59 lines
2.0 KiB
C++
59 lines
2.0 KiB
C++
/* Copyright 2019 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 "mlir-hlo/Dialect/mhlo/IR/hlo_ops.h"
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#include "mlir-hlo/Dialect/mhlo/transforms/rewriters.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/IR/MLIRContext.h"
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#include "mlir/IR/Operation.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/DialectConversion.h"
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namespace mlir {
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namespace mhlo {
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namespace {
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struct TestMaterializeBroadcastsPass
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: public PassWrapper<TestMaterializeBroadcastsPass, FunctionPass> {
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void runOnFunction() override {
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ConversionTarget conversionTarget(getContext());
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OwningRewritePatternList conversionPatterns;
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// Consider the mhlo dialect legal for tests.
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conversionTarget.addLegalDialect<MhloDialect>();
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// The conversion uses helpers from the Standard dialect.
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conversionTarget.addLegalDialect<mlir::StandardOpsDialect>();
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SetupMaterializeBroadcastsLegality(&getContext(), &conversionTarget);
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PopulateMaterializeBroadcastsPatterns(&getContext(), &conversionPatterns);
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if (failed(applyPartialConversion(getFunction(), conversionTarget,
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conversionPatterns))) {
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return signalPassFailure();
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}
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}
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};
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} // namespace
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std::unique_ptr<::mlir::Pass> createTestMaterializeBroadcastsPass() {
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return std::make_unique<TestMaterializeBroadcastsPass>();
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}
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} // namespace mhlo
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} // namespace mlir
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