| /* Copyright 2019 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/decode_constant.h" |
| #include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h" |
| #include "tensorflow/compiler/mlir/tfjs/transforms/passes.h" |
| |
| namespace mlir { |
| /// Create a pass to convert from the TFExecutor to the TF control dialect. |
| std::unique_ptr<OperationPass<FuncOp>> |
| CreateTFExecutorToControlDialectConversion(); |
| } // namespace mlir |
| |
| 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()); |
| } |
| |
| } // namespace tensorflow |