9816edc9f3
The vector.extract assembly format currently only contains the source type, for example: %1 = vector.extract %0[1] : vector<3x7x8xf32> it's not immediately obvious if this is the source or result type. This patch improves the assembly format to make this clearer, so the above becomes: %1 = vector.extract %0[1] : vector<7x8xf32> from vector<3x7x8xf32>
361 lines
14 KiB
C++
361 lines
14 KiB
C++
//===- LowerVectorShapeCast.cpp - Lower 'vector.shape_cast' 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.shape_cast' operation.
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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/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/SCF/IR/SCF.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Utils/IndexingUtils.h"
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#include "mlir/Dialect/Utils/StructuredOpsUtils.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/VectorRewritePatterns.h"
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#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
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#include "mlir/IR/BuiltinAttributeInterfaces.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/ImplicitLocOpBuilder.h"
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#include "mlir/IR/Location.h"
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#include "mlir/IR/Matchers.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/TypeUtilities.h"
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#include "mlir/Interfaces/VectorInterfaces.h"
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#include "mlir/Support/LogicalResult.h"
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#define DEBUG_TYPE "vector-shape-cast-lowering"
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using namespace mlir;
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using namespace mlir::vector;
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namespace {
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/// ShapeOp 2D -> 1D downcast serves the purpose of flattening 2-D to 1-D
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/// vectors progressively on the way to target llvm.matrix intrinsics.
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/// This iterates over the most major dimension of the 2-D vector and performs
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/// rewrites into:
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/// vector.extract from 2-D + vector.insert_strided_slice offset into 1-D
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class ShapeCastOp2DDownCastRewritePattern
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: public OpRewritePattern<vector::ShapeCastOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::ShapeCastOp op,
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PatternRewriter &rewriter) const override {
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auto sourceVectorType = op.getSourceVectorType();
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auto resultVectorType = op.getResultVectorType();
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if (sourceVectorType.isScalable() || resultVectorType.isScalable())
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return failure();
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if (sourceVectorType.getRank() != 2 || resultVectorType.getRank() != 1)
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return failure();
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auto loc = op.getLoc();
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Value desc = rewriter.create<arith::ConstantOp>(
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loc, resultVectorType, rewriter.getZeroAttr(resultVectorType));
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unsigned mostMinorVectorSize = sourceVectorType.getShape()[1];
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for (int64_t i = 0, e = sourceVectorType.getShape().front(); i != e; ++i) {
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Value vec = rewriter.create<vector::ExtractOp>(loc, op.getSource(), i);
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desc = rewriter.create<vector::InsertStridedSliceOp>(
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loc, vec, desc,
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/*offsets=*/i * mostMinorVectorSize, /*strides=*/1);
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}
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rewriter.replaceOp(op, desc);
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return success();
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}
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};
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/// ShapeOp 1D -> 2D upcast serves the purpose of unflattening 2-D from 1-D
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/// vectors progressively.
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/// This iterates over the most major dimension of the 2-D vector and performs
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/// rewrites into:
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/// vector.extract_strided_slice from 1-D + vector.insert into 2-D
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/// Note that 1-D extract_strided_slice are lowered to efficient vector.shuffle.
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class ShapeCastOp2DUpCastRewritePattern
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: public OpRewritePattern<vector::ShapeCastOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::ShapeCastOp op,
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PatternRewriter &rewriter) const override {
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auto sourceVectorType = op.getSourceVectorType();
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auto resultVectorType = op.getResultVectorType();
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if (sourceVectorType.isScalable() || resultVectorType.isScalable())
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return failure();
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if (sourceVectorType.getRank() != 1 || resultVectorType.getRank() != 2)
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return failure();
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auto loc = op.getLoc();
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Value desc = rewriter.create<arith::ConstantOp>(
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loc, resultVectorType, rewriter.getZeroAttr(resultVectorType));
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unsigned mostMinorVectorSize = resultVectorType.getShape()[1];
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for (int64_t i = 0, e = resultVectorType.getShape().front(); i != e; ++i) {
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Value vec = rewriter.create<vector::ExtractStridedSliceOp>(
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loc, op.getSource(), /*offsets=*/i * mostMinorVectorSize,
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/*sizes=*/mostMinorVectorSize,
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/*strides=*/1);
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desc = rewriter.create<vector::InsertOp>(loc, vec, desc, i);
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}
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rewriter.replaceOp(op, desc);
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return success();
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}
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};
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static void incIdx(llvm::MutableArrayRef<int64_t> idx, VectorType tp,
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int dimIdx, int initialStep = 1) {
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int step = initialStep;
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for (int d = dimIdx; d >= 0; d--) {
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idx[d] += step;
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if (idx[d] >= tp.getDimSize(d)) {
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idx[d] = 0;
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step = 1;
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} else {
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break;
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}
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}
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}
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// We typically should not lower general shape cast operations into data
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// movement instructions, since the assumption is that these casts are
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// optimized away during progressive lowering. For completeness, however,
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// we fall back to a reference implementation that moves all elements
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// into the right place if we get here.
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class ShapeCastOpRewritePattern : public OpRewritePattern<vector::ShapeCastOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::ShapeCastOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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auto sourceVectorType = op.getSourceVectorType();
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auto resultVectorType = op.getResultVectorType();
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if (sourceVectorType.isScalable() || resultVectorType.isScalable())
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return failure();
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// Special case 2D / 1D lowerings with better implementations.
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// TODO: make is ND / 1D to allow generic ND -> 1D -> MD.
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int64_t srcRank = sourceVectorType.getRank();
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int64_t resRank = resultVectorType.getRank();
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if ((srcRank == 2 && resRank == 1) || (srcRank == 1 && resRank == 2))
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return failure();
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// Generic ShapeCast lowering path goes all the way down to unrolled scalar
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// extract/insert chains.
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// TODO: consider evolving the semantics to only allow 1D source or dest and
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// drop this potentially very expensive lowering.
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// Compute number of elements involved in the reshape.
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int64_t numElts = 1;
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for (int64_t r = 0; r < srcRank; r++)
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numElts *= sourceVectorType.getDimSize(r);
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// Replace with data movement operations:
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// x[0,0,0] = y[0,0]
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// x[0,0,1] = y[0,1]
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// x[0,1,0] = y[0,2]
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// etc., incrementing the two index vectors "row-major"
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// within the source and result shape.
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SmallVector<int64_t> srcIdx(srcRank);
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SmallVector<int64_t> resIdx(resRank);
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Value result = rewriter.create<arith::ConstantOp>(
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loc, resultVectorType, rewriter.getZeroAttr(resultVectorType));
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for (int64_t i = 0; i < numElts; i++) {
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if (i != 0) {
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incIdx(srcIdx, sourceVectorType, srcRank - 1);
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incIdx(resIdx, resultVectorType, resRank - 1);
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}
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Value extract;
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if (srcRank == 0) {
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// 0-D vector special case
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assert(srcIdx.empty() && "Unexpected indices for 0-D vector");
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extract = rewriter.create<vector::ExtractElementOp>(
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loc, op.getSourceVectorType().getElementType(), op.getSource());
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} else {
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extract =
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rewriter.create<vector::ExtractOp>(loc, op.getSource(), srcIdx);
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}
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if (resRank == 0) {
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// 0-D vector special case
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assert(resIdx.empty() && "Unexpected indices for 0-D vector");
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result = rewriter.create<vector::InsertElementOp>(loc, extract, result);
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} else {
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result =
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rewriter.create<vector::InsertOp>(loc, extract, result, resIdx);
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}
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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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/// A shape_cast lowering for scalable vectors with a single trailing scalable
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/// dimension. This is similar to the general shape_cast lowering but makes use
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/// of vector.scalable.insert and vector.scalable.extract to move elements a
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/// subvector at a time.
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///
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/// E.g.:
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/// ```
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/// // Flatten scalable vector
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/// %0 = vector.shape_cast %arg0 : vector<2x1x[4]xi32> to vector<[8]xi32>
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/// ```
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/// is rewritten to:
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/// ```
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/// // Flatten scalable vector
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/// %c = arith.constant dense<0> : vector<[8]xi32>
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/// %0 = vector.extract %arg0[0, 0] : vector<[4]xi32> from vector<2x1x[4]xi32>
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/// %1 = vector.scalable.insert %0, %c[0] : vector<[4]xi32> into vector<[8]xi32>
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/// %2 = vector.extract %arg0[1, 0] : vector<[4]xi32> from vector<2x1x[4]xi32>
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/// %3 = vector.scalable.insert %2, %1[4] : vector<[4]xi32> into vector<[8]xi32>
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/// ```
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/// or:
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/// ```
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/// // Un-flatten scalable vector
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/// %0 = vector.shape_cast %arg0 : vector<[8]xi32> to vector<2x1x[4]xi32>
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/// ```
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/// is rewritten to:
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/// ```
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/// // Un-flatten scalable vector
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/// %c = arith.constant dense<0> : vector<2x1x[4]xi32>
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/// %0 = vector.scalable.extract %arg0[0] : vector<[4]xi32> from vector<[8]xi32>
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/// %1 = vector.insert %0, %c [0, 0] : vector<[4]xi32> into vector<2x1x[4]xi32>
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/// %2 = vector.scalable.extract %arg0[4] : vector<[4]xi32> from vector<[8]xi32>
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/// %3 = vector.insert %2, %1 [1, 0] : vector<[4]xi32> into vector<2x1x[4]xi32>
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/// ```
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class ScalableShapeCastOpRewritePattern
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: public OpRewritePattern<vector::ShapeCastOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(vector::ShapeCastOp op,
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PatternRewriter &rewriter) const override {
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Location loc = op.getLoc();
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auto sourceVectorType = op.getSourceVectorType();
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auto resultVectorType = op.getResultVectorType();
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auto srcRank = sourceVectorType.getRank();
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auto resRank = resultVectorType.getRank();
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// This can only lower shape_casts where both the source and result types
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// have a single trailing scalable dimension. This is because there are no
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// legal representation of other scalable types in LLVM (and likely won't be
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// soon). There are also (currently) no operations that can index or extract
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// from >= 2D scalable vectors or scalable vectors of fixed vectors.
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if (!isTrailingDimScalable(sourceVectorType) ||
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!isTrailingDimScalable(resultVectorType)) {
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return failure();
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}
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// The sizes of the trailing dimension of the source and result vectors, the
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// size of subvector to move, and the number of elements in the vectors.
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// These are "min" sizes as they are the size when vscale == 1.
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auto minSourceTrailingSize = sourceVectorType.getShape().back();
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auto minResultTrailingSize = resultVectorType.getShape().back();
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auto minExtractionSize =
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std::min(minSourceTrailingSize, minResultTrailingSize);
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int64_t minNumElts = 1;
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for (auto size : sourceVectorType.getShape())
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minNumElts *= size;
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// The subvector type to move from the source to the result. Note that this
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// is a scalable vector. This rewrite will generate code in terms of the
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// "min" size (vscale == 1 case), that scales to any vscale.
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auto extractionVectorType = VectorType::get(
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{minExtractionSize}, sourceVectorType.getElementType(), {true});
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Value result = rewriter.create<arith::ConstantOp>(
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loc, resultVectorType, rewriter.getZeroAttr(resultVectorType));
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SmallVector<int64_t> srcIdx(srcRank);
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SmallVector<int64_t> resIdx(resRank);
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// TODO: Try rewriting this with StaticTileOffsetRange (from IndexingUtils)
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// once D150000 lands.
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Value currentResultScalableVector;
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Value currentSourceScalableVector;
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for (int64_t i = 0; i < minNumElts; i += minExtractionSize) {
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// 1. Extract a scalable subvector from the source vector.
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if (!currentSourceScalableVector) {
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if (srcRank != 1) {
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currentSourceScalableVector = rewriter.create<vector::ExtractOp>(
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loc, op.getSource(), llvm::ArrayRef(srcIdx).drop_back());
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} else {
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currentSourceScalableVector = op.getSource();
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}
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}
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Value sourceSubVector = currentSourceScalableVector;
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if (minExtractionSize < minSourceTrailingSize) {
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sourceSubVector = rewriter.create<vector::ScalableExtractOp>(
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loc, extractionVectorType, sourceSubVector, srcIdx.back());
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}
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// 2. Insert the scalable subvector into the result vector.
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if (!currentResultScalableVector) {
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if (minExtractionSize == minResultTrailingSize) {
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currentResultScalableVector = sourceSubVector;
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} else if (resRank != 1) {
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currentResultScalableVector = rewriter.create<vector::ExtractOp>(
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loc, result, llvm::ArrayRef(resIdx).drop_back());
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} else {
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currentResultScalableVector = result;
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}
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}
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if (minExtractionSize < minResultTrailingSize) {
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currentResultScalableVector = rewriter.create<vector::ScalableInsertOp>(
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loc, sourceSubVector, currentResultScalableVector, resIdx.back());
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}
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// 3. Update the source and result scalable vectors if needed.
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if (resIdx.back() + minExtractionSize >= minResultTrailingSize &&
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currentResultScalableVector != result) {
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// Finished row of result. Insert complete scalable vector into result
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// (n-D) vector.
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result = rewriter.create<vector::InsertOp>(
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loc, currentResultScalableVector, result,
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llvm::ArrayRef(resIdx).drop_back());
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currentResultScalableVector = {};
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}
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if (srcIdx.back() + minExtractionSize >= minSourceTrailingSize) {
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// Finished row of source.
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currentSourceScalableVector = {};
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}
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// 4. Increment the insert/extract indices, stepping by minExtractionSize
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// for the trailing dimensions.
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incIdx(srcIdx, sourceVectorType, srcRank - 1, minExtractionSize);
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incIdx(resIdx, resultVectorType, resRank - 1, minExtractionSize);
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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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static bool isTrailingDimScalable(VectorType type) {
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return type.getRank() >= 1 && type.getScalableDims().back() &&
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!llvm::is_contained(type.getScalableDims().drop_back(), true);
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}
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};
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} // namespace
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void mlir::vector::populateVectorShapeCastLoweringPatterns(
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RewritePatternSet &patterns, PatternBenefit benefit) {
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patterns.add<ShapeCastOp2DDownCastRewritePattern,
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ShapeCastOp2DUpCastRewritePattern, ShapeCastOpRewritePattern,
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ScalableShapeCastOpRewritePattern>(patterns.getContext(),
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benefit);
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}
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