Wei Xu's comments, set up right inouts.

paddle/function/ContextProjectionOp.cpp

@@ -70,10 +70,11 @@ void ContextProjectionForward<DEVICE_TYPE_CPU>(CpuMatrix& out_mat,
 }
 
 /**
+ * \param outputs[0] output value.
+ *
  * \param inputs[0] input value.
  * \param inputs[1] input weight.
  * \param inputs[2] input sequence.
- * \param outputs[0] output value.
  */
 template <DeviceType Device>
 class ContextProjectionForwardFunc : public FunctionBase {
@@ -123,7 +124,8 @@ private:
 };
 
 template <>
-void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
+<<<<<<< HEAD
+void ContextProjectionBackward<DEVICE_TYPE_CPU>(const CpuMatrix& out_grad_mat,
                                                 CpuMatrix& in_grad_mat,
                                                 CpuMatrix& w_grad_mat,
                                                 const CpuIVector& seq_vec,
@@ -176,10 +178,10 @@ void ContextProjectionBackward<DEVICE_TYPE_CPU>(CpuMatrix& out_grad_mat,
 }
 
 /**
- * \param inputs[0] input grad.
- * \param inputs[1] weight grad.
- * \param inputs[2] input sequence.
- * \param outputs[0] output value.
+ * \param inputs[0]     input sequence.
+ * \param inputs[1]     output grad.
+ * \param inouts[0]     input grad.
+ * \param inouts[1]     weight grad.
  */
 template <DeviceType Device>
 class ContextProjectionBackwardFunc : public FunctionBase {
@@ -192,6 +194,7 @@ public:
     total_pad_ = config.get<size_t>("total_pad");
   }
 
+<<<<<<< HEAD
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
     CHECK_EQ((size_t)3, inputs.size());
     CHECK_EQ((size_t)1, outputs.size());
@@ -210,6 +213,42 @@ public:
     CHECK_EQ(outputs[0].shape()[1], inputs[0].shape()[1] * context_length_);
 
     CHECK_EQ(outputs[0].getArgType(), ADD_TO);
+=======
+  void calc(const Arguments& inputs,
+            const Arguments& outputs,
+            const Arguments& inouts) override {
+    CHECK_EQ(2, inputs.size());
+    CHECK_EQ(0, outputs.size());
+    CHECK_EQ(2, inouts.size());
+
+    CHECK(inputs[0].getData() && inputs[1].getData());
+    CHECK_EQ(inputs[0].dims_.size(), 1);
+    CHECK_EQ(inputs[1].dims_.size(), 2);
+    CHECK_EQ(inouts[0].dims_.size(), 2);
+    CHECK_EQ(inouts[1].dims_.size(), 2);
+
+    /// dim of input grad == dim of weight grad
+    CHECK_EQ(inouts[0].dims_[1], inouts[1].dims_[1]);
+    /// input grad and output grad have the same batch_size
+    CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]);
+    /// dim of output = dim of input * context_length
+    CHECK_EQ(inputs[1].dims_[1], inputs[0].dims_[1] * context_length_);
+
+    typename SequenceT<Device>::type seq_vec(
+        inputs[0].dims_[0], reinterpret_cast<int*>(inputs[0].getData()));
+    const auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
+        inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]);
+    auto in_grad_mat =
+        !inouts[0].getData()
+            ? nullptr
+            : std::make_shared<typename MatrixT<Device>::type>(
+                  inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]);
+    auto w_grad_mat =
+        !inouts[1].getData()
+            ? nullptr
+            : std::make_shared<typename MatrixT<Device>::type>(
+                  inouts[1].getData(), inouts[1].dims_[0], inouts[1].dims_[1]);
+>>>>>>> Wei Xu's comments, set up right inouts.
 
     auto out_grad_mat = outputs[0].matrix<Device>();
     auto in_grad_mat =
@@ -240,9 +279,9 @@ private:
 
 #if 0
 /**
- * \param inputs[0] input grad.
- * \param inputs[1] input sequence.
- * \param outputs[0] output grad.
+ * \param inouts[0]    input grad.
+ * \param inputs[0]    input sequence.
+ * \param inputs[1]    output grad.
  */
 template <DeviceType Device>
 class ContextProjectionBackwardDataFunc : public FunctionBase {
@@ -255,23 +294,24 @@ public:
   void calc(const Arguments& inputs,
             const Arguments& outputs,
             const Arguments& inouts) override {
-    CHECK_EQ(2, static_cast<int>(inputs.size()));
-    CHECK_EQ(1, static_cast<int>(outputs.size()));
-    CHECK_EQ(0, static_cast<int>(inouts.size()));
-    CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData());
-    CHECK_EQ(static_cast<int>(outputs[0].dims_.size()), 2);
-    CHECK_EQ(static_cast<int>(inputs[0].dims_.size()), 2);
-    CHECK_EQ(static_cast<int>(inputs[1].dims_.size()), 1);
-    CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_);
-    /// input and output has the same batch_size
-    CHECK_EQ(inputs[0].dims_[0], outputs[0].dims_[0]);
+    CHECK_EQ(2, inputs.size());
+    CHECK_EQ(0, outputs.size());
+    CHECK_EQ(1, inouts.size());
+
+    CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData());
+    CHECK_EQ(inputs[0].dims_.size(), 1);
+    CHECK_EQ(inputs[1].dims_.size(), 2);
+    CHECK_EQ(inouts[0].dims_.size(), 2);
+    CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_);
+    /// input and output grad have the same batch_size
+    CHECK_EQ(inouts[0].dims_[0], inputs[1].dims_[0]);
 
-    auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
-        outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]);
-    const auto in_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
-        inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]);
     typename SequenceT<Device>::type seq_vec(
-        inputs[1].dims_[0], reinterpret_cast<int*>(inputs[1].getData()));
+        inputs[0].dims_[0], reinterpret_cast<int*>(inputs[0].getData()));
+    const auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
+        inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]);
+    auto in_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
+        inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]);
 
     ContextProjectionBackwardData<Device>(out_grad_mat.get(),
                                           in_grad_mat.get(),
@@ -286,9 +326,9 @@ private:
 };
 
 /**
- * \param inputs[0] weight grad.
- * \param inputs[1] input sequence.
- * \param outputs[0] output grad.
+ * \param inouts[0]    weight grad.
+ * \param inputs[0]    input sequence.
+ * \param inputs[1]    output grad.
  */
 template <DeviceType Device>
 class ContextProjectionBackwardWeightFunc : public FunctionBase {
@@ -303,22 +343,22 @@ public:
   void calc(const Arguments& inputs,
             const Arguments& outputs,
             const Arguments& inouts) override {
-    CHECK_EQ(2, static_cast<int>(inputs.size()));
-    CHECK_EQ(1, static_cast<int>(outputs.size()));
-    CHECK_EQ(0, static_cast<int>(inouts.size()));
-
-    CHECK(inputs[0].getData() && outputs[0].getData() && inputs[1].getData());
-    CHECK_EQ(static_cast<int>(outputs[0].dims_.size()), 2);
-    CHECK_EQ(static_cast<int>(inputs[0].dims_.size()), 2);
-    CHECK_EQ(static_cast<int>(inputs[1].dims_.size()), 1);
-    CHECK_EQ(outputs[0].dims_[1], inputs[0].dims_[1] * context_length_);
-
-    auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
-        outputs[0].getData(), outputs[0].dims_[0], outputs[0].dims_[1]);
-    auto w_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
-        inputs[0].getData(), inputs[0].dims_[0], inputs[0].dims_[1]);
+    CHECK_EQ(2, inputs.size());
+    CHECK_EQ(0, outputs.size());
+    CHECK_EQ(1, inouts.size());
+
+    CHECK(inouts[0].getData() && inputs[0].getData() && inputs[1].getData());
+    CHECK_EQ(inputs[0].dims_.size(), 1);
+    CHECK_EQ(inputs[1].dims_.size(), 2);
+    CHECK_EQ(inouts[0].dims_.size(), 2);
+    CHECK_EQ(inputs[1].dims_[1], inouts[0].dims_[1] * context_length_);
+
     typename SequenceT<Device>::type seq_vec(
-        inputs[1].dims_[0], reinterpret_cast<int*>(inputs[1].getData()));
+        inputs[0].dims_[0], reinterpret_cast<int*>(inputs[0].getData()));
+    const auto out_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
+        inputs[1].getData(), inputs[1].dims_[0], inputs[1].dims_[1]);
+    auto w_grad_mat = std::make_shared<typename MatrixT<Device>::type>(
+        inouts[0].getData(), inouts[0].dims_[0], inouts[0].dims_[1]);
 
     ContextProjectionBackwardWeight<Device>(out_grad_mat.get(),
                                             w_grad_mat.get(),

paddle/function/ContextProjectionOp.h

@@ -21,14 +21,14 @@ namespace paddle {
 /**
  * \brief   Context Projection Forward.
  *
- * \param[out]  outputs           output data.
- * \param[in]   input             input data.
- * \param[in]   weight            input weight.
- * \param[in]   sequence          input data.
- * \param[in]   context_length    consecutive rows for concatenation.
- * \param[in]   context_start     context start position.
- * \param[in]   begin_pad         begining pad position.
- * \param[in]   is_padding        whether padding 0 or not.
+ * \param[in/out]  outputs           output data.
+ * \param[in]      input             input data.
+ * \param[in]      weight            input weight.
+ * \param[in]      sequence          input data.
+ * \param[in]      context_length    consecutive rows for concatenation.
+ * \param[in]      context_start     context start position.
+ * \param[in]      begin_pad         begining pad position.
+ * \param[in]      is_padding        whether padding 0 or not.
  *
  */
 template <DeviceType DType>
@@ -68,7 +68,7 @@ void ContextProjectionBackward(
 
 template <DeviceType DType>
 void ContextProjectionBackwardData(
-    typename Tensor<real, DType>::Matrix& out_grad,
+    const typename Tensor<real, DType>::Matrix& out_grad,
     typename Tensor<real, DType>::Matrix& in_grad,
     const typename Tensor<int, DType>::Vector& sequence,
     size_t context_length,
@@ -76,7 +76,7 @@ void ContextProjectionBackwardData(
 
 template <DeviceType DType>
 void ContextProjectionBackwardWeight(
-    typename Tensor<real, DType>::Matrix& out_grad,
+    const typename Tensor<real, DType>::Matrix& out_grad,
     typename Tensor<real, DType>::Matrix& w_grad,
     const typename Tensor<int, DType>::Vector& seq_vec,
     size_t context_length,

paddle/function/ContextProjectionOpGpu.cu

@@ -138,10 +138,10 @@ void ContextProjectionForward<DEVICE_TYPE_GPU>(GpuMatrix& output,
                                 begin_pad);
 }
 
-__global__ void KeContextProjectionBackwardData(real* out_grad,
+__global__ void KeContextProjectionBackwardData(const real* out_grad,
                                                 const int* sequence,
                                                 real* in_grad,
-                                                int input_dim,
+                                                size_t input_dim,
                                                 int context_length,
                                                 int context_start) {
   int idx = threadIdx.x;
@@ -152,7 +152,8 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
   real value = 0;
 
   int instances = seq_end - seq_start + context_length - 1;
-  out_grad += seq_start * input_dim * context_length;
+  auto out = const_cast<real*>(out_grad);
+  out += seq_start * input_dim * context_length;
   in_grad += seq_start * input_dim;
   for (int k = 0; k <= input_dim / block_size; k++) {
     if (idx < input_dim) {
@@ -169,7 +170,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
         int outx = (i - context_length) < 0 ? i : (context_length - 1);
         int outy = (i - context_length) < 0 ? 0 : (i - (context_length - 1));
         real* output_r =
-          out_grad + outy * input_dim * context_length + outx * input_dim;
+          out + outy * input_dim * context_length + outx * input_dim;
         for (int j = outy; j < seq_end - seq_start; j++) {
           value += output_r[idx];
           if (j - outy == outx) break;
@@ -194,7 +195,7 @@ __global__ void KeContextProjectionBackwardData(real* out_grad,
  * @param[in]   context_start    context start.
  *
  */
-void hl_context_projection_backward_data(real* out_grad,
+void hl_context_projection_backward_data(const real* out_grad,
                                          const int* sequence,
                                          real* input_grad,
                                          size_t num_sequences,
@@ -216,7 +217,8 @@ void hl_context_projection_backward_data(real* out_grad,
 }
 
 template <>
-void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
+<<<<<<< HEAD
+void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                                     GpuMatrix& in_grad,
                                                     const GpuIVector& sequence,
                                                     size_t context_length,
@@ -231,7 +233,7 @@ void ContextProjectionBackwardData<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
 }
 
 template<int THREADS_X, int THREADS_Y>
-__global__ void KeContextProjectionBackwardWeight(real* out_grad,
+__global__ void KeContextProjectionBackwardWeight(const real* out_grad,
                                                   const int* sequence,
                                                   real* w_grad,
                                                   int num_sequences,
@@ -254,7 +256,8 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
     for (int seqId = idy; seqId < num_sequences; seqId += THREADS_Y) {
       int seq_start = sequence[seqId];
       int seq_end = sequence[seqId+1];
-      output_r = out_grad + seq_start * w_dim * context_length;
+      output_r = const_cast<real*>(out_grad) 
+                    + seq_start * w_dim * context_length;
 
       if (context_start < 0) {
         if (padId + context_start < 0) {
@@ -318,7 +321,7 @@ __global__ void KeContextProjectionBackwardWeight(real* out_grad,
  * beginning.
  *
  */
-void hl_context_projection_backward_weight(real* out_grad,
+void hl_context_projection_backward_weight(const real* out_grad,
                                            const int* sequence,
                                            real* w_grad,
                                            size_t num_sequences,
@@ -346,7 +349,7 @@ void hl_context_projection_backward_weight(real* out_grad,
 
 template <>
 void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
-        GpuMatrix& out_grad,
+        const GpuMatrix& out_grad,
         GpuMatrix& w_grad,
         const GpuIVector& seq_vec,
         size_t context_length,
@@ -365,7 +368,7 @@ void ContextProjectionBackwardWeight<DEVICE_TYPE_GPU>(
 }
 
 template <>
-void ContextProjectionBackward<DEVICE_TYPE_GPU>(GpuMatrix& out_grad,
+void ContextProjectionBackward<DEVICE_TYPE_GPU>(const GpuMatrix& out_grad,
                                                 GpuMatrix& in_grad,
                                                 GpuMatrix& w_grad,
                                                 const GpuIVector& sequence,

paddle/function/ContextProjectionOpTest.cpp

@@ -62,16 +62,18 @@ void testMatrixProjectionForward(int context_start,
               Dims{pad, input_dim}),
        Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
               Dims{cpu_seq->getSize()})},
-      {Tensor(cpu_out.getData(), Dims{batch_size, input_dim * context_length})},
-      {});
+      {},
+      {Tensor(cpu_out.getData(),
+              Dims{batch_size, input_dim * context_length})});
   compare.getGpuFunction()->calc(
       {Tensor(gpu_in.getData(), Dims{batch_size, input_dim}),
        Tensor(gpu_weight ? gpu_weight->getData() : nullptr,
               Dims{pad, input_dim}),
        Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
               Dims{gpu_seq->getSize()})},
-      {Tensor(gpu_out.getData(), Dims{batch_size, input_dim * context_length})},
-      {});
+      {},
+      {Tensor(gpu_out.getData(),
+              Dims{batch_size, input_dim * context_length})});
 
   autotest::TensorCheckEqual(cpu_out, gpu_out);
 }
@@ -118,24 +120,24 @@ void testMatrixProjectionBackward(int context_start,
   }
 
   compare.getCpuFunction()->calc(
+      {Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
+              Dims{cpu_seq->getSize()}),
+       Tensor(cpu_out_grad.getData(),
+              Dims{batch_size, input_dim * context_length})},
+      {},
       {Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}),
        Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr,
-              Dims{pad, input_dim}),
-       Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
-              Dims{cpu_seq->getSize()})},
-      {Tensor(cpu_out_grad.getData(),
-              Dims{batch_size, input_dim * context_length})},
-      {});
+              Dims{pad, input_dim})});
 
   compare.getGpuFunction()->calc(
+      {Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
+              Dims{gpu_seq->getSize()}),
+       Tensor(gpu_out_grad.getData(),
+              Dims{batch_size, input_dim * context_length})},
+      {},
       {Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}),
        Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr,
-              Dims{pad, input_dim}),
-       Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
-              Dims{gpu_seq->getSize()})},
-      {Tensor(gpu_out_grad.getData(),
-              Dims{batch_size, input_dim * context_length})},
-      {});
+              Dims{pad, input_dim})});
 
   autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad);
   if (is_padding) {