wehub-resource-sync
97 lines
3.6 KiB
Plaintext
97 lines
3.6 KiB
Plaintext
/* Copyright (c) 2022 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 "paddle/phi/kernels/sparse/addmm_grad_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/sparse/matmul_grad_kernel.h"
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namespace phi {
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namespace sparse {
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template <typename T, typename Context>
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void AddmmCooDenseGradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const SparseCooTensor& x,
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const DenseTensor& y,
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const DenseTensor& dout,
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float alpha,
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float beta,
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DenseTensor* dinput,
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SparseCooTensor* dx,
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DenseTensor* dy) {
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auto blas = funcs::GetBlas<Context, T>(dev_ctx);
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if (dinput) {
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dinput->Resize(input.dims());
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dev_ctx.template Alloc<T>(dinput);
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blas.VCOPY(input.numel(), dout.data<T>(), dinput->data<T>());
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blas.SCAL(input.numel(), beta, dinput->data<T>());
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}
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DenseTensor dout_scale = EmptyLike<T, Context>(dev_ctx, dout);
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blas.VCOPY(dout.numel(), dout.data<T>(), dout_scale.data<T>());
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blas.SCAL(dout.numel(), alpha, dout_scale.data<T>());
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MatmulCooDenseGradKernel<T, Context>(dev_ctx, x, y, dout_scale, dx, dy);
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}
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// Backward of "DENSE + CSR @ DENSE -> DENSE"
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template <typename T, typename Context>
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void AddmmCsrDenseGradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const SparseCsrTensor& x,
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const DenseTensor& y,
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const DenseTensor& dout,
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float alpha,
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float beta,
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DenseTensor* dinput,
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SparseCsrTensor* dx,
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DenseTensor* dy) {
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auto blas = funcs::GetBlas<Context, T>(dev_ctx);
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if (dinput) {
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dinput->Resize(input.dims());
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dev_ctx.template Alloc<T>(dinput);
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blas.VCOPY(input.numel(), dout.data<T>(), dinput->data<T>());
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blas.SCAL(input.numel(), beta, dinput->data<T>());
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}
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DenseTensor dout_scale = EmptyLike<T, Context>(dev_ctx, dout);
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blas.VCOPY(dout.numel(), dout.data<T>(), dout_scale.data<T>());
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blas.SCAL(dout.numel(), alpha, dout_scale.data<T>());
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MatmulCsrDenseGradKernel<T, Context>(dev_ctx, x, y, dout_scale, dx, dy);
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}
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} // namespace sparse
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} // namespace phi
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PD_REGISTER_KERNEL(addmm_coo_dense_grad,
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GPU,
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ALL_LAYOUT,
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phi::sparse::AddmmCooDenseGradKernel,
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float,
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double) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
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}
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PD_REGISTER_KERNEL(addmm_csr_dense_grad,
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GPU,
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ALL_LAYOUT,
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phi::sparse::AddmmCsrDenseGradKernel,
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float,
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double) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
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}
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