/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #pragma once #include "paddle/operators/math/detail/lstm_kernel.h" #include "paddle/operators/math/lstm_compute.h" #include "paddle/platform/cuda_helper.h" namespace paddle { namespace operators { namespace math { namespace detail { /* * threads(framePerBlock, batchPerBlock) * grid(frameBlocks, batchBlocks) */ template __global__ void KeLstmForward(Op op, lstm_value value, int frameSize, int batchSize, activation_mode_t active_node, activation_mode_t active_gate, activation_mode_t active_state) { const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x; if (frameIdx >= frameSize) return; int batchIdx = 0; if (isBatch) { batchIdx = blockIdx.y * blockDim.y + threadIdx.y; if (batchIdx >= batchSize) return; value.gateValue += batchIdx * frameSize * 4; value.outputValue += batchIdx * frameSize; value.stateValue += batchIdx * frameSize; value.stateActiveValue += batchIdx * frameSize; } T rState; T rPrevState = 0; T rStateAtv; T rOut; T rValueIn; T rValueIg; T rValueFg; T rValueOg; T rCheckI = value.checkIg[frameIdx]; T rCheckF = value.checkFg[frameIdx]; T rCheckO = value.checkOg[frameIdx]; rValueIn = value.gateValue[frameIdx]; rValueIg = value.gateValue[frameIdx + frameSize]; rValueFg = value.gateValue[frameIdx + frameSize * 2]; rValueOg = value.gateValue[frameIdx + frameSize * 3]; if (value.prevStateValue) { if (isBatch) value.prevStateValue += batchIdx * frameSize; rPrevState = value.prevStateValue[frameIdx]; } op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv, rOut, rCheckI, rCheckF, rCheckO, hppl::gpu::forward[active_node], hppl::gpu::forward[active_gate], hppl::gpu::forward[active_state]); value.gateValue[frameIdx] = rValueIn; value.gateValue[frameIdx + frameSize] = rValueIg; value.gateValue[frameIdx + frameSize * 2] = rValueFg; value.gateValue[frameIdx + frameSize * 3] = rValueOg; value.stateValue[frameIdx] = rState; value.stateActiveValue[frameIdx] = rStateAtv; value.outputValue[frameIdx] = rOut; } /* * threads(framePerBlock, batchPerBlock) * grid(frameBlocks, batchBlocks) */ template __global__ void KeLstmBackward(Op op, lstm_value value, lstm_grad grad, int frameSize, int batchSize, activation_mode_t active_node, activation_mode_t active_gate, activation_mode_t active_state) { const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x; if (frameIdx >= frameSize) return; int batchIdx = 0; if (isBatch) { batchIdx = blockIdx.y * blockDim.y + threadIdx.y; if (batchIdx >= batchSize) return; value.gateValue += batchIdx * frameSize * 4; value.stateValue += batchIdx * frameSize; value.stateActiveValue += batchIdx * frameSize; grad.gateGrad += batchIdx * frameSize * 4; grad.stateGrad += batchIdx * frameSize; grad.outputGrad += batchIdx * frameSize; } T rValueIn; T rValueIg; T rValueFg; T rValueOg; T rGradIn; T rGradIg; T rGradFg; T rGradOg; T rPrevState = 0; T rPrevStateGrad; T rState; T rStateGrad; T rStateAtv; T rOutputGrad; T rCheckI = value.checkIg[frameIdx]; T rCheckF = value.checkFg[frameIdx]; T rCheckO = value.checkOg[frameIdx]; T rCheckIGrad; T rCheckFGrad; T rCheckOGrad; rValueIn = value.gateValue[frameIdx]; rValueIg = value.gateValue[frameIdx + frameSize]; rValueFg = value.gateValue[frameIdx + frameSize * 2]; rValueOg = value.gateValue[frameIdx + frameSize * 3]; rState = value.stateValue[frameIdx]; rStateAtv = value.stateActiveValue[frameIdx]; rOutputGrad = grad.outputGrad[frameIdx]; rStateGrad = grad.stateGrad[frameIdx]; if (value.prevStateValue) { if (isBatch) value.prevStateValue += batchIdx * frameSize; rPrevState = value.prevStateValue[frameIdx]; } op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg, rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv, rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad, hppl::gpu::backward[active_node], hppl::gpu::backward[active_gate], hppl::gpu::backward[active_state]); grad.gateGrad[frameIdx] = rGradIn; grad.gateGrad[frameIdx + frameSize] = rGradIg; grad.gateGrad[frameIdx + frameSize * 2] = rGradFg; grad.gateGrad[frameIdx + frameSize * 3] = rGradOg; grad.stateGrad[frameIdx] = rStateGrad; if (grad.prevStateGrad) { if (isBatch) grad.prevStateGrad += batchIdx * frameSize; grad.prevStateGrad[frameIdx] = rPrevStateGrad; } if (isBatch) { if (value.prevStateValue) { if (grad.checkIgGrad) paddle::platform::CudaAtomicAdd(grad.checkIgGrad + frameIdx, rCheckIGrad); if (grad.checkFgGrad) paddle::platform::CudaAtomicAdd(grad.checkFgGrad + frameIdx, rCheckFGrad); } if (grad.checkOgGrad) paddle::platform::CudaAtomicAdd(grad.checkOgGrad + frameIdx, rCheckOGrad); } else { if (value.prevStateValue) { if (grad.checkIgGrad) grad.checkIgGrad[frameIdx] += rCheckIGrad; if (grad.checkFgGrad) grad.checkFgGrad[frameIdx] += rCheckFGrad; } if (grad.checkOgGrad) grad.checkOgGrad[frameIdx] += rCheckOGrad; } } template void gpu_lstm_forward(Op op, lstm_value value, int frameSize, int batchSize, activation_mode_t active_node, activation_mode_t active_gate, activation_mode_t active_state) { dim3 threads; dim3 grid; if (batchSize == 1) { int framePerBlock = frameSize <= 1024 ? frameSize : 1024; int frameBlocks = (frameSize + 1024 - 1) / 1024; threads = dim3(framePerBlock, 1); grid = dim3(frameBlocks, 1); } else { /* framePerBlock = 32 batchPerBlock = 32 */ threads = dim3(32, 32); grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32); } if (batchSize == 1) { KeLstmForward<<>>( op, value, frameSize, batchSize, active_node, active_gate, active_state); } else { KeLstmForward<<>>( op, value, frameSize, batchSize, active_node, active_gate, active_state); } } template void gpu_lstm_backward(Op op, lstm_value value, lstm_grad grad, int frameSize, int batchSize, activation_mode_t active_node, activation_mode_t active_gate, activation_mode_t active_state) { dim3 threads; dim3 grid; if (batchSize == 1) { int framePerBlock = frameSize <= 1024 ? frameSize : 1024; int frameBlocks = (frameSize + 1024 - 1) / 1024; threads = dim3(framePerBlock, 1); grid = dim3(frameBlocks, 1); } else { /* framePerBlock = 32 batchPerBlock = 32 */ threads = dim3(32, 32); grid = dim3((frameSize + 32 - 1) / 32, (batchSize + 32 - 1) / 32); } if (batchSize == 1) { KeLstmBackward<<>>( op, value, grad, frameSize, batchSize, active_node, active_gate, active_state); } else { KeLstmBackward<<>>( op, value, grad, frameSize, batchSize, active_node, active_gate, active_state); } } } // namespace detail } // namespace math } // namespace operators } // namespace paddle