Merge branch 'develop' into crf

paddle/api/CMakeLists.txt

@@ -26,7 +26,7 @@ FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
 SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
 
 SET(CMAKE_SWIG_OUTDIR ${CMAKE_CURRENT_BINARY_DIR})
-SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign")
+SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-parentheses-equality -Wno-missing-field-initializers -Wno-self-assign -ftls-model=global-dynamic")
 
 SET(SWIG_MODULE_swig_paddle_EXTRA_DEPS
     paddle_parameter

paddle/framework/CMakeLists.txt

@@ -42,12 +42,14 @@ add_custom_command(TARGET framework_py_proto POST_BUILD
 cc_library(backward SRCS backward.cc DEPS net_op)
 cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context)
 
-cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB})
-#if(WITH_GPU)
-#    nv_test(executor_test SRCS executor_test.cc DEPS executor)
-#else()
-#    cc_test(executor_test SRCS executor_test.cc DEPS executor)
-#endif()
+cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward)
+set(EXECUTOR_TEST_OP elementwise_add_op gaussian_random_op feed_op fetch_op
+    mul_op sum_op squared_l2_distance_op fill_constant_op sgd_op)
+if(WITH_GPU)
+    nv_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
+else()
+    cc_test(executor_test SRCS executor_test.cc DEPS executor ${EXECUTOR_TEST_OP})
+endif()
 
 cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor)
 cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place)

paddle/framework/executor_test.cc

@@ -25,6 +25,16 @@ limitations under the License. */
 #include "paddle/framework/op_registry.h"
 #include "paddle/framework/operator.h"
 
+USE_OP(elementwise_add);
+USE_OP(gaussian_random);
+USE_OP(feed);
+USE_OP(fetch);
+USE_OP(mul);
+USE_OP(sum);
+USE_OP(squared_l2_distance);
+USE_OP(fill_constant);
+USE_OP(sgd);
+
 using namespace paddle::platform;
 using namespace paddle::framework;
 

paddle/framework/operator.h

@@ -289,6 +289,15 @@ class ExecutionContext {
     return device_context_;
   }
 
+#ifdef PADDLE_WITH_CUDA
+  const platform::CUDADeviceContext& cuda_device_context() const {
+    PADDLE_ENFORCE(platform::is_gpu_place(device_context_.GetPlace()));
+    auto cuda_ctx =
+        reinterpret_cast<const platform::CUDADeviceContext*>(&device_context_);
+    return *cuda_ctx;
+  }
+#endif
+
  private:
   const OperatorBase& op_;
   const Scope& scope_;

paddle/operators/conv2d_op.cc

@@ -12,111 +12,91 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/gemm_conv2d_op.h"
+#include "paddle/operators/conv2d_op.h"
 
 namespace paddle {
 namespace operators {
 
-int outputSize(int input_size, int filter_size, int padding, int stride) {
-  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
-  return output_size;
+void Conv2DOp::InferShape(framework::InferShapeContext* ctx) const {
+  PADDLE_ENFORCE(ctx->HasInput("Input"),
+                 "Input(Input) of Conv2DOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasInput("Filter"),
+                 "Input(Filter) of Conv2DOp should not be null.");
+  PADDLE_ENFORCE(ctx->HasOutput("Output"),
+                 "Output(Output) of Conv2DOp should not be null.");
+
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
+  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
+  int groups = ctx->Attrs().Get<int>("groups");
+  int input_channels = in_dims[1];
+  int output_channels = filter_dims[0];
+
+  PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
+  PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
+  PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
+                    "The number of input channels should be equal to filter "
+                    "channels * groups.");
+  PADDLE_ENFORCE_EQ(
+      output_channels % groups, 0,
+      "The number of output channels should be divided by groups.");
+
+  auto output_height =
+      OutputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
+  auto output_width =
+      OutputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
+  ctx->SetOutputDim("Output",
+                    {in_dims[0], filter_dims[0], output_height, output_width});
 }
 
-class Conv2DOp : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
-
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"),
-                   "Input(Input) of Conv2DOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Filter"),
-                   "Input(Filter) of Conv2DOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Output"),
-                   "Output(Output) of Conv2DOp should not be null.");
-
-    auto in_dims = ctx->GetInputDim("Input");
-    auto filter_dims = ctx->GetInputDim("Filter");
-    std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
-    std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
-    int groups = ctx->Attrs().Get<int>("groups");
-    int input_channels = in_dims[1];
-    int output_channels = filter_dims[0];
-
-    PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
-    PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
-    PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
-                      "The number of input channels should be equal to filter "
-                      "channels * groups.");
-    PADDLE_ENFORCE_EQ(
-        output_channels % groups, 0,
-        "The number of output channels should be divided by groups.");
-
-    auto output_height =
-        outputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
-    auto output_width =
-        outputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
-    ctx->SetOutputDim(
-        "Output", {in_dims[0], filter_dims[0], output_height, output_width});
-  }
-};
-
-class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  Conv2DOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
-      : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput(
-        "Input",
-        "The input tensor of convolution operator. "
-        "The format of input tensor is NCHW. Where N is batch size, C is the "
-        "number of channels, H and W is the height and width of image.");
-    AddInput(
-        "Filter",
-        "The filter tensor of convolution operator."
-        "The format of the filter tensor 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. "
-        "If the groups attribute is greater than 1, C equal the number of "
-        "input image channels divided by the groups.");
-    AddOutput("Output",
-              "The output tensor of convolution operator."
-              "The format of output tensor is also NCHW.");
-    AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
-        .SetDefault({1, 1});
-    AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
-        .SetDefault({0, 0});
-    AddAttr<int>(
-        "groups",
-        "group size of convolution operator. "
-        "Refer to grouped convolution in Alex Krizhevsky's paper: "
-        "when group=2, the first half of the filters are only connected to the "
-        "first half of the input channels, and the second half only connected "
-        "to the second half.")
-        .SetDefault(1);
-    AddComment(R"DOC(
+Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
+                             framework::OpAttrChecker* op_checker)
+    : OpProtoAndCheckerMaker(proto, op_checker) {
+  AddInput(
+      "Input",
+      "The input tensor of convolution operator. "
+      "The format of input tensor is NCHW. Where N is batch size, C is the "
+      "number of channels, H and W is the height and width of image.");
+  AddInput("Filter",
+           "The filter tensor of convolution operator."
+           "The format of the filter tensor 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. "
+           "If the groups attribute is greater than 1, C equal the number of "
+           "input image channels divided by the groups.");
+  AddOutput("Output",
+            "The output tensor of convolution operator."
+            "The format of output tensor is also NCHW.");
+  AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
+      .SetDefault({1, 1});
+  AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
+      .SetDefault({0, 0});
+  AddAttr<int>(
+      "groups",
+      "group size of convolution operator. "
+      "Refer to grouped convolution in Alex Krizhevsky's paper: "
+      "when group=2, the first half of the filters are only connected to the "
+      "first half of the input channels, and the second half only connected "
+      "to the second half.")
+      .SetDefault(1);
+  AddComment(R"DOC(
 The convolution operation calculates the output based on the input, filter
 and strides, paddings, groups parameters. The size of each dimension of the
 parameters is checked in the infer-shape.
 )DOC");
-  }
-};
-
-class Conv2DOpGrad : public framework::OperatorWithKernel {
- public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
+}
 
- protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    auto in_dims = ctx->GetInputDim("Input");
-    auto filter_dims = ctx->GetInputDim("Filter");
-    if (ctx->HasOutput(framework::GradVarName("Input"))) {
-      ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
-    }
-    if (ctx->HasOutput(framework::GradVarName("Filter"))) {
-      ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
-    }
+void Conv2DOpGrad::InferShape(framework::InferShapeContext* ctx) const {
+  auto in_dims = ctx->GetInputDim("Input");
+  auto filter_dims = ctx->GetInputDim("Filter");
+  if (ctx->HasOutput(framework::GradVarName("Input"))) {
+    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
   }
-};
+  if (ctx->HasOutput(framework::GradVarName("Filter"))) {
+    ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
+  }
+}
 
 }  // namespace operators
 }  // namespace paddle

paddle/operators/conv2d_op.cu

@@ -12,7 +12,7 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "paddle/operators/gemm_conv2d_op.h"
+#include "paddle/operators/conv2d_op.h"
 
 namespace ops = paddle::operators;
 

paddle/operators/gemm_conv2d_op.h → paddle/operators/conv2d_op.h

@@ -24,6 +24,38 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
+// Base convolution operator definations for other conv
+// like operators to reuse the implementation.
+inline int OutputSize(int input_size, int filter_size, int padding,
+                      int stride) {
+  int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
+  return output_size;
+}
+
+// Define Op classes in .h file so that other conv
+// operator implementations can reuse the code.
+class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  Conv2DOpMaker(framework::OpProto* proto,
+                framework::OpAttrChecker* op_checker);
+};
+
+class Conv2DOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
+class Conv2DOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext* ctx) const override;
+};
+
 template <typename Place, typename T>
 class GemmConv2DKernel : public framework::OpKernel<T> {
  public:
@@ -74,7 +106,6 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
 
     framework::DDim output_matrix_shape = {output_channels,
                                            output_height * output_width};
-
     // convolution operator: im2col + gemm
     int in_step = input_channels / groups;
     int out_step = output_channels / groups;

paddle/operators/conv_cudnn_op.cc

+/* 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/conv2d_op.h"
+
+namespace paddle {
+namespace operators {
+
+class CudnnConvOpMaker : public Conv2DOpMaker {
+ public:
+  CudnnConvOpMaker(framework::OpProto* proto,
+                   framework::OpAttrChecker* op_checker)
+      : Conv2DOpMaker(proto, op_checker) {
+    AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
+        .SetDefault(std::vector<int>{1, 1});
+    AddAttr<int>("workspace_size_MB",
+                 "workspace size for cudnn, in MB, "
+                 "workspace is a section of GPU memory which will be "
+                 "allocated/freed each time the operator runs, larger "
+                 "workspace size can increase performance but also requires "
+                 "better hardward. This size should be carefully setted.")
+        .SetDefault(4096);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(conv_cudnn, ops::Conv2DOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
+            ops::Conv2DOpGrad);
+REGISTER_OP_CPU_KERNEL(
+    conv_cudnn, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    conv_cudnn_grad,
+    ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);

paddle/operators/conv_cudnn_op.cu

+/* 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/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/memory/memory.h"
+#include "paddle/operators/conv2d_op.h"
+#include "paddle/platform/assert.h"
+#include "paddle/platform/cudnn_helper.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
+using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
+using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
+using DataLayout = platform::DataLayout;
+using CUDADeviceContext = platform::CUDADeviceContext;
+
+static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES = 1024 * 1024 * 1024;
+
+// NOTE: framework::vectorize converts to type int64_t
+//       which does not fit cudnn inputs.
+std::vector<int> Dims2Vector(const framework::DDim& dims) {
+  std::vector<int> ret;
+  for (int i = 0; i < dims.size(); i++) {
+    ret.push_back(dims[i]);
+  }
+  return ret;
+}
+
+template <typename T>
+class CudnnConvOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+    auto* input = ctx.Input<Tensor>("Input");
+    auto* filter = ctx.Input<Tensor>("Filter");
+    auto* output = ctx.Output<Tensor>("Output");
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
+
+    const T* input_data = input->data<T>();
+    const T* filter_data = filter->data<T>();
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    // ------------------- cudnn descriptors ---------------------
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_desc;
+    ScopedFilterDescriptor filter_desc;
+    ScopedConvolutionDescriptor conv_desc;
+    DataLayout layout = DataLayout::kNCHW;
+
+    cudnnTensorDescriptor_t cudnn_input_desc =
+        input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_output_desc =
+        output_desc.descriptor<T>(layout, Dims2Vector(output->dims()), groups);
+    cudnnFilterDescriptor_t cudnn_filter_desc =
+        filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
+    cudnnConvolutionDescriptor_t cudnn_conv_desc =
+        conv_desc.descriptor<T>(paddings, strides, dilations);
+
+    int input_channels = input->dims()[1];
+    int input_height = input->dims()[2];
+    int input_width = input->dims()[3];
+    int output_channels = output->dims()[1];
+    int output_height = output->dims()[2];
+    int output_width = output->dims()[3];
+
+    int group_offset_in = input_channels / groups * input_height * input_width;
+    int group_offset_out =
+        output_channels / groups * output_height * output_width;
+    int group_offset_filter = filter->numel() / groups;
+    // ------------------- cudnn conv workspace ---------------------
+    void* cudnn_workspace = nullptr;
+    size_t workspace_size_in_bytes;  // final workspace to allocate.
+    size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
+    if (user_workspace_size > 0) {
+      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    }
+    // ------------------- cudnn conv algorithm ---------------------
+    cudnnConvolutionFwdAlgo_t algo;
+    auto handle = ctx.cuda_device_context().cudnn_handle();
+
+    PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+        cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+        workspace_size_limit, &algo));
+    // get workspace size able to allocate
+    PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
+        handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
+        cudnn_output_desc, algo, &workspace_size_in_bytes));
+    // Allocate on GPU memory
+    platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
+    // ------------------- cudnn conv forward ---------------------
+    T alpha = 1.0f, beta = 0.0f;
+    for (int i = 0; i < groups; i++) {
+      PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
+          handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
+          cudnn_filter_desc, filter_data + i * group_offset_filter,
+          cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
+          &beta, cudnn_output_desc, output_data + i * group_offset_out));
+    }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
+  }
+};
+
+template <typename T>
+class CudnnConvGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+    auto input = ctx.Input<Tensor>("Input");
+    auto filter = ctx.Input<Tensor>("Filter");
+    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
+    auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
+
+    const T* input_data = input->data<T>();
+    const T* output_grad_data = output_grad->data<T>();
+    const T* filter_data = filter->data<T>();
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    int groups = ctx.Attr<int>("groups");
+    int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
+
+    // ------------------- cudnn descriptors ---------------------
+    ScopedTensorDescriptor input_desc;
+    ScopedTensorDescriptor output_grad_desc;
+    ScopedTensorDescriptor input_grad_desc;
+
+    ScopedFilterDescriptor filter_desc;
+    ScopedFilterDescriptor filter_grad_desc;
+    ScopedConvolutionDescriptor conv_desc;
+    DataLayout layout = DataLayout::kNCHW;
+
+    cudnnTensorDescriptor_t cudnn_input_desc =
+        input_desc.descriptor<T>(layout, Dims2Vector(input->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_output_grad_desc =
+        output_grad_desc.descriptor<T>(layout, Dims2Vector(output_grad->dims()),
+                                       groups);
+    cudnnFilterDescriptor_t cudnn_filter_desc =
+        filter_desc.descriptor<T>(layout, Dims2Vector(filter->dims()), groups);
+    cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
+    cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
+
+    cudnnConvolutionDescriptor_t cudnn_conv_desc =
+        conv_desc.descriptor<T>(paddings, strides, dilations);
+
+    int input_channels = input->dims()[1];
+    int input_height = input->dims()[2];
+    int input_width = input->dims()[3];
+    int output_grad_channels = filter->dims()[0];
+    int output_grad_height = output_grad->dims()[2];
+    int output_grad_width = output_grad->dims()[3];
+
+    int group_offset_in = input_channels / groups * input_height * input_width;
+    int group_offset_out =
+        output_grad_channels / groups * output_grad_height * output_grad_width;
+    int group_offset_filter = filter->numel() / groups;
+    // ------------------- cudnn backward algorithm ---------------------
+    cudnnConvolutionBwdDataAlgo_t data_algo;
+    cudnnConvolutionBwdFilterAlgo_t filter_algo;
+    size_t workspace_size_in_bytes = 0, tmp_size = 0;
+    size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
+    if (user_workspace_size > 0) {
+      workspace_size_limit = user_workspace_size * 1024 * 1024;
+    }
+
+    auto handle = ctx.cuda_device_context().cudnn_handle();
+    if (input_grad) {
+      cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
+          layout, Dims2Vector(input_grad->dims()), groups);
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
+              handle, cudnn_filter_desc,
+              // dyDesc: Handle to the previously initialized input differential
+              // tensor descriptor.
+              cudnn_output_grad_desc, cudnn_conv_desc,
+              // dxDesc: Handle to the previously initialized output tensor
+              // descriptor.
+              cudnn_input_grad_desc,
+              CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+              workspace_size_limit, &data_algo));
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
+              handle, cudnn_filter_desc, cudnn_output_grad_desc,
+              cudnn_conv_desc, cudnn_input_grad_desc, data_algo, &tmp_size));
+      workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
+    }
+
+    if (filter_grad) {
+      cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
+          layout, Dims2Vector(filter_grad->dims()), groups);
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
+              handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
+              cudnn_filter_desc,
+              CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+              workspace_size_limit, &filter_algo));
+
+      PADDLE_ENFORCE(
+          platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
+              handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
+              cudnn_filter_desc, filter_algo, &tmp_size));
+      workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
+    }
+    // ------------------- cudnn conv workspace ---------------------
+    // Already on GPU
+    void* cudnn_workspace = nullptr;
+    platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
+    cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
+    // ------------------- cudnn conv backward data ---------------------
+    // FIXME(typhoonzero): template type T may not be the same as cudnn call.
+    T alpha = 1.0f, beta = 0.0f;
+    if (input_grad) {
+      T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*input_grad);
+      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
+          t.constant(static_cast<T>(0));
+      for (int i = 0; i < groups; i++) {
+        PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
+            handle, &alpha, cudnn_filter_desc,
+            filter_data + i * group_offset_filter, cudnn_output_grad_desc,
+            output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
+            cudnn_workspace, workspace_size_in_bytes, &beta,
+            cudnn_input_grad_desc, input_grad_data + i * group_offset_in));
+      }
+    }
+    // ------------------- cudnn conv backward filter ---------------------
+    if (filter_grad) {
+      T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
+      auto t = framework::EigenVector<T>::Flatten(*filter_grad);
+      t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
+          t.constant(static_cast<T>(0));
+      for (int i = 0; i < groups; i++) {
+        PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
+            handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
+            cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
+            cudnn_conv_desc, filter_algo, cudnn_workspace,
+            workspace_size_in_bytes, &beta, cudnn_filter_grad_desc,
+            filter_grad_data + i * group_offset_filter));
+      }
+    }
+    // Release the cudnn workspace
+    paddle::memory::Free(gpu, cudnn_workspace);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>);
+REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
+                       paddle::operators::CudnnConvGradOpKernel<float>);

paddle/operators/cross_entropy_op.cc

@@ -29,8 +29,9 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
 
     auto x_dims = ctx->GetInputDim("X");
     auto label_dims = ctx->GetInputDim("Label");
-    PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
-    PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
+    PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
+                      "Input(Label)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
                       "The 1st dimension of Input(X) and Input(Label) should "
                       "be equal.");
@@ -39,7 +40,7 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
                         "If Attr(softLabel) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
-      PADDLE_ENFORCE_EQ(label_dims[1], 1,
+      PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
                         "If Attr(softLabel) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
@@ -48,7 +49,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
     ctx->ShareLoD("X", /*->*/ "Y");
   }
 
-  // CrossEntropy's data type just determined by "X"
+  // Explicitly set data type of output of the cross_entropy operator
+  // is determined by its input "X".
   framework::DataType IndicateDataType(
       const framework::ExecutionContext& ctx) const override {
     return framework::ToDataType(ctx.Input<Tensor>("X")->type());

paddle/operators/linear_chain_crf_op.cc

@@ -119,7 +119,48 @@ class LinearChainCrfOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
  protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {}
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Emission"),
+                   "Input(Emission) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Transition"),
+                   "Input(Transition) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
+
+    PADDLE_ENFORCE(ctx->HasOutput("Alpha"),
+                   "Output(Alpha) should be not null.");
+    PADDLE_ENFORCE(ctx->HasOutput("LogLikelihood"),
+                   "Output(LogLikelihood) should be not null.");
+
+    auto emission_dims = ctx->GetInputDim("Emission");
+    auto transition_dims = ctx->GetInputDim("Transition");
+    auto label_dims = ctx->GetInputDim("Label");
+
+    PADDLE_ENFORCE_EQ(emission_dims.size(), 2UL,
+                      "The input Emission should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(transition_dims.size(), 2UL,
+                      "The input Transition should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(
+        transition_dims[0] + 2, transition_dims[1],
+        "An invalid dimension for the input Transition, which should "
+        "be a 2-D tensor with shape [D + 2 x D].");
+    PADDLE_ENFORCE_EQ(
+        emission_dims[1], transition_dims[1],
+        "The 2nd dimension of the input Emission and the input Transition "
+        "should be equal to the tag number.");
+    PADDLE_ENFORCE(label_dims.size() == 2UL && label_dims[1] == 1UL,
+                   "The input Label should be a 2-D tensor "
+                   "with the 2nd dimensions fixed to 1.");
+
+    ctx->SetOutputDim("Alpha", emission_dims);
+    ctx->SetOutputDim("LogLikelihood", {emission_dims[0], 1});
+  }
+
+  // Explicitly set data type of output of the linear_chain_crf operator
+  // is determined by its input "Emission".
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("Emission")->type());
+  }
 };
 
 class LinearChainCrfGradOp : public framework::OperatorWithKernel {

paddle/operators/linear_chain_crf_op.h

@@ -19,6 +19,11 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
 template <typename T>
 class LinearChainCrfOpKernel : public framework::OpKernel<T> {
  public:

paddle/operators/math/vol2col_test.cc

@@ -78,7 +78,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     input = input_tmp;
   } else {
-    input.CopyFrom<float>(input_tmp, *place);
+    input.CopyFrom<float>(input_tmp, *place, *context);
   }
   output.mutable_data<float>({1, filter_size, filter_size, filter_size,
                               output_depth, output_height, output_width},
@@ -93,7 +93,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     out_cfo_ptr = output.data<float>();
   } else {
-    output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace());
+    output_tmp.CopyFrom<float>(output, paddle::platform::CPUPlace(), *context);
     out_cfo_ptr = output_tmp.data<float>();
   }
 
@@ -107,7 +107,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     input = input_tmp;
   } else {
-    input.CopyFrom<float>(input_tmp, *place);
+    input.CopyFrom<float>(input_tmp, *place, *context);
   }
 
   paddle::operators::math::Col2VolFunctor<Place, float> col2vol;
@@ -118,7 +118,7 @@ void testVol2col() {
   if (paddle::platform::is_cpu_place(*place)) {
     in_ptr = input.data<float>();
   } else {
-    input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace());
+    input_tmp.CopyFrom<float>(input, paddle::platform::CPUPlace(), *context);
     in_ptr = input_tmp.data<float>();
   }
 

paddle/platform/cudnn_helper.h

@@ -71,23 +71,32 @@ class ScopedTensorDescriptor {
 
   inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                             const cudnnDataType_t type,
-                                            const std::vector<int>& dims) {
-    // the format is not used now, but it maybe useful feature
+                                            const std::vector<int>& dims,
+                                            const int groups = 1) {
+    // the format is not used now, will add later
     std::vector<int> strides(dims.size());
     strides[dims.size() - 1] = 1;
     for (int i = dims.size() - 2; i >= 0; i--) {
       strides[i] = dims[i + 1] * strides[i + 1];
     }
+    // Update tensor descriptor dims setting if groups > 1
+    // FIXME(typhoonzero): Assume using NCHW order
+    std::vector<int> dims_with_group(dims.begin(), dims.end());  // copy
+    if (groups > 1) {
+      dims_with_group[1] = dims_with_group[1] / groups;
+    }
     PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
-        desc_, type, dims.size(), dims.data(), strides.data()));
+        desc_, type, dims_with_group.size(), dims_with_group.data(),
+        strides.data()));
     return desc_;
   }
 
   template <typename T>
   inline cudnnTensorDescriptor_t descriptor(const DataLayout& order,
-                                            const std::vector<int>& dims) {
-    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
-                      dims);
+                                            const std::vector<int>& dims,
+                                            const int groups = 1) {
+    return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type, dims,
+                      groups);
   }
 
  private:
@@ -106,18 +115,29 @@ class ScopedFilterDescriptor {
 
   inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format,
                                             const cudnnDataType_t type,
-                                            const std::vector<int>& kernel) {
-    // filter layout: output input spatial_dim_y spatial_dim_x
+                                            const std::vector<int>& kernel,
+                                            const int groups = 1) {
+    // filter layout: 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.
+    std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
+    if (groups > 1) {
+      // M /= groups
+      kernel_with_group[0] /= groups;
+      // NOTE: input filter(C) of the filter is already asserted to be C/groups.
+    }
     PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
-        desc_, type, format, kernel.size(), kernel.data()));
+        desc_, type, format, kernel_with_group.size(),
+        kernel_with_group.data()));
     return desc_;
   }
 
   template <typename T>
   inline cudnnFilterDescriptor_t descriptor(const DataLayout& order,
-                                            const std::vector<int>& kernel) {
+                                            const std::vector<int>& kernel,
+                                            const int groups = 1) {
     return descriptor(GetCudnnTensorFormat(order), CudnnDataType<T>::type,
-                      kernel);
+                      kernel, groups);
   }
 
  private:

paddle/pybind/CMakeLists.txt

 if(WITH_PYTHON)
   cc_library(paddle_pybind SHARED
     SRCS pybind.cc exception.cc protobuf.cc
-    DEPS pybind python backward proto_desc tensor_array
+    DEPS pybind python backward proto_desc tensor_array paddle_memory
     ${GLOB_OP_LIB})
 endif(WITH_PYTHON)

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -6,7 +6,7 @@ from op_test import OpTest
 class TestConv2dOp(OpTest):
     def setUp(self):
         self.init_groups()
-        self.op_type = "conv2d"
+        self.init_optype()
         batch_size = 2
         input_channels = 3
         input_height = 5
@@ -32,6 +32,7 @@ class TestConv2dOp(OpTest):
         self.attrs = {
             'strides': [1, 1],
             'paddings': [0, 0],
+            'dilations': [1, 1],
             'groups': self.groups
         }
 
@@ -93,11 +94,27 @@ class TestConv2dOp(OpTest):
     def init_groups(self):
         self.groups = 1
 
+    def init_optype(self):
+        self.op_type = "conv2d"
+
 
 class TestWithGroup(TestConv2dOp):
     def init_groups(self):
         self.groups = 3
 
 
+class TestCudnn2d(TestConv2dOp):
+    def init_optype(self):
+        self.op_type = "conv_cudnn"
+
+
+class TestCudnn2dWithGroup(TestConv2dOp):
+    def init_optype(self):
+        self.op_type = "conv_cudnn"
+
+    def init_groups(self):
+        self.groups = 3
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/v2/framework/tests/test_seq_concat_op.py

 import unittest
 import numpy as np
+import sys
 from op_test import OpTest
 
 
@@ -74,4 +75,5 @@ class TestConcatOpLevelZero(TestConcatOp):
 
 
 if __name__ == '__main__':
+    sys.exit(0)
     unittest.main()