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      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #include <stdio.h>
     17 #include <memory>
     18 #include <string>
     19 
     20 #include "tensorflow/compiler/xla/client/client.h"
     21 #include "tensorflow/compiler/xla/client/client_library.h"
     22 #include "tensorflow/compiler/xla/client/computation.h"
     23 #include "tensorflow/compiler/xla/client/local_client.h"
     24 #include "tensorflow/compiler/xla/service/computation_tracker.h"
     25 #include "tensorflow/compiler/xla/service/service.h"
     26 #include "tensorflow/compiler/xla/service/session.pb.h"
     27 #include "tensorflow/compiler/xla/statusor.h"
     28 #include "tensorflow/compiler/xla/types.h"
     29 #include "tensorflow/compiler/xla/xla_data.pb.h"
     30 #include "tensorflow/core/lib/gtl/array_slice.h"
     31 #include "tensorflow/core/platform/env.h"
     32 #include "tensorflow/core/platform/init_main.h"
     33 #include "tensorflow/core/platform/logging.h"
     34 
     35 namespace xla {
     36 namespace tools {
     37 
     38 void RealMain(tensorflow::gtl::ArraySlice<char*> args, bool compile) {
     39   LocalClient* client = ClientLibrary::LocalClientOrDie();
     40   LocalService* local_service =
     41       ClientLibrary::GetXlaService(client->platform());
     42   for (char* arg : args) {
     43     SessionModule session_module;
     44     TF_CHECK_OK(tensorflow::ReadBinaryProto(tensorflow::Env::Default(), arg,
     45                                             &session_module));
     46     auto computation_status = client->LoadSnapshot(session_module);
     47     if (!computation_status.ok()) {
     48       fprintf(stderr, "could not load snapshot for %s: %s\n", arg,
     49               computation_status.status().ToString().c_str());
     50       continue;
     51     }
     52     Computation computation = computation_status.ConsumeValueOrDie();
     53 
     54     if (compile) {
     55       std::unique_ptr<ProgramShape> program_shape =
     56           client->GetComputationShape(computation).ConsumeValueOrDie();
     57 
     58       std::vector<const Shape*> layouts;
     59       layouts.reserve(program_shape->parameters_size());
     60       for (int i = 0; i < program_shape->parameters_size(); ++i) {
     61         layouts.push_back(&program_shape->parameters(i));
     62       }
     63 
     64       ExecutableBuildOptions build_options;
     65       build_options.set_device_ordinal(0);
     66       build_options.set_result_layout(program_shape->result());
     67       StatusOr<std::unique_ptr<Executable>> executable =
     68           local_service->CompileExecutable(computation.handle(), layouts,
     69                                            build_options);
     70 
     71       const HloModule& module = executable.ValueOrDie()->module();
     72 
     73       fprintf(stdout, "HLO compiled for %s backend:\n%s\n",
     74               local_service->backend().platform()->Name().c_str(),
     75               module.ToString(HloPrintOptions::ShortParsable()).c_str());
     76     } else {
     77       const ComputationTracker& tracker = local_service->computation_tracker();
     78       UserComputation* user_computation =
     79           tracker.Resolve(computation.handle()).ConsumeValueOrDie();
     80       VersionedComputationHandle versioned_handle =
     81           user_computation->GetVersionedHandle();
     82       std::unique_ptr<HloModule> module =
     83           tracker.BuildHloModule(versioned_handle, HloModuleConfig())
     84               .ConsumeValueOrDie();
     85 
     86       fprintf(stdout, "%s\n",
     87               module->ToString(HloPrintOptions::ShortParsable()).c_str());
     88     }
     89   }
     90 }
     91 
     92 }  // namespace tools
     93 }  // namespace xla
     94 
     95 int main(int argc, char** argv) {
     96   bool compile = false;
     97   std::vector<tensorflow::Flag> flag_list = {
     98       {"compile", &compile,
     99        "If true, compile the computation using the default client before "
    100        "dumping the HLO. Otherwise dump the raw (uncompiled) HLO."},
    101   };
    102   const xla::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
    103   bool parsed_flags_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
    104   QCHECK(parsed_flags_ok) << "\n" << usage;
    105 
    106   tensorflow::port::InitMain(usage.c_str(), &argc, &argv);
    107   QCHECK(argc > 1) << "\nERROR: must specify at least one module\n" << usage;
    108 
    109   tensorflow::gtl::ArraySlice<char*> args(argv, argc);
    110   args.pop_front();  // Pop off the binary name, argv[0]
    111   xla::tools::RealMain(args, compile);
    112   return 0;
    113 }
    114