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122 lines
3.8 KiB
122 lines
3.8 KiB
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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#include "nnet/nnet_producer.h"
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#include "matrix/kaldi-matrix.h"
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namespace ppspeech {
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using kaldi::BaseFloat;
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using std::vector;
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NnetProducer::NnetProducer(std::shared_ptr<NnetBase> nnet,
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std::shared_ptr<FrontendInterface> frontend)
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: nnet_(nnet), frontend_(frontend) {
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abort_ = false;
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Reset();
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if (nnet_ != nullptr) thread_ = std::thread(RunNnetEvaluation, this);
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}
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void NnetProducer::Accept(const std::vector<kaldi::BaseFloat>& inputs) {
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frontend_->Accept(inputs);
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condition_variable_.notify_one();
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}
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void NnetProducer::WaitProduce() {
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std::unique_lock<std::mutex> lock(read_mutex_);
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while (frontend_->IsFinished() == false && cache_.empty()) {
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condition_read_ready_.wait(lock);
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}
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return;
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}
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void NnetProducer::RunNnetEvaluation(NnetProducer* me) {
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me->RunNnetEvaluationInteral();
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}
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void NnetProducer::RunNnetEvaluationInteral() {
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bool result = false;
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LOG(INFO) << "NnetEvaluationInteral begin";
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while (!abort_) {
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std::unique_lock<std::mutex> lock(mutex_);
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condition_variable_.wait(lock);
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do {
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result = Compute();
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} while (result);
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if (frontend_->IsFinished() == true) {
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if (cache_.empty()) finished_ = true;
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}
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}
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LOG(INFO) << "NnetEvaluationInteral exit";
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}
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void NnetProducer::Acceptlikelihood(
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const kaldi::Matrix<BaseFloat>& likelihood) {
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std::vector<BaseFloat> prob;
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prob.resize(likelihood.NumCols());
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for (size_t idx = 0; idx < likelihood.NumRows(); ++idx) {
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for (size_t col = 0; col < likelihood.NumCols(); ++col) {
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prob[col] = likelihood(idx, col);
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}
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cache_.push_back(prob);
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}
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}
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bool NnetProducer::Read(std::vector<kaldi::BaseFloat>* nnet_prob) {
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bool flag = cache_.pop(nnet_prob);
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condition_variable_.notify_one();
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return flag;
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}
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bool NnetProducer::ReadandCompute(std::vector<kaldi::BaseFloat>* nnet_prob) {
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Compute();
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if (frontend_->IsFinished() && cache_.empty()) finished_ = true;
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bool flag = cache_.pop(nnet_prob);
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return flag;
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}
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bool NnetProducer::Compute() {
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vector<BaseFloat> features;
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if (frontend_ == NULL || frontend_->Read(&features) == false) {
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// no feat or frontend_ not init.
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VLOG(2) << "no feat avalible";
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return false;
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}
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CHECK_GE(frontend_->Dim(), 0);
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VLOG(1) << "Forward in " << features.size() / frontend_->Dim() << " feats.";
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NnetOut out;
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nnet_->FeedForward(features, frontend_->Dim(), &out);
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int32& vocab_dim = out.vocab_dim;
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size_t nframes = out.logprobs.size() / vocab_dim;
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VLOG(1) << "Forward out " << nframes << " decoder frames.";
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for (size_t idx = 0; idx < nframes; ++idx) {
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std::vector<BaseFloat> logprob(
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out.logprobs.data() + idx * vocab_dim,
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out.logprobs.data() + (idx + 1) * vocab_dim);
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cache_.push_back(logprob);
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condition_read_ready_.notify_one();
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}
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return true;
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}
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void NnetProducer::AttentionRescoring(const std::vector<std::vector<int>>& hyps,
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float reverse_weight,
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std::vector<float>* rescoring_score) {
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nnet_->AttentionRescoring(hyps, reverse_weight, rescoring_score);
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}
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} // namespace ppspeech
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