affine_grid_cudnn_op.cu.cc

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

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. */

#ifndef PADDLE_WITH_HIP
// HIP not support cudnnSpatialTfGridGeneratorForward

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using ScopedSpatialTransformerDescriptor =
    platform::ScopedSpatialTransformerDescriptor;

template <typename T>
class CUDNNAffineGridOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE_EQ(
        platform::is_gpu_place(ctx.GetPlace()), true,
        platform::errors::InvalidArgument(
            "Only support for CUDAPlace.Please switch your context from "
            "CPUPlace to CUDAPlace or update your cudnn."));
    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    auto handle = dev_ctx.cudnn_handle();
    auto* theta = ctx.Input<Tensor>("Theta");
    auto* output = ctx.Output<Tensor>("Output");
    const T* theta_data = theta->data<T>();

    int n = theta->dims()[0];
    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
    Tensor h_sizes;
    int* h_size_data;
    if (size_attr.size() == 0) {
      auto* output_shape = ctx.Input<Tensor>("OutputShape");
      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
      h_size_data = h_sizes.data<int>();
    } else {
      h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
      h_size_data[0] = n;
      h_size_data[1] = size_attr[1];
      h_size_data[2] = size_attr[2];
      h_size_data[3] = size_attr[3];
    }

    T* output_data = output->mutable_data<T>(
        {n, h_size_data[2], h_size_data[3], 2}, ctx.GetPlace());
    ScopedSpatialTransformerDescriptor st_desc;
    cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
        st_desc.descriptor<T>(4, h_size_data);

    PADDLE_ENFORCE_EQ(
        platform::dynload::cudnnSpatialTfGridGeneratorForward(
            handle, cudnn_st_desc, theta_data, output_data),
        0, platform::errors::Fatal("Some errors has occurred "
                                   "during forward computation in cudnn."));
  }
};

template <typename T>
class CUDNNAffineGridGradOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
                      platform::errors::InvalidArgument(
                          "Only "
                          "support for CUDAPlace. Please switch "
                          "your context from CPUPlace to "
                          "CUDAPlace or update your cudnn."));
    auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
    auto handle = dev_ctx.cudnn_handle();
    auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
    auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));

    int n = output_grad->dims()[0];
    auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
    Tensor h_sizes;
    int* h_size_data;
    if (size_attr.size() == 0) {
      auto* output_shape = ctx.Input<Tensor>("OutputShape");
      framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
      h_size_data = h_sizes.data<int>();
    } else {
      h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
      h_size_data[0] = n;
      h_size_data[1] = size_attr[1];
      h_size_data[2] = size_attr[2];
      h_size_data[3] = size_attr[3];
    }

    ScopedSpatialTransformerDescriptor st_desc;
    cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
        st_desc.descriptor<T>(4, h_size_data);

    const T* output_grad_data = output_grad->data<T>();
    T* theta_grad_data = theta_grad->mutable_data<T>(ctx.GetPlace());

    PADDLE_ENFORCE_GPU_SUCCESS(
        platform::dynload::cudnnSpatialTfGridGeneratorBackward(
            handle, cudnn_st_desc, output_grad_data, theta_grad_data));
  }
};

}  // namespace operators
}  // namespace paddle

namespace plat = paddle::platform;
REGISTER_OP_KERNEL(affine_grid, CUDNN, plat::CUDAPlace,
                   paddle::operators::CUDNNAffineGridOpKernel<float>,
                   paddle::operators::CUDNNAffineGridOpKernel<double>);
REGISTER_OP_KERNEL(affine_grid_grad, CUDNN, plat::CUDAPlace,
                   paddle::operators::CUDNNAffineGridGradOpKernel<float>,
                   paddle::operators::CUDNNAffineGridGradOpKernel<double>);

#endif  // not PADDLE_WITH_HIP