oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning

Annotated version: LSTM RNN Primitive Example

* Copyright 2020 Intel Corporation
* 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
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* See the License for the specific language governing permissions and
* limitations under the License.
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using namespace dnnl;
void lstm_example(dnnl::engine::kind engine_kind) {
// Create execution dnnl::engine.
dnnl::engine engine(engine_kind, 0);
// Create dnnl::stream.
dnnl::stream engine_stream(engine);
// Tensor dimensions.
const memory::dim N = 26, // batch size
T = 6, // time steps
C = 12, // channels
G = 4, // gates
L = 4, // layers
D = 1; // directions
// Source (src), weights, bias, and destination (dst) tensors
// dimensions.
memory::dims src_dims = {T, N, C};
memory::dims weights_dims = {L, D, C, G, C};
memory::dims bias_dims = {L, D, G, C};
memory::dims dst_dims = {T, N, C};
// Allocate buffers.
std::vector<float> src_layer_data(product(src_dims));
std::vector<float> weights_layer_data(product(weights_dims));
std::vector<float> weights_iter_data(product(weights_dims));
std::vector<float> dst_layer_data(product(dst_dims));
std::vector<float> bias_data(product(bias_dims));
// Initialize src, weights, and bias tensors.
std::generate(src_layer_data.begin(), src_layer_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
std::generate(weights_layer_data.begin(), weights_layer_data.end(), []() {
static int i = 0;
return std::sin(i++ * 2.f);
std::generate(bias_data.begin(), bias_data.end(), []() {
static int i = 0;
return std::tanh(i++);
// Create memory descriptors and memory objects for src, bias, and dst.
auto src_layer_md = memory::desc(src_dims, dt::f32, tag::tnc);
auto bias_md = memory::desc(bias_dims, dt::f32, tag::ldgo);
auto dst_layer_md = memory::desc(dst_dims, dt::f32, tag::tnc);
auto src_layer_mem = memory(src_layer_md, engine);
auto bias_mem = memory(bias_md, engine);
auto dst_layer_mem = memory(dst_layer_md, engine);
// Create memory objects for weights using user's memory layout. In this
// example, LDIGO is assumed.
auto user_weights_layer_mem
= memory({weights_dims, dt::f32, tag::ldigo}, engine);
auto user_weights_iter_mem
= memory({weights_dims, dt::f32, tag::ldigo}, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(, src_layer_mem);
write_to_dnnl_memory(, bias_mem);
write_to_dnnl_memory(, user_weights_layer_mem);
write_to_dnnl_memory(, user_weights_iter_mem);
// Create memory descriptors for weights with format_tag::any. This enables
// the LSTM primitive to choose the optimized memory layout.
auto lstm_weights_layer_md = memory::desc(weights_dims, dt::f32, tag::any);
auto lstm_weights_iter_md = memory::desc(weights_dims, dt::f32, tag::any);
// Optional memory descriptors for recurrent data.
auto src_iter_md = memory::desc();
auto src_iter_c_md = memory::desc();
auto dst_iter_md = memory::desc();
auto dst_iter_c_md = memory::desc();
// Create operation descriptor.
rnn_direction::unidirectional_left2right, src_layer_md, src_iter_md,
src_iter_c_md, lstm_weights_layer_md, lstm_weights_iter_md, bias_md,
dst_layer_md, dst_iter_md, dst_iter_c_md);
// Create primitive descriptor.
auto lstm_pd = lstm_forward::primitive_desc(lstm_desc, engine);
// For now, assume that the weights memory layout generated by the primitive
// and the ones provided by the user are identical.
auto lstm_weights_layer_mem = user_weights_layer_mem;
auto lstm_weights_iter_mem = user_weights_iter_mem;
// Reorder the data in case the weights memory layout generated by the
// primitive and the one provided by the user are different. In this case,
// we create additional memory objects with internal buffers that will
// contain the reordered data.
if (lstm_pd.weights_desc() != user_weights_layer_mem.get_desc()) {
lstm_weights_layer_mem = memory(lstm_pd.weights_desc(), engine);
reorder(user_weights_layer_mem, lstm_weights_layer_mem)
.execute(engine_stream, user_weights_layer_mem,
if (lstm_pd.weights_iter_desc() != user_weights_iter_mem.get_desc()) {
lstm_weights_iter_mem = memory(lstm_pd.weights_iter_desc(), engine);
reorder(user_weights_iter_mem, lstm_weights_iter_mem)
.execute(engine_stream, user_weights_iter_mem,
// Create the memory objects from the primitive descriptor. A workspace is
// also required for LSTM.
// NOTE: Here, the workspace is required for later usage in backward
// propagation mode.
auto src_iter_mem = memory(lstm_pd.src_iter_desc(), engine);
auto src_iter_c_mem = memory(lstm_pd.src_iter_c_desc(), engine);
auto weights_iter_mem = memory(lstm_pd.weights_iter_desc(), engine);
auto dst_iter_mem = memory(lstm_pd.dst_iter_desc(), engine);
auto dst_iter_c_mem = memory(lstm_pd.dst_iter_c_desc(), engine);
auto workspace_mem = memory(lstm_pd.workspace_desc(), engine);
// Create the primitive.
auto lstm_prim = lstm_forward(lstm_pd);
// Primitive arguments
std::unordered_map<int, memory> lstm_args;
lstm_args.insert({DNNL_ARG_SRC_LAYER, src_layer_mem});
lstm_args.insert({DNNL_ARG_WEIGHTS_LAYER, lstm_weights_layer_mem});
lstm_args.insert({DNNL_ARG_WEIGHTS_ITER, lstm_weights_iter_mem});
lstm_args.insert({DNNL_ARG_BIAS, bias_mem});
lstm_args.insert({DNNL_ARG_DST_LAYER, dst_layer_mem});
lstm_args.insert({DNNL_ARG_SRC_ITER, src_iter_mem});
lstm_args.insert({DNNL_ARG_SRC_ITER_C, src_iter_c_mem});
lstm_args.insert({DNNL_ARG_DST_ITER, dst_iter_mem});
lstm_args.insert({DNNL_ARG_DST_ITER_C, dst_iter_c_mem});
lstm_args.insert({DNNL_ARG_WORKSPACE, workspace_mem});
// Primitive execution: LSTM.
lstm_prim.execute(engine_stream, lstm_args);
// Wait for the computation to finalize.
// Read data from memory object's handle.
read_from_dnnl_memory(, dst_layer_mem);
int main(int argc, char **argv) {
return handle_example_errors(lstm_example, parse_engine_kind(argc, argv));