296 lines
8.6 KiB
C++
296 lines
8.6 KiB
C++
// sherpa-onnx/csrc/rknn/online-zipformer-ctc-model-rknn.cc
|
|
//
|
|
// Copyright (c) 2025 Xiaomi Corporation
|
|
|
|
#include "sherpa-onnx/csrc/rknn/online-zipformer-ctc-model-rknn.h"
|
|
|
|
#include <memory>
|
|
#include <sstream>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
#include <utility>
|
|
#include <vector>
|
|
|
|
#if __ANDROID_API__ >= 9
|
|
#include "android/asset_manager.h"
|
|
#include "android/asset_manager_jni.h"
|
|
#endif
|
|
|
|
#if __OHOS__
|
|
#include "rawfile/raw_file_manager.h"
|
|
#endif
|
|
|
|
#include "sherpa-onnx/csrc/file-utils.h"
|
|
#include "sherpa-onnx/csrc/rknn/macros.h"
|
|
#include "sherpa-onnx/csrc/rknn/utils.h"
|
|
#include "sherpa-onnx/csrc/text-utils.h"
|
|
|
|
namespace sherpa_onnx {
|
|
|
|
class OnlineZipformerCtcModelRknn::Impl {
|
|
public:
|
|
~Impl() {
|
|
auto ret = rknn_destroy(ctx_);
|
|
if (ret != RKNN_SUCC) {
|
|
SHERPA_ONNX_LOGE("Failed to destroy the context");
|
|
}
|
|
}
|
|
|
|
explicit Impl(const OnlineModelConfig &config) : config_(config) {
|
|
{
|
|
auto buf = ReadFile(config.zipformer2_ctc.model);
|
|
Init(buf.data(), buf.size());
|
|
}
|
|
|
|
SetCoreMask(ctx_, config_.num_threads);
|
|
}
|
|
|
|
template <typename Manager>
|
|
Impl(Manager *mgr, const OnlineModelConfig &config) : config_(config) {
|
|
{
|
|
auto buf = ReadFile(mgr, config.zipformer2_ctc.model);
|
|
Init(buf.data(), buf.size());
|
|
}
|
|
|
|
SetCoreMask(ctx_, config_.num_threads);
|
|
}
|
|
|
|
// TODO(fangjun): Support Android
|
|
|
|
std::vector<std::vector<uint8_t>> GetInitStates() const {
|
|
// input_attrs_[0] is for the feature
|
|
// input_attrs_[1:] is for states
|
|
// so we use -1 here
|
|
std::vector<std::vector<uint8_t>> states(input_attrs_.size() - 1);
|
|
|
|
int32_t i = -1;
|
|
for (auto &attr : input_attrs_) {
|
|
i += 1;
|
|
if (i == 0) {
|
|
// skip processing the attr for features.
|
|
continue;
|
|
}
|
|
|
|
if (attr.type == RKNN_TENSOR_FLOAT16) {
|
|
states[i - 1].resize(attr.n_elems * sizeof(float));
|
|
} else if (attr.type == RKNN_TENSOR_INT64) {
|
|
states[i - 1].resize(attr.n_elems * sizeof(int64_t));
|
|
} else {
|
|
SHERPA_ONNX_LOGE("Unsupported tensor type: %d, %s", attr.type,
|
|
get_type_string(attr.type));
|
|
SHERPA_ONNX_EXIT(-1);
|
|
}
|
|
}
|
|
|
|
return states;
|
|
}
|
|
|
|
std::pair<std::vector<float>, std::vector<std::vector<uint8_t>>> Run(
|
|
std::vector<float> features,
|
|
std::vector<std::vector<uint8_t>> states) const {
|
|
std::vector<rknn_input> inputs(input_attrs_.size());
|
|
|
|
for (int32_t i = 0; i < static_cast<int32_t>(inputs.size()); ++i) {
|
|
auto &input = inputs[i];
|
|
auto &attr = input_attrs_[i];
|
|
input.index = attr.index;
|
|
|
|
if (attr.type == RKNN_TENSOR_FLOAT16) {
|
|
input.type = RKNN_TENSOR_FLOAT32;
|
|
} else if (attr.type == RKNN_TENSOR_INT64) {
|
|
input.type = RKNN_TENSOR_INT64;
|
|
} else {
|
|
SHERPA_ONNX_LOGE("Unsupported tensor type %d, %s", attr.type,
|
|
get_type_string(attr.type));
|
|
SHERPA_ONNX_EXIT(-1);
|
|
}
|
|
|
|
input.fmt = attr.fmt;
|
|
if (i == 0) {
|
|
input.buf = reinterpret_cast<void *>(features.data());
|
|
input.size = features.size() * sizeof(float);
|
|
} else {
|
|
input.buf = reinterpret_cast<void *>(states[i - 1].data());
|
|
input.size = states[i - 1].size();
|
|
}
|
|
}
|
|
|
|
std::vector<float> out(output_attrs_[0].n_elems);
|
|
|
|
// Note(fangjun): We can reuse the memory from input argument `states`
|
|
// auto next_states = GetInitStates();
|
|
auto &next_states = states;
|
|
|
|
std::vector<rknn_output> outputs(output_attrs_.size());
|
|
for (int32_t i = 0; i < outputs.size(); ++i) {
|
|
auto &output = outputs[i];
|
|
auto &attr = output_attrs_[i];
|
|
output.index = attr.index;
|
|
output.is_prealloc = 1;
|
|
|
|
if (attr.type == RKNN_TENSOR_FLOAT16) {
|
|
output.want_float = 1;
|
|
} else if (attr.type == RKNN_TENSOR_INT64) {
|
|
output.want_float = 0;
|
|
} else {
|
|
SHERPA_ONNX_LOGE("Unsupported tensor type %d, %s", attr.type,
|
|
get_type_string(attr.type));
|
|
SHERPA_ONNX_EXIT(-1);
|
|
}
|
|
|
|
if (i == 0) {
|
|
output.size = out.size() * sizeof(float);
|
|
output.buf = reinterpret_cast<void *>(out.data());
|
|
} else {
|
|
output.size = next_states[i - 1].size();
|
|
output.buf = reinterpret_cast<void *>(next_states[i - 1].data());
|
|
}
|
|
}
|
|
|
|
auto ret = rknn_inputs_set(ctx_, inputs.size(), inputs.data());
|
|
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to set inputs");
|
|
|
|
ret = rknn_run(ctx_, nullptr);
|
|
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to run the model");
|
|
|
|
ret = rknn_outputs_get(ctx_, outputs.size(), outputs.data(), nullptr);
|
|
SHERPA_ONNX_RKNN_CHECK(ret, "Failed to get model output");
|
|
|
|
for (int32_t i = 0; i < next_states.size(); ++i) {
|
|
const auto &attr = input_attrs_[i + 1];
|
|
if (attr.n_dims == 4) {
|
|
// TODO(fangjun): The transpose is copied from
|
|
// https://github.com/airockchip/rknn_model_zoo/blob/main/examples/zipformer/cpp/process.cc#L22
|
|
// I don't understand why we need to do that.
|
|
std::vector<uint8_t> dst(next_states[i].size());
|
|
int32_t n = attr.dims[0];
|
|
int32_t h = attr.dims[1];
|
|
int32_t w = attr.dims[2];
|
|
int32_t c = attr.dims[3];
|
|
ConvertNCHWtoNHWC(
|
|
reinterpret_cast<const float *>(next_states[i].data()), n, c, h, w,
|
|
reinterpret_cast<float *>(dst.data()));
|
|
next_states[i] = std::move(dst);
|
|
}
|
|
}
|
|
|
|
return {std::move(out), std::move(next_states)};
|
|
}
|
|
|
|
int32_t ChunkSize() const { return T_; }
|
|
|
|
int32_t ChunkShift() const { return decode_chunk_len_; }
|
|
|
|
int32_t VocabSize() const { return vocab_size_; }
|
|
|
|
rknn_tensor_attr GetOutAttr() const { return output_attrs_[0]; }
|
|
|
|
private:
|
|
void Init(void *model_data, size_t model_data_length) {
|
|
InitContext(model_data, model_data_length, config_.debug, &ctx_);
|
|
|
|
InitInputOutputAttrs(ctx_, config_.debug, &input_attrs_, &output_attrs_);
|
|
|
|
rknn_custom_string custom_string = GetCustomString(ctx_, config_.debug);
|
|
|
|
auto meta = Parse(custom_string, config_.debug);
|
|
|
|
if (meta.count("T")) {
|
|
T_ = atoi(meta.at("T").c_str());
|
|
}
|
|
|
|
if (meta.count("decode_chunk_len")) {
|
|
decode_chunk_len_ = atoi(meta.at("decode_chunk_len").c_str());
|
|
}
|
|
|
|
vocab_size_ = output_attrs_[0].dims[2];
|
|
|
|
if (config_.debug) {
|
|
#if __OHOS__
|
|
SHERPA_ONNX_LOGE("T: %{public}d", T_);
|
|
SHERPA_ONNX_LOGE("decode_chunk_len_: %{public}d", decode_chunk_len_);
|
|
SHERPA_ONNX_LOGE("vocab_size: %{public}d", vocab_size);
|
|
#else
|
|
SHERPA_ONNX_LOGE("T: %d", T_);
|
|
SHERPA_ONNX_LOGE("decode_chunk_len_: %d", decode_chunk_len_);
|
|
SHERPA_ONNX_LOGE("vocab_size: %d", vocab_size_);
|
|
#endif
|
|
}
|
|
|
|
if (T_ == 0) {
|
|
SHERPA_ONNX_LOGE(
|
|
"Invalid T. Please use the script from icefall to export your model");
|
|
SHERPA_ONNX_EXIT(-1);
|
|
}
|
|
|
|
if (decode_chunk_len_ == 0) {
|
|
SHERPA_ONNX_LOGE(
|
|
"Invalid decode_chunk_len. Please use the script from icefall to "
|
|
"export your model");
|
|
SHERPA_ONNX_EXIT(-1);
|
|
}
|
|
}
|
|
|
|
private:
|
|
OnlineModelConfig config_;
|
|
rknn_context ctx_ = 0;
|
|
|
|
std::vector<rknn_tensor_attr> input_attrs_;
|
|
std::vector<rknn_tensor_attr> output_attrs_;
|
|
|
|
int32_t T_ = 0;
|
|
int32_t decode_chunk_len_ = 0;
|
|
int32_t vocab_size_ = 0;
|
|
};
|
|
|
|
OnlineZipformerCtcModelRknn::~OnlineZipformerCtcModelRknn() = default;
|
|
|
|
OnlineZipformerCtcModelRknn::OnlineZipformerCtcModelRknn(
|
|
const OnlineModelConfig &config)
|
|
: impl_(std::make_unique<Impl>(config)) {}
|
|
|
|
template <typename Manager>
|
|
OnlineZipformerCtcModelRknn::OnlineZipformerCtcModelRknn(
|
|
Manager *mgr, const OnlineModelConfig &config)
|
|
: impl_(std::make_unique<Impl>(mgr, config)) {}
|
|
|
|
std::vector<std::vector<uint8_t>> OnlineZipformerCtcModelRknn::GetInitStates()
|
|
const {
|
|
return impl_->GetInitStates();
|
|
}
|
|
|
|
std::pair<std::vector<float>, std::vector<std::vector<uint8_t>>>
|
|
OnlineZipformerCtcModelRknn::Run(
|
|
std::vector<float> features,
|
|
std::vector<std::vector<uint8_t>> states) const {
|
|
return impl_->Run(std::move(features), std::move(states));
|
|
}
|
|
|
|
int32_t OnlineZipformerCtcModelRknn::ChunkSize() const {
|
|
return impl_->ChunkSize();
|
|
}
|
|
|
|
int32_t OnlineZipformerCtcModelRknn::ChunkShift() const {
|
|
return impl_->ChunkShift();
|
|
}
|
|
|
|
int32_t OnlineZipformerCtcModelRknn::VocabSize() const {
|
|
return impl_->VocabSize();
|
|
}
|
|
|
|
rknn_tensor_attr OnlineZipformerCtcModelRknn::GetOutAttr() const {
|
|
return impl_->GetOutAttr();
|
|
}
|
|
|
|
#if __ANDROID_API__ >= 9
|
|
template OnlineZipformerCtcModelRknn::OnlineZipformerCtcModelRknn(
|
|
AAssetManager *mgr, const OnlineModelConfig &config);
|
|
#endif
|
|
|
|
#if __OHOS__
|
|
template OnlineZipformerCtcModelRknn::OnlineZipformerCtcModelRknn(
|
|
NativeResourceManager *mgr, const OnlineModelConfig &config);
|
|
#endif
|
|
|
|
} // namespace sherpa_onnx
|