| //===- XeGPUOptimizeBlockLoads.cpp - XeGPU optimize block loads -*- C++ -*-===// |
| // |
| // 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 |
| // |
| //===----------------------------------------------------------------------===// |
| |
| #include "mlir/Dialect/Arith/IR/Arith.h" |
| #include "mlir/Dialect/GPU/IR/GPUDialect.h" |
| #include "mlir/Dialect/MemRef/IR/MemRef.h" |
| #include "mlir/Dialect/SCF/Transforms/Patterns.h" |
| #include "mlir/Dialect/Utils/IndexingUtils.h" |
| #include "mlir/Dialect/Utils/StaticValueUtils.h" |
| #include "mlir/Dialect/Vector/IR/VectorOps.h" |
| #include "mlir/Dialect/XeGPU/IR/XeGPU.h" |
| #include "mlir/Dialect/XeGPU/Transforms/Passes.h" |
| #include "mlir/Dialect/XeGPU/Transforms/Transforms.h" |
| #include "mlir/Dialect/XeGPU/Utils/XeGPUUtils.h" |
| #include "mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h" |
| #include "mlir/Dialect/XeGPU/uArch/uArchBase.h" |
| #include "mlir/IR/BuiltinTypes.h" |
| #include "mlir/IR/OpDefinition.h" |
| #include "mlir/IR/Types.h" |
| #include "mlir/IR/Value.h" |
| #include "mlir/Transforms/DialectConversion.h" |
| #include "llvm/ADT/STLExtras.h" |
| #include "llvm/ADT/SmallVector.h" |
| #include <optional> |
| |
| namespace mlir { |
| namespace xegpu { |
| #define GEN_PASS_DEF_XEGPUOPTIMIZEBLOCKLOADS |
| #include "mlir/Dialect/XeGPU/Transforms/Passes.h.inc" |
| } // namespace xegpu |
| } // namespace mlir |
| |
| #define DEBUG_TYPE "xegpu-optimize-block-loads" |
| #define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE "]: ") |
| |
| using namespace mlir; |
| |
| namespace { |
| |
| /// Get the 2D lane data from a tensor desc type if it exists. |
| static std::optional<SmallVector<int64_t>> |
| getMaybeLaneData(xegpu::TensorDescType tdescType) { |
| auto layout = tdescType.getLayoutAttr(); |
| if (!layout) |
| return std::nullopt; |
| auto laneData = layout.getEffectiveLaneDataAsInt(); |
| if (laneData.size() != 2) |
| return std::nullopt; |
| return laneData; |
| } |
| |
| /// Get the 2D lane layout from a tensor desc type if it exists. |
| static std::optional<SmallVector<int64_t>> |
| getMaybeLaneLayout(xegpu::TensorDescType tdescType) { |
| auto layout = tdescType.getLayoutAttr(); |
| if (!layout) |
| return std::nullopt; |
| auto laneLayout = layout.getEffectiveLaneLayoutAsInt(); |
| if (laneLayout.size() != 2) |
| return std::nullopt; |
| return laneLayout; |
| } |
| |
| /// A layout can be optimized if its lane layout is transposed (lane[0] != 1 && |
| /// lane[1] == 1), but inner lane data is not equal to [1, 1]. |
| /// Example: |
| /// !xegpu.tensor_desc<16x16xf16, |
| /// #xegpu.layout<lane_layout = [16, 1], lane_data = [1, 2]>> |
| /// In this case, lane layout is transposed (from the usual [1, SG_SIZE] form) |
| /// indicating that this is a load that requires transpose effect. However, |
| /// lane data is [1, 2], meaning that each lane must grab 2 f16 elements from |
| /// the inner dimension. We convert this to a optimized form by converting the |
| /// tensor_desc to i32 type such that lane data becomes [1, 1]. This makes the |
| /// later lowering easily use the load with transpose instruction. |
| static bool canBeOptimizedForTranspose(ArrayRef<int64_t> laneLayout, |
| ArrayRef<int64_t> laneData) { |
| if (laneLayout.size() != 2 || laneData.size() != 2) |
| return false; |
| if (laneLayout[0] == 1 || laneLayout[1] != 1) |
| return false; |
| if (laneData[0] != 1 || laneData[1] == 1) |
| return false; |
| return true; |
| } |
| |
| /// A tensor desc type can be optimized if its element type is less than 32 bits |
| /// and its layout can be optimized. |
| static bool canBeOptimizedForTranspose(xegpu::TensorDescType tdescType) { |
| // If the dtype is greater or equal to 32 bits, layout must be valid. |
| int elementTyBitwidth = tdescType.getElementType().getIntOrFloatBitWidth(); |
| if (elementTyBitwidth >= 32) |
| return false; |
| auto maybeLaneLayout = getMaybeLaneLayout(tdescType); |
| auto maybeLaneData = getMaybeLaneData(tdescType); |
| if (!maybeLaneData || !maybeLaneLayout) |
| return false; |
| return canBeOptimizedForTranspose(*maybeLaneLayout, *maybeLaneData); |
| } |
| |
| /// Check if a tensor desc type can be optimized for transpose, if so return the |
| /// new optimized tensor desc type with a valid transpose layout. |
| static xegpu::TensorDescType tryOptimize(xegpu::TensorDescType tdescType, |
| const uArch *targetuArch) { |
| if (!canBeOptimizedForTranspose(tdescType)) |
| return tdescType; |
| auto laneData = getMaybeLaneData(tdescType) |
| .value(); // Lane data must exist if we reach here. |
| int64_t innerLaneData = laneData[1]; |
| int elementTyBitwidth = tdescType.getElementType().getIntOrFloatBitWidth(); |
| // Required shape is total shape of the vector result that this tensor desc |
| // must eventually load after adjusting for the new bitwidth and array |
| // length. |
| SmallVector<int64_t> requiredShape(tdescType.getShape()); |
| requiredShape.back() = |
| requiredShape.back() * tdescType.getArrayLength() / innerLaneData; |
| int newBitWidth = elementTyBitwidth * innerLaneData; |
| Type newElemTy = IntegerType::get(tdescType.getContext(), newBitWidth); |
| // Supported shape is the max transpose shape that can be supported by |
| // hardware that is less than or equal to required shape. |
| auto *blockLoadTarget = dyn_cast<Subgroup2DBlockLoadInstruction>( |
| targetuArch->getInstruction(InstructionKind::Subgroup2DBlockLoad)); |
| auto maybeHWParams = blockLoadTarget->getBlockWidthHeightCount( |
| newElemTy, /** has transform */ false, /** has transpose */ true); |
| // If no HW params found, return the original type. |
| if (!maybeHWParams) |
| return tdescType; |
| auto [widths, heights, counts] = maybeHWParams.value(); |
| // TODO: Currently we expect array length to be 1 for transpose case. |
| if (counts.size() != 1 || counts[0] != 1) |
| return tdescType; |
| int arrayLen = counts[0]; |
| int supportedHeight = |
| xegpu::getLargestDivisor(static_cast<int>(requiredShape[0]), heights); |
| int supportedWidth = |
| xegpu::getLargestDivisor(static_cast<int>(requiredShape[1]), widths); |
| // If no supported height or width found, return the original type. |
| if (supportedHeight == -1 || supportedWidth == -1) |
| return tdescType; |
| |
| SmallVector<int64_t> supportedShape = {supportedHeight, supportedWidth}; |
| xegpu::LayoutAttr newLayout = xegpu::LayoutAttr::get( |
| tdescType.getContext(), |
| tdescType.getLayoutAttr().getLaneLayout().asArrayRef(), {1, 1}); |
| // Array length can not be larger than 1 for transpose case. |
| return xegpu::TensorDescType::get(supportedShape, newElemTy, arrayLen, |
| tdescType.getBoundaryCheck(), |
| tdescType.getMemorySpace(), newLayout); |
| } |
| |
| /// Helper to convert an OpFoldResult to Value. |
| static Value convertToValue(ConversionPatternRewriter &rewriter, Location loc, |
| OpFoldResult ofr) { |
| std::optional<int64_t> mayBeInt = getConstantIntValue(ofr); |
| if (mayBeInt) |
| return arith::ConstantIndexOp::create(rewriter, loc, *mayBeInt).getResult(); |
| return llvm::cast<Value>(ofr); |
| } |
| |
| /// Helper to divide a Value by a constant integer. |
| static Value divideByConstant(ConversionPatternRewriter &rewriter, Location loc, |
| Value val, int64_t constant) { |
| // If the constant is a power of 2, use right shift for division. |
| if (llvm::isPowerOf2_64(constant)) { |
| int64_t shiftAmount = llvm::Log2_64(constant); |
| return arith::ShRUIOp::create( |
| rewriter, loc, val, |
| arith::ConstantIndexOp::create(rewriter, loc, shiftAmount) |
| .getResult()) |
| .getResult(); |
| } |
| auto constantOp = |
| arith::ConstantIndexOp::create(rewriter, loc, constant).getResult(); |
| return arith::DivUIOp::create(rewriter, loc, val, constantOp).getResult(); |
| } |
| |
| /// This function takes a larger register block `data` and generates multiple |
| /// smaller loads (size given by `newTensorDesc`) to fill in the `data` block |
| /// starting from `offsets`. |
| static Value generateLoads(ConversionPatternRewriter &rewriter, |
| TypedValue<VectorType> data, |
| SmallVector<OpFoldResult> offsets, |
| TypedValue<xegpu::TensorDescType> newTensorDesc, |
| xegpu::LoadNdOp origLoadOp) { |
| Location loc = data.getLoc(); |
| assert(offsets.size() >= 2 && "Expecting at least 2 offsets for 2D LoadNdOp"); |
| Value offsetDim0 = convertToValue(rewriter, loc, offsets[offsets.size() - 2]); |
| Value offsetDim1 = convertToValue(rewriter, loc, offsets[offsets.size() - 1]); |
| SmallVector<int64_t> supportedShape(newTensorDesc.getType().getShape()); |
| // Compute the ratio between original shape and supported shape. We need to |
| // generate loads in this ratio arrangement. |
| auto shapeRatio = computeShapeRatio(data.getType().getShape(), |
| supportedShape) |
| .value(); // `ratio` must be defined if we reach here. |
| for (int64_t h = 0; h < shapeRatio[0]; ++h) { |
| for (int64_t w = 0; w < shapeRatio[1]; ++w) { |
| int64_t localOffsetDim0 = h * supportedShape[0]; |
| int64_t localOffsetDim1 = w * supportedShape[1]; |
| Value loadOffsetX = arith::AddIOp::create( |
| rewriter, loc, offsetDim0, |
| arith::ConstantIndexOp::create(rewriter, loc, localOffsetDim0) |
| .getResult()); |
| Value loadOffsetY = arith::AddIOp::create( |
| rewriter, loc, offsetDim1, |
| arith::ConstantIndexOp::create(rewriter, loc, localOffsetDim1) |
| .getResult()); |
| auto loadOp = xegpu::LoadNdOp::create( |
| rewriter, loc, |
| VectorType::get(supportedShape, data.getType().getElementType()), |
| newTensorDesc, ArrayRef<OpFoldResult>{loadOffsetX, loadOffsetY}, |
| origLoadOp.getPackedAttr(), origLoadOp.getTransposeAttr(), |
| origLoadOp.getL1HintAttr(), origLoadOp.getL2HintAttr(), |
| origLoadOp.getL3HintAttr()); |
| // Set the layout for the loadOp. |
| auto layoutAttr = newTensorDesc.getType().getLayoutAttr(); |
| xegpu::setDistributeLayoutAttr(loadOp->getOpResult(0), layoutAttr); |
| // Insert the loaded block into the right position in data. |
| auto insertOp = vector::InsertStridedSliceOp::create( |
| rewriter, loc, loadOp.getResult(), data, |
| ArrayRef<int64_t>{localOffsetDim0, localOffsetDim1}, |
| ArrayRef<int64_t>{1, 1}); |
| // InsertOp must have the same layout as newTensorDesc. |
| xegpu::setDistributeLayoutAttr(insertOp->getOpResult(0), layoutAttr); |
| data = insertOp.getResult(); |
| } |
| } |
| return data; |
| } |
| |
| /// Checks if a CreateNdDescOp can be optimized for transpose, if so creates a |
| /// new CreateNdDescOp with optimized tensor desc type. This involves extracting |
| /// the base pointer from the original memory source and adjusting the shape and |
| /// strides of the tensor desc to fit with the new optimized transpose layout. |
| class XeGPUCreateNdDescOpPattern final |
| : public OpConversionPattern<xegpu::CreateNdDescOp> { |
| public: |
| using OpConversionPattern<xegpu::CreateNdDescOp>::OpConversionPattern; |
| LogicalResult |
| matchAndRewrite(xegpu::CreateNdDescOp createNdOp, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override { |
| auto tdescTy = createNdOp.getType(); |
| // Get the target uArch info. |
| auto chipStr = xegpu::getChipStr(createNdOp); |
| // Check if the chip is supported. |
| assert( |
| chipStr && (chipStr.value() == "pvc" || chipStr.value() == "bmg") && |
| "Expecting target chip to be pvc or bmg for transpose optimization."); |
| const uArch *targetuArch = xegpu::uArch::getUArch(chipStr.value()); |
| |
| auto convertType = tryOptimize(tdescTy, targetuArch); |
| if (convertType == tdescTy) |
| return failure(); |
| auto strides = createNdOp.getMixedStrides(); |
| auto maybeConstInnerStride = getConstantIntValue(strides.back()); |
| // Only row-major memrefs are expected for now. |
| if (!maybeConstInnerStride || *maybeConstInnerStride != 1) |
| return rewriter.notifyMatchFailure( |
| createNdOp, "Expecting row-major memref for transpose optimization."); |
| Value source = createNdOp.getSource(); |
| auto optionalLaneData = getMaybeLaneData(tdescTy); |
| assert(optionalLaneData && "Expected 2D lane data"); |
| auto laneData = optionalLaneData.value(); |
| int64_t innerLaneData = laneData[1]; |
| auto memrefType = dyn_cast<MemRefType>(source.getType()); |
| // Inner dimension of the shape must be adjusted based on innerLaneData. |
| SmallVector<OpFoldResult> modifiedShape(createNdOp.getMixedSizes()); |
| modifiedShape.back() = divideByConstant( |
| rewriter, createNdOp.getLoc(), |
| convertToValue(rewriter, createNdOp.getLoc(), modifiedShape.back()), |
| innerLaneData); |
| // Similarly, second to last stride must be adjusted. |
| assert(strides.size() >= 2 && |
| "Expected at least 2 strides for CreateNdDescOp"); |
| SmallVector<OpFoldResult> modifiedStrides(strides); |
| modifiedStrides[modifiedStrides.size() - 2] = divideByConstant( |
| rewriter, createNdOp.getLoc(), |
| convertToValue(rewriter, createNdOp.getLoc(), |
| modifiedStrides[modifiedStrides.size() - 2]), |
| innerLaneData); |
| |
| // If the source is a static memref, we need to extract the pointer to |
| // base address. |
| if (memrefType && memrefType.hasStaticShape()) { |
| auto extractOp = memref::ExtractAlignedPointerAsIndexOp::create( |
| rewriter, createNdOp.getLoc(), source); |
| source = arith::IndexCastOp::create(rewriter, createNdOp.getLoc(), |
| rewriter.getI64Type(), |
| extractOp.getResult()) |
| .getResult(); |
| } |
| // Create a new CreateNdDescOp with the modified shape and converted type. |
| auto newCreateNdDescOp = xegpu::CreateNdDescOp::create( |
| rewriter, createNdOp.getLoc(), convertType, source, modifiedShape, |
| modifiedStrides); |
| rewriter.replaceOp(createNdOp, newCreateNdDescOp.getResult()); |
| return success(); |
| } |
| }; |
| |
| /// Checks if a LoadNdOp consumes a tensor desc type that was rewritten for |
| /// tranpose optimization. If so, rewrites the LoadNdOp to to align with the |
| /// adjusted tensor desc type. This can result in multiple LoadNdOps being |
| /// generated to fill in the original load shape. |
| class XeGPULoadNdDescOpPattern final |
| : public OpConversionPattern<xegpu::LoadNdOp> { |
| public: |
| using OpConversionPattern<xegpu::LoadNdOp>::OpConversionPattern; |
| LogicalResult |
| matchAndRewrite(xegpu::LoadNdOp loadNdOp, OpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override { |
| auto origTensorDescType = loadNdOp.getTensorDescType(); |
| auto adaptorType = |
| cast<xegpu::TensorDescType>(adaptor.getTensorDesc().getType()); |
| if (adaptorType == origTensorDescType) |
| return failure(); |
| // Offsets must be adjusted based on innerLaneData. |
| auto laneData = getMaybeLaneData(loadNdOp.getTensorDescType()).value(); |
| int64_t innerLaneData = laneData[1]; |
| auto offsets = loadNdOp.getMixedOffsets(); |
| if (offsets.empty()) |
| return rewriter.notifyMatchFailure(loadNdOp, |
| "Expecting offsets in LoadNd"); |
| SmallVector<OpFoldResult> modifiedOffsets(offsets); |
| modifiedOffsets.back() = divideByConstant( |
| rewriter, loadNdOp.getLoc(), |
| convertToValue(rewriter, loadNdOp.getLoc(), modifiedOffsets.back()), |
| innerLaneData); |
| // Get the 2D data shape of this loadNdOp in its original type including |
| // array length. |
| SmallVector<int64_t> origDataShape(origTensorDescType.getShape()); |
| // Adjust the data shape based on innerLaneData. |
| origDataShape.back() /= innerLaneData; |
| // HW supported shape is the new tensor desc shape after conversion. |
| SmallVector<int64_t> hwSupportedShape(adaptorType.getShape()); |
| VectorType origVectorType = |
| VectorType::get(origDataShape, adaptorType.getElementType()); |
| Value data; |
| // Orig data shape is 3D for the array length case. |
| if (origTensorDescType.getArrayLength() > 1) { |
| SmallVector<Value> arraySlices; |
| for (int64_t i = 0; i < origTensorDescType.getArrayLength(); ++i) { |
| Value slice = arith::ConstantOp::create( |
| rewriter, loadNdOp->getLoc(), origVectorType, |
| rewriter.getZeroAttr(origVectorType)); |
| // Increase the Y offset for each array slice. |
| Value offsetY = convertToValue(rewriter, loadNdOp->getLoc(), |
| modifiedOffsets.back()); |
| modifiedOffsets.back() = |
| arith::AddIOp::create( |
| rewriter, loadNdOp->getLoc(), offsetY, |
| arith::ConstantIndexOp::create(rewriter, loadNdOp->getLoc(), |
| i * origDataShape[1]) |
| .getResult()) |
| .getResult(); |
| slice = generateLoads( |
| rewriter, cast<TypedValue<VectorType>>(slice), modifiedOffsets, |
| cast<TypedValue<xegpu::TensorDescType>>(adaptor.getTensorDesc()), |
| loadNdOp); |
| // BitCast back to original load shape without array length. |
| auto bitcastType = VectorType::get(origTensorDescType.getShape(), |
| origTensorDescType.getElementType()); |
| auto bitCastOp = vector::BitCastOp::create(rewriter, loadNdOp->getLoc(), |
| bitcastType, slice); |
| // BitCastOp must have the same layout as the original loadNdOp. |
| xegpu::setDistributeLayoutAttr(bitCastOp->getOpResult(0), |
| origTensorDescType.getLayoutAttr()); |
| arraySlices.push_back(bitCastOp.getResult()); |
| } |
| rewriter.replaceOpWithMultiple(loadNdOp, {arraySlices}); |
| return success(); |
| } |
| data = arith::ConstantOp::create( |
| rewriter, loadNdOp->getLoc(), |
| VectorType::get(origDataShape, adaptorType.getElementType()), |
| rewriter.getZeroAttr(origVectorType)); |
| data = generateLoads( |
| rewriter, cast<TypedValue<VectorType>>(data), modifiedOffsets, |
| cast<TypedValue<xegpu::TensorDescType>>(adaptor.getTensorDesc()), |
| loadNdOp); |
| auto bitCastOp = vector::BitCastOp::create(rewriter, loadNdOp->getLoc(), |
| loadNdOp.getType(), data); |
| // BitCastOp must have the same layout as the original loadNdOp. |
| xegpu::setDistributeLayoutAttr(bitCastOp->getOpResult(0), |
| origTensorDescType.getLayoutAttr()); |
| rewriter.replaceOp(loadNdOp, bitCastOp); |
| return success(); |
| } |
| }; |
| |
| /// Vector ExtractOp must be processed if the original tensor desc type has |
| /// array length greater than 1. In this case, the LoadNdOp is replaced with |
| /// multiple LoadNdOps for each array slice making the extraction unnecessary. |
| /// In this case, we simply remove the ExtractOp. |
| class VectorExtractOpPattern final |
| : public OpConversionPattern<vector::ExtractOp> { |
| public: |
| using OpConversionPattern<vector::ExtractOp>::OpConversionPattern; |
| LogicalResult |
| matchAndRewrite(vector::ExtractOp extractOp, OneToNOpAdaptor adaptor, |
| ConversionPatternRewriter &rewriter) const override { |
| // Check if the source of the extraction is split to multiple values. |
| if (adaptor.getSource().size() == 1) |
| return failure(); |
| auto mixedPos = extractOp.getMixedPosition(); |
| if (mixedPos.size() != 1) |
| return failure(); |
| auto mayBeInt = getConstantIntValue(mixedPos[0]); |
| if (!mayBeInt) |
| return failure(); |
| rewriter.replaceOp(extractOp, adaptor.getSource()[*mayBeInt]); |
| return success(); |
| } |
| }; |
| |
| } // namespace |
| |
| void xegpu::populateXeGPUOptimizeBlockLoadsPatterns( |
| RewritePatternSet &patterns) { |
| patterns.add<XeGPUCreateNdDescOpPattern, XeGPULoadNdDescOpPattern, |
| VectorExtractOpPattern>(patterns.getContext()); |
| } |
| |
| namespace { |
| |
| struct XeGPUOptimizeBlockLoadsPass final |
| : public xegpu::impl::XeGPUOptimizeBlockLoadsBase< |
| XeGPUOptimizeBlockLoadsPass> { |
| void runOnOperation() override { |
| MLIRContext &context = getContext(); |
| TypeConverter converter; |
| RewritePatternSet patterns(&context); |
| ConversionTarget target(context); |
| |
| // This pass is only meant for PVC and BMG targets. If unsupported target |
| // is found, exit early. |
| bool isTargetSupported = false; |
| getOperation()->walk([&](gpu::GPUFuncOp funcOp) { |
| auto chipStr = xegpu::getChipStr(funcOp); |
| if (chipStr && (chipStr.value() == "pvc" || chipStr.value() == "bmg")) |
| isTargetSupported = true; |
| }); |
| |
| if (!isTargetSupported) { |
| DBGS() << "XeGPUOptimizeBlockLoadsPass only supports PVC and BMG targets." |
| << "\n"; |
| return; |
| } |
| |
| // CreateNdDescOp and LoadNdOp with optimizable tensor desc types must be |
| // converted. |
| target.addDynamicallyLegalOp<xegpu::CreateNdDescOp>( |
| [&](xegpu::CreateNdDescOp createNdOp) { |
| return !canBeOptimizedForTranspose(createNdOp.getType()); |
| }); |
| target.addDynamicallyLegalOp<xegpu::LoadNdOp>( |
| [&](xegpu::LoadNdOp loadNdOp) { |
| return !canBeOptimizedForTranspose(loadNdOp.getTensorDescType()); |
| }); |
| // Vector ExtractOps can have optimizable layouts if they extract from |
| // LoadNdOps with array length greater than 1. These ExtractOps must be |
| // converted. |
| target.addDynamicallyLegalOp<vector::ExtractOp>( |
| [&](vector::ExtractOp extractOp) { |
| auto layout = xegpu::getDistributeLayoutAttr(extractOp.getResult()); |
| if (!layout) |
| return true; |
| auto laneLayout = layout.getEffectiveLaneLayoutAsInt(); |
| auto laneData = layout.getEffectiveLaneDataAsInt(); |
| return !canBeOptimizedForTranspose(laneLayout, laneData); |
| }); |
| converter.addConversion([](Type type) { return type; }); |
| |
| target.addLegalDialect<arith::ArithDialect, memref::MemRefDialect, |
| vector::VectorDialect>(); |
| scf::populateSCFStructuralTypeConversionsAndLegality(converter, patterns, |
| target); |
| xegpu::populateXeGPUOptimizeBlockLoadsPatterns(patterns); |
| if (failed(applyPartialConversion(getOperation(), target, |
| std::move(patterns)))) { |
| DBGS() << "Optimize block loads pass failed.\n"; |
| return signalPassFailure(); |
| } |
| } |
| }; |
| |
| } // namespace |
