wehub-resource-sync
105 lines
3.3 KiB
C++
105 lines
3.3 KiB
C++
/* 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/funcs/fc_functor.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/jit/kernels.h"
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namespace phi {
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namespace funcs {
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template <typename DeviceContext, typename T>
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void FCFunctor<DeviceContext, T>::operator()(const DeviceContext& dev_ctx,
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const int M,
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const int N,
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const int K,
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const T* X,
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const T* W,
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T* Y,
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const T* B,
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bool relu,
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bool padding_weights) {
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auto blas = GetBlas<DeviceContext, T>(dev_ctx);
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DenseTensor Y1;
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T* Y1_data = nullptr;
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if (padding_weights) {
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const int NN = N + 4;
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const int KK = K + 4;
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DenseTensor X1;
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X1.Resize({M * KK});
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T* X1_data = dev_ctx.template HostAlloc<T>(&X1);
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Y1.Resize({M * (N + 4)});
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Y1_data = dev_ctx.template HostAlloc<T>(&Y1);
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#if defined(PADDLE_WITH_MKLML) || defined(PADDLE_WITH_HML)
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#pragma omp parallel for
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#endif
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for (int i = 0; i < M; i++) {
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memcpy(X1_data + i * KK, X + i * K, K * sizeof(T));
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}
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blas.GEMM(false,
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false,
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M,
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N,
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K,
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static_cast<T>(1.0),
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X1_data,
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KK,
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W,
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NN,
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static_cast<T>(0.0),
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Y1_data,
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NN);
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} else {
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blas.MatMul(M, N, K, X, W, Y);
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}
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if (B == nullptr) {
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if (padding_weights) {
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#if defined(PADDLE_WITH_MKLML) || defined(PADDLE_WITH_HML)
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#pragma omp parallel for
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#endif
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for (int i = 0; i < M; i++) {
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memcpy(Y + i * N, Y1_data + i * (N + 4), N * sizeof(T));
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}
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}
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PADDLE_ENFORCE_EQ(
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relu,
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false,
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errors::PermissionDenied("When bias is NULL, relu can not be true."));
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return;
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}
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auto compute =
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relu
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? phi::jit::KernelFuncs<phi::jit::VAddReluTuple<T>, CPUPlace>::Cache()
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.At(N)
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: phi::jit::KernelFuncs<phi::jit::VAddTuple<T>, CPUPlace>::Cache().At(
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N);
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#if defined(PADDLE_WITH_MKLML) || defined(PADDLE_WITH_HML)
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#pragma omp parallel for
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#endif
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for (int i = 0; i < M; i++) {
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T* dst = Y + i * N;
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T* src = (padding_weights) ? Y1_data + i * (N + 4) : dst;
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compute(B, src, dst, N);
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}
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}
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template class FCFunctor<CPUContext, float>;
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template class FCFunctor<CPUContext, double>;
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} // namespace funcs
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} // namespace phi
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