118 lines
3.8 KiB
C++
118 lines
3.8 KiB
C++
// sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc
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//
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// Copyright (c) 2024 Xiaomi Corporation
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#include "sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.h"
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#include <algorithm>
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#include <iterator>
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#include <utility>
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/onnx-utils.h"
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namespace sherpa_onnx {
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static std::pair<Ort::Value, Ort::Value> BuildDecoderInput(
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int32_t token, OrtAllocator *allocator) {
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std::array<int64_t, 2> shape{1, 1};
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Ort::Value decoder_input =
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Ort::Value::CreateTensor<int32_t>(allocator, shape.data(), shape.size());
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std::array<int64_t, 1> length_shape{1};
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Ort::Value decoder_input_length = Ort::Value::CreateTensor<int32_t>(
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allocator, length_shape.data(), length_shape.size());
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int32_t *p = decoder_input.GetTensorMutableData<int32_t>();
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int32_t *p_length = decoder_input_length.GetTensorMutableData<int32_t>();
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p[0] = token;
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p_length[0] = 1;
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return {std::move(decoder_input), std::move(decoder_input_length)};
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}
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static OfflineTransducerDecoderResult DecodeOne(
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const float *p, int32_t num_rows, int32_t num_cols,
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OfflineTransducerNeMoModel *model, float blank_penalty) {
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auto memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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OfflineTransducerDecoderResult ans;
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int32_t vocab_size = model->VocabSize();
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int32_t blank_id = vocab_size - 1;
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auto decoder_input_pair = BuildDecoderInput(blank_id, model->Allocator());
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std::pair<Ort::Value, std::vector<Ort::Value>> decoder_output_pair =
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model->RunDecoder(std::move(decoder_input_pair.first),
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std::move(decoder_input_pair.second),
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model->GetDecoderInitStates(1));
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std::array<int64_t, 3> encoder_shape{1, num_cols, 1};
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for (int32_t t = 0; t != num_rows; ++t) {
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Ort::Value cur_encoder_out = Ort::Value::CreateTensor(
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memory_info, const_cast<float *>(p) + t * num_cols, num_cols,
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encoder_shape.data(), encoder_shape.size());
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Ort::Value logit = model->RunJoiner(std::move(cur_encoder_out),
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View(&decoder_output_pair.first));
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float *p_logit = logit.GetTensorMutableData<float>();
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if (blank_penalty > 0) {
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p_logit[blank_id] -= blank_penalty;
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}
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auto y = static_cast<int32_t>(std::distance(
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static_cast<const float *>(p_logit),
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std::max_element(static_cast<const float *>(p_logit),
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static_cast<const float *>(p_logit) + vocab_size)));
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if (y != blank_id) {
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ans.tokens.push_back(y);
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ans.timestamps.push_back(t);
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decoder_input_pair = BuildDecoderInput(y, model->Allocator());
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decoder_output_pair =
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model->RunDecoder(std::move(decoder_input_pair.first),
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std::move(decoder_input_pair.second),
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std::move(decoder_output_pair.second));
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} // if (y != blank_id)
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} // for (int32_t i = 0; i != num_rows; ++i)
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return ans;
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}
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std::vector<OfflineTransducerDecoderResult>
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OfflineTransducerGreedySearchNeMoDecoder::Decode(
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Ort::Value encoder_out, Ort::Value encoder_out_length,
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OfflineStream ** /*ss = nullptr*/, int32_t /*n= 0*/) {
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auto shape = encoder_out.GetTensorTypeAndShapeInfo().GetShape();
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int32_t batch_size = static_cast<int32_t>(shape[0]);
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int32_t dim1 = static_cast<int32_t>(shape[1]);
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int32_t dim2 = static_cast<int32_t>(shape[2]);
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const int64_t *p_length = encoder_out_length.GetTensorData<int64_t>();
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const float *p = encoder_out.GetTensorData<float>();
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std::vector<OfflineTransducerDecoderResult> ans(batch_size);
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for (int32_t i = 0; i != batch_size; ++i) {
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const float *this_p = p + dim1 * dim2 * i;
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int32_t this_len = p_length[i];
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ans[i] = DecodeOne(this_p, this_len, dim2, model_, blank_penalty_);
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}
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return ans;
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}
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} // namespace sherpa_onnx
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