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88 lines
2.9 KiB
88 lines
2.9 KiB
/* 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 "paddle/fluid/operators/math/fc.h"
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#include "paddle/fluid/operators/jit/kernels.h"
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#include "paddle/fluid/operators/math/blas.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename T>
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class FCFunctor<platform::CPUDeviceContext, T> {
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public:
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void operator()(const platform::CPUDeviceContext& context, const int M,
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const int N, const int K, const T* X, const T* W, T* Y,
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const T* B = nullptr, bool relu = false,
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bool padding_weights = false) {
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auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
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framework::Tensor 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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framework::Tensor X1;
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T* X1_data = X1.mutable_data<T>({M * KK}, platform::CPUPlace());
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Y1_data = Y1.mutable_data<T>({M * (N + 4)}, platform::CPUPlace());
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#ifdef PADDLE_WITH_MKLML
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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, false, M, N, K, static_cast<T>(1.0), X1_data, KK, W, NN,
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static_cast<T>(0.0), Y1_data, 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 == NULL) {
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if (padding_weights) {
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#ifdef PADDLE_WITH_MKLML
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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(relu, false,
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platform::errors::PermissionDenied(
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"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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? jit::KernelFuncs<jit::VAddReluTuple<T>,
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platform::CPUPlace>::Cache()
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.At(N)
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: jit::KernelFuncs<jit::VAddTuple<T>, platform::CPUPlace>::Cache()
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.At(N);
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#ifdef PADDLE_WITH_MKLML
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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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};
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template class FCFunctor<platform::CPUDeviceContext, float>;
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template class FCFunctor<platform::CPUDeviceContext, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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