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 001/204] 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 002/204] 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 003/204] 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 004/204] 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 005/204] 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 006/204] 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 007/204] 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 008/204] 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 009/204] 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 010/204] 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 011/204] 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 012/204] 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 013/204] 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 014/204] 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 cb34f6a230bf51cc6cb0b8b2ef93b3e13ed3f516 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 22 Jan 2018 14:45:06 +0800
Subject: [PATCH 015/204] 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 016/204] 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 017/204] 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 018/204] 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 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 019/204] 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 020/204] ../../../../../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 021/204] 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 ca0177190f75a4f39482b8fe1c8e929ab8e1a381 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Mon, 22 Jan 2018 15:18:47 +0800
Subject: [PATCH 022/204] 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 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 023/204] 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 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 024/204] 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 ae0ea5415902f3187c7883016c3798ee5ec64fab Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Wed, 24 Jan 2018 14:14:42 +0800
Subject: [PATCH 025/204] 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 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 026/204] 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 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 027/204] 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 4ce397964b788f1e9bbbe08a8e5f4d4ce21dd2f8 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sat, 27 Jan 2018 12:31:30 +0800
Subject: [PATCH 028/204] 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 72eccb238ebea3bb0509da7bf6e9ff18db088763 Mon Sep 17 00:00:00 2001
From: gaoyuan <gaoyuan18@baidu.com>
Date: Sun, 28 Jan 2018 15:38:54 +0800
Subject: [PATCH 029/204] 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 030/204] 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 0f47703dd5db01a7510031e810f963e09a8c9c13 Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Sun, 28 Jan 2018 16:41:18 +0800
Subject: [PATCH 031/204] 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 1b8bc3c5b3d3884dad4310d58eb949c0cfbf0d60 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 29 Jan 2018 16:58:01 +0800
Subject: [PATCH 032/204] 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 bd64719a2f012af82dcac731179a998764d432b9 Mon Sep 17 00:00:00 2001
From: typhoonzero <typhoonzero1986@gmail.com>
Date: Mon, 29 Jan 2018 20:42:29 +0800
Subject: [PATCH 033/204] 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 034/204] 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 035/204] 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 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 036/204] 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 037/204] 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 038/204] 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 039/204] 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 040/204] 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 041/204] 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 042/204] 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 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 043/204] 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 044/204] 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 045/204] 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 046/204] 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 047/204] 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 048/204] 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 049/204] 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 050/204] 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 051/204] 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 052/204] 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 263e01970d4f1923a5ee92e8d9b615a529bfb29e Mon Sep 17 00:00:00 2001
From: chengduoZH <zhaochengduo@163.com>
Date: Tue, 30 Jan 2018 19:43:26 +0800
Subject: [PATCH 053/204] 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 054/204] 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 055/204] 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 056/204] 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 057/204] 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 058/204] 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 059/204] 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 060/204] 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 061/204] 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 062/204] "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 063/204] 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 064/204] 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 065/204] 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 066/204] "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 067/204] 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 068/204] 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 069/204] 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 070/204] 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 071/204] 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 072/204] 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 073/204] 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 074/204] 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 075/204] 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 076/204] 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 077/204] 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 078/204] 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 079/204] 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 080/204] 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 081/204] 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 082/204] 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 083/204] 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 084/204] 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 085/204] 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 086/204] 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 087/204] 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 088/204] 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 089/204] 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 090/204] 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 091/204] 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 092/204] 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 093/204] 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 094/204] 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 095/204] 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 096/204] 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 097/204] 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 098/204] 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 099/204] 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 100/204] 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 101/204] 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 102/204] 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 103/204] 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 104/204] 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 105/204] 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 106/204] 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 107/204] 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 108/204] 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 109/204] 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 110/204] 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 111/204] 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 112/204] 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 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 113/204] 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 114/204] 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 115/204] 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 116/204] 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 117/204] 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 118/204] 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 119/204] 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 120/204] 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 121/204] 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 122/204] 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 123/204] 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 124/204] 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 125/204] 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 126/204] 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 127/204] 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 128/204] 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 129/204] 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 130/204] 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 131/204] 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 132/204] 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 133/204] 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 134/204] 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 135/204] 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 136/204] 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 137/204] 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 37a251ebafce61776b2fea7a2fb2ee16defd14ea Mon Sep 17 00:00:00 2001
From: xuwei06 <xuwei06@baidu.com>
Date: Fri, 2 Feb 2018 15:46:51 -0800
Subject: [PATCH 138/204] 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 139/204] 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 140/204] 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 b60da6729fa2484506869bc29271761de91676b7 Mon Sep 17 00:00:00 2001
From: chengduo <zhaochengduo@baidu.com>
Date: Sat, 3 Feb 2018 23:32:56 +0800
Subject: [PATCH 141/204] 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 326fa176ea6401f171e9325aa29fb0b5cf6f7a29 Mon Sep 17 00:00:00 2001
From: wanghaoshuang <wanghaoshuang@baidu.com>
Date: Sun, 4 Feb 2018 22:45:47 +0800
Subject: [PATCH 142/204] 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 143/204] 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 144/204] 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 145/204] 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 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 146/204] 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 147/204] 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 7dabee27960b5e043b85aca3ee51568443b326f4 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Mon, 5 Feb 2018 15:00:03 +0800
Subject: [PATCH 148/204] 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 149/204] 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 150/204] "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 151/204] "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 152/204] "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 153/204] 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 154/204] "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 155/204] "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 156/204] 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 157/204] 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 158/204] 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 159/204] 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 160/204] 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 161/204] 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 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 162/204] 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 163/204] 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 164/204] 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 9a1fa890a0c510ca1863eea358423bc89fd4fdef Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 6 Feb 2018 11:10:34 +0800
Subject: [PATCH 165/204] 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 1010e39bdf738029fcb78b0d388a91dfdebdda2f Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Tue, 6 Feb 2018 12:39:51 +0800
Subject: [PATCH 166/204] 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 167/204] 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 168/204] 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 169/204] 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 170/204] "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 171/204] "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 172/204] "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 173/204] 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 174/204] 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 175/204] 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 176/204] 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 177/204] 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 178/204] 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 179/204] 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 180/204] 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 181/204] 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 182/204] 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 183/204] 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 184/204] 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 185/204] "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 186/204] 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
rename to doc/howto/cluster/cluster_train_cn.md
diff --git a/doc/howto/usage/cluster/cluster_train_en.md b/doc/howto/cluster/cluster_train_en.md
similarity index 100%
rename from doc/howto/usage/cluster/cluster_train_en.md
rename to doc/howto/cluster/cluster_train_en.md
diff --git a/doc/howto/usage/cluster/fabric_cn.md b/doc/howto/cluster/fabric_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/fabric_cn.md
rename to doc/howto/cluster/fabric_cn.md
diff --git a/doc/howto/usage/cluster/fabric_en.md b/doc/howto/cluster/fabric_en.md
similarity index 100%
rename from doc/howto/usage/cluster/fabric_en.md
rename to doc/howto/cluster/fabric_en.md
diff --git a/doc/howto/usage/cluster/fluid_cluster_train_en.md b/doc/howto/cluster/fluid_cluster_train_en.md
similarity index 100%
rename from doc/howto/usage/cluster/fluid_cluster_train_en.md
rename to doc/howto/cluster/fluid_cluster_train_en.md
diff --git a/doc/howto/usage/cluster/k8s_aws_cn.md b/doc/howto/cluster/k8s_aws_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/k8s_aws_cn.md
rename to doc/howto/cluster/k8s_aws_cn.md
diff --git a/doc/howto/usage/cluster/k8s_aws_en.md b/doc/howto/cluster/k8s_aws_en.md
similarity index 100%
rename from doc/howto/usage/cluster/k8s_aws_en.md
rename to doc/howto/cluster/k8s_aws_en.md
diff --git a/doc/howto/usage/cluster/k8s_cn.md b/doc/howto/cluster/k8s_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/k8s_cn.md
rename to doc/howto/cluster/k8s_cn.md
diff --git a/doc/howto/usage/cluster/k8s_distributed_cn.md b/doc/howto/cluster/k8s_distributed_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/k8s_distributed_cn.md
rename to doc/howto/cluster/k8s_distributed_cn.md
diff --git a/doc/howto/usage/cluster/k8s_en.md b/doc/howto/cluster/k8s_en.md
similarity index 100%
rename from doc/howto/usage/cluster/k8s_en.md
rename to doc/howto/cluster/k8s_en.md
diff --git a/doc/howto/usage/cluster/openmpi_cn.md b/doc/howto/cluster/openmpi_cn.md
similarity index 100%
rename from doc/howto/usage/cluster/openmpi_cn.md
rename to doc/howto/cluster/openmpi_cn.md
diff --git a/doc/howto/usage/cluster/openmpi_en.md b/doc/howto/cluster/openmpi_en.md
similarity index 100%
rename from doc/howto/usage/cluster/openmpi_en.md
rename to doc/howto/cluster/openmpi_en.md
diff --git a/doc/howto/usage/cluster/src/Dockerfile b/doc/howto/cluster/src/Dockerfile
similarity index 100%
rename from doc/howto/usage/cluster/src/Dockerfile
rename to doc/howto/cluster/src/Dockerfile
diff --git a/doc/howto/usage/cluster/src/add_security_group.png b/doc/howto/cluster/src/add_security_group.png
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rename from doc/howto/usage/cluster/src/add_security_group.png
rename to doc/howto/cluster/src/add_security_group.png
diff --git a/doc/howto/usage/cluster/src/create_efs.png b/doc/howto/cluster/src/create_efs.png
similarity index 100%
rename from doc/howto/usage/cluster/src/create_efs.png
rename to doc/howto/cluster/src/create_efs.png
diff --git a/doc/howto/usage/cluster/src/efs_mount.png b/doc/howto/cluster/src/efs_mount.png
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rename from doc/howto/usage/cluster/src/efs_mount.png
rename to doc/howto/cluster/src/efs_mount.png
diff --git a/doc/howto/usage/cluster/src/k8s-paddle-arch.png b/doc/howto/cluster/src/k8s-paddle-arch.png
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rename from doc/howto/usage/cluster/src/k8s-paddle-arch.png
rename to doc/howto/cluster/src/k8s-paddle-arch.png
diff --git a/doc/howto/usage/cluster/src/k8s_data/Dockerfile b/doc/howto/cluster/src/k8s_data/Dockerfile
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_data/Dockerfile
rename to doc/howto/cluster/src/k8s_data/Dockerfile
diff --git a/doc/howto/usage/cluster/src/k8s_data/README.md b/doc/howto/cluster/src/k8s_data/README.md
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_data/README.md
rename to doc/howto/cluster/src/k8s_data/README.md
diff --git a/doc/howto/usage/cluster/src/k8s_data/get_data.sh b/doc/howto/cluster/src/k8s_data/get_data.sh
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_data/get_data.sh
rename to doc/howto/cluster/src/k8s_data/get_data.sh
diff --git a/doc/howto/usage/cluster/src/k8s_train/Dockerfile b/doc/howto/cluster/src/k8s_train/Dockerfile
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_train/Dockerfile
rename to doc/howto/cluster/src/k8s_train/Dockerfile
diff --git a/doc/howto/usage/cluster/src/k8s_train/README.md b/doc/howto/cluster/src/k8s_train/README.md
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_train/README.md
rename to doc/howto/cluster/src/k8s_train/README.md
diff --git a/doc/howto/usage/cluster/src/k8s_train/start.sh b/doc/howto/cluster/src/k8s_train/start.sh
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_train/start.sh
rename to doc/howto/cluster/src/k8s_train/start.sh
diff --git a/doc/howto/usage/cluster/src/k8s_train/start_paddle.py b/doc/howto/cluster/src/k8s_train/start_paddle.py
similarity index 100%
rename from doc/howto/usage/cluster/src/k8s_train/start_paddle.py
rename to doc/howto/cluster/src/k8s_train/start_paddle.py
diff --git a/doc/howto/usage/cluster/src/managed_policy.png b/doc/howto/cluster/src/managed_policy.png
similarity index 100%
rename from doc/howto/usage/cluster/src/managed_policy.png
rename to doc/howto/cluster/src/managed_policy.png
diff --git a/doc/howto/usage/cluster/src/pserver_and_trainer.png b/doc/howto/cluster/src/pserver_and_trainer.png
similarity index 100%
rename from doc/howto/usage/cluster/src/pserver_and_trainer.png
rename to doc/howto/cluster/src/pserver_and_trainer.png
diff --git a/doc/howto/usage/cluster/src/route53_create_recordset.png b/doc/howto/cluster/src/route53_create_recordset.png
similarity index 100%
rename from doc/howto/usage/cluster/src/route53_create_recordset.png
rename to doc/howto/cluster/src/route53_create_recordset.png
diff --git a/doc/howto/usage/cluster/src/route53_create_zone.png b/doc/howto/cluster/src/route53_create_zone.png
similarity index 100%
rename from doc/howto/usage/cluster/src/route53_create_zone.png
rename to doc/howto/cluster/src/route53_create_zone.png
diff --git a/doc/howto/usage/cluster/src/trainer.png b/doc/howto/cluster/src/trainer.png
similarity index 100%
rename from doc/howto/usage/cluster/src/trainer.png
rename to doc/howto/cluster/src/trainer.png
diff --git a/doc/howto/usage/cluster/src/trainer_cn.png b/doc/howto/cluster/src/trainer_cn.png
similarity index 100%
rename from doc/howto/usage/cluster/src/trainer_cn.png
rename to doc/howto/cluster/src/trainer_cn.png
diff --git a/doc/howto/usage/cluster/src/word2vec/api_train_v2.py b/doc/howto/cluster/src/word2vec/api_train_v2.py
similarity index 100%
rename from doc/howto/usage/cluster/src/word2vec/api_train_v2.py
rename to doc/howto/cluster/src/word2vec/api_train_v2.py
diff --git a/doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py b/doc/howto/cluster/src/word2vec/api_train_v2_cluster.py
similarity index 100%
rename from doc/howto/usage/cluster/src/word2vec/api_train_v2_cluster.py
rename to doc/howto/cluster/src/word2vec/api_train_v2_cluster.py
diff --git a/doc/howto/usage/cluster/src/word2vec/prepare.py b/doc/howto/cluster/src/word2vec/prepare.py
similarity index 100%
rename from doc/howto/usage/cluster/src/word2vec/prepare.py
rename to doc/howto/cluster/src/word2vec/prepare.py
diff --git a/doc/howto/usage/cluster/src/worker_security_group.png b/doc/howto/cluster/src/worker_security_group.png
similarity index 100%
rename from doc/howto/usage/cluster/src/worker_security_group.png
rename to doc/howto/cluster/src/worker_security_group.png
diff --git a/doc/howto/usage/cmd_parameter/arguments_cn.md b/doc/howto/cmd_parameter/arguments_cn.md
similarity index 100%
rename from doc/howto/usage/cmd_parameter/arguments_cn.md
rename to doc/howto/cmd_parameter/arguments_cn.md
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 187/204] 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 188/204] 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 189/204] 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 190/204] 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 ee7d8421907affb362b9ed9baa0150f734d2c33c Mon Sep 17 00:00:00 2001
From: dangqingqing <dangqingqing@baidu.com>
Date: Wed, 7 Feb 2018 15:01:23 +0800
Subject: [PATCH 191/204] 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 192/204] 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 5210ff015870ecd002b1048a1aa45c94116e6bbc Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Wed, 7 Feb 2018 16:04:12 +0800
Subject: [PATCH 193/204] 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 194/204] 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 195/204] 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 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 196/204] 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 197/204] 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 198/204] 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 199/204] 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 200/204] 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 201/204] 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 202/204] 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 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 203/204] 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 204/204] 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