f8655c08cf
Derive HloModulePass and HloModuleGroupPass from HloPassInterface which run module-scoped and module-group-scoped respectively. Replace all existing uses of HloPassInterface with HloModulePass because all existing passes are module-scoped. Also rewrite HloPassPipeline to support both module-scoped and module-group-scoped passes. PiperOrigin-RevId: 213629604
41 lines
1.5 KiB
C++
41 lines
1.5 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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// Flatten the call graph for an HLO module into a tree.
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#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_FLATTEN_CALL_GRAPH_H_
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#define TENSORFLOW_COMPILER_XLA_SERVICE_FLATTEN_CALL_GRAPH_H_
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#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
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#include "tensorflow/compiler/xla/statusor.h"
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namespace xla {
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// Flattening associates each call site with a unique computation (for
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// sequential calling contexts) This simplifies buffer assignment and
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// points-to analysis (see b/36865746 for details).
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class FlattenCallGraph : public HloModulePass {
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public:
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absl::string_view name() const override { return "flatten-call-graph"; }
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// Duplicates computations called from multiple call- or while-nodes to
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// flatten the call graph.
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StatusOr<bool> Run(HloModule* module) override;
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};
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_SERVICE_FLATTEN_CALL_GRAPH_H_
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