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https://github.com/uxlfoundation/oneDNN.git
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207 lines
8.4 KiB
C++
207 lines
8.4 KiB
C++
/*******************************************************************************
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* Copyright 2024-2025 Intel Corporation
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*******************************************************************************/
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/// @example lbr_gru.cpp
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/// > Annotated version: @ref lbr_gru_example_cpp
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/// @page lbr_gru_example_cpp_brief
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/// @brief This C++ API example demonstrates how to create and execute a
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/// [Linear-Before-Reset GRU RNN](@ref dev_guide_rnn) primitive in forward
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/// training propagation mode.
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/// @page lbr_gru_example_cpp Linear-Before-Reset GRU RNN Primitive Example
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/// \copybrief lbr_gru_example_cpp_brief
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///
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/// Key optimizations included in this example:
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/// - Creation of optimized memory format from the primitive descriptor.
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///
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/// @include lbr_gru.cpp
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#include <algorithm>
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#include <cmath>
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#include <iostream>
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#include <string>
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#include <vector>
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#include "dnnl.hpp"
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#include "example_utils.hpp"
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using namespace dnnl;
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void lbr_gru_example(dnnl::engine::kind engine_kind) {
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// Create execution dnnl::engine.
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dnnl::engine engine(engine_kind, 0);
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// Create dnnl::stream.
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dnnl::stream engine_stream(engine);
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// Tensor dimensions.
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const memory::dim N = 2, // batch size
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T = 3, // time steps
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IC = 2, // src channels
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OC = 3, // dst channels
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G = 3, // gates
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L = 1, // layers
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D = 1, // directions
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E = 1; // extra Bias number. Extra Bias for u' gate
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// Source (src), weights, bias, attention, and destination (dst) tensors
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// dimensions.
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memory::dims src_dims = {T, N, IC};
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memory::dims weights_layer_dims = {L, D, IC, G, OC};
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memory::dims weights_iter_dims = {L, D, OC, G, OC};
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memory::dims bias_dims = {L, D, G + E, OC};
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memory::dims dst_layer_dims = {T, N, OC};
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memory::dims dst_iter_dims = {L, D, N, OC};
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// Allocate buffers.
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std::vector<float> src_layer_data(product(src_dims));
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std::vector<float> weights_layer_data(product(weights_layer_dims));
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std::vector<float> weights_iter_data(product(weights_iter_dims));
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std::vector<float> bias_data(product(bias_dims));
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std::vector<float> dst_layer_data(product(dst_layer_dims));
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std::vector<float> dst_iter_data(product(dst_iter_dims));
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// Initialize src, weights, and bias tensors.
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std::generate(src_layer_data.begin(), src_layer_data.end(), []() {
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static int i = 0;
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return std::cos(i++ / 10.f);
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});
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std::generate(weights_layer_data.begin(), weights_layer_data.end(), []() {
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static int i = 0;
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return std::sin(i++ * 2.f);
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});
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std::generate(weights_iter_data.begin(), weights_iter_data.end(), []() {
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static int i = 0;
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return std::sin(i++ * 2.f);
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});
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std::generate(bias_data.begin(), bias_data.end(), []() {
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static int i = 0;
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return std::tanh(float(i++));
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});
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// Create memory descriptors and memory objects for src, bias, and dst.
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auto src_layer_md = memory::desc(
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src_dims, memory::data_type::f32, memory::format_tag::tnc);
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auto bias_md = memory::desc(
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bias_dims, memory::data_type::f32, memory::format_tag::ldgo);
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auto dst_layer_md = memory::desc(
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dst_layer_dims, memory::data_type::f32, memory::format_tag::tnc);
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auto src_layer_mem = memory(src_layer_md, engine);
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auto bias_mem = memory(bias_md, engine);
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auto dst_layer_mem = memory(dst_layer_md, engine);
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// Create memory objects for weights using user's memory layout. In this
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// example, LDIGO (num_layers, num_directions, input_channels, num_gates,
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// output_channels) is assumed.
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auto user_weights_layer_mem
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= memory({weights_layer_dims, memory::data_type::f32,
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memory::format_tag::ldigo},
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engine);
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auto user_weights_iter_mem
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= memory({weights_iter_dims, memory::data_type::f32,
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memory::format_tag::ldigo},
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engine);
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// Write data to memory object's handle.
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// For GRU cells, the gates order is update, reset and output
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// gate except the bias. For the bias tensor, the gates order is
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// u, r, o and u' gate.
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write_to_dnnl_memory(src_layer_data.data(), src_layer_mem);
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write_to_dnnl_memory(bias_data.data(), bias_mem);
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write_to_dnnl_memory(weights_layer_data.data(), user_weights_layer_mem);
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write_to_dnnl_memory(weights_iter_data.data(), user_weights_iter_mem);
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// Create memory descriptors for weights with format_tag::any. This enables
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// the lbr_gru primitive to choose the optimized memory layout.
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auto weights_layer_md = memory::desc(weights_layer_dims,
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memory::data_type::f32, memory::format_tag::any);
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auto weights_iter_md = memory::desc(
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weights_iter_dims, memory::data_type::f32, memory::format_tag::any);
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// Optional memory descriptors for recurrent data.
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// Default memory descriptor for initial hidden states of the GRU cells
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auto src_iter_md = memory::desc();
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auto dst_iter_md = memory::desc();
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// Create primitive descriptor.
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auto lbr_gru_pd = lbr_gru_forward::primitive_desc(engine,
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prop_kind::forward_training,
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rnn_direction::unidirectional_left2right, src_layer_md, src_iter_md,
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weights_layer_md, weights_iter_md, bias_md, dst_layer_md,
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dst_iter_md);
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// For now, assume that the weights memory layout generated by the primitive
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// and the ones provided by the user are identical.
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auto weights_layer_mem = user_weights_layer_mem;
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auto weights_iter_mem = user_weights_iter_mem;
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// Reorder the data in case the weights memory layout generated by the
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// primitive and the one provided by the user are different. In this case,
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// we create additional memory objects with internal buffers that will
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// contain the reordered data.
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if (lbr_gru_pd.weights_desc() != user_weights_layer_mem.get_desc()) {
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weights_layer_mem = memory(lbr_gru_pd.weights_desc(), engine);
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reorder(user_weights_layer_mem, weights_layer_mem)
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.execute(engine_stream, user_weights_layer_mem,
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weights_layer_mem);
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}
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if (lbr_gru_pd.weights_iter_desc() != user_weights_iter_mem.get_desc()) {
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weights_iter_mem = memory(lbr_gru_pd.weights_iter_desc(), engine);
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reorder(user_weights_iter_mem, weights_iter_mem)
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.execute(
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engine_stream, user_weights_iter_mem, weights_iter_mem);
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}
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// Create the memory objects from the primitive descriptor. A workspace is
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// also required for Linear-Before-Reset GRU RNN.
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// NOTE: Here, the workspace is required for later usage in backward
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// propagation mode.
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auto src_iter_mem = memory(lbr_gru_pd.src_iter_desc(), engine);
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auto dst_iter_mem = memory(lbr_gru_pd.dst_iter_desc(), engine);
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auto workspace_mem = memory(lbr_gru_pd.workspace_desc(), engine);
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// Create the primitive.
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auto lbr_gru_prim = lbr_gru_forward(lbr_gru_pd);
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// Primitive arguments
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std::unordered_map<int, memory> lbr_gru_args;
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lbr_gru_args.insert({DNNL_ARG_SRC_LAYER, src_layer_mem});
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lbr_gru_args.insert({DNNL_ARG_WEIGHTS_LAYER, weights_layer_mem});
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lbr_gru_args.insert({DNNL_ARG_WEIGHTS_ITER, weights_iter_mem});
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lbr_gru_args.insert({DNNL_ARG_BIAS, bias_mem});
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lbr_gru_args.insert({DNNL_ARG_DST_LAYER, dst_layer_mem});
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lbr_gru_args.insert({DNNL_ARG_SRC_ITER, src_iter_mem});
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lbr_gru_args.insert({DNNL_ARG_DST_ITER, dst_iter_mem});
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lbr_gru_args.insert({DNNL_ARG_WORKSPACE, workspace_mem});
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// Primitive execution: lbr_gru.
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lbr_gru_prim.execute(engine_stream, lbr_gru_args);
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// Wait for the computation to finalize.
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engine_stream.wait();
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// Read data from memory object's handle.
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read_from_dnnl_memory(dst_layer_data.data(), dst_layer_mem);
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}
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int main(int argc, char **argv) {
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return handle_example_errors(
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lbr_gru_example, parse_engine_kind(argc, argv));
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}
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