eda6f907d2
Now that dialect constructors are generated in the .cpp file, we can drop all of the dependent dialect includes from the .h file. Differential Revision: https://reviews.llvm.org/D124298
51 lines
1.7 KiB
C++
51 lines
1.7 KiB
C++
//===- TestPadFusion.cpp - Test fusion of pad op with Linalg ops ---------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements a pass for testing fusion of pad ops with its producer
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// Linalg op.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Pass/PassManager.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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using namespace mlir;
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namespace {
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struct TestPadFusionPass
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: public PassWrapper<TestPadFusionPass, OperationPass<>> {
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MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestPadFusionPass)
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry
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.insert<AffineDialect, linalg::LinalgDialect, tensor::TensorDialect>();
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}
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StringRef getArgument() const final { return "test-linalg-pad-fusion"; }
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StringRef getDescription() const final { return "Test PadOp fusion"; }
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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linalg::populateFuseTensorPadWithProducerLinalgOpPatterns(patterns);
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if (failed(
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applyPatternsAndFoldGreedily(getOperation(), std::move(patterns))))
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return signalPassFailure();
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}
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};
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} // namespace
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namespace mlir {
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namespace test {
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void registerTestPadFusion() { PassRegistration<TestPadFusionPass>(); }
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} // namespace test
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} // namespace mlir
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