Developer Guide and Reference

  • 2022.1
  • 04/11/2022
  • Public Content

AUGRU RNN Primitive Example

This C++ API example demonstrates how to create and execute an AUGRU RNN primitive in forward training propagation mode.
Key optimizations included in this example:
  • Creation of optimized memory format from the primitive descriptor.
/******************************************************************************* * Copyright 2022 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, * 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. *******************************************************************************/ #include <algorithm> #include <cmath> #include <iostream> #include <string> #include <vector> #include "example_utils.hpp" #include "oneapi/dnnl/dnnl.hpp" using namespace dnnl; using tag = memory::format_tag; using dt = memory::data_type; void augru_example(dnnl::engine::kind engine_kind) { if (engine_kind == engine::kind::gpu) throw example_allows_unimplemented { "No AUGRU implementation is available for GPU.\n"}; // 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 = 3, // gates L = 1, // layers D = 1; // directions // Source (src), weights, bias, attention, and destination (dst) tensors // dimensions. memory::dims src_dims = {T, N, C}; memory::dims attention_dims = {T, N, 1}; 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> attention_data(product(attention_dims)); std::vector<float> weights_layer_data(product(weights_dims)); std::vector<float> weights_iter_data(product(weights_dims)); std::vector<float> bias_data(product(bias_dims)); std::vector<float> dst_layer_data(product(dst_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(attention_data.begin(), attention_data.end(), []() { static int i = 0; return std::sin(i++ * 2.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(float(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 attention_md = memory::desc(attention_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 attention_mem = memory(attention_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(, attention_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 AUGRU primitive to choose the optimized memory layout. auto augru_weights_layer_md = memory::desc(weights_dims, dt::f32, tag::any); auto augru_weights_iter_md = memory::desc(weights_dims, dt::f32, tag::any); // Optional memory descriptors for recurrent data. auto src_iter_md = memory::desc(); auto dst_iter_md = memory::desc(); // Create operation descriptor. auto augru_desc = augru_forward::desc(prop_kind::forward_training, rnn_direction::unidirectional_left2right, src_layer_md, src_iter_md, attention_md, augru_weights_layer_md, augru_weights_iter_md, bias_md, dst_layer_md, dst_iter_md); // Create primitive descriptor. auto augru_pd = augru_forward::primitive_desc(augru_desc, engine); // For now, assume that the weights memory layout generated by the primitive // and the ones provided by the user are identical. auto augru_weights_layer_mem = user_weights_layer_mem; auto augru_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 (augru_pd.weights_desc() != user_weights_layer_mem.get_desc()) { augru_weights_layer_mem = memory(augru_pd.weights_desc(), engine); reorder(user_weights_layer_mem, augru_weights_layer_mem) .execute(engine_stream, user_weights_layer_mem, augru_weights_layer_mem); } if (augru_pd.weights_iter_desc() != user_weights_iter_mem.get_desc()) { augru_weights_iter_mem = memory(augru_pd.weights_iter_desc(), engine); reorder(user_weights_iter_mem, augru_weights_iter_mem) .execute(engine_stream, user_weights_iter_mem, augru_weights_iter_mem); } // Create the memory objects from the primitive descriptor. A workspace is // also required for AUGRU. // NOTE: Here, the workspace is required for later usage in backward // propagation mode. auto src_iter_mem = memory(augru_pd.src_iter_desc(), engine); auto weights_iter_mem = memory(augru_pd.weights_iter_desc(), engine); auto dst_iter_mem = memory(augru_pd.dst_iter_desc(), engine); auto workspace_mem = memory(augru_pd.workspace_desc(), engine); // Create the primitive. auto augru_prim = augru_forward(augru_pd); // Primitive arguments std::unordered_map<int, memory> augru_args; augru_args.insert({DNNL_ARG_SRC_LAYER, src_layer_mem}); augru_args.insert({DNNL_ARG_AUGRU_ATTENTION, attention_mem}); augru_args.insert({DNNL_ARG_WEIGHTS_LAYER, augru_weights_layer_mem}); augru_args.insert({DNNL_ARG_WEIGHTS_ITER, augru_weights_iter_mem}); augru_args.insert({DNNL_ARG_BIAS, bias_mem}); augru_args.insert({DNNL_ARG_DST_LAYER, dst_layer_mem}); augru_args.insert({DNNL_ARG_SRC_ITER, src_iter_mem}); augru_args.insert({DNNL_ARG_DST_ITER, dst_iter_mem}); augru_args.insert({DNNL_ARG_WORKSPACE, workspace_mem}); // Primitive execution: AUGRU. augru_prim.execute(engine_stream, augru_args); // Wait for the computation to finalize. engine_stream.wait(); // Read data from memory object's handle. read_from_dnnl_memory(, dst_layer_mem); } int main(int argc, char **argv) { return handle_example_errors(augru_example, parse_engine_kind(argc, argv)); }

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