deconv2d cudnn

paddle/operators/conv2dtranspose_cudnn_op.cu

@@ -12,7 +12,6 @@
    See the License for the specific language governing permissions and
    limitations under the License. */
 
-#include "glog/logging.h"
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
 #include "paddle/memory/memory.h"
@@ -69,13 +68,6 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
     cudnnConvolutionDescriptor_t cudnn_conv_desc =
         conv_desc.descriptor<T>(paddings, strides, dilations);
 
-    int input_channels = input->dims()[1];    // M
-    int input_height = input->dims()[2];      // H
-    int input_width = input->dims()[3];       // W
-    int output_channels = output->dims()[1];  // C
-    int output_height = output->dims()[2];    // O_H
-    int output_width = output->dims()[3];     // O_W
-
     // ------------------- cudnn conv workspace ---------------------
     void* cudnn_workspace = nullptr;
     size_t workspace_size_in_bytes;  // final workspace to allocate.
@@ -118,7 +110,6 @@ class CudnnConvTransposeOpKernel : public framework::OpKernel<T> {
   }
 };
 
-/*
 template <typename T>
 class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
  public:
@@ -130,7 +121,6 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
     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>();
@@ -138,47 +128,33 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<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;
-
+    ScopedTensorDescriptor output_desc;
     ScopedFilterDescriptor filter_desc;
-    ScopedFilterDescriptor filter_grad_desc;
     ScopedConvolutionDescriptor conv_desc;
     DataLayout layout = DataLayout::kNCHW;
 
+    // Input: (N, M, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
-        layout, framework::vectorize2int(input->dims()), groups);
-    cudnnTensorDescriptor_t cudnn_output_grad_desc =
-        output_grad_desc.descriptor<T>(
-            layout, framework::vectorize2int(output_grad->dims()), groups);
+        layout, framework::vectorize2int(input->dims()));
+    // Output: (N, C, O_H, O_W)
+    cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
+        layout, framework::vectorize2int(output_grad->dims()));
+    // Filter (M, C, K_H, K_W)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
-        layout, framework::vectorize2int(filter->dims()), groups);
-    cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
-    cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
+        layout, framework::vectorize2int(filter->dims()));
 
     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;
+    cudnnConvolutionFwdAlgo_t data_algo;
     cudnnConvolutionBwdFilterAlgo_t filter_algo;
-    size_t workspace_size_in_bytes = 0, tmp_size = 0;
+    size_t bwd_filter_ws_size, fwd_ws_size;
+    size_t workspace_size_in_bytes = 0;
     size_t workspace_size_limit = kCONV_CUDNN_WORKSPACE_LIMIT_BYTES;
     if (user_workspace_size > 0) {
       workspace_size_limit = user_workspace_size * 1024 * 1024;
@@ -186,42 +162,35 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
 
     auto handle = ctx.cuda_device_context().cudnn_handle();
     if (input_grad) {
-      cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
-          layout, framework::vectorize2int(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,
+      // choose backward algorithm for data
+      PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
+          handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
+          cudnn_input_desc, CUDNN_CONVOLUTION_FWD_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);
+      PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
+          handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
+          cudnn_input_desc, data_algo, &fwd_ws_size));
+      workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size);
     }
 
     if (filter_grad) {
-      cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
-          layout, framework::vectorize2int(filter_grad->dims()), groups);
+      // choose backward algorithm for filter
       PADDLE_ENFORCE(
           platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
-              handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
+              handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
               cudnn_filter_desc,
               CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
               workspace_size_limit, &filter_algo));
 
+      // get workspace for backwards filter algorithm
       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);
+              handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
+              cudnn_filter_desc, filter_algo, &bwd_filter_ws_size));
+      workspace_size_in_bytes =
+          std::max(workspace_size_in_bytes, bwd_filter_ws_size);
     }
+
     // ------------------- cudnn conv workspace ---------------------
     // Already on GPU
     void* cudnn_workspace = nullptr;
@@ -235,35 +204,30 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
       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));
-      }
+
+      PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
+          handle, &alpha, cudnn_output_desc, output_grad_data,
+          cudnn_filter_desc, filter_data, cudnn_conv_desc, data_algo,
+          cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
+          input_grad_data));
     }
+
     // ------------------- 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++) {
+      // Gradient with respect to the filter
       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));
-      }
+          handle, &alpha, cudnn_output_desc, output_grad_data, cudnn_input_desc,
+          input_data, cudnn_conv_desc, filter_algo, cudnn_workspace,
+          workspace_size_in_bytes, &beta, cudnn_filter_desc, filter_grad_data));
     }
     // Release the cudnn workspace
     paddle::memory::Free(gpu, cudnn_workspace);
   }
 };
-*/
 
 }  // namespace operators
 }  // namespace paddle
@@ -272,5 +236,5 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_GPU_KERNEL(conv2dtranspose_cudnn,
                        ops::CudnnConvTransposeOpKernel<float>);
-// REGISTER_OP_GPU_KERNEL(conv2dtranspose_cudnn_grad,
-//     ops::CudnnConvTransposeGradOpKernel<float>);
+REGISTER_OP_GPU_KERNEL(conv2dtranspose_cudnn_grad,
+                       ops::CudnnConvTransposeGradOpKernel<float>);

python/paddle/v2/framework/tests/test_conv2dtranspose_op.py

@@ -56,27 +56,9 @@ class TestConv2dTransposeOp(OpTest):
         self.outputs = {'Output': output}
 
     def test_check_output(self):
-        print 'check output here'
+        print 'check output here for', self.op_type
         self.check_output()
 
-    def test_check_grad(self):
-        self.check_grad(
-            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
-
-    def test_check_grad_no_filter(self):
-        self.check_grad(
-            ['Input'],
-            'Output',
-            max_relative_error=0.05,
-            no_grad_set=set(['Filter']))
-
-    def test_check_grad_no_input(self):
-        self.check_grad(
-            ['Filter'],
-            'Output',
-            max_relative_error=0.05,
-            no_grad_set=set(['Input']))
-
     def init_test_case(self):
         self.pad = [0, 0]
         self.stride = [1, 1]
@@ -88,15 +70,29 @@ class TestConv2dTransposeOp(OpTest):
     def init_op_type(self):
         self.op_type = "conv2dtranspose"
 
+    def test_check_grad_no_input(self):
+        self.check_grad(
+            ['Filter'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Input']))
+
+    def test_check_grad_no_filter(self):
+        self.check_grad(
+            ['Input'],
+            'Output',
+            max_relative_error=0.05,
+            no_grad_set=set(['Filter']))
 
-"""
-class TestCudnn(TestConv2dOp):
-    def init_group(self):
-        self.groups = 1
+    def test_check_grad(self):
+        self.check_grad(
+            set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
 
+
+class TestCudnn(TestConv2dTransposeOp):
     def init_op_type(self):
-        self.op_type = "conv_cudnn"
-"""
+        self.op_type = "conv2dtranspose_cudnn"
+
 
 if __name__ == '__main__':
     unittest.main()