wehub-resource-sync
142 lines
4.8 KiB
Plaintext
142 lines
4.8 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_kernel.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/sparse/sparse_blas.h"
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namespace phi {
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namespace sparse {
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template <typename T, typename Context, typename TensorType>
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void AddmmKernelImpl(const Context& dev_ctx,
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const DenseTensor& input,
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const TensorType& x,
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const DenseTensor& y,
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float beta,
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float alpha,
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DenseTensor* out) {
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#if defined(PADDLE_WITH_CUDA)
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std::vector<int64_t> input_dim = vectorize(input.dims());
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std::vector<int64_t> x_dim = vectorize(x.dims());
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std::vector<int64_t> y_dim = vectorize(y.dims());
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auto rank = input_dim.size();
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PADDLE_ENFORCE_GE(
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rank,
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2,
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common::errors::InvalidArgument(
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"the dims size of input must be greater than or equal to 2."));
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PADDLE_ENFORCE_EQ(
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x_dim.size(),
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rank,
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common::errors::PreconditionNotMet(
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"The dims size of Input(input) and Input(x) must be equal."));
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PADDLE_ENFORCE_GE(
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y_dim.size(),
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rank,
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common::errors::InvalidArgument(
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"the dims size of Input(input) and Input(y) must be equal."));
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for (size_t i = 0; i < rank - 2; ++i) {
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PADDLE_ENFORCE_EQ(input_dim[i],
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x_dim[i],
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common::errors::InvalidArgument(
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"input.dim[%d] and x.dim[%d] must be equal.", i, i));
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PADDLE_ENFORCE_EQ(input_dim[i],
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y_dim[i],
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common::errors::InvalidArgument(
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"input.dim[%d] and y.dim[%d] must be equal.", i, i));
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}
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PADDLE_ENFORCE_GE(
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input_dim[rank - 2],
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x_dim[rank - 2],
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common::errors::PreconditionNotMet(
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"The shape of Input(input) and Input(x) is not suitable for matmul "
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"operation, input_dim[-2] must be equal to x_dim[-2]."));
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PADDLE_ENFORCE_GE(
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input_dim[rank - 1],
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y_dim[rank - 1],
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common::errors::PreconditionNotMet(
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"The shape of Input(input) and Input(y) is not suitable for matmul "
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"operation, input_dim[-1] must be equal to y_dim[-1]."));
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PADDLE_ENFORCE_GE(
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x_dim[rank - 1],
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y_dim[rank - 2],
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common::errors::PreconditionNotMet(
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"The shape of Input(x) and Input(y) is not suitable for matmul "
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"operation, x_dim[-1] must be equal to y_dim[-2]."));
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phi::Copy(dev_ctx, input, dev_ctx.GetPlace(), false, out);
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auto sparse_blas = funcs::sparse::GetSparseBlas<Context, T>(dev_ctx);
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sparse_blas.SPMM(
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false, false, static_cast<T>(alpha), x, y, static_cast<T>(beta), out);
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#endif
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}
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template <typename T, typename Context>
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void AddmmCooDenseKernel(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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float beta,
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float alpha,
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DenseTensor* out) {
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AddmmKernelImpl<T>(dev_ctx, input, x, y, beta, alpha, out);
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}
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template <typename T, typename Context>
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void AddmmCsrDenseKernel(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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float beta,
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float alpha,
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DenseTensor* out) {
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AddmmKernelImpl<T>(dev_ctx, input, x, y, beta, alpha, out);
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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,
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GPU,
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ALL_LAYOUT,
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phi::sparse::AddmmCooDenseKernel,
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float,
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double,
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phi::float16) {
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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,
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GPU,
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ALL_LAYOUT,
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phi::sparse::AddmmCsrDenseKernel,
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float,
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double,
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phi::float16) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_CSR);
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}
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