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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 "caffe2/core/blob_serialization.h"
#include "caffe2/core/init.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor_int8.h"
#ifdef CAFFE2_OPTIMIZER
#include "caffe2/opt/optimizer.h"
#endif
#include "caffe2/proto/caffe2_pb.h"
#include "caffe2/utils/proto_utils.h"
#include "caffe2/utils/string_utils.h"
C10_DEFINE_string(net, "", "The given net to benchmark.");
C10_DEFINE_string(init_net, "", "The given net to initialize any parameters.");
C10_DEFINE_string(
input,
"",
"Input that is needed for running the network. If "
"multiple input needed, use comma separated string.");
C10_DEFINE_string(
input_file,
"",
"Input file that contain the serialized protobuf for "
"the input blobs. If multiple input needed, use comma "
"separated string. Must have the same number of items "
"as input does.");
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(
output,
"",
"Output that should be dumped after the execution "
"finishes. If multiple outputs are needed, use comma "
"separated string. If you want to dump everything, pass "
"'*' as the output value.");
C10_DEFINE_string(
output_folder,
"",
"The folder that the output should be written to. This "
"folder must already exist in the file system.");
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_int(opt, 0, "The level of optimization to run automatically.");
C10_DEFINE_bool(
run_individual,
false,
"Whether to benchmark individual operators.");
C10_DEFINE_bool(force_engine, false, "Force engine field for all operators");
C10_DEFINE_string(engine, "", "Forced engine field value");
C10_DEFINE_bool(force_algo, false, "Force algo arg for all operators");
C10_DEFINE_string(algo, "", "Forced algo arg value");
using std::string;
using std::unique_ptr;
using std::vector;
int main(int argc, char** argv) {
caffe2::GlobalInit(&argc, &argv);
unique_ptr<caffe2::Workspace> workspace(new caffe2::Workspace());
// Run initialization network.
caffe2::NetDef net_def;
CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_init_net, &net_def));
CAFFE_ENFORCE(workspace->RunNetOnce(net_def));
// Load input.
if (FLAGS_input.size()) {
vector<string> input_names = caffe2::split(',', FLAGS_input);
if (FLAGS_input_file.size()) {
vector<string> input_files = caffe2::split(',', FLAGS_input_file);
CAFFE_ENFORCE_EQ(
input_names.size(),
input_files.size(),
"Input name and file should have the same number.");
for (int i = 0; i < input_names.size(); ++i) {
caffe2::BlobProto blob_proto;
CAFFE_ENFORCE(caffe2::ReadProtoFromFile(input_files[i], &blob_proto));
DeserializeBlob(blob_proto, workspace->CreateBlob(input_names[i]));
}
} else if (FLAGS_input_dims.size() || FLAGS_input_type.size()) {
CAFFE_ENFORCE_GE(
FLAGS_input_dims.size(),
0,
"Input dims must be specified when input tensors are used.");
CAFFE_ENFORCE_GE(
FLAGS_input_type.size(),
0,
"Input type must be specified when input tensors are used.");
vector<string> input_dims_list = caffe2::split(';', FLAGS_input_dims);
CAFFE_ENFORCE_EQ(
input_names.size(),
input_dims_list.size(),
"Input name and dims should have the same number of items.");
vector<string> input_type_list = caffe2::split(';', FLAGS_input_type);
CAFFE_ENFORCE_EQ(
input_names.size(),
input_type_list.size(),
"Input name and type should have the same number of items.");
for (size_t i = 0; i < input_names.size(); ++i) {
vector<string> input_dims_str = caffe2::split(',', input_dims_list[i]);
vector<int> input_dims;
for (const string& s : input_dims_str) {
input_dims.push_back(c10::stoi(s));
}
caffe2::Blob* blob = workspace->GetBlob(input_names[i]);
if (blob == nullptr) {
blob = workspace->CreateBlob(input_names[i]);
}
if (input_type_list[i] == "uint8_t") {
caffe2::int8::Int8TensorCPU* tensor =
blob->GetMutable<caffe2::int8::Int8TensorCPU>();
TORCH_CHECK_NOTNULL(tensor);
tensor->t.Resize(input_dims);
tensor->t.mutable_data<uint8_t>();
} else if (input_type_list[i] == "float") {
caffe2::TensorCPU* tensor = BlobGetMutableTensor(blob, caffe2::CPU);
TORCH_CHECK_NOTNULL(tensor);
tensor->Resize(input_dims);
tensor->mutable_data<float>();
} else {
CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
}
}
} else {
CAFFE_THROW(
"You requested input tensors, but neither input_file nor "
"input_dims is set.");
}
}
// Run main network.
CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_net, &net_def));
if (!net_def.has_name()) {
net_def.set_name("benchmark");
}
// force changing engine and algo
if (FLAGS_force_engine) {
LOG(INFO) << "force engine be: " << FLAGS_engine;
for (const auto& op : net_def.op()) {
const_cast<caffe2::OperatorDef*>(&op)->set_engine(FLAGS_engine);
}
}
if (FLAGS_force_algo) {
LOG(INFO) << "force algo be: " << FLAGS_algo;
for (const auto& op : net_def.op()) {
caffe2::GetMutableArgument(
"algo", true, const_cast<caffe2::OperatorDef*>(&op))
->set_s(FLAGS_algo);
}
}
if (FLAGS_opt) {
#ifdef CAFFE2_OPTIMIZER
net_def = caffe2::opt::optimize(net_def, workspace.get(), FLAGS_opt);
#else
LOG(WARNING) << "Caffe2 not compiled with optimization passes.";
#endif
}
caffe2::NetBase* net = workspace->CreateNet(net_def);
TORCH_CHECK_NOTNULL(net);
CAFFE_ENFORCE(net->Run());
net->TEST_Benchmark(FLAGS_warmup, FLAGS_iter, FLAGS_run_individual);
string output_prefix =
FLAGS_output_folder.size() ? FLAGS_output_folder + "/" : "";
if (FLAGS_output.size()) {
vector<string> output_names = caffe2::split(',', FLAGS_output);
if (FLAGS_output == "*") {
output_names = workspace->Blobs();
}
for (const string& name : output_names) {
CAFFE_ENFORCE(
workspace->HasBlob(name),
"You requested a non-existing blob: ",
name);
string serialized = SerializeBlob(*workspace->GetBlob(name), name);
string output_filename = output_prefix + name;
caffe2::WriteStringToFile(serialized, output_filename.c_str());
}
}
return 0;
}
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