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Wrap remaining unfused instructions in fusions before conversion to L…
…HLO. After this, `HloToLhloModule` no longer creates additional fusions, which means we can easily produce a mapping from MLIR operation to corresponding HLO instruction (and their types will match). After this, we can remove the MLIR->HLO conversion step from `ir_emitter_unnested`, which means codegen will again have access to valid (properly connected) HLO. This should be an NFC. PiperOrigin-RevId: 558699182
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| Original file line number | Diff line number | Diff line change |
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| /* Copyright 2023 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
| #include "tensorflow/compiler/xla/service/gpu/fusion_wrapper.h" | ||
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| #include <functional> | ||
| #include <vector> | ||
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| #include "absl/container/flat_hash_set.h" | ||
| #include "absl/strings/string_view.h" | ||
| #include "tensorflow/compiler/xla/hlo/ir/hlo_instruction.h" | ||
| #include "tensorflow/compiler/xla/hlo/ir/hlo_opcode.h" | ||
| #include "tensorflow/compiler/xla/service/gpu/gpu_fusible.h" | ||
| #include "tensorflow/compiler/xla/status_macros.h" | ||
| #include "tensorflow/compiler/xla/statusor.h" | ||
| #include "tensorflow/tsl/platform/errors.h" | ||
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| namespace xla { | ||
| namespace gpu { | ||
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| StatusOr<bool> FusionWrapper::Run( | ||
| HloModule* module, | ||
| const absl::flat_hash_set<absl::string_view>& execution_threads) { | ||
| auto instructions = module->entry_computation()->MakeInstructionPostOrder(); | ||
| bool changed = false; | ||
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| std::function<Status(HloInstruction*)> handle_instruction; | ||
| handle_instruction = [&](HloInstruction* instruction) -> Status { | ||
| switch (instruction->opcode()) { | ||
| case HloOpcode::kConditional: | ||
| case HloOpcode::kWhile: | ||
| for (auto* computation : instruction->called_computations()) { | ||
| for (auto* inner_instruction : | ||
| computation->MakeInstructionPostOrder()) { | ||
| TF_RETURN_IF_ERROR(handle_instruction(inner_instruction)); | ||
| } | ||
| } | ||
| break; | ||
| case HloOpcode::kAbs: | ||
| case HloOpcode::kAdd: | ||
| case HloOpcode::kAnd: | ||
| case HloOpcode::kAtan2: | ||
| case HloOpcode::kBitcastConvert: | ||
| case HloOpcode::kBroadcast: | ||
| case HloOpcode::kCeil: | ||
| case HloOpcode::kCbrt: | ||
| case HloOpcode::kClamp: | ||
| case HloOpcode::kClz: | ||
| case HloOpcode::kCompare: | ||
| case HloOpcode::kComplex: | ||
| case HloOpcode::kConcatenate: | ||
| case HloOpcode::kConvert: | ||
| case HloOpcode::kCos: | ||
| case HloOpcode::kDivide: | ||
| case HloOpcode::kDot: | ||
| case HloOpcode::kDynamicSlice: | ||
| case HloOpcode::kDynamicUpdateSlice: | ||
| case HloOpcode::kExp: | ||
| case HloOpcode::kExpm1: | ||
| case HloOpcode::kFloor: | ||
| case HloOpcode::kGather: | ||
| case HloOpcode::kImag: | ||
| case HloOpcode::kIota: | ||
| case HloOpcode::kIsFinite: | ||
| case HloOpcode::kLog: | ||
| case HloOpcode::kLog1p: | ||
| case HloOpcode::kMap: | ||
| case HloOpcode::kMaximum: | ||
| case HloOpcode::kMinimum: | ||
| case HloOpcode::kMultiply: | ||
| case HloOpcode::kNegate: | ||
| case HloOpcode::kNot: | ||
| case HloOpcode::kOr: | ||
| case HloOpcode::kPad: | ||
| case HloOpcode::kPopulationCount: | ||
| case HloOpcode::kPower: | ||
| case HloOpcode::kReal: | ||
| case HloOpcode::kReshape: | ||
| case HloOpcode::kReducePrecision: | ||
| case HloOpcode::kReduceWindow: | ||
| case HloOpcode::kRemainder: | ||
| case HloOpcode::kReverse: | ||
| case HloOpcode::kRoundNearestAfz: | ||
| case HloOpcode::kRoundNearestEven: | ||
| case HloOpcode::kRsqrt: | ||
| case HloOpcode::kSelect: | ||
| case HloOpcode::kShiftLeft: | ||
| case HloOpcode::kShiftRightLogical: | ||
| case HloOpcode::kShiftRightArithmetic: | ||
| case HloOpcode::kSign: | ||
| case HloOpcode::kSin: | ||
| case HloOpcode::kSlice: | ||
| case HloOpcode::kSqrt: | ||
| case HloOpcode::kSubtract: | ||
| case HloOpcode::kStochasticConvert: | ||
| case HloOpcode::kTan: | ||
| case HloOpcode::kTanh: | ||
| case HloOpcode::kTranspose: | ||
| case HloOpcode::kXor: | ||
| case HloOpcode::kCopy: | ||
| case HloOpcode::kReduce: { | ||
| auto* computation = instruction->parent(); | ||
| auto* fusion_instruction = | ||
| computation->AddInstruction(HloInstruction::CreateFusion( | ||
| instruction->shape(), | ||
| ChooseFusionKind(*instruction /*unused but required*/, | ||
| *instruction), | ||
| instruction)); | ||
| instruction->GetModule()->SetAndUniquifyInstrName( | ||
| fusion_instruction, absl::StrCat("wrapped_", instruction->name())); | ||
| if (module->has_schedule()) { | ||
| module->schedule().replace_instruction(computation, instruction, | ||
| fusion_instruction); | ||
| } | ||
| TF_RETURN_IF_ERROR( | ||
| fusion_instruction->CopyAllControlDepsFrom(instruction)); | ||
| TF_RETURN_IF_ERROR(instruction->DropAllControlDeps()); | ||
| TF_RETURN_IF_ERROR(instruction->ReplaceAllUsesWith(fusion_instruction)); | ||
| TF_RETURN_IF_ERROR(computation->RemoveInstruction(instruction)); | ||
| changed = true; | ||
| break; | ||
| } | ||
| default: | ||
| break; | ||
| } | ||
| return OkStatus(); | ||
| }; | ||
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| for (auto* instruction : instructions) { | ||
| TF_RETURN_IF_ERROR(handle_instruction(instruction)); | ||
| } | ||
| return changed; | ||
| } | ||
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| } // namespace gpu | ||
| } // namespace xla |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,42 @@ | ||
| /* Copyright 2023 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
| #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_FUSION_WRAPPER_H_ | ||
| #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_FUSION_WRAPPER_H_ | ||
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| #include "absl/container/flat_hash_set.h" | ||
| #include "absl/strings/string_view.h" | ||
| #include "tensorflow/compiler/xla/hlo/ir/hlo_module.h" | ||
| #include "tensorflow/compiler/xla/service/hlo_pass_interface.h" | ||
| #include "tensorflow/compiler/xla/statusor.h" | ||
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| namespace xla { | ||
| namespace gpu { | ||
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| // Wraps leftover unfused instruction that are in the entry computation that | ||
| // have no LHLO equivalent in fusions containing just that instruction. | ||
| class FusionWrapper : public HloModulePass { | ||
| public: | ||
| absl::string_view name() const override { return "fusion-wrapper"; } | ||
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| using HloPassInterface::Run; | ||
| StatusOr<bool> Run( | ||
| HloModule* module, | ||
| const absl::flat_hash_set<absl::string_view>& execution_threads) override; | ||
| }; | ||
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| } // namespace gpu | ||
| } // namespace xla | ||
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| #endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_FUSION_WRAPPER_H_ |
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