The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ ``` Differential Revision: https://reviews.llvm.org/D150123
312 lines
12 KiB
C++
312 lines
12 KiB
C++
//===- LowerVectorMask.cpp - Lower 'vector.mask' operation ----------------===//
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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 target-independent rewrites and utilities to lower the
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// 'vector.mask' operation.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Vector/IR/VectorOps.h"
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#include "mlir/Dialect/Vector/Transforms/LoweringPatterns.h"
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#include "mlir/Dialect/Vector/Transforms/Passes.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#define DEBUG_TYPE "lower-vector-mask"
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namespace mlir {
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namespace vector {
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#define GEN_PASS_DEF_LOWERVECTORMASKPASS
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#include "mlir/Dialect/Vector/Transforms/Passes.h.inc"
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} // namespace vector
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} // namespace mlir
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using namespace mlir;
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using namespace mlir::vector;
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//===----------------------------------------------------------------------===//
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// populateVectorMaskOpLoweringPatterns
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//===----------------------------------------------------------------------===//
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namespace {
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/// Progressive lowering of CreateMaskOp.
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/// One:
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/// %x = vector.create_mask %a, ... : vector<dx...>
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/// is replaced by:
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/// %l = vector.create_mask ... : vector<...> ; one lower rank
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/// %0 = arith.cmpi "slt", %ci, %a |
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/// %1 = select %0, %l, %zeroes |
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/// %r = vector.insert %1, %pr [i] | d-times
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/// %x = ....
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/// until a one-dimensional vector is reached.
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class CreateMaskOpLowering : public OpRewritePattern<vector::CreateMaskOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::CreateMaskOp op,
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PatternRewriter &rewriter) const override {
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auto dstType = cast<VectorType>(op.getResult().getType());
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int64_t rank = dstType.getRank();
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if (rank <= 1)
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return rewriter.notifyMatchFailure(
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op, "0-D and 1-D vectors are handled separately");
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auto loc = op.getLoc();
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auto eltType = dstType.getElementType();
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int64_t dim = dstType.getDimSize(0);
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Value idx = op.getOperand(0);
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VectorType lowType =
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VectorType::get(dstType.getShape().drop_front(), eltType);
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Value trueVal = rewriter.create<vector::CreateMaskOp>(
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loc, lowType, op.getOperands().drop_front());
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Value falseVal = rewriter.create<arith::ConstantOp>(
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loc, lowType, rewriter.getZeroAttr(lowType));
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Value result = rewriter.create<arith::ConstantOp>(
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loc, dstType, rewriter.getZeroAttr(dstType));
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for (int64_t d = 0; d < dim; d++) {
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Value bnd =
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rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(d));
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Value val = rewriter.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt,
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bnd, idx);
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Value sel = rewriter.create<arith::SelectOp>(loc, val, trueVal, falseVal);
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auto pos = rewriter.getI64ArrayAttr(d);
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result =
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rewriter.create<vector::InsertOp>(loc, dstType, sel, result, pos);
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}
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rewriter.replaceOp(op, result);
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return success();
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}
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};
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/// Progressive lowering of ConstantMaskOp.
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/// One:
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/// %x = vector.constant_mask [a,b]
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/// is replaced by:
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/// %z = zero-result
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/// %l = vector.constant_mask [b]
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/// %4 = vector.insert %l, %z[0]
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/// ..
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/// %x = vector.insert %l, %..[a-1]
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/// until a one-dimensional vector is reached. All these operations
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/// will be folded at LLVM IR level.
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class ConstantMaskOpLowering : public OpRewritePattern<vector::ConstantMaskOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::ConstantMaskOp op,
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PatternRewriter &rewriter) const override {
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auto loc = op.getLoc();
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auto dstType = op.getType();
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auto eltType = dstType.getElementType();
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auto dimSizes = op.getMaskDimSizes();
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int64_t rank = dstType.getRank();
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if (rank == 0) {
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assert(dimSizes.size() == 1 &&
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"Expected exactly one dim size for a 0-D vector");
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bool value = cast<IntegerAttr>(dimSizes[0]).getInt() == 1;
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(
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op, dstType,
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DenseIntElementsAttr::get(
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VectorType::get(ArrayRef<int64_t>{}, rewriter.getI1Type()),
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ArrayRef<bool>{value}));
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return success();
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}
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// Scalable constant masks can only be lowered for the "none set" case.
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if (cast<VectorType>(dstType).isScalable()) {
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(
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op, DenseElementsAttr::get(dstType, false));
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return success();
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}
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int64_t trueDim = std::min(dstType.getDimSize(0),
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cast<IntegerAttr>(dimSizes[0]).getInt());
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if (rank == 1) {
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// Express constant 1-D case in explicit vector form:
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// [T,..,T,F,..,F].
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SmallVector<bool> values(dstType.getDimSize(0));
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for (int64_t d = 0; d < trueDim; d++)
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values[d] = true;
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rewriter.replaceOpWithNewOp<arith::ConstantOp>(
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op, dstType, rewriter.getBoolVectorAttr(values));
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return success();
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}
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VectorType lowType =
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VectorType::get(dstType.getShape().drop_front(), eltType);
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SmallVector<int64_t> newDimSizes;
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for (int64_t r = 1; r < rank; r++)
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newDimSizes.push_back(cast<IntegerAttr>(dimSizes[r]).getInt());
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Value trueVal = rewriter.create<vector::ConstantMaskOp>(
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loc, lowType, rewriter.getI64ArrayAttr(newDimSizes));
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Value result = rewriter.create<arith::ConstantOp>(
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loc, dstType, rewriter.getZeroAttr(dstType));
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for (int64_t d = 0; d < trueDim; d++) {
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auto pos = rewriter.getI64ArrayAttr(d);
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result =
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rewriter.create<vector::InsertOp>(loc, dstType, trueVal, result, pos);
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}
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rewriter.replaceOp(op, result);
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return success();
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}
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};
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} // namespace
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void mlir::vector::populateVectorMaskOpLoweringPatterns(
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RewritePatternSet &patterns, PatternBenefit benefit) {
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patterns.add<CreateMaskOpLowering, ConstantMaskOpLowering>(
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patterns.getContext(), benefit);
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}
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//===----------------------------------------------------------------------===//
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// populateVectorMaskLoweringPatternsForSideEffectingOps
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//===----------------------------------------------------------------------===//
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namespace {
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/// The `MaskOpRewritePattern` implements a pattern that follows a two-fold
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/// matching:
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/// 1. It matches a `vector.mask` operation.
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/// 2. It invokes `matchAndRewriteMaskableOp` on `MaskableOpInterface` nested
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/// in the matched `vector.mask` operation.
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///
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/// It is required that the replacement op in the pattern replaces the
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/// `vector.mask` operation and not the nested `MaskableOpInterface`. This
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/// approach allows having patterns that "stop" at every `vector.mask` operation
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/// and actually match the traits of its the nested `MaskableOpInterface`.
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template <class SourceOp>
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struct MaskOpRewritePattern : OpRewritePattern<MaskOp> {
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using OpRewritePattern<MaskOp>::OpRewritePattern;
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private:
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LogicalResult matchAndRewrite(MaskOp maskOp,
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PatternRewriter &rewriter) const final {
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auto maskableOp = cast<MaskableOpInterface>(maskOp.getMaskableOp());
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SourceOp sourceOp = dyn_cast<SourceOp>(maskableOp.getOperation());
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if (!sourceOp)
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return failure();
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return matchAndRewriteMaskableOp(sourceOp, maskOp, rewriter);
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}
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protected:
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virtual LogicalResult
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matchAndRewriteMaskableOp(SourceOp sourceOp, MaskingOpInterface maskingOp,
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PatternRewriter &rewriter) const = 0;
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};
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/// Lowers a masked `vector.transfer_read` operation.
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struct MaskedTransferReadOpPattern
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: public MaskOpRewritePattern<TransferReadOp> {
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public:
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using MaskOpRewritePattern<TransferReadOp>::MaskOpRewritePattern;
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LogicalResult
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matchAndRewriteMaskableOp(TransferReadOp readOp, MaskingOpInterface maskingOp,
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PatternRewriter &rewriter) const override {
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// TODO: The 'vector.mask' passthru is a vector and 'vector.transfer_read'
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// expects a scalar. We could only lower one to the other for cases where
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// the passthru is a broadcast of a scalar.
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if (maskingOp.hasPassthru())
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return rewriter.notifyMatchFailure(
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maskingOp, "Can't lower passthru to vector.transfer_read");
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// Replace the `vector.mask` operation.
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rewriter.replaceOpWithNewOp<TransferReadOp>(
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maskingOp.getOperation(), readOp.getVectorType(), readOp.getSource(),
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readOp.getIndices(), readOp.getPermutationMap(), readOp.getPadding(),
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maskingOp.getMask(), readOp.getInBounds().value_or(ArrayAttr()));
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return success();
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}
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};
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/// Lowers a masked `vector.transfer_write` operation.
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struct MaskedTransferWriteOpPattern
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: public MaskOpRewritePattern<TransferWriteOp> {
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public:
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using MaskOpRewritePattern<TransferWriteOp>::MaskOpRewritePattern;
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LogicalResult
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matchAndRewriteMaskableOp(TransferWriteOp writeOp,
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MaskingOpInterface maskingOp,
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PatternRewriter &rewriter) const override {
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Type resultType =
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writeOp.getResult() ? writeOp.getResult().getType() : Type();
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// Replace the `vector.mask` operation.
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rewriter.replaceOpWithNewOp<TransferWriteOp>(
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maskingOp.getOperation(), resultType, writeOp.getVector(),
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writeOp.getSource(), writeOp.getIndices(), writeOp.getPermutationMap(),
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maskingOp.getMask(), writeOp.getInBounds().value_or(ArrayAttr()));
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return success();
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}
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};
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/// Lowers a masked `vector.gather` operation.
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struct MaskedGatherOpPattern : public MaskOpRewritePattern<GatherOp> {
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public:
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using MaskOpRewritePattern<GatherOp>::MaskOpRewritePattern;
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LogicalResult
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matchAndRewriteMaskableOp(GatherOp gatherOp, MaskingOpInterface maskingOp,
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PatternRewriter &rewriter) const override {
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Value passthru = maskingOp.hasPassthru()
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? maskingOp.getPassthru()
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: rewriter.create<arith::ConstantOp>(
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gatherOp.getLoc(),
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rewriter.getZeroAttr(gatherOp.getVectorType()));
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// Replace the `vector.mask` operation.
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rewriter.replaceOpWithNewOp<GatherOp>(
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maskingOp.getOperation(), gatherOp.getVectorType(), gatherOp.getBase(),
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gatherOp.getIndices(), gatherOp.getIndexVec(), maskingOp.getMask(),
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passthru);
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return success();
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}
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};
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struct LowerVectorMaskPass
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: public vector::impl::LowerVectorMaskPassBase<LowerVectorMaskPass> {
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using Base::Base;
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void runOnOperation() override {
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Operation *op = getOperation();
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MLIRContext *context = op->getContext();
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RewritePatternSet loweringPatterns(context);
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populateVectorMaskLoweringPatternsForSideEffectingOps(loweringPatterns);
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if (failed(applyPatternsAndFoldGreedily(op, std::move(loweringPatterns))))
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signalPassFailure();
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}
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<vector::VectorDialect>();
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}
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};
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} // namespace
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/// Populates instances of `MaskOpRewritePattern` to lower masked operations
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/// with `vector.mask`. Patterns should rewrite the `vector.mask` operation and
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/// not its nested `MaskableOpInterface`.
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void vector::populateVectorMaskLoweringPatternsForSideEffectingOps(
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RewritePatternSet &patterns) {
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patterns.add<MaskedTransferReadOpPattern, MaskedTransferWriteOpPattern,
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MaskedGatherOpPattern>(patterns.getContext());
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}
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std::unique_ptr<Pass> mlir::vector::createLowerVectorMaskPass() {
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return std::make_unique<LowerVectorMaskPass>();
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}
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