models.h
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#include <map>
#include <vector>
#include <iostream>
#include <algorithm>
#include <sys/stat.h>
#include "utils_onnx.h"
struct Model
{
public:
const char* encoder_path;
const char* decoder_path;
const char* joiner_path;
const char* joiner_encoder_proj_path;
const char* joiner_decoder_proj_path;
const char* tokens_path;
Ort::Session encoder = load_model(encoder_path);
Ort::Session decoder = load_model(decoder_path);
Ort::Session joiner = load_model(joiner_path);
Ort::Session joiner_encoder_proj = load_model(joiner_encoder_proj_path);
Ort::Session joiner_decoder_proj = load_model(joiner_decoder_proj_path);
std::map<int, std::string> tokens_map = get_token_map(tokens_path);
int32_t blank_id;
int32_t unk_id;
int32_t context_size;
std::vector<Ort::Value> encoder_forward(std::vector<float> in_vector,
std::vector<int64_t> in_vector_length,
std::vector<int64_t> feature_dims,
std::vector<int64_t> feature_length_dims,
Ort::MemoryInfo &memory_info){
std::vector<Ort::Value> encoder_inputTensors;
encoder_inputTensors.push_back(Ort::Value::CreateTensor<float>(memory_info, in_vector.data(), in_vector.size(), feature_dims.data(), feature_dims.size()));
encoder_inputTensors.push_back(Ort::Value::CreateTensor<int64_t>(memory_info, in_vector_length.data(), in_vector_length.size(), feature_length_dims.data(), feature_length_dims.size()));
std::vector<const char*> encoder_inputNames = {encoder.GetInputName(0, allocator), encoder.GetInputName(1, allocator)};
std::vector<const char*> encoder_outputNames = {encoder.GetOutputName(0, allocator)};
auto out = encoder.Run(Ort::RunOptions{nullptr},
encoder_inputNames.data(),
encoder_inputTensors.data(),
encoder_inputTensors.size(),
encoder_outputNames.data(),
encoder_outputNames.size());
return out;
}
std::vector<Ort::Value> decoder_forward(std::vector<int64_t> in_vector,
std::vector<int64_t> dims,
Ort::MemoryInfo &memory_info){
std::vector<Ort::Value> inputTensors;
inputTensors.push_back(Ort::Value::CreateTensor<int64_t>(memory_info, in_vector.data(), in_vector.size(), dims.data(), dims.size()));
std::vector<const char*> inputNames {decoder.GetInputName(0, allocator)};
std::vector<const char*> outputNames {decoder.GetOutputName(0, allocator)};
auto out = decoder.Run(Ort::RunOptions{nullptr},
inputNames.data(),
inputTensors.data(),
inputTensors.size(),
outputNames.data(),
outputNames.size());
return out;
}
std::vector<Ort::Value> joiner_forward(std::vector<float> projected_encoder_out,
std::vector<float> decoder_out,
std::vector<int64_t> projected_encoder_out_dims,
std::vector<int64_t> decoder_out_dims,
Ort::MemoryInfo &memory_info){
std::vector<Ort::Value> inputTensors;
inputTensors.push_back(Ort::Value::CreateTensor<float>(memory_info, projected_encoder_out.data(), projected_encoder_out.size(), projected_encoder_out_dims.data(), projected_encoder_out_dims.size()));
inputTensors.push_back(Ort::Value::CreateTensor<float>(memory_info, decoder_out.data(), decoder_out.size(), decoder_out_dims.data(), decoder_out_dims.size()));
std::vector<const char*> inputNames = {joiner.GetInputName(0, allocator), joiner.GetInputName(1, allocator)};
std::vector<const char*> outputNames = {joiner.GetOutputName(0, allocator)};
auto out = joiner.Run(Ort::RunOptions{nullptr},
inputNames.data(),
inputTensors.data(),
inputTensors.size(),
outputNames.data(),
outputNames.size());
return out;
}
std::vector<Ort::Value> joiner_encoder_proj_forward(std::vector<float> in_vector,
std::vector<int64_t> dims,
Ort::MemoryInfo &memory_info){
std::vector<Ort::Value> inputTensors;
inputTensors.push_back(Ort::Value::CreateTensor<float>(memory_info, in_vector.data(), in_vector.size(), dims.data(), dims.size()));
std::vector<const char*> inputNames {joiner_encoder_proj.GetInputName(0, allocator)};
std::vector<const char*> outputNames {joiner_encoder_proj.GetOutputName(0, allocator)};
auto out = joiner_encoder_proj.Run(Ort::RunOptions{nullptr},
inputNames.data(),
inputTensors.data(),
inputTensors.size(),
outputNames.data(),
outputNames.size());
return out;
}
std::vector<Ort::Value> joiner_decoder_proj_forward(std::vector<float> in_vector,
std::vector<int64_t> dims,
Ort::MemoryInfo &memory_info){
std::vector<Ort::Value> inputTensors;
inputTensors.push_back(Ort::Value::CreateTensor<float>(memory_info, in_vector.data(), in_vector.size(), dims.data(), dims.size()));
std::vector<const char*> inputNames {joiner_decoder_proj.GetInputName(0, allocator)};
std::vector<const char*> outputNames {joiner_decoder_proj.GetOutputName(0, allocator)};
auto out = joiner_decoder_proj.Run(Ort::RunOptions{nullptr},
inputNames.data(),
inputTensors.data(),
inputTensors.size(),
outputNames.data(),
outputNames.size());
return out;
}
Ort::Session load_model(const char* path){
struct stat buffer;
if (stat(path, &buffer) != 0){
std::cout << "File does not exist!: " << path << std::endl;
exit(0);
}
std::cout << "loading " << path << std::endl;
Ort::Session onnx_model(env, path, session_options);
return onnx_model;
}
void extract_constant_lm_parameters(){
/*
all_in_one contains these params. We should trace all_in_one and find 'constants_lm' nodes to extract these params
For now, these params are set staticaly.
in: Ort::Session &all_in_one
out: {blank_id, unk_id, context_size}
should return std::vector<int32_t>
*/
blank_id = 0;
unk_id = 0;
context_size = 2;
}
std::map<int, std::string> get_token_map(const char* token_path){
std::ifstream inFile;
inFile.open(token_path);
if (inFile.fail())
std::cerr << "Could not find token file" << std::endl;
std::map<int, std::string> token_map;
std::string line;
while (std::getline(inFile, line))
{
int id;
std::string token;
std::istringstream iss(line);
iss >> token;
iss >> id;
token_map[id] = token;
}
return token_map;
}
};
Model get_model(std::string exp_path, char* tokens_path){
Model model{
(exp_path + "/encoder_simp.onnx").c_str(),
(exp_path + "/decoder_simp.onnx").c_str(),
(exp_path + "/joiner_simp.onnx").c_str(),
(exp_path + "/joiner_encoder_proj_simp.onnx").c_str(),
(exp_path + "/joiner_decoder_proj_simp.onnx").c_str(),
tokens_path,
};
model.extract_constant_lm_parameters();
return model;
}
Model get_model(char* encoder_path,
char* decoder_path,
char* joiner_path,
char* joiner_encoder_proj_path,
char* joiner_decoder_proj_path,
char* tokens_path){
Model model{
encoder_path,
decoder_path,
joiner_path,
joiner_encoder_proj_path,
joiner_decoder_proj_path,
tokens_path,
};
model.extract_constant_lm_parameters();
return model;
}
void doWarmup(Model *model, int numWarmup = 5){
std::cout << "Warmup is started" << std::endl;
std::vector<float> encoder_warmup_sample (500 * 80, 1.0);
for (int i=0; i<numWarmup; i++)
auto encoder_out = model->encoder_forward(encoder_warmup_sample,
std::vector<int64_t> {500},
std::vector<int64_t> {1, 500, 80},
std::vector<int64_t> {1},
memory_info);
std::vector<int64_t> decoder_warmup_sample {1, 1};
for (int i=0; i<numWarmup; i++)
auto decoder_out = model->decoder_forward(decoder_warmup_sample,
std::vector<int64_t> {1, 2},
memory_info);
std::vector<float> joiner_warmup_sample1 (512, 1.0);
std::vector<float> joiner_warmup_sample2 (512, 1.0);
for (int i=0; i<numWarmup; i++)
auto logits = model->joiner_forward(joiner_warmup_sample1,
joiner_warmup_sample2,
std::vector<int64_t> {1, 1, 1, 512},
std::vector<int64_t> {1, 1, 1, 512},
memory_info);
std::vector<float> joiner_encoder_proj_warmup_sample (100 * 512, 1.0);
for (int i=0; i<numWarmup; i++)
auto projected_encoder_out = model->joiner_encoder_proj_forward(joiner_encoder_proj_warmup_sample,
std::vector<int64_t> {100, 512},
memory_info);
std::vector<float> joiner_decoder_proj_warmup_sample (512, 1.0);
for (int i=0; i<numWarmup; i++)
auto projected_decoder_out = model->joiner_decoder_proj_forward(joiner_decoder_proj_warmup_sample,
std::vector<int64_t> {1, 512},
memory_info);
std::cout << "Warmup is done" << std::endl;
}