TaoLv commented on a change in pull request #14713: MKLDNN RNN Inference Integration(fp32 LSTM and vRNN with tanh and relu) URL: https://github.com/apache/incubator-mxnet/pull/14713#discussion_r277168188
########## File path: src/operator/nn/mkldnn/mkldnn_rnn_impl.h ########## @@ -0,0 +1,710 @@ +/* + * 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(); +} + +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 AdjustGruGateOrder(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; + } +} +// since there is different sematics of MKLDNN's Fused RNN and Mxnet FusedRNN, +// bidirectional will be fused layer by layer, +// unidirectional will be done by fused 1 + fused (L - 1) layers or fused L layers(when I = H) + +template <typename DType> +void MKLDNNRNNForwardSingleLayerBi(bool state_outputs, + const int T, + const int N, + const int I, + const int H, + DType* x_ptr, + mkldnn::memory user_src_layer_memory, + DType* hx_ptr, + DType* cx_ptr, + DType* w_ptr, + DType* b_ptr, + DType* y_ptr, + DType* hy_ptr, + DType* cy_ptr, + std::vector<mkldnn::memory> *concat_weight_memory, + std::vector<mkldnn::memory> *concat_iter_memory, + std::vector<mkldnn::memory> *x_memory, + std::vector<mkldnn::memory> *hcx_memory, + std::vector<mkldnn::memory> *wx_memory, + std::vector<mkldnn::memory> *wh_memory, + std::vector<mkldnn::memory> *bias_memory, + std::vector<mkldnn::memory> *y_memory, + std::vector<mkldnn::memory> *hcy_memory, + std::vector<primitive> *rnn_forward_prim, + int layer_index, + bool *has_cache, + int lvalue, + int dtype, + bool is_train, + int mode) { + int ngates = 0, nstates = 0; + algorithm nalgorithm = GetMKLDNNRNNAlgo(mode, &ngates, &nstates); + mkldnn::memory::data_type mkldnn_dtype = get_mkldnn_type(dtype); + const int single_cell_size = N * H; + const int single_b_size = ngates * H; + DType* wx = w_ptr; // ngates * H, I + DType* wh = w_ptr + I * H * ngates; // ngates * H, H + DType* back_wx = w_ptr + ngates * H * (I + H); + DType* back_wh = back_wx + I * H * ngates; + DType* bx = b_ptr; + DType* bh = b_ptr + H * ngates; + DType* back_bx = b_ptr + single_b_size * 2; + DType* back_bh = back_bx + H * ngates; + const int omp_threads = mxnet::engine::OpenMP::Get()->GetRecommendedOMPThreadCount(); + auto cpu_engine = CpuEngine::Get()->get_engine(); + auto null_memory_ = null_memory(cpu_engine); + int offset1 = 0, offset2 = 0; + bool cached = *has_cache; + mkldnn::memory::dims src_layer_tz = {T, N, I}; + mkldnn::memory::dims dst_layer_tz = {T, N, 2 * H}; + mkldnn::memory::dims weights_layer_tz = {1, 2, I, ngates, H}; // ldigo + mkldnn::memory::dims weights_layer_r_tz = {1, 1, I, ngates, H}; // ldigo for reorder + mkldnn::memory::dims weights_iter_tz = {1, 2, H, ngates, H}; // ldigo + mkldnn::memory::dims weights_iter_r_tz = {1, 1, H, ngates, H}; // ldigo for reorder + mkldnn::memory::dims bias_tz = {1, 2, ngates, H}; + mkldnn::memory::dims src_iter_tz = {1, 2, nstates, N, H}; // ldsnc + mkldnn::memory::dims dst_iter_tz = {1, 2, nstates, N, H}; // ldsnc + + std::vector<float> weights_scales(ngates * H); Review comment: Seems it's not used? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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