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[mlir][vector] Add leading unit dim folding patterns for masked transfers #71466

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Nov 7, 2023
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[mlir][vector] Add leading unit dim folding patterns for masked transfers #71466

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69 changes: 59 additions & 10 deletions mlir/lib/Dialect/Vector/Transforms/VectorDropLeadUnitDim.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@
//
//===----------------------------------------------------------------------===//

#include <numeric>

#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
Expand Down Expand Up @@ -208,9 +210,6 @@ struct CastAwayTransferReadLeadingOneDim
if (read.getTransferRank() == 0)
return failure();

if (read.getMask())
return failure();

auto shapedType = cast<ShapedType>(read.getSource().getType());
if (shapedType.getElementType() != read.getVectorType().getElementType())
return failure();
Expand All @@ -233,10 +232,18 @@ struct CastAwayTransferReadLeadingOneDim
inBoundsAttr = rewriter.getArrayAttr(
read.getInBoundsAttr().getValue().take_back(newType.getRank()));

Value mask = Value();
if (read.getMask()) {
// The mask shape must always match the shape of the written vector, so we
// can safely use the same extraction indices.
int64_t dropDim = oldType.getRank() - newType.getRank();
mask = rewriter.create<vector::ExtractOp>(read.getLoc(), read.getMask(),
splatZero(dropDim));
}

auto newRead = rewriter.create<vector::TransferReadOp>(
read.getLoc(), newType, read.getSource(), read.getIndices(),
AffineMapAttr::get(newMap), read.getPadding(), /*mask=*/Value(),
inBoundsAttr);
AffineMapAttr::get(newMap), read.getPadding(), mask, inBoundsAttr);
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(read, oldType, newRead);

return success();
Expand All @@ -256,9 +263,6 @@ struct CastAwayTransferWriteLeadingOneDim
if (write.getTransferRank() == 0)
return failure();

if (write.getMask())
return failure();

auto shapedType = dyn_cast<ShapedType>(write.getSource().getType());
if (shapedType.getElementType() != write.getVectorType().getElementType())
return failure();
Expand All @@ -283,10 +287,21 @@ struct CastAwayTransferWriteLeadingOneDim

auto newVector = rewriter.create<vector::ExtractOp>(
write.getLoc(), write.getVector(), splatZero(dropDim));

if (write.getMask()) {
// The mask shape must always match the shape of the written vector, so we
// can safely use the same extraction indices.
auto newMask = rewriter.create<vector::ExtractOp>(
write.getLoc(), write.getMask(), splatZero(dropDim));
rewriter.replaceOpWithNewOp<vector::TransferWriteOp>(
write, newVector, write.getSource(), write.getIndices(),
AffineMapAttr::get(newMap), newMask, inBoundsAttr);
return success();
}

rewriter.replaceOpWithNewOp<vector::TransferWriteOp>(
write, newVector, write.getSource(), write.getIndices(),
AffineMapAttr::get(newMap), inBoundsAttr);

return success();
}
};
Expand Down Expand Up @@ -467,14 +482,48 @@ class CastAwayElementwiseLeadingOneDim : public RewritePattern {
}
};

// Drops leading 1 dimensions from vector.constant_mask and inserts a
// vector.broadcast back to the original shape.
struct CastAwayConstantMaskLeadingOneDim
: public OpRewritePattern<vector::ConstantMaskOp> {
using OpRewritePattern::OpRewritePattern;

LogicalResult matchAndRewrite(vector::ConstantMaskOp mask,
PatternRewriter &rewriter) const override {
VectorType oldType = mask.getType();
VectorType newType = trimLeadingOneDims(oldType);

if (newType == oldType)
return failure();

int64_t dropDim = oldType.getRank() - newType.getRank();
SmallVector<int64_t> dimSizes;
for (auto attr : mask.getMaskDimSizes())
dimSizes.push_back(llvm::cast<IntegerAttr>(attr).getInt());

// If any of the dropped unit dims has a size of `0`, the entire mask is a
// zero mask, else the unit dim has no effect on the mask.
int64_t flatLeadingSize =
std::accumulate(dimSizes.begin(), dimSizes.begin() + dropDim + 1,
static_cast<int64_t>(1), std::multiplies<int64_t>());
SmallVector<int64_t> newDimSizes({flatLeadingSize});
newDimSizes.append(dimSizes.begin() + dropDim + 1, dimSizes.end());

auto newMask = rewriter.create<vector::ConstantMaskOp>(
mask.getLoc(), newType, rewriter.getI64ArrayAttr(newDimSizes));
rewriter.replaceOpWithNewOp<vector::BroadcastOp>(mask, oldType, newMask);
return success();
}
};

} // namespace

void mlir::vector::populateCastAwayVectorLeadingOneDimPatterns(
RewritePatternSet &patterns, PatternBenefit benefit) {
patterns
.add<CastAwayExtractStridedSliceLeadingOneDim,
CastAwayInsertStridedSliceLeadingOneDim, CastAwayInsertLeadingOneDim,
CastAwayTransferReadLeadingOneDim,
CastAwayConstantMaskLeadingOneDim, CastAwayTransferReadLeadingOneDim,
CastAwayTransferWriteLeadingOneDim, CastAwayElementwiseLeadingOneDim,
CastAwayContractionLeadingOneDim>(patterns.getContext(), benefit);
populateShapeCastFoldingPatterns(patterns, benefit);
Expand Down
35 changes: 35 additions & 0 deletions mlir/test/Dialect/Vector/vector-dropleadunitdim-transforms.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -209,6 +209,20 @@ func.func @cast_away_transfer_read_leading_one_dims(%arg0: memref<1x4x8x16xf16>)
return %0: vector<1x4xf16>
}

// CHECK-LABEL: func @cast_away_masked_transfer_read_leading_one_dims
func.func @cast_away_masked_transfer_read_leading_one_dims(%arg0: memref<1x4x8x16xf16>, %arg1: vector<1x4xi1>) -> vector<1x4xf16> {
// CHECK: %[[C0:.+]] = arith.constant 0 : index
%c0 = arith.constant 0 : index
// CHECK: %[[F0:.+]] = arith.constant 0.000000e+00 : f16
%f0 = arith.constant 0. : f16
// CHECK: %[[MASK_CAST:.+]] = vector.extract %{{.*}}[0] : vector<4xi1> from vector<1x4xi1>
// CHECK: %[[READ:.+]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]], %[[C0]], %[[C0]]], %[[F0]], %[[MASK_CAST]] {in_bounds = [true]} : memref<1x4x8x16xf16>, vector<4xf16>
// CHECK: %[[CAST:.+]] = vector.broadcast %[[READ]] : vector<4xf16> to vector<1x4xf16>
%0 = vector.transfer_read %arg0[%c0, %c0, %c0, %c0], %f0, %arg1 {in_bounds = [true, true]} : memref<1x4x8x16xf16>, vector<1x4xf16>
// CHECK: return %[[CAST]]
return %0: vector<1x4xf16>
}

// CHECK-LABEL: func @cast_away_transfer_read_leading_one_dims_one_element
func.func @cast_away_transfer_read_leading_one_dims_one_element(%arg0: memref<1x1x1x1xf16>) -> vector<1x1xf16> {
%c0 = arith.constant 0 : index
Expand All @@ -229,6 +243,18 @@ func.func @cast_away_transfer_write_leading_one_dims(%arg0: memref<1x4x8x16xf16>
return
}

// CHECK-LABEL: func @cast_away_masked_transfer_write_leading_one_dims
func.func @cast_away_masked_transfer_write_leading_one_dims(%arg0: memref<1x4x8x16xf16>, %arg1: vector<1x4xf16>, %arg2: vector<1x4xi1>) {
// CHECK: %[[C0:.+]] = arith.constant 0 : index
%c0 = arith.constant 0 : index
// CHECK: %[[CAST:.+]] = vector.extract %{{.*}}[0] : vector<4xf16> from vector<1x4xf16>
// CHECK: %[[MASK_CAST:.+]] = vector.extract %{{.*}}[0] : vector<4xi1> from vector<1x4xi1>
// CHECK: vector.transfer_write %[[CAST]], %{{.*}}[%[[C0]], %[[C0]], %[[C0]], %[[C0]]], %[[MASK_CAST]] {in_bounds = [true]} : vector<4xf16>, memref<1x4x8x16xf16>

vector.transfer_write %arg1, %arg0[%c0, %c0, %c0, %c0], %arg2 {in_bounds = [true, true]} : vector<1x4xf16>, memref<1x4x8x16xf16>
return
}

// CHECK-LABEL: func @cast_away_transfer_write_leading_one_dims_one_element
func.func @cast_away_transfer_write_leading_one_dims_one_element(%arg0: memref<1x1x1x1xf16>, %arg1: vector<1x1xf16>) {
%c0 = arith.constant 0 : index
Expand Down Expand Up @@ -410,3 +436,12 @@ func.func @cast_away_insert_leading_one_dims_one_two_dest_scalable(%s: vector<1x
%0 = vector.insert %s, %v [0, 0, 7] : vector<1x[8]xi1> into vector<1x1x8x1x[8]xi1>
return %0: vector<1x1x8x1x[8]xi1>
}

// CHECK-LABEL: func.func @cast_away_constant_mask() -> vector<1x1x8x2x1xi1> {
// CHECK: %[[MASK:.*]] = vector.constant_mask [6, 1, 1] : vector<8x2x1xi1>
// CHECK: %[[BCAST:.*]] = vector.broadcast %[[MASK]] : vector<8x2x1xi1> to vector<1x1x8x2x1xi1>
// CHECK: return %[[BCAST]] : vector<1x1x8x2x1xi1>
func.func @cast_away_constant_mask() -> vector<1x1x8x2x1xi1> {
%0 = vector.constant_mask [1, 1, 6, 1, 1] : vector<1x1x8x2x1xi1>
return %0: vector<1x1x8x2x1xi1>
}