Revert some changes

apex/contrib/csrc/bottleneck/bottleneck.cpp

@@ -2158,73 +2158,6 @@ at::Tensor bottleneck_backward_grad_out2(bool explicit_nhwc, int stride_1X1, std
   return grad_out2;
 }
 
-// compute dgrad of 3x3 convolution without fusing with drelu and dscale
-at::Tensor bottleneck_backward_dgrad1(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs, at::Tensor grad_out2) {
-
-  bool requires_grad = inputs[0].requires_grad();
-
-  std::cout << std::fixed;
-  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
-
-  // dgrad
-  at::Half* dy2 = grad_out2.data_ptr<at::Half>();
-
-  // dgrad
-  auto dgrad1 = at::empty(backward_state.outdim1, inputs[0].type(), output_format);
-  at::Half* dy1 = dgrad1.data_ptr<at::Half>();
-  at::Half* w = inputs[2].data_ptr<at::Half>();
-  at::Half* z = inputs[4].data_ptr<at::Half>();
-
-  at::Half* relu1 = inputs[12].data_ptr<at::Half>();
-  //printf("relu.shape = [%d,%d,%d,%d]\n",inputs[12].size(0),inputs[12].size(1),inputs[12].size(2),inputs[12].size(3));
-
-  // dgrad
-  run_dconv(backward_state.outdimA1,
-            backward_state.padA1,
-            backward_state.convstrideA,
-            backward_state.dilationA,
-            backward_state.filterdimA2,
-            backward_state.outdimA2,
-            CUDNN_DATA_HALF,
-            dy1,
-            w,
-            dy2,
-            CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR);
-
-  return dgrad1;
-}
-
-at::Tensor bottleneck_backward_dgrad1_halo(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs, at::Tensor grad_out2_halo) {
-
-  bool requires_grad = inputs[0].requires_grad();
-
-  std::cout << std::fixed;
-  auto output_format = explicit_nhwc ? at::MemoryFormat::Contiguous : at::MemoryFormat::ChannelsLast;
-
-  // dgrad
-  at::Half* dy2h = grad_out2_halo.data_ptr<at::Half>();
-
-  // dgrad
-  auto dgrad1_halo = at::empty(backward_state.outdim1h, inputs[0].type(), output_format);
-  at::Half* dy1h = dgrad1_halo.data_ptr<at::Half>();
-  at::Half* w = inputs[2].data_ptr<at::Half>();
-
-  // dgrad
-  run_dconv(backward_state.outdimA1h,
-            backward_state.padA1,
-            backward_state.convstrideA,
-            backward_state.dilationA,
-            backward_state.filterdimA2,
-            backward_state.outdimA2h,
-            CUDNN_DATA_HALF,
-            dy1h,
-            w,
-            dy2h,
-            CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR);
-
-  return dgrad1_halo;
-}
-
 at::Tensor bottleneck_backward_grad_out1(bool explicit_nhwc, int stride_1X1, std::vector<at::Tensor> inputs, std::vector<at::Tensor> outputs, at::Tensor grad_out2) {
 
   bool requires_grad = inputs[0].requires_grad();
@@ -2547,8 +2480,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("backward_grad_out2", &bottleneck_backward_grad_out2, "Bottleneck block backward");
   m.def("backward_grad_out1", &bottleneck_backward_grad_out1, "Bottleneck block backward");
   m.def("backward_grad_out1_halo", &bottleneck_backward_grad_out1_halo, "Bottleneck block backward");
-  m.def("backward_dgrad1", &bottleneck_backward_dgrad1, "Bottleneck block backward");
-  m.def("backward_dgrad1_halo", &bottleneck_backward_dgrad1_halo, "Bottleneck block backward");
   m.def("backward_wgrad2", &bottleneck_backward_wgrad2, "Bottleneck block backward");
   m.def("backward_wgrad2_halo", &bottleneck_backward_wgrad2_halo, "Bottleneck block backward");
   m.def("backward_rest", &bottleneck_backward_rest, "Bottleneck block backward");