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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/05/12 02:45:49 UTC
[GitHub] [incubator-mxnet] pengzhao-intel commented on a change in pull
request #14713: [WIP]MKLDNN RNN Inference Integration(fp32 LSTM and vRNN
with tanh and relu)
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_r283117182
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File path: src/operator/nn/mkldnn/mkldnn_rnn_impl.h
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@@ -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,
+// 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
+
+ if (!cached) {
+ if (mode == rnn_enum::kGru) {
+ AdjustGruWeightGateOrder(wx, I, H);
+ AdjustGruWeightGateOrder(back_wx, I, H);
+ AdjustGruWeightGateOrder(wh, H, H);
+ AdjustGruWeightGateOrder(back_wh, H, H);
+ AdjustGruBiasGateOrder(bx, H);
+ AdjustGruBiasGateOrder(back_bx, H);
+ AdjustGruBiasGateOrder(bh, H);
+ AdjustGruBiasGateOrder(back_bh, H);
+ }
+ auto src_wx = (*concat_weight_memory)[2 * layer_index];
+ auto src_wh = (*concat_weight_memory)[2 * layer_index + 1];
+ std::vector<void*> srcs_data1;
+ srcs_data1.push_back(wx);
+ srcs_data1.push_back(back_wx);
+ ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
+ {weights_layer_r_tz, weights_layer_r_tz}, weights_layer_tz,
+ mkldnn_dtype, 1, srcs_data1, src_wx);
+ srcs_data1.clear();
+ srcs_data1.push_back(wh);
+ srcs_data1.push_back(back_wh);
+ ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
+ {weights_iter_r_tz, weights_iter_r_tz}, weights_iter_tz,
+ mkldnn_dtype, 1, srcs_data1, src_wh);
+ int tmpvalue = 0;
+ if (lvalue > 0) {
+ tmpvalue = lvalue + 1;
+ }
+ MKLDNNStream::Get()->RegisterPrim(reorder(src_wx, (*wx_memory)[tmpvalue]));
+ MKLDNNStream::Get()->RegisterPrim(reorder(src_wh, (*wh_memory)[tmpvalue]));
+
+ DType* user_bias = reinterpret_cast<DType *>
+ ((*bias_memory)[tmpvalue].get_data_handle());
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int j = 0; j < single_b_size; j++) {
+ user_bias[j] = bx[j] + bh[j];
+ user_bias[single_b_size + j] = back_bx[j] + back_bh[j];
+ }
+ }
+ if (lvalue > 0) {
+ (*wx_memory)[layer_index].set_data_handle((*wx_memory)[lvalue + 1].get_data_handle());
+ (*wh_memory)[layer_index].set_data_handle((*wh_memory)[lvalue + 1].get_data_handle());
+ (*bias_memory)[layer_index].set_data_handle((*bias_memory)[lvalue + 1].get_data_handle());
+ }
+
+ auto src_layer_md = mkldnn::memory::desc(
+ { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
+ auto weight_layer_md = mkldnn::memory::desc(
+ { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
+ auto weight_iter_md = mkldnn::memory::desc(
+ { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
+ auto dst_layer_md = mkldnn::memory::desc(
+ { dst_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
+ auto dst_iter_md = mkldnn::memory::desc(
+ { dst_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldsnc);
+ auto src_iter_md = mkldnn::memory::desc(
+ {src_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
+ auto bias_md = mkldnn::memory::desc({bias_tz},
+ mkldnn_dtype, mkldnn::memory::format::ldgo);
+
+ auto user_src_iter_memory = (*concat_iter_memory)[2];
+ if (mode == rnn_enum::kLstm) {
+ std::vector<void*> srcs_data1;
+ srcs_data1.push_back(hx_ptr);
+ srcs_data1.push_back(cx_ptr);
+ auto tmp1_src_iter_memory = (*concat_iter_memory)[0];
+ ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
+ {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype, 2,
+ srcs_data1, tmp1_src_iter_memory);
+ std::vector<void*> srcs_data2;
+ srcs_data2.push_back(hx_ptr + single_cell_size);
+ srcs_data2.push_back(cx_ptr + single_cell_size);
+ auto tmp2_src_iter_memory = (*concat_iter_memory)[1];
+ ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
+ {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype, 2,
+ srcs_data2, tmp2_src_iter_memory);
+ std::vector<void*> srcs_data3;
+ srcs_data3.push_back(reinterpret_cast<DType *>(tmp1_src_iter_memory.get_data_handle()));
+ srcs_data3.push_back(reinterpret_cast<DType *>(tmp2_src_iter_memory.get_data_handle()));
+ ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
+ {{1, 1, nstates, N, H}, {1, 1, nstates, N, H}}, {1, 2, nstates, N, H},
+ mkldnn_dtype, 1, srcs_data3, user_src_iter_memory);
+ } else {
+ user_src_iter_memory.set_data_handle(hx_ptr);
+ }
+ (*hcx_memory)[layer_index].set_data_handle(user_src_iter_memory.get_data_handle());
+
+ rnn_cell::desc rnn_cell(nalgorithm,
+ mode == rnn_enum::kRnnRelu ? algorithm::eltwise_relu : algorithm::eltwise_tanh);
+
+ rnn_forward::desc layer_desc(prop_kind::forward_inference, rnn_cell,
+ rnn_direction::bidirectional_concat, src_layer_md,
+ src_iter_md, weight_layer_md, weight_iter_md,
+ bias_md, dst_layer_md, dst_iter_md);
+
+ auto prim_desc
+ = rnn_forward::primitive_desc(layer_desc, cpu_engine);
+
+ if (x_ptr && layer_index == 0) {
+ (*x_memory)[layer_index].set_data_handle(x_ptr);
+ } else {
+ (*x_memory)[layer_index].set_data_handle((*user_src_layer_memory).get_data_handle());
+ }
+ (*y_memory)[layer_index].set_data_handle(y_ptr);
+
+ if (rnn_forward_prim->size() <= (size_t)layer_index) {
+ primitive rnn_prim = rnn_forward(prim_desc, (*x_memory)[layer_index],
+ (*hcx_memory)[layer_index], (*wx_memory)[layer_index],
+ (*wh_memory)[layer_index], (*bias_memory)[layer_index],
+ (*y_memory)[layer_index],
+ (*hcy_memory)[layer_index], null_memory_);
+ rnn_forward_prim->push_back(rnn_prim);
+ }
+ MKLDNNStream::Get()->RegisterPrim((*rnn_forward_prim)[layer_index]);
+ MKLDNNStream::Get()->Submit();
+
+ if (state_outputs) {
+ DType* dst_hcy = reinterpret_cast<DType *> ((*hcy_memory)[layer_index].get_data_handle());
+ if (mode == rnn_enum::kLstm) {
+ offset1 = nstates * single_cell_size;
+ offset2 = (nstates + 1) * single_cell_size;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int n = 0; n < single_cell_size; n++) {
+ hy_ptr[n] = dst_hcy[n];
+ hy_ptr[n + single_cell_size] = dst_hcy[n + offset1];
+ cy_ptr[n] = dst_hcy[n + single_cell_size];
+ cy_ptr[n + single_cell_size] = dst_hcy[n + offset2];
+ }
+ } else {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int n = 0; n < 2 * single_cell_size; n++) {
+ hy_ptr[n] = dst_hcy[n];
+ }
+ }
+ }
+}
+
+
+template <typename DType>
+void MKLDNNRNNForwardUnidi(bool state_outputs,
+ const int L,
+ 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 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 cell_size = N * H;
+ const int single_cell_size = N * H;
+ const int single_b_size = ngates * H;
+ int w_size = (I + H) * 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, H};
+ mkldnn::memory::dims weights_layer_tz = {L, 1, I, ngates, H}; // ldigo
+ mkldnn::memory::dims weights_iter_tz = {L, 1, H, ngates, H}; // ldigo
+ mkldnn::memory::dims bias_tz = {L, 1, ngates, H};
+ mkldnn::memory::dims src_iter_tz = {L, 1, nstates, N, H}; // ldsnc
+ mkldnn::memory::dims dst_iter_tz = {L, 1, nstates, N, H}; // ldsnc
+ mkldnn::memory::dims weights_layer_r_tz = {1, 1, I, ngates, H}; // ldigo for reorder
+ mkldnn::memory::dims weights_iter_r_tz = {1, 1, H, ngates, H}; // ldigo for reorder
+
+ auto weight_layer_md = mkldnn::memory::desc(
+ { weights_layer_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
+ auto weight_iter_md = mkldnn::memory::desc(
+ { weights_iter_tz }, mkldnn_dtype, mkldnn::memory::format::ldigo);
+ auto src_layer_md = mkldnn::memory::desc(
+ { src_layer_tz }, mkldnn_dtype, mkldnn::memory::format::tnc);
+ auto dst_layer_md = mkldnn::memory::desc(
+ {dst_layer_tz}, mkldnn_dtype, mkldnn::memory::format::tnc);
+ auto src_iter_md = mkldnn::memory::desc(
+ {src_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
+ auto bias_md = mkldnn::memory::desc({bias_tz},
+ mkldnn_dtype, mkldnn::memory::format::ldgo);
+ auto dst_iter_md = mkldnn::memory::desc(
+ {dst_iter_tz}, mkldnn_dtype, mkldnn::memory::format::ldsnc);
+
+ for (int l = 0; l < L; l++) {
+ if (mode == rnn_enum::kLstm) {
+ std::vector<void*> srcs_data;
+ srcs_data.push_back(hx_ptr);
+ srcs_data.push_back(cx_ptr);
+ auto tmp_src_iter_memory = (*concat_iter_memory)[l + layer_index];
+ ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc,
+ {{1, 1, 1, N, H}, {1, 1, 1, N, H}}, {1, 1, nstates, N, H}, mkldnn_dtype,
+ 2, srcs_data, tmp_src_iter_memory);
+ } else {
+ (*concat_iter_memory)[l + layer_index].set_data_handle(hx_ptr);
+ }
+ hx_ptr += cell_size;
+ if (mode == rnn_enum::kLstm) {
+ cx_ptr += cell_size;
+ }
+ }
+
+ auto user_src_iter_memory = null_memory_;
+ if (L == 1) {
+ user_src_iter_memory = (*concat_iter_memory)[layer_index];
+ } else {
+ user_src_iter_memory = (*concat_iter_memory)[L + layer_index];
+ std::vector<void*> src_l_data;
+ std::vector<mkldnn::memory::dims> src_l_dim;
+ for (int l = 0; l < L; l++) {
+ src_l_data.push_back(reinterpret_cast<DType *>
+ ((*concat_iter_memory)[l + layer_index].get_data_handle()));
+ src_l_dim.push_back({1, 1, nstates, N, H});
+ }
+ ConcatData(mkldnn::memory::format::ldsnc, mkldnn::memory::format::ldsnc, src_l_dim,
+ {L, 1, nstates, N, H}, mkldnn_dtype, 0, src_l_data, user_src_iter_memory);
+ }
+ (*hcx_memory)[layer_index].set_data_handle(user_src_iter_memory.get_data_handle());
+
+ auto src_wx_f = (*concat_weight_memory)[2 * layer_index];
+ auto src_wh_f = (*concat_weight_memory)[2 * layer_index + 1];
+
+ std::vector<void*> srcs_data_x;
+ std::vector<void*> srcs_data_h;
+ std::vector<mkldnn::memory::dims> src_l_dim_x;
+ std::vector<mkldnn::memory::dims> src_l_dim_h;
+ if (!cached) {
+ if (L == 1) {
+ DType* wx = w_ptr;
+ DType* wh = w_ptr + I * H * ngates;
+ if (mode == rnn_enum::kGru) {
+ AdjustGruWeightGateOrder(wx, I, H);
+ AdjustGruWeightGateOrder(wh, H, H);
+ }
+ src_wx_f.set_data_handle(wx);
+ src_wh_f.set_data_handle(wh);
+ } else {
+ for (int l = 0; l < L; l++) {
+ DType* wx = w_ptr;
+ DType* wh = w_ptr + I * H * ngates;
+ DType* bx = b_ptr + l * ngates * H * 2;
+ DType* bh = b_ptr + l * ngates * H * 2 + H * ngates;
+ if (mode == rnn_enum::kGru) {
+ AdjustGruWeightGateOrder(wx, I, H);
+ AdjustGruWeightGateOrder(wh, H, H);
+ AdjustGruBiasGateOrder(bx, H);
+ AdjustGruBiasGateOrder(bh, H);
+ }
+ srcs_data_x.push_back(wx);
+ srcs_data_h.push_back(wh);
+ src_l_dim_x.push_back(weights_layer_r_tz);
+ src_l_dim_h.push_back(weights_iter_r_tz);
+ w_ptr = w_ptr + w_size;
+ }
+ ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
+ src_l_dim_x, weights_layer_tz, mkldnn_dtype, 0, srcs_data_x, src_wx_f);
+ ConcatData(mkldnn::memory::format::ldgoi, mkldnn::memory::format::ldgoi,
+ src_l_dim_h, weights_iter_tz, mkldnn_dtype, 0, srcs_data_h, src_wh_f);
+ }
+ MKLDNNStream::Get()->RegisterPrim(reorder(src_wx_f, (*wx_memory)[layer_index]));
+ MKLDNNStream::Get()->RegisterPrim(reorder(src_wh_f, (*wh_memory)[layer_index]));
+
+ DType* user_bias_f = reinterpret_cast<DType *> ((*bias_memory)[layer_index].get_data_handle());
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int j = 0; j < L * single_b_size; j++) {
+ int k = j / single_b_size;
+ user_bias_f[j] = b_ptr[j + k * single_b_size] + b_ptr[j + k * single_b_size + single_b_size];
+ }
+ }
+
+ rnn_cell::desc rnn_cell(nalgorithm,
+ mode == rnn_enum::kRnnRelu ? algorithm::eltwise_relu : algorithm::eltwise_tanh);
+
+ rnn_forward::desc layer_desc(prop_kind::forward_inference, rnn_cell,
+ rnn_direction::unidirectional, src_layer_md,
+ src_iter_md, weight_layer_md, weight_iter_md,
+ bias_md, dst_layer_md, dst_iter_md);
+
+ auto prim_desc
+ = rnn_forward::primitive_desc(layer_desc, cpu_engine);
+
+ if (x_ptr && layer_index == 0) {
+ (*x_memory)[layer_index].set_data_handle(x_ptr);
+ } else {
+ (*x_memory)[layer_index].set_data_handle((*user_src_layer_memory).get_data_handle());
+ }
+ (*y_memory)[layer_index].set_data_handle(y_ptr);
+
+ if (rnn_forward_prim->size() <= (size_t)layer_index) {
+ primitive rnn_prim = rnn_forward(prim_desc, (*x_memory)[layer_index],
+ (*hcx_memory)[layer_index], (*wx_memory)[layer_index],
+ (*wh_memory)[layer_index], (*bias_memory)[layer_index],
+ (*y_memory)[layer_index],
+ (*hcy_memory)[layer_index], null_memory_);
+ rnn_forward_prim->push_back(rnn_prim);
+ }
+ MKLDNNStream::Get()->RegisterPrim((*rnn_forward_prim)[layer_index]);
+ MKLDNNStream::Get()->Submit();
+
+ if (state_outputs) {
+ DType* dst_hcy = reinterpret_cast<DType *> ((*hcy_memory)[layer_index].get_data_handle());
+ if (mode == rnn_enum::kLstm) {
+ for (int l = 0; l < L; l++) {
+ offset1 = l * single_cell_size;
+ offset2 = l * nstates * single_cell_size;
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int n = 0; n < single_cell_size; n++) {
+ hy_ptr[offset1 + n] = dst_hcy[offset2 + n];
+ cy_ptr[offset1 + n] = dst_hcy[offset2 + n + single_cell_size];
+ }
+ }
+ } else {
+ #pragma omp parallel for num_threads(omp_threads)
+ for (int n = 0; n < L * single_cell_size; n++) {
+ hy_ptr[n] = dst_hcy[n];
+ }
+ }
+ }
+}
+
+template <typename DType>
+void MKLDNNRNNForward(bool state_outputs,
+ const int L,
+ const int D,
+ const int T,
+ const int N,
+ const int I,
+ const int H,
+ DType* x_ptr,
+ 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,
+ bool *has_cache,
+ int dtype,
+ bool is_train,
+ int mode) {
+ int ngates = 0, nstates = 0;
+ GetMKLDNNRNNAlgo(mode, &ngates, &nstates);
+ const int b_size = 2 * H * ngates * D;
+ const int cell_size = N * H * D;
+ // First layer
+ int w_size = (I + H) * H * ngates * D;
+ auto cpu_engine = CpuEngine::Get()->get_engine();
+ auto null_memory_ = null_memory(cpu_engine);
+ DType* tmpNull = NULL;
+ // when D = 1 and I == H, L layers can be fused together
+ if (D == 1 && I == H) {
+ MKLDNNRNNForwardUnidi(state_outputs, L, T, N, I, H, x_ptr, &null_memory_,
Review comment:
Too many sub-function for different cases and I'm afraid it's really error-prone.
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