[Mlir-commits] [mlir] c577f91 - [mlir][vector] Add support for linearizing Extract, ExtractStridedSlice, Shuffle VectorOps in VectorLinearize (#88204)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Apr 18 11:13:54 PDT 2024
Author: Charitha Saumya
Date: 2024-04-18T21:13:49+03:00
New Revision: c577f91d266b74d1b5df475fa2dce7c47fc6c57e
URL: https://github.com/llvm/llvm-project/commit/c577f91d266b74d1b5df475fa2dce7c47fc6c57e
DIFF: https://github.com/llvm/llvm-project/commit/c577f91d266b74d1b5df475fa2dce7c47fc6c57e.diff
LOG: [mlir][vector] Add support for linearizing Extract, ExtractStridedSlice, Shuffle VectorOps in VectorLinearize (#88204)
This PR adds support for converting `vector.extract_strided_slice` and
`vector.extract` operations to equivalent `vector.shuffle` operations
that operates on linearized (1-D) vectors. `vector.shuffle` operations
operating on n-D (n > 1) are also converted to equivalent shuffle
operations working on linearized vectors.
Added:
Modified:
mlir/include/mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h
mlir/lib/Dialect/Vector/Transforms/VectorLinearize.cpp
mlir/test/Dialect/Vector/linearize.mlir
mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h b/mlir/include/mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h
index 453fa73429dd1a..fa2912a3e577d1 100644
--- a/mlir/include/mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h
+++ b/mlir/include/mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h
@@ -389,6 +389,13 @@ void populateVectorLinearizeTypeConversionsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target, unsigned targetBitWidth);
+/// Populates patterns for linearizing ND (N >= 2) vector operations to 1D
+/// vector shuffle operations.
+void populateVectorLinearizeShuffleLikeOpsPatterns(TypeConverter &typeConverter,
+ RewritePatternSet &patterns,
+ ConversionTarget &target,
+ unsigned targetBitWidth);
+
} // namespace vector
} // namespace mlir
diff --git a/mlir/lib/Dialect/Vector/Transforms/VectorLinearize.cpp b/mlir/lib/Dialect/Vector/Transforms/VectorLinearize.cpp
index b59e9062e5a08e..69999f0918c103 100644
--- a/mlir/lib/Dialect/Vector/Transforms/VectorLinearize.cpp
+++ b/mlir/lib/Dialect/Vector/Transforms/VectorLinearize.cpp
@@ -13,9 +13,16 @@
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h"
+#include "mlir/IR/Attributes.h"
+#include "mlir/IR/BuiltinAttributes.h"
+#include "mlir/IR/Operation.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
+#include "mlir/Support/LogicalResult.h"
#include "mlir/Transforms/DialectConversion.h"
+#include "llvm/ADT/ArrayRef.h"
+#include <cstdint>
+#include <numeric>
using namespace mlir;
@@ -103,6 +110,251 @@ struct LinearizeVectorizable final
return success();
}
+private:
+ unsigned targetVectorBitWidth;
+};
+
+/// This pattern converts the ExtractStridedSliceOp into a ShuffleOp that works
+/// on a linearized vector.
+/// Following,
+/// vector.extract_strided_slice %source
+/// { offsets = [..], strides = [..], sizes = [..] }
+/// is converted to :
+/// %source_1d = vector.shape_cast %source
+/// %out_1d = vector.shuffle %source_1d, %source_1d [ shuffle_indices_1d ]
+/// %out_nd = vector.shape_cast %out_1d
+/// `shuffle_indices_1d` is computed using the offsets and sizes of the
+/// extraction.
+struct LinearizeVectorExtractStridedSlice final
+ : public mlir::OpConversionPattern<mlir::vector::ExtractStridedSliceOp> {
+ using OpConversionPattern::OpConversionPattern;
+ LinearizeVectorExtractStridedSlice(
+ const TypeConverter &typeConverter, MLIRContext *context,
+ unsigned targetVectBitWidth = std::numeric_limits<unsigned>::max(),
+ PatternBenefit benefit = 1)
+ : OpConversionPattern(typeConverter, context, benefit),
+ targetVectorBitWidth(targetVectBitWidth) {}
+
+ LogicalResult
+ matchAndRewrite(vector::ExtractStridedSliceOp extractOp, OpAdaptor adaptor,
+ ConversionPatternRewriter &rewriter) const override {
+ Type dstType = getTypeConverter()->convertType(extractOp.getType());
+ assert(!(extractOp.getVector().getType().isScalable() ||
+ dstType.cast<VectorType>().isScalable()) &&
+ "scalable vectors are not supported.");
+ if (!isLessThanTargetBitWidth(extractOp, targetVectorBitWidth))
+ return rewriter.notifyMatchFailure(
+ extractOp, "Can't flatten since targetBitWidth <= OpSize");
+
+ ArrayAttr offsets = extractOp.getOffsets();
+ ArrayAttr sizes = extractOp.getSizes();
+ ArrayAttr strides = extractOp.getStrides();
+ if (!isConstantIntValue(strides[0], 1))
+ return rewriter.notifyMatchFailure(
+ extractOp, "Strided slice with stride != 1 is not supported.");
+ Value srcVector = adaptor.getVector();
+ // If kD offsets are specified for nD source vector (n > k), the granularity
+ // of the extraction is greater than 1. In this case last (n-k) dimensions
+ // form the extraction granularity.
+ // Example :
+ // vector.extract_strided_slice %src {
+ // offsets = [0, 0], sizes = [2, 2], strides = [1, 1]} :
+ // vector<4x8x8xf32> to vector<2x2x8xf32>
+ // Here, extraction granularity is 8.
+ int64_t extractGranularitySize = 1;
+ int64_t nD = extractOp.getSourceVectorType().getRank();
+ int64_t kD = (int64_t)offsets.size();
+ int64_t k = kD;
+ while (k < nD) {
+ extractGranularitySize *= extractOp.getSourceVectorType().getShape()[k];
+ ++k;
+ }
+ // Get total number of extracted slices.
+ int64_t nExtractedSlices = 1;
+ for (Attribute size : sizes) {
+ nExtractedSlices *= size.cast<IntegerAttr>().getInt();
+ }
+ // Compute the strides of the source vector considering first k dimensions.
+ llvm::SmallVector<int64_t, 4> sourceStrides(kD, extractGranularitySize);
+ for (int i = kD - 2; i >= 0; --i) {
+ sourceStrides[i] = sourceStrides[i + 1] *
+ extractOp.getSourceVectorType().getShape()[i + 1];
+ }
+ // Final shuffle indices has nExtractedSlices * extractGranularitySize
+ // elements.
+ llvm::SmallVector<int64_t, 4> indices(nExtractedSlices *
+ extractGranularitySize);
+ // Compute the strides of the extracted kD vector.
+ llvm::SmallVector<int64_t, 4> extractedStrides(kD, 1);
+ // Compute extractedStrides.
+ for (int i = kD - 2; i >= 0; --i) {
+ extractedStrides[i] =
+ extractedStrides[i + 1] * sizes[i + 1].cast<IntegerAttr>().getInt();
+ }
+ // Iterate over all extracted slices from 0 to nExtractedSlices - 1
+ // and compute the multi-dimensional index and the corresponding linearized
+ // index within the source vector.
+ for (int64_t i = 0; i < nExtractedSlices; ++i) {
+ int64_t index = i;
+ // Compute the corresponding multi-dimensional index.
+ llvm::SmallVector<int64_t, 4> multiDimIndex(kD, 0);
+ for (int64_t j = 0; j < kD; ++j) {
+ multiDimIndex[j] = (index / extractedStrides[j]);
+ index -= multiDimIndex[j] * extractedStrides[j];
+ }
+ // Compute the corresponding linearized index in the source vector
+ // i.e. shift the multiDimIndex by the offsets.
+ int64_t linearizedIndex = 0;
+ for (int64_t j = 0; j < kD; ++j) {
+ linearizedIndex +=
+ (offsets[j].cast<IntegerAttr>().getInt() + multiDimIndex[j]) *
+ sourceStrides[j];
+ }
+ // Fill the indices array form linearizedIndex to linearizedIndex +
+ // extractGranularitySize.
+ for (int64_t j = 0; j < extractGranularitySize; ++j) {
+ indices[i * extractGranularitySize + j] = linearizedIndex + j;
+ }
+ }
+ // Perform a shuffle to extract the kD vector.
+ rewriter.replaceOpWithNewOp<vector::ShuffleOp>(
+ extractOp, dstType, srcVector, srcVector,
+ rewriter.getI64ArrayAttr(indices));
+ return success();
+ }
+
+private:
+ unsigned targetVectorBitWidth;
+};
+
+/// This pattern converts the ShuffleOp that works on nD (n > 1)
+/// vectors to a ShuffleOp that works on linearized vectors.
+/// Following,
+/// vector.shuffle %v1, %v2 [ shuffle_indices ]
+/// is converted to :
+/// %v1_1d = vector.shape_cast %v1
+/// %v2_1d = vector.shape_cast %v2
+/// %out_1d = vector.shuffle %v1_1d, %v2_1d [ shuffle_indices_1d ]
+/// %out_nd = vector.shape_cast %out_1d
+// `shuffle_indices_1d` is computed using the sizes and `shuffle_indices`
+/// of the original shuffle operation.
+struct LinearizeVectorShuffle final
+ : public OpConversionPattern<vector::ShuffleOp> {
+ using OpConversionPattern::OpConversionPattern;
+ LinearizeVectorShuffle(
+ const TypeConverter &typeConverter, MLIRContext *context,
+ unsigned targetVectBitWidth = std::numeric_limits<unsigned>::max(),
+ PatternBenefit benefit = 1)
+ : OpConversionPattern(typeConverter, context, benefit),
+ targetVectorBitWidth(targetVectBitWidth) {}
+
+ LogicalResult
+ matchAndRewrite(vector::ShuffleOp shuffleOp, OpAdaptor adaptor,
+ ConversionPatternRewriter &rewriter) const override {
+ Type dstType = getTypeConverter()->convertType(shuffleOp.getType());
+ assert(!(shuffleOp.getV1VectorType().isScalable() ||
+ shuffleOp.getV2VectorType().isScalable() ||
+ dstType.cast<VectorType>().isScalable()) &&
+ "scalable vectors are not supported.");
+ if (!isLessThanTargetBitWidth(shuffleOp, targetVectorBitWidth))
+ return rewriter.notifyMatchFailure(
+ shuffleOp, "Can't flatten since targetBitWidth <= OpSize");
+
+ Value vec1 = adaptor.getV1();
+ Value vec2 = adaptor.getV2();
+ int shuffleSliceLen = 1;
+ int rank = shuffleOp.getV1().getType().getRank();
+
+ // If rank > 1, we need to do the shuffle in the granularity of slices
+ // instead of scalars. Size of the slice is equal to the rank-1 innermost
+ // dims. Mask of the shuffle op specifies which slice to take from the
+ // outermost dim.
+ if (rank > 1) {
+ llvm::ArrayRef<int64_t> shape = shuffleOp.getV1().getType().getShape();
+ for (unsigned i = 1; i < shape.size(); ++i) {
+ shuffleSliceLen *= shape[i];
+ }
+ }
+
+ // For each value in the mask, we generate the indices of the source vectors
+ // that needs to be shuffled to the destination vector. If shuffleSliceLen >
+ // 1 we need to shuffle the slices (consecutive shuffleSliceLen number of
+ // elements) instead of scalars.
+ ArrayAttr mask = shuffleOp.getMask();
+ int64_t totalSizeOfShuffledElmnts = mask.size() * shuffleSliceLen;
+ llvm::SmallVector<int64_t, 2> indices(totalSizeOfShuffledElmnts);
+ for (auto [i, value] :
+ llvm::enumerate(mask.getAsValueRange<IntegerAttr>())) {
+
+ int64_t v = value.getZExtValue();
+ std::iota(indices.begin() + shuffleSliceLen * i,
+ indices.begin() + shuffleSliceLen * (i + 1),
+ shuffleSliceLen * v);
+ }
+
+ rewriter.replaceOpWithNewOp<vector::ShuffleOp>(
+ shuffleOp, dstType, vec1, vec2, rewriter.getI64ArrayAttr(indices));
+ return success();
+ }
+
+private:
+ unsigned targetVectorBitWidth;
+};
+
+/// This pattern converts the ExtractOp to a ShuffleOp that works on a
+/// linearized vector.
+/// Following,
+/// vector.extract %source [ position ]
+/// is converted to :
+/// %source_1d = vector.shape_cast %source
+/// %out_1d = vector.shuffle %source_1d, %source_1d [ shuffle_indices_1d ]
+/// %out_nd = vector.shape_cast %out_1d
+/// `shuffle_indices_1d` is computed using the position of the original extract.
+struct LinearizeVectorExtract final
+ : public OpConversionPattern<vector::ExtractOp> {
+ using OpConversionPattern::OpConversionPattern;
+ LinearizeVectorExtract(
+ const TypeConverter &typeConverter, MLIRContext *context,
+ unsigned targetVectBitWidth = std::numeric_limits<unsigned>::max(),
+ PatternBenefit benefit = 1)
+ : OpConversionPattern(typeConverter, context, benefit),
+ targetVectorBitWidth(targetVectBitWidth) {}
+ LogicalResult
+ matchAndRewrite(vector::ExtractOp extractOp, OpAdaptor adaptor,
+ ConversionPatternRewriter &rewriter) const override {
+ Type dstTy = getTypeConverter()->convertType(extractOp.getType());
+ assert(!(extractOp.getVector().getType().isScalable() ||
+ dstTy.cast<VectorType>().isScalable()) &&
+ "scalable vectors are not supported.");
+ if (!isLessThanTargetBitWidth(extractOp, targetVectorBitWidth))
+ return rewriter.notifyMatchFailure(
+ extractOp, "Can't flatten since targetBitWidth <= OpSize");
+
+ // Dynamic position is not supported.
+ if (extractOp.hasDynamicPosition())
+ return rewriter.notifyMatchFailure(extractOp,
+ "dynamic position is not supported.");
+
+ llvm::ArrayRef<int64_t> shape = extractOp.getVector().getType().getShape();
+ int64_t size = extractOp.getVector().getType().getNumElements();
+
+ // Compute linearized offset.
+ int64_t linearizedOffset = 0;
+ llvm::ArrayRef<int64_t> offsets = extractOp.getStaticPosition();
+ for (auto [i, off] : llvm::enumerate(offsets)) {
+ size /= shape[i];
+ linearizedOffset += offsets[i] * size;
+ }
+
+ llvm::SmallVector<int64_t, 2> indices(size);
+ std::iota(indices.begin(), indices.end(), linearizedOffset);
+ rewriter.replaceOpWithNewOp<vector::ShuffleOp>(
+ extractOp, dstTy, adaptor.getVector(), adaptor.getVector(),
+ rewriter.getI64ArrayAttr(indices));
+
+ return success();
+ }
+
private:
unsigned targetVectorBitWidth;
};
@@ -145,3 +397,21 @@ void mlir::vector::populateVectorLinearizeTypeConversionsAndLegality(
patterns.add<LinearizeConstant, LinearizeVectorizable>(
typeConverter, patterns.getContext(), targetBitWidth);
}
+
+void mlir::vector::populateVectorLinearizeShuffleLikeOpsPatterns(
+ TypeConverter &typeConverter, RewritePatternSet &patterns,
+ ConversionTarget &target, unsigned int targetBitWidth) {
+ target.addDynamicallyLegalOp<vector::ShuffleOp>(
+ [=](vector::ShuffleOp shuffleOp) -> bool {
+ return isLessThanTargetBitWidth(shuffleOp, targetBitWidth)
+ ? (typeConverter.isLegal(shuffleOp) &&
+ shuffleOp.getResult()
+ .getType()
+ .cast<mlir::VectorType>()
+ .getRank() == 1)
+ : true;
+ });
+ patterns.add<LinearizeVectorShuffle, LinearizeVectorExtract,
+ LinearizeVectorExtractStridedSlice>(
+ typeConverter, patterns.getContext(), targetBitWidth);
+}
diff --git a/mlir/test/Dialect/Vector/linearize.mlir b/mlir/test/Dialect/Vector/linearize.mlir
index 22be78cd682057..b29ceab5783d7a 100644
--- a/mlir/test/Dialect/Vector/linearize.mlir
+++ b/mlir/test/Dialect/Vector/linearize.mlir
@@ -153,3 +153,95 @@ func.func @test_0d_vector() -> vector<f32> {
// ALL: return %[[CST]]
return %0 : vector<f32>
}
+
+// -----
+// ALL-LABEL: test_extract_strided_slice_1
+// ALL-SAME: (%[[ORIG_ARG:.*]]: vector<4x8xf32>) -> vector<2x2xf32> {
+func.func @test_extract_strided_slice_1(%arg0 : vector<4x8xf32>) -> vector<2x2xf32> {
+ // DEFAULT: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<4x8xf32> to vector<32xf32>
+ // DEFAULT: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // DEFAULT-SAME: [4, 5, 12, 13] : vector<32xf32>, vector<32xf32>
+ // DEFAULT: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<4xf32> to vector<2x2xf32>
+ // DEFAULT: return %[[RES]] : vector<2x2xf32
+
+ // BW-128: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<4x8xf32> to vector<32xf32>
+ // BW-128: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // BW-128-SAME: [4, 5, 12, 13] : vector<32xf32>, vector<32xf32>
+ // BW-128: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<4xf32> to vector<2x2xf32>
+ // BW-128: return %[[RES]] : vector<2x2xf32>
+
+ // BW-0: %[[RES:.*]] = vector.extract_strided_slice %[[ARG:.*]] {offsets = [0, 4], sizes = [2, 2], strides = [1, 1]} : vector<4x8xf32> to vector<2x2xf32>
+ // BW-0: return %[[RES]] : vector<2x2xf32>
+ %0 = vector.extract_strided_slice %arg0 { sizes = [2, 2], strides = [1, 1], offsets = [0, 4]}
+ : vector<4x8xf32> to vector<2x2xf32>
+ return %0 : vector<2x2xf32>
+}
+
+// -----
+// ALL-LABEL: test_extract_strided_slice_2
+// ALL-SAME: (%[[ORIG_ARG:.*]]: vector<2x8x2xf32>) -> vector<1x4x2xf32> {
+func.func @test_extract_strided_slice_2(%arg0 : vector<2x8x2xf32>) -> vector<1x4x2xf32> {
+ // DEFAULT: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<2x8x2xf32> to vector<32xf32>
+ // DEFAULT: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // DEFAULT-SAME: [20, 21, 22, 23, 24, 25, 26, 27] : vector<32xf32>, vector<32xf32>
+ // DEFAULT: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<8xf32> to vector<1x4x2xf32>
+ // DEFAULT: return %[[RES]] : vector<1x4x2xf32>
+
+ // BW-128: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<2x8x2xf32> to vector<32xf32>
+ // BW-128: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // BW-128-SAME: [20, 21, 22, 23, 24, 25, 26, 27] : vector<32xf32>, vector<32xf32>
+ // BW-128: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<8xf32> to vector<1x4x2xf32>
+ // BW-128: return %[[RES]] : vector<1x4x2xf32>
+
+ // BW-0: %[[RES:.*]] = vector.extract_strided_slice %[[ORIG_ARG]] {offsets = [1, 2], sizes = [1, 4], strides = [1, 1]} : vector<2x8x2xf32> to vector<1x4x2xf32>
+ // BW-0: return %[[RES]] : vector<1x4x2xf32>
+ %0 = vector.extract_strided_slice %arg0 { offsets = [1, 2], strides = [1, 1], sizes = [1, 4] }
+ : vector<2x8x2xf32> to vector<1x4x2xf32>
+ return %0 : vector<1x4x2xf32>
+}
+
+// -----
+// ALL-LABEL: test_vector_shuffle
+// ALL-SAME: (%[[ORIG_ARG0:.*]]: vector<4x2xf32>, %[[ORIG_ARG1:.*]]: vector<4x2xf32>) -> vector<8x2xf32> {
+func.func @test_vector_shuffle(%arg0: vector<4x2xf32>, %arg1: vector<4x2xf32>) -> vector<8x2xf32> {
+ // DEFAULT: %[[ARG0:.*]] = vector.shape_cast %[[ORIG_ARG0]] : vector<4x2xf32> to vector<8xf32>
+ // DEFAULT: %[[ARG1:.*]] = vector.shape_cast %[[ORIG_ARG1]] : vector<4x2xf32> to vector<8xf32>
+ // DEFAULT: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG0]], %[[ARG1]]
+ // DEFAULT-SAME: [0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15] : vector<8xf32>, vector<8xf32>
+ // DEFAULT: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<16xf32> to vector<8x2xf32>
+ // DEFAULT: return %[[RES]] : vector<8x2xf32>
+
+ // BW-128: %[[ARG0:.*]] = vector.shape_cast %[[ORIG_ARG0]] : vector<4x2xf32> to vector<8xf32>
+ // BW-128: %[[ARG1:.*]] = vector.shape_cast %[[ORIG_ARG1]] : vector<4x2xf32> to vector<8xf32>
+ // BW-128: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG0]], %[[ARG1]]
+ // BW-128-SAME: [0, 1, 8, 9, 2, 3, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15] : vector<8xf32>, vector<8xf32>
+ // BW-128: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<16xf32> to vector<8x2xf32>
+ // BW-128: return %[[RES]] : vector<8x2xf32>
+
+ // BW-0: %[[RES:.*]] = vector.shuffle %[[ORIG_ARG0]], %[[ORIG_ARG1]] [0, 4, 1, 5, 2, 6, 3, 7] : vector<4x2xf32>, vector<4x2xf32>
+ // BW-0: return %[[RES]] : vector<8x2xf32>
+ %0 = vector.shuffle %arg0, %arg1 [0, 4, 1, 5, 2, 6, 3, 7] : vector<4x2xf32>, vector<4x2xf32>
+ return %0 : vector<8x2xf32>
+}
+
+// -----
+// ALL-LABEL: test_vector_extract
+// ALL-SAME: (%[[ORIG_ARG:.*]]: vector<2x8x2xf32>) -> vector<8x2xf32> {
+func.func @test_vector_extract(%arg0: vector<2x8x2xf32>) -> vector<8x2xf32> {
+ // DEFAULT: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<2x8x2xf32> to vector<32xf32>
+ // DEFAULT: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // DEFAULT-SAME: [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] : vector<32xf32>, vector<32xf32>
+ // DEFAULT: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<16xf32> to vector<8x2xf32>
+ // DEFAULT: return %[[RES]] : vector<8x2xf32>
+
+ // BW-128: %[[ARG:.*]] = vector.shape_cast %[[ORIG_ARG]] : vector<2x8x2xf32> to vector<32xf32>
+ // BW-128: %[[SHUFFLE:.*]] = vector.shuffle %[[ARG]], %[[ARG]]
+ // BW-128-SAME: [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] : vector<32xf32>, vector<32xf32>
+ // BW-128: %[[RES:.*]] = vector.shape_cast %[[SHUFFLE]] : vector<16xf32> to vector<8x2xf32>
+ // BW-128: return %[[RES]] : vector<8x2xf32>
+
+ // BW-0: %[[RES:.*]] = vector.extract %[[ORIG_ARG]][1] : vector<8x2xf32> from vector<2x8x2xf32>
+ // BW-0: return %[[RES]] : vector<8x2xf32>
+ %0 = vector.extract %arg0[1]: vector<8x2xf32> from vector<2x8x2xf32>
+ return %0 : vector<8x2xf32>
+}
diff --git a/mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp b/mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp
index 00622599910567..c978699e179fca 100644
--- a/mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp
+++ b/mlir/test/lib/Dialect/Vector/TestVectorTransforms.cpp
@@ -867,6 +867,8 @@ struct TestVectorLinearize final
vector::populateVectorLinearizeTypeConversionsAndLegality(
typeConverter, patterns, target, targetVectorBitwidth);
+ vector::populateVectorLinearizeShuffleLikeOpsPatterns(
+ typeConverter, patterns, target, targetVectorBitwidth);
if (failed(applyPartialConversion(getOperation(), target,
std::move(patterns))))
return signalPassFailure();
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