wehub-resource-sync
77 lines
3.2 KiB
C++
77 lines
3.2 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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#ifndef TENSORFLOW_COMPILER_TF2XLA_MLIR_BRIDGE_PASS_H_
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#define TENSORFLOW_COMPILER_TF2XLA_MLIR_BRIDGE_PASS_H_
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#include <string>
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#include "tensorflow/compiler/mlir/tf2xla/mlir_bridge_rollout_policy.h"
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#include "absl/status/status.h"
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#include "llvm/ADT/StringRef.h"
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#include "mlir/IR/BuiltinOps.h" // from @llvm-project
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#include "tensorflow/compiler/jit/flags.h"
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#include "tensorflow/compiler/mlir/mlir_graph_optimization_pass.h"
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#include "tensorflow/core/common_runtime/device_set.h"
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#include "tensorflow/core/common_runtime/optimization_registry.h"
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#include "tensorflow/core/framework/function.h"
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#include "tensorflow/core/graph/graph.h"
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#include "tensorflow/core/platform/status.h"
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#include "tensorflow/core/protobuf/config.pb.h"
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namespace tensorflow {
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// This pass uses MLIR to implement all the conversion steps to target XLA from
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// a TensorFlow Function Graph. It is meant to expose a very limited set of
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// functionalities during the bring-up of MLIR-based bridge.
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class MlirBridgePass : public MlirOptimizationPass {
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public:
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llvm::StringRef name() const override { return "bridge"; }
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MlirOptimizationPassState GetPassState(
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const DeviceSet* device_set, const ConfigProto& config_proto,
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const Graph& graph,
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const FunctionLibraryDefinition& function_library) const override;
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// This should be used as a thin mapper around mlir::ModulePass::runOnModule
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// API integrated with the Tensorflow runtime.
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absl::Status Run(const std::string& function_name,
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const ConfigProto& config_proto, mlir::ModuleOp module,
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const Graph& graph,
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const FunctionLibraryDefinition& function_library) override;
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};
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// This pass uses MLIR to implement all the conversion steps to target XLA from
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// a TensorFlow V1 Graph. It is meant to expose a very limited set of
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// functionalities during the bring-up of MLIR-based bridge.
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class MlirBridgeV1CompatPass : public MlirV1CompatOptimizationPass {
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public:
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llvm::StringRef name() const override { return "bridge"; }
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MlirOptimizationPassState GetPassState(
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const DeviceSet* device_set, const ConfigProto& config_proto,
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const Graph& graph,
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const FunctionLibraryDefinition& function_library) const override;
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// This should be used as a thin mapper around mlir::ModulePass::runOnModule
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// API integrated with the Tensorflow runtime.
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absl::Status Run(const GraphOptimizationPassOptions& options,
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mlir::ModuleOp module) override;
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};
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} // namespace tensorflow
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#endif // TENSORFLOW_COMPILER_TF2XLA_MLIR_BRIDGE_PASS_H_
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