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clang-p2996/mlir/lib/Dialect/ArmSME/Transforms/VectorLegalization.cpp
Matthias Springer 8c4bc1e75d [mlir][Transforms] Merge 1:1 and 1:N type converters (#113032) 
The 1:N type converter derived from the 1:1 type converter and extends
it with 1:N target materializations. This commit merges the two type
converters and stores 1:N target materializations in the 1:1 type
converter. This is in preparation of merging the 1:1 and 1:N dialect
conversion infrastructures.

1:1 target materializations (producing a single `Value`) will remain
valid. An additional API is added to the type converter to register 1:N
target materializations (producing a `SmallVector<Value>`). Internally,
all target materializations are stored as 1:N materializations.

The 1:N type converter is removed.

Note for LLVM integration: If you are using the `OneToNTypeConverter`,
simply switch all occurrences to `TypeConverter`.

---------

Co-authored-by: Markus Böck <markus.boeck02@gmail.com>
2024-10-25 11:44:20 -07:00

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//===- VectorLegalization.cpp - Legalize vectors for lowering to ArmSME ---===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This pass legalizes vector operations so they can be lowered to ArmSME.
//
// Note: In the context of this pass 'tile' always refers to an SME tile.
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/ArmSME/IR/ArmSME.h"
#include "mlir/Dialect/ArmSME/Transforms/Passes.h"
#include "mlir/Dialect/ArmSME/Utils/Utils.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Func/Transforms/OneToNFuncConversions.h"
#include "mlir/Dialect/Index/IR/IndexDialect.h"
#include "mlir/Dialect/Index/IR/IndexOps.h"
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/SCF/IR/SCF.h"
#include "mlir/Dialect/SCF/Transforms/Patterns.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
#include "mlir/Transforms/OneToNTypeConversion.h"
#define DEBUG_TYPE "arm-sme-vector-legalization"
namespace mlir::arm_sme {
#define GEN_PASS_DEF_VECTORLEGALIZATION
#include "mlir/Dialect/ArmSME/Transforms/Passes.h.inc"
} // namespace mlir::arm_sme
using namespace mlir;
using namespace mlir::arm_sme;
namespace {
//===----------------------------------------------------------------------===//
// Decomposition of vector operations larger than an SME tile
//===----------------------------------------------------------------------===//
// Common match failure reasons.
static constexpr StringLiteral kMatchFailureNotSMETileTypeMultiple(
"op vector size is not multiple of SME tiles");
static constexpr StringLiteral kMatchFailureUnsupportedMaskOp(
"op mask is unsupported for legalization/decomposition");
static constexpr StringLiteral
kMatchFailureNonPermutationMap("op affine map is not a permutation");
static constexpr StringLiteral kMatchFailureNotIllegalToLegal(
"expected transpose from illegal type to legal type");
/// An SMESubTile represents a single SME-sized sub-tile from decomposing a
/// larger vector type. The (`row`, `col`) are the position of the tile in the
/// original vector type. For example for an [8]x[8] tile with four [4]x[4]
/// sub-tiles, we would have:
///
/// 8 x vscale
/// ┌─────────────┬─────────────┐
/// │(0,0) │(0,4) │
/// │ │ │
/// ├─────────────┼─────────────┤ 8 x vscale
/// │(4,0) │(4,4) │
/// │ │ │
/// └─────────────┴─────────────┘
struct SMESubTile {
// Note: The units of (row, col) are vscale (as SME tiles are scalable).
int row{0};
int col{0};
// The SME tile type.
VectorType type;
};
/// Adds a constant elementwise scalable offset to `indices` (which are of equal
/// length). For example, in the 2D case this would return:
// { indices[0] + offset[0] * vscale, indices[1] + offset[1] * vscale }
SmallVector<Value, 2> addConstantScalableOffset(OpBuilder &builder,
Location loc,
ValueRange indices,
ArrayRef<int> scalableOffsets) {
auto vscale = builder.create<vector::VectorScaleOp>(loc);
return llvm::map_to_vector(
llvm::zip_equal(indices, scalableOffsets), [&](auto pair) -> Value {
auto [index, base] = pair;
auto offset = builder.create<arith::MulIOp>(
loc, builder.create<arith::ConstantIndexOp>(loc, base), vscale);
return builder.create<arith::AddIOp>(loc, index, offset);
});
}
/// Adjusts `indices` (e.g. from a load/store) for a larger vector type to
/// indices for one of the SME sub-tiles it will decompose into.
///
/// For example, if you were to decompose an 8x8 load into four 4x4 tiles, the
/// indices for each tile would need to be adjusted as follows:
///
/// initial indices = [a,b], inital size = 8x8, target size = 4x4
/// ┌─────────────┬─────────────┐
/// │[a,b] │[a,b+4] │
/// │ │ │
/// ├─────────────┼─────────────┤
/// │[a+4,b] │[a+4,b+4] │
/// │ │ │
/// └─────────────┴─────────────┘
SmallVector<Value, 2> getSMESubTileIndices(OpBuilder &builder, Location loc,
ValueRange indices,
SMESubTile smeTile) {
return addConstantScalableOffset(builder, loc, indices,
{smeTile.row, smeTile.col});
}
/// Returns true if `mask` is generated by an operation that can be decomposed
/// for SME. Currently, that is just no mask, or vector.create_mask.
/// TODO: Add support for vector.constant_mask once required for SME.
bool isSupportedMaskOp(Value mask) {
return !mask || mask.getDefiningOp<vector::CreateMaskOp>();
}
/// Extracts a mask for an SME sub-tile from the mask of a larger vector type.
Value extractSMEMask(OpBuilder &builder, Location loc, Value mask,
SMESubTile smeTile) {
assert(isSupportedMaskOp(mask));
if (!mask)
return Value{};
auto createMask = mask.getDefiningOp<vector::CreateMaskOp>();
// The operands of `vector.create_mask` (from a 2D perspective) are the
// coordinates where the mask ends. So we subtract where this tile starts,
// from the mask operands to get the parameters for this sub-tile.
auto smeTileMaskDims = addConstantScalableOffset(
builder, loc, createMask.getOperands(), {-smeTile.row, -smeTile.col});
auto smeTileCreateMask = builder.create<vector::CreateMaskOp>(
loc, smeTile.type.clone(builder.getI1Type()), smeTileMaskDims);
return smeTileCreateMask.getResult();
}
/// Constructs an iterator that returns each SME tile (with coordinates)
/// contained within a VectorType. For example, if decomposing an [8]x[8] into
/// [4]x[4] tiles, the iterator would yield the tiles: (0, 0), (0, 4), (4, 0),
/// (4, 4).
auto decomposeToSMETiles(OpBuilder &builder, VectorType type,
VectorType smeTileType,
bool transposeIndices = false) {
return llvm::map_range(
StaticTileOffsetRange(
type.getShape(),
{std::min(type.getDimSize(0), smeTileType.getDimSize(0)),
std::min(type.getDimSize(1), smeTileType.getDimSize(1))}),
[=](auto indices) {
int row = int(indices[0]);
int col = int(indices[1]);
if (transposeIndices)
std::swap(row, col);
return SMESubTile{row, col, smeTileType};
});
}
/// Returns the number of SME tiles that fit into the (2D-scalable) vector type
/// `type`.
int getNumberOfSMETilesForVectorType(VectorType type) {
assert(isMultipleOfSMETileVectorType(type) &&
"`type` not multiple of SME tiles");
int64_t vectorRows = type.getDimSize(0);
int64_t vectorCols = type.getDimSize(1);
auto elementType = type.getElementType();
unsigned minNumElts = getSMETileSliceMinNumElts(elementType);
return (vectorRows * vectorCols) / (minNumElts * minNumElts);
}
/// Legalize `arith.constant dense<value>` splat operations to fit within SME
/// tiles by decomposing them into tile-sized operations.
struct LegalizeArithConstantOpsByDecomposition
: public OneToNOpConversionPattern<arith::ConstantOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(arith::ConstantOp constantOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
auto vectorType = dyn_cast<VectorType>(constantOp.getType());
auto denseAttr = dyn_cast<DenseElementsAttr>(constantOp.getValueAttr());
if (!vectorType || !denseAttr || !denseAttr.isSplat())
return failure();
if (!isMultipleOfSMETileVectorType(vectorType))
return rewriter.notifyMatchFailure(constantOp,
kMatchFailureNotSMETileTypeMultiple);
auto smeTileType = getSMETileTypeForElement(vectorType.getElementType());
auto tileCount = getNumberOfSMETilesForVectorType(vectorType);
auto tileSplat = rewriter.create<arith::ConstantOp>(
constantOp.getLoc(), denseAttr.resizeSplat(smeTileType));
rewriter.replaceOp(constantOp, SmallVector<Value>(tileCount, tileSplat),
adaptor.getResultMapping());
return success();
}
};
/// Legalize `vector.outerproduct` operations to fit within SME tiles by
/// decomposing them into tile-sized operations.
struct LegalizeVectorOuterProductOpsByDecomposition
: public OneToNOpConversionPattern<vector::OuterProductOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(vector::OuterProductOp outerProductOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
auto vectorType = outerProductOp.getResultVectorType();
if (!isMultipleOfSMETileVectorType(vectorType))
return rewriter.notifyMatchFailure(outerProductOp,
kMatchFailureNotSMETileTypeMultiple);
Value mask;
Operation *rootOp = outerProductOp;
auto loc = outerProductOp.getLoc();
if (outerProductOp.isMasked()) {
auto maskOp = outerProductOp.getMaskingOp();
mask = maskOp.getMask();
rootOp = maskOp;
}
if (!isSupportedMaskOp(mask))
return rewriter.notifyMatchFailure(outerProductOp,
kMatchFailureUnsupportedMaskOp);
ValueRange accSMETiles = adaptor.getAcc();
auto smeTileType = getSMETileTypeForElement(vectorType.getElementType());
VectorType sliceType = VectorType::Builder(smeTileType).dropDim(0);
SmallVector<Value> resultSMETiles;
for (auto [index, smeTile] : llvm::enumerate(
decomposeToSMETiles(rewriter, vectorType, smeTileType))) {
auto smeMask = extractSMEMask(rewriter, loc, mask, smeTile);
auto lhs = rewriter.create<vector::ScalableExtractOp>(
loc, sliceType, outerProductOp.getLhs(), smeTile.row);
auto rhs = rewriter.create<vector::ScalableExtractOp>(
loc, sliceType, outerProductOp.getRhs(), smeTile.col);
auto smeOuterProduct = rewriter.create<vector::OuterProductOp>(
loc, smeTileType, lhs, rhs,
!accSMETiles.empty() ? accSMETiles[index] : Value{},
outerProductOp.getKind());
auto maskedOuterProduct =
vector::maskOperation(rewriter, smeOuterProduct, smeMask);
resultSMETiles.push_back(maskedOuterProduct->getResult(0));
}
rewriter.replaceOp(rootOp, resultSMETiles, adaptor.getResultMapping());
return success();
}
};
// Workaround for `vector.mask`. We want to match on `vector.outerproduct` (to
// get the help of the type conversion), but doing so results in the type
// conversion adding target materializations in the `vector.mask` region
// (invalid). This pattern matches on `vector.mask` then calls into the
// `vector.outerproduct` pattern to work around this issue.
struct LegalizeMaskedVectorOuterProductOpsByDecomposition
: public OneToNOpConversionPattern<vector::MaskOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(vector::MaskOp maskOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
if (auto outerProductOp =
llvm::dyn_cast<vector::OuterProductOp>(maskOp.getMaskableOp())) {
LegalizeVectorOuterProductOpsByDecomposition pattern(*getTypeConverter(),
getContext());
return static_cast<RewritePattern &>(pattern).matchAndRewrite(
outerProductOp, rewriter);
}
return failure();
}
};
/// Legalize `vector.transfer_read` operations to fit within SME tiles by
/// decomposing them into tile-sized operations.
struct LegalizeTransferReadOpsByDecomposition
: public OneToNOpConversionPattern<vector::TransferReadOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(vector::TransferReadOp readOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
auto vectorType = readOp.getVectorType();
if (!isMultipleOfSMETileVectorType(vectorType))
return rewriter.notifyMatchFailure(readOp,
kMatchFailureNotSMETileTypeMultiple);
auto mask = readOp.getMask();
if (!isSupportedMaskOp(mask))
return rewriter.notifyMatchFailure(readOp,
kMatchFailureUnsupportedMaskOp);
auto permutationMap = readOp.getPermutationMap();
if (!permutationMap.isPermutation())
return rewriter.notifyMatchFailure(readOp,
kMatchFailureNonPermutationMap);
// Note: For 2D vector types the only non-identity permutation is a simple
// tranpose [1, 0].
bool transposed = !permutationMap.isIdentity();
auto loc = readOp.getLoc();
auto smeTileType = getSMETileTypeForElement(vectorType.getElementType());
SmallVector<Value> resultSMETiles;
for (SMESubTile smeTile :
decomposeToSMETiles(rewriter, vectorType, smeTileType, transposed)) {
auto smeMask = extractSMEMask(rewriter, loc, mask, smeTile);
auto smeRead = rewriter.create<vector::TransferReadOp>(
loc, smeTileType, readOp.getSource(),
getSMESubTileIndices(rewriter, loc, readOp.getIndices(), smeTile),
readOp.getPermutationMapAttr(), readOp.getPadding(), smeMask,
readOp.getInBoundsAttr());
resultSMETiles.push_back(smeRead);
}
rewriter.replaceOp(readOp, resultSMETiles, adaptor.getResultMapping());
return success();
}
};
/// Legalize `vector.transfer_write` operations to fit within SME tiles by
/// decomposing them into tile-sized operations.
struct LegalizeTransferWriteOpsByDecomposition
: public OneToNOpConversionPattern<vector::TransferWriteOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(vector::TransferWriteOp writeOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
auto vectorType = writeOp.getVectorType();
if (!isMultipleOfSMETileVectorType(vectorType))
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureNotSMETileTypeMultiple);
auto mask = writeOp.getMask();
if (!isSupportedMaskOp(mask))
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureUnsupportedMaskOp);
auto permutationMap = writeOp.getPermutationMap();
if (!permutationMap.isPermutation())
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureNonPermutationMap);
// Note: For 2D vector types the only non-identity permutation is a simple
// tranpose [1, 0].
bool transposed = !permutationMap.isIdentity();
auto loc = writeOp.getLoc();
auto smeTileType = getSMETileTypeForElement(vectorType.getElementType());
auto inputSMETiles = adaptor.getVector();
Value destTensorOrMemref = writeOp.getSource();
for (auto [index, smeTile] : llvm::enumerate(decomposeToSMETiles(
rewriter, vectorType, smeTileType, transposed))) {
auto smeMask = extractSMEMask(rewriter, loc, mask, smeTile);
auto smeWrite = rewriter.create<vector::TransferWriteOp>(
loc, inputSMETiles[index], destTensorOrMemref,
getSMESubTileIndices(rewriter, loc, writeOp.getIndices(), smeTile),
writeOp.getPermutationMapAttr(), smeMask, writeOp.getInBoundsAttr());
if (writeOp.hasPureTensorSemantics())
destTensorOrMemref = smeWrite.getResult();
}
if (writeOp.hasPureTensorSemantics())
rewriter.replaceOp(writeOp, destTensorOrMemref);
else
rewriter.eraseOp(writeOp);
return success();
}
};
/// Legalize a multi-tile transfer_write as a single store loop. This is done as
/// part of type decomposition as at this level we know each tile write is
/// disjoint, but that information is lost after decomposition (without analysis
/// to reconstruct it).
///
/// Example (pseudo-MLIR):
///
/// ```
/// vector.transfer_write %vector, %dest[%y, %x], %mask
/// : vector<[16]x[8]xi16>, memref<?x?xi16>
/// ```
/// Is rewritten to:
/// ```
/// scf.for %slice_idx = %c0 to %c8_vscale step %c1 {
/// %upper_slice_mask = vector.extract %mask[%slice_idx] ─┐
/// : vector<[8]xi1> from vector<[16]x[8]xi1> |
/// %upper_slice = vector.extract %upper_tile[%slice_idx] |- Store upper tile
/// : vector<[8]xi16> from vector<[8]x[8]xi16> |
/// vector.transfer_write %upper_slice, |
/// %dest[%slice_idx + %y, %x], %upper_slice_mask |
/// : vector<[8]xi16>, memref<?x?xi16> ┘
/// %lower_slice_idx = %slice_idx + %c8_vscale ─┐
/// %lower_slice_mask = vector.extract %mask[%lower_slice_idx] |
/// : vector<[8]xi1> from vector<[16]x[8]xi1> |
/// %lower_slice = vector.extract %lower_tile[%slice_idx] |- Store lower
/// : vector<[8]xi16> from vector<[8]x[8]xi16> | tile
/// vector.transfer_write %lower_slice, |
/// %dest[%lower_slice_idx + %y, %x], %lower_slice_mask |
/// : vector<[8]xi16>, memref<?x?xi16> ┘
/// }
/// ```
struct LegalizeMultiTileTransferWriteAsStoreLoop
: public OneToNOpConversionPattern<vector::TransferWriteOp> {
using OneToNOpConversionPattern::OneToNOpConversionPattern;
LogicalResult
matchAndRewrite(vector::TransferWriteOp writeOp, OpAdaptor adaptor,
OneToNPatternRewriter &rewriter) const override {
if (writeOp.hasPureTensorSemantics())
return rewriter.notifyMatchFailure(
writeOp, "TODO: tensor semantics are unsupported");
auto permutationMap = writeOp.getPermutationMap();
if (!permutationMap.isPermutation())
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureNonPermutationMap);
bool transposed = !permutationMap.isIdentity();
if (transposed)
return rewriter.notifyMatchFailure(writeOp,
"TODO: transpose unsupported");
auto vectorType = writeOp.getVectorType();
if (!isMultipleOfSMETileVectorType(vectorType))
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureNotSMETileTypeMultiple);
// Note: We also disallow masks where any dimension is > 16 because that
// prevents the masking from being lowered to use arm_sve.psel.
auto mask = writeOp.getMask();
if (!isSupportedMaskOp(mask) || (mask && (vectorType.getDimSize(0) > 16 ||
vectorType.getDimSize(1) > 16)))
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureUnsupportedMaskOp);
auto loc = writeOp.getLoc();
auto createVscaleMultiple =
vector::makeVscaleConstantBuilder(rewriter, loc);
// Get SME tile and slice types.
auto smeTileType = getSMETileTypeForElement(vectorType.getElementType());
auto minTileSlices = smeTileType.getDimSize(0);
VectorType sliceMaskType =
VectorType::get(minTileSlices, rewriter.getI1Type(), true);
// Create loop over all tile slices.
auto lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
auto upperBound = createVscaleMultiple(minTileSlices);
auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
auto storeLoop =
rewriter.create<scf::ForOp>(loc, lowerBound, upperBound, step);
rewriter.setInsertionPointToStart(storeLoop.getBody());
// For each sub-tile of the multi-tile `vectorType`.
auto inputSMETiles = adaptor.getVector();
auto tileSliceIndex = storeLoop.getInductionVar();
for (auto [index, smeTile] : llvm::enumerate(
decomposeToSMETiles(rewriter, vectorType, smeTileType))) {
// The coordinates of the tile within `vectorType`.
auto tileRow = createVscaleMultiple(smeTile.row);
auto tileCol = createVscaleMultiple(smeTile.col);
// The current slice of `vectorType` we are processing.
auto sliceIndex =
rewriter.create<arith::AddIOp>(loc, tileRow, tileSliceIndex);
// Where in the destination memref the current slice will be stored.
auto storeRow = rewriter.create<arith::AddIOp>(loc, sliceIndex,
writeOp.getIndices()[0]);
auto storeCol =
rewriter.create<arith::AddIOp>(loc, tileCol, writeOp.getIndices()[1]);
// Extract the mask for the current slice.
Value sliceMask = nullptr;
if (mask) {
sliceMask = rewriter.create<vector::ExtractOp>(
loc, mask, OpFoldResult(sliceIndex));
if (sliceMaskType != sliceMask.getType())
sliceMask = rewriter.create<vector::ScalableExtractOp>(
loc, sliceMaskType, sliceMask, smeTile.col);
}
// Extract and store the current slice.
Value tile = inputSMETiles[index];
auto slice =
rewriter.create<vector::ExtractOp>(loc, tile, tileSliceIndex);
rewriter.create<vector::TransferWriteOp>(
loc, slice, writeOp.getSource(), ValueRange{storeRow, storeCol},
AffineMapAttr::get(writeOp.getPermutationMap().dropResult(0)),
sliceMask,
rewriter.getBoolArrayAttr(
ArrayRef<bool>(writeOp.getInBoundsValues()).drop_front()));
}
rewriter.eraseOp(writeOp);
return success();
}
};
//===----------------------------------------------------------------------===//
// ArmSME-specific fixup canonicalizations/folds
//===----------------------------------------------------------------------===//
/// Folds an extract from a 3D `vector.create_mask` (which is a vector of
/// SME-like masks), into a compare and a 2D `vector.create_mask`. This is
/// necessary for the mask to be lowered to ArmSME.
///
/// Example:
///
/// BEFORE:
/// ```mlir
/// %mask = vector.create_mask %nonConstantDim, %a, %b : vector<4x[4]x[4]xi1>
/// %subMask = vector.extract %mask[2]
/// : vector<[4]x[4]xi1> from vector<4x[4]x[4]xi1>
/// ```
///
/// AFTER:
/// ```mlir
/// %extractionInTrueRegion = arith.cmpi slt, %c2, %nonConstantDim : index
/// %newMaskFrontDim = arith.select %extractionInTrueRegion, %a, %c0 : index
/// %subMask = vector.create_mask %newMaskFrontDim, %b : vector<[4]x[4]xi1>
/// ```
struct FoldExtractFromVectorOfSMELikeCreateMasks
: public OpRewritePattern<vector::ExtractOp> {
using OpRewritePattern<vector::ExtractOp>::OpRewritePattern;
LogicalResult matchAndRewrite(vector::ExtractOp extractOp,
PatternRewriter &rewriter) const override {
auto loc = extractOp.getLoc();
auto createMaskOp =
extractOp.getVector().getDefiningOp<vector::CreateMaskOp>();
if (!createMaskOp)
return rewriter.notifyMatchFailure(
extractOp, "extract not from vector.create_mask op");
VectorType extractedMaskType =
llvm::dyn_cast<VectorType>(extractOp.getResult().getType());
if (!extractedMaskType)
return rewriter.notifyMatchFailure(extractOp,
"extracted type is not a vector type");
auto numScalable = extractedMaskType.getNumScalableDims();
if (numScalable != 2)
return rewriter.notifyMatchFailure(
extractOp, "expected extracted type to be an SME-like mask");
// TODO: Support multiple extraction indices.
if (extractOp.getStaticPosition().size() != 1)
return rewriter.notifyMatchFailure(
extractOp, "only a single extraction index is supported");
auto frontMaskDim = createMaskOp.getOperand(0);
if (frontMaskDim.getDefiningOp<arith::ConstantOp>())
return rewriter.notifyMatchFailure(
extractOp,
"constant vector.create_masks dims should be folded elsewhere");
auto zero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
auto extractionIndex = getValueOrCreateConstantIndexOp(
rewriter, loc, extractOp.getMixedPosition()[0]);
auto extractionInTrueRegion = rewriter.create<arith::CmpIOp>(
loc, rewriter.getI1Type(), arith::CmpIPredicate::slt, extractionIndex,
frontMaskDim);
auto newMaskFrontDim = rewriter.create<arith::SelectOp>(
loc, extractionInTrueRegion, createMaskOp.getOperand(1), zero);
rewriter.replaceOpWithNewOp<vector::CreateMaskOp>(
extractOp, extractedMaskType,
ValueRange{newMaskFrontDim, createMaskOp.getOperand(2)});
return success();
}
};
/// A vector type where no fixed dimension comes after a scalable dimension.
bool isLegalVectorType(VectorType vType) {
bool seenFixedDim = false;
for (bool scalableFlag : llvm::reverse(vType.getScalableDims())) {
seenFixedDim |= !scalableFlag;
if (seenFixedDim && scalableFlag)
return false;
}
return true;
}
/// Lifts an illegal vector.transpose and vector.transfer_read to a
/// memref.subview + memref.transpose, followed by a legal read.
///
/// 'Illegal' here means a leading scalable dimension and a fixed trailing
/// dimension, which has no valid lowering.
///
/// The memref.transpose is metadata-only transpose that produces a strided
/// memref, which eventually becomes a loop reading individual elements.
///
/// Example:
///
/// BEFORE:
/// ```mlir
/// %illegalRead = vector.transfer_read %memref[%a, %b]
/// : memref<?x?xf32>, vector<[8]x4xf32>
/// %legalType = vector.transpose %illegalRead, [1, 0]
/// : vector<[8]x4xf32> to vector<4x[8]xf32>
/// ```
///
/// AFTER:
/// ```mlir
/// %readSubview = memref.subview %memref[%a, %b] [%c8_vscale, %c4] [%c1, %c1]
/// : memref<?x?xf32> to memref<?x?xf32>
/// %transpose = memref.transpose %readSubview (d0, d1) -> (d1, d0)
/// : memref<?x?xf32> to memref<?x?xf32>
/// %legalType = vector.transfer_read %transpose[%c0, %c0]
/// : memref<?x?xf32>, vector<4x[8]xf32>
/// ```
struct LiftIllegalVectorTransposeToMemory
: public OpRewritePattern<vector::TransposeOp> {
using OpRewritePattern<vector::TransposeOp>::OpRewritePattern;
static Value getExtensionSource(Operation *op) {
if (isa_and_present<arith::ExtSIOp, arith::ExtUIOp, arith::ExtFOp>(op))
return op->getOperand(0);
return {};
}
LogicalResult matchAndRewrite(vector::TransposeOp transposeOp,
PatternRewriter &rewriter) const override {
auto sourceType = transposeOp.getSourceVectorType();
auto resultType = transposeOp.getResultVectorType();
if (isLegalVectorType(sourceType) || !isLegalVectorType(resultType))
return rewriter.notifyMatchFailure(transposeOp,
kMatchFailureNotIllegalToLegal);
// Look through extend for transfer_read.
Value maybeRead = transposeOp.getVector();
auto *transposeSourceOp = maybeRead.getDefiningOp();
Operation *extendOp = nullptr;
if (Value extendSource = getExtensionSource(transposeSourceOp)) {
maybeRead = extendSource;
extendOp = transposeSourceOp;
}
auto illegalRead = maybeRead.getDefiningOp<vector::TransferReadOp>();
if (!illegalRead)
return rewriter.notifyMatchFailure(
transposeOp,
"expected source to be (possibly extended) transfer_read");
if (!illegalRead.getPermutationMap().isIdentity())
return rewriter.notifyMatchFailure(
illegalRead, "expected read to have identity permutation map");
auto loc = transposeOp.getLoc();
auto zero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
auto one = rewriter.create<arith::ConstantIndexOp>(loc, 1);
// Create a subview that matches the size of the illegal read vector type.
auto readType = illegalRead.getVectorType();
auto readSizes = llvm::map_to_vector(
llvm::zip_equal(readType.getShape(), readType.getScalableDims()),
[&](auto dim) -> Value {
auto [size, isScalable] = dim;
auto dimSize = rewriter.create<arith::ConstantIndexOp>(loc, size);
if (!isScalable)
return dimSize;
auto vscale = rewriter.create<vector::VectorScaleOp>(loc);
return rewriter.create<arith::MulIOp>(loc, vscale, dimSize);
});
SmallVector<Value> strides(readType.getRank(), Value(one));
auto readSubview = rewriter.create<memref::SubViewOp>(
loc, illegalRead.getSource(), illegalRead.getIndices(), readSizes,
strides);
// Apply the transpose to all values/attributes of the transfer_read:
// - The mask
Value mask = illegalRead.getMask();
if (mask) {
// Note: The transpose for the mask should fold into the
// vector.create_mask/constant_mask op, which will then become legal.
mask = rewriter.create<vector::TransposeOp>(loc, mask,
transposeOp.getPermutation());
}
// - The source memref
mlir::AffineMap transposeMap = AffineMap::getPermutationMap(
transposeOp.getPermutation(), getContext());
auto transposedSubview = rewriter.create<memref::TransposeOp>(
loc, readSubview, AffineMapAttr::get(transposeMap));
ArrayAttr inBoundsAttr = illegalRead.getInBoundsAttr();
// - The `in_bounds` attribute
if (inBoundsAttr) {
SmallVector<Attribute> inBoundsValues(inBoundsAttr.begin(),
inBoundsAttr.end());
applyPermutationToVector(inBoundsValues, transposeOp.getPermutation());
inBoundsAttr = rewriter.getArrayAttr(inBoundsValues);
}
VectorType legalReadType = resultType.clone(readType.getElementType());
// Note: The indices are all zero as the subview is already offset.
SmallVector<Value> readIndices(illegalRead.getIndices().size(), zero);
auto legalRead = rewriter.create<vector::TransferReadOp>(
loc, legalReadType, transposedSubview, readIndices,
illegalRead.getPermutationMapAttr(), illegalRead.getPadding(), mask,
inBoundsAttr);
// Replace the transpose with the new read, extending the result if
// necessary.
rewriter.replaceOp(transposeOp, [&]() -> Operation * {
if (extendOp)
return rewriter.create(loc, extendOp->getName().getIdentifier(),
Value(legalRead), resultType);
return legalRead;
}());
return success();
}
};
/// A rewrite to turn unit dim transpose-like vector.shape_casts into
/// vector.transposes. The shape_cast has to be from an illegal vector type to a
/// legal one (as defined by isLegalVectorType).
///
/// The reasoning for this is if we've got to this pass and we still have
/// shape_casts of illegal types, then they likely will not cancel out. Turning
/// them into transposes gives LiftIllegalVectorTransposeToMemory a chance to
/// eliminate them.
///
/// Example:
///
/// BEFORE:
/// ```mlir
/// %0 = vector.shape_cast %a : vector<[4]x1xf32> to vector<1x[4]xf32>
/// ```
///
/// AFTER:
/// ```mlir
/// %0 = vector.transpose %0, [1, 0] : vector<[4]x1xf32> to vector<1x[4]xf32>
/// ```
struct ConvertIllegalShapeCastOpsToTransposes
: public OpRewritePattern<vector::ShapeCastOp> {
using OpRewritePattern<vector::ShapeCastOp>::OpRewritePattern;
LogicalResult matchAndRewrite(vector::ShapeCastOp shapeCastOp,
PatternRewriter &rewriter) const override {
auto sourceType = shapeCastOp.getSourceVectorType();
auto resultType = shapeCastOp.getResultVectorType();
if (isLegalVectorType(sourceType) || !isLegalVectorType(resultType))
return rewriter.notifyMatchFailure(shapeCastOp,
kMatchFailureNotIllegalToLegal);
// Note: If we know that `sourceType` is an illegal vector type (and 2D)
// then dim 0 is scalable and dim 1 is fixed.
if (sourceType.getRank() != 2 || sourceType.getDimSize(1) != 1)
return rewriter.notifyMatchFailure(
shapeCastOp, "expected source to be a 2D scalable vector with a "
"trailing unit dim");
auto loc = shapeCastOp.getLoc();
auto transpose = rewriter.create<vector::TransposeOp>(
loc, shapeCastOp.getSource(), ArrayRef<int64_t>{1, 0});
if (resultType.getRank() == 1)
rewriter.replaceOpWithNewOp<vector::ShapeCastOp>(shapeCastOp, resultType,
transpose);
else
rewriter.replaceOp(shapeCastOp, transpose);
return success();
}
};
/// Rewrites an illegal/unsupported SVE transfer_write(transpose) to instead use
/// the ZA state. This workaround rewrite to support these transposes when ZA is
/// available.
///
/// Example:
///
/// BEFORE:
/// ```mlir
/// %transpose = vector.transpose %vec, [1, 0]
/// : vector<2x[4]xf32> to vector<[4]x2xf32>
/// vector.transfer_write %transpose, %dest[%y, %x]
/// : vector<[4]x2xf32>, memref<?x?xf32>
/// ```
///
/// AFTER:
/// ```mlir
/// %0 = arm_sme.get_tile : vector<[4]x[4]xf32>
/// %1 = vector.extract %vec[0] : vector<[4]xf32> from vector<2x[4]xf32>
/// %2 = vector.insert %1, %0 [0] : vector<[4]xf32> into vector<[4]x[4]xf32>
/// %3 = vector.extract %vec[1] : vector<[4]xf32> from vector<2x[4]xf32>
/// %4 = vector.insert %3, %2 [1] : vector<[4]xf32> into vector<[4]x[4]xf32>
/// %c4_vscale = arith.muli %vscale, %c4 : index
/// %mask = vector.create_mask %c4_vscale, %c2 : vector<[4]x[4]xi1>
/// vector.transfer_write %4, %dest[%y, %x], %mask
/// {permutation_map = affine_map<(d0, d1) -> (d1, d0)>}
/// : vector<[4]x[4]xf32>, memref<?x?xf32>
/// ```
///
/// Values larger than a single tile are supported via decomposition.
struct LowerIllegalTransposeStoreViaZA
: public OpRewritePattern<vector::TransferWriteOp> {
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(vector::TransferWriteOp writeOp,
PatternRewriter &rewriter) const override {
if (!isSupportedMaskOp(writeOp.getMask()))
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureUnsupportedMaskOp);
auto permutationMap = writeOp.getPermutationMap();
if (!permutationMap.isIdentity())
return rewriter.notifyMatchFailure(writeOp,
kMatchFailureNonPermutationMap);
auto transposeOp = writeOp.getVector().getDefiningOp<vector::TransposeOp>();
if (!transposeOp)
return failure();
auto sourceType = transposeOp.getSourceVectorType();
auto resultType = transposeOp.getResultVectorType();
if (resultType.getRank() != 2)
return rewriter.notifyMatchFailure(transposeOp, "TransposeOp not rank 2");
if (!isLegalVectorType(sourceType) || isLegalVectorType(resultType))
return rewriter.notifyMatchFailure(
transposeOp, "not illegal/unsupported SVE transpose");
auto smeTileType = getSMETileTypeForElement(resultType.getElementType());
VectorType smeSliceType = VectorType::Builder(smeTileType).dropDim(0);
if (sourceType.getDimSize(0) <= 1 ||
sourceType.getDimSize(1) % smeSliceType.getDimSize(0) != 0)
return rewriter.notifyMatchFailure(writeOp, "unsupported source shape");
auto loc = writeOp.getLoc();
auto createVscaleMultiple =
vector::makeVscaleConstantBuilder(rewriter, loc);
auto transposeMap = AffineMapAttr::get(
AffineMap::getPermutationMap(ArrayRef<int64_t>{1, 0}, getContext()));
// Note: We need to use `get_tile` as there's no vector-level `undef`.
Value undefTile = rewriter.create<arm_sme::GetTileOp>(loc, smeTileType);
Value destTensorOrMemref = writeOp.getSource();
auto numSlicesPerTile =
std::min(sourceType.getDimSize(0), smeTileType.getDimSize(0));
auto numSlices =
rewriter.create<arith::ConstantIndexOp>(loc, numSlicesPerTile);
for (auto [index, smeTile] : llvm::enumerate(
decomposeToSMETiles(rewriter, sourceType, smeTileType))) {
// 1. _Deliberately_ drop a scalable dimension and insert a fixed number
// of slices from the source type into the SME tile. Without checking
// vscale (and emitting multiple implementations) we can't make use of the
// rows of the tile after 1*vscale rows.
Value tile = undefTile;
for (int d = 0; d < numSlicesPerTile; ++d) {
Value vector = rewriter.create<vector::ExtractOp>(
loc, transposeOp.getVector(),
rewriter.getIndexAttr(d + smeTile.row));
if (vector.getType() != smeSliceType) {
vector = rewriter.create<vector::ScalableExtractOp>(
loc, smeSliceType, vector, smeTile.col);
}
tile = rewriter.create<vector::InsertOp>(loc, vector, tile, d);
}
// 2. Transpose the tile position.
auto transposedRow = createVscaleMultiple(smeTile.col);
auto transposedCol =
rewriter.create<arith::ConstantIndexOp>(loc, smeTile.row);
// 3. Compute mask for tile store.
Value maskRows;
Value maskCols;
if (auto mask = writeOp.getMask()) {
auto createMask = mask.getDefiningOp<vector::CreateMaskOp>();
maskRows = rewriter.create<arith::SubIOp>(loc, createMask.getOperand(0),
transposedRow);
maskCols = rewriter.create<arith::SubIOp>(loc, createMask.getOperand(1),
transposedCol);
maskCols = rewriter.create<index::MinSOp>(loc, maskCols, numSlices);
} else {
maskRows = createVscaleMultiple(smeTileType.getDimSize(0));
maskCols = numSlices;
}
auto subMask = rewriter.create<vector::CreateMaskOp>(
loc, smeTileType.clone(rewriter.getI1Type()),
ValueRange{maskRows, maskCols});
// 4. Emit a transposed tile write.
auto writeIndices = writeOp.getIndices();
Value destRow =
rewriter.create<arith::AddIOp>(loc, transposedRow, writeIndices[0]);
Value destCol =
rewriter.create<arith::AddIOp>(loc, transposedCol, writeIndices[1]);
auto smeWrite = rewriter.create<vector::TransferWriteOp>(
loc, tile, destTensorOrMemref, ValueRange{destRow, destCol},
transposeMap, subMask, writeOp.getInBounds());
if (writeOp.hasPureTensorSemantics())
destTensorOrMemref = smeWrite.getResult();
}
if (writeOp.hasPureTensorSemantics())
rewriter.replaceOp(writeOp, destTensorOrMemref);
else
rewriter.eraseOp(writeOp);
return success();
}
};
struct VectorLegalizationPass
: public arm_sme::impl::VectorLegalizationBase<VectorLegalizationPass> {
void runOnOperation() override {
auto *context = &getContext();
TypeConverter converter;
RewritePatternSet patterns(context);
converter.addConversion([](Type type) { return type; });
converter.addConversion(
[](VectorType vectorType,
SmallVectorImpl<Type> &types) -> std::optional<LogicalResult> {
if (!isMultipleOfSMETileVectorType(vectorType))
return std::nullopt;
auto smeTileCount = getNumberOfSMETilesForVectorType(vectorType);
auto smeTileType =
getSMETileTypeForElement(vectorType.getElementType());
types = SmallVector<Type>(smeTileCount, smeTileType);
return success();
});
patterns.add<FoldExtractFromVectorOfSMELikeCreateMasks,
LiftIllegalVectorTransposeToMemory,
ConvertIllegalShapeCastOpsToTransposes,
LowerIllegalTransposeStoreViaZA>(context);
// Note: These two patterns are added with a high benefit to ensure:
// - Masked outer products are handled before unmasked ones
// - Multi-tile writes are lowered as a store loop (if possible)
patterns.add<LegalizeMaskedVectorOuterProductOpsByDecomposition,
LegalizeMultiTileTransferWriteAsStoreLoop>(converter, context,
/*benefit=*/1024);
patterns.add<LegalizeArithConstantOpsByDecomposition,
LegalizeVectorOuterProductOpsByDecomposition,
LegalizeTransferReadOpsByDecomposition,
LegalizeTransferWriteOpsByDecomposition>(converter, context);
populateFuncTypeConversionPatterns(converter, patterns);
scf::populateSCFStructuralOneToNTypeConversions(converter, patterns);
if (failed(applyPartialOneToNConversion(getOperation(), converter,
std::move(patterns))))
return signalPassFailure();
}
};
} // namespace
std::unique_ptr<Pass> mlir::arm_sme::createVectorLegalizationPass() {
return std::make_unique<VectorLegalizationPass>();
}
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