Enable multiple groups for cudnn conv transpose

paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc

@@ -44,6 +44,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     // cudnn v5 does not support dilations
     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>();
@@ -64,13 +65,13 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
 
     // (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
-        layout, framework::vectorize2int(input->dims()));
+        layout, framework::vectorize2int(input->dims()), groups);
     // (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
-        layout, framework::vectorize2int(output->dims()));
+        layout, framework::vectorize2int(output->dims()), groups);
     // (M, C, K_h, K_w) or (M, C, K_d, K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
-        layout, framework::vectorize2int(filter->dims()));
+        layout, framework::vectorize2int(filter->dims()), groups);
     cudnnConvolutionDescriptor_t cudnn_conv_desc =
         conv_desc.descriptor<T>(paddings, strides, dilations);
 
@@ -104,11 +105,17 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
     cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
 
     // ------------------- cudnn conv transpose forward ---------------------
+    int input_offset = input->numel() / input->dims()[0] / groups;
+    int output_offset = output->numel() / output->dims()[0] / groups;
+    int filter_offset = filter->numel() / groups;
     T alpha = 1.0f, beta = 0.0f;
+    for (int g = 0; g < groups; g++) {
       PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
-        handle, &alpha, cudnn_filter_desc, filter_data, cudnn_input_desc,
-        input_data, cudnn_conv_desc, algo, cudnn_workspace,
-        workspace_size_in_bytes, &beta, cudnn_output_desc, output_data));
+          handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
+          cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
+          algo, cudnn_workspace, workspace_size_in_bytes, &beta,
+          cudnn_output_desc, output_data + output_offset * g));
+    }
 
     // Release the cudnn workspace
     paddle::memory::Free(gpu, cudnn_workspace);
@@ -134,6 +141,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     // cudnn v5 does not support dilations
     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 ---------------------
@@ -145,13 +153,13 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
 
     // Input: (N, M, H, W) or (N, M, D, H, W)
     cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
-        layout, framework::vectorize2int(input->dims()));
+        layout, framework::vectorize2int(input->dims()), groups);
     // Output: (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
     cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
-        layout, framework::vectorize2int(output_grad->dims()));
+        layout, framework::vectorize2int(output_grad->dims()), groups);
     // Filter (M, C, K_h, K_w) or (M, C, K_d K_h, K_w)
     cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
-        layout, framework::vectorize2int(filter->dims()));
+        layout, framework::vectorize2int(filter->dims()), groups);
 
     cudnnConvolutionDescriptor_t cudnn_conv_desc =
         conv_desc.descriptor<T>(paddings, strides, dilations);
@@ -205,15 +213,22 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
     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.
+    int input_offset = input->numel() / input->dims()[0] / groups;
+    int output_grad_offset =
+        output_grad->numel() / output_grad->dims()[0] / groups;
+    int filter_offset = filter->numel() / groups;
     T alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.
+      for (int g = 0; g < groups; g++) {
         PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
-          handle, &alpha, cudnn_output_desc, output_grad_data,
-          cudnn_filter_desc, filter_data, cudnn_conv_desc, data_algo,
+            handle, &alpha, cudnn_output_desc,
+            output_grad_data + output_grad_offset * g, cudnn_filter_desc,
+            filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
             cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
-          input_grad_data));
+            input_grad_data + input_offset * g));
+      }
     }
 
     // ------------------- cudnn conv backward filter ---------------------
@@ -221,11 +236,16 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
       T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset filter_grad.
       // Gradient with respect to the filter
+      for (int g = 0; g < groups; g++) {
         PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
-          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));
+            handle, &alpha, cudnn_output_desc,
+            output_grad_data + output_grad_offset * g, cudnn_input_desc,
+            input_data + input_offset * g, cudnn_conv_desc, filter_algo,
+            cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_filter_desc,
+            filter_grad_data + filter_offset * g));
       }
+    }
+
     // Release the cudnn workspace
     paddle::memory::Free(gpu, cudnn_workspace);
   }

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py

@@ -227,6 +227,21 @@ class TestCUDNNWithStride(TestWithStride):
         self.op_type = "conv2d_transpose"
 
 
+class TestCUDNNWithGroups(TestWithGroups):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 4, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 3, 3, 3]
+
+    def init_op_type(self):
+        self.use_cudnn = True
+        self.op_type = "conv2d_transpose"
+
+
 # Please Don't remove the following code.
 # Currently, CI use cudnn V5.0 which not support dilation conv.
 # class TestCUDNNWithDilation(TestWithDilation):