pengzhao-intel commented on a change in pull request #14713: [WIP]MKLDNN RNN Inference Integration(fp32 LSTM and vRNN with tanh and relu) URL: https://github.com/apache/incubator-mxnet/pull/14713#discussion_r285424536
########## File path: src/operator/nn/mkldnn/mkldnn_rnn_impl.h ########## @@ -0,0 +1,739 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +#ifndef MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_IMPL_H_ +#define MXNET_OPERATOR_NN_MKLDNN_MKLDNN_RNN_IMPL_H_ +#if MXNET_USE_MKLDNN == 1 +#include <dmlc/logging.h> +#include <dmlc/parameter.h> +#include <mxnet/operator.h> +#include <mxnet/storage.h> +#include <algorithm> +#include <map> +#include <vector> +#include <utility> +#include <string> +#include "../../math.h" +#include "../../math_functions-inl.h" +#include "../../operator_common.h" +#include "../../rnn_impl.h" +#include "../../rnn-inl.h" +#include "mkldnn.hpp" +#include "./mkldnn_base-inl.h" + +namespace mxnet { +namespace op { + +algorithm GetMKLDNNRNNAlgo(int mode, + int* ngates, + int* nstates) { + algorithm algo = algorithm::vanilla_rnn; + switch (mode) { + case rnn_enum::kLstm: + *ngates = 4; + *nstates = 2; + algo = algorithm::vanilla_lstm; + break; + case rnn_enum::kGru: + *ngates = 3; + *nstates = 1; + algo = algorithm::vanilla_gru; + break; + case rnn_enum::kRnnRelu: + case rnn_enum::kRnnTanh: + *ngates = 1; + *nstates = 1; + algo = algorithm::vanilla_rnn; + break; + default: + LOG(FATAL) << "unsupported RNN mode:" << mode; + break; + } + return algo; +} + +void ConcatData(mkldnn::memory::format src_format, + mkldnn::memory::format dst_format, + std::vector<mkldnn::memory::dims> srcs_cds, + mkldnn::memory::dims dst_cds, + mkldnn::memory::data_type mkldnn_dtype, + int concat_dimension, + std::vector<void*> srcs_data, + const mkldnn::memory &dst) { + auto cpu_engine = CpuEngine::Get()->get_engine(); + std::vector<mkldnn::memory::primitive_desc> srcs_pd; + std::vector<mkldnn::memory> srcs; + for (size_t i = 0; i < srcs_cds.size(); i++) { + auto desc = mkldnn::memory::desc(srcs_cds[i], mkldnn_dtype, src_format); + auto mpd = mkldnn::memory::primitive_desc(desc, cpu_engine); + auto src_memory = mkldnn::memory(mpd, srcs_data[i]); + srcs_pd.push_back(mpd); + srcs.push_back(src_memory); + } + std::vector<primitive::at> inputs; + for (size_t i = 0; i < srcs_cds.size(); i++) { + inputs.push_back(srcs[i]); + } + auto dst_desc = mkldnn::memory::desc(dst_cds, mkldnn_dtype, dst_format); + auto concat_pd = concat::primitive_desc(dst_desc, concat_dimension, srcs_pd); + MKLDNNStream::Get()->RegisterPrim(concat(concat_pd, inputs, dst)); + MKLDNNStream::Get()->Submit(); +} + +// cached mkldnn memory +// first layer wx, wh with next L - 1 layers wx and wh +// with L layers hx and cx, src and dst data/iter etc. +// it will prepare memory on before and after reorder and concat. +// for unidirectional, it will fused as dim like 1 + (L - 1) when I != H. +// for bidirectional, it will fused as data + back_data (weight, bias, iter etc), +// also need to identify first layer and next layers +inline size_t GetMKLDNNRNNCacheMemorySize(int L, + int D, + int T, + int N, + int I, + int H, + int mode) { + size_t size = 0; + switch (mode) { + case rnn_enum::kLstm: + size = 2 * (D * (I + H) * 4 * H + (L - 1) * D * (D * H + H) * 4 * H + + L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 4 * H + (L + 2) * D * 2 * N * H + + 6 * D * (I + H + 2) * 4 * H + T * N * I * 2; + break; + case rnn_enum::kGru: + size = 2 * (D * (I + H) * 3 * H + (L - 1) * D * (D * H + H) * 3 * H + + L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 3 * H + (L + 2) * D * 2 * N * H + + 6 * D * (I + H + 2) * 3 * H + T * N * I * 2; + break; + case rnn_enum::kRnnRelu: + case rnn_enum::kRnnTanh: + size = 2 * (D * (I + H) * 1 * H + (L - 1) * D * (D * H + H) * 1 * H + + L * D * 2 * N * H) + T * N * D * H + L * 2 * D * 1 * H + (L + 2) * D * 2 * N * H + + 6 * D * (I + H + 2) * 1 * H + T * N * I * 2; + break; + default: + LOG(FATAL) << "unknown RNN mode " << mode; + break; + } + return size; +} + +template <typename DType> +void AdjustGruWeightGateOrder(DType* weight, + const int I, + const int H) { + // mxnet gru gate order is reset, update and new gates + // mkldnn gru gate order is update, reset and new gates + const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount(); + DType* weight_reset = weight; + DType* weight_update = weight + I * H; + #pragma omp parallel for num_threads(omp_threads) + for (int i = 0; i < I * H; i++) { + DType tmp = weight_update[i]; + weight_update[i] = weight_reset[i]; + weight_reset[i] = tmp; + } +} + +template <typename DType> +void AdjustGruBiasGateOrder(DType* bias, + const int H) { + // mxnet gru gate order is reset, update and new gates + // mkldnn gru gate order is update, reset and new gates + const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount(); + DType* bias_reset = bias; + DType* bias_update = bias + H; + #pragma omp parallel for num_threads(omp_threads) + for (int i = 0; i < H; i++) { + DType tmp = bias_update[i]; + bias_update[i] = bias_reset[i]; + bias_reset[i] = tmp; + } +} +// since there is different sematics of MKLDNN's Fused RNN and Mxnet FusedRNN, Review comment: Mxnet to MXNet ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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