Modify the arguments description of ConvFunctionBase. And add the definition...

Modify the arguments description of ConvFunctionBase. And add the definition of backward input and backward filter function.

paddle/function/ConvOp.h

@@ -19,20 +19,36 @@ limitations under the License. */
 namespace paddle {
 
 /*
- * Function Arguments:
+ * \brief Based on the ConvFunctionBase class, the forward calculation,
+ *        backward input calculation and backward filter calculation
+ *        of convolution operations can be implemented.
  *
- * \param inputs[0]  Input image data, is NCHW format, where N is batch size,
- *                   C is the number of channels, H and W is the height and
- *                   width of input image.
- * \param inputs[1]  Filter data, is MCHW, where M is the number of output
- *                   channels, C is the number of input channels, H and W
- *                   is height and width of filter.
- * \param outputs[0] Output image data, is NCHW format, where N is batch size,
- *                   C is the number of channels, H and W is the height and
- *                   width of output image.
+ * Arguments of forward and backward calculation:
+ *   1. Forward calculation of convolution.
+ *      inputs = {INPUT, FILTER}, outputs = {OUTPUT}
+ *      The first and second input arguments are input image and filter data.
+ *      The output argument is output image.
  *
- * \note Implemented based on the ConvFunctionBase class only supports
- *       input data in the NCHW format.
+ *   2. Backward input calculation of convolution.
+ *      inputs = {OUTPUT_GRAD, FILTER}, outputs = {INPUT_GRAD}
+ *      The first and second input arguments are output grad image
+ *      and filter data.
+ *      The output argument is input grad image.
+ *
+ *   3. Backward filter calculation of convolution.
+ *      inputs = {OUTPUT_GRAD, INPUT}, outputs = {FILTER_GRAD}
+ *      The first and second input arguments are output grad image
+ *      and input image.
+ *      The output argument is filter grad.
+ *
+ * Arguments format of input, filter and output:
+ *   1. Input image, output image, input image gradient, output image gradient
+ *      are all NCHW format. Where N is batch size, C is the number of channels,
+ *      H and W is the height and width of image or image gradient.
+ *
+ *   2. The format of the filter data is MCHW, where M is the number of
+ *      output image channels, C is the number of input image channels,
+ *      H and W is height and width of filter.
  */
 class ConvFunctionBase : public FunctionBase {
 public:
@@ -49,17 +65,25 @@ public:
 
   virtual void calc(const BufferArgs& inputs, const BufferArgs& outputs) {}
 
-  void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
-    CHECK_EQ(numInputs_, inputs.size());
-    CHECK_EQ(numOutputs_, outputs.size());
-
-    CHECK_EQ(inputs[0].shape().ndims(), (size_t)4);
-    CHECK_EQ(inputs[1].shape().ndims(), (size_t)4);
-    CHECK_EQ(outputs[0].shape().ndims(), (size_t)4);
-
-    CHECK(inputs[0].shape()[0] == outputs[0].shape()[0]);
-    CHECK(inputs[0].shape()[1] / groups_ == inputs[1].shape()[1]);
-    CHECK(outputs[0].shape()[1] == inputs[1].shape()[0]);
+  // input can be INPUT and INPUT_GRAD
+  // filter can be FILTER and FILTER_GRAD
+  // output can be OUTPUT and OUTPUT_GRAD
+  void check(const TensorShape& input,
+             const TensorShape& filter,
+             const TensorShape& output) {
+    // inputs and outputs arguments should be 4-dimensional.
+    CHECK_EQ(input.ndims(), (size_t)4);
+    CHECK_EQ(filter.ndims(), (size_t)4);
+    CHECK_EQ(output.ndims(), (size_t)4);
+
+    // The batchSize of the input needs to be equal to
+    // the batchSize of the output.
+    CHECK_EQ(input[0], output[0]);
+
+    // The input and output channel dimensions are the second and first
+    // dimensions of the filter shape.
+    CHECK_EQ(input[1] / groups_, filter[1]);
+    CHECK_EQ(output[1], filter[0]);
   }
 
 protected:

paddle/function/GemmConvOp.cpp

@@ -68,17 +68,7 @@ public:
 };
 
 /*
- * Function Arguments:
- *
- * \param inputs[0]  Input image data, is NCHW format, where N is batch size,
- *                   C is the number of channels, H and W is the height and
- *                   width of input image.
- * \param inputs[1]  Filter data, is MCHW, where M is the number of output
- *                   channels, C is the number of input channels, H and W
- *                   is height and width of filter.
- * \param outputs[0] Output image data, is NCHW format, where N is batch size,
-  *                  C is the number of channels, H and W is the height and
- *                   width of output image.
+ * \brief Forward calculation of convolution.
  */
 template <DeviceType Device>
 class GemmConvFunction : public ConvFunctionBase {
@@ -88,8 +78,21 @@ public:
   }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
-    check(inputs, outputs);
-    CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
+    CHECK_EQ(numInputs_, inputs.size());
+    CHECK_EQ(numOutputs_, outputs.size());
+    // TODO(hedaoyuan): Need to define some index macros,
+    // to avoid useing 0 and 1.
+    const TensorShape& input = inputs[0].shape();
+    const TensorShape& filter = inputs[1].shape();
+    const TensorShape& output = outputs[0].shape();
+    check(input, filter, output);
+
+    real beta;
+    if (outputs[0].getArgType() == ADD_TO) {
+      beta = 1.0;
+    } else {
+      beta = 0.0;
+    }
 
     size_t batchSize = inputs[0].shape()[0];
     size_t inputChannels = inputs[0].shape()[1];
@@ -143,7 +146,7 @@ public:
              K,
              colData,
              N,
-             0.0f,
+             beta,
              outputData + g * outputOffset,
              N);
       }
@@ -166,9 +169,53 @@ private:
   MemoryHandlePtr memory_;
 };
 
+/*
+ * \brief Backward input calculation of convolution.
+ */
+template <DeviceType Device>
+class GemmConvGradInputFunction : public ConvFunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    ConvFunctionBase::init(config);
+  }
+
+  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
+    CHECK_EQ(numInputs_, inputs.size());
+    CHECK_EQ(numOutputs_, outputs.size());
+    const TensorShape& outputGrad = inputs[0].shape();
+    const TensorShape& filter = inputs[1].shape();
+    const TensorShape& inputGrad = outputs[0].shape();
+    check(inputGrad, filter, outputGrad);
+  }
+};
+
+/*
+ * \brief Backward filter calculation of convolution.
+ */
+template <DeviceType Device>
+class GemmConvGradFilterFunction : public ConvFunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    ConvFunctionBase::init(config);
+  }
+
+  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
+    CHECK_EQ(numInputs_, inputs.size());
+    CHECK_EQ(numOutputs_, outputs.size());
+    const TensorShape& outputGrad = inputs[0].shape();
+    const TensorShape& input = inputs[1].shape();
+    const TensorShape& filterGrad = outputs[0].shape();
+    check(input, filterGrad, outputGrad);
+  }
+};
+
 REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvFunction);
+REGISTER_TYPED_FUNC(GemmConvGradInput, CPU, GemmConvGradInputFunction);
+REGISTER_TYPED_FUNC(GemmConvGradFilter, CPU, GemmConvGradFilterFunction);
 #ifndef PADDLE_ONLY_CPU
 REGISTER_TYPED_FUNC(GemmConv, GPU, GemmConvFunction);
+REGISTER_TYPED_FUNC(GemmConvGradInput, GPU, GemmConvGradInputFunction);
+REGISTER_TYPED_FUNC(GemmConvGradFilter, GPU, GemmConvGradFilterFunction);
 #endif
 
 }  // namespace paddle

paddle/function/NaiveConvOp.cpp

@@ -91,7 +91,12 @@ public:
   }
 
   void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
-    check(inputs, outputs);
+    CHECK_EQ(numInputs_, inputs.size());
+    CHECK_EQ(numOutputs_, outputs.size());
+    const TensorShape& input = inputs[0].shape();
+    const TensorShape& filter = inputs[1].shape();
+    const TensorShape& output = outputs[0].shape();
+    check(input, filter, output);
     CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
 
     size_t batchSize = inputs[0].shape()[0];