wehub-resource-sync
165 lines
7.1 KiB
Plaintext
165 lines
7.1 KiB
Plaintext
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <vector>
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_dnn.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/softmax.h"
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#include "paddle/phi/kernels/funcs/softmax_impl.h"
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namespace phi {
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namespace funcs {
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using ScopedTensorDescriptor = phi::backends::gpu::ScopedTensorDescriptor;
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template <typename T>
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using CudnnDataType = phi::backends::gpu::CudnnDataType<T>;
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template <typename T, typename DeviceContext>
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void SoftmaxCUDNNFunctor<T, DeviceContext>::operator()(
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const DeviceContext& dev_ctx, const DenseTensor* X, DenseTensor* Y) {
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// ------------------- cudnn descriptors ---------------------
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ScopedTensorDescriptor xDesc;
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ScopedTensorDescriptor yDesc;
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std::vector<int> cudnn_tensor_dims = vectorize<int>(X->dims());
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DataLayout layout = DataLayout::NCHW;
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if (cudnn_tensor_dims.size() == 5) {
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layout = DataLayout::NCDHW;
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}
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// NOTE(*) : cudnn softmax only support >= 4D DenseTensor,
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// fill 1 at unused dims
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if (cudnn_tensor_dims.size() <= 2) {
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cudnn_tensor_dims.resize(4, 1);
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}
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#ifdef PADDLE_WITH_HIP
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miopenTensorDescriptor_t cudnn_x_desc =
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xDesc.descriptor<T>(layout, cudnn_tensor_dims);
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miopenTensorDescriptor_t cudnn_y_desc =
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xDesc.descriptor<T>(layout, cudnn_tensor_dims);
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenSoftmaxForward_V2(dev_ctx.cudnn_handle(),
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CudnnDataType<T>::kOne(),
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cudnn_x_desc,
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X->data<T>(),
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CudnnDataType<T>::kZero(),
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cudnn_y_desc,
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dev_ctx.template Alloc<T>(Y),
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MIOPEN_SOFTMAX_ACCURATE,
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MIOPEN_SOFTMAX_MODE_INSTANCE));
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#else
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cudnnTensorDescriptor_t cudnn_x_desc =
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xDesc.descriptor<T>(layout, cudnn_tensor_dims);
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cudnnTensorDescriptor_t cudnn_y_desc =
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xDesc.descriptor<T>(layout, cudnn_tensor_dims);
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::cudnnSoftmaxForward(dev_ctx.cudnn_handle(),
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CUDNN_SOFTMAX_ACCURATE,
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CUDNN_SOFTMAX_MODE_INSTANCE,
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CudnnDataType<T>::kOne(),
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cudnn_x_desc,
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X->data<T>(),
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CudnnDataType<T>::kZero(),
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cudnn_y_desc,
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dev_ctx.template Alloc<T>(Y)));
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#endif
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}
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template <typename T, typename DeviceContext>
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void SoftmaxGradCUDNNFunctor<T, DeviceContext>::operator()(
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const DeviceContext& dev_ctx,
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const DenseTensor* Y,
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const DenseTensor* YGrad,
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DenseTensor* XGrad) {
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// ------------------- cudnn descriptors ---------------------
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ScopedTensorDescriptor yDesc;
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ScopedTensorDescriptor dyDesc;
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ScopedTensorDescriptor dxDesc;
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std::vector<int> cudnn_tensor_dims = vectorize<int>(Y->dims());
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DataLayout layout = DataLayout::NCHW;
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if (cudnn_tensor_dims.size() == 5) {
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layout = DataLayout::NCDHW;
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}
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// NOTE(*) : cudnn softmax only support >= 4D DenseTensor,
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// fill 1 at unused dims
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if (cudnn_tensor_dims.size() <= 2) {
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cudnn_tensor_dims.resize(4, 1);
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}
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#ifdef PADDLE_WITH_HIP
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miopenTensorDescriptor_t cudnn_y_desc =
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yDesc.descriptor<T>(layout, cudnn_tensor_dims);
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miopenTensorDescriptor_t cudnn_xgrad_desc =
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dxDesc.descriptor<T>(layout, cudnn_tensor_dims);
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miopenTensorDescriptor_t cudnn_ygrad_desc =
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dyDesc.descriptor<T>(layout, cudnn_tensor_dims);
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::miopenSoftmaxBackward_V2(dev_ctx.cudnn_handle(),
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CudnnDataType<T>::kOne(),
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cudnn_y_desc,
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Y->data<T>(),
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cudnn_ygrad_desc,
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YGrad->data<T>(),
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CudnnDataType<T>::kZero(),
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cudnn_xgrad_desc,
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dev_ctx.template Alloc<T>(XGrad),
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MIOPEN_SOFTMAX_ACCURATE,
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MIOPEN_SOFTMAX_MODE_INSTANCE));
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#else
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cudnnTensorDescriptor_t cudnn_y_desc =
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yDesc.descriptor<T>(layout, cudnn_tensor_dims);
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cudnnTensorDescriptor_t cudnn_xgrad_desc =
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dxDesc.descriptor<T>(layout, cudnn_tensor_dims);
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cudnnTensorDescriptor_t cudnn_ygrad_desc =
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dyDesc.descriptor<T>(layout, cudnn_tensor_dims);
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PADDLE_ENFORCE_GPU_SUCCESS(
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phi::dynload::cudnnSoftmaxBackward(dev_ctx.cudnn_handle(),
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CUDNN_SOFTMAX_ACCURATE,
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CUDNN_SOFTMAX_MODE_INSTANCE,
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CudnnDataType<T>::kOne(),
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cudnn_y_desc,
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Y->data<T>(),
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cudnn_ygrad_desc,
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YGrad->data<T>(),
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CudnnDataType<T>::kZero(),
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cudnn_xgrad_desc,
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dev_ctx.template Alloc<T>(XGrad)));
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#endif
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}
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template class SoftmaxCUDNNFunctor<float, GPUContext>;
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template class SoftmaxCUDNNFunctor<phi::float16, GPUContext>;
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template class SoftmaxGradCUDNNFunctor<float, GPUContext>;
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template class SoftmaxGradCUDNNFunctor<phi::float16, GPUContext>;
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#if CUDNN_VERSION_MIN(8, 1, 0)
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template class SoftmaxCUDNNFunctor<phi::bfloat16, GPUContext>;
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template class SoftmaxGradCUDNNFunctor<phi::bfloat16, GPUContext>;
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#endif
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// MIOPEN do not support double
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#ifndef PADDLE_WITH_HIP
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template class SoftmaxCUDNNFunctor<double, GPUContext>;
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template class SoftmaxGradCUDNNFunctor<double, GPUContext>;
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#endif
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template class SoftmaxFunctor<GPUContext, phi::float16>;
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template class SoftmaxFunctor<GPUContext, phi::bfloat16>;
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template class SoftmaxFunctor<GPUContext, float>;
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template class SoftmaxFunctor<GPUContext, double>;
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template class SoftmaxGradFunctor<GPUContext, float>;
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template class SoftmaxGradFunctor<GPUContext, double>;
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template class SoftmaxGradFunctor<GPUContext, phi::float16>;
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template class SoftmaxGradFunctor<GPUContext, phi::bfloat16>;
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} // namespace funcs
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} // namespace phi
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