9041215899
We don't allow implicit broadcasts anymore at HLO level, so we can simplify/remove a lot of code. PiperOrigin-RevId: 237024811
59 lines
2.0 KiB
C++
59 lines
2.0 KiB
C++
/* Copyright 2018 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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#include "tensorflow/compiler/xla/service/despecializer.h"
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#include "tensorflow/compiler/xla/service/bfloat16_normalization.h"
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#include "tensorflow/compiler/xla/service/defuser.h"
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#include "tensorflow/compiler/xla/service/hlo_memory_scheduler.h"
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namespace xla {
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namespace {
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// Pass which strips control dependencies from all instructions in the module.
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class ControlDepRemover : public HloModulePass {
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public:
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ControlDepRemover() = default;
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absl::string_view name() const override { return "control-dep-remover"; }
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StatusOr<bool> Run(HloModule* module) override {
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bool changed = false;
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for (HloComputation* computation : module->computations()) {
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for (HloInstruction* instruction : computation->instructions()) {
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changed = changed || !instruction->control_predecessors().empty();
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TF_RETURN_IF_ERROR(instruction->DropAllControlDeps());
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}
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}
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return changed;
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}
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};
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} // namespace
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Despecializer::Despecializer() : pipeline_("despecializer") {
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// TODO(b/70588125): Also deal with window reversal in a fast way.
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pipeline_.AddPass<HloDescheduler>();
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pipeline_.AddPass<ControlDepRemover>();
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pipeline_.AddPass<Defuser>();
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pipeline_.AddPass<BFloat16MixedPrecisionRemoval>();
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}
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StatusOr<bool> Despecializer::Run(HloModule* module) {
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return pipeline_.Run(module);
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}
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} // namespace xla
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