rnnlm-core-compute.cc
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// rnnlm/rnnlm-core-compute.cc
// Copyright 2017 Daniel Povey
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include <numeric>
#include "rnnlm/rnnlm-core-compute.h"
#include "rnnlm/rnnlm-example-utils.h"
#include "nnet3/nnet-utils.h"
namespace kaldi {
namespace rnnlm {
BaseFloat RnnlmCoreComputer::Compute(
const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
BaseFloat *weight,
CuMatrixBase<BaseFloat> *word_embedding_deriv) {
using namespace nnet3;
bool need_model_derivative = false;
bool need_input_derivative = (word_embedding_deriv != NULL);
bool store_component_stats = false;
ComputationRequest request;
GetRnnlmComputationRequest(minibatch, need_model_derivative,
need_input_derivative,
store_component_stats,
&request);
std::shared_ptr<const NnetComputation> computation = compiler_.Compile(request);
NnetComputeOptions compute_opts;
NnetComputer computer(compute_opts, *computation, nnet_, NULL);
ProvideInput(minibatch, derived, word_embedding, &computer);
computer.Run(); // This is the forward pass.
BaseFloat ans = ProcessOutput(minibatch, derived, word_embedding,
&computer, word_embedding_deriv, weight);
if (word_embedding_deriv != NULL) {
computer.Run(); // This is the backward pass.
CuMatrix<BaseFloat> input_deriv;
computer.GetOutputDestructive("input", &input_deriv);
word_embedding_deriv->AddMatSmat(1.0, input_deriv,
derived.input_words_smat,
kTrans, 1.0);
}
num_minibatches_processed_++;
return ans;
}
void RnnlmCoreComputer::ProvideInput(
const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
nnet3::NnetComputer *computer) {
int32 embedding_dim = word_embedding.NumCols();
CuMatrix<BaseFloat> input_embeddings(derived.cu_input_words.Dim(),
embedding_dim,
kUndefined);
input_embeddings.CopyRows(word_embedding,
derived.cu_input_words);
computer->AcceptInput("input", &input_embeddings);
}
RnnlmCoreComputer::RnnlmCoreComputer(const nnet3::Nnet &nnet):
nnet_(nnet),
compiler_(nnet), // for now we don't make available other options
num_minibatches_processed_(0),
objf_info_(10) { }
BaseFloat RnnlmCoreComputer::ProcessOutput(
const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
nnet3::NnetComputer *computer,
CuMatrixBase<BaseFloat> *word_embedding_deriv,
BaseFloat *weight_out) {
// 'output' is the output of the neural network. The row-index
// combines the time (with higher stride) and the member 'n'
// of the minibatch (with stride 1); the number of columns is
// the word-embedding dimension.
CuMatrix<BaseFloat> output;
CuMatrix<BaseFloat> output_deriv;
computer->GetOutputDestructive("output", &output);
output_deriv.Resize(output.NumRows(), output.NumCols());
BaseFloat weight, objf_num, objf_den, objf_den_exact;
RnnlmObjectiveOptions objective_opts; // Use the defaults; we're not training
// so they won't matter.
ProcessRnnlmOutput(objective_opts, minibatch, derived, word_embedding,
output, word_embedding_deriv, &output_deriv,
&weight, &objf_num, &objf_den,
&objf_den_exact);
objf_info_.AddStats(weight, objf_num, objf_den, objf_den_exact);
if (weight_out)
*weight_out = weight;
return objf_num + objf_den;
}
} // namespace rnnlm
} // namespace kaldi