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
55 lines
2.0 KiB
C++
55 lines
2.0 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_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
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#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
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#include "tensorflow/compiler/xla/service/cpu/target_machine_features.h"
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#include "tensorflow/compiler/xla/service/hlo_module.h"
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#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
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namespace xla {
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namespace cpu {
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// An HLO pass that canonicalizes the dimension numbers of all top-level
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// convolutions in the given module.
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//
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// In order to hit the fast path of using Eigen's convolution implementation, a
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// convolution's dimension numbers need to satisfy certain constraints (so
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// called canonical convolutions). This pass expands non-canonical convolutions
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// into reshapes and canonical convolutions, so that these non-canonical
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// convolutions can run faster.
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class ConvCanonicalization : public HloModulePass {
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public:
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explicit ConvCanonicalization(
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const TargetMachineFeatures* target_machine_features)
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: target_machine_features_(*target_machine_features) {}
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~ConvCanonicalization() override {}
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absl::string_view name() const override {
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return "convolution-canonicalization";
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}
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StatusOr<bool> Run(HloModule* module) override;
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private:
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const TargetMachineFeatures& target_machine_features_;
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};
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} // namespace cpu
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CONV_CANONICALIZATION_H_
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