Merge branch 'develop' into stride

paddle/cuda/include/hl_sequence.h

@@ -159,4 +159,10 @@ extern void hl_sequence_avg_forward(real* dst,
                                     int width,
                                     const int mode);
 
+extern void hl_sequence_avg_backward(real* dst,
+                                     real* src,
+                                     const int* starts,
+                                     int height,
+                                     int width,
+                                     const int mode);
 #endif /* HL_SEQUENCE_H_ */

paddle/cuda/include/stub/hl_sequence_stub.h

@@ -57,4 +57,10 @@ inline void hl_sequence_avg_forward(real* dst,
                                     int width,
                                     const int mode) {}
 
+inline void hl_sequence_avg_backward(real* dst,
+                                     real* src,
+                                     const int* starts,
+                                     int height,
+                                     int width,
+                                     const int mode) {}
 #endif  // HL_SEQUENCE_STUB_H_

paddle/cuda/src/hl_cuda_sequence.cu

@@ -325,12 +325,12 @@ __global__ void KeSequenceAvgForward(real* dst,
     int seqLength = end - start;
     if (seqLength == 0) return;
     real sum = 0.0;
-    for (int i = 0; i < seqLength; i++) {
-      sum += src[(start + i) * width + col];
+    for (int i = start; i < end; i++) {
+      sum += src[i * width + col];
     }
     sum = mode == 1 ? sum :
         (mode == 0 ? sum / seqLength : sum * my_rsqrt((real)seqLength));
-    dst[row * width + col] = sum;
+    dst[gid] = sum;
   }
 }
 
@@ -354,3 +354,48 @@ void hl_sequence_avg_forward(real* dst,
            (dst, src, starts, height, width, mode);
   CHECK_SYNC("hl_sequence_avg_forward failed");
 }
+
+__global__ void KeSequenceAvgBackward(real* dst,
+                                      real* src,
+                                      const int* starts,
+                                      int height,
+                                      int width,
+                                      const int mode) {
+  int gid = blockIdx.x * blockDim.x + threadIdx.x;
+  int row = gid / width;
+  int col = gid % width;
+
+  if (gid < height * width) {
+    int start = starts[row];
+    int end = starts[row + 1];
+    int seqLength = end - start;
+    if (seqLength == 0) return;
+    real grad = src[gid];
+    grad = mode == 1 ? grad :
+        (mode == 0 ? grad / seqLength : grad * my_rsqrt((real)seqLength));
+    for (int i = start; i < end; i++) {
+      dst[i * width + col] += grad;
+    }
+  }
+}
+
+void hl_sequence_avg_backward(real* dst,
+                              real* src,
+                              const int* starts,
+                              int height,
+                              int width,
+                              const int mode) {
+  CHECK_NOTNULL(dst);
+  CHECK_NOTNULL(src);
+  CHECK_NOTNULL(starts);
+
+  int block = 512;
+  int grid = DIVUP(width * height, 512);
+
+  CHECK(mode == 0 || mode == 1 || mode == 2)
+    << "mode error in hl_sequence_avg_backward!";
+
+  KeSequenceAvgBackward<<< grid, block, 0, STREAM_DEFAULT >>>
+           (dst, src, starts, height, width, mode);
+  CHECK_SYNC("hl_sequence_avg_backward failed");
+}

paddle/gserver/layers/AverageLayer.cpp

@@ -26,8 +26,6 @@ bool AverageLayer::init(const LayerMap& layerMap,
                         const ParameterMap& parameterMap) {
   SequencePoolLayer::init(layerMap, parameterMap);
 
-  dataMtx_ = Matrix::create(nullptr, 1, 1, false, useGpu_);
-  outMtx_ = Matrix::create(nullptr, 1, getSize(), false, useGpu_);
   // average strategy
   if (config_.average_strategy() == "average") {
     mode_ = kAverage;
@@ -60,43 +58,9 @@ void AverageLayer::forward(PassType passType) {
 void AverageLayer::backward(const UpdateCallback& callback) {
   SequencePoolLayer::backward(callback);
 
-  const int* starts = startPositions_->getData(false);
-  MatrixPtr grad = getInputGrad(0);
-
-  if (grad) {
-    size_t dim = getSize();
-    real* gradientData = getInputGrad(0)->getData();
-    real* gradient = getOutputGrad()->getData();
-    size_t numSequences = startPositions_->getSize() - 1;
-    for (size_t sequenceId = 0; sequenceId < numSequences; ++sequenceId) {
-      // TODO(Dangqingqing) optimization for GPU
-      int sequenceLength = starts[sequenceId + 1] - starts[sequenceId];
-      if (0 == sequenceLength) {
-        // empty sequence
-        continue;
-      }
-      dataMtx_->setData(
-          gradientData + starts[sequenceId] * dim, sequenceLength, dim);
-      outMtx_->setData(gradient + sequenceId * dim);
-      switch (mode_) {
-        case kAverage: {
-          // plain average
-          dataMtx_->addBias(*outMtx_, 1.0f / sequenceLength);
-          break;
-        }
-        case kSum: {
-          // sum instead of average
-          dataMtx_->addBias(*outMtx_, 1.0f);
-          break;
-        }
-        case kAverageSquareRootN: {
-          // divide by square root of sequenceLength
-          dataMtx_->addBias(*outMtx_, 1.0f / sqrt(sequenceLength));
-          break;
-        }
-        default: { LOG(FATAL) << "should not reach here"; }
-      }
-    }
+  if (getInputGrad(0)) {
+    getInputGrad(0)->sequenceAvgBackward(
+        *getOutputGrad(), *startPositions_->getVector(useGpu_), mode_);
   }
 }
 

paddle/gserver/layers/AverageLayer.h

@@ -45,8 +45,6 @@ public:
   void backward(const UpdateCallback& callback = nullptr) override;
 
 protected:
-  MatrixPtr outMtx_;
-  MatrixPtr dataMtx_;
   int mode_;
 };
 }  // namespace paddle

paddle/math/Matrix.cpp

@@ -483,6 +483,20 @@ void GpuMatrix::sequenceAvgForward(Matrix& a,
   hl_sequence_avg_forward(dst, src, starts, height, width, mode);
 }
 
+void GpuMatrix::sequenceAvgBackward(Matrix& a,
+                                    const IVector& startsPos,
+                                    int mode) {
+  size_t height = a.getHeight();
+  size_t width = getWidth();
+  CHECK_EQ(height, startsPos.getSize() - 1);
+  CHECK_EQ(width, a.getWidth());
+  real* dst = getData();
+  real* src = a.getData();
+  const int* starts = startsPos.getData();
+
+  hl_sequence_avg_backward(dst, src, starts, height, width, mode);
+}
+
 /* this = scaleAB*(a*b) +  scaleT*this */
 void GpuMatrix::mul(const GpuMatrix& a,
                     const GpuMatrix& b,
@@ -2304,6 +2318,41 @@ void CpuMatrix::sequenceAvgForward(Matrix& a,
   }
 }
 
+void CpuMatrix::sequenceAvgBackward(Matrix& a,
+                                    const IVector& startsPos,
+                                    int mode) {
+  size_t height = a.getHeight();
+  size_t width = getWidth();
+  CHECK_EQ(height, startsPos.getSize() - 1);
+  CHECK_EQ(width, a.getWidth());
+  real* dst = getData();
+  real* src = a.getData();
+  const int* starts = startsPos.getData();
+  MatrixPtr outMtx = Matrix::create(nullptr, 1, width, false, false);
+  MatrixPtr dataMtx = Matrix::create(nullptr, 1, width, false, false);
+  for (size_t i = 0; i < height; ++i) {
+    int sequenceLength = starts[i + 1] - starts[i];
+    if (0 == sequenceLength) {
+      // empty sequence
+      continue;
+    }
+    outMtx->setData(dst + starts[i] * width, sequenceLength, width);
+    dataMtx->setData(src + i * width);
+    if (mode == 0) {
+      // plain average
+      outMtx->addBias(*dataMtx, 1.0f / sequenceLength);
+    } else if (mode == 1) {
+      // sum instead of average
+      outMtx->addBias(*dataMtx, 1.0f);
+    } else if (mode == 2) {
+      // divide by square root of sequenceLength
+      outMtx->addBias(*dataMtx, 1.0f / std::sqrt(sequenceLength));
+    } else {
+      LOG(FATAL) << "should not reach here";
+    }
+  }
+}
+
 /* this = scaleAB*(a*b) + scaleT*this*/
 void CpuMatrix::mul(const Matrix& a,
                     const Matrix& b,

paddle/math/Matrix.h

@@ -461,6 +461,12 @@ public:
     LOG(FATAL) << "Not implemented";
   }
 
+  virtual void sequenceAvgBackward(Matrix& a,
+                                   const IVector& startsPos,
+                                   int mode) {
+    LOG(FATAL) << "Not implemented";
+  }
+
   /**
    * @code
    * this = scaleAB*(a*b) + scaleT*this
@@ -1203,6 +1209,7 @@ public:
   void collectSharedBias(Matrix& a, real scale);
 
   void sequenceAvgForward(Matrix& a, const IVector& startsPos, int mode);
+  void sequenceAvgBackward(Matrix& a, const IVector& startsPos, int mode);
 
   /**
    * @code
@@ -1619,6 +1626,7 @@ public:
   void collectSharedBias(Matrix& a, real scale);
 
   void sequenceAvgForward(Matrix& a, const IVector& startsPos, int mode);
+  void sequenceAvgBackward(Matrix& a, const IVector& startsPos, int mode);
 
   /**
    * @code

paddle/math/tests/test_matrixCompare.cpp

@@ -685,7 +685,7 @@ TEST(SMatrix, topK) {
   }
 }
 
-void testMatrixSequenceAvgForward(int batchSize, int inputDim, int mode) {
+void testMatrixSequenceAvg(int batchSize, int inputDim, int mode) {
   MatrixPtr cpuInput = std::make_shared<CpuMatrix>(batchSize, inputDim);
   MatrixPtr gpuInput = std::make_shared<GpuMatrix>(batchSize, inputDim);
   cpuInput->randomizeUniform();
@@ -706,15 +706,25 @@ void testMatrixSequenceAvgForward(int batchSize, int inputDim, int mode) {
   gpuOutput->sequenceAvgForward(*gpuInput, *gpuSequence, mode);
 
   TensorCheckErr(*cpuOutput, *gpuOutput);
+
+  MatrixPtr cpuInGrad = std::make_shared<CpuMatrix>(batchSize, inputDim);
+  MatrixPtr gpuInGrad = std::make_shared<GpuMatrix>(batchSize, inputDim);
+  cpuInGrad->randomizeUniform();
+  gpuInGrad->copyFrom(*cpuInGrad);
+
+  cpuInGrad->sequenceAvgBackward(*cpuOutput, *cpuSequence, mode);
+  gpuInGrad->sequenceAvgBackward(*gpuOutput, *gpuSequence, mode);
+
+  TensorCheckErr(*cpuInGrad, *gpuInGrad);
 }
 
-TEST(Matrix, sequenceAvgForward) {
+TEST(Matrix, sequenceAvg) {
   for (auto batchSize : {10, 128, 6000}) {
     for (auto inputDim : {32, 100, 512}) {
       for (auto mode : {0, 1, 2}) {
         VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim
                 << " mode=" << mode;
-        testMatrixSequenceAvgForward(batchSize, inputDim, mode);
+        testMatrixSequenceAvg(batchSize, inputDim, mode);
       }
     }
   }

python/paddle/v2/plot/plot_curve.py

@@ -0,0 +1,48 @@
+from IPython import display
+import os
+
+
+class PlotCost(object):
+    """
+    append train and test cost in event_handle and then call plot.
+    """
+
+    def __init__(self):
+        self.train_costs = ([], [])
+        self.test_costs = ([], [])
+
+        self.__disable_plot__ = os.environ.get("DISABLE_PLOT")
+        if not self.__plot_is_disabled__():
+            import matplotlib.pyplot as plt
+            self.plt = plt
+
+    def __plot_is_disabled__(self):
+        return self.__disable_plot__ == "True"
+
+    def plot(self):
+        if self.__plot_is_disabled__():
+            return
+
+        self.plt.plot(*self.train_costs)
+        self.plt.plot(*self.test_costs)
+        title = []
+        if len(self.train_costs[0]) > 0:
+            title.append('Train Cost')
+        if len(self.test_costs[0]) > 0:
+            title.append('Test Cost')
+        self.plt.legend(title, loc='upper left')
+        display.clear_output(wait=True)
+        display.display(self.plt.gcf())
+        self.plt.gcf().clear()
+
+    def append_train_cost(self, step, cost):
+        self.train_costs[0].append(step)
+        self.train_costs[1].append(cost)
+
+    def append_test_cost(self, step, cost):
+        self.test_costs[0].append(step)
+        self.test_costs[1].append(cost)
+
+    def reset(self):
+        self.train_costs = ([], [])
+        self.test_costs = ([], [])