text: /* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  
  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 "tensorflow/compiler/mlir/tfjs/tf_tfjs_passes.h"
  
  #include <memory>
  
  #include "mlir/Pass/Pass.h"  // from @llvm-project
  #include "mlir/Pass/PassManager.h"  // from @llvm-project
  #include "mlir/Transforms/Passes.h"  // from @llvm-project
  #include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
  #include "tensorflow/compiler/mlir/tfjs/transforms/passes.h"
  
  namespace tensorflow {
  
  void AddTFToTFJSConversionPasses(mlir::OpPassManager* pm) {
    // Then we pass the MLIR module through the TF standard pipeline, which for
    mlir::TF::StandardPipelineOptions tf_options;
    tf_options.enable_inliner = true;
    mlir::TF::CreateTFStandardPipeline(*pm, tf_options);
  
    // freeze global tensors.
    pm->addPass(mlir::tf_saved_model::CreateFreezeGlobalTensorsPass());
  
    // TFJS dialect passes.
    pm->addPass(mlir::tfjs::CreateOptimizePass());
  
    // Canonicalize, CSE etc.
    pm->addNestedPass<mlir::FuncOp>(mlir::createCanonicalizerPass());
    pm->addNestedPass<mlir::FuncOp>(mlir::createCSEPass());
  
    // raise to executor dialect in order to use GraphDef converter
    pm->addNestedPass<mlir::FuncOp>(
        mlir::CreateFunctionalToExecutorDialectConversionPass());
    pm->addNestedPass<mlir::FuncOp>(mlir::CreateBreakUpIslandsPass());
  }
  
  }  // namespace tensorflow
  