wehub-resource-sync
341 lines
12 KiB
Plaintext
341 lines
12 KiB
Plaintext
/* 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/phi/kernels/funcs/gru_compute.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/detail/gru_gpu_kernel.h"
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#include "paddle/phi/kernels/funcs/detail/gru_kernel.h"
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namespace phi {
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namespace funcs {
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template <typename T>
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struct GRUUnitFunctor<GPUContext, T> {
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static void compute(const GPUContext &dev_ctx,
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GRUMetaValue<T> value,
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int frame_size,
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int batch_size,
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const funcs::detail::ActivationType active_node,
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const funcs::detail::ActivationType active_gate,
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bool origin_mode) {
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auto stream = dev_ctx.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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if (dev_ctx.GetComputeCapability() >= 70) {
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if (frame_size < 16) {
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constexpr int tiled_size = 8;
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int frame_blocks = (frame_size * 2 + tiled_size - 1) / tiled_size;
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threads = dim3(tiled_size, 1);
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grid = dim3(frame_blocks, 1);
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detail::KeFastCollectiveGruGate<T, tiled_size>
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<<<grid, threads, 0, stream>>>(value.gate_value,
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value.prev_out_value,
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value.gate_weight,
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value.reset_output_value,
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frame_size,
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active_gate);
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frame_blocks = (frame_size + tiled_size - 1) / tiled_size;
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grid = dim3(frame_blocks, 1);
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detail::KeFastCollectiveGruOut<T, tiled_size>
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<<<grid, threads, 0, stream>>>(value.state_weight,
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value.prev_out_value,
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value.output_value,
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value.gate_value,
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value.reset_output_value,
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frame_size,
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active_node,
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origin_mode);
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} else {
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constexpr int tiled_size = 16;
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int frame_blocks = (frame_size * 2 + tiled_size - 1) / tiled_size;
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threads = dim3(tiled_size, 1);
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grid = dim3(frame_blocks, 1);
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detail::KeFastCollectiveGruGate<T, tiled_size>
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<<<grid, threads, 0, stream>>>(value.gate_value,
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value.prev_out_value,
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value.gate_weight,
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value.reset_output_value,
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frame_size,
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active_gate);
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frame_blocks = (frame_size + tiled_size - 1) / tiled_size;
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grid = dim3(frame_blocks, 1);
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detail::KeFastCollectiveGruOut<T, tiled_size>
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<<<grid, threads, 0, stream>>>(value.state_weight,
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value.prev_out_value,
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value.output_value,
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value.gate_value,
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value.reset_output_value,
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frame_size,
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active_node,
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origin_mode);
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}
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return;
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} else {
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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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}
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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 = funcs::GetBlas<GPUContext, T>(dev_ctx);
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if (value.prev_out_value) {
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blas.GEMM(false,
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false,
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batch_size,
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frame_size * 2,
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frame_size,
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1,
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value.prev_out_value,
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frame_size,
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value.gate_weight,
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frame_size * 2,
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1,
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value.gate_value,
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frame_size * 3);
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}
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if (batch_size == 1) {
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detail::KeGruForwardResetOutput<
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funcs::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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funcs::detail::forward::gru_resetOutput<T>(),
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value.gate_value,
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value.reset_output_value,
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value.prev_out_value,
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frame_size,
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batch_size,
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active_gate);
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} else {
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detail::KeGruForwardResetOutput<
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funcs::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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funcs::detail::forward::gru_resetOutput<T>(),
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value.gate_value,
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value.reset_output_value,
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value.prev_out_value,
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frame_size,
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batch_size,
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active_gate);
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}
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if (value.prev_out_value) {
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blas.GEMM(false,
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false,
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batch_size,
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frame_size,
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frame_size,
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1,
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value.reset_output_value,
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frame_size,
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value.state_weight,
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frame_size,
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1,
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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<
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funcs::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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funcs::detail::forward::gru_finalOutput<T>(),
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value.gate_value,
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value.prev_out_value,
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value.output_value,
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frame_size,
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batch_size,
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active_node,
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origin_mode);
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} else {
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detail::KeGruForwardFinalOutput<
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funcs::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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funcs::detail::forward::gru_finalOutput<T>(),
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value.gate_value,
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value.prev_out_value,
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value.output_value,
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frame_size,
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batch_size,
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active_node,
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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<GPUContext, T> {
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static void compute(const GPUContext &dev_ctx,
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GRUMetaValue<T> value,
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GRUMetaGrad<T> grad,
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int frame_size,
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int batch_size,
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const funcs::detail::ActivationType active_node,
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const funcs::detail::ActivationType active_gate,
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bool origin_mode) {
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auto stream = dev_ctx.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<funcs::detail::backward::gru_stateGrad<T>,
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/* is_batch= */ false>
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<<<grid, threads, 0, stream>>>(
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funcs::detail::backward::gru_stateGrad<T>(),
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value.gate_value,
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grad.gate_grad,
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value.prev_out_value,
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grad.prev_out_grad,
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grad.output_grad,
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frame_size,
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batch_size,
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active_node,
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origin_mode);
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} else {
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detail::KeGruBackwardStateGrad<funcs::detail::backward::gru_stateGrad<T>,
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/* is_batch= */ true>
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<<<grid, threads, 0, stream>>>(
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funcs::detail::backward::gru_stateGrad<T>(),
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value.gate_value,
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grad.gate_grad,
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value.prev_out_value,
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grad.prev_out_grad,
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grad.output_grad,
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frame_size,
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batch_size,
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active_node,
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origin_mode);
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}
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auto blas = funcs::GetBlas<GPUContext, T>(dev_ctx);
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if (value.prev_out_value && grad.prev_out_grad) {
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blas.GEMM(false,
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true,
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batch_size,
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frame_size,
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frame_size,
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1,
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grad.gate_grad + frame_size * 2,
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frame_size * 3,
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value.state_weight,
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frame_size,
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0,
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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,
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false,
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frame_size,
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frame_size,
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batch_size,
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1,
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value.reset_output_value,
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frame_size,
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grad.gate_grad + frame_size * 2,
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frame_size * 3,
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1,
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grad.state_weight_grad,
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frame_size);
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}
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}
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if (batch_size == 1) {
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detail::KeGruBackwardResetGrad<funcs::detail::backward::gru_resetGrad<T>,
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/* is_batch= */ false>
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<<<grid, threads, 0, stream>>>(
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funcs::detail::backward::gru_resetGrad<T>(),
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value.gate_value,
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grad.gate_grad,
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value.prev_out_value,
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grad.prev_out_grad,
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grad.reset_output_grad,
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frame_size,
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batch_size,
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active_gate);
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} else {
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detail::KeGruBackwardResetGrad<funcs::detail::backward::gru_resetGrad<T>,
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/* is_batch= */ true>
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<<<grid, threads, 0, stream>>>(
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funcs::detail::backward::gru_resetGrad<T>(),
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value.gate_value,
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grad.gate_grad,
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value.prev_out_value,
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grad.prev_out_grad,
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grad.reset_output_grad,
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frame_size,
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batch_size,
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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,
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true,
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batch_size,
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frame_size,
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frame_size * 2,
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1,
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grad.gate_grad,
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frame_size * 3,
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value.gate_weight,
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frame_size * 2,
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1,
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grad.prev_out_grad,
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frame_size);
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if (grad.gate_weight_grad) {
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blas.GEMM(true,
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false,
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frame_size,
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frame_size * 2,
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batch_size,
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1,
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value.prev_out_value,
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frame_size,
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grad.gate_grad,
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frame_size * 3,
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1,
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grad.gate_weight_grad,
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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<GPUContext, float>;
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template struct GRUUnitFunctor<GPUContext, double>;
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template struct GRUUnitGradFunctor<GPUContext, float>;
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template struct GRUUnitGradFunctor<GPUContext, double>;
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} // namespace funcs
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} // namespace phi
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