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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 <string>
#include <sstream>
#include "torch/script.h"
#include "torch/csrc/jit/api/module.h"
#include <torch/csrc/jit/passes/metal_rewrite.h>
#include "torch/csrc/jit/passes/vulkan_rewrite.h"
#include "torch/csrc/jit/passes/xnnpack_rewrite.h"
#include "torch/csrc/jit/serialization/import.h"
#include "torch/csrc/jit/serialization/export.h"
C10_DEFINE_string(model, "", "The torch script model to optimize.");
C10_DEFINE_string(
output,
"",
"Name of the output model to be saved.");
C10_DEFINE_string(backend, "", "The backend to be optimized");
C10_DEFINE_string(preserved_methods, "", "Methods to be preserved")
int main(int argc, char** argv) {
c10::SetUsageMessage(
"\nRun optimization pass for pytorch model. Example usage:\n"
"./optimize_for_mobile"
" --model=<model_file>"
" [--output=<output_file_name>]"
" [--backend=<cpu|vulkan|metal>]"
" [--preserved_methods=<method_names>]"
);
if (!c10::ParseCommandLineFlags(&argc, &argv)) {
std::cerr << "Failed to parse command line flags!" << std::endl;
std::cout << c10::UsageMessage() << std::endl;
return 1;
}
CAFFE_ENFORCE(FLAGS_model != "", c10::UsageMessage());
std::string output_model_name =
FLAGS_model.substr(0, FLAGS_model.find(".")) + "_optimized.ptl";
if (FLAGS_output != "") {
output_model_name = FLAGS_output;
}
std::vector<std::string> preserved_methods;
if(FLAGS_preserved_methods != ""){
std::stringstream ss(FLAGS_preserved_methods);
std::string m;
while(std::getline(ss, m, ';')){
if(m != ""){
preserved_methods.emplace_back(std::move(m));
}
}
std::cout<<"The following methods will be preserved:"<<std::endl;
for(auto& str : preserved_methods){
std::cout<<str<<std::endl;
}
}
auto module = torch::jit::load(FLAGS_model);
auto ops = torch::jit::export_opnames(module);
std::cout << "\npt_operator_library(" << std::endl;
std::cout << "\tname = \"old_op_library\"," << std::endl;
std::cout << "\tops = [" << std::endl;
for (auto const& op: ops) {
std::cout << "\t\t\"" << op << "\"," << std::endl;
}
std::cout << "\t],\n)\n" << std::endl;
torch::jit::Module optimized_module;
if (FLAGS_backend == "" || FLAGS_backend == "cpu") {
optimized_module = torch::jit::optimizeForMobile(module);
} else if (FLAGS_backend == "vulkan") {
optimized_module = torch::jit::vulkanOptimizeForMobile(module, preserved_methods);
} else if (FLAGS_backend == "metal"){
optimized_module = torch::jit::metalOptimizeForMobile(module, preserved_methods);
}else{
CAFFE_ENFORCE(false, "Unknown backend: " + FLAGS_backend);
}
auto new_ops = torch::jit::export_opnames(optimized_module);
std::cout << "\npt_operator_library(" << std::endl;
std::cout << "\tname = \"new_op_library\"," << std::endl;
std::cout << "\tops = [" << std::endl;
for (auto const& op: new_ops) {
std::cout << "\t\t\"" << op << "\"," << std::endl;
}
std::cout << "\t],\n)\n" << std::endl;
optimized_module._save_for_mobile(output_model_name);
std::cout << "The optimized model for lite interpreter was saved to " << output_model_name << std::endl;
return 0;
}
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