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103 lines
4.1 KiB
103 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/math/gru_compute.h"
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#include "paddle/operators/math/detail/gru_cpu_kernel.h"
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#include "paddle/operators/math/detail/gru_kernel.h"
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#include "paddle/operators/math/math_function.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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struct GRUUnitFunctor<platform::CPUPlace, T> {
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static void compute(const platform::DeviceContext &context,
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hl_gru_value<T> value, int frameSize, int batchSize,
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activation_mode_t active_node,
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activation_mode_t active_gate) {
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#ifndef __NVCC__
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if (value.prevOutValue) {
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math::gemm<platform::CPUPlace, T>(
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context, false, false, batchSize, frameSize * 2, frameSize, 1,
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value.prevOutValue, frameSize, value.gateWeight, frameSize * 2, 1,
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value.gateValue, frameSize * 3);
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}
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detail::forward_reset_output(detail::forward::gru_resetOutput<T>(), value,
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frameSize, batchSize, active_gate);
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if (value.prevOutValue) {
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math::gemm<platform::CPUPlace, T>(
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context, false, false, batchSize, frameSize, frameSize, 1,
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value.resetOutputValue, frameSize, value.stateWeight, frameSize, 1,
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value.gateValue + frameSize * 2, frameSize * 3);
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}
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detail::forward_final_output(detail::forward::gru_finalOutput<T>(), value,
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frameSize, batchSize, active_node);
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#endif
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}
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};
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template <typename T>
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struct GRUUnitGradFunctor<platform::CPUPlace, T> {
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static void compute(const platform::DeviceContext &context,
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hl_gru_value<T> value, hl_gru_grad<T> grad, int frameSize,
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int batchSize, activation_mode_t active_node,
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activation_mode_t active_gate) {
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#ifndef __NVCC__
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detail::backward_state_grad(detail::backward::gru_stateGrad<T>(), value,
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grad, frameSize, batchSize, active_node);
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if (value.prevOutValue && grad.prevOutGrad) {
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math::gemm<platform::CPUPlace, T>(
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context, false, true, batchSize, frameSize, frameSize, 1,
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grad.gateGrad + frameSize * 2, frameSize * 3, value.stateWeight,
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frameSize, 0, grad.resetOutputGrad, frameSize);
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if (grad.stateWeightGrad) {
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math::gemm<platform::CPUPlace, T>(
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context, true, false, frameSize, frameSize, batchSize, 1,
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value.resetOutputValue, frameSize, grad.gateGrad + frameSize * 2,
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frameSize * 3, 1, grad.stateWeightGrad, frameSize);
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}
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}
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detail::backward_reset_grad(detail::backward::gru_resetGrad<T>(), value,
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grad, frameSize, batchSize, active_gate);
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if (grad.prevOutGrad && value.prevOutValue) {
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math::gemm<platform::CPUPlace, T>(
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context, false, true, batchSize, frameSize, frameSize * 2, 1,
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grad.gateGrad, frameSize * 3, value.gateWeight, frameSize * 2, 1,
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grad.prevOutGrad, frameSize);
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if (grad.gateWeightGrad) {
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math::gemm<platform::CPUPlace, T>(
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context, true, false, frameSize, frameSize * 2, batchSize, 1,
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value.prevOutValue, frameSize, grad.gateGrad, frameSize * 3, 1,
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grad.gateWeightGrad, frameSize * 2);
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}
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}
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#endif
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}
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};
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template struct GRUUnitFunctor<platform::CPUPlace, float>;
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template struct GRUUnitFunctor<platform::CPUPlace, double>;
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template struct GRUUnitGradFunctor<platform::CPUPlace, float>;
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template struct GRUUnitGradFunctor<platform::CPUPlace, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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