diff --git a/paddle/fluid/operators/fusion_gru_op.cc b/paddle/fluid/operators/fusion_gru_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..2559a75256d5bca1c8ba0867bef9b747fef4b74c --- /dev/null +++ b/paddle/fluid/operators/fusion_gru_op.cc @@ -0,0 +1,303 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +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. */ + +#include "paddle/fluid/operators/fusion_gru_op.h" +#include +#include "paddle/fluid/framework/eigen.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/operators/math/detail/activation_functions.h" +#include "paddle/fluid/operators/math/gru_compute.h" +#include "paddle/fluid/operators/math/math_function.h" +#include "paddle/fluid/operators/math/sequence2batch.h" + +namespace paddle { +namespace operators { + +void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE(ctx->HasInput("Input"), + "Input(%s) of GRUOp should not be null.", "Input"); + PADDLE_ENFORCE(ctx->HasInput("Weight"), + "Input(%s) of GRUOp should not be null.", "Weight"); + PADDLE_ENFORCE(ctx->HasOutput("BatchGate"), + "Output(%s) of GRUOp should not be null.", "BatchGate"); + PADDLE_ENFORCE(ctx->HasOutput("BatchResetHiddenPrev"), + "Output(%s) of GRUOp should not be null.", + "BatchResetHiddenPrev"); + PADDLE_ENFORCE(ctx->HasOutput("BatchHidden"), + "Output(%s) of GRUOp should not be null.", "BatchHidden"); + PADDLE_ENFORCE(ctx->HasOutput("Hidden"), + "Output(%s) of GRUOp should not be null.", "Hidden"); + auto input_dims = ctx->GetInputDim("Input"); + auto weight_dims = ctx->GetInputDim("Weight"); + int input_size = input_dims[1]; + int frame_size = weight_dims[0]; + PADDLE_ENFORCE_EQ(input_size, frame_size * 3, + "The input_size must be 3 times of frame_size in GRUOp."); + PADDLE_ENFORCE_EQ( + weight_dims[1], frame_size * 3, + "The shape of Weight matrix must be [frame_size, frame_size * 3]."); + if (ctx->HasInput("H0")) { + auto h0_dims = ctx->GetInputDim("H0"); + PADDLE_ENFORCE_EQ(h0_dims[1], frame_size, + "The width of H0 must be equal to frame_size."); + } + if (ctx->HasInput("Bias")) { + auto bias_dims = ctx->GetInputDim("Bias"); + int bias_height = bias_dims[0]; + int bias_width = bias_dims[1]; + PADDLE_ENFORCE_EQ(bias_height, 1, + "The shape of Bias must be [1, frame_size * 3]."); + PADDLE_ENFORCE_EQ(bias_width, frame_size * 3, + "The shape of Bias must be [1, frame_size * 3]."); + } + ctx->SetOutputDim("BatchGate", input_dims); + ctx->SetOutputDim("BatchResetHiddenPrev", {input_dims[0], frame_size}); + ctx->SetOutputDim("BatchHidden", {input_dims[0], frame_size}); + ctx->SetOutputDim("Hidden", {input_dims[0], frame_size}); + ctx->ShareLoD("Input", "Hidden"); +} + +framework::OpKernelType FusionGRUOp::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + return framework::OpKernelType( + framework::ToDataType(ctx.Input("X")->type()), + ctx.device_context()); +} + +void FusionGRUOpMaker::Make() { + AddInput("Input", + "(LoDTensor) The first input is a LodTensor, which supports " + "variable-time length input sequence. The underlying tensor in " + "this LoDTenosr is a matrix with shape (T X 3D), where, T is the " + "total time steps in this mini-batch, D is the hidden size."); + AddInput("H0", + "(Tensor, optional) The initial hidden state is an optional " + "input. This is a tensor with shape (N x D), where N is the " + "batch size, D is the hidden size.") + .AsDispensable(); + AddInput( + "Weight", + "(Tensor) The learnable hidden-hidden weight matrix with shape " + "(D x 3D), where D is the hidden size. The elements continuous in " + "memory can be divided into two parts. The first part are weights of " + "the update gate and reset gate with shape (D x 2D), and the second " + "part are weights of output candidate with shape (D x D)."); + AddInput("Bias", + "(Tensor, optional) Bias vector with shape (1 x 3D) concating " + "bias of the update gate, reset gate and output candidate.") + .AsDispensable(); + AddOutput("BatchGate", + "(LoDTensor) To compute with batches, sequence data will be " + "reorganized into several successive batches each containing " + "data from the same time step. The LoDTensor BatchGate contains " + "the update gate, reset gate and output candidate values " + "organized in batches. The LoD size is 2. The first LoD contains " + "the batch offsets and the second LoD contains the indexes in " + "the raw sequence data.") + .AsIntermediate(); + AddOutput( + "BatchResetHiddenPrev", + "(LoDTensor) The reseted hidden state LoDTensor organized in batches. " + "This LoDTensor is a matrix with shape (T X D) and has the same LoD " + "with `BatchGate`.") + .AsIntermediate(); + AddOutput( + "BatchHidden", + "(LoDTensor) The hidden state LoDTensor organized in batches. " + "This LoDTensor is a matrix with shape (T X D) and has the same LoD " + "with `BatchGate`.") + .AsIntermediate(); + AddOutput( + "Hidden", + "(LoDTensor) the hidden state LoDTensor organized in sequences. " + "This LoDTensor is a matrix with shape (T X D) and has the same LoD " + "with `BatchGate`."); + AddAttr("activation", + "(string, default tanh) " + "The activation type used for output candidate {h}_t.") + .SetDefault("tanh"); + AddAttr( + "gate_activation", + "(string, default sigmoid) " + "The activation type used in update gate and reset gate.") + .SetDefault("sigmoid"); + AddAttr("is_reverse", + "(bool, defalut: False) " + "whether to compute reversed GRU.") + .SetDefault(false); + AddComment(R"DOC( +The Fusion complete GRU Operator. +This operator fuse the fully-connected operator into GRU, +more details can refer to GRU op. +)DOC"); +} + +template +inline void ReorderInitState(const DeviceContext& ctx, + const framework::Tensor& src, + framework::Vector index_lod, + framework::Tensor* dst, bool indexed_src) { + math::CopyMatrixRowsFunctor row_shuffle; + dst->mutable_data(src.dims(), ctx.GetPlace()); + row_shuffle(ctx, src, index_lod, dst, indexed_src); +} + +template +class FusionGRUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* x = context.Input("X"); + auto* h = context.Input("H"); + auto* h0 = context.Input("H0"); + auto* x_weight = context.Input("XWeight"); // x_dim*3D + auto* gate_weight = context.Input("HWeight"); // D*3D + auto* bias = context.Input("Bias"); // 1*3D + + auto hidden_dims = hidden->dims(); + + bool is_reverse = context.Attr("is_reverse"); + math::LoDTensor2BatchFunctor to_batch; + auto& dev_ctx = context.template device_context(); + to_batch(dev_ctx, *input, batch_gate, true, is_reverse); + + if (bias) { + math::RowwiseAdd add_bias; + add_bias(dev_ctx, *batch_gate, *bias, batch_gate); + } + + int frame_size = hidden_dims[1]; + math::GRUMetaValue gru_value; + gru_value.gate_weight = const_cast(weight_data); + gru_value.state_weight = + const_cast(weight_data + 2 * frame_size * frame_size); + Tensor ordered_h0; + + framework::Vector order(batch_gate->lod()[2]); + + if (h0) { + // Since the batch computing for GRU reorders the input sequences + // according to their length. The initialized cell state also needs + // to reorder. + ReorderInitState( + context.template device_context(), *h0, order, + &ordered_h0, true); + gru_value.prev_out_value = ordered_h0.data(); + } else { + gru_value.prev_out_value = nullptr; + } + auto batch_starts = batch_gate->lod()[0]; + size_t seq_len = batch_starts.size() - 1; + auto active_node = math::detail::GetActivationType( + context.Attr("activation")); + auto active_gate = math::detail::GetActivationType( + context.Attr("gate_activation")); + +#ifdef PADDLE_WITH_MKLML + // use MKL packed to speedup GEMM + if (FLAGS_paddle_num_threads >= 4) { + auto blas = math::GetBlas(dev_ctx); + T* packed_gate = blas.GEMM_ALLOC(CblasBMatrix, 1 /*height of C*/, + frame_size * 2 /*width of weight*/, + frame_size /*height of height*/); + PADDLE_ENFORCE(packed_gate); + blas.GEMM_PACK(CblasBMatrix, CblasNoTrans, 1 /*cur bs?*/, frame_size * 2, + frame_size, T(1.0), gru_value.gate_weight, frame_size * 2, + packed_gate); + T* packed_state = blas.GEMM_ALLOC(CblasBMatrix, 1 /*height of C*/, + frame_size /*width of weight*/, + frame_size /*height of height*/); + PADDLE_ENFORCE(packed_state); + blas.GEMM_PACK(CblasBMatrix, CblasNoTrans, 1 /*cur bs?*/, frame_size, + frame_size, T(1.0), gru_value.state_weight, frame_size, + packed_state); + for (size_t n = 0; n < seq_len; n++) { + int bstart = static_cast(batch_starts[n]); + int bend = static_cast(batch_starts[n + 1]); + int cur_batch_size = bend - bstart; + + Tensor gate_t = batch_gate->Slice(bstart, bend); + Tensor reset_hidden_prev_t = + batch_reset_hidden_prev->Slice(bstart, bend); + Tensor hidden_t = batch_hidden->Slice(bstart, bend); + gru_value.output_value = hidden_t.data(); + gru_value.gate_value = gate_t.data(); + gru_value.reset_output_value = reset_hidden_prev_t.data(); + + if (gru_value.prev_out_value) { + blas.GEMM_COMPUTE( + CblasNoTrans, CblasPacked, cur_batch_size, frame_size * 2, + frame_size, gru_value.prev_out_value, frame_size, packed_gate, + frame_size * 2, T(1), gru_value.gate_value, frame_size * 3); + } + + math::detail::forward_reset_output( + math::detail::forward::gru_resetOutput(), gru_value, frame_size, + cur_batch_size, active_gate); + + if (gru_value.prev_out_value) { + blas.GEMM_COMPUTE( + CblasNoTrans, CblasPacked, cur_batch_size, frame_size, frame_size, + gru_value.reset_output_value, frame_size, packed_state, + frame_size, T(1), gru_value.gate_value + frame_size * 2, + frame_size * 3); + } + + math::detail::forward_final_output( + math::detail::forward::gru_finalOutput(), gru_value, frame_size, + cur_batch_size, active_node); + + gru_value.prev_out_value = gru_value.output_value; + } + + blas.GEMM_FREE(packed_gate); + blas.GEMM_FREE(packed_state); + } else { +#endif + for (size_t n = 0; n < seq_len; n++) { + int bstart = static_cast(batch_starts[n]); + int bend = static_cast(batch_starts[n + 1]); + int cur_batch_size = bend - bstart; + + Tensor gate_t = batch_gate->Slice(bstart, bend); + Tensor reset_hidden_prev_t = + batch_reset_hidden_prev->Slice(bstart, bend); + Tensor hidden_t = batch_hidden->Slice(bstart, bend); + gru_value.output_value = hidden_t.data(); + gru_value.gate_value = gate_t.data(); + gru_value.reset_output_value = reset_hidden_prev_t.data(); + + math::GRUUnitFunctor::compute( + dev_ctx, gru_value, frame_size, cur_batch_size, active_node, + active_gate); + + gru_value.prev_out_value = gru_value.output_value; + } +#ifdef PADDLE_WITH_MKLML + } +#endif + math::Batch2LoDTensorFunctor to_seq; + batch_hidden->set_lod(batch_gate->lod()); + to_seq(dev_ctx, *batch_hidden, hidden); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OPERATOR(fusion_gru, ops::FusionGRUOp, ops::FusionGRUOpMaker, + paddle::framework::DefaultGradOpDescMaker); +REGISTER_OP_CPU_KERNEL( + fusion_gru, ops::FusionGRUKernel, + ops::GRUKernel); diff --git a/paddle/fluid/operators/fusion_gru_op.h b/paddle/fluid/operators/fusion_gru_op.h new file mode 100644 index 0000000000000000000000000000000000000000..eaa59cd412f8f2fd0089428f5e25202c70f032c7 --- /dev/null +++ b/paddle/fluid/operators/fusion_gru_op.h @@ -0,0 +1,41 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +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/fluid/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using LoDTensor = framework::LoDTensor; +using Tensor = framework::Tensor; + +class FusionGRUOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override; + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override; +}; + +class FusionGRUOpMaker : public framework::OpProtoAndCheckerMaker { + public: + void Make() override; +}; + +} // namespace operators +} // namespace paddle