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180 lines
7.7 KiB
180 lines
7.7 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/platform/device_context.h>
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#include "paddle/fluid/operators/math/blas.h"
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#include "paddle/fluid/operators/math/detail/gru_gpu_kernel.h"
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#include "paddle/fluid/operators/math/detail/gru_kernel.h"
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#include "paddle/fluid/operators/math/gru_compute.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::CUDADeviceContext, T> {
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static void compute(const platform::CUDADeviceContext &context,
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GRUMetaValue<T> value, int frame_size, int batch_size,
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const detail::ActivationType active_node,
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const detail::ActivationType active_gate,
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bool origin_mode) {
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auto stream = context.stream();
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dim3 threads;
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dim3 grid;
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if (batch_size == 1) {
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int frame_per_block = frame_size <= 1024 ? frame_size : 1024;
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int frame_blocks = (frame_size + 1024 - 1) / 1024;
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threads = dim3(frame_per_block, 1);
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grid = dim3(frame_blocks, 1);
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} else {
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threads = dim3(32, 32);
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grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32);
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}
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auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context);
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if (value.prev_out_value) {
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blas.GEMM(false, false, batch_size, frame_size * 2, frame_size, 1,
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value.prev_out_value, frame_size, value.gate_weight,
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frame_size * 2, 1, value.gate_value, frame_size * 3);
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}
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if (batch_size == 1) {
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detail::KeGruForwardResetOutput<detail::forward::gru_resetOutput<T>,
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/* is_batch= */ false,
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T><<<grid, threads, 0, stream>>>(
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detail::forward::gru_resetOutput<T>(), value.gate_value,
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value.reset_output_value, value.prev_out_value, frame_size,
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batch_size, active_gate);
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} else {
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detail::KeGruForwardResetOutput<detail::forward::gru_resetOutput<T>,
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/* is_batch= */ true,
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T><<<grid, threads, 0, stream>>>(
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detail::forward::gru_resetOutput<T>(), value.gate_value,
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value.reset_output_value, value.prev_out_value, frame_size,
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batch_size, active_gate);
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}
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if (value.prev_out_value) {
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blas.GEMM(false, false, batch_size, frame_size, frame_size, 1,
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value.reset_output_value, frame_size, value.state_weight,
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frame_size, 1, value.gate_value + frame_size * 2,
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frame_size * 3);
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}
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if (batch_size == 1) {
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detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
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/* is_batch= */ false,
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T><<<grid, threads, 0, stream>>>(
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detail::forward::gru_finalOutput<T>(), value.gate_value,
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value.prev_out_value, value.output_value, frame_size, batch_size,
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active_node, origin_mode);
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} else {
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detail::KeGruForwardFinalOutput<detail::forward::gru_finalOutput<T>,
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/* is_batch= */ true,
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T><<<grid, threads, 0, stream>>>(
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detail::forward::gru_finalOutput<T>(), value.gate_value,
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value.prev_out_value, value.output_value, frame_size, batch_size,
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active_node, origin_mode);
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}
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}
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};
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template <typename T>
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struct GRUUnitGradFunctor<platform::CUDADeviceContext, T> {
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static void compute(const platform::CUDADeviceContext &context,
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GRUMetaValue<T> value, GRUMetaGrad<T> grad,
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int frame_size, int batch_size,
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const detail::ActivationType active_node,
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const detail::ActivationType active_gate,
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bool origin_mode) {
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auto stream = context.stream();
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dim3 threads;
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dim3 grid;
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if (batch_size == 1) {
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int frame_per_block = frame_size <= 1024 ? frame_size : 1024;
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int frame_blocks = (frame_size + 1024 - 1) / 1024;
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threads = dim3(frame_per_block, 1);
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grid = dim3(frame_blocks, 1);
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} else {
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threads = dim3(32, 32);
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grid = dim3((frame_size + 32 - 1) / 32, (batch_size + 32 - 1) / 32);
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}
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if (batch_size == 1) {
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detail::KeGruBackwardStateGrad<
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detail::backward::gru_stateGrad<T>,
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/* is_batch= */ false><<<grid, threads, 0, stream>>>(
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detail::backward::gru_stateGrad<T>(), value.gate_value,
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grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
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grad.output_grad, frame_size, batch_size, active_node, origin_mode);
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} else {
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detail::KeGruBackwardStateGrad<
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detail::backward::gru_stateGrad<T>,
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/* is_batch= */ true><<<grid, threads, 0, stream>>>(
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detail::backward::gru_stateGrad<T>(), value.gate_value,
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grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
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grad.output_grad, frame_size, batch_size, active_node, origin_mode);
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}
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auto blas = math::GetBlas<platform::CUDADeviceContext, T>(context);
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if (value.prev_out_value && grad.prev_out_grad) {
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blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,
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grad.gate_grad + frame_size * 2, frame_size * 3,
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value.state_weight, frame_size, 0, grad.reset_output_grad,
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frame_size);
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if (grad.state_weight_grad) {
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blas.GEMM(true, false, frame_size, frame_size, batch_size, 1,
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value.reset_output_value, frame_size,
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grad.gate_grad + frame_size * 2, frame_size * 3, 1,
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grad.state_weight_grad, frame_size);
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}
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}
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if (batch_size == 1) {
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detail::KeGruBackwardResetGrad<
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detail::backward::gru_resetGrad<T>,
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/* is_batch= */ false><<<grid, threads, 0, stream>>>(
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detail::backward::gru_resetGrad<T>(), value.gate_value,
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grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
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grad.reset_output_grad, frame_size, batch_size, active_gate);
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} else {
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detail::KeGruBackwardResetGrad<
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detail::backward::gru_resetGrad<T>,
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/* is_batch= */ true><<<grid, threads, 0, stream>>>(
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detail::backward::gru_resetGrad<T>(), value.gate_value,
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grad.gate_grad, value.prev_out_value, grad.prev_out_grad,
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grad.reset_output_grad, frame_size, batch_size, active_gate);
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}
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if (grad.prev_out_grad && value.prev_out_value) {
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blas.GEMM(false, true, batch_size, frame_size, frame_size * 2, 1,
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grad.gate_grad, frame_size * 3, value.gate_weight,
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frame_size * 2, 1, grad.prev_out_grad, frame_size);
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if (grad.gate_weight_grad) {
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blas.GEMM(true, false, frame_size, frame_size * 2, batch_size, 1,
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value.prev_out_value, frame_size, grad.gate_grad,
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frame_size * 3, 1, grad.gate_weight_grad, frame_size * 2);
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}
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}
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}
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};
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template struct GRUUnitFunctor<platform::CUDADeviceContext, float>;
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template struct GRUUnitFunctor<platform::CUDADeviceContext, double>;
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template struct GRUUnitGradFunctor<platform::CUDADeviceContext, float>;
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template struct GRUUnitGradFunctor<platform::CUDADeviceContext, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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