rnnlm-core-compute.h
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// rnnlm/rnnlm-core-compute.h
// Copyright 2017 Johns Hopkins University (author: 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.
#ifndef KALDI_RNNLM_RNNLM_CORE_COMPUTE_H_
#define KALDI_RNNLM_RNNLM_CORE_COMPUTE_H_
#include "rnnlm/rnnlm-example-utils.h"
#include "rnnlm/rnnlm-core-training.h"
namespace kaldi {
namespace rnnlm {
/** This class has a similar interface to RnnlmCoreTrainer, but it doesn't
actually train the RNNLM; it's for computing likelihoods and (optionally)
derivatives w.r.t. the embedding, in situations where you are not training
the core part of the RNNLM. It reads egs-- it's not for rescoring lattices
and similar purposes.
*/
class RnnlmCoreComputer {
public:
/** Constructor.
@param [in] nnet The neural network that is to be used to evaluate
likelihoods (and possibly derivatives).
*/
RnnlmCoreComputer(const nnet3::Nnet &nnet);
/* Compute the objective on one minibatch (and possibly also derivatives
w.r.t. the embedding).
@param [in] minibatch The RNNLM minibatch to evalute, containing
a number of parallel word sequences. It will not
necessarily contain words with the 'original'
numbering, it will in most circumstances contain
just the ones we used; see RenumberRnnlmMinibatch().
@param [in] derived Derived parameters of the minibatch, computed
by previously calling GetRnnlmExampleDerived()
with suitable arguments.
@param [in] word_embedding The matrix giving the embedding of words, of
dimension minibatch.vocab_size by the embedding dimension.
The numbering of the words does not have to be the 'real'
numbering of words, it can consist of words renumbered
by RenumberRnnlmMinibatch(); it just has to be
consistent with the word-ids present in 'minibatch'.
@para [out] weight If non-NULL, the total weight of the words in the
minibatch will be written to here (this is just the sum
of minibatch.output_weights).
@param [out] word_embedding_deriv If supplied, the derivative of the
objective function w.r.t. the word embedding will be
*added* to this location; it must have the same
dimension as 'word_embedding'.
@return objf The total objective function for this minibatch; divide
this by '*weight' to normalize it (i.e. get the average
log-prob per word).
*/
BaseFloat Compute(const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
BaseFloat *weight = NULL,
CuMatrixBase<BaseFloat> *word_embedding_deriv = NULL);
private:
void ProvideInput(const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
nnet3::NnetComputer *computer);
/** Process the output of the neural net and compute the objective function;
store stats from the objective function in objf_info_.
@param [in] minibatch The minibatch for which we're proessing the output.
@param [in] derived Derived quantities from the minibatch.
@param [in] word_embedding The word embedding, with the same numbering as
used in the minibatch (may be subsampled at this point).
@param [out] word_embedding_deriv If non-NULL, the part of the derivative
w.r.t. the word-embedding that arises from the output
computation will be *added* to here.
@param [out] weight If non-NULL, this function will output to this location
the total weight of the output words, which can be used as
the normalizer for the (returned) objective function.
@return Returns the total objective function (of the form:
\sum_i weight(i) * ( num_term(i) + den_term(i) ), see rnnlm-example-utils.h
for more information about this.
*/
BaseFloat ProcessOutput(const RnnlmExample &minibatch,
const RnnlmExampleDerived &derived,
const CuMatrixBase<BaseFloat> &word_embedding,
nnet3::NnetComputer *computer,
CuMatrixBase<BaseFloat> *word_embedding_deriv,
BaseFloat *weight);
const nnet3::Nnet &nnet_;
nnet3::CachingOptimizingCompiler compiler_;
int32 num_minibatches_processed_;
ObjectiveTracker objf_info_;
};
} // namespace rnnlm
} // namespace kaldi
#endif //KALDI_RNNLM_RNNLM_CORE_COMPUTE_H_