65066c0277
This aims to clean-up confusing uses of builder.createOrFold<ConstantOp> since folding of constants fails.
94 lines
3.5 KiB
C++
94 lines
3.5 KiB
C++
//===- ConcatOpPatterns.cpp - Patterns related to tensor.concat lowering --===//
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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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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Arith/Utils/Utils.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tensor/Transforms/Transforms.h"
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#include "mlir/IR/PatternMatch.h"
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using namespace mlir;
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using namespace mlir::tensor;
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namespace {
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/// Decompose `tensor.concat` into `tensor.empty` and a chain of slice inserts.
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///
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/// %concat = tensor.concat dim(1) %0, %1 :
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/// (tensor<2x3xf32>, tensor<2x4xf32>) -> tensor<2x7xf32>
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///
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/// Becomes
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///
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/// %empty = tensor.empty() : tensor<2x7xf32>
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/// %insert0 = tensor.insert_slice %0 into %empty[0, 0][2, 3][1, 1]
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/// %concat = tensor.insert_slice %1 into %insert0[0, 3][2, 4][1, 1]
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struct DecomposeTensorConcatOp : public OpRewritePattern<ConcatOp> {
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using OpRewritePattern<ConcatOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(ConcatOp concatOp,
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PatternRewriter &rewriter) const override {
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Location loc = concatOp.getLoc();
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FailureOr<Value> dest =
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tensor::getOrCreateDestination(rewriter, loc, concatOp->getResult(0));
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if (failed(dest))
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return failure();
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auto empty = dest->getDefiningOp<tensor::EmptyOp>();
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if (!empty)
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return failure();
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int64_t dim = concatOp.getDim();
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Value dimValue =
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rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(dim));
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int64_t rank = concatOp.getResultType().getRank();
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SmallVector<OpFoldResult> strides(rank, rewriter.getIndexAttr(1));
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SmallVector<OpFoldResult> offsets(rank, rewriter.getIndexAttr(0));
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// Compute the partial sums for the slice offsets.
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AffineExpr sum = rewriter.getAffineDimExpr(0);
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SmallVector<AffineExpr> partialSums = {sum};
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SmallVector<OpFoldResult> offsetStrides = {rewriter.getIndexAttr(0)};
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for (auto [idx, input] :
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llvm::enumerate(concatOp.getInputs().drop_back())) {
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sum = sum + rewriter.getAffineDimExpr(idx + 1);
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partialSums.push_back(sum);
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offsetStrides.push_back(
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rewriter.createOrFold<tensor::DimOp>(loc, input, dimValue));
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}
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auto partialSumMap = AffineMap::get(concatOp.getInputs().size(), 0,
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partialSums, rewriter.getContext());
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SmallVector<OpFoldResult> dimOffsets =
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affine::makeComposedFoldedMultiResultAffineApply(
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rewriter, loc, partialSumMap, offsetStrides);
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// Construct the chain of insert_slice ops into the destination.
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Value result = *dest;
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for (auto [input, offset] :
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llvm::zip_equal(concatOp.getInputs(), dimOffsets)) {
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SmallVector<OpFoldResult> sizes =
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tensor::getMixedSizes(rewriter, loc, input);
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offsets[dim] = offset;
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result = rewriter.createOrFold<tensor::InsertSliceOp>(
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loc, input, result, offsets, sizes, strides);
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}
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rewriter.replaceOpWithNewOp<tensor::CastOp>(
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concatOp, concatOp.getResultType(), 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::tensor::populateDecomposeTensorConcatPatterns(
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RewritePatternSet &patterns) {
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patterns.add<DecomposeTensorConcatOp>(patterns.getContext());
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}
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