1914c410b2
Update eager/context.py and tfe_wrapper to support returning the real value of mlir_bridge_rollout (enabled/disabled/unspecified) instead of a bool. This gives users a clearer signal of whether or not the mlir bridge is being used. At the moment, the mlir bridge is only enabled when mlir_bridge_rollout is set to enabled but this will change in the future. PiperOrigin-RevId: 338124102 Change-Id: I5c93cbdd2815a698e6b41244db8eed716f4988e6
73 lines
3.1 KiB
C++
73 lines
3.1 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 "llvm/ADT/StringRef.h"
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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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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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bool IsEnabled(const ConfigProto& config_proto) const override {
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return config_proto.experimental().enable_mlir_bridge() ||
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config_proto.experimental().mlir_bridge_rollout() ==
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tensorflow::ConfigProto::Experimental::
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MLIR_BRIDGE_ROLLOUT_ENABLED ||
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tensorflow::GetMlirCommonFlags()->tf_mlir_enable_mlir_bridge ==
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tensorflow::ConfigProto::Experimental::
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MLIR_BRIDGE_ROLLOUT_ENABLED;
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}
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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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Status Run(const ConfigProto& config_proto, mlir::ModuleOp module) 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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bool IsEnabled(const ConfigProto& config_proto) const override {
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return config_proto.experimental().enable_mlir_bridge() ||
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config_proto.experimental().mlir_bridge_rollout() ==
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tensorflow::ConfigProto::Experimental::
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MLIR_BRIDGE_ROLLOUT_ENABLED ||
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tensorflow::GetMlirCommonFlags()->tf_mlir_enable_mlir_bridge ==
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tensorflow::ConfigProto::Experimental::
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MLIR_BRIDGE_ROLLOUT_ENABLED;
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}
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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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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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