2bbf57217f
This is due to issues around inf/nan behavior on the cpu. tf_library still enables all fast math options though with the observation that currently most users of this are desiring performance and have tested their code already. PiperOrigin-RevId: 311787817 Change-Id: Iab012d49435845dc5b7a5fcedca89bf159ec65a3
97 lines
3.6 KiB
C++
97 lines
3.6 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "absl/strings/match.h"
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#include "absl/strings/str_join.h"
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#include "absl/strings/string_view.h"
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#include "tensorflow/compiler/aot/codegen.h"
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#include "tensorflow/compiler/aot/compile.h"
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#include "tensorflow/compiler/aot/flags.h"
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#include "tensorflow/compiler/tf2xla/tf2xla.pb.h"
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#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
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#include "tensorflow/compiler/xla/debug_options_flags.h"
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#include "tensorflow/compiler/xla/service/compiler.h"
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#include "tensorflow/core/framework/function.h"
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#include "tensorflow/core/framework/graph.pb.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/framework/types.h"
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#include "tensorflow/core/graph/graph.h"
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#include "tensorflow/core/graph/tensor_id.h"
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#include "tensorflow/core/lib/core/errors.h"
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#include "tensorflow/core/lib/strings/numbers.h"
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#include "tensorflow/core/platform/env.h"
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#include "tensorflow/core/platform/init_main.h"
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#include "tensorflow/core/platform/logging.h"
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#include "tensorflow/core/platform/protobuf.h"
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#include "tensorflow/core/util/command_line_flags.h"
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namespace tensorflow {
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namespace tfcompile {
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const char kUsageHeader[] =
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"tfcompile performs ahead-of-time compilation of a TensorFlow graph,\n"
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"resulting in an object file compiled for your target architecture, and a\n"
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"header file that gives access to the functionality in the object file.\n"
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"A typical invocation looks like this:\n"
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"\n"
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" $ tfcompile --graph=mygraph.pb --config=myfile.pbtxt "
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"--cpp_class=\"mynamespace::MyComputation\"\n"
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"\n";
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} // end namespace tfcompile
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} // end namespace tensorflow
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int main(int argc, char** argv) {
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tensorflow::tfcompile::MainFlags flags;
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flags.target_triple = "x86_64-pc-linux";
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flags.out_function_object = "out_model.o";
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flags.out_metadata_object = "out_helper.o";
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flags.out_header = "out.h";
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flags.entry_point = "entry";
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flags.debug_info_path_begin_marker = "";
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// Note that tfcompile.bzl's tf_library macro sets fast math flags as that is
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// generally the preferred case.
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std::vector<tensorflow::Flag> flag_list;
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AppendMainFlags(&flag_list, &flags);
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xla::AppendDebugOptionsFlags(&flag_list);
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tensorflow::string usage = tensorflow::tfcompile::kUsageHeader;
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usage += tensorflow::Flags::Usage(argv[0], flag_list);
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if (argc > 1 && absl::string_view(argv[1]) == "--help") {
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std::cerr << usage << "\n";
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return 0;
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}
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bool parsed_flags_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
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QCHECK(parsed_flags_ok) << "\n" << usage;
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tensorflow::port::InitMain(usage.c_str(), &argc, &argv);
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QCHECK(argc == 1) << "\nERROR: This command does not take any arguments "
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"other than flags. See --help.\n\n";
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tensorflow::Status status = tensorflow::tfcompile::Main(flags);
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if (status.code() == tensorflow::error::INVALID_ARGUMENT) {
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std::cerr << "INVALID ARGUMENTS: " << status.error_message() << "\n\n";
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return 1;
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} else {
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TF_QCHECK_OK(status);
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}
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return 0;
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}
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