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speed_benchmark_torch.cc
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speed_benchmark_torch.cc
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/**
* Copyright (c) 2016-present, Facebook, Inc.
*
* 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
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <fstream>
#include <string>
#include <vector>
#include "ATen/ATen.h"
#include "caffe2/core/timer.h"
#include "caffe2/utils/string_utils.h"
#include "torch/csrc/autograd/grad_mode.h"
#include "torch/csrc/jit/serialization/import.h"
#include "torch/script.h"
#include <chrono>
using namespace std::chrono;
C10_DEFINE_string(model, "", "The given torch script model to benchmark.");
C10_DEFINE_string(
input_dims,
"",
"Alternate to input_files, if all inputs are simple "
"float TensorCPUs, specify the dimension using comma "
"separated numbers. If multiple input needed, use "
"semicolon to separate the dimension of different "
"tensors.");
C10_DEFINE_string(input_type, "", "Input type (uint8_t/float)");
C10_DEFINE_string(input_file, "", "Input file");
C10_DEFINE_bool(
print_output,
false,
"Whether to print output with all one input tensor.");
C10_DEFINE_int(warmup, 0, "The number of iterations to warm up.");
C10_DEFINE_int(iter, 10, "The number of iterations to run.");
C10_DEFINE_bool(
report_pep,
false,
"Whether to print performance stats for AI-PEP.");
C10_DEFINE_int(pytext_len, 0, "Length of input sequence.");
std::vector<std::string>
split(char separator, const std::string& string, bool ignore_empty = true) {
std::vector<std::string> pieces;
std::stringstream ss(string);
std::string item;
while (getline(ss, item, separator)) {
if (!ignore_empty || !item.empty()) {
pieces.push_back(std::move(item));
}
}
return pieces;
}
std::vector<std::vector<c10::IValue>> nlu_process(std::string file_path) {
std::vector<std::vector<c10::IValue>> nlu_inputs;
std::ifstream input_file(FLAGS_input_file);
for (std::string line; getline(input_file, line);) {
std::vector<c10::IValue> nlu_input;
c10::List<std::string> tokens(split(' ', line));
nlu_input.push_back(tokens);
auto len = torch::jit::IValue(static_cast<int64_t>(tokens.size()));
nlu_input.push_back({});
nlu_input.push_back(len);
nlu_inputs.emplace_back(std::move(nlu_input));
std::cout << line << std::endl;
}
return nlu_inputs;
}
int main(int argc, char** argv) {
c10::SetUsageMessage(
"Run speed benchmark for pytorch model.\n"
"Example usage:\n"
"./speed_benchmark_torch"
" --model=<model_file>"
" --input_dims=\"1,3,224,224\""
" --input_type=float"
" --warmup=5"
" --iter=20");
if (!c10::ParseCommandLineFlags(&argc, &argv)) {
std::cerr << "Failed to parse command line flags!" << std::endl;
return 1;
}
CAFFE_ENFORCE_GE(FLAGS_input_dims.size(), 0, "Input dims must be specified.");
CAFFE_ENFORCE_GE(FLAGS_input_type.size(), 0, "Input type must be specified.");
std::vector<std::string> input_dims_list = split(';', FLAGS_input_dims);
std::vector<std::string> input_type_list = split(';', FLAGS_input_type);
CAFFE_ENFORCE_EQ(
input_dims_list.size(),
input_type_list.size(),
"Input dims and type should have the same number of items.");
std::vector<std::vector<c10::IValue>> inputs;
if (input_type_list[0] == "NLUType"){
inputs = nlu_process(FLAGS_input_file);
} else {
inputs.push_back(std::vector<c10::IValue>());
for (size_t i = 0; i < input_dims_list.size(); ++i) {
auto input_dims_str = split(',', input_dims_list[i]);
std::vector<int64_t> input_dims;
for (const auto& s : input_dims_str) {
input_dims.push_back(c10::stoi(s));
}
if (input_type_list[i] == "float") {
inputs[0].push_back(torch::ones(input_dims, at::ScalarType::Float));
} else if (input_type_list[i] == "uint8_t") {
inputs[0].push_back(torch::ones(input_dims, at::ScalarType::Byte));
} else if (input_type_list[i] == "int64") {
inputs[0].push_back(torch::ones(input_dims, torch::kI64));
} else {
CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
}
}
}
if (FLAGS_pytext_len > 0) {
auto stensor = FLAGS_pytext_len * at::ones({1}, torch::kI64);
inputs[0].push_back(stensor);
}
torch::autograd::AutoGradMode guard(false);
torch::jit::GraphOptimizerEnabledGuard no_optimizer_guard(false);
auto module = torch::jit::load(FLAGS_model);
module.eval();
if (FLAGS_print_output) {
std::cout << module.forward(inputs[0]) << std::endl;
}
std::cout << "Starting benchmark." << std::endl;
std::cout << "Running warmup runs." << std::endl;
CAFFE_ENFORCE(
FLAGS_warmup >= 0,
"Number of warm up runs should be non negative, provided ",
FLAGS_warmup,
".");
for (unsigned int i = 0; i < FLAGS_warmup; ++i) {
for (const auto& input : inputs) {
module.forward(input);
}
}
std::cout << "Main runs." << std::endl;
CAFFE_ENFORCE(
FLAGS_iter >= 0,
"Number of main runs should be non negative, provided ",
FLAGS_iter,
".");
caffe2::Timer timer;
std::vector<float> times;
auto millis = timer.MilliSeconds();
for (int i = 0; i < FLAGS_iter; ++i) {
for (const std::vector<c10::IValue>& input: inputs) {
auto start = high_resolution_clock::now();
module.forward(input);
auto stop = high_resolution_clock::now();
auto duration = duration_cast<milliseconds>(stop - start);
times.push_back(duration.count());
}
}
millis = timer.MilliSeconds();
if (FLAGS_report_pep) {
for (auto t : times) {
std::cout << "PyTorchObserver {\"type\": \"NET\", \"unit\": \"us\", \"metric\": \"latency\", \"value\": \"" << t << "\"}" << std::endl;
}
}
std::cout << "Main run finished. Milliseconds per iter: "
<< millis / FLAGS_iter
<< ". Iters per second: " << 1000.0 * FLAGS_iter / millis
<< std::endl;
return 0;
}