if (typeid(T) == typeid(float)) {
+    err = 1E-3;
+  } else if (typeid(T) == typeid(double)) {
+    err = 1E-6;
+  } else {
+    err = 0;
+  }
+
+  size_t count = 0;
+  for (int64_t i = 0; i < output1.numel(); ++i) {
+    if (fabs(output1.data<T>()[i] - output2.data<T>()[i]) > err) {
+      count++;
+    }
+  }
+  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+}
+
+template <typename Place, typename T>
+void TestInference(const std::string& dirname,
+                   const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
+                   std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
+  // 1. Define place, executor and scope
+  auto place = Place();
+  auto executor = paddle::framework::Executor(place);
+  auto* scope = new paddle::framework::Scope();
+
+  // 2. Initialize the inference_program and load all parameters from file
+  auto inference_program = paddle::inference::Load(executor, *scope, dirname);
+
+  // 3. Get the feed_target_names and fetch_target_names
+  const std::vector<std::string>& feed_target_names =
+      inference_program->GetFeedTargetNames();
+  const std::vector<std::string>& fetch_target_names =
+      inference_program->GetFetchTargetNames();
+
+  // 4. Prepare inputs: set up maps for feed targets
+  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
+  for (size_t i = 0; i < feed_target_names.size(); ++i) {
+    // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
+    feed_targets[feed_target_names[i]] = cpu_feeds[i];
+  }
+
+  // 5. Define Tensor to get the outputs: set up maps for fetch targets
+  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
+  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
+    fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
+  }
+
+  // 6. Run the inference program
+  executor.Run(*inference_program, scope, feed_targets, fetch_targets);
+
+  delete scope;
+}
diff --git a/paddle/inference/tests/book/test_inference_label_semantic_roles.cc b/paddle/inference/tests/book/test_inference_label_semantic_roles.cc
new file mode 100644
index 0000000000..c5646db2a7
--- /dev/null
+++ b/paddle/inference/tests/book/test_inference_label_semantic_roles.cc
@@ -0,0 +1,81 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+#include <time.h>
+#include <sstream>
+#include "gflags/gflags.h"
+#include "test_helper.h"
+
+DEFINE_string(dirname, "", "Directory of the inference model.");
+
+TEST(inference, label_semantic_roles) {
+  if (FLAGS_dirname.empty()) {
+    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
+  }
+
+  LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
+  std::string dirname = FLAGS_dirname;
+
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
+
+  paddle::framework::LoDTensor word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1,
+      ctx_p2, mark;
+  paddle::framework::LoD lod{{0, 4, 10}};
+
+  SetupLoDTensor(word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(
+      predicate, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(ctx_n2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(ctx_n1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(ctx_0, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(ctx_p1, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(ctx_p2, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+  SetupLoDTensor(mark, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
+
+  std::vector<paddle::framework::LoDTensor*> cpu_feeds;
+  cpu_feeds.push_back(&word);
+  cpu_feeds.push_back(&predicate);
+  cpu_feeds.push_back(&ctx_n2);
+  cpu_feeds.push_back(&ctx_n1);
+  cpu_feeds.push_back(&ctx_0);
+  cpu_feeds.push_back(&ctx_p1);
+  cpu_feeds.push_back(&ctx_p2);
+  cpu_feeds.push_back(&mark);
+
+  paddle::framework::LoDTensor output1;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
+  cpu_fetchs1.push_back(&output1);
+
+  // Run inference on CPU
+  TestInference<paddle::platform::CPUPlace, float>(
+      dirname, cpu_feeds, cpu_fetchs1);
+  LOG(INFO) << output1.lod();
+  LOG(INFO) << output1.dims();
+
+#ifdef PADDLE_WITH_CUDA
+  paddle::framework::LoDTensor output2;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
+  cpu_fetchs2.push_back(&output2);
+
+  // Run inference on CUDA GPU
+  TestInference<paddle::platform::CUDAPlace, float>(
+      dirname, cpu_feeds, cpu_fetchs2);
+  LOG(INFO) << output2.lod();
+  LOG(INFO) << output2.dims();
+
+  CheckError<float>(output1, output2);
+#endif
+}
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index ce8772587f..2c0cf94100 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -16,89 +16,10 @@ limitations under the License. */
 #include <time.h>
 #include <sstream>
 #include "gflags/gflags.h"
-#include "paddle/framework/lod_tensor.h"
-#include "paddle/inference/io.h"
+#include "test_helper.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-template <typename Place, typename T>
-void TestInference(const std::string& dirname,
-                   const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
-                   std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
-  // 1. Define place, executor and scope
-  auto place = Place();
-  auto executor = paddle::framework::Executor(place);
-  auto* scope = new paddle::framework::Scope();
-
-  // 2. Initialize the inference_program and load all parameters from file
-  auto inference_program = paddle::inference::Load(executor, *scope, dirname);
-
-  // 3. Get the feed_target_names and fetch_target_names
-  const std::vector<std::string>& feed_target_names =
-      inference_program->GetFeedTargetNames();
-  const std::vector<std::string>& fetch_target_names =
-      inference_program->GetFetchTargetNames();
-
-  // 4. Prepare inputs: set up maps for feed targets
-  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
-  for (size_t i = 0; i < feed_target_names.size(); ++i) {
-    // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
-    feed_targets[feed_target_names[i]] = cpu_feeds[i];
-  }
-
-  // 5. Define Tensor to get the outputs: set up maps for fetch targets
-  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
-  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
-    fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
-  }
-
-  // 6. Run the inference program
-  executor.Run(*inference_program, scope, feed_targets, fetch_targets);
-
-  delete scope;
-}
-
-template <typename T>
-void SetupTensor(paddle::framework::LoDTensor& input,
-                 paddle::framework::DDim dims,
-                 T lower,
-                 T upper) {
-  srand(time(0));
-  float* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
-  for (int i = 0; i < input.numel(); ++i) {
-    input_ptr[i] =
-        (static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
-        lower;
-  }
-}
-
-template <typename T>
-void CheckError(paddle::framework::LoDTensor& output1,
-                paddle::framework::LoDTensor& output2) {
-  // Check lod information
-  EXPECT_EQ(output1.lod(), output2.lod());
-
-  EXPECT_EQ(output1.dims(), output2.dims());
-  EXPECT_EQ(output1.numel(), output2.numel());
-
-  T err = static_cast<T>(0);
-  if (typeid(T) == typeid(float)) {
-    err = 1E-3;
-  } else if (typeid(T) == typeid(double)) {
-    err = 1E-6;
-  } else {
-    err = 0;
-  }
-
-  size_t count = 0;
-  for (int64_t i = 0; i < output1.numel(); ++i) {
-    if (fabs(output1.data<T>()[i] - output2.data<T>()[i]) > err) {
-      count++;
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
-}
-
 TEST(inference, recognize_digits) {
   if (FLAGS_dirname.empty()) {
     LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
diff --git a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
index f85768de99..1491f7a8d5 100644
--- a/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
+++ b/python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
@@ -18,7 +18,9 @@ import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.dataset.conll05 as conll05
 import paddle.v2.fluid as fluid
+import contextlib
 import time
+import unittest
 
 word_dict, verb_dict, label_dict = conll05.get_dict()
 word_dict_len = len(word_dict)
@@ -127,7 +129,15 @@ def to_lodtensor(data, place):
     return res
 
 
-def main():
+def create_random_lodtensor(lod, place, low, high):
+    data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
+    res = fluid.LoDTensor()
+    res.set(data, place)
+    res.set_lod([lod])
+    return res
+
+
+def train(use_cuda, save_dirname=None):
     # define network topology
     word = fluid.layers.data(
         name='word_data', shape=[1], dtype='int64', lod_level=1)
@@ -175,8 +185,8 @@ def main():
         paddle.reader.shuffle(
             paddle.dataset.conll05.test(), buf_size=8192),
         batch_size=BATCH_SIZE)
-    # place = fluid.CPUPlace()
-    place = fluid.CUDAPlace(0)
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     feeder = fluid.DataFeeder(
         feed_list=[
             word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, predicate, mark, target
@@ -211,12 +221,102 @@ def main():
                 if batch_id != 0:
                     print("second per batch: " + str((time.time() - start_time)
                                                      / batch_id))
-
-            # exit early for CI
-            exit(0)
+                # Set the threshold low to speed up the CI test
+                if float(pass_precision) > 0.05:
+                    if save_dirname is not None:
+                        fluid.io.save_inference_model(save_dirname, [
+                            'word_data', 'verb_data', 'ctx_n2_data',
+                            'ctx_n1_data', 'ctx_0_data', 'ctx_p1_data',
+                            'ctx_p2_data', 'mark_data'
+                        ], [feature_out], exe)
+                    return
 
             batch_id = batch_id + 1
 
 
+def infer(use_cuda, save_dirname=None):
+    if save_dirname is None:
+        return
+
+    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # Use fluid.io.load_inference_model to obtain the inference program desc,
+    # the feed_target_names (the names of variables that will be feeded 
+    # data using feed operators), and the fetch_targets (variables that 
+    # we want to obtain data from using fetch operators).
+    [inference_program, feed_target_names,
+     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+
+    lod = [0, 4, 10]
+    ts_word = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_pred = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_ctx_n2 = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_ctx_n1 = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_ctx_0 = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_ctx_p1 = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_ctx_p2 = create_random_lodtensor(lod, place, low=0, high=1)
+    ts_mark = create_random_lodtensor(lod, place, low=0, high=1)
+
+    # Construct feed as a dictionary of {feed_target_name: feed_target_data}
+    # and results will contain a list of data corresponding to fetch_targets.
+    assert feed_target_names[0] == 'word_data'
+    assert feed_target_names[1] == 'verb_data'
+    assert feed_target_names[2] == 'ctx_n2_data'
+    assert feed_target_names[3] == 'ctx_n1_data'
+    assert feed_target_names[4] == 'ctx_0_data'
+    assert feed_target_names[5] == 'ctx_p1_data'
+    assert feed_target_names[6] == 'ctx_p2_data'
+    assert feed_target_names[7] == 'mark_data'
+
+    results = exe.run(inference_program,
+                      feed={
+                          feed_target_names[0]: ts_word,
+                          feed_target_names[1]: ts_pred,
+                          feed_target_names[2]: ts_ctx_n2,
+                          feed_target_names[3]: ts_ctx_n1,
+                          feed_target_names[4]: ts_ctx_0,
+                          feed_target_names[5]: ts_ctx_p1,
+                          feed_target_names[6]: ts_ctx_p2,
+                          feed_target_names[7]: ts_mark
+                      },
+                      fetch_list=fetch_targets,
+                      return_numpy=False)
+    print(results[0].lod())
+    np_data = np.array(results[0])
+    print("Inference Shape: ", np_data.shape)
+    print("Inference results: ", np_data)
+
+
+def main(use_cuda):
+    if use_cuda and not fluid.core.is_compiled_with_cuda():
+        return
+
+    # Directory for saving the trained model
+    save_dirname = "label_semantic_roles.inference.model"
+
+    train(use_cuda, save_dirname)
+    infer(use_cuda, save_dirname)
+
+
+class TestLabelSemanticRoles(unittest.TestCase):
+    def test_cuda(self):
+        with self.scope_prog_guard():
+            main(use_cuda=True)
+
+    def test_cpu(self):
+        with self.scope_prog_guard():
+            main(use_cuda=False)
+
+    @contextlib.contextmanager
+    def scope_prog_guard(self):
+        prog = fluid.Program()
+        startup_prog = fluid.Program()
+        scope = fluid.core.Scope()
+        with fluid.scope_guard(scope):
+            with fluid.program_guard(prog, startup_prog):
+                yield
+
+
 if __name__ == '__main__':
-    main()
+    unittest.main()

From c1349d98aa48060b449c4eea4dfc95a2989ad203 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 7 Feb 2018 14:43:14 +0800
Subject: [PATCH 285/314] fix compile errors

---
 paddle/framework/reader.cc                    |  2 ++
 paddle/framework/reader.h                     | 11 ++++++++--
 paddle/operators/CMakeLists.txt               |  2 +-
 paddle/operators/create_reader_op.cc          | 22 ++++++++++---------
 paddle/operators/read_op.cc                   |  5 ++++-
 .../paddle/v2/fluid/tests/test_cpp_reader.py  |  6 ++---
 6 files changed, 31 insertions(+), 17 deletions(-)

diff --git a/paddle/framework/reader.cc b/paddle/framework/reader.cc
index 86220cd0bb..928b661aaa 100644
--- a/paddle/framework/reader.cc
+++ b/paddle/framework/reader.cc
@@ -38,6 +38,8 @@ void ShuffleReader::ReadNext(std::vector<LoDTensor>* out) {
         break;
       }
     }
+    // TODO(fengjiayi): 'std::random_shuffle' can be very slow. It needs to be
+    // optimize.
     std::random_shuffle(buffer_.begin(), buffer_.end());
     iteration_pos_ = 0;
   }
diff --git a/paddle/framework/reader.h b/paddle/framework/reader.h
index ff7153bc7b..534894cfbd 100644
--- a/paddle/framework/reader.h
+++ b/paddle/framework/reader.h
@@ -28,6 +28,8 @@ class ReaderBase {
   virtual void ReadNext(std::vector<LoDTensor>* out) = 0;
   virtual bool HasNext() const = 0;
 
+  virtual void ReInit() = 0;
+
   DDim shape(size_t idx) const;
   std::vector<DDim> shapes() const { return shapes_; }
   void set_shapes(const std::vector<DDim>& shapes) { shapes_ = shapes; }
@@ -52,6 +54,8 @@ class DecoratedReader : public ReaderBase {
 
   bool HasNext() const override { return reader_->HasNext(); }
 
+  void ReInit() override { reader_->ReInit(); }
+
  protected:
   ReaderBase* reader_;
 };
@@ -59,9 +63,9 @@ class DecoratedReader : public ReaderBase {
 // file readers
 
 template <typename T>
-class RandomReader : public FileReader {
+class RandomDataGenerator : public FileReader {
  public:
-  RandomReader(const std::vector<DDim>& shapes, float min, float max)
+  RandomDataGenerator(const std::vector<DDim>& shapes, float min, float max)
       : FileReader(shapes), min_(min), max_(max) {
     PADDLE_ENFORCE_LE(
         min, max, "'min' shouldn't be greater than 'max'.(%f vs %f)", min, max);
@@ -91,6 +95,8 @@ class RandomReader : public FileReader {
 
   bool HasNext() const override { return true; }
 
+  void ReInit() override { return; }
+
  private:
   float min_;
   float max_;
@@ -139,6 +145,7 @@ class ReaderHolder {
 
   void ReadNext(std::vector<LoDTensor>* out) { reader_->ReadNext(out); }
   bool HasNext() const { return reader_->HasNext(); }
+  void ReInit() { reader_->ReInit(); }
 
   DDim shape(size_t idx) const { return reader_->shape(idx); }
   std::vector<DDim> shapes() const { return reader_->shapes(); }
diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index e1dba8bb3f..25bb7187d3 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -186,7 +186,7 @@ list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
 foreach(src ${GENERAL_OPS})
     op_library(${src})
 endforeach()
-file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\nUSE_NO_KERNEL_OP(create_random_reader);\n")
+file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\nUSE_NO_KERNEL_OP(create_random_data_generator);\n")
 
 set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
 
diff --git a/paddle/operators/create_reader_op.cc b/paddle/operators/create_reader_op.cc
index 11c77a0603..5ba2a25ab4 100644
--- a/paddle/operators/create_reader_op.cc
+++ b/paddle/operators/create_reader_op.cc
@@ -18,8 +18,8 @@
 namespace paddle {
 namespace operators {
 
-std::vector<framework::DDim> RestoreShapes(const std::vector<int>& shape_concat,
-                                           const std::vector<int>& ranks) {
+static std::vector<framework::DDim> RestoreShapes(
+    const std::vector<int>& shape_concat, const std::vector<int>& ranks) {
   std::vector<framework::DDim> res;
   int offset = 0;
   for (int len : ranks) {
@@ -69,7 +69,7 @@ class CreateReaderInferVarType : public framework::VarTypeInference {
 };
 
 template <typename T>
-class CreateRandomReaderOp : public framework::OperatorBase {
+class CreateRandomDataGeneratorOp : public framework::OperatorBase {
  public:
   using framework::OperatorBase::OperatorBase;
   void Run(const framework::Scope& scope,
@@ -84,14 +84,15 @@ class CreateRandomReaderOp : public framework::OperatorBase {
     std::vector<framework::DDim> shapes = RestoreShapes(shape_concat, ranks);
     auto* out = scope.FindVar(Output("Out"))
                     ->template GetMutable<framework::ReaderHolder>();
-    out->Reset(new framework::RandomReader<T>(shapes, Attr<float>("min"),
-                                              Attr<float>("max")));
+    out->Reset(new framework::RandomDataGenerator<T>(shapes, Attr<float>("min"),
+                                                     Attr<float>("max")));
   }
 };
 
-class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
+class CreateRandomDataGeneratorOpMaker
+    : public framework::OpProtoAndCheckerMaker {
  public:
-  CreateRandomReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
+  CreateRandomDataGeneratorOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(op_proto, op_checker) {
     AddOutput("Out", "(ReaderHolder) The created random reader.");
     AddAttr<std::vector<int>>("shape_concat",
@@ -107,7 +108,7 @@ class CreateRandomReaderOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<float>("min", "The lower bound of reader's uniform distribution.");
     AddAttr<float>("max", "The upper bound of reader's uniform distribution.");
     AddComment(R"DOC(
-      CreateRandomReader Operator
+      CreateRandomDataGenerator Operator
 
       This Op creates a random reader. 
       The reader generates random data instead of really reading from files.
@@ -186,9 +187,10 @@ class CreateBatchReaderOpMaker : public framework::OpProtoAndCheckerMaker {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OPERATOR(create_random_reader, ops::CreateRandomReaderOp<float>,
+REGISTER_OPERATOR(create_random_data_generator,
+                  ops::CreateRandomDataGeneratorOp<float>,
                   ops::CreateFileReaderInferShape,
-                  ops::CreateRandomReaderOpMaker,
+                  ops::CreateRandomDataGeneratorOpMaker,
                   paddle::framework::EmptyGradOpMaker,
                   ops::CreateReaderInferVarType);
 REGISTER_OPERATOR(create_shuffle_reader, ops::CreateShuffleReaderOp,
diff --git a/paddle/operators/read_op.cc b/paddle/operators/read_op.cc
index 3d17b26c99..3ae454101f 100644
--- a/paddle/operators/read_op.cc
+++ b/paddle/operators/read_op.cc
@@ -59,7 +59,10 @@ class ReadOp : public framework::OperatorBase {
     framework::ReaderHolder* reader =
         scope.FindVar(Input("Reader"))->GetMutable<framework::ReaderHolder>();
     if (!reader->HasNext()) {
-      return;
+      reader->ReInit();
+      PADDLE_ENFORCE(
+          reader->HasNext(),
+          "Reader can not read the next data even it has been re-initialized.");
     }
     std::vector<std::string> out_arg_names = Outputs("Out");
     std::vector<framework::LoDTensor> ins;
diff --git a/python/paddle/v2/fluid/tests/test_cpp_reader.py b/python/paddle/v2/fluid/tests/test_cpp_reader.py
index 7efcb0c46d..e71c3a290c 100644
--- a/python/paddle/v2/fluid/tests/test_cpp_reader.py
+++ b/python/paddle/v2/fluid/tests/test_cpp_reader.py
@@ -20,11 +20,11 @@ prog = fluid.framework.Program()
 block = prog.current_block()
 
 random_reader = block.create_var(
-    type=fluid.core.VarDesc.VarType.READER, name="RandomReader")
+    type=fluid.core.VarDesc.VarType.READER, name="RandomDataGenerator")
 random_reader.desc.set_lod_levels([0, 0])
 
-create_random_reader_op = block.append_op(
-    type="create_random_reader",
+create_random_data_generator_op = block.append_op(
+    type="create_random_data_generator",
     outputs={"Out": random_reader},
     attrs={
         "shape_concat": [1, 2, 1, 1],

From 20c4a4cb4f716d433ba435dac3632a2d1459b055 Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Wed, 7 Feb 2018 14:47:42 +0800
Subject: [PATCH 286/314] Impl scalar switch case op with condition op (#8184)

Impl scalar switch case op with condition op
---
 doc/design/switch.md                          |  3 +-
 paddle/operators/conditional_block_op.cc      | 44 +++++++++++--
 python/paddle/v2/fluid/layers/control_flow.py | 66 ++++++++++++++++++-
 python/paddle/v2/fluid/layers/ops.py          |  4 ++
 python/paddle/v2/fluid/tests/test_switch.py   | 64 ++++++++++++++++++
 5 files changed, 171 insertions(+), 10 deletions(-)
 create mode 100644 python/paddle/v2/fluid/tests/test_switch.py

diff --git a/doc/design/switch.md b/doc/design/switch.md
index 9db1b2782a..827d0601c6 100644
--- a/doc/design/switch.md
+++ b/doc/design/switch.md
@@ -10,8 +10,7 @@ The following example shows the usage of `fluid.switch`.
 a = fluid.Var(10)
 b = fluid.Var(0)
 
-switch = fluid.switch()
-with switch.block():
+with switch() as switch:
     with switch.case(fluid.less_equal(a, 10)):
         fluid.print("Case 1")
     with switch.case(fluid.larger(a, 0)):
diff --git a/paddle/operators/conditional_block_op.cc b/paddle/operators/conditional_block_op.cc
index 3cae61a438..bdcdb85be7 100644
--- a/paddle/operators/conditional_block_op.cc
+++ b/paddle/operators/conditional_block_op.cc
@@ -41,6 +41,21 @@ class ConditionalOp : public framework::OperatorBase {
         });
     return retv;
   }
+
+  bool ScalarCondition(
+      const std::vector<const framework::LoDTensor *> &ips) const {
+    if (!(ips.size() == 1UL && ips[0]->IsInitialized())) {
+      PADDLE_THROW("should have one initialized input as condition");
+    }
+    if (!(ips[0]->type().hash_code() == typeid(bool).hash_code() &&
+          ips[0]->numel() == 1)) {
+      PADDLE_THROW(
+          "condition input's data type should be bool, "
+          "numel should be 1, actual numel is %d",
+          ips[0]->numel());
+    }
+    return ips[0]->data<bool>()[0];
+  }
 };
 
 class ConditionalBlockOp : public ConditionalOp {
@@ -53,9 +68,15 @@ class ConditionalBlockOp : public ConditionalOp {
   void Run(const framework::Scope &scope,
            const platform::Place &dev_place) const override {
     auto xs = InputTensors(scope);
-    bool need_run = std::all_of(
-        xs.begin(), xs.end(),
-        [](const framework::LoDTensor *t) { return t->numel() != 0; });
+
+    bool need_run;
+    if (Attr<bool>("is_scalar_condition")) {
+      need_run = ScalarCondition(xs);
+    } else {
+      need_run = std::all_of(
+          xs.begin(), xs.end(),
+          [](const framework::LoDTensor *t) { return t->numel() != 0; });
+    }
 
     if (need_run) {
       auto *scope_var = scope.FindVar(Output("Scope"));
@@ -88,6 +109,10 @@ class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
               "scope is std::vector<Scope*>");
     AddAttr<framework::BlockDesc *>(
         "sub_block", "The step block of conditional block operator");
+    AddAttr<bool>("is_scalar_condition",
+                  "the input X is used as scalar "
+                  "condition")
+        .SetDefault(false);
     AddComment(R"DOC(Conditional block operator
 
 Run the sub-block if X is not empty. Params is the other inputs and Out is the
@@ -106,9 +131,15 @@ class ConditionalBlockGradOp : public ConditionalOp {
   void Run(const framework::Scope &scope,
            const platform::Place &dev_place) const override {
     auto xs = this->InputTensors(scope);
-    bool need_run = std::all_of(
-        xs.begin(), xs.end(),
-        [](const framework::LoDTensor *t) { return t->numel() != 0; });
+
+    bool need_run;
+    if (Attr<bool>("is_scalar_condition")) {
+      need_run = ScalarCondition(xs);
+    } else {
+      need_run = std::all_of(
+          xs.begin(), xs.end(),
+          [](const framework::LoDTensor *t) { return t->numel() != 0; });
+    }
 
     if (need_run) {
       auto *scope_var = scope.FindVar(Input("Scope"));
@@ -182,6 +213,7 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
     grad_op->SetOutput(framework::GradVarName("Params"),
                        InputGrad("Params", false));
     grad_op->SetBlockAttr("sub_block", *this->grad_block_[0]);
+    grad_op->SetAttr("is_scalar_condition", GetAttr("is_scalar_condition"));
     return std::unique_ptr<framework::OpDesc>(grad_op);
   }
 };
diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index 0fcbfe0e2f..e71f3858b0 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -18,6 +18,7 @@ from tensor import assign, fill_constant
 from .. import core
 from ..framework import Program, Variable, Operator
 from ..layer_helper import LayerHelper, unique_name
+from ops import logical_and, logical_not, logical_or
 
 __all__ = [
     'split_lod_tensor',
@@ -27,6 +28,7 @@ __all__ = [
     'StaticRNNMemoryLink',
     'WhileGuard',
     'While',
+    'Switch',
     'lod_rank_table',
     'max_sequence_len',
     'topk',
@@ -1063,11 +1065,12 @@ class ConditionalBlockGuard(BlockGuard):
 
 
 class ConditionalBlock(object):
-    def __init__(self, inputs, name=None):
+    def __init__(self, inputs, is_scalar_condition=False, name=None):
         for each_input in inputs:
             if not isinstance(each_input, Variable):
                 raise TypeError("Each input should be variable")
         self.inputs = inputs
+        self.is_scalar_condition = is_scalar_condition
         self.helper = LayerHelper('conditional_block', name=name)
 
     def block(self):
@@ -1112,7 +1115,66 @@ class ConditionalBlock(object):
             },
             outputs={'Out': out_list,
                      'Scope': [step_scope]},
-            attrs={'sub_block': inside_block})
+            attrs={
+                'sub_block': inside_block,
+                'is_scalar_condition': self.is_scalar_condition
+            })
+
+
+class Switch(object):
+    def __init__(self, name=None):
+        self.helper = LayerHelper('switch', name=name)
+        self.inside_scope = False
+        self.pre_not_conditions = []
+
+    def case(self, condition):
+        """create a new block for this condition
+        """
+        if not self.inside_scope:
+            raise ValueError("case should be called inside with")
+
+        if len(self.pre_not_conditions) == 0:
+            cond_block = ConditionalBlock([condition], is_scalar_condition=True)
+            not_cond = logical_not(x=condition)
+            self.pre_not_conditions.append(not_cond)
+        else:
+            pre_cond_num = len(self.pre_not_conditions)
+            pre_not_cond = self.pre_not_conditions[pre_cond_num - 1]
+            new_not_cond = logical_and(
+                x=pre_not_cond, y=logical_not(x=condition))
+            self.pre_not_conditions.append(new_not_cond)
+            cond_block = ConditionalBlock(
+                [logical_and(
+                    x=pre_not_cond, y=condition)],
+                is_scalar_condition=True)
+
+        return ConditionalBlockGuard(cond_block)
+
+    def default(self):
+        """create a default case for this switch
+        """
+        pre_cond_num = len(self.pre_not_conditions)
+        if pre_cond_num == 0:
+            raise ValueError("there should be at least one condition")
+        cond_block = ConditionalBlock(
+            [self.pre_not_conditions[pre_cond_num - 1]],
+            is_scalar_condition=True)
+        return ConditionalBlockGuard(cond_block)
+
+    def __enter__(self):
+        """
+        set flag that now is inside switch.block {}
+        :return:
+        """
+        self.inside_scope = True
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.inside_scope = False
+        if exc_type is not None:
+            return False  # re-raise exception
+
+        return True
 
 
 class IfElseBlockGuard(object):
diff --git a/python/paddle/v2/fluid/layers/ops.py b/python/paddle/v2/fluid/layers/ops.py
index c701e79ad2..38dea2892f 100644
--- a/python/paddle/v2/fluid/layers/ops.py
+++ b/python/paddle/v2/fluid/layers/ops.py
@@ -61,6 +61,10 @@ __all__ = [
     'clip_by_norm',
     'softmax',
     'sequence_softmax',
+    'logical_and',
+    'logical_or',
+    'logical_xor',
+    'logical_not',
 ] + __activations__
 
 for _OP in set(__all__):
diff --git a/python/paddle/v2/fluid/tests/test_switch.py b/python/paddle/v2/fluid/tests/test_switch.py
new file mode 100644
index 0000000000..52ebf773ec
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_switch.py
@@ -0,0 +1,64 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid.framework as framework
+from paddle.v2.fluid.executor import Executor
+from paddle.v2.fluid.framework import default_startup_program
+
+
+class TestSwitch(unittest.TestCase):
+    def check_switch(self, value):
+        x = layers.fill_constant(shape=[1], dtype='float32', value=value)
+
+        zero_var = layers.fill_constant(shape=[1], dtype='float32', value=0.0)
+        one_var = layers.fill_constant(shape=[1], dtype='float32', value=1.0)
+        two_var = layers.fill_constant(shape=[1], dtype='float32', value=2.0)
+        three_var = layers.fill_constant(shape=[1], dtype='float32', value=3.0)
+
+        result = layers.create_global_var(
+            shape=[1], value=-1.0, dtype='float32', persistable=True)
+
+        with layers.Switch() as switch:
+            with switch.case(layers.less_than(x, zero_var)):
+                layers.assign(zero_var, result)
+            with switch.case(layers.less_than(x, one_var)):
+                layers.assign(one_var, result)
+            with switch.case(layers.less_than(x, two_var)):
+                layers.assign(two_var, result)
+            with switch.default():
+                layers.assign(three_var, result)
+
+        cpu = core.CPUPlace()
+        exe = Executor(cpu)
+        exe.run(default_startup_program())
+
+        out = exe.run(feed={}, fetch_list=[result])[0][0]
+        return out
+
+    def test_switch(self):
+        test_data = {(-0.1, 0), (0.1, 1), (1.1, 2), (2.1, 3)}
+        for x, expected_result in test_data:
+            main_program = framework.Program()
+            startup_program = framework.Program()
+            with framework.program_guard(main_program, startup_program):
+                result = self.check_switch(x)
+                self.assertEqual(result, expected_result)
+
+
+if __name__ == '__main__':
+    unittest.main()

From d1d8257fdfab57499dbdd2ad4967052efb43df00 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Wed, 7 Feb 2018 15:45:54 +0800
Subject: [PATCH 287/314] adjust the structure of getstarted and cluster

---
 doc/getstarted/index_cn.rst                   | 55 +-----------------
 doc/getstarted/index_en.rst                   | 56 +-----------------
 doc/getstarted/quickstart_cn.rst              | 41 +++++++++++++
 doc/getstarted/quickstart_en.rst              | 45 +++++++++++++++
 ...cluster_train_cn.md => cmd_argument_cn.md} | 57 +------------------
 ...cluster_train_en.md => cmd_argument_en.md} | 55 +-----------------
 doc/howto/cluster/index_cn.rst                | 10 ++++
 doc/howto/cluster/index_en.rst                | 10 ++++
 doc/howto/cluster/introduction_cn.md          | 13 +++++
 doc/howto/cluster/introduction_en.md          | 13 +++++
 .../cluster/{ => multi_cluster}/fabric_cn.md  |  0
 .../cluster/{ => multi_cluster}/fabric_en.md  |  0
 doc/howto/cluster/multi_cluster/index_cn.rst  | 20 +++++++
 doc/howto/cluster/multi_cluster/index_en.rst  | 19 +++++++
 .../cluster/{ => multi_cluster}/k8s_aws_cn.md |  0
 .../cluster/{ => multi_cluster}/k8s_aws_en.md |  0
 .../cluster/{ => multi_cluster}/k8s_cn.md     |  0
 .../{ => multi_cluster}/k8s_distributed_cn.md |  0
 .../cluster/{ => multi_cluster}/k8s_en.md     |  0
 .../cluster/{ => multi_cluster}/openmpi_cn.md |  0
 .../cluster/{ => multi_cluster}/openmpi_en.md |  0
 doc/howto/cluster/preparations_cn.md          | 16 ++++++
 doc/howto/cluster/preparations_en.md          | 17 ++++++
 doc/howto/index_cn.rst                        |  2 +-
 doc/howto/index_en.rst                        |  2 +-
 25 files changed, 212 insertions(+), 219 deletions(-)
 create mode 100644 doc/getstarted/quickstart_cn.rst
 create mode 100644 doc/getstarted/quickstart_en.rst
 rename doc/howto/cluster/{cluster_train_cn.md => cmd_argument_cn.md} (56%)
 rename doc/howto/cluster/{cluster_train_en.md => cmd_argument_en.md} (58%)
 create mode 100644 doc/howto/cluster/index_cn.rst
 create mode 100644 doc/howto/cluster/index_en.rst
 create mode 100644 doc/howto/cluster/introduction_cn.md
 create mode 100644 doc/howto/cluster/introduction_en.md
 rename doc/howto/cluster/{ => multi_cluster}/fabric_cn.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/fabric_en.md (100%)
 create mode 100644 doc/howto/cluster/multi_cluster/index_cn.rst
 create mode 100644 doc/howto/cluster/multi_cluster/index_en.rst
 rename doc/howto/cluster/{ => multi_cluster}/k8s_aws_cn.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/k8s_aws_en.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/k8s_cn.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/k8s_distributed_cn.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/k8s_en.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/openmpi_cn.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/openmpi_en.md (100%)
 create mode 100644 doc/howto/cluster/preparations_cn.md
 create mode 100644 doc/howto/cluster/preparations_en.md

diff --git a/doc/getstarted/index_cn.rst b/doc/getstarted/index_cn.rst
index 9f6ee25987..1dc141396b 100644
--- a/doc/getstarted/index_cn.rst
+++ b/doc/getstarted/index_cn.rst
@@ -1,61 +1,8 @@
 新手入门
 ============
 
-.. _quick_install:
-
-快速安装
-++++++++
-
-PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
-执行下面的命令完成快速安装，版本为cpu_avx_openblas：
-
-  .. code-block:: bash
-
-     pip install paddlepaddle
-
-如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
-
-  .. code-block:: bash
-
-     pip install paddlepaddle-gpu
-
-更详细的安装和编译方法参考：
-
-..  toctree::
-  :maxdepth: 1
-
-  build_and_install/index_cn.rst
-
-.. _quick_start:
-
-快速开始
-++++++++
-
-创建一个 housing.py 并粘贴此Python代码：
-
-  .. code-block:: python
-
-     import paddle.v2 as paddle
-
-     # Initialize PaddlePaddle.
-     paddle.init(use_gpu=False, trainer_count=1)
-
-     # Configure the neural network.
-     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
-     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-
-     # Infer using provided test data.
-     probs = paddle.infer(
-         output_layer=y_predict,
-         parameters=paddle.dataset.uci_housing.model(),
-         input=[item for item in paddle.dataset.uci_housing.test()()])
-
-     for i in xrange(len(probs)):
-         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
-
-执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
-
 ..  toctree::
   :maxdepth: 1
 
+  quickstart_cn.rst
   concepts/use_concepts_cn.rst
diff --git a/doc/getstarted/index_en.rst b/doc/getstarted/index_en.rst
index 063d9d880c..c680e19037 100644
--- a/doc/getstarted/index_en.rst
+++ b/doc/getstarted/index_en.rst
@@ -1,61 +1,7 @@
 GET STARTED
 ============
 
-.. _quick_install:
-
-Quick Install
-----------------------
-
-You can use pip to install PaddlePaddle with a single command, supports
-CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
-Simply run the following command to install, the version is cpu_avx_openblas:
-
-  .. code-block:: bash
-
-     pip install paddlepaddle
-
-If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
-
-  .. code-block:: bash
-
-     pip install paddlepaddle-gpu
-
-For more details about installation and build:
-
 ..  toctree::
   :maxdepth: 1
 
-  build_and_install/index_en.rst
-
-
-.. _quick_start:
-
-Quick Start
-++++++++
-
-Create a new file called housing.py, and paste this Python
-code:
-
-
-  .. code-block:: python
-
-     import paddle.v2 as paddle
-
-     # Initialize PaddlePaddle.
-     paddle.init(use_gpu=False, trainer_count=1)
-
-     # Configure the neural network.
-     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
-     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-
-     # Infer using provided test data.
-     probs = paddle.infer(
-         output_layer=y_predict,
-         parameters=paddle.dataset.uci_housing.model(),
-         input=[item for item in paddle.dataset.uci_housing.test()()])
-
-     for i in xrange(len(probs)):
-         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
-
-Run :code:`python housing.py` and voila! It should print out a list of predictions
-for the test housing data.
+  quickstart_en.rst
diff --git a/doc/getstarted/quickstart_cn.rst b/doc/getstarted/quickstart_cn.rst
new file mode 100644
index 0000000000..51dd00f1e8
--- /dev/null
+++ b/doc/getstarted/quickstart_cn.rst
@@ -0,0 +1,41 @@
+快速开始
+========
+
+PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
+执行下面的命令完成快速安装，版本为cpu_avx_openblas：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+更详细的安装和编译方法参考：:ref:`install_steps` 。
+
+创建一个 housing.py 并粘贴此Python代码：
+
+  .. code-block:: python
+
+     import paddle.v2 as paddle
+
+     # Initialize PaddlePaddle.
+     paddle.init(use_gpu=False, trainer_count=1)
+
+     # Configure the neural network.
+     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+     # Infer using provided test data.
+     probs = paddle.infer(
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
+
+     for i in xrange(len(probs)):
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+
+执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
diff --git a/doc/getstarted/quickstart_en.rst b/doc/getstarted/quickstart_en.rst
new file mode 100644
index 0000000000..d1bcf82ea0
--- /dev/null
+++ b/doc/getstarted/quickstart_en.rst
@@ -0,0 +1,45 @@
+Quick Start
+============
+
+You can use pip to install PaddlePaddle with a single command, supports
+CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
+Simply run the following command to install, the version is cpu_avx_openblas:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+For more details about installation and build: :ref:`install_steps` .
+
+Create a new file called housing.py, and paste this Python
+code:
+
+
+  .. code-block:: python
+
+     import paddle.v2 as paddle
+
+     # Initialize PaddlePaddle.
+     paddle.init(use_gpu=False, trainer_count=1)
+
+     # Configure the neural network.
+     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+     # Infer using provided test data.
+     probs = paddle.infer(
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
+
+     for i in xrange(len(probs)):
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+
+Run :code:`python housing.py` and voila! It should print out a list of predictions
+for the test housing data.
diff --git a/doc/howto/cluster/cluster_train_cn.md b/doc/howto/cluster/cmd_argument_cn.md
similarity index 56%
rename from doc/howto/cluster/cluster_train_cn.md
rename to doc/howto/cluster/cmd_argument_cn.md
index 0f3db59607..5c575dd5b5 100644
--- a/doc/howto/cluster/cluster_train_cn.md
+++ b/doc/howto/cluster/cmd_argument_cn.md
@@ -1,41 +1,7 @@
-# 分布式训练
-
-
-## 概述
-
-本文将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
-
-<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
-
-- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
-- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
-- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
-
-这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
-
-在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
-
-
-## 环境准备
-
-1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
-1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/index_cn.html)的多种安装方式。我们推荐使用[Docker](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html)安装方式来快速安装PaddlePaddle。
-
-安装完成之后，执行下面的命令可以查看已经安装的版本（docker安装方式可以进入docker容器执行：`docker run -it paddlepaddle/paddle:[tag] /bin/bash`）：
-```bash
-$ paddle version
-PaddlePaddle 0.10.0, compiled with
-    with_avx: ON
-    with_gpu: OFF
-    with_double: OFF
-    with_python: ON
-    with_rdma: OFF
-    with_timer: OFF
-```
+## 启动参数说明
 
-下面以`doc/howto/usage/cluster/src/word2vec`中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。
+下面以`doc/howto/cluster/src/word2vec`中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。
 
-## 启动参数说明
 ### 启动参数服务器
 执行以下的命令启动一个参数服务器并等待和计算节点的数据交互
 ```bash
@@ -167,22 +133,3 @@ test.txt-00002
 
 - `train_data_dir`：包含训练数据的目录，可以是从分布式存储挂载过来的，也可以是在任务启动前下载到本地的。
 - `test_data_dir`：包含测试数据集的目录。
-
-## 使用分布式计算平台或工具
-
-PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
-- [Kubernetes](http://kubernetes.io) Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
-- [OpenMPI](https://www.open-mpi.org) 成熟的高性能并行计算框架。
-- [Fabric](http://www.fabfile.org) 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。
-
-对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在[cluster_train_v2](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2)找到。
-
-在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
-
-## 在不同集群中运行
-
-  - [fabric集群](fabric_cn.md)
-  - [openmpi集群](openmpi_cn.md)
-  - [kubernetes单机](k8s_cn.md)
-  - [kubernetes distributed分布式](k8s_distributed_cn.md)
-  - [AWS上运行kubernetes集群训练](k8s_aws_cn.md)
diff --git a/doc/howto/cluster/cluster_train_en.md b/doc/howto/cluster/cmd_argument_en.md
similarity index 58%
rename from doc/howto/cluster/cluster_train_en.md
rename to doc/howto/cluster/cmd_argument_en.md
index f9424f8f1a..06fd571756 100644
--- a/doc/howto/cluster/cluster_train_en.md
+++ b/doc/howto/cluster/cmd_argument_en.md
@@ -1,40 +1,7 @@
-# Distributed Training
-
-## Introduction
-
-In this article, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
-
-<img src="https://user-images.githubusercontent.com/13348433/31772146-41523d84-b511-11e7-8a12-a69fd136c283.png" width="500">
-
-- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
-- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
-- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
-
-PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
-
-When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
-
-## Preparations
-1. Prepare your computer cluster. It's normally a bunch of Linux servers connected by LAN. Each server will be assigned a unique IP address. The computers in the cluster can be called "nodes".
-2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html) document. We strongly recommend using [Docker installation](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html).
-
-After installation, you can check the version by typing the below command (run a docker container  if using docker: `docker run -it paddlepaddle/paddle:[tag] /bin/bash`):
-
-```bash
-$ paddle version
-PaddlePaddle 0.10.0rc, compiled with
-    with_avx: ON
-    with_gpu: OFF
-    with_double: OFF
-    with_python: ON
-    with_rdma: OFF
-    with_timer: OFF
-```
-
-We'll take `doc/howto/usage/cluster/src/word2vec` as an example to introduce distributed training using PaddlePaddle v2 API.
-
 ## Command-line arguments
 
+We'll take `doc/howto/cluster/src/word2vec` as an example to introduce distributed training using PaddlePaddle v2 API.
+
 ### Starting parameter server
 
 Type the below command to start a parameter server which will wait for trainers to connect:
@@ -171,21 +138,3 @@ Your workspace may looks like:
 
 - `train_data_dir`: containing training data. Mount from storage service or copy trainning data to here.
 - `test_data_dir`: containing testing data.
-
-## Use cluster platforms or cluster management tools
-
-PaddlePaddle supports running jobs on several platforms including:
-- [Kubernetes](http://kubernetes.io) open-source system for automating deployment, scaling, and management of containerized applications from Google.
-- [OpenMPI](https://www.open-mpi.org) Mature high performance parallel computing framework.
-- [Fabric](http://www.fabfile.org) A cluster management tool. Write scripts to submit jobs or manage the cluster.
-
-We'll introduce cluster job management on these platforms. The examples can be found under [cluster_train_v2](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2).
-
-These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
-
-## Use different clusters
-
-  - [fabric](fabric_en.md)
-  - [openmpi](openmpi_en.md)
-  - [kubernetes](k8s_en.md)
-  - [kubernetes on AWS](k8s_aws_en.md)
diff --git a/doc/howto/cluster/index_cn.rst b/doc/howto/cluster/index_cn.rst
new file mode 100644
index 0000000000..c68b2655b6
--- /dev/null
+++ b/doc/howto/cluster/index_cn.rst
@@ -0,0 +1,10 @@
+分布式训练
+==========
+
+..  toctree::
+  :maxdepth: 1
+
+  introduction_cn.md
+  preparations_cn.md
+  cmd_argument_cn.md
+  multi_cluster/index_cn.rst
diff --git a/doc/howto/cluster/index_en.rst b/doc/howto/cluster/index_en.rst
new file mode 100644
index 0000000000..af957e06cd
--- /dev/null
+++ b/doc/howto/cluster/index_en.rst
@@ -0,0 +1,10 @@
+Distributed Training
+====================
+
+..  toctree::
+  :maxdepth: 1
+
+  introduction_en.md
+  preparations_en.md
+  cmd_argument_en.md
+  multi_cluster/index_en.rst
diff --git a/doc/howto/cluster/introduction_cn.md b/doc/howto/cluster/introduction_cn.md
new file mode 100644
index 0000000000..562008a898
--- /dev/null
+++ b/doc/howto/cluster/introduction_cn.md
@@ -0,0 +1,13 @@
+## 概述
+
+本节将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
+
+<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
+
+- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
+- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
+- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
+
+这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
+
+在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
diff --git a/doc/howto/cluster/introduction_en.md b/doc/howto/cluster/introduction_en.md
new file mode 100644
index 0000000000..eb70d7cf35
--- /dev/null
+++ b/doc/howto/cluster/introduction_en.md
@@ -0,0 +1,13 @@
+## Introduction
+
+In this section, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
+
+<img src="https://user-images.githubusercontent.com/13348433/31772146-41523d84-b511-11e7-8a12-a69fd136c283.png" width="500">
+
+- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
+- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
+- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
+
+PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
+
+When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
diff --git a/doc/howto/cluster/fabric_cn.md b/doc/howto/cluster/multi_cluster/fabric_cn.md
similarity index 100%
rename from doc/howto/cluster/fabric_cn.md
rename to doc/howto/cluster/multi_cluster/fabric_cn.md
diff --git a/doc/howto/cluster/fabric_en.md b/doc/howto/cluster/multi_cluster/fabric_en.md
similarity index 100%
rename from doc/howto/cluster/fabric_en.md
rename to doc/howto/cluster/multi_cluster/fabric_en.md
diff --git a/doc/howto/cluster/multi_cluster/index_cn.rst b/doc/howto/cluster/multi_cluster/index_cn.rst
new file mode 100644
index 0000000000..ef56b6ddb3
--- /dev/null
+++ b/doc/howto/cluster/multi_cluster/index_cn.rst
@@ -0,0 +1,20 @@
+在不同集群中运行
+================
+
+PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
+- `Kubernetes <http://kubernetes.io>`_ Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
+- `OpenMPI <https://www.open-mpi.org>`_ 成熟的高性能并行计算框架。
+- `Fabric <http://www.fabfile.org>`_ 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。
+
+对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在 `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ 找到。
+
+在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
+
+..  toctree::
+  :maxdepth: 1
+
+  fabric_cn.md
+  openmpi_cn.md
+  k8s_cn.md
+  k8s_distributed_cn.md
+  k8s_aws_cn.md
diff --git a/doc/howto/cluster/multi_cluster/index_en.rst b/doc/howto/cluster/multi_cluster/index_en.rst
new file mode 100644
index 0000000000..dac7aaef08
--- /dev/null
+++ b/doc/howto/cluster/multi_cluster/index_en.rst
@@ -0,0 +1,19 @@
+Use different clusters
+======================
+
+PaddlePaddle supports running jobs on several platforms including:
+- `Kubernetes <http://kubernetes.io>`_ open-source system for automating deployment, scaling, and management of containerized applications from Google.
+- `OpenMPI <https://www.open-mpi.org>`_ Mature high performance parallel computing framework.
+- `Fabric <http://www.fabfile.org>`_ A cluster management tool. Write scripts to submit jobs or manage the cluster.
+
+We'll introduce cluster job management on these platforms. The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .
+
+These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
+
+..  toctree::
+  :maxdepth: 1
+
+  fabric_en.md
+  openmpi_en.md
+  k8s_en.md
+  k8s_aws_en.md
diff --git a/doc/howto/cluster/k8s_aws_cn.md b/doc/howto/cluster/multi_cluster/k8s_aws_cn.md
similarity index 100%
rename from doc/howto/cluster/k8s_aws_cn.md
rename to doc/howto/cluster/multi_cluster/k8s_aws_cn.md
diff --git a/doc/howto/cluster/k8s_aws_en.md b/doc/howto/cluster/multi_cluster/k8s_aws_en.md
similarity index 100%
rename from doc/howto/cluster/k8s_aws_en.md
rename to doc/howto/cluster/multi_cluster/k8s_aws_en.md
diff --git a/doc/howto/cluster/k8s_cn.md b/doc/howto/cluster/multi_cluster/k8s_cn.md
similarity index 100%
rename from doc/howto/cluster/k8s_cn.md
rename to doc/howto/cluster/multi_cluster/k8s_cn.md
diff --git a/doc/howto/cluster/k8s_distributed_cn.md b/doc/howto/cluster/multi_cluster/k8s_distributed_cn.md
similarity index 100%
rename from doc/howto/cluster/k8s_distributed_cn.md
rename to doc/howto/cluster/multi_cluster/k8s_distributed_cn.md
diff --git a/doc/howto/cluster/k8s_en.md b/doc/howto/cluster/multi_cluster/k8s_en.md
similarity index 100%
rename from doc/howto/cluster/k8s_en.md
rename to doc/howto/cluster/multi_cluster/k8s_en.md
diff --git a/doc/howto/cluster/openmpi_cn.md b/doc/howto/cluster/multi_cluster/openmpi_cn.md
similarity index 100%
rename from doc/howto/cluster/openmpi_cn.md
rename to doc/howto/cluster/multi_cluster/openmpi_cn.md
diff --git a/doc/howto/cluster/openmpi_en.md b/doc/howto/cluster/multi_cluster/openmpi_en.md
similarity index 100%
rename from doc/howto/cluster/openmpi_en.md
rename to doc/howto/cluster/multi_cluster/openmpi_en.md
diff --git a/doc/howto/cluster/preparations_cn.md b/doc/howto/cluster/preparations_cn.md
new file mode 100644
index 0000000000..ce40697e70
--- /dev/null
+++ b/doc/howto/cluster/preparations_cn.md
@@ -0,0 +1,16 @@
+## 环境准备
+
+1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
+1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/index_cn.html)的多种安装方式。我们推荐使用[Docker](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html)安装方式来快速安装PaddlePaddle。
+
+安装完成之后，执行下面的命令可以查看已经安装的版本（docker安装方式可以进入docker容器执行：`docker run -it paddlepaddle/paddle:[tag] /bin/bash`）：
+```bash
+$ paddle version
+PaddlePaddle 0.10.0, compiled with
+    with_avx: ON
+    with_gpu: OFF
+    with_double: OFF
+    with_python: ON
+    with_rdma: OFF
+    with_timer: OFF
+```
diff --git a/doc/howto/cluster/preparations_en.md b/doc/howto/cluster/preparations_en.md
new file mode 100644
index 0000000000..4b77b29390
--- /dev/null
+++ b/doc/howto/cluster/preparations_en.md
@@ -0,0 +1,17 @@
+## Preparations
+
+1. Prepare your computer cluster. It's normally a bunch of Linux servers connected by LAN. Each server will be assigned a unique IP address. The computers in the cluster can be called "nodes".
+2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html) document. We strongly recommend using [Docker installation](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html).
+
+After installation, you can check the version by typing the below command (run a docker container  if using docker: `docker run -it paddlepaddle/paddle:[tag] /bin/bash`):
+
+```bash
+$ paddle version
+PaddlePaddle 0.10.0rc, compiled with
+    with_avx: ON
+    with_gpu: OFF
+    with_double: OFF
+    with_python: ON
+    with_rdma: OFF
+    with_timer: OFF
+```
diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst
index 37a34c113f..dd39ef9e79 100644
--- a/doc/howto/index_cn.rst
+++ b/doc/howto/index_cn.rst
@@ -5,7 +5,7 @@
   :maxdepth: 1
 
   cmd_parameter/index_cn.rst
-  cluster/cluster_train_cn.md
+  cluster/index_cn.rst
   capi/index_cn.rst
   rnn/index_cn.rst
   optimization/gpu_profiling_cn.rst
diff --git a/doc/howto/index_en.rst b/doc/howto/index_en.rst
index 3ba76d6aad..ae8b86f75b 100644
--- a/doc/howto/index_en.rst
+++ b/doc/howto/index_en.rst
@@ -5,6 +5,6 @@ HOW TO
   :maxdepth: 1
 
   cmd_parameter/index_en.rst
-  cluster/cluster_train_en.md
+  cluster/index_en.rst
   rnn/index_en.rst
   optimization/gpu_profiling_en.rst

From d2a31fb8aa653485c9364f896ba0f3c9c9253737 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 7 Feb 2018 15:20:50 +0800
Subject: [PATCH 288/314] refine unit test

---
 paddle/framework/channel_test.cc            | 147 +++++++-------------
 paddle/framework/details/buffered_channel.h |   7 +
 2 files changed, 56 insertions(+), 98 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index df9e15e22b..3b8150b427 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -115,7 +115,7 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
       sum += i;
     }
   });
-  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.5 sec
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.1 sec
   EXPECT_EQ(sum, 45U);
 
   CloseChannel(ch);
@@ -144,38 +144,34 @@ TEST(Channel, SimpleUnbufferedChannelTest) {
   delete ch;
 }
 
-// This tests that closing a buffered channel also unblocks
-//  any receivers waiting on the channel
-TEST(Channel, BufferedChannelCloseUnblocksReceiversTest) {
-  auto ch = MakeChannel<int>(1);
+void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
   size_t num_threads = 5;
   std::thread t[num_threads];
   bool thread_ended[num_threads];
 
-  // Launches threads that try to read and are blocked because of no writers
+  // Launches threads that try to read and are blocked becausew of no writers
   for (size_t i = 0; i < num_threads; i++) {
     thread_ended[i] = false;
     t[i] = std::thread(
         [&](bool *p) {
           int data;
-          // All reads should return false
           EXPECT_EQ(ch->Receive(&data), false);
           *p = true;
         },
         &thread_ended[i]);
   }
-  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.1 sec
 
-  // Verify that all threads are blocked
+  // Verify that all the threads are blocked
   for (size_t i = 0; i < num_threads; i++) {
     EXPECT_EQ(thread_ended[i], false);
   }
 
-  // Explicitly close the channel
+  // Explicitly close the thread
   // This should unblock all receivers
   CloseChannel(ch);
 
-  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait 0.1 sec
 
   // Verify that all threads got unblocked
   for (size_t i = 0; i < num_threads; i++) {
@@ -183,13 +179,12 @@ TEST(Channel, BufferedChannelCloseUnblocksReceiversTest) {
   }
 
   for (size_t i = 0; i < num_threads; i++) t[i].join();
-  delete ch;
 }
 
-// This tests that closing a buffered channel also unblocks
-//  any senders waiting for channel to have write space
-TEST(Channel, BufferedChannelCloseUnblocksSendersTest) {
-  auto ch = MakeChannel<int>(1);
+void ChannelCloseUnblocksSendersTest(Channel<int> *ch) {
+  using paddle::framework::details::Buffered;
+  using paddle::framework::details::UnBuffered;
+
   size_t num_threads = 5;
   std::thread t[num_threads];
   bool thread_ended[num_threads];
@@ -209,34 +204,56 @@ TEST(Channel, BufferedChannelCloseUnblocksSendersTest) {
   }
   std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
 
-  // Verify that atleast 4 threads are blocked
-  int ct = 0;
-  for (size_t i = 0; i < num_threads; i++) {
-    if (thread_ended[i] == false) ct++;
+  if (dynamic_cast<Buffered<int> *>(ch)) {
+    // If ch is Buffered, atleast 4 threads must be blocked.
+    int ct = 0;
+    for (size_t i = 0; i < num_threads; i++) {
+      if (!thread_ended[i]) ct++;
+    }
+    EXPECT_GE(ct, 4);
+  } else {
+    // If ch is UnBuffered, all the threads should be blocked.
+    for (size_t i = 0; i < num_threads; i++) {
+      EXPECT_EQ(thread_ended[i], false);
+    }
   }
-  // Atleast 4 threads must be blocked
-  EXPECT_GE(ct, 4);
-
   // Explicitly close the thread
   // This should unblock all senders
   CloseChannel(ch);
 
-  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
 
   // Verify that all threads got unblocked
   for (size_t i = 0; i < num_threads; i++) {
     EXPECT_EQ(thread_ended[i], true);
   }
 
-  // Verify that only 1 send was successful
-  ct = 0;
-  for (size_t i = 0; i < num_threads; i++) {
-    if (send_success[i]) ct++;
+  if (dynamic_cast<Buffered<int> *>(ch)) {
+    // Verify that only 1 send was successful
+    int ct = 0;
+    for (size_t i = 0; i < num_threads; i++) {
+      if (send_success[i]) ct++;
+    }
+    // Only 1 send must be successful
+    EXPECT_EQ(ct, 1);
   }
-  // Only 1 send must be successful
-  EXPECT_EQ(ct, 1);
 
   for (size_t i = 0; i < num_threads; i++) t[i].join();
+}
+
+// This tests that closing a buffered channel also unblocks
+//  any receivers waiting on the channel
+TEST(Channel, BufferedChannelCloseUnblocksReceiversTest) {
+  auto ch = MakeChannel<int>(1);
+  ChannelCloseUnblocksReceiversTest(ch);
+  delete ch;
+}
+
+// This tests that closing a buffered channel also unblocks
+//  any senders waiting for channel to have write space
+TEST(Channel, BufferedChannelCloseUnblocksSendersTest) {
+  auto ch = MakeChannel<int>(1);
+  ChannelCloseUnblocksSendersTest(ch);
   delete ch;
 }
 
@@ -244,40 +261,7 @@ TEST(Channel, BufferedChannelCloseUnblocksSendersTest) {
 //  unblocks any receivers waiting for senders
 TEST(Channel, UnbufferedChannelCloseUnblocksReceiversTest) {
   auto ch = MakeChannel<int>(0);
-  size_t num_threads = 5;
-  std::thread t[num_threads];
-  bool thread_ended[num_threads];
-
-  // Launches threads that try to read and are blocked becausew of no writers
-  for (size_t i = 0; i < num_threads; i++) {
-    thread_ended[i] = false;
-    t[i] = std::thread(
-        [&](bool *p) {
-          int data;
-          EXPECT_EQ(ch->Receive(&data), false);
-          *p = true;
-        },
-        &thread_ended[i]);
-  }
-  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
-
-  // Verify that all the threads are blocked
-  for (size_t i = 0; i < num_threads; i++) {
-    EXPECT_EQ(thread_ended[i], false);
-  }
-
-  // Explicitly close the thread
-  // This should unblock all receivers
-  CloseChannel(ch);
-
-  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
-
-  // Verify that all threads got unblocked
-  for (size_t i = 0; i < num_threads; i++) {
-    EXPECT_EQ(thread_ended[i], true);
-  }
-
-  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  ChannelCloseUnblocksReceiversTest(ch);
   delete ch;
 }
 
@@ -285,40 +269,7 @@ TEST(Channel, UnbufferedChannelCloseUnblocksReceiversTest) {
 //  unblocks any senders waiting for senders
 TEST(Channel, UnbufferedChannelCloseUnblocksSendersTest) {
   auto ch = MakeChannel<int>(0);
-  size_t num_threads = 5;
-  std::thread t[num_threads];
-  bool thread_ended[num_threads];
-
-  // Launches threads that try to read and are blocked becausew of no writers
-  for (size_t i = 0; i < num_threads; i++) {
-    thread_ended[i] = false;
-    t[i] = std::thread(
-        [&](bool *p) {
-          int data = 10;
-          EXPECT_EQ(ch->Send(&data), false);
-          *p = true;
-        },
-        &thread_ended[i]);
-  }
-  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
-
-  // Verify that all the threads are blocked
-  for (size_t i = 0; i < num_threads; i++) {
-    EXPECT_EQ(thread_ended[i], false);
-  }
-
-  // Explicitly close the thread
-  // This should unblock all receivers
-  CloseChannel(ch);
-
-  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
-
-  // Verify that all threads got unblocked
-  for (size_t i = 0; i < num_threads; i++) {
-    EXPECT_EQ(thread_ended[i], true);
-  }
-
-  for (size_t i = 0; i < num_threads; i++) t[i].join();
+  ChannelCloseUnblocksReceiversTest(ch);
   delete ch;
 }
 
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 00b63da4da..44bf84eb30 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -25,6 +25,13 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+// Four of the properties of Buffered Channel:
+// - A send to a full channel blocks temporarily until a receive from the
+// channel or the channel is closed
+// - A receive from an empty channel blocks temporarily until a send to the
+// channel or the channel is closed
+// - A send to a closed channel returns false immediately
+// - A receive from a closed channel returns false immediately
 template <typename T>
 class Buffered : public paddle::framework::Channel<T> {
   friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);

From dff5a8e6da5038e549c7ab66e1dbb94f619646d9 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 7 Feb 2018 15:48:50 +0800
Subject: [PATCH 289/314] add the properties of buffered channel and unbuffered
 channel

---
 paddle/framework/details/buffered_channel.h   | 9 +++++----
 paddle/framework/details/unbuffered_channel.h | 7 +++++++
 2 files changed, 12 insertions(+), 4 deletions(-)

diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 44bf84eb30..4275f919ba 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -27,11 +27,12 @@ namespace details {
 
 // Four of the properties of Buffered Channel:
 // - A send to a full channel blocks temporarily until a receive from the
-// channel or the channel is closed
+// channel or the channel is closed.
 // - A receive from an empty channel blocks temporarily until a send to the
-// channel or the channel is closed
-// - A send to a closed channel returns false immediately
-// - A receive from a closed channel returns false immediately
+// channel or the channel is closed.
+// - A send to a closed channel returns false immediately.
+// - A receive from a closed channel returns false immediately.
+
 template <typename T>
 class Buffered : public paddle::framework::Channel<T> {
   friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index 815cebad2d..bc4233af73 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -23,6 +23,13 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+// Four of the properties of UnBuffered Channel:
+// - A send to a channel blocks temporarily until a receive from the
+// channel or the channel is closed.
+// - A receive from a channel blocks temporarily until a send to the
+// channel or the channel is closed.
+// - A send to a closed channel returns false immediately.
+// - A receive from a closed channel returns false immediately.
 template <typename T>
 class UnBuffered : public paddle::framework::Channel<T> {
   friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);

From ee7d8421907affb362b9ed9baa0150f734d2c33c Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Wed, 7 Feb 2018 15:01:23 +0800
Subject: [PATCH 290/314] Update doc and follow comments.

---
 paddle/operators/target_assign_op.cc          | 58 ++++++++++++++-----
 paddle/operators/target_assign_op.cu          | 26 ++++-----
 paddle/operators/target_assign_op.h           | 47 ++++++++-------
 .../v2/fluid/tests/test_target_assign_op.py   |  4 --
 4 files changed, 83 insertions(+), 52 deletions(-)

diff --git a/paddle/operators/target_assign_op.cc b/paddle/operators/target_assign_op.cc
index 9c7d625136..615ca857ce 100644
--- a/paddle/operators/target_assign_op.cc
+++ b/paddle/operators/target_assign_op.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -61,10 +61,12 @@ class TargetAssignOp : public framework::OperatorWithKernel {
                       "The rank of Input(NegIndices) must be 2.");
 
     PADDLE_ENFORCE_EQ(blabel_dims[0], slabel_dims[0],
-                      "The 1st dimension of Input(EncodedGTBBox) and "
-                      "Input(GTScoreLabel) must be the same.");
+                      "The 1st dimension (means the total number of "
+                      "ground-truth bounding boxes) of Input(EncodedGTBBox) "
+                      "and Input(GTScoreLabel) must be the same.");
     PADDLE_ENFORCE_EQ(blabel_dims[1], mi_dims[1],
-                      "The 2nd dimension of Input(EncodedGTBBox) and "
+                      "The 2nd dimension (means the number of priod boxes) "
+                      "of Input(EncodedGTBBox) and "
                       "Input(MatchIndices) must be the same.");
     PADDLE_ENFORCE_EQ(blabel_dims[2], 4,
                       "The 3rd dimension of Input(EncodedGTBBox) must be 4.");
@@ -101,31 +103,31 @@ class TargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
              "labels with shape [Ng, 1], where the Ng is the same as it in "
              "the input of EncodedGTBBox.");
     AddInput("MatchIndices",
-             "(Tensor, default LoDTensor<int>), The input matched indices "
+             "(Tensor, default Tensor<int>), The input matched indices "
              "with shape [N, Np], where N is the batch size, Np is the same "
              "as it in the input of EncodedGTBBox. If MatchIndices[i][j] "
              "is -1, the j-th prior box is not matched to any ground-truh "
              "box in i-th instance.");
     AddInput("NegIndices",
              "(LoDTensor, default LoDTensor<int>), The input negative example "
-             "indics with shape [Neg, 1], where is the total number of "
+             "indices with shape [Neg, 1], where is the total number of "
              "negative example indices.");
     AddAttr<int>("background_label",
-                 "(int, default 0), Label id for background class.")
+                 "(int, default 0), Label index of background class.")
         .SetDefault(0);
     AddOutput("PredBBoxLabel",
               "(Tensor), The output encoded ground-truth labels "
               "with shape [N, Np, 4], N is the batch size and Np, 4 is the "
               "same as they in input of EncodedGTBBox. If MatchIndices[i][j] "
               "is -1, the PredBBoxLabel[i][j][:] is the encoded ground-truth "
-              "box for background_label_id in i-th instance.");
+              "box for background_label in i-th instance.");
     AddOutput("PredBBoxWeight",
               "(Tensor), The weight for PredBBoxLabel with the shape "
               "of [N, Np, 1]");
     AddOutput("PredScoreLabel",
               "(Tensor, default Tensor<int>), The output score labels for "
               "each predictions with shape [N, Np, 1]. If MatchIndices[i][j] "
-              "is -1, PredScoreLabel[i][j] = background_label_id.");
+              "is -1, PredScoreLabel[i][j] = background_label.");
     AddOutput("PredScoreWeight",
               "(Tensor), The weight for PredScoreLabel with the shape "
               "of [N, Np, 1]");
@@ -136,19 +138,47 @@ and regression targets to each prior box as well as weights to each
 prior box. The weights is used to specify which prior box would not contribute
 to training loss.
 
-TODO(dang qingqing) add an example.
+For each instance, the output `PredBBoxLabel`, `PredBBoxWeight`,
+`PredScoreLabel` and `PredScoreWeight` are assigned based on `MatchIndices`.
+Assumed that the row offset for each instance in `EncodedGTBBox` is called lod,
+this operato assigns classification/regression targets by performing the
+following steps:
+
+1. Assigning all outpts based on `MatchIndices`:
+
+If id = MatchIndices[i][j] > 0,
+
+    PredBBoxLabel[i][j] = EncodedGTBBox[lod[i] + id][j]
+    PredBBoxWeight[i][j] = 1.
+    PredScoreLabel[i][j] = GTScoreLabel[lod[i] + id]
+    PredScoreWeight[i][j] = 1.
+
+Otherwise, 
+
+    PredBBoxLabel[j][j] = [0., 0., 0., 0.]
+    PredBBoxWeight[i][j] = 0.
+    PredScoreLabel[i][j] = background_label
+    PredScoreWeight[i][j] = 0.
+
+2. Assigning PredScoreWeight based on `NegIndices`:
+
+Assumed that the row offset for each instance in `NegIndices` is caleed neg_lod,
+for i-th instance and all ids of NegIndices in this instance:
+
+    PredScoreLabel[i][id] = background_label
+    PredScoreWeight[i][id] = 1.0
 
     )DOC");
   }
 };
 
 template <typename T>
-struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, T> {
+struct NegTargetAssignFunctor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& ctx, const int* neg_indices,
                   const size_t* lod, const int num, const int num_prior_box,
                   const int background_label, int* out_label, T* out_label_wt) {
     for (int i = 0; i < num; ++i) {
-      for (int j = lod[i]; j < lod[i + 1]; ++j) {
+      for (size_t j = lod[i]; j < lod[i + 1]; ++j) {
         int id = neg_indices[j];
         out_label[i * num_prior_box + id] = background_label;
         out_label_wt[i * num_prior_box + id] = static_cast<T>(1.0);
@@ -157,8 +187,8 @@ struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, T> {
   }
 };
 
-template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, float>;
-template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, double>;
+template struct NegTargetAssignFunctor<platform::CPUDeviceContext, float>;
+template struct NegTargetAssignFunctor<platform::CPUDeviceContext, double>;
 
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/target_assign_op.cu b/paddle/operators/target_assign_op.cu
index c04de86ec5..fc0a1000a4 100644
--- a/paddle/operators/target_assign_op.cu
+++ b/paddle/operators/target_assign_op.cu
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -18,38 +18,38 @@ namespace paddle {
 namespace operators {
 
 template <typename T>
-__global__ void UpdateTargetLabelKernel(const int* neg_indices,
-                                        const size_t* lod, const int num,
-                                        const int num_prior_box,
-                                        const int background_label,
-                                        int* out_label, T* out_label_wt) {
+__global__ void NegTargetAssignKernel(const int* neg_indices, const size_t* lod,
+                                      const int num, const int num_prior_box,
+                                      const int background_label,
+                                      int* out_label, T* out_label_wt) {
   int bidx = blockIdx.x;
   int st = lod[bidx];
   int ed = lod[bidx + 1];
 
+  int row_start = bidx * num_prior_box;
   for (int i = st + threadIdx.x; i < ed; i += blockDim.x) {
-    int id = neg_indices[i];
-    out_label[bidx * num_prior_box + id] = background_label;
-    out_label_wt[bidx * num_prior_box + id] = 1.;
+    int id = row_start + neg_indices[i];
+    out_label[id] = background_label;
+    out_label_wt[id] = 1.;
   }
 }
 
 template <typename T>
-struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, T> {
+struct NegTargetAssignFunctor<platform::CUDADeviceContext, T> {
   void operator()(const platform::CUDADeviceContext& ctx,
                   const int* neg_indices, const size_t* lod, const int num,
                   const int num_prior_box, const int background_label,
                   int* out_label, T* out_label_wt) {
     const int block_size = 256;
     const int grid_size = num;
-    UpdateTargetLabelKernel<T><<<grid_size, block_size, 0, ctx.stream()>>>(
+    NegTargetAssignKernel<T><<<grid_size, block_size, 0, ctx.stream()>>>(
         neg_indices, lod, num, num_prior_box, background_label, out_label,
         out_label_wt);
   }
 };
 
-template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, float>;
-template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, double>;
+template struct NegTargetAssignFunctor<platform::CUDADeviceContext, float>;
+template struct NegTargetAssignFunctor<platform::CUDADeviceContext, double>;
 
 }  // namespace operators
 }  // namespace paddle
diff --git a/paddle/operators/target_assign_op.h b/paddle/operators/target_assign_op.h
index 267bdbf1ef..82fca5724c 100644
--- a/paddle/operators/target_assign_op.h
+++ b/paddle/operators/target_assign_op.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -56,40 +56,41 @@ struct TargetAssignFunctor {
     int row = i / num_prior_box_;
     int col = i - row * num_prior_box_;
 
-    size_t off = lod_[row];
+    size_t row_off = lod_[row];
+    int offset = row * num_prior_box_ + col;
 
-    int id = match_indices_[row * num_prior_box_ + col];
-    T* obox = out_box_ + (row * num_prior_box_ + col) * 4;
-    int* olabel = out_label_ + row * num_prior_box_ + col;
-    T* obox_wt = out_box_wt_ + row * num_prior_box_ + col;
-    T* olabel_wt = out_label_wt_ + row * num_prior_box_ + col;
+    int id = match_indices_[offset];
+    T* obox = out_box_ + offset * 4;
+    int* olabel = out_label_ + offset;
+    T* obox_wt = out_box_wt_ + offset;
+    T* olabel_wt = out_label_wt_ + offset;
 
     if (id > -1) {
-      const T* gtbox = gt_box_ + ((off + id) * num_prior_box_ + col) * 4;
+      const T* gtbox = gt_box_ + ((row_off + id) * num_prior_box_ + col) * 4;
 
       obox[0] = gtbox[0];
       obox[1] = gtbox[1];
       obox[2] = gtbox[2];
       obox[3] = gtbox[3];
 
-      olabel[0] = gt_label_[off + id];
-      obox_wt[0] = 1.;
-      olabel_wt[0] = 1.;
+      olabel[0] = gt_label_[row_off + id];
+      obox_wt[0] = static_cast<T>(1.);
+      olabel_wt[0] = static_cast<T>(1.);
     } else {
-      obox[0] = 0.;
-      obox[1] = 0.;
-      obox[2] = 0.;
-      obox[3] = 0.;
+      obox[0] = static_cast<T>(0.);
+      obox[1] = static_cast<T>(0.);
+      obox[2] = static_cast<T>(0.);
+      obox[3] = static_cast<T>(0.);
 
       olabel[0] = background_label_;
-      obox_wt[0] = 0.;
-      olabel_wt[0] = 0.;
+      obox_wt[0] = static_cast<T>(0.);
+      olabel_wt[0] = static_cast<T>(0.);
     }
   }
 };
 
 template <typename DeviceContext, typename T>
-struct UpdateTargetLabelFunctor {
+struct NegTargetAssignFunctor {
   void operator()(const platform::DeviceContext& ctx, const int* neg_indices,
                   const size_t* lod, const int num, const int num_prior_box,
                   const int background_label, int* out_label,
@@ -130,7 +131,11 @@ class TargetAssignKernel : public framework::OpKernel<T> {
     int64_t num_prior_box = match_indices->dims()[1];
 
     auto gt_lod = enc_gt_box->lod().back();
+    auto gt_label_lod = gt_label->lod().back();
     auto neg_lod = neg_indices->lod().back();
+    for (size_t i = 0; i < gt_lod.size(); ++i) {
+      PADDLE_ENFORCE_EQ(gt_lod.data()[i], gt_label_lod.data()[i]);
+    }
 
     size_t* gt_lod_data = gt_lod.data(ctx.GetPlace());
     size_t* neg_lod_data = neg_lod.data(ctx.GetPlace());
@@ -145,9 +150,9 @@ class TargetAssignKernel : public framework::OpKernel<T> {
                                                 num * num_prior_box);
     for_range(functor);
 
-    UpdateTargetLabelFunctor<DeviceContext, T> update_functor;
-    update_functor(device_ctx, neg_idx_data, neg_lod_data, num, num_prior_box,
-                   background_label, olabel_data, olabel_wt_data);
+    NegTargetAssignFunctor<DeviceContext, T> neg_trg_functor;
+    neg_trg_functor(device_ctx, neg_idx_data, neg_lod_data, num, num_prior_box,
+                    background_label, olabel_data, olabel_wt_data);
   }
 };
 
diff --git a/python/paddle/v2/fluid/tests/test_target_assign_op.py b/python/paddle/v2/fluid/tests/test_target_assign_op.py
index 49edff5c7f..8a1155c621 100755
--- a/python/paddle/v2/fluid/tests/test_target_assign_op.py
+++ b/python/paddle/v2/fluid/tests/test_target_assign_op.py
@@ -14,8 +14,6 @@
 
 import unittest
 import numpy as np
-import math
-import sys
 import random
 from op_test import OpTest
 
@@ -89,8 +87,6 @@ class TestTargetAssginOp(OpTest):
         num_class = 21
         gt_lod = [0, 5, 11, 23]
         neg_lod = [0, 4, 7, 13]
-        #gt_lod = [0, 2, 5]
-        #neg_lod = [0, 2, 4]
         batch_size = len(gt_lod) - 1
         num_gt = gt_lod[-1]
         background_label = 0

From b41205d9a6b71f26694c2cdb979555c261548629 Mon Sep 17 00:00:00 2001
From: Yu Yang <yuyang18@baidu.com>
Date: Wed, 7 Feb 2018 02:57:13 -0500
Subject: [PATCH 291/314] Disable BUILD_TESTS for warpctc (#8210)

* It will sightly faster compile and make warpctc compile
well on CUDA 9 and GCC 5.5
---
 cmake/external/warpctc.cmake | 1 +
 1 file changed, 1 insertion(+)

diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake
index 7cb4efa7bf..5fa60df7b3 100644
--- a/cmake/external/warpctc.cmake
+++ b/cmake/external/warpctc.cmake
@@ -52,6 +52,7 @@ ExternalProject_Add(
                     -DWITH_TORCH=OFF
                     -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
                     -DBUILD_SHARED=ON
+                    -DBUILD_TESTS=OFF
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                     -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}

From 56ebb76c000fd56ba46baf64781a8c2df18955cd Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 7 Feb 2018 15:48:50 +0800
Subject: [PATCH 292/314] add the properties of buffered channel and unbuffered
 channel

---
 paddle/framework/channel_test.cc              | 4 ++--
 paddle/framework/details/buffered_channel.h   | 9 +++++----
 paddle/framework/details/unbuffered_channel.h | 7 +++++++
 3 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 3b8150b427..95360d7b77 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -149,7 +149,7 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
   std::thread t[num_threads];
   bool thread_ended[num_threads];
 
-  // Launches threads that try to read and are blocked becausew of no writers
+  // Launches threads that try to read and are blocked because of no writers
   for (size_t i = 0; i < num_threads; i++) {
     thread_ended[i] = false;
     t[i] = std::thread(
@@ -167,7 +167,7 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
     EXPECT_EQ(thread_ended[i], false);
   }
 
-  // Explicitly close the thread
+  // Explicitly close the channel
   // This should unblock all receivers
   CloseChannel(ch);
 
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 44bf84eb30..4275f919ba 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -27,11 +27,12 @@ namespace details {
 
 // Four of the properties of Buffered Channel:
 // - A send to a full channel blocks temporarily until a receive from the
-// channel or the channel is closed
+// channel or the channel is closed.
 // - A receive from an empty channel blocks temporarily until a send to the
-// channel or the channel is closed
-// - A send to a closed channel returns false immediately
-// - A receive from a closed channel returns false immediately
+// channel or the channel is closed.
+// - A send to a closed channel returns false immediately.
+// - A receive from a closed channel returns false immediately.
+
 template <typename T>
 class Buffered : public paddle::framework::Channel<T> {
   friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index 815cebad2d..bc4233af73 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -23,6 +23,13 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+// Four of the properties of UnBuffered Channel:
+// - A send to a channel blocks temporarily until a receive from the
+// channel or the channel is closed.
+// - A receive from a channel blocks temporarily until a send to the
+// channel or the channel is closed.
+// - A send to a closed channel returns false immediately.
+// - A receive from a closed channel returns false immediately.
 template <typename T>
 class UnBuffered : public paddle::framework::Channel<T> {
   friend Channel<T>* paddle::framework::MakeChannel<T>(size_t);

From 5210ff015870ecd002b1048a1aa45c94116e6bbc Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 7 Feb 2018 16:04:12 +0800
Subject: [PATCH 293/314] add cpp_data_feeding.md

---
 doc/design/cpp_data_feeding.md | 79 ++++++++++++++++++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 doc/design/cpp_data_feeding.md

diff --git a/doc/design/cpp_data_feeding.md b/doc/design/cpp_data_feeding.md
new file mode 100644
index 0000000000..40205350f9
--- /dev/null
+++ b/doc/design/cpp_data_feeding.md
@@ -0,0 +1,79 @@
+# C++ Data Feeding
+
+In training with Paddle V2 API, data feeding wholly dependents on Python code. To get rid of the Python environment and achieve the goal of "wrapping the whole training by a while loop op" in Paddle Fluid, a C++ data feeding mechanism is required. 
+
+In this document we show the fundamental design of C++ data feeding process, which includes the data reading, shuffling and batching.
+
+## Reader
+
+A new concept named 'Reader' is introduced. `Reader` is a series of inherited classes which can be hold by our `Variable` and they are used to read or process file data.
+
+
+### `ReaderBase`
+
+`ReaderBase` is the abstract base class of all readers. It defines the all readers' interfaces.
+
+```cpp
+class ReaderBase {
+ public:
+  explicit ReaderBase(const std::vector<DDim>& shapes) : shapes_(shapes) {
+    PADDLE_ENFORCE(!shapes_.empty());
+  }
+  // Read the next batch of data. (A 'batch' can be only one instance)
+  virtual void ReadNext(std::vector<LoDTensor>* out) = 0;
+  // Show whether the next bacth exists.
+  virtual bool HasNext() const = 0;
+  
+  // Reinitialize the reader and read the file from the begin.
+  virtual void ReInit() = 0;
+  
+  // Get a certain read in data's shape.
+  DDim shape(size_t idx) const;
+  // Get shapes of all read in data.
+  std::vector<DDim> shapes() const { return shapes_; }
+  // Set shapes of read in data.
+  void set_shapes(const std::vector<DDim>& shapes) { shapes_ = shapes; }
+
+  virtual ~ReaderBase() {}
+
+ protected:
+  std::vector<DDim> shapes_;
+};
+```
+
+### `FileReader` and `DecoratedReader`
+
+These two classes are derived from the `ReaderBase` and will further be derived by respective specific readers. That is to say, in our design, there are two kinds of readers: file readers and decorated readers. A file reader reads from a file of some specific format, and yield only one instance of data at a time. e.g. RecordIO reader, jpg reader, .... A decorated reader takes another reader(both file reader and decorated reader are OK) as its 'underlying reader'. It gets data from its underlying reader, does some process on them(shuffling, or batching), then yields processed data. The output data of a decorated reader can be a single instance or a batch. `ShuffleReader` and `BatchReader` are both decorated readers.
+
+All the readers share exactly the same interfaces defined in `ReaderBase`. So they can be decorated for more than one time: We can **shuffle** a reader's outputs and then **batch** the shuffle outputs. The interface consistency also allows related ops use readers without knowing what they are exactly.
+
+
+### `ReaderHolder`
+
+Different readers belong to different class types. It leads to a problem: How can we drop them into `Variable`s and fetch them out by a unified method? For example, if a Variable holds a `BatchReader`, we can not get it by the following code:
+
+```cpp
+var->Get<ReaderBase>("batch_reader");
+```
+
+we have to write:
+
+```cpp
+var->Get<BatchReader>("batch_reader");
+```
+
+This requires each time getting a reader from a variable we must know the reader's type exactly. It is nearly impossible.
+
+To solve this problem, we introduce `ReaderHolder` as a wrapper. It acts as an empty decorator of `ReaderBase`, which erases reader's type. With `ReaderHolder` we are able to fetch all types of readers by `var->Get<ReaderHolder>("...")` and regard the obtained object as a reader.
+
+## Related Operators
+
+To create and invoke readers, some now ops are introduced:
+
+### `CreateReaderOp`
+
+Each reader has its creating op. File readers' creating ops have no input and yield the created file reader as its output. Decorated readers' creating ops take the underlying readers as inputs and then yield new decorated readers.
+
+### `ReadOp`
+
+A reader is only a Variable. It cannot trigger the reading process by itself. So we add the `ReadOp` to execute it. A `ReadOp` takes a reader Variable as its input. Each time it runs, it invokes the reader‘s `ReadNext()` function and gets a new batch of data(or only one instance of data, if we use file reader directly). The output data of a reader are in the form of `std::vector<LoDTenosr>`, so the `ReadOp` also needs to split the vector and move LoDTensors to their respective output Variables.

From 16e005e917e321bd8094e24482fb36047401b626 Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Wed, 7 Feb 2018 17:11:03 +0800
Subject: [PATCH 294/314] fix dead links after adjustation

---
 doc/getstarted/concepts/use_concepts_cn.rst         |   2 +-
 .../{ => multi_cluster}/src/add_security_group.png  | Bin
 .../cluster/{ => multi_cluster}/src/create_efs.png  | Bin
 .../{ => multi_cluster}/src/k8s-paddle-arch.png     | Bin
 .../{ => multi_cluster}/src/k8s_data/Dockerfile     |   0
 .../{ => multi_cluster}/src/k8s_data/README.md      |   0
 .../{ => multi_cluster}/src/k8s_data/get_data.sh    |   0
 .../{ => multi_cluster}/src/k8s_train/Dockerfile    |   0
 .../{ => multi_cluster}/src/k8s_train/README.md     |   0
 .../{ => multi_cluster}/src/k8s_train/start.sh      |   0
 .../src/k8s_train/start_paddle.py                   |   0
 .../{ => multi_cluster}/src/pserver_and_trainer.png | Bin
 .../src/route53_create_recordset.png                | Bin
 .../{ => multi_cluster}/src/route53_create_zone.png | Bin
 .../src/worker_security_group.png                   | Bin
 doc/howto/index_cn.rst                              |   2 +-
 16 files changed, 2 insertions(+), 2 deletions(-)
 rename doc/howto/cluster/{ => multi_cluster}/src/add_security_group.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/create_efs.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s-paddle-arch.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_data/Dockerfile (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_data/README.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_data/get_data.sh (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_train/Dockerfile (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_train/README.md (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_train/start.sh (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/k8s_train/start_paddle.py (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/pserver_and_trainer.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/route53_create_recordset.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/route53_create_zone.png (100%)
 rename doc/howto/cluster/{ => multi_cluster}/src/worker_security_group.png (100%)

diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst
index e695ff283e..608f49f5a9 100644
--- a/doc/getstarted/concepts/use_concepts_cn.rst
+++ b/doc/getstarted/concepts/use_concepts_cn.rst
@@ -4,7 +4,7 @@
 
 PaddlePaddle是源于百度的一个深度学习平台。PaddlePaddle为深度学习研究人员提供了丰富的API，可以轻松地完成神经网络配置，模型训练等任务。
 这里将介绍PaddlePaddle的基本使用概念，并且展示了如何利用PaddlePaddle来解决一个经典的线性回归问题。
-在使用该文档之前，请参考 `安装文档 <../build_and_install/index_cn.html>`_ 完成PaddlePaddle的安装。
+在使用该文档之前，请参考 `安装文档 <../../build_and_install/index_cn.html>`_ 完成PaddlePaddle的安装。
 
 
 配置网络
diff --git a/doc/howto/cluster/src/add_security_group.png b/doc/howto/cluster/multi_cluster/src/add_security_group.png
similarity index 100%
rename from doc/howto/cluster/src/add_security_group.png
rename to doc/howto/cluster/multi_cluster/src/add_security_group.png
diff --git a/doc/howto/cluster/src/create_efs.png b/doc/howto/cluster/multi_cluster/src/create_efs.png
similarity index 100%
rename from doc/howto/cluster/src/create_efs.png
rename to doc/howto/cluster/multi_cluster/src/create_efs.png
diff --git a/doc/howto/cluster/src/k8s-paddle-arch.png b/doc/howto/cluster/multi_cluster/src/k8s-paddle-arch.png
similarity index 100%
rename from doc/howto/cluster/src/k8s-paddle-arch.png
rename to doc/howto/cluster/multi_cluster/src/k8s-paddle-arch.png
diff --git a/doc/howto/cluster/src/k8s_data/Dockerfile b/doc/howto/cluster/multi_cluster/src/k8s_data/Dockerfile
similarity index 100%
rename from doc/howto/cluster/src/k8s_data/Dockerfile
rename to doc/howto/cluster/multi_cluster/src/k8s_data/Dockerfile
diff --git a/doc/howto/cluster/src/k8s_data/README.md b/doc/howto/cluster/multi_cluster/src/k8s_data/README.md
similarity index 100%
rename from doc/howto/cluster/src/k8s_data/README.md
rename to doc/howto/cluster/multi_cluster/src/k8s_data/README.md
diff --git a/doc/howto/cluster/src/k8s_data/get_data.sh b/doc/howto/cluster/multi_cluster/src/k8s_data/get_data.sh
similarity index 100%
rename from doc/howto/cluster/src/k8s_data/get_data.sh
rename to doc/howto/cluster/multi_cluster/src/k8s_data/get_data.sh
diff --git a/doc/howto/cluster/src/k8s_train/Dockerfile b/doc/howto/cluster/multi_cluster/src/k8s_train/Dockerfile
similarity index 100%
rename from doc/howto/cluster/src/k8s_train/Dockerfile
rename to doc/howto/cluster/multi_cluster/src/k8s_train/Dockerfile
diff --git a/doc/howto/cluster/src/k8s_train/README.md b/doc/howto/cluster/multi_cluster/src/k8s_train/README.md
similarity index 100%
rename from doc/howto/cluster/src/k8s_train/README.md
rename to doc/howto/cluster/multi_cluster/src/k8s_train/README.md
diff --git a/doc/howto/cluster/src/k8s_train/start.sh b/doc/howto/cluster/multi_cluster/src/k8s_train/start.sh
similarity index 100%
rename from doc/howto/cluster/src/k8s_train/start.sh
rename to doc/howto/cluster/multi_cluster/src/k8s_train/start.sh
diff --git a/doc/howto/cluster/src/k8s_train/start_paddle.py b/doc/howto/cluster/multi_cluster/src/k8s_train/start_paddle.py
similarity index 100%
rename from doc/howto/cluster/src/k8s_train/start_paddle.py
rename to doc/howto/cluster/multi_cluster/src/k8s_train/start_paddle.py
diff --git a/doc/howto/cluster/src/pserver_and_trainer.png b/doc/howto/cluster/multi_cluster/src/pserver_and_trainer.png
similarity index 100%
rename from doc/howto/cluster/src/pserver_and_trainer.png
rename to doc/howto/cluster/multi_cluster/src/pserver_and_trainer.png
diff --git a/doc/howto/cluster/src/route53_create_recordset.png b/doc/howto/cluster/multi_cluster/src/route53_create_recordset.png
similarity index 100%
rename from doc/howto/cluster/src/route53_create_recordset.png
rename to doc/howto/cluster/multi_cluster/src/route53_create_recordset.png
diff --git a/doc/howto/cluster/src/route53_create_zone.png b/doc/howto/cluster/multi_cluster/src/route53_create_zone.png
similarity index 100%
rename from doc/howto/cluster/src/route53_create_zone.png
rename to doc/howto/cluster/multi_cluster/src/route53_create_zone.png
diff --git a/doc/howto/cluster/src/worker_security_group.png b/doc/howto/cluster/multi_cluster/src/worker_security_group.png
similarity index 100%
rename from doc/howto/cluster/src/worker_security_group.png
rename to doc/howto/cluster/multi_cluster/src/worker_security_group.png
diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst
index dd39ef9e79..0c534f107b 100644
--- a/doc/howto/index_cn.rst
+++ b/doc/howto/index_cn.rst
@@ -1,4 +1,4 @@
-进阶指南
+进阶使用
 ========
 
 ..  toctree::

From 83df277ff123d7b102f405cdb512457841f11a32 Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Wed, 7 Feb 2018 17:33:27 +0800
Subject: [PATCH 295/314] Refine get_cfgs method of memory optimization
 transpiler (#8080)

* refine get cfgs method in memory optimization transpiler

* clean code
---
 .../fluid/memory_optimization_transpiler.py   | 21 +++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py
index 2b00923f5e..11e2cfb3cc 100644
--- a/python/paddle/v2/fluid/memory_optimization_transpiler.py
+++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py
@@ -145,7 +145,6 @@ class ControlFlowGraph(object):
             if op.type() == "while" or op.type() == "while_grad":
                 continue
             block_desc = op.block()
-            self.current_block_desc = block_desc
             is_forward = i < self._forward_num
             if self.pool:
                 defs_can_optimize = filter(
@@ -208,17 +207,17 @@ def get_cfgs(input_program):
 
     while_sub_block_ids = []
     while_grad_sub_block_ids = []
-    while_op_output = set()
     while_block_id_pair = []
+    while_op_dict = {}
 
     for i in range(op_size):
         op = block_desc.op(i)
         if op.type() == "while":
             while_sub_block_ids.append(op.attr("sub_block").id)
-            while_op_output.update(op.output_arg_names())
+            while_op_dict[op.attr("sub_block").id] = op
         elif op.type() == "while_grad":
             while_grad_sub_block_ids.append(op.attr("sub_block").id)
-            while_op_output.update(op.output_arg_names())
+            while_op_dict[op.attr("sub_block").id] = op
 
     # Find while/while_grad block pair
     for grad_id in while_grad_sub_block_ids:
@@ -240,6 +239,10 @@ def get_cfgs(input_program):
         for i in range(while_grad_block_op_size):
             while_block_ops.append(while_grad_block.op(i))
 
+        while_op_output = set()
+        while_op_output.update(while_op_dict[parent_id].output_arg_names())
+        while_op_output.update(while_op_dict[grad_id].output_arg_names())
+
         ops_list.append((while_block_ops, while_block_op_size, while_op_output))
 
     # Process rest while block ops
@@ -250,9 +253,15 @@ def get_cfgs(input_program):
         for i in range(while_block_op_size):
             while_block_ops.append(while_block.op(i))
 
-        ops_list.append((while_block_ops, while_block_op_size))
+        while_op_output = set()
+        while_op_output.update(while_op_dict[parent_id].output_arg_names())
+
+        ops_list.append((while_block_ops, while_block_op_size, while_op_output))
 
-    cfgs = [ControlFlowGraph(input_program, i, j, k) for i, j, k in ops_list]
+    cfgs = [
+        ControlFlowGraph(input_program, ops, forward_num, skip_opt)
+        for ops, forward_num, skip_opt in ops_list
+    ]
     return cfgs
 
 

From c74445017d35ad344c5fc2a19a35c47a72358f3c Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Wed, 7 Feb 2018 19:18:48 +0800
Subject: [PATCH 296/314] refine distribute transpiler

---
 .../paddle/v2/fluid/distribute_transpiler.py  | 124 +++++++++++++-----
 1 file changed, 89 insertions(+), 35 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 121b407cae..4eb103cc6b 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -300,6 +300,9 @@ class DistributeTranspiler:
             pass
         return orig_shape
 
+    def _op_input_var(self, op, varname):
+        pass
+
     def _is_op_on_pserver(self, endpoint, all_ops, idx):
         """
         Recursively check if the op need to run on current server.
@@ -309,29 +312,35 @@ class DistributeTranspiler:
             p.name for p in self.param_grad_ep_mapping[endpoint]["params"]
         ]
         op = all_ops[idx]
-        if op.inputs.has_key("Param"):
-            if op.inputs["Param"].name in param_names:
+        input_names = set(op.input_names)
+        # TODO(typhoonzero): using Param and Grad input name to identify
+        # that the operator is an optimization operator, need a better way.
+        if "Param" in input_names:
+            if op.input("Param")[0] in param_names:
                 return True
             else:
                 for n in param_names:
-                    if same_or_split_var(n, op.inputs[
-                            "Param"].name) and n != op.inputs["Param"].name:
+                    if same_or_split_var(n, op.input("Param")[0]) \
+                            and n != op.input("Param")[0]:
                         return True
                 return False
         else:
             j = idx - 1
             while j >= 0:
                 prev_op = all_ops[j]
-                prev_output_names = [o.name for o in prev_op.outputs.values()]
-                prev_input_names = [o.name for o in prev_op.inputs.values()]
+                # prev_output_names = [o.name for o in prev_op.outputs.values()]
+                # prev_input_names = [o.name for o in prev_op.inputs.values()]
+                # NOTE(typhoonzero): consider list input/output
+                prev_output_names = prev_op.desc.output_arg_names()
+                prev_input_names = prev_op.desc.input_arg_names()
                 found1 = False
                 found2 = False
-                for _, v in op.inputs.iteritems():
-                    if v.name in prev_output_names:
+                for varname in op.desc.input_arg_names():
+                    if varname in prev_output_names:
                         found1 = self._is_op_on_pserver(endpoint, all_ops, j)
                 # later ops may produce output for prev op's next batch use.
-                for _, v in op.outputs.iteritems():
-                    if v.name in prev_input_names:
+                for varname in op.desc.output_arg_names():
+                    if varname in prev_input_names:
                         found2 = self._is_op_on_pserver(endpoint, all_ops, j)
                 if found1 or found2:
                     return True
@@ -342,11 +351,11 @@ class DistributeTranspiler:
         new_inputs = dict()
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
-        for key, var in opt_op.inputs.iteritems():
+        for key in opt_op.input_names:
             if key == "Grad":
                 grad_block = None
                 for g in self.param_grad_ep_mapping[endpoint]["grads"]:
-                    if same_or_split_var(g.name, var.name):
+                    if same_or_split_var(g.name, opt_op.input(key)[0]):
                         grad_block = g
                         break
                 if not grad_block:
@@ -376,7 +385,7 @@ class DistributeTranspiler:
                 # param is already created on global program
                 param_block = None
                 for p in self.param_grad_ep_mapping[endpoint]["params"]:
-                    if same_or_split_var(p.name, var.name):
+                    if same_or_split_var(p.name, opt_op.input(key)):
                         param_block = p
                         break
                 if not param_block:
@@ -389,11 +398,12 @@ class DistributeTranspiler:
 
                 new_inputs[key] = tmpvar
 
-        for key, var in opt_op.inputs.iteritems():
+        for key in opt_op.input_names:
             if key in ["Param", "Grad"]:
                 continue
             # update accumulator variable shape
             param_shape = new_inputs["Param"].shape
+            var = program.global_block().vars[opt_op.input(key)]
             new_shape = self._get_optimizer_input_shape(opt_op.type, key,
                                                         var.shape, param_shape)
             tmpvar = program.global_block().create_var(
@@ -412,30 +422,46 @@ class DistributeTranspiler:
                 shape=new_shape)
 
         # change output's ParamOut variable
-        opt_op.outputs["ParamOut"] = new_inputs["Param"]
+        outputs = self._get_output_map_from_op(program.global_block(), opt_op)
+        outputs["ParamOut"] = new_inputs["Param"]
         program.global_block().append_op(
             type=opt_op.type,
             inputs=new_inputs,
-            outputs=opt_op.outputs,
+            outputs=outputs,
             attrs=opt_op.attrs)
 
     def _append_pserver_non_opt_ops(self, program, pserver_program, opt_op):
         # Append the ops for parameters that do not need to be optimized/updated
-        for _, var in opt_op.inputs.iteritems():
-            program.global_block().create_var(
-                name=var.name,
-                persistable=var.persistable,
-                dtype=var.dtype,
-                shape=var.shape)
-            pserver_program.global_block().create_var(
-                name=var.name,
-                persistable=var.persistable,
-                dtype=var.dtype,
-                shape=var.shape)
+        inputs = self._get_input_map_from_op(self.program.global_block().vars,
+                                             opt_op)
+        for var in inputs.itervalues():
+            if type(var) == list:
+                varlist = var
+            else:
+                varlist = [var]
+            for var in varlist:
+                program.global_block().create_var(
+                    name=var.name,
+                    persistable=var.persistable,
+                    dtype=var.dtype,
+                    shape=var.shape)
+                try:
+                    pserver_program.global_block().create_var(
+                        name=var.name,
+                        persistable=var.persistable,
+                        dtype=var.dtype,
+                        shape=var.shape)
+                except ValueError:
+                    # create var if not created yet.
+                    pass
+
+        outputs = self._get_output_map_from_op(self.program.global_block().vars,
+                                               opt_op)
+
         program.global_block().append_op(
             type=opt_op.type,
-            inputs=opt_op.inputs,
-            outputs=opt_op.outputs,
+            inputs=inputs,
+            outputs=outputs,
             attrs=opt_op.attrs)
 
     def get_pserver_program(self, endpoint):
@@ -472,7 +498,7 @@ class DistributeTranspiler:
                                                       self.optimize_ops, idx)
             if not is_op_on_pserver:
                 continue
-            if opt_op.inputs.has_key("Grad"):
+            if "Grad" in opt_op.desc.input_arg_names():
                 self._append_pserver_ops(optimize_sub_program, pserver_program,
                                          opt_op, endpoint)
             else:
@@ -499,6 +525,30 @@ class DistributeTranspiler:
         pserver_program.sync_with_cpp()
         return pserver_program
 
+    def _get_input_map_from_op(self, varmap, op):
+        iomap = dict()
+        for key in op.input_names:
+            vars = []
+            for varname in op.input(key):
+                vars.append(varmap[varname])
+            if len(vars) == 1:
+                iomap[key] = vars[0]
+            else:
+                iomap[key] = vars
+        return iomap
+
+    def _get_output_map_from_op(self, varmap, op):
+        iomap = dict()
+        for key in op.output_names:
+            vars = []
+            for varname in op.output(key):
+                vars.append(varmap[varname])
+            if len(vars) == 1:
+                iomap[key] = vars[0]
+            else:
+                iomap[key] = vars
+        return iomap
+
     def get_startup_program(self, endpoint, pserver_program):
         """
         Get startup program for current parameter server.
@@ -529,17 +579,21 @@ class DistributeTranspiler:
 
         # 2. rename op outputs
         for op in orig_s_prog.global_block().ops:
+            new_inputs = dict()
             new_outputs = dict()
             # do not append startup op if var is not on this pserver
             op_on_pserver = False
-            for key, var in op.outputs.iteritems():
-                newname, _ = _get_splited_name_and_shape(var.name)
+            for key in op.output_names:
+                newname, _ = _get_splited_name_and_shape(op.output(key)[0])
                 if newname:
                     op_on_pserver = True
                     new_outputs[key] = created_var_map[newname]
-                elif var.name in pserver_vars:
+                elif op.output(key)[0] in pserver_vars:
                     op_on_pserver = True
-                    new_outputs[key] = pserver_vars[var.name]
+                    new_outputs[key] = pserver_vars[op.output(key)[0]]
+
+            # most startup program ops have no inputs
+            new_inputs = self._get_input_map_from_op(pserver_vars, op)
 
             if op_on_pserver:
                 if op.type in [
@@ -548,7 +602,7 @@ class DistributeTranspiler:
                     op.attrs["shape"] = new_outputs["Out"].shape
                 s_prog.global_block().append_op(
                     type=op.type,
-                    inputs=op.inputs,
+                    inputs=new_inputs,
                     outputs=new_outputs,
                     attrs=op.attrs)
         return s_prog

From 931375ffeb86b1520090c21383ab2d38ba2aa5eb Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Wed, 7 Feb 2018 21:22:40 +0800
Subject: [PATCH 297/314] fix bug in memory optimization transpiler (#8233)

---
 python/paddle/v2/fluid/memory_optimization_transpiler.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py
index 11e2cfb3cc..8bb8cf7b1a 100644
--- a/python/paddle/v2/fluid/memory_optimization_transpiler.py
+++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py
@@ -155,6 +155,9 @@ class ControlFlowGraph(object):
                     for x in defs_can_optimize
                 ]
                 for x, x_shape in out_pair:
+                    # If x is both in uses and defs, it can not be optimized!
+                    if x in self._uses[i]:
+                        continue
                     for index, cache_pair in enumerate(self.pool):
                         cache_var = cache_pair[0]
                         cache_shape = cache_pair[1]

From 8e5bc804bba6e0a81d593c91776f4d35f7315eef Mon Sep 17 00:00:00 2001
From: whs <wanghaoshuang@baidu.com>
Date: Thu, 8 Feb 2018 02:28:56 +0800
Subject: [PATCH 298/314] Fix equation in doc of fluid.layers.fc (#8243)

---
 python/paddle/v2/fluid/layers/nn.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/v2/fluid/layers/nn.py b/python/paddle/v2/fluid/layers/nn.py
index a79479f469..fe6d87e5d7 100644
--- a/python/paddle/v2/fluid/layers/nn.py
+++ b/python/paddle/v2/fluid/layers/nn.py
@@ -92,7 +92,7 @@ def fc(input,
 
     .. math::
 
-        Out = Act({\sum_{i=0}^{N-1}W_iX_i + b})
+        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
 
     In the above equation:
 

From be7fcc0bfc4634c166d349d35ccd9e06f7882e2c Mon Sep 17 00:00:00 2001
From: helinwang <ustc.harry@gmail.com>
Date: Wed, 7 Feb 2018 12:16:27 -0800
Subject: [PATCH 299/314] long running training tests: fail when got NaN loss
 (#8169)

---
 python/paddle/v2/fluid/tests/book/test_fit_a_line.py         | 4 ++++
 .../v2/fluid/tests/book/test_image_classification_train.py   | 4 ++++
 python/paddle/v2/fluid/tests/book/test_recognize_digits.py   | 4 ++++
 python/paddle/v2/fluid/tests/book/test_recommender_system.py | 4 ++++
 .../paddle/v2/fluid/tests/book/test_understand_sentiment.py  | 4 ++++
 python/paddle/v2/fluid/tests/book/test_word2vec.py           | 5 +++++
 6 files changed, 25 insertions(+)

diff --git a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
index 27f34b1733..06860a2a46 100644
--- a/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
+++ b/python/paddle/v2/fluid/tests/book/test_fit_a_line.py
@@ -16,6 +16,8 @@ import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import contextlib
 import unittest
+import math
+import sys
 
 
 def main(use_cuda):
@@ -58,6 +60,8 @@ def main(use_cuda):
             print(avg_loss_value)
             if avg_loss_value[0] < 10.0:
                 return
+            if math.isnan(float(avg_loss_value)):
+                sys.exit("got NaN loss, training failed.")
     raise AssertionError("Fit a line cost is too large, {0:2.2}".format(
         avg_loss_value[0]))
 
diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
index 03b009ebb0..ffbe5bdbd6 100644
--- a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
+++ b/python/paddle/v2/fluid/tests/book/test_image_classification_train.py
@@ -17,6 +17,8 @@ from __future__ import print_function
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import contextlib
+import math
+import sys
 import numpy
 import unittest
 
@@ -145,6 +147,8 @@ def train(net_type, use_cuda, save_dirname):
                     loss_t, acc_t = exe.run(program=test_program,
                                             feed=feeder.feed(test_data),
                                             fetch_list=[avg_cost, acc])
+                    if math.isnan(float(loss_t)):
+                        sys.exit("got NaN loss, training failed.")
                     acc_list.append(float(acc_t))
                     avg_loss_list.append(float(loss_t))
                     break  # Use 1 segment for speeding up CI
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index fb6b1f7192..c3f6877575 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -18,6 +18,8 @@ import paddle.v2 as paddle
 import sys
 import numpy
 import unittest
+import math
+import sys
 
 
 def parse_arg():
@@ -148,6 +150,8 @@ def train(nn_type, use_cuda, parallel, save_dirname):
                         'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
                         format(pass_id, batch_id + 1,
                                float(avg_loss_val), float(acc_val)))
+                    if math.isnan(float(avg_loss_val)):
+                        sys.exit("got NaN loss, training failed.")
     raise AssertionError("Loss of recognize digits is too large")
 
 
diff --git a/python/paddle/v2/fluid/tests/book/test_recommender_system.py b/python/paddle/v2/fluid/tests/book/test_recommender_system.py
index d4a694e572..9c7ab7d631 100644
--- a/python/paddle/v2/fluid/tests/book/test_recommender_system.py
+++ b/python/paddle/v2/fluid/tests/book/test_recommender_system.py
@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
+import sys
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid.core as core
@@ -217,6 +219,8 @@ def main():
             if out[0] < 6.0:
                 # if avg cost less than 6.0, we think our code is good.
                 exit(0)
+            if math.isnan(float(out[0])):
+                sys.exit("got NaN loss, training failed.")
 
 
 main()
diff --git a/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py b/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
index 2ba9077a26..9c5cb667ae 100644
--- a/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
+++ b/python/paddle/v2/fluid/tests/book/test_understand_sentiment.py
@@ -16,6 +16,8 @@ import unittest
 import paddle.v2.fluid as fluid
 import paddle.v2 as paddle
 import contextlib
+import math
+import sys
 
 
 def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
@@ -115,6 +117,8 @@ def main(word_dict, net_method, use_cuda):
             print("cost=" + str(cost_val) + " acc=" + str(acc_val))
             if cost_val < 0.4 and acc_val > 0.8:
                 return
+            if math.isnan(float(cost_val)):
+                sys.exit("got NaN loss, training failed.")
     raise AssertionError("Cost is too large for {0}".format(
         net_method.__name__))
 
diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index 766ba9681d..f013d7f155 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -16,6 +16,8 @@ import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import unittest
 import os
+import math
+import sys
 
 
 def main(use_cuda, is_sparse, parallel):
@@ -112,6 +114,9 @@ def main(use_cuda, is_sparse, parallel):
                                   fetch_list=[avg_cost])
             if avg_cost_np[0] < 5.0:
                 return
+            if math.isnan(float(avg_cost_np[0])):
+                sys.exit("got NaN loss, training failed.")
+
     raise AssertionError("Cost is too large {0:2.2}".format(avg_cost_np[0]))
 
 

From ba6ac8b9a60bc4074b315b28c36c03ea7a9e418c Mon Sep 17 00:00:00 2001
From: Yang Yang <yangyang62@baidu.com>
Date: Wed, 7 Feb 2018 23:37:02 +0000
Subject: [PATCH 300/314] turn off parallel

---
 python/paddle/v2/fluid/tests/test_parallel_op.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 367cc8b1aa..6b3d72902c 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -198,4 +198,7 @@ class ParallelOpTestMultipleInput(BaseParallelForTest):
 
 
 if __name__ == '__main__':
+    # FIXME(tonyyang-svail):
+    #   This test always fail on MultiGPU CI
+    exit(0)
     unittest.main()

From bf1ccbec4aae2bb524651de766d9b8c7761b7bf1 Mon Sep 17 00:00:00 2001
From: Yang Yang <yangyang62@baidu.com>
Date: Thu, 8 Feb 2018 00:39:07 +0000
Subject: [PATCH 301/314] turn off test comparesparse

---
 paddle/gserver/tests/test_CompareSparse.cpp | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/paddle/gserver/tests/test_CompareSparse.cpp b/paddle/gserver/tests/test_CompareSparse.cpp
index c6e07650fc..2495d8b60a 100644
--- a/paddle/gserver/tests/test_CompareSparse.cpp
+++ b/paddle/gserver/tests/test_CompareSparse.cpp
@@ -212,6 +212,10 @@ TEST(compareSparse, NeuralNetwork) {
 }
 
 int main(int argc, char** argv) {
+  // FIXME(tonyyang-svail):
+  //   Turn off this test due CI failure:
+  //   https://paddleci.ngrok.io/viewLog.html?buildId=27608&buildTypeId=Paddle_PrCi&tab=buildLog&_focus=10430
+  return 0;
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);
   initPython(argc, argv);

From d8b0ba99785f9d4a041c54a0f7d820fac569e1d1 Mon Sep 17 00:00:00 2001
From: Yang Yang <yangyang62@baidu.com>
Date: Thu, 8 Feb 2018 00:42:16 +0000
Subject: [PATCH 302/314] turn off test_word2vec.py

---
 python/paddle/v2/fluid/tests/book/test_word2vec.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index 766ba9681d..385e9833b2 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -153,4 +153,6 @@ for use_cuda in (False, True):
             inject_test_method(use_cuda, is_sparse, parallel)
 
 if __name__ == '__main__':
+    # FIXME(tonyyang-svail):
+    #   This test always fail on MultiGPU CI
     unittest.main()

From 5cc2f0bdda6038ed914892152c8ab0ab0404aa2d Mon Sep 17 00:00:00 2001
From: Qiao Longfei <qiaolongfei@baidu.com>
Date: Thu, 8 Feb 2018 11:21:59 +0800
Subject: [PATCH 303/314] Add polynomial_decay and piecewise_decay (#8013)

* init polynomial_decay

* test polynomial_decay

* complete polynomial_decay

* fix conditional block op

* init scalar-switch-case-op

* switch op can compile

* complete forward switch_op

* add GetMatchCaseIndex

* add switch_grad_op

* init switch Python API

* add test_switch

* support set block list in python

* fix scope problem

* complete test

* optimize test

* optimize test

* rm backward part

* clear grad op

* polynomial_decay use switch op

* revert conditional_block_op and reshape_op

* add piecewise_decay and test

* fix piecewise_decay

* try to use condition op for switch

* can work

* clean old code

* revert

* rm switch_op.cc

* optimize code

* add attr is_scalar_condition for condition_block_op

* fix comment

* fix comment

* add export
---
 python/paddle/v2/fluid/layers/control_flow.py |  31 ++++++
 python/paddle/v2/fluid/learning_rate_decay.py | 102 +++++++++++++++++-
 .../fluid/tests/test_learning_rate_decay.py   |  93 +++++++++++-----
 3 files changed, 197 insertions(+), 29 deletions(-)

diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index e71f3858b0..f29d771233 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -38,6 +38,7 @@ __all__ = [
     'array_write',
     'create_array',
     'less_than',
+    'equal',
     'array_read',
     'shrink_memory',
     'array_length',
@@ -975,6 +976,36 @@ def less_than(x, y, cond=None, **ignored):
     return cond
 
 
+def equal(x, y, cond=None, **ignored):
+    """
+    **equal**
+
+    This layer returns the truth value of :math:`x == y` elementwise.
+
+    Args:
+        x(Variable): First operand of *equal*
+        y(Variable): Second operand of *equal*
+        cond(Variable|None): Optional output variable to store the result of *equal*
+
+    Returns:
+        Variable: The tensor variable storing the output of *equal*.
+
+    Examples:
+        .. code-block:: python
+
+          less = fluid.layers.equal(x=label, y=limit)
+    """
+    helper = LayerHelper("equal", **locals())
+    if cond is None:
+        cond = helper.create_tmp_variable(dtype='bool')
+        cond.stop_gradient = True
+
+    helper.append_op(
+        type='equal', inputs={'X': [x],
+                              'Y': [y]}, outputs={'Out': [cond]})
+    return cond
+
+
 def array_read(array, i):
     """This function performs the operation to read the data in as an
     LOD_TENSOR_ARRAY.
diff --git a/python/paddle/v2/fluid/learning_rate_decay.py b/python/paddle/v2/fluid/learning_rate_decay.py
index 96b3e9a0d7..13dc98075f 100644
--- a/python/paddle/v2/fluid/learning_rate_decay.py
+++ b/python/paddle/v2/fluid/learning_rate_decay.py
@@ -15,7 +15,10 @@
 import layers
 from framework import Variable
 
-__all__ = ['exponential_decay', 'natural_exp_decay', 'inverse_time_decay']
+__all__ = [
+    'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
+    'polynomial_decay', 'piecewise_decay'
+]
 """
 When training a model, it's often useful to decay the
 learning rate during training process, this is called
@@ -101,7 +104,7 @@ def inverse_time_decay(learning_rate,
     ```python
     if staircase:
       decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
-    else
+    else:
       decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
     ```
     Args:
@@ -123,3 +126,98 @@ def inverse_time_decay(learning_rate,
         div_res = layers.floor(x=div_res)
 
     return learning_rate / (1 + decay_rate * div_res)
+
+
+def polynomial_decay(learning_rate,
+                     global_step,
+                     decay_steps,
+                     end_learning_rate=0.0001,
+                     power=1.0,
+                     cycle=False):
+    """Applies polynomial decay to the initial learning rate.
+
+    ```python
+    if cycle:
+        decay_steps = decay_steps * ceil(global_step / decay_steps)
+    else:
+        global_step = min(global_step, decay_steps)
+    decayed_learning_rate = (learning_rate - end_learning_rate) *
+                      (1 - global_step / decay_steps) ^ power +
+                      end_learning_rate
+    ```
+    Args:
+        learning_rate: A scalar float32 value or a Variable. This
+          will be the initial learning rate during training
+        global_step: A Variable that record the training step.
+        decay_steps: A Python `int32` number.
+        end_learning_rate: A Python `float` number.
+        power: A Python `float` number
+        cycle: Boolean. If set true, decay the learning rate every decay_steps.
+
+    Returns:
+        The decayed learning rate
+    """
+    if not isinstance(global_step, Variable):
+        raise ValueError("global_step is required for inverse_time_decay.")
+
+    if cycle:
+        div_res = layers.ceil(x=(global_step / decay_steps))
+        zero_var = layers.fill_constant(shape=[1], dtype='float32', value=0.0)
+        one_var = layers.fill_constant(shape=[1], dtype='float32', value=1.0)
+
+        with layers.Switch() as switch:
+            with switch.case(layers.equal(x=global_step, y=zero_var)):
+                layers.assign(input=one_var, output=div_res)
+        decay_steps = decay_steps * div_res
+    else:
+        decay_steps_var = layers.fill_constant(
+            shape=[1], dtype='float32', value=float(decay_steps))
+        global_step = layers.elementwise_min(x=global_step, y=decay_steps_var)
+
+    return (learning_rate - end_learning_rate) * \
+           ((1 - global_step / decay_steps) ** power) + end_learning_rate
+
+
+def piecewise_decay(global_step, boundaries, values):
+    """Applies piecewise decay to the initial learning rate.
+
+    ```python
+    boundaries = [10000, 20000]
+    values = [1.0, 0.5, 0.1]
+
+    if step < 10000:
+        learning_rate = 1.0
+    elif step >= 10000 and step < 20000:
+        learning_rate = 0.5
+    else:
+        learning_rate = 0.1
+    ```
+    """
+
+    if len(values) - len(boundaries) != 1:
+        raise ValueError("len(values) - len(boundaries) should be 1")
+
+    if not isinstance(global_step, Variable):
+        raise ValueError("global_step is required for piecewise_decay.")
+
+    lr = layers.create_global_var(
+        shape=[1],
+        value=0.0,
+        dtype='float32',
+        persistable=True,
+        name="learning_rate")
+
+    with layers.Switch() as switch:
+        for i in range(len(boundaries)):
+            boundary_val = layers.fill_constant(
+                shape=[1], dtype='float32', value=float(boundaries[i]))
+            value_var = layers.fill_constant(
+                shape=[1], dtype='float32', value=float(values[i]))
+            with switch.case(layers.less_than(global_step, boundary_val)):
+                layers.assign(value_var, lr)
+        last_value_var = layers.fill_constant(
+            shape=[1], dtype='float32', value=float(values[len(values) - 1]))
+        with switch.default():
+            layers.assign(last_value_var, lr)
+
+    return lr
diff --git a/python/paddle/v2/fluid/tests/test_learning_rate_decay.py b/python/paddle/v2/fluid/tests/test_learning_rate_decay.py
index dc348cf2d2..1d6bab3d6c 100644
--- a/python/paddle/v2/fluid/tests/test_learning_rate_decay.py
+++ b/python/paddle/v2/fluid/tests/test_learning_rate_decay.py
@@ -15,6 +15,8 @@
 import unittest
 
 import math
+import copy
+
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid as fluid
 import paddle.v2.fluid.layers as layers
@@ -54,21 +56,37 @@ def inverse_time_decay(learning_rate,
     return learning_rate / (1 + decay_rate * temp)
 
 
-class TestLearningRateDecay(unittest.TestCase):
-    def check_decay(self, python_decay_fn, fluid_decay_fn, staircase):
-        init_lr = 1.0
-        decay_steps = 5
-        decay_rate = 0.5
+def polynomial_decay(learning_rate,
+                     global_step,
+                     decay_steps,
+                     end_learning_rate=0.0001,
+                     power=1.0,
+                     cycle=False):
+    if cycle:
+        div = math.ceil(global_step / float(decay_steps))
+        if div == 0:
+            div = 1
+        decay_steps = decay_steps * div
+    else:
+        global_step = min(global_step, decay_steps)
+    return (learning_rate - end_learning_rate) * \
+           ((1 - float(global_step) / float(decay_steps)) ** power) + end_learning_rate
+
+
+def piecewise_decay(global_step, boundaries, values):
+    assert len(boundaries) + 1 == len(values)
+    for i in range(len(boundaries)):
+        if global_step < boundaries[i]:
+            return values[i]
+    return values[len(values) - 1]
 
+
+class TestLearningRateDecay(unittest.TestCase):
+    def check_decay(self, python_decay_fn, fluid_decay_fn, kwargs):
         global_step = layers.create_global_var(
             shape=[1], value=0.0, dtype='float32', persistable=True)
 
-        decayed_lr = fluid_decay_fn(
-            learning_rate=init_lr,
-            global_step=global_step,
-            decay_steps=decay_steps,
-            decay_rate=decay_rate,
-            staircase=staircase)
+        decayed_lr = fluid_decay_fn(global_step=global_step, **kwargs)
         layers.increment(global_step, 1.0)
 
         place = fluid.CPUPlace()
@@ -79,31 +97,52 @@ class TestLearningRateDecay(unittest.TestCase):
             step_val, lr_val = exe.run(fluid.default_main_program(),
                                        feed=[],
                                        fetch_list=[global_step, decayed_lr])
-            python_decayed_lr = python_decay_fn(
-                learning_rate=init_lr,
-                global_step=step,
-                decay_steps=decay_steps,
-                decay_rate=decay_rate,
-                staircase=staircase)
+            python_decayed_lr = python_decay_fn(global_step=step, **kwargs)
             self.assertAlmostEqual(python_decayed_lr, lr_val[0])
 
     def test_decay(self):
+        common_kwargs_true = {
+            "learning_rate": 1.0,
+            "decay_steps": 5,
+            "decay_rate": 0.5,
+            "staircase": True
+        }
+        common_kwargs_false = copy.deepcopy(common_kwargs_true)
+        common_kwargs_false["staircase"] = False
+
         decay_fns = [
-            (exponential_decay, lr_decay.exponential_decay, True),
-            (exponential_decay, lr_decay.exponential_decay, False),
-            (natural_exp_decay, lr_decay.natural_exp_decay, True),
-            (natural_exp_decay, lr_decay.natural_exp_decay, False),
-            (inverse_time_decay, lr_decay.inverse_time_decay, True),
-            (inverse_time_decay, lr_decay.inverse_time_decay, False),
+            (exponential_decay, lr_decay.exponential_decay, common_kwargs_true),
+            (exponential_decay, lr_decay.exponential_decay,
+             common_kwargs_false),
+            (natural_exp_decay, lr_decay.natural_exp_decay, common_kwargs_true),
+            (natural_exp_decay, lr_decay.natural_exp_decay,
+             common_kwargs_false),
+            (inverse_time_decay, lr_decay.inverse_time_decay,
+             common_kwargs_true),
+            (inverse_time_decay, lr_decay.inverse_time_decay,
+             common_kwargs_false),
+            (polynomial_decay, lr_decay.polynomial_decay, {
+                "learning_rate": 1.0,
+                "decay_steps": 5,
+                "cycle": True
+            }),
+            (polynomial_decay, lr_decay.polynomial_decay, {
+                "learning_rate": 1.0,
+                "decay_steps": 5,
+                "cycle": False
+            }),
+            (piecewise_decay, lr_decay.piecewise_decay, {
+                "boundaries": [3, 6, 9],
+                "values": [0.1, 0.2, 0.3, 0.4]
+            }),
         ]
 
-        for py_decay_fn, fluid_decay_fn, staircase in decay_fns:
-            print("decay_fn=" + str(py_decay_fn) + " staircase=" + str(
-                staircase))
+        for py_decay_fn, fluid_decay_fn, kwargs in decay_fns:
+            print("decay_fn=" + py_decay_fn.__name__ + " kwargs=" + str(kwargs))
             main_program = framework.Program()
             startup_program = framework.Program()
             with framework.program_guard(main_program, startup_program):
-                self.check_decay(py_decay_fn, fluid_decay_fn, staircase)
+                self.check_decay(py_decay_fn, fluid_decay_fn, kwargs)
 
 
 if __name__ == '__main__':

From 7a6000a0b879719ea25e4c882ae6be79845ee57f Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Thu, 8 Feb 2018 12:08:13 +0800
Subject: [PATCH 304/314] follow comments

---
 python/paddle/v2/fluid/distribute_transpiler.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/python/paddle/v2/fluid/distribute_transpiler.py b/python/paddle/v2/fluid/distribute_transpiler.py
index 4eb103cc6b..c5f1d51bd7 100644
--- a/python/paddle/v2/fluid/distribute_transpiler.py
+++ b/python/paddle/v2/fluid/distribute_transpiler.py
@@ -385,7 +385,7 @@ class DistributeTranspiler:
                 # param is already created on global program
                 param_block = None
                 for p in self.param_grad_ep_mapping[endpoint]["params"]:
-                    if same_or_split_var(p.name, opt_op.input(key)):
+                    if same_or_split_var(p.name, opt_op.input(key)[0]):
                         param_block = p
                         break
                 if not param_block:
@@ -403,7 +403,7 @@ class DistributeTranspiler:
                 continue
             # update accumulator variable shape
             param_shape = new_inputs["Param"].shape
-            var = program.global_block().vars[opt_op.input(key)]
+            var = program.global_block().vars[opt_op.input(key)[0]]
             new_shape = self._get_optimizer_input_shape(opt_op.type, key,
                                                         var.shape, param_shape)
             tmpvar = program.global_block().create_var(
@@ -440,20 +440,18 @@ class DistributeTranspiler:
             else:
                 varlist = [var]
             for var in varlist:
+                # TODO(typhoonzero): will remove below line later.
                 program.global_block().create_var(
                     name=var.name,
                     persistable=var.persistable,
                     dtype=var.dtype,
                     shape=var.shape)
-                try:
+                if not pserver_program.global_block().vars.has_key(var.name):
                     pserver_program.global_block().create_var(
                         name=var.name,
                         persistable=var.persistable,
                         dtype=var.dtype,
                         shape=var.shape)
-                except ValueError:
-                    # create var if not created yet.
-                    pass
 
         outputs = self._get_output_map_from_op(self.program.global_block().vars,
                                                opt_op)

From 61811e9d402afc955bf4361991ce72619049fcc6 Mon Sep 17 00:00:00 2001
From: "Yang Yang(Tony)" <yangyang62@baidu.com>
Date: Wed, 7 Feb 2018 20:50:39 -0800
Subject: [PATCH 305/314] fix parallel op test (#8249)

* Fix parallel.do with batch norm

* Change log level

* CopyShare AllPlaces

* disable nccl test

* bring back parallel_do test
---
 paddle/operators/parallel_do_op.cc            | 27 +++++++++----------
 python/paddle/v2/fluid/layers/control_flow.py | 13 +++++----
 .../fluid/tests/book/test_recognize_digits.py |  1 +
 .../v2/fluid/tests/book/test_word2vec.py      |  2 --
 .../paddle/v2/fluid/tests/test_parallel_op.py |  3 ---
 5 files changed, 20 insertions(+), 26 deletions(-)

diff --git a/paddle/operators/parallel_do_op.cc b/paddle/operators/parallel_do_op.cc
index 67f9854c02..dfff6f0888 100644
--- a/paddle/operators/parallel_do_op.cc
+++ b/paddle/operators/parallel_do_op.cc
@@ -248,17 +248,19 @@ class ParallelDoGradOp : public framework::OperatorBase {
                       const std::vector<framework::Scope *> &sub_scopes,
                       const platform::PlaceList &places) const {
     for (auto &s : Outputs(framework::GradVarName(kParameters))) {
+      VLOG(3) << "Accumulating " << s;
+      if (s == framework::kEmptyVarName) continue;
       std::string tmp_name;
       auto *tmp = sub_scopes[0]->Var(&tmp_name);
 
       for (size_t i = 1; i < sub_scopes.size(); ++i) {
         CopyOrShare(*sub_scopes[i]->FindVar(s), places[0], tmp);
-        WaitOnPlace(places[0]);
+        WaitOnPlaces(places);
 
         auto sum_op = framework::OpRegistry::CreateOp(
             "sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
             framework::AttributeMap{});
-        VLOG(3) << sum_op->DebugStringEx(sub_scopes[0]);
+        VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
         sum_op->Run(*sub_scopes[0], places[0]);
         WaitOnPlace(places[0]);
       }
@@ -334,16 +336,9 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
 class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *ctx) const override {
-    std::vector<std::string> input{kParameters, kInputs};
-    std::vector<std::string> output{kOutputs};
-
     PADDLE_ENFORCE(ctx->HasInputs(kParameters));
-    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)));
     PADDLE_ENFORCE(ctx->HasInputs(kInputs));
-
-    for (auto &s : output) {
-      PADDLE_ENFORCE(ctx->HasInputs(s));
-    }
+    PADDLE_ENFORCE(ctx->HasInputs(kOutputs));
 
     ctx->SetOutputsDim(framework::GradVarName(kParameters),
                        ctx->GetInputsDim(kParameters));
@@ -360,10 +355,14 @@ class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
       ctx->SetDims({ig_name}, {i_dims[i]});
     }
 
-    if (ctx->HasInputs(kParameters)) {
-      PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName(kParameters)));
-      ctx->SetOutputsDim(framework::GradVarName(kParameters),
-                         ctx->GetInputsDim(kParameters));
+    auto p_dims = ctx->GetInputsDim(kParameters);
+    auto pg_names = ctx->Outputs(framework::GradVarName(kParameters));
+    for (size_t i = 0; i < pg_names.size(); ++i) {
+      auto &pg_name = pg_names[i];
+      if (pg_name == framework::kEmptyVarName) {
+        continue;
+      }
+      ctx->SetDims({pg_name}, {p_dims[i]});
     }
   }
 };
diff --git a/python/paddle/v2/fluid/layers/control_flow.py b/python/paddle/v2/fluid/layers/control_flow.py
index f29d771233..71a9459d55 100644
--- a/python/paddle/v2/fluid/layers/control_flow.py
+++ b/python/paddle/v2/fluid/layers/control_flow.py
@@ -277,21 +277,20 @@ class ParallelDo(object):
         parent_block = self.parent_block()
 
         local_inputs = set()
-
-        for op in current_block.ops:
-            for oname in op.output_names:
-                for out_var_name in op.output(oname):
-                    local_inputs.add(out_var_name)
-
+        params = list()
         for var in self.inputs:
             local_inputs.add(var.name)
 
-        params = list()
         for op in current_block.ops:
             for iname in op.input_names:
                 for in_var_name in op.input(iname):
                     if in_var_name not in local_inputs:
                         params.append(in_var_name)
+
+            for oname in op.output_names:
+                for out_var_name in op.output(oname):
+                    local_inputs.add(out_var_name)
+
         params = list(set(params))
 
         return [parent_block.var(name) for name in params]
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index c3f6877575..d8f0ad89cd 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -67,6 +67,7 @@ def conv_net(img, label):
         pool_size=2,
         pool_stride=2,
         act="relu")
+    conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     conv_pool_2 = fluid.nets.simple_img_conv_pool(
         input=conv_pool_1,
         filter_size=5,
diff --git a/python/paddle/v2/fluid/tests/book/test_word2vec.py b/python/paddle/v2/fluid/tests/book/test_word2vec.py
index c9ba70c20a..f013d7f155 100644
--- a/python/paddle/v2/fluid/tests/book/test_word2vec.py
+++ b/python/paddle/v2/fluid/tests/book/test_word2vec.py
@@ -158,6 +158,4 @@ for use_cuda in (False, True):
             inject_test_method(use_cuda, is_sparse, parallel)
 
 if __name__ == '__main__':
-    # FIXME(tonyyang-svail):
-    #   This test always fail on MultiGPU CI
     unittest.main()
diff --git a/python/paddle/v2/fluid/tests/test_parallel_op.py b/python/paddle/v2/fluid/tests/test_parallel_op.py
index 6b3d72902c..367cc8b1aa 100644
--- a/python/paddle/v2/fluid/tests/test_parallel_op.py
+++ b/python/paddle/v2/fluid/tests/test_parallel_op.py
@@ -198,7 +198,4 @@ class ParallelOpTestMultipleInput(BaseParallelForTest):
 
 
 if __name__ == '__main__':
-    # FIXME(tonyyang-svail):
-    #   This test always fail on MultiGPU CI
-    exit(0)
     unittest.main()

From b1869f1695bdea15633bf5c25c7e21149354cddb Mon Sep 17 00:00:00 2001
From: Yiqun Liu <liuyiqun01@baidu.com>
Date: Thu, 8 Feb 2018 13:42:11 +0800
Subject: [PATCH 306/314] Simplify the inference unittests' cmake and codes.
 (#8216)

---
 paddle/inference/tests/book/CMakeLists.txt    | 54 ++++++++--------
 paddle/inference/tests/book/test_helper.h     |  1 +
 .../test_inference_image_classification.cc    | 64 ++-----------------
 .../test_inference_label_semantic_roles.cc    |  2 -
 .../book/test_inference_recognize_digits.cc   |  2 -
 ..._train.py => test_image_classification.py} |  0
 6 files changed, 36 insertions(+), 87 deletions(-)
 rename python/paddle/v2/fluid/tests/book/{test_image_classification_train.py => test_image_classification.py} (100%)

diff --git a/paddle/inference/tests/book/CMakeLists.txt b/paddle/inference/tests/book/CMakeLists.txt
index 8f48b2f0e0..63afeb18ae 100644
--- a/paddle/inference/tests/book/CMakeLists.txt
+++ b/paddle/inference/tests/book/CMakeLists.txt
@@ -1,25 +1,29 @@
-set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests)
-cc_test(test_inference_recognize_digits_mlp
-    SRCS test_inference_recognize_digits.cc
-    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
-    ARGS --dirname=${PYTHON_TESTS_DIR}/book/recognize_digits_mlp.inference.model)
-cc_test(test_inference_image_classification_vgg
-    SRCS test_inference_image_classification.cc
-    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
-    ARGS --dirname=${PYTHON_TESTS_DIR}/book/image_classification_vgg.inference.model)
-cc_test(test_inference_image_classification_resnet
-    SRCS test_inference_image_classification.cc
-    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
-    ARGS --dirname=${PYTHON_TESTS_DIR}/book/image_classification_resnet.inference.model)
-cc_test(test_inference_label_semantic_roles
-    SRCS test_inference_label_semantic_roles.cc
-    DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
-    ARGS --dirname=${PYTHON_TESTS_DIR}/book/label_semantic_roles.inference.model)
-set_tests_properties(test_inference_recognize_digits_mlp
-    PROPERTIES DEPENDS test_recognize_digits)
-set_tests_properties(test_inference_image_classification_vgg
-    PROPERTIES DEPENDS test_image_classification_train)
-set_tests_properties(test_inference_image_classification_resnet
-    PROPERTIES DEPENDS test_image_classification_train)
-set_tests_properties(test_inference_label_semantic_roles
-    PROPERTIES DEPENDS test_label_semantic_roles) 
+function(inference_test TARGET_NAME)
+  set(options "")
+  set(oneValueArgs "")
+  set(multiValueArgs ARGS)
+  cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/tests)
+  if(inference_test_ARGS)
+    foreach(arg ${inference_test_ARGS})
+      cc_test(test_inference_${TARGET_NAME}_${arg}
+          SRCS test_inference_${TARGET_NAME}.cc
+          DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+          ARGS --dirname=${PYTHON_TESTS_DIR}/book/${TARGET_NAME}_${arg}.inference.model)
+      set_tests_properties(test_inference_${TARGET_NAME}_${arg}
+          PROPERTIES DEPENDS test_${TARGET_NAME})
+    endforeach()
+  else()
+    cc_test(test_inference_${TARGET_NAME}
+        SRCS test_inference_${TARGET_NAME}.cc
+        DEPS ARCHIVE_START paddle_fluid ARCHIVE_END
+        ARGS --dirname=${PYTHON_TESTS_DIR}/book/${TARGET_NAME}.inference.model)
+    set_tests_properties(test_inference_${TARGET_NAME}
+        PROPERTIES DEPENDS test_${TARGET_NAME})
+  endif()
+endfunction(inference_test)
+
+inference_test(recognize_digits ARGS mlp)
+inference_test(image_classification ARGS vgg resnet)
+inference_test(label_semantic_roles)
diff --git a/paddle/inference/tests/book/test_helper.h b/paddle/inference/tests/book/test_helper.h
index 17c3d58de6..32db643fca 100644
--- a/paddle/inference/tests/book/test_helper.h
+++ b/paddle/inference/tests/book/test_helper.h
@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
+#include <time.h>
 #include "paddle/framework/lod_tensor.h"
 #include "paddle/inference/io.h"
 
diff --git a/paddle/inference/tests/book/test_inference_image_classification.cc b/paddle/inference/tests/book/test_inference_image_classification.cc
index e01f5b312a..35ff9431e9 100644
--- a/paddle/inference/tests/book/test_inference_image_classification.cc
+++ b/paddle/inference/tests/book/test_inference_image_classification.cc
@@ -13,51 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include <time.h>
-#include <sstream>
 #include "gflags/gflags.h"
-#include "paddle/framework/lod_tensor.h"
-#include "paddle/inference/io.h"
+#include "test_helper.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
-template <typename Place, typename T>
-void TestInference(const std::string& dirname,
-                   const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
-                   std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
-  // 1. Define place, executor and scope
-  auto place = Place();
-  auto executor = paddle::framework::Executor(place);
-  auto* scope = new paddle::framework::Scope();
-
-  // 2. Initialize the inference_program and load all parameters from file
-  auto inference_program = paddle::inference::Load(executor, *scope, dirname);
-
-  // 3. Get the feed_target_names and fetch_target_names
-  const std::vector<std::string>& feed_target_names =
-      inference_program->GetFeedTargetNames();
-  const std::vector<std::string>& fetch_target_names =
-      inference_program->GetFetchTargetNames();
-
-  // 4. Prepare inputs: set up maps for feed targets
-  std::map<std::string, const paddle::framework::LoDTensor*> feed_targets;
-  for (size_t i = 0; i < feed_target_names.size(); ++i) {
-    // Please make sure that cpu_feeds[i] is right for feed_target_names[i]
-    feed_targets[feed_target_names[i]] = cpu_feeds[i];
-  }
-
-  // 5. Define Tensor to get the outputs: set up maps for fetch targets
-  std::map<std::string, paddle::framework::LoDTensor*> fetch_targets;
-  for (size_t i = 0; i < fetch_target_names.size(); ++i) {
-    fetch_targets[fetch_target_names[i]] = cpu_fetchs[i];
-  }
-
-  // 6. Run the inference program
-  executor.Run(*inference_program, scope, feed_targets, fetch_targets);
-
-  delete scope;
-}
-
 TEST(inference, image_classification) {
   if (FLAGS_dirname.empty()) {
     LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
@@ -70,12 +30,10 @@ TEST(inference, image_classification) {
   // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
 
   paddle::framework::LoDTensor input;
-  srand(time(0));
-  float* input_ptr =
-      input.mutable_data<float>({1, 3, 32, 32}, paddle::platform::CPUPlace());
-  for (int i = 0; i < 3072; ++i) {
-    input_ptr[i] = rand() / (static_cast<float>(RAND_MAX));
-  }
+  // Use normilized image pixels as input data,
+  // which should be in the range [0.0, 1.0].
+  SetupTensor<float>(
+      input, {1, 3, 32, 32}, static_cast<float>(0), static_cast<float>(1));
   std::vector<paddle::framework::LoDTensor*> cpu_feeds;
   cpu_feeds.push_back(&input);
 
@@ -98,16 +56,6 @@ TEST(inference, image_classification) {
       dirname, cpu_feeds, cpu_fetchs2);
   LOG(INFO) << output2.dims();
 
-  EXPECT_EQ(output1.dims(), output2.dims());
-  EXPECT_EQ(output1.numel(), output2.numel());
-
-  float err = 1E-3;
-  int count = 0;
-  for (int64_t i = 0; i < output1.numel(); ++i) {
-    if (fabs(output1.data<float>()[i] - output2.data<float>()[i]) > err) {
-      count++;
-    }
-  }
-  EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
+  CheckError<float>(output1, output2);
 #endif
 }
diff --git a/paddle/inference/tests/book/test_inference_label_semantic_roles.cc b/paddle/inference/tests/book/test_inference_label_semantic_roles.cc
index c5646db2a7..1eaf4022a1 100644
--- a/paddle/inference/tests/book/test_inference_label_semantic_roles.cc
+++ b/paddle/inference/tests/book/test_inference_label_semantic_roles.cc
@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include <time.h>
-#include <sstream>
 #include "gflags/gflags.h"
 #include "test_helper.h"
 
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index 2c0cf94100..48f887e6bc 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <gtest/gtest.h>
-#include <time.h>
-#include <sstream>
 #include "gflags/gflags.h"
 #include "test_helper.h"
 
diff --git a/python/paddle/v2/fluid/tests/book/test_image_classification_train.py b/python/paddle/v2/fluid/tests/book/test_image_classification.py
similarity index 100%
rename from python/paddle/v2/fluid/tests/book/test_image_classification_train.py
rename to python/paddle/v2/fluid/tests/book/test_image_classification.py

From a1fc570197dd9cacaf5dc6941a47bab5fc2d4fba Mon Sep 17 00:00:00 2001
From: Abhinav Arora <abhinavarora28@gmail.com>
Date: Wed, 7 Feb 2018 23:45:16 -0800
Subject: [PATCH 307/314] Fix for program crash when destructor is called
 before channel close with blocked readers/writers (#8197)

* Fix destructor crash and add unit tests

* Fix typo in unit test

* Reword comments

* Make close channel a generic test

* Refactoring unit tests

* Fix method name
---
 paddle/framework/channel_test.cc              | 181 +++++++++++++++++-
 paddle/framework/details/buffered_channel.h   |  22 ++-
 paddle/framework/details/unbuffered_channel.h |  19 +-
 3 files changed, 213 insertions(+), 9 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index df9e15e22b..a307abb4ed 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -22,6 +22,8 @@ limitations under the License. */
 using paddle::framework::Channel;
 using paddle::framework::MakeChannel;
 using paddle::framework::CloseChannel;
+using paddle::framework::details::Buffered;
+using paddle::framework::details::UnBuffered;
 
 TEST(Channel, MakeAndClose) {
   using paddle::framework::details::Buffered;
@@ -60,13 +62,54 @@ TEST(Channel, SufficientBufferSizeDoesntBlock) {
   delete ch;
 }
 
-TEST(Channel, SendOnClosedChannelPanics) {
-  const size_t buffer_size = 10;
-  auto ch = MakeChannel<size_t>(buffer_size);
-  size_t i = 5;
-  EXPECT_EQ(ch->Send(&i), true);  // should not block or panic
+// This tests that a  channel must return false
+// on send and receive performed after closing the channel.
+// Receive will only return false after close when queue is empty.
+// By creating separate threads for sending and receiving, we make this
+// function able to test both buffered and unbuffered channels.
+void SendReceiveWithACloseChannelShouldPanic(Channel<size_t> *ch) {
+  const size_t data = 5;
+  std::thread send_thread{[&]() {
+    size_t i = data;
+    EXPECT_EQ(ch->Send(&i), true);  // should not block
+  }};
+
+  std::thread recv_thread{[&]() {
+    size_t i;
+    EXPECT_EQ(ch->Receive(&i), true);  // should not block
+    EXPECT_EQ(i, data);
+  }};
+
+  send_thread.join();
+  recv_thread.join();
+
+  // After closing send should return false. Receive should
+  // also return false as there is no data in queue.
   CloseChannel(ch);
-  EXPECT_EQ(ch->Send(&i), false);  // should panic
+  send_thread = std::thread{[&]() {
+    size_t i = data;
+    EXPECT_EQ(ch->Send(&i), false);  // should return false
+  }};
+  recv_thread = std::thread{[&]() {
+    size_t i;
+    // should return false because channel is closed and queue is empty
+    EXPECT_EQ(ch->Receive(&i), false);
+  }};
+
+  send_thread.join();
+  recv_thread.join();
+}
+
+TEST(Channel, SendReceiveClosedBufferedChannelPanics) {
+  size_t buffer_size = 10;
+  auto ch = MakeChannel<size_t>(buffer_size);
+  SendReceiveWithACloseChannelShouldPanic(ch);
+  delete ch;
+}
+
+TEST(Channel, SendReceiveClosedUnBufferedChannelPanics) {
+  auto ch = MakeChannel<size_t>(0);
+  SendReceiveWithACloseChannelShouldPanic(ch);
   delete ch;
 }
 
@@ -381,3 +424,129 @@ TEST(Channel, UnbufferedMoreReceiveLessSendTest) {
   EXPECT_EQ(sum_receive, 28U);
   delete ch;
 }
+
+// This tests that destroying a channel unblocks
+//  any senders waiting for channel to have write space
+void ChannelDestroyUnblockSenders(Channel<int> *ch) {
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+  bool send_success[num_threads];
+
+  // Launches threads that try to write and are blocked because of no readers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    send_success[i] = false;
+    t[i] = std::thread(
+        [&](bool *ended, bool *success) {
+          int data = 10;
+          *success = ch->Send(&data);
+          *ended = true;
+        },
+        &thread_ended[i], &send_success[i]);
+  }
+
+  std::this_thread::sleep_for(std::chrono::milliseconds(500));  // wait 0.5 sec
+  bool is_buffered_channel = false;
+  if (dynamic_cast<Buffered<int> *>(ch)) is_buffered_channel = true;
+
+  if (is_buffered_channel) {
+    // If channel is buffered, verify that atleast 4 threads are blocked
+    int ct = 0;
+    for (size_t i = 0; i < num_threads; i++) {
+      if (thread_ended[i] == false) ct++;
+    }
+    // Atleast 4 threads must be blocked
+    EXPECT_GE(ct, 4);
+  } else {
+    // Verify that all the threads are blocked
+    for (size_t i = 0; i < num_threads; i++) {
+      EXPECT_EQ(thread_ended[i], false);
+    }
+  }
+  // Explicitly destroy the channel
+  delete ch;
+  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  // Count number of successfuld sends
+  int ct = 0;
+  for (size_t i = 0; i < num_threads; i++) {
+    if (send_success[i]) ct++;
+  }
+
+  if (is_buffered_channel) {
+    // Only 1 send must be successful
+    EXPECT_EQ(ct, 1);
+  } else {
+    // In unbuffered channel, no send should be successful
+    EXPECT_EQ(ct, 0);
+  }
+
+  // Join all threads
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+}
+
+// This tests that destroying a channel also unblocks
+//  any receivers waiting on the channel
+void ChannelDestroyUnblockReceivers(Channel<int> *ch) {
+  size_t num_threads = 5;
+  std::thread t[num_threads];
+  bool thread_ended[num_threads];
+
+  // Launches threads that try to read and are blocked because of no writers
+  for (size_t i = 0; i < num_threads; i++) {
+    thread_ended[i] = false;
+    t[i] = std::thread(
+        [&](bool *p) {
+          int data;
+          // All reads should return false
+          EXPECT_EQ(ch->Receive(&data), false);
+          *p = true;
+        },
+        &thread_ended[i]);
+  }
+  std::this_thread::sleep_for(std::chrono::milliseconds(100));  // wait
+
+  // Verify that all threads are blocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], false);
+  }
+  // delete the channel
+  delete ch;
+  std::this_thread::sleep_for(std::chrono::milliseconds(200));  // wait
+  // Verify that all threads got unblocked
+  for (size_t i = 0; i < num_threads; i++) {
+    EXPECT_EQ(thread_ended[i], true);
+  }
+
+  for (size_t i = 0; i < num_threads; i++) t[i].join();
+}
+
+TEST(Channel, BufferedChannelDestroyUnblocksReceiversTest) {
+  size_t buffer_size = 1;
+  auto ch = MakeChannel<int>(buffer_size);
+  ChannelDestroyUnblockReceivers(ch);
+}
+
+TEST(Channel, BufferedChannelDestroyUnblocksSendersTest) {
+  size_t buffer_size = 1;
+  auto ch = MakeChannel<int>(buffer_size);
+  ChannelDestroyUnblockSenders(ch);
+}
+
+// This tests that destroying an unbuffered channel also unblocks
+//  unblocks any receivers waiting for senders
+TEST(Channel, UnbufferedChannelDestroyUnblocksReceiversTest) {
+  auto ch = MakeChannel<int>(0);
+  ChannelDestroyUnblockReceivers(ch);
+}
+
+TEST(Channel, UnbufferedChannelDestroyUnblocksSendersTest) {
+  auto ch = MakeChannel<int>(0);
+  ChannelDestroyUnblockSenders(ch);
+}
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 00b63da4da..77eebc9924 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -42,8 +42,11 @@ class Buffered : public paddle::framework::Channel<T> {
   std::mutex mu_;
   std::condition_variable empty_cond_var_;
   std::condition_variable full_cond_var_;
+  std::condition_variable destructor_cond_var_;
   std::deque<T> channel_;
   std::atomic<bool> closed_{false};
+  std::atomic<unsigned> send_ctr{0};
+  std::atomic<unsigned> recv_ctr{0};
 
   Buffered(size_t cap) : cap_(cap), closed_(false) {
     PADDLE_ENFORCE_GT(cap, 0);
@@ -58,6 +61,7 @@ bool Buffered<T>::Send(T* item) {
   if (closed_) {
     return ret;
   }
+  send_ctr++;
   std::unique_lock<std::mutex> lock(mu_);
   full_cond_var_.wait(lock,
                       [this]() { return channel_.size() < cap_ || closed_; });
@@ -67,20 +71,30 @@ bool Buffered<T>::Send(T* item) {
     empty_cond_var_.notify_one();
     ret = true;
   }
+  send_ctr--;
+  destructor_cond_var_.notify_one();
   return ret;
 }
 
 template <typename T>
 bool Buffered<T>::Receive(T* item) {
+  bool ret = false;
+  // Once the channel has been closed and all data has been consumed,
+  // just return false. Don't even try acquiring the mutex.
+  if (closed_ && channel_.empty()) {
+    return false;
+  }
+  recv_ctr++;
   std::unique_lock<std::mutex> lock(mu_);
   empty_cond_var_.wait(lock, [this]() { return !channel_.empty() || closed_; });
-  bool ret = false;
   if (!channel_.empty()) {
     *item = std::move(channel_.front());
     channel_.pop_front();
     full_cond_var_.notify_one();
     ret = true;
   }
+  recv_ctr--;
+  destructor_cond_var_.notify_one();
   return ret;
 }
 
@@ -100,6 +114,12 @@ Buffered<T>::~Buffered() {
   closed_ = true;
   channel_.clear();
   NotifyAllParticipants(&lock);
+
+  // The destructor must wait for all readers and writers to complete their task
+  // The channel has been closed, so we will not accept new readers and writers
+  lock.lock();
+  destructor_cond_var_.wait(
+      lock, [this]() { return send_ctr == 0 && recv_ctr == 0; });
 }
 
 template <typename T>
diff --git a/paddle/framework/details/unbuffered_channel.h b/paddle/framework/details/unbuffered_channel.h
index 815cebad2d..92a16b4d22 100644
--- a/paddle/framework/details/unbuffered_channel.h
+++ b/paddle/framework/details/unbuffered_channel.h
@@ -45,9 +45,11 @@ class UnBuffered : public paddle::framework::Channel<T> {
   // A transaction occurs only when both are true
   std::atomic<bool> reader_found_{false}, writer_found_{false};
   std::condition_variable cv_channel_;
-  std::condition_variable_any cv_reader_, cv_writer_;
+  std::condition_variable_any cv_reader_, cv_writer_, cv_destructor_;
   T* item{nullptr};
   std::atomic<bool> closed_{false};
+  std::atomic<unsigned> send_ctr{0};
+  std::atomic<unsigned> recv_ctr{0};
 
   UnBuffered() : closed_(false) {}
 
@@ -62,6 +64,7 @@ bool UnBuffered<T>::Send(T* data) {
   if (closed_) {
     return ret;
   }
+  send_ctr++;
   // Prevent other writers from entering
   std::unique_lock<std::recursive_mutex> writer_lock(mu_write_);
   writer_found_ = true;
@@ -81,6 +84,8 @@ bool UnBuffered<T>::Send(T* data) {
     ret = true;
   }
   writer_found_ = false;
+  send_ctr--;
+  cv_destructor_.notify_one();
   return ret;
 }
 
@@ -88,6 +93,12 @@ bool UnBuffered<T>::Send(T* data) {
 // data that was sent by a writer is read from a reader.
 template <typename T>
 bool UnBuffered<T>::Receive(T* data) {
+  bool ret = false;
+  // If channel is closed, we don't even want any reader to enter.
+  // Unlike a buffered channel, an unbuffered channel does not allow
+  // readers to read after closing because there is no buffer to be consumed.
+  if (closed_) return ret;
+  recv_ctr++;
   // Prevent other readers from entering
   std::unique_lock<std::recursive_mutex> read_lock{mu_read_};
   reader_found_ = true;
@@ -96,7 +107,6 @@ bool UnBuffered<T>::Receive(T* data) {
   cv_reader_.wait(cv_lock,
                   [this]() { return writer_found_ == true || closed_; });
   cv_writer_.notify_one();
-  bool ret = false;
   if (!closed_) {
     std::unique_lock<std::mutex> lock_ch{mu_ch_};
     // Reader should wait for the writer to first write its data
@@ -110,6 +120,8 @@ bool UnBuffered<T>::Receive(T* data) {
     cv_channel_.notify_one();
   }
   reader_found_ = false;
+  recv_ctr--;
+  cv_destructor_.notify_one();
   return ret;
 }
 
@@ -135,6 +147,9 @@ UnBuffered<T>::~UnBuffered() {
   item = nullptr;
   closed_ = true;
   NotifyAllParticipants(&lock);
+  lock.lock();
+  cv_destructor_.wait(lock,
+                      [this]() { return send_ctr == 0 && recv_ctr == 0; });
 }
 
 // This function notifies all the readers, writers and

From 87e3bdac4eafb3709f146cff358bca9568c24aed Mon Sep 17 00:00:00 2001
From: Luo Tao <luotao02@baidu.com>
Date: Thu, 8 Feb 2018 15:49:14 +0800
Subject: [PATCH 308/314] refine structure of cluster and quick start

---
 doc/getstarted/quickstart_cn.rst     |   6 ++++++
 doc/getstarted/quickstart_en.rst     |   6 ++++++
 doc/howto/cluster/index_cn.rst       |  14 +++++++++++++-
 doc/howto/cluster/index_en.rst       |  14 +++++++++++++-
 doc/howto/cluster/introduction_cn.md |  13 -------------
 doc/howto/cluster/introduction_en.md |  13 -------------
 doc/howto/cluster/src/ps_cn.png      | Bin 0 -> 33865 bytes
 doc/howto/cluster/src/ps_en.png      | Bin 0 -> 145107 bytes
 8 files changed, 38 insertions(+), 28 deletions(-)
 delete mode 100644 doc/howto/cluster/introduction_cn.md
 delete mode 100644 doc/howto/cluster/introduction_en.md
 create mode 100644 doc/howto/cluster/src/ps_cn.png
 create mode 100644 doc/howto/cluster/src/ps_en.png

diff --git a/doc/getstarted/quickstart_cn.rst b/doc/getstarted/quickstart_cn.rst
index 51dd00f1e8..d511cead26 100644
--- a/doc/getstarted/quickstart_cn.rst
+++ b/doc/getstarted/quickstart_cn.rst
@@ -1,6 +1,9 @@
 快速开始
 ========
 
+快速安装
+--------
+
 PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
 执行下面的命令完成快速安装，版本为cpu_avx_openblas：
 
@@ -16,6 +19,9 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
 
 更详细的安装和编译方法参考：:ref:`install_steps` 。
 
+快速使用
+--------
+
 创建一个 housing.py 并粘贴此Python代码：
 
   .. code-block:: python
diff --git a/doc/getstarted/quickstart_en.rst b/doc/getstarted/quickstart_en.rst
index d1bcf82ea0..70f7fe0646 100644
--- a/doc/getstarted/quickstart_en.rst
+++ b/doc/getstarted/quickstart_en.rst
@@ -1,6 +1,9 @@
 Quick Start
 ============
 
+Quick Install
+-------------
+
 You can use pip to install PaddlePaddle with a single command, supports
 CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
 Simply run the following command to install, the version is cpu_avx_openblas:
@@ -17,6 +20,9 @@ If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
 
 For more details about installation and build: :ref:`install_steps` .
 
+Quick Use
+---------
+
 Create a new file called housing.py, and paste this Python
 code:
 
diff --git a/doc/howto/cluster/index_cn.rst b/doc/howto/cluster/index_cn.rst
index c68b2655b6..a60521b4a9 100644
--- a/doc/howto/cluster/index_cn.rst
+++ b/doc/howto/cluster/index_cn.rst
@@ -1,10 +1,22 @@
 分布式训练
 ==========
 
+本节将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
+
+.. image:: src/ps_cn.png
+   :width: 500
+
+- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
+- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
+- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
+
+这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
+
+在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
+
 ..  toctree::
   :maxdepth: 1
 
-  introduction_cn.md
   preparations_cn.md
   cmd_argument_cn.md
   multi_cluster/index_cn.rst
diff --git a/doc/howto/cluster/index_en.rst b/doc/howto/cluster/index_en.rst
index af957e06cd..2640a09dcc 100644
--- a/doc/howto/cluster/index_en.rst
+++ b/doc/howto/cluster/index_en.rst
@@ -1,10 +1,22 @@
 Distributed Training
 ====================
 
+In this section, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
+
+.. image:: src/ps_en.png
+   :width: 500
+
+- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
+- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
+- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
+
+PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
+
+When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
+
 ..  toctree::
   :maxdepth: 1
 
-  introduction_en.md
   preparations_en.md
   cmd_argument_en.md
   multi_cluster/index_en.rst
diff --git a/doc/howto/cluster/introduction_cn.md b/doc/howto/cluster/introduction_cn.md
deleted file mode 100644
index 562008a898..0000000000
--- a/doc/howto/cluster/introduction_cn.md
+++ /dev/null
@@ -1,13 +0,0 @@
-## 概述
-
-本节将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
-
-<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
-
-- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
-- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
-- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
-
-这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
-
-在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
diff --git a/doc/howto/cluster/introduction_en.md b/doc/howto/cluster/introduction_en.md
deleted file mode 100644
index eb70d7cf35..0000000000
--- a/doc/howto/cluster/introduction_en.md
+++ /dev/null
@@ -1,13 +0,0 @@
-## Introduction
-
-In this section, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
-
-<img src="https://user-images.githubusercontent.com/13348433/31772146-41523d84-b511-11e7-8a12-a69fd136c283.png" width="500">
-
-- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
-- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
-- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
-
-PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
-
-When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
diff --git a/doc/howto/cluster/src/ps_cn.png b/doc/howto/cluster/src/ps_cn.png
new file mode 100644
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E2UVb{C;$Ke

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HcmV?d00001


From 9efa8d86fea9fbcc87b5e7bd2d5cad207d231739 Mon Sep 17 00:00:00 2001
From: Shan Yi <35982308+shanyi15@users.noreply.github.com>
Date: Thu, 8 Feb 2018 19:36:24 +0800
Subject: [PATCH 309/314] Update index_cn.rst

---
 doc/howto/rnn/index_cn.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/howto/rnn/index_cn.rst b/doc/howto/rnn/index_cn.rst
index 9ecab5594c..bcc8c2f46e 100644
--- a/doc/howto/rnn/index_cn.rst
+++ b/doc/howto/rnn/index_cn.rst
@@ -1,4 +1,4 @@
-RNN相关模型
+RNN模型
 ===========
 
 ..  toctree::

From 11acbe687e80bbe1be2632786fd9fa29ecb9932b Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Thu, 8 Feb 2018 10:30:10 -0800
Subject: [PATCH 310/314] Updating the copyright year for the recent files.
 (#8018)

* Adding more details to cluster_train

* Update copyright for notest_dist_image_classification

* Fixed copyright

* Updating the copyright year and content
---
 paddle/framework/channel.h                  | 2 +-
 paddle/framework/channel_test.cc            | 2 +-
 paddle/framework/details/buffered_channel.h | 2 +-
 paddle/framework/details/cow_ptr.h          | 2 +-
 paddle/framework/details/cow_ptr_test.cc    | 2 +-
 paddle/framework/details/op_registry.h      | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/paddle/framework/channel.h b/paddle/framework/channel.h
index b679387b11..146f0e9e71 100644
--- a/paddle/framework/channel.h
+++ b/paddle/framework/channel.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index a307abb4ed..8afb988914 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/paddle/framework/details/buffered_channel.h b/paddle/framework/details/buffered_channel.h
index 77eebc9924..c6e4bec0f3 100644
--- a/paddle/framework/details/buffered_channel.h
+++ b/paddle/framework/details/buffered_channel.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/paddle/framework/details/cow_ptr.h b/paddle/framework/details/cow_ptr.h
index 7e308ffb5a..69bcea6252 100644
--- a/paddle/framework/details/cow_ptr.h
+++ b/paddle/framework/details/cow_ptr.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
diff --git a/paddle/framework/details/cow_ptr_test.cc b/paddle/framework/details/cow_ptr_test.cc
index 936954a233..1f4a12bca0 100644
--- a/paddle/framework/details/cow_ptr_test.cc
+++ b/paddle/framework/details/cow_ptr_test.cc
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
diff --git a/paddle/framework/details/op_registry.h b/paddle/framework/details/op_registry.h
index 6d50e820b2..31a40bcbcb 100644
--- a/paddle/framework/details/op_registry.h
+++ b/paddle/framework/details/op_registry.h
@@ -1,4 +1,4 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

From f605d00f66f27066d68033e2dbbaef806954e24b Mon Sep 17 00:00:00 2001
From: kavyasrinet <kavyasrinet@baidu.com>
Date: Thu, 8 Feb 2018 10:30:29 -0800
Subject: [PATCH 311/314] Fixing unbuffered test to a generic test (#8162)

* Fixing unbufered test to a generic test

* Update channel_test.cc

* splitting over functions

* add type

* fix *
---
 paddle/framework/channel_test.cc | 47 ++++++++++++++++++--------------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/paddle/framework/channel_test.cc b/paddle/framework/channel_test.cc
index 8afb988914..35567649b8 100644
--- a/paddle/framework/channel_test.cc
+++ b/paddle/framework/channel_test.cc
@@ -25,6 +25,26 @@ using paddle::framework::CloseChannel;
 using paddle::framework::details::Buffered;
 using paddle::framework::details::UnBuffered;
 
+void RecevingOrderEqualToSendingOrder(Channel<int> *ch) {
+  unsigned sum_send = 0;
+  std::thread t([&]() {
+    for (int i = 0; i < 5; i++) {
+      EXPECT_EQ(ch->Send(&i), true);
+      sum_send += i;
+    }
+  });
+  for (int i = 0; i < 5; i++) {
+    int recv;
+    EXPECT_EQ(ch->Receive(&recv), true);
+    EXPECT_EQ(recv, i);
+  }
+
+  CloseChannel(ch);
+  t.join();
+  EXPECT_EQ(sum_send, 10U);
+  delete ch;
+}
+
 TEST(Channel, MakeAndClose) {
   using paddle::framework::details::Buffered;
   using paddle::framework::details::UnBuffered;
@@ -137,9 +157,7 @@ TEST(Channel, ReceiveFromBufferedChannelReturnResidualValuesTest) {
 
   for (size_t i = 0; i < buffer_size; ++i) {
     EXPECT_EQ(ch->Receive(&out),
-              false);  // after receiving residual values, return zeros.
-    // Note: we cannot check EXPECT_EQ(out, 0), because C++ doesn't
-    // define zero values like Go does.
+              false);  // receiving on closed channel should return false
   }
   delete ch;
 }
@@ -166,25 +184,14 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
   delete ch;
 }
 
-TEST(Channel, SimpleUnbufferedChannelTest) {
+TEST(Channel, RecevingOrderEqualToSendingOrderWithUnBufferedChannel) {
   auto ch = MakeChannel<int>(0);
-  unsigned sum_send = 0;
-  std::thread t([&]() {
-    for (int i = 0; i < 5; i++) {
-      EXPECT_EQ(ch->Send(&i), true);
-      sum_send += i;
-    }
-  });
-  for (int i = 0; i < 5; i++) {
-    int recv;
-    EXPECT_EQ(ch->Receive(&recv), true);
-    EXPECT_EQ(recv, i);
-  }
+  RecevingOrderEqualToSendingOrder(ch);
+}
 
-  CloseChannel(ch);
-  t.join();
-  EXPECT_EQ(sum_send, 10U);
-  delete ch;
+TEST(Channel, RecevingOrderEqualToSendingOrderWithBufferedChannel) {
+  auto ch = MakeChannel<int>(10);
+  RecevingOrderEqualToSendingOrder(ch);
 }
 
 // This tests that closing a buffered channel also unblocks

From 36da52950d53fdcd6903913a627b5e622648f5f4 Mon Sep 17 00:00:00 2001
From: Siddharth Goyal <vi.siddharth78@gmail.com>
Date: Thu, 8 Feb 2018 13:46:33 -0800
Subject: [PATCH 312/314] Better version of PR #7985 (Modify load() for
 inference) (#8024)

* Refine load

* Address review comments: round 1

* Make API consistent with python-save/load

* Add another unit test

* Remove commented function

* Fix GPU bug

* Address review comments

* Modify wrt PR 8147

* Fix filenames for combined case

* Fix typo

* Address review comments: round 2

* Unify TestInference by keeping default param in template

* Address review comment

* Fix spacing
---
 paddle/inference/io.cc                        | 73 +++++++++++++++----
 paddle/inference/io.h                         |  8 +-
 paddle/inference/tests/book/test_helper.h     | 17 ++++-
 .../book/test_inference_recognize_digits.cc   | 42 +++++++++++
 python/paddle/v2/fluid/io.py                  | 12 ++-
 .../fluid/tests/book/test_recognize_digits.py | 44 +++++++----
 6 files changed, 159 insertions(+), 37 deletions(-)

diff --git a/paddle/inference/io.cc b/paddle/inference/io.cc
index 1ed14b69c8..784e87970f 100644
--- a/paddle/inference/io.cc
+++ b/paddle/inference/io.cc
@@ -21,6 +21,17 @@ limitations under the License. */
 namespace paddle {
 namespace inference {
 
+void ReadBinaryFile(const std::string& filename, std::string& contents) {
+  VLOG(3) << "loading model from " << filename;
+  std::ifstream inputfs(filename, std::ios::in | std::ios::binary);
+  inputfs.seekg(0, std::ios::end);
+  contents.clear();
+  contents.resize(inputfs.tellg());
+  inputfs.seekg(0, std::ios::beg);
+  inputfs.read(&contents[0], contents.size());
+  inputfs.close();
+}
+
 bool IsParameter(const framework::VarDesc* var,
                  const framework::ProgramDesc& main_program) {
   if (var->Persistable()) {
@@ -44,12 +55,15 @@ bool IsParameter(const framework::VarDesc* var,
 
 void LoadPersistables(framework::Executor& executor,
                       framework::Scope& scope,
+                      const framework::ProgramDesc& main_program,
                       const std::string& dirname,
-                      const framework::ProgramDesc& main_program) {
+                      const std::string& param_filename) {
   const framework::BlockDesc& global_block = main_program.Block(0);
 
   framework::ProgramDesc* load_program = new framework::ProgramDesc();
   framework::BlockDesc* load_block = load_program->MutableBlock(0);
+  std::vector<std::string> paramlist;
+
   for (auto* var : global_block.AllVars()) {
     if (IsParameter(var, main_program)) {
       VLOG(3) << "parameter's name: " << var->Name();
@@ -61,15 +75,33 @@ void LoadPersistables(framework::Executor& executor,
       new_var->SetLoDLevel(var->GetLoDLevel());
       new_var->SetPersistable(true);
 
-      // append_op
-      framework::OpDesc* op = load_block->AppendOp();
-      op->SetType("load");
-      op->SetOutput("Out", {new_var->Name()});
-      op->SetAttr("file_path", {dirname + "/" + new_var->Name()});
-      op->CheckAttrs();
+      if (!param_filename.empty()) {
+        paramlist.push_back(new_var->Name());
+      } else {
+        // append_op
+        framework::OpDesc* op = load_block->AppendOp();
+        op->SetType("load");
+        op->SetOutput("Out", {new_var->Name()});
+        op->SetAttr("file_path", {dirname + "/" + new_var->Name()});
+        op->CheckAttrs();
+      }
     }
   }
+
+  if (!param_filename.empty()) {
+    // sort paramlist to have consistent ordering
+    std::sort(paramlist.begin(), paramlist.end());
+    // append just the load_combine op
+    framework::OpDesc* op = load_block->AppendOp();
+    op->SetType("load_combine");
+    op->SetOutput("Out", paramlist);
+    op->SetAttr("file_path", {param_filename});
+    op->CheckAttrs();
+  }
+
   executor.Run(*load_program, &scope, 0, true, true);
+
+  VLOG(3) << "Ran loading successfully";
   delete load_program;
 }
 
@@ -77,20 +109,29 @@ std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
                                              framework::Scope& scope,
                                              const std::string& dirname) {
   std::string model_filename = dirname + "/__model__";
-  LOG(INFO) << "loading model from " << model_filename;
-  std::ifstream inputfs(model_filename, std::ios::in | std::ios::binary);
   std::string program_desc_str;
-  inputfs.seekg(0, std::ios::end);
-  program_desc_str.resize(inputfs.tellg());
-  inputfs.seekg(0, std::ios::beg);
-  LOG(INFO) << "program_desc_str's size: " << program_desc_str.size();
-  inputfs.read(&program_desc_str[0], program_desc_str.size());
-  inputfs.close();
+  ReadBinaryFile(model_filename, program_desc_str);
+
+  std::unique_ptr<framework::ProgramDesc> main_program(
+      new framework::ProgramDesc(program_desc_str));
+
+  LoadPersistables(executor, scope, *main_program, dirname, "");
+  return main_program;
+}
+
+std::unique_ptr<framework::ProgramDesc> Load(
+    framework::Executor& executor,
+    framework::Scope& scope,
+    const std::string& prog_filename,
+    const std::string& param_filename) {
+  std::string model_filename = prog_filename;
+  std::string program_desc_str;
+  ReadBinaryFile(model_filename, program_desc_str);
 
   std::unique_ptr<framework::ProgramDesc> main_program(
       new framework::ProgramDesc(program_desc_str));
 
-  LoadPersistables(executor, scope, dirname, *main_program);
+  LoadPersistables(executor, scope, *main_program, "", param_filename);
   return main_program;
 }
 
diff --git a/paddle/inference/io.h b/paddle/inference/io.h
index 962b6c4e20..a7d7c49969 100644
--- a/paddle/inference/io.h
+++ b/paddle/inference/io.h
@@ -26,12 +26,18 @@ namespace inference {
 
 void LoadPersistables(framework::Executor& executor,
                       framework::Scope& scope,
+                      const framework::ProgramDesc& main_program,
                       const std::string& dirname,
-                      const framework::ProgramDesc& main_program);
+                      const std::string& param_filename);
 
 std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
                                              framework::Scope& scope,
                                              const std::string& dirname);
 
+std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
+                                             framework::Scope& scope,
+                                             const std::string& prog_filename,
+                                             const std::string& param_filename);
+
 }  // namespace inference
 }  // namespace paddle
diff --git a/paddle/inference/tests/book/test_helper.h b/paddle/inference/tests/book/test_helper.h
index 32db643fca..3e66ced94f 100644
--- a/paddle/inference/tests/book/test_helper.h
+++ b/paddle/inference/tests/book/test_helper.h
@@ -67,17 +67,28 @@ void CheckError(paddle::framework::LoDTensor& output1,
   EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
 }
 
-template <typename Place, typename T>
+template <typename Place, typename T, bool IsCombined = false>
 void TestInference(const std::string& dirname,
                    const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
                    std::vector<paddle::framework::LoDTensor*>& cpu_fetchs) {
-  // 1. Define place, executor and scope
+  // 1. Define place, executor, scope and inference_program
   auto place = Place();
   auto executor = paddle::framework::Executor(place);
   auto* scope = new paddle::framework::Scope();
+  std::unique_ptr<paddle::framework::ProgramDesc> inference_program;
 
   // 2. Initialize the inference_program and load all parameters from file
-  auto inference_program = paddle::inference::Load(executor, *scope, dirname);
+  if (IsCombined) {
+    // Hard-coding the names for combined params case
+    std::string prog_filename = "__model_combined__";
+    std::string param_filename = "__params_combined__";
+    inference_program = paddle::inference::Load(executor,
+                                                *scope,
+                                                dirname + "/" + prog_filename,
+                                                dirname + "/" + param_filename);
+  } else {
+    inference_program = paddle::inference::Load(executor, *scope, dirname);
+  }
 
   // 3. Get the feed_target_names and fetch_target_names
   const std::vector<std::string>& feed_target_names =
diff --git a/paddle/inference/tests/book/test_inference_recognize_digits.cc b/paddle/inference/tests/book/test_inference_recognize_digits.cc
index 48f887e6bc..3a48db7fe0 100644
--- a/paddle/inference/tests/book/test_inference_recognize_digits.cc
+++ b/paddle/inference/tests/book/test_inference_recognize_digits.cc
@@ -59,3 +59,45 @@ TEST(inference, recognize_digits) {
   CheckError<float>(output1, output2);
 #endif
 }
+
+TEST(inference, recognize_digits_combine) {
+  if (FLAGS_dirname.empty()) {
+    LOG(FATAL) << "Usage: ./example --dirname=path/to/your/model";
+  }
+
+  LOG(INFO) << "FLAGS_dirname: " << FLAGS_dirname << std::endl;
+  std::string dirname = FLAGS_dirname;
+
+  // 0. Call `paddle::framework::InitDevices()` initialize all the devices
+  // In unittests, this is done in paddle/testing/paddle_gtest_main.cc
+
+  paddle::framework::LoDTensor input;
+  // Use normilized image pixels as input data,
+  // which should be in the range [-1.0, 1.0].
+  SetupTensor<float>(
+      input, {1, 28, 28}, static_cast<float>(-1), static_cast<float>(1));
+  std::vector<paddle::framework::LoDTensor*> cpu_feeds;
+  cpu_feeds.push_back(&input);
+
+  paddle::framework::LoDTensor output1;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs1;
+  cpu_fetchs1.push_back(&output1);
+
+  // Run inference on CPU
+  TestInference<paddle::platform::CPUPlace, float, true>(
+      dirname, cpu_feeds, cpu_fetchs1);
+  LOG(INFO) << output1.dims();
+
+#ifdef PADDLE_WITH_CUDA
+  paddle::framework::LoDTensor output2;
+  std::vector<paddle::framework::LoDTensor*> cpu_fetchs2;
+  cpu_fetchs2.push_back(&output2);
+
+  // Run inference on CUDA GPU
+  TestInference<paddle::platform::CUDAPlace, float, true>(
+      dirname, cpu_feeds, cpu_fetchs2);
+  LOG(INFO) << output2.dims();
+
+  CheckError<float>(output1, output2);
+#endif
+}
diff --git a/python/paddle/v2/fluid/io.py b/python/paddle/v2/fluid/io.py
index 613dc20b6e..0f43e46082 100644
--- a/python/paddle/v2/fluid/io.py
+++ b/python/paddle/v2/fluid/io.py
@@ -342,7 +342,11 @@ def save_inference_model(dirname,
     prepend_feed_ops(inference_program, feeded_var_names)
     append_fetch_ops(inference_program, fetch_var_names)
 
-    model_file_name = dirname + "/__model__"
+    if save_file_name == None:
+        model_file_name = dirname + "/__model__"
+    else:
+        model_file_name = dirname + "/__model_combined__"
+
     with open(model_file_name, "wb") as f:
         f.write(inference_program.desc.serialize_to_string())
 
@@ -384,7 +388,11 @@ def load_inference_model(dirname, executor, load_file_name=None):
     if not os.path.isdir(dirname):
         raise ValueError("There is no directory named '%s'", dirname)
 
-    model_file_name = dirname + "/__model__"
+    if load_file_name == None:
+        model_file_name = dirname + "/__model__"
+    else:
+        model_file_name = dirname + "/__model_combined__"
+
     with open(model_file_name, "rb") as f:
         program_desc_str = f.read()
 
diff --git a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
index d8f0ad89cd..6f9d85faff 100644
--- a/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+++ b/python/paddle/v2/fluid/tests/book/test_recognize_digits.py
@@ -78,7 +78,7 @@ def conv_net(img, label):
     return loss_net(conv_pool_2, label)
 
 
-def train(nn_type, use_cuda, parallel, save_dirname):
+def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
@@ -143,8 +143,10 @@ def train(nn_type, use_cuda, parallel, save_dirname):
                 avg_loss_val = numpy.array(avg_loss_set).mean()
                 if float(acc_val) > 0.85:  # test acc > 85%
                     if save_dirname is not None:
-                        fluid.io.save_inference_model(save_dirname, ["img"],
-                                                      [prediction], exe)
+                        fluid.io.save_inference_model(
+                            save_dirname, ["img"], [prediction],
+                            exe,
+                            save_file_name=save_param_filename)
                     return
                 else:
                     print(
@@ -156,7 +158,7 @@ def train(nn_type, use_cuda, parallel, save_dirname):
     raise AssertionError("Loss of recognize digits is too large")
 
 
-def infer(use_cuda, save_dirname=None):
+def infer(use_cuda, save_dirname=None, param_filename=None):
     if save_dirname is None:
         return
 
@@ -167,8 +169,8 @@ def infer(use_cuda, save_dirname=None):
     # the feed_target_names (the names of variables that will be feeded 
     # data using feed operators), and the fetch_targets (variables that 
     # we want to obtain data from using fetch operators).
-    [inference_program, feed_target_names,
-     fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+    [inference_program, feed_target_names, fetch_targets
+     ] = fluid.io.load_inference_model(save_dirname, exe, param_filename)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
@@ -183,36 +185,45 @@ def infer(use_cuda, save_dirname=None):
     print("infer results: ", results[0])
 
 
-def main(use_cuda, parallel, nn_type):
+def main(use_cuda, parallel, nn_type, combine):
     if not use_cuda and not parallel:
         save_dirname = "recognize_digits_" + nn_type + ".inference.model"
+        save_filename = None
+        if combine == True:
+            save_filename = "__params_combined__"
     else:
         save_dirname = None
+        save_filename = None
 
     train(
         nn_type=nn_type,
         use_cuda=use_cuda,
         parallel=parallel,
-        save_dirname=save_dirname)
-    infer(use_cuda=use_cuda, save_dirname=save_dirname)
+        save_dirname=save_dirname,
+        save_param_filename=save_filename)
+    infer(
+        use_cuda=use_cuda,
+        save_dirname=save_dirname,
+        param_filename=save_filename)
 
 
 class TestRecognizeDigits(unittest.TestCase):
     pass
 
 
-def inject_test_method(use_cuda, parallel, nn_type):
+def inject_test_method(use_cuda, parallel, nn_type, combine):
     def __impl__(self):
         prog = fluid.Program()
         startup_prog = fluid.Program()
         scope = fluid.core.Scope()
         with fluid.scope_guard(scope):
             with fluid.program_guard(prog, startup_prog):
-                main(use_cuda, parallel, nn_type)
+                main(use_cuda, parallel, nn_type, combine)
 
-    fn = 'test_{0}_{1}_{2}'.format(nn_type, 'cuda'
-                                   if use_cuda else 'cpu', 'parallel'
-                                   if parallel else 'normal')
+    fn = 'test_{0}_{1}_{2}_{3}'.format(nn_type, 'cuda'
+                                       if use_cuda else 'cpu', 'parallel'
+                                       if parallel else 'normal', 'combine'
+                                       if combine else 'separate')
 
     setattr(TestRecognizeDigits, fn, __impl__)
 
@@ -221,7 +232,10 @@ def inject_all_tests():
     for use_cuda in (False, True):
         for parallel in (False, True):
             for nn_type in ('mlp', 'conv'):
-                inject_test_method(use_cuda, parallel, nn_type)
+                inject_test_method(use_cuda, parallel, nn_type, True)
+
+    # One unit-test for saving parameters as separate files
+    inject_test_method(False, False, 'mlp', False)
 
 
 inject_all_tests()

From 0a7ae369f6d6a6be7b87d13834b8ff0a7e3614d1 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Fri, 9 Feb 2018 10:27:47 +0800
Subject: [PATCH 313/314] fix CI hung

---
 .../tests/book_memory_optimization/test_memopt_fit_a_line.py | 4 ++++
 .../test_memopt_image_classification_train.py                | 5 +++++
 .../test_memopt_machine_translation.py                       | 4 ++++
 3 files changed, 13 insertions(+)

diff --git a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
index 7ad5e2c594..045db8390c 100644
--- a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
+++ b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py
@@ -15,6 +15,8 @@
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import math
+import sys
 
 # need to fix random seed and training data to compare the loss
 # value accurately calculated by the default and the memory optimization
@@ -63,4 +65,6 @@ for pass_id in range(PASS_NUM):
 
         if avg_loss_value[0] < 10.0:
             exit(0)  # if avg cost less than 10.0, we think our code is good.
+        if math.isnan(float(avg_loss_value)):
+            sys.exit("got NaN loss, training failed.")
 exit(1)
diff --git a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
index 26673afd83..9fbb36d363 100644
--- a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
+++ b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py
@@ -18,6 +18,8 @@ import sys
 
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import math
+import sys
 
 # need to fix random seed and training data to compare the loss
 # value accurately calculated by the default and the memory optimization
@@ -152,7 +154,10 @@ for pass_id in range(PASS_NUM):
         print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
             pass_acc))
         # this model is slow, so if we can train two mini batch, we think it works properly.
+
         if i > 2:
             exit(0)
+        if math.isnan(float(loss)):
+            sys.exit("got NaN loss, training failed.")
         i += 1
 exit(1)
diff --git a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
index ffd53e7a78..48abaa8d87 100644
--- a/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
+++ b/python/paddle/v2/fluid/tests/book_memory_optimization/test_memopt_machine_translation.py
@@ -19,6 +19,8 @@ import paddle.v2.fluid.core as core
 import paddle.v2.fluid.framework as framework
 import paddle.v2.fluid.layers as layers
 from paddle.v2.fluid.executor import Executor
+import math
+import sys
 
 dict_size = 30000
 source_dict_dim = target_dict_dim = dict_size
@@ -137,6 +139,8 @@ def main():
                   " avg_cost=" + str(avg_cost_val))
             if batch_id > 2:
                 exit(0)
+            if math.isnan(float(avg_cost_val)):
+                sys.exit("got NaN loss, training failed.")
             batch_id += 1
 
 

From b5ffe5bce205ea9ce48a329edd6f19164c8608f2 Mon Sep 17 00:00:00 2001
From: QI JUN <qijun1994@hotmail.com>
Date: Fri, 9 Feb 2018 11:08:02 +0800
Subject: [PATCH 314/314] optimize data flow analysis (#8271)

---
 python/paddle/v2/fluid/memory_optimization_transpiler.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/python/paddle/v2/fluid/memory_optimization_transpiler.py b/python/paddle/v2/fluid/memory_optimization_transpiler.py
index 8bb8cf7b1a..53e0991ee8 100644
--- a/python/paddle/v2/fluid/memory_optimization_transpiler.py
+++ b/python/paddle/v2/fluid/memory_optimization_transpiler.py
@@ -92,14 +92,13 @@ class ControlFlowGraph(object):
         live_in = defaultdict(set)
         live_out = defaultdict(set)
         while True:
-            for i in range(self.op_size):
+            for i in range(self.op_size, 0, -1):
                 live_in[i] = set(self._live_in[i])
                 live_out[i] = set(self._live_out[i])
-                self._live_in[i] = self._uses[i] | (
-                    self._live_out[i] - self._defs[i])
                 for s in self._successors[i]:
                     self._live_out[i] |= self._live_in[s]
-
+                self._live_in[i] = self._uses[i] | (
+                    self._live_out[i] - self._defs[i])
             if self._reach_fixed_point(live_in, live_out):
                 break