644 lines
29 KiB
C++
644 lines
29 KiB
C++
/*******************************************************************************
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* Copyright 2018 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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#include <assert.h>
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#include <cstring>
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#include <iostream>
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#include <math.h>
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#include <numeric>
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#include <string>
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#include "mkldnn.hpp"
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// MSVC doesn't support collapse clause in omp parallel
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#if defined(_MSC_VER) && !defined(__clang__) && !defined(__INTEL_COMPILER)
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#define collapse(x)
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#endif
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using namespace mkldnn;
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using dim_t = mkldnn::memory::dim;
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const dim_t batch = 128;
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const dim_t src_seq_length_max = 28;
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const dim_t tgt_seq_length_max = 28;
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const dim_t feature_size = 1024;
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const dim_t enc_bidir_n_layers = 1;
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const dim_t enc_unidir_n_layers = 7;
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const dim_t dec_n_layers = 8;
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const int lstm_n_gates = 4;
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const int lstm_n_states = 2;
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std::vector<float> weighted_src_layer(batch *feature_size, 1.0f);
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std::vector<float> alignment_model(
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src_seq_length_max *batch *feature_size, 1.0f);
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std::vector<float> alignments(src_seq_length_max *batch, 1.0f);
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std::vector<float> exp_sums(batch, 1.0f);
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const float onef = 1.0, zerof = 0.0;
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const dim_t onei = 1;
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void compute_weighted_annotations(float *weighted_annotations,
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dim_t src_seq_length_max, dim_t batch, dim_t feature_size,
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float *weights_annot, float *annotations) {
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// annotations(aka enc_dst_layer) is (t, n, 2c)
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// weights_annot is (2c, c)
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// annotation[i] = GEMM(weights_annot, enc_dst_layer[i]);
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dim_t num_weighted_annotations = src_seq_length_max * batch;
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mkldnn_sgemm("N", "N", &feature_size, &num_weighted_annotations,
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&feature_size, &onef, weights_annot, &feature_size, annotations,
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&feature_size, &zerof, weighted_annotations, &feature_size);
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}
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void compute_attention(float *context_vectors, dim_t src_seq_length_max,
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dim_t batch, dim_t feature_size, float *weights_src_layer,
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float *dec_src_layer, float *annotations, float *weighted_annotations,
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float *weights_alignments) {
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// dst_iter : (n, c) matrix
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// src_layer: (n, c) matrix
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// weighted_annotations (t, n, c)
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// weights_yi is (c, c)
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// weights_ai is (c, 1)
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// tmp[i] is (n, c)
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// a[i] is (n, 1)
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// p is (n, 1)
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// first we precompute the weighted_dec_src_layer
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mkldnn_sgemm("N", "N", &feature_size, &batch, &feature_size, &onef,
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weights_src_layer, &feature_size, dec_src_layer, &feature_size,
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&zerof, weighted_src_layer.data(), &feature_size);
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// then we compute the alignment model
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float *alignment_model_ptr = alignment_model.data();
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#ifdef _OPENMP
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#pragma omp parallel for collapse(2)
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#endif
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for (dim_t i = 0; i < src_seq_length_max; i++) {
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for (dim_t j = 0; j < batch * feature_size; j++)
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alignment_model_ptr[i * batch * feature_size + j] = tanhf(
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weighted_src_layer.data()[j]
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+ weighted_annotations[i * batch * feature_size + j]);
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}
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// gemv with alignments weights. the resulting alignments are in alignments
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dim_t num_weighted_annotations = src_seq_length_max * batch;
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mkldnn_sgemm("N", "N", &onei, &num_weighted_annotations, &feature_size,
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&onef, weights_alignments, &onei, alignment_model_ptr,
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&feature_size, &zerof, alignments.data(), &onei);
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// softmax on alignments. the resulting context weights are in alignments
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#ifdef _OPENMP
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#pragma omp parallel for
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#endif
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for (dim_t i = 0; i < batch; i++)
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exp_sums[i] = 0.0f;
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#ifdef _OPENMP
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#pragma omp parallel for collapse(2)
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#endif
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for (dim_t i = 0; i < src_seq_length_max; i++) {
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for (dim_t j = 0; j < batch; j++) {
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alignments[i * batch + j] = expf(alignments[i * batch + j]);
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exp_sums[j] += alignments[i * batch + j];
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}
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}
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#ifdef _OPENMP
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#pragma omp parallel for collapse(2)
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#endif
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for (dim_t i = 0; i < src_seq_length_max; i++)
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for (dim_t j = 0; j < batch; j++)
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alignments[i * batch + j] /= exp_sums[j];
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// then we compute the context vectors
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#ifdef _OPENMP
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#pragma omp parallel for collapse(2)
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#endif
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for (dim_t i = 0; i < batch; i++)
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for (dim_t j = 0; j < feature_size; j++)
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context_vectors[i * (feature_size + feature_size) + feature_size
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+ j]
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= 0.0f;
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#ifdef _OPENMP
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#pragma omp parallel for collapse(3)
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#endif
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for (dim_t i = 0; i < batch; i++)
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for (dim_t k = 0; k < src_seq_length_max; k++)
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for (dim_t j = 0; j < feature_size; j++)
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context_vectors[i * (feature_size + feature_size) + feature_size
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+ j]
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+= alignments[k * batch + i]
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* annotations[j + feature_size * (i + batch * k)];
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}
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void copy_context(float *src_iter, dim_t n_layers, dim_t n_states, dim_t batch,
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dim_t feature_size) {
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// we copy the context from the first layer to all other layers
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#ifdef _OPENMP
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#pragma omp parallel for collapse(3)
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#endif
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for (dim_t k = 1; k < n_layers; k++)
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for (dim_t j = 0; j < batch; j++)
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for (dim_t i = 0; i < feature_size; i++)
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src_iter[(k * n_states * batch + j)
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* (feature_size + feature_size)
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+ i]
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= src_iter[j * (feature_size + feature_size) + i];
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}
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void simple_net() {
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auto cpu_engine = engine(engine::cpu, 0);
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stream s(cpu_engine);
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/*
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GNMT Example.
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Note, we do not implement connection yet.
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For the encoder we use:
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- one primitive for the bidirectional layer of the encoder
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- one primitive for all remaining unidirectional layers in the encoder
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For the decoder we use:
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- one primitive for the first iteration
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- one primitive for all subsequent iterations in the decoder. Note that
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in this example, this primitive computes the states in place.
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- the attention mechanism is implemented separately as there is no support
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for the context vectors in MKL-DNN yet
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*/
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std::vector<primitive> encoder_net, decoder_net;
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std::vector<std::unordered_map<int, memory>> encoder_net_args,
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decoder_net_args;
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std::vector<float> net_src(batch * src_seq_length_max * feature_size, 1.0f);
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std::vector<float> net_dst(batch * tgt_seq_length_max * feature_size, 1.0f);
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/* Encoder */
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memory::dims enc_bidir_src_layer_tz
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= { src_seq_length_max, batch, feature_size };
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memory::dims enc_bidir_weights_layer_tz = { enc_bidir_n_layers, 2,
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feature_size, lstm_n_gates, feature_size };
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memory::dims enc_bidir_weights_iter_tz = { enc_bidir_n_layers, 2,
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feature_size, lstm_n_gates, feature_size };
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memory::dims enc_bidir_bias_tz
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= { enc_bidir_n_layers, 2, lstm_n_gates, feature_size };
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memory::dims enc_bidir_dst_layer_tz
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= { src_seq_length_max, batch, 2 * feature_size };
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/* GNMT encoder: 1 bidirectional layer and 7 unidirectional layers */
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std::vector<float> user_enc_bidir_wei_layer(
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enc_bidir_n_layers * 2 * feature_size * lstm_n_gates * feature_size,
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1.0f);
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std::vector<float> user_enc_bidir_wei_iter(
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enc_bidir_n_layers * 2 * feature_size * lstm_n_gates * feature_size,
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1.0f);
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std::vector<float> user_enc_bidir_bias(
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enc_bidir_n_layers * 2 * lstm_n_gates * feature_size, 1.0f);
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/* Create the memory for user data */
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auto user_enc_bidir_src_layer_md = mkldnn::memory::desc(
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{ enc_bidir_src_layer_tz }, mkldnn::memory::data_type::f32,
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mkldnn::memory::format_tag::tnc);
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auto user_enc_bidir_wei_layer_md = mkldnn::memory::desc(
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{ enc_bidir_weights_layer_tz }, mkldnn::memory::data_type::f32,
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mkldnn::memory::format_tag::ldigo);
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auto user_enc_bidir_wei_iter_md = mkldnn::memory::desc(
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{ enc_bidir_weights_iter_tz }, mkldnn::memory::data_type::f32,
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mkldnn::memory::format_tag::ldigo);
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auto user_enc_bidir_bias_md = mkldnn::memory::desc({ enc_bidir_bias_tz },
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mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldgo);
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auto user_enc_bidir_src_layer_memory = mkldnn::memory(
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user_enc_bidir_src_layer_md, cpu_engine, net_src.data());
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auto user_enc_bidir_wei_layer_memory
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= mkldnn::memory(user_enc_bidir_wei_layer_md, cpu_engine,
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user_enc_bidir_wei_layer.data());
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auto user_enc_bidir_wei_iter_memory
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= mkldnn::memory(user_enc_bidir_wei_iter_md, cpu_engine,
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user_enc_bidir_wei_iter.data());
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auto user_enc_bidir_bias_memory = mkldnn::memory(
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user_enc_bidir_bias_md, cpu_engine, user_enc_bidir_bias.data());
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/* Create memory descriptors for RNN data w/o specified layout */
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auto enc_bidir_wei_layer_md = memory::desc({ enc_bidir_weights_layer_tz },
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memory::data_type::f32, memory::format_tag::any);
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auto enc_bidir_wei_iter_md = memory::desc({ enc_bidir_weights_iter_tz },
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memory::data_type::f32, memory::format_tag::any);
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auto enc_bidir_dst_layer_md = memory::desc({ enc_bidir_dst_layer_tz },
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memory::data_type::f32, memory::format_tag::any);
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/* Create bidirectional RNN */
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rnn_cell::desc bi_cell(algorithm::vanilla_lstm);
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rnn_forward::desc bi_layer_desc(prop_kind::forward_inference, bi_cell,
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rnn_direction::bidirectional_concat, user_enc_bidir_src_layer_md,
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memory::desc(), enc_bidir_wei_layer_md, enc_bidir_wei_iter_md,
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user_enc_bidir_bias_md, enc_bidir_dst_layer_md, memory::desc());
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auto enc_bidir_prim_desc
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= mkldnn::rnn_forward::primitive_desc(bi_layer_desc, cpu_engine);
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/* Create memory for input data and use reorders to reorder user data
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* to internal representation */
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auto enc_bidir_wei_layer_memory
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= memory(enc_bidir_prim_desc.weights_layer_desc(), cpu_engine);
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auto enc_bidir_wei_layer_reorder_pd = reorder::primitive_desc(
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user_enc_bidir_wei_layer_memory, enc_bidir_wei_layer_memory);
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reorder(enc_bidir_wei_layer_reorder_pd)
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.execute(s, user_enc_bidir_wei_layer_memory,
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enc_bidir_wei_layer_memory);
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auto enc_bidir_wei_iter_memory
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= memory(enc_bidir_prim_desc.weights_iter_desc(), cpu_engine);
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auto enc_bidir_wei_iter_reorder_pd = reorder::primitive_desc(
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user_enc_bidir_wei_iter_memory, enc_bidir_wei_iter_memory);
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reorder(enc_bidir_wei_iter_reorder_pd)
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.execute(s, user_enc_bidir_wei_iter_memory,
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enc_bidir_wei_iter_memory);
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auto enc_bidir_dst_layer_memory
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= mkldnn::memory(enc_bidir_prim_desc.dst_layer_desc(), cpu_engine);
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encoder_net.push_back(rnn_forward(enc_bidir_prim_desc));
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encoder_net_args.push_back(
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{ { MKLDNN_ARG_SRC_LAYER, user_enc_bidir_src_layer_memory },
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{ MKLDNN_ARG_WEIGHTS_LAYER, enc_bidir_wei_layer_memory },
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{ MKLDNN_ARG_WEIGHTS_ITER, enc_bidir_wei_iter_memory },
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{ MKLDNN_ARG_BIAS, user_enc_bidir_bias_memory },
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{ MKLDNN_ARG_DST_LAYER, enc_bidir_dst_layer_memory } });
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/* GNMT encoder: unidirectional layers */
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// First unidirectinal layer scales 2 * feature_size output of bidirectional
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// layer to feature_size output
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std::vector<float> user_enc_uni_first_wei_layer(
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1 * 1 * 2 * feature_size * lstm_n_gates * feature_size, 1.0f);
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std::vector<float> user_enc_uni_first_wei_iter(
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1 * 1 * feature_size * lstm_n_gates * feature_size, 1.0f);
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std::vector<float> user_enc_uni_first_bias(
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1 * 1 * lstm_n_gates * feature_size, 1.0f);
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memory::dims user_enc_uni_first_wei_layer_dims
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= { 1, 1, 2 * feature_size, lstm_n_gates, feature_size };
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memory::dims user_enc_uni_first_wei_iter_dims
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= { 1, 1, feature_size, lstm_n_gates, feature_size };
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memory::dims user_enc_uni_first_bias_dims
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= { 1, 1, lstm_n_gates, feature_size };
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memory::dims enc_uni_first_dst_layer_dims
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= { src_seq_length_max, batch, feature_size };
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auto user_enc_uni_first_wei_layer_md = mkldnn::memory::desc(
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{ user_enc_uni_first_wei_layer_dims },
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mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldigo);
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auto user_enc_uni_first_wei_iter_md = mkldnn::memory::desc(
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{ user_enc_uni_first_wei_iter_dims },
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mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldigo);
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auto user_enc_uni_first_bias_md = mkldnn::memory::desc(
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{ user_enc_uni_first_bias_dims }, mkldnn::memory::data_type::f32,
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mkldnn::memory::format_tag::ldgo);
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auto user_enc_uni_first_wei_layer_memory
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= mkldnn::memory(user_enc_uni_first_wei_layer_md, cpu_engine,
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user_enc_uni_first_wei_layer.data());
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auto user_enc_uni_first_wei_iter_memory
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= mkldnn::memory(user_enc_uni_first_wei_iter_md, cpu_engine,
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user_enc_uni_first_wei_iter.data());
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auto user_enc_uni_first_bias_memory
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= mkldnn::memory(user_enc_uni_first_bias_md, cpu_engine,
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user_enc_uni_first_bias.data());
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auto enc_uni_first_wei_layer_md
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= memory::desc({ user_enc_uni_first_wei_layer_dims },
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memory::data_type::f32, memory::format_tag::any);
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auto enc_uni_first_wei_iter_md
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= memory::desc({ user_enc_uni_first_wei_iter_dims },
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memory::data_type::f32, memory::format_tag::any);
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auto enc_uni_first_dst_layer_md
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= memory::desc({ enc_uni_first_dst_layer_dims },
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memory::data_type::f32, memory::format_tag::any);
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/// @todo add suport for residual connections
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/// should it be a set residual in op_desc or a field to set manually?
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/// should be an integer to specify at which layer to start
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rnn_cell::desc enc_uni_first_cell(algorithm::vanilla_lstm);
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rnn_forward::desc enc_uni_first_layer_desc(prop_kind::forward_inference,
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enc_uni_first_cell, rnn_direction::unidirectional_left2right,
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enc_bidir_dst_layer_md, memory::desc(), enc_uni_first_wei_layer_md,
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enc_uni_first_wei_iter_md, user_enc_uni_first_bias_md,
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enc_uni_first_dst_layer_md, memory::desc());
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auto enc_uni_first_prim_desc = mkldnn::rnn_forward::primitive_desc(
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enc_uni_first_layer_desc, cpu_engine);
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auto enc_uni_first_wei_layer_memory
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= memory(enc_uni_first_prim_desc.weights_layer_desc(), cpu_engine);
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auto enc_uni_first_wei_layer_reorder_pd
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= reorder::primitive_desc(user_enc_uni_first_wei_layer_memory,
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enc_uni_first_wei_layer_memory);
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reorder(enc_uni_first_wei_layer_reorder_pd)
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.execute(s, user_enc_uni_first_wei_layer_memory,
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enc_uni_first_wei_layer_memory);
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auto enc_uni_first_wei_iter_memory
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= memory(enc_uni_first_prim_desc.weights_iter_desc(), cpu_engine);
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auto enc_uni_first_wei_iter_reorder_pd = reorder::primitive_desc(
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user_enc_uni_first_wei_iter_memory, enc_uni_first_wei_iter_memory);
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reorder(enc_uni_first_wei_iter_reorder_pd)
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.execute(s, user_enc_uni_first_wei_iter_memory,
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enc_uni_first_wei_iter_memory);
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auto enc_uni_first_dst_layer_memory = mkldnn::memory(
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enc_uni_first_prim_desc.dst_layer_desc(), cpu_engine);
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/// @todo add a reorder when they will be available
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encoder_net.push_back(rnn_forward(enc_uni_first_prim_desc));
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encoder_net_args.push_back({ { MKLDNN_ARG_SRC_LAYER,
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enc_bidir_dst_layer_memory },
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{ MKLDNN_ARG_WEIGHTS_LAYER, enc_uni_first_wei_layer_memory },
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{ MKLDNN_ARG_WEIGHTS_ITER, enc_uni_first_wei_iter_memory },
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{ MKLDNN_ARG_BIAS, user_enc_uni_first_bias_memory },
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{ MKLDNN_ARG_DST_LAYER, enc_uni_first_dst_layer_memory } });
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/* Remainging unidirectional layers */
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std::vector<float> user_enc_uni_wei_layer((enc_unidir_n_layers - 1) * 1
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* feature_size * lstm_n_gates * feature_size,
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1.0f);
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std::vector<float> user_enc_uni_wei_iter((enc_unidir_n_layers - 1) * 1
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* feature_size * lstm_n_gates * feature_size,
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1.0f);
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std::vector<float> user_enc_uni_bias(
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(enc_unidir_n_layers - 1) * 1 * lstm_n_gates * feature_size, 1.0f);
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memory::dims user_enc_uni_wei_layer_dims = { (enc_unidir_n_layers - 1), 1,
|
|
feature_size, lstm_n_gates, feature_size };
|
|
memory::dims user_enc_uni_wei_iter_dims = { (enc_unidir_n_layers - 1), 1,
|
|
feature_size, lstm_n_gates, feature_size };
|
|
memory::dims user_enc_uni_bias_dims
|
|
= { (enc_unidir_n_layers - 1), 1, lstm_n_gates, feature_size };
|
|
memory::dims enc_dst_layer_dims
|
|
= { src_seq_length_max, batch, feature_size };
|
|
auto user_enc_uni_wei_layer_md = mkldnn::memory::desc(
|
|
{ user_enc_uni_wei_layer_dims }, mkldnn::memory::data_type::f32,
|
|
mkldnn::memory::format_tag::ldigo);
|
|
auto user_enc_uni_wei_iter_md = mkldnn::memory::desc(
|
|
{ user_enc_uni_wei_iter_dims }, mkldnn::memory::data_type::f32,
|
|
mkldnn::memory::format_tag::ldigo);
|
|
auto user_enc_uni_bias_md = mkldnn::memory::desc({ user_enc_uni_bias_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldgo);
|
|
auto user_enc_uni_wei_layer_memory
|
|
= mkldnn::memory(user_enc_uni_wei_layer_md, cpu_engine,
|
|
user_enc_uni_wei_layer.data());
|
|
auto user_enc_uni_wei_iter_memory = mkldnn::memory(
|
|
user_enc_uni_wei_iter_md, cpu_engine, user_enc_uni_wei_iter.data());
|
|
auto user_enc_uni_bias_memory = mkldnn::memory(
|
|
user_enc_uni_bias_md, cpu_engine, user_enc_uni_bias.data());
|
|
|
|
auto enc_uni_wei_layer_md = memory::desc({ user_enc_uni_wei_layer_dims },
|
|
memory::data_type::f32, memory::format_tag::any);
|
|
auto enc_uni_wei_iter_md = memory::desc({ user_enc_uni_wei_iter_dims },
|
|
memory::data_type::f32, memory::format_tag::any);
|
|
auto enc_dst_layer_md = memory::desc({ enc_dst_layer_dims },
|
|
memory::data_type::f32, memory::format_tag::any);
|
|
|
|
/// @todo add suport for residual connections
|
|
/// should it be a set residual in op_desc or a field to set manually?
|
|
/// should be an integer to specify at which layer to start
|
|
rnn_cell::desc enc_uni_cell(algorithm::vanilla_lstm);
|
|
rnn_forward::desc enc_uni_layer_desc(prop_kind::forward_inference,
|
|
enc_uni_cell, rnn_direction::unidirectional_left2right,
|
|
enc_uni_first_dst_layer_md, memory::desc(), enc_uni_wei_layer_md,
|
|
enc_uni_wei_iter_md, user_enc_uni_bias_md, enc_dst_layer_md,
|
|
memory::desc());
|
|
auto enc_uni_prim_desc = mkldnn::rnn_forward::primitive_desc(
|
|
enc_uni_layer_desc, cpu_engine);
|
|
|
|
auto enc_uni_wei_layer_memory
|
|
= memory(enc_uni_prim_desc.weights_layer_desc(), cpu_engine);
|
|
auto enc_uni_wei_layer_reorder_pd = reorder::primitive_desc(
|
|
user_enc_uni_wei_layer_memory, enc_uni_wei_layer_memory);
|
|
reorder(enc_uni_wei_layer_reorder_pd)
|
|
.execute(
|
|
s, user_enc_uni_wei_layer_memory, enc_uni_wei_layer_memory);
|
|
|
|
auto enc_uni_wei_iter_memory
|
|
= memory(enc_uni_prim_desc.weights_iter_desc(), cpu_engine);
|
|
auto enc_uni_wei_iter_reorder_pd = reorder::primitive_desc(
|
|
user_enc_uni_wei_iter_memory, enc_uni_wei_iter_memory);
|
|
reorder(enc_uni_wei_iter_reorder_pd)
|
|
.execute(s, user_enc_uni_wei_iter_memory, enc_uni_wei_iter_memory);
|
|
|
|
auto enc_dst_layer_memory
|
|
= mkldnn::memory(enc_uni_prim_desc.dst_layer_desc(), cpu_engine);
|
|
|
|
/// @todo add a reorder when they will be available
|
|
encoder_net.push_back(rnn_forward(enc_uni_prim_desc));
|
|
encoder_net_args.push_back(
|
|
{ { MKLDNN_ARG_SRC_LAYER, enc_uni_first_dst_layer_memory },
|
|
{ MKLDNN_ARG_WEIGHTS_LAYER, enc_uni_wei_layer_memory },
|
|
{ MKLDNN_ARG_WEIGHTS_ITER, enc_uni_wei_iter_memory },
|
|
{ MKLDNN_ARG_BIAS, user_enc_uni_bias_memory },
|
|
{ MKLDNN_ARG_DST_LAYER, enc_dst_layer_memory } });
|
|
|
|
/* GNMT: decoder with attention mechanism */
|
|
std::vector<float> user_dec_wei_layer(
|
|
dec_n_layers * 1 * feature_size * lstm_n_gates * feature_size,
|
|
1.0f);
|
|
std::vector<float> user_dec_wei_iter(dec_n_layers * 1
|
|
* (feature_size + feature_size) * lstm_n_gates
|
|
* feature_size,
|
|
1.0f);
|
|
std::vector<float> user_dec_bias(
|
|
dec_n_layers * 1 * lstm_n_gates * feature_size, 1.0f);
|
|
std::vector<float> user_dec_dst(
|
|
tgt_seq_length_max * batch * feature_size, 1.0f);
|
|
std::vector<float> user_weights_attention_src_layer(
|
|
feature_size * feature_size, 1.0f);
|
|
std::vector<float> user_weights_annotation(
|
|
feature_size * feature_size, 1.0f);
|
|
std::vector<float> user_weights_alignments(feature_size, 1.0f);
|
|
|
|
memory::dims user_dec_wei_layer_dims
|
|
= { dec_n_layers, 1, feature_size, lstm_n_gates, feature_size };
|
|
memory::dims user_dec_wei_iter_dims = { dec_n_layers, 1,
|
|
feature_size + feature_size, lstm_n_gates, feature_size };
|
|
memory::dims user_dec_bias_dims
|
|
= { dec_n_layers, 1, lstm_n_gates, feature_size };
|
|
|
|
memory::dims dec_src_layer_dims = { 1, batch, feature_size };
|
|
memory::dims dec_dst_layer_dims = { 1, batch, feature_size };
|
|
|
|
// We will use the same memory for dec_src_iter and dec_dst_iter
|
|
// However, dec_src_iter has a context vector but not
|
|
// dec_dst_iter.
|
|
// To resolve this we will create one memory that holds the
|
|
// context vector as well as the both the hidden and cell states.
|
|
// The dst_iter will be a sub-memory of this memory.
|
|
// Note that the cell state will be padded by
|
|
// feature_size values. However, we do not compute or
|
|
// access those.
|
|
memory::dims dec_dst_iter_dims = { dec_n_layers, 1, lstm_n_states, batch,
|
|
feature_size + feature_size };
|
|
memory::dims dec_dst_iter_noctx_dims
|
|
= { dec_n_layers, 1, lstm_n_states, batch, feature_size };
|
|
|
|
auto user_dec_wei_layer_md = mkldnn::memory::desc(
|
|
{ user_dec_wei_layer_dims }, mkldnn::memory::data_type::f32,
|
|
mkldnn::memory::format_tag::ldigo);
|
|
auto user_dec_wei_iter_md = mkldnn::memory::desc({ user_dec_wei_iter_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldigo);
|
|
auto user_dec_bias_md = mkldnn::memory::desc({ user_dec_bias_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldgo);
|
|
auto dec_dst_layer_md = mkldnn::memory::desc({ dec_dst_layer_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::tnc);
|
|
auto dec_src_layer_md = mkldnn::memory::desc({ dec_src_layer_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::tnc);
|
|
auto dec_dst_iter_md = mkldnn::memory::desc({ dec_dst_iter_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::ldsnc);
|
|
auto user_dec_wei_layer_memory = mkldnn::memory(
|
|
user_dec_wei_layer_md, cpu_engine, user_dec_wei_layer.data());
|
|
auto user_dec_wei_iter_memory = mkldnn::memory(
|
|
user_dec_wei_iter_md, cpu_engine, user_dec_wei_iter.data());
|
|
auto user_dec_bias_memory = mkldnn::memory(
|
|
user_dec_bias_md, cpu_engine, user_dec_bias.data());
|
|
auto user_dec_dst_layer_memory
|
|
= mkldnn::memory(dec_dst_layer_md, cpu_engine, user_dec_dst.data());
|
|
auto dec_src_layer_memory = mkldnn::memory(dec_src_layer_md, cpu_engine);
|
|
|
|
auto dec_wei_layer_md = mkldnn::memory::desc({ user_dec_wei_layer_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::any);
|
|
auto dec_wei_iter_md = mkldnn::memory::desc({ user_dec_wei_iter_dims },
|
|
mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::any);
|
|
|
|
// As mentioned above, we create a view without context out of the
|
|
// memory with context.
|
|
auto dec_dst_iter_memory = mkldnn::memory(dec_dst_iter_md, cpu_engine);
|
|
auto dec_dst_iter_noctx_md = dec_dst_iter_md.submemory_desc(
|
|
dec_dst_iter_noctx_dims, { 0, 0, 0, 0, 0 });
|
|
|
|
/// @todo add suport for residual connections
|
|
/// should it be a set residual in op_desc or a field to set manually?
|
|
/// should be an integer to specify at which layer to start
|
|
rnn_cell::desc dec_cell(algorithm::vanilla_lstm);
|
|
rnn_forward::desc dec_ctx_desc(prop_kind::forward_inference, dec_cell,
|
|
rnn_direction::unidirectional_left2right, dec_src_layer_md,
|
|
dec_dst_iter_md, dec_wei_layer_md, dec_wei_iter_md,
|
|
user_dec_bias_md, dec_dst_layer_md, dec_dst_iter_noctx_md);
|
|
auto dec_ctx_prim_desc
|
|
= mkldnn::rnn_forward::primitive_desc(dec_ctx_desc, cpu_engine);
|
|
|
|
auto dec_wei_layer_memory
|
|
= memory(dec_ctx_prim_desc.weights_layer_desc(), cpu_engine);
|
|
auto dec_wei_layer_reorder_pd = reorder::primitive_desc(
|
|
user_dec_wei_layer_memory, dec_wei_layer_memory);
|
|
reorder(dec_wei_layer_reorder_pd)
|
|
.execute(s, user_dec_wei_layer_memory, dec_wei_layer_memory);
|
|
|
|
auto dec_wei_iter_memory
|
|
= memory(dec_ctx_prim_desc.weights_iter_desc(), cpu_engine);
|
|
auto dec_wei_iter_reorder_pd = reorder::primitive_desc(
|
|
user_dec_wei_iter_memory, dec_wei_iter_memory);
|
|
reorder(dec_wei_iter_reorder_pd)
|
|
.execute(s, user_dec_wei_iter_memory, dec_wei_iter_memory);
|
|
|
|
/// @todo add a reorder when they will be available
|
|
decoder_net.push_back(rnn_forward(dec_ctx_prim_desc));
|
|
decoder_net_args.push_back({ { MKLDNN_ARG_SRC_LAYER, dec_src_layer_memory },
|
|
{ MKLDNN_ARG_SRC_ITER, dec_dst_iter_memory },
|
|
{ MKLDNN_ARG_WEIGHTS_LAYER, dec_wei_layer_memory },
|
|
{ MKLDNN_ARG_WEIGHTS_ITER, dec_wei_iter_memory },
|
|
{ MKLDNN_ARG_BIAS, user_dec_bias_memory },
|
|
{ MKLDNN_ARG_DST_LAYER, user_dec_dst_layer_memory },
|
|
{ MKLDNN_ARG_DST_ITER, dec_dst_iter_memory } });
|
|
|
|
// allocating temporary buffer for attention mechanism
|
|
std::vector<float> weighted_annotations(
|
|
src_seq_length_max * batch * feature_size, 1.0f);
|
|
|
|
/*
|
|
Execution
|
|
*/
|
|
auto execute = [&]() {
|
|
// run encoder (1 stream)
|
|
assert(encoder_net.size() == encoder_net_args.size()
|
|
&& "something is missing");
|
|
for (size_t p = 0; p < encoder_net.size(); ++p)
|
|
encoder_net.at(p).execute(s, encoder_net_args.at(p));
|
|
|
|
// we compute the weighted annotations once before the decoder
|
|
compute_weighted_annotations(weighted_annotations.data(),
|
|
src_seq_length_max, batch, feature_size,
|
|
user_weights_annotation.data(),
|
|
(float *)enc_dst_layer_memory.get_data_handle());
|
|
|
|
// We initialise src_layer to the embedding of </s>, which
|
|
// are assumed to be 0 here
|
|
memset(dec_src_layer_memory.get_data_handle(), 0,
|
|
dec_src_layer_memory.get_desc().get_size());
|
|
// From now on, src points to the output of the last iteration
|
|
|
|
for (dim_t i = 0; i < tgt_seq_length_max; i++) {
|
|
float *src_att_layer_handle
|
|
= (float *)dec_src_layer_memory.get_data_handle();
|
|
float *src_att_iter_handle
|
|
= (float *)dec_dst_iter_memory.get_data_handle();
|
|
|
|
// Compute attention context vector into the first layer src_iter
|
|
compute_attention(src_att_iter_handle, src_seq_length_max, batch,
|
|
feature_size, user_weights_attention_src_layer.data(),
|
|
src_att_layer_handle,
|
|
(float *)enc_bidir_dst_layer_memory.get_data_handle(),
|
|
weighted_annotations.data(),
|
|
user_weights_alignments.data());
|
|
|
|
// copy the context vectors to all layers of src_iter
|
|
copy_context(src_att_iter_handle, dec_n_layers, lstm_n_states,
|
|
batch, feature_size);
|
|
|
|
// run the decoder iteration
|
|
assert(decoder_net.size() == decoder_net_args.size()
|
|
&& "something is missing");
|
|
for (size_t p = 0; p < decoder_net.size(); ++p)
|
|
decoder_net.at(p).execute(s, decoder_net_args.at(p));
|
|
|
|
// Move the handle on the src/dst layer to the next iteration
|
|
auto dst_layer_handle
|
|
= (float *)user_dec_dst_layer_memory.get_data_handle();
|
|
dec_src_layer_memory.set_data_handle(dst_layer_handle);
|
|
user_dec_dst_layer_memory.set_data_handle(
|
|
dst_layer_handle + batch * feature_size);
|
|
}
|
|
|
|
};
|
|
|
|
execute();
|
|
}
|
|
|
|
int main(int argc, char **argv) {
|
|
try {
|
|
simple_net();
|
|
std::cout << "ok\n";
|
|
} catch (error &e) {
|
|
std::cerr << "status: " << e.status << std::endl;
|
|
std::cerr << "message: " << e.message << std::endl;
|
|
return 1;
|
|
}
|
|
return 0;
|
|
}
|