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Support index element type in VectorLinearize pass #966

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Nov 21, 2024
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Support index element type in VectorLinearize pass #966

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0d06825
Support index element type in VectorLinearize pass
ayzhuang Nov 21, 2024
7188e91
Delete nd-ops.mlir
ayzhuang Nov 21, 2024
44efd07
Fix vector-linearize.mlir format
ayzhuang Nov 21, 2024
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27 changes: 0 additions & 27 deletions lib/Conversion/XeGPUToVC/XeGPUToVC.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -661,22 +661,6 @@ class VectorShapeCastPattern : public OpConversionPattern<ShapeCastOp> {
}
};

template <typename OpTy>
class IndexCastPattern : public OpConversionPattern<OpTy> {
public:
using OpConversionPattern<OpTy>::OpConversionPattern;
LogicalResult
matchAndRewrite(OpTy indexCastOp, typename OpTy::Adaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
auto *converter = OpConversionPattern<OpTy>::getTypeConverter();
Type dstType = converter->convertType(indexCastOp.getType());
if (!dstType)
return failure();
rewriter.replaceOpWithNewOp<OpTy>(indexCastOp, dstType, adaptor.getIn());
return success();
}
};

class SCFForPattern : public OpConversionPattern<ForOp> {
public:
using OpConversionPattern<ForOp>::OpConversionPattern;
Expand Down Expand Up @@ -823,14 +807,6 @@ struct XeGPUToVCPass : public imex::impl::ConvertXeGPUToVCBase<XeGPUToVCPass> {
target.addDynamicallyLegalDialect<scf::SCFDialect>(
[&](Operation *op) { return isLegalXeGPUSCFOp(op, typeConverter); });

target.addDynamicallyLegalOp<arith::IndexCastOp, arith::IndexCastUIOp>(
[&](Operation *op) {
if (auto vecTy = dyn_cast<VectorType>(op->getResult(0).getType())) {
return typeConverter.isLegal(vecTy);
}
return true;
});

target.addIllegalOp<ShapeCastOp>();

// TODO: can we change it to addDynamicLegalOp?
Expand Down Expand Up @@ -883,9 +859,6 @@ struct XeGPUToVCPass : public imex::impl::ConvertXeGPUToVCBase<XeGPUToVCPass> {
patterns.add<VectorShapeCastPattern, SCFForPattern>(typeConverter,
patterns.getContext());

patterns.add<IndexCastPattern<arith::IndexCastOp>,
IndexCastPattern<arith::IndexCastUIOp>>(typeConverter,
patterns.getContext());
// Ops to llvm.genx only Patterns
patterns.add<NbarrierWaitPattern, CompilerHintPattern, DpasPattern,
NbarrierArrivePattern>(patterns.getContext());
Expand Down
123 changes: 82 additions & 41 deletions lib/Transforms/VectorLinearize.cpp
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Expand Up @@ -34,33 +34,93 @@ namespace imex {

namespace {

// rewrite arith.constant op in form of vector<1xmxindex> into 1D form
// (vector<mxindex>)
struct ArithConstantOpConversion final
// Cloned from upstream with isLessThanTargetBitWidth check removed.
struct ConstantOpConversion final
: public mlir::OpConversionPattern<mlir::arith::ConstantOp> {
using mlir::OpConversionPattern<mlir::arith::ConstantOp>::OpConversionPattern;
using OpConversionPattern::OpConversionPattern;
mlir::LogicalResult
matchAndRewrite(mlir::arith::ConstantOp constOp, OpAdaptor adaptor,
mlir::ConversionPatternRewriter &rewriter) const override {
auto value = llvm::dyn_cast<mlir::DenseElementsAttr>(constOp.getValue());
if (!value || value.getType().getRank() != 2)
return mlir::failure();
auto type = value.getType();
auto shape = type.getShape();
auto elemTy = type.getElementType();
if (shape[0] != 1 || !elemTy.isIndex())
auto resType =
getTypeConverter()->convertType<mlir::VectorType>(constOp.getType());

if (resType.isScalable() &&
!mlir::isa<mlir::SplatElementsAttr>(constOp.getValue()))
return rewriter.notifyMatchFailure(
constOp,
"Cannot linearize a constant scalable vector that's not a splat");

if (!resType)
return rewriter.notifyMatchFailure(constOp, "can't convert return type");
auto dstElementsAttr =
mlir::dyn_cast<mlir::DenseElementsAttr>(constOp.getValue());
if (!dstElementsAttr)
return rewriter.notifyMatchFailure(constOp, "unsupported attr type");

dstElementsAttr = dstElementsAttr.reshape(resType);
rewriter.replaceOpWithNewOp<mlir::arith::ConstantOp>(constOp, resType,
dstElementsAttr);
return mlir::success();
}
};

// Cloned from upstream with isLessThanTargetBitWidth check removed.
struct VectorizableOpConversion final
: public mlir::OpTraitConversionPattern<mlir::OpTrait::Vectorizable> {
using OpTraitConversionPattern::OpTraitConversionPattern;
mlir::LogicalResult
matchAndRewrite(mlir::Operation *op, llvm::ArrayRef<mlir::Value> operands,
mlir::ConversionPatternRewriter &rewriter) const override {
mlir::FailureOr<mlir::Operation *> newOp =
convertOpResultTypes(op, operands, *getTypeConverter(), rewriter);
if (failed(newOp))
return mlir::failure();
auto newTy = mlir::VectorType::get({shape[1]}, elemTy);
value = value.reshape(newTy);
auto newOp =
rewriter.create<mlir::arith::ConstantOp>(constOp.getLoc(), value);
auto castOp = rewriter.create<mlir::vector::ShapeCastOp>(constOp.getLoc(),
type, newOp);
rewriter.replaceOp(constOp, castOp);

rewriter.replaceOp(op, (*newOp)->getResults());
return mlir::success();
}
};

// Cloned from upstream with isLessThanTargetBitWidth check removed.
static void populateVectorLinearizeTypeConversionsAndLegality(
mlir::TypeConverter &typeConverter, mlir::RewritePatternSet &patterns,
mlir::ConversionTarget &target) {

typeConverter.addConversion(
[](mlir::VectorType type) -> std::optional<mlir::Type> {
if (!mlir::vector::isLinearizableVector(type))
return type;

return mlir::VectorType::get(type.getNumElements(),
type.getElementType(), type.isScalable());
});

auto materializeCast = [](mlir::OpBuilder &builder, mlir::Type type,
mlir::ValueRange inputs,
mlir::Location loc) -> mlir::Value {
if (inputs.size() != 1 ||
!mlir::isa<mlir::VectorType>(inputs.front().getType()) ||
!mlir::isa<mlir::VectorType>(type))
return nullptr;

return builder.create<mlir::vector::ShapeCastOp>(loc, type, inputs.front());
};
typeConverter.addArgumentMaterialization(materializeCast);
typeConverter.addSourceMaterialization(materializeCast);
typeConverter.addTargetMaterialization(materializeCast);
target.markUnknownOpDynamicallyLegal(
[=](mlir::Operation *op) -> std::optional<bool> {
if ((mlir::isa<mlir::arith::ConstantOp>(op) ||
op->hasTrait<mlir::OpTrait::Vectorizable>())) {
return typeConverter.isLegal(op);
}
return std::nullopt;
});

patterns.add<ConstantOpConversion, VectorizableOpConversion>(
typeConverter, patterns.getContext());
}

struct VectorLoadOpConversion final
: public mlir::OpConversionPattern<mlir::vector::LoadOp> {
using mlir::OpConversionPattern<mlir::vector::LoadOp>::OpConversionPattern;
Expand Down Expand Up @@ -513,38 +573,19 @@ struct VectorLinearizePass final
return (op && op.getAggregate().getType().getRank() == 1);
});

// borrowed from upstream with hacking for index type. Currently
// we only target vector<1xmxindex> to vector<mxindex> conversion. It is
// unclear whether others are valid or not; thus they are left untouched.
target.addDynamicallyLegalOp<mlir::arith::ConstantOp>(
[&](mlir::arith::ConstantOp op) -> bool {
auto vecTy = mlir::dyn_cast<mlir::VectorType>(op.getType());
if (!vecTy || vecTy.getRank() == 0)
return true;

auto elemTy = vecTy.getElementType();
if (elemTy.isIndex()) {
if (vecTy.getRank() == 2 && vecTy.getShape()[0] == 1)
return false;
return true;
}
return !mlir::vector::isLinearizableVector(vecTy);
});

patterns.add<VectorExtractStridedSliceConversion, VectorShffleOpConversion,
VectorExtractOpConversion, VectorInsertOpConversion,
VectorSplatOpConversion, VectorLoadOpConversion,
VectorStoreOpConversion, VectorCreateMaskOpConversion,
ArithConstantOpConversion>(typeConverter, context);
VectorStoreOpConversion, VectorCreateMaskOpConversion>(
typeConverter, context);

// Shuffle16x16 will fallback to Shuffle1D for non 16x16 sizes.
mlir::vector::populateVectorTransposeLoweringPatterns(
patterns,
mlir::vector::VectorTransformsOptions().setVectorTransposeLowering(
mlir::vector::VectorTransposeLowering::Shuffle16x16));
unsigned targetVectBitWidth = std::numeric_limits<unsigned>::max();
mlir::vector::populateVectorLinearizeTypeConversionsAndLegality(
typeConverter, patterns, target, targetVectBitWidth);
populateVectorLinearizeTypeConversionsAndLegality(typeConverter, patterns,
target);
if (mlir::failed(mlir::applyPartialConversion(getOperation(), target,
std::move(patterns))))
return signalPassFailure();
Expand Down
92 changes: 92 additions & 0 deletions test/Transforms/vector-linearize.mlir
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Expand Up @@ -284,3 +284,95 @@ func.func @test_vector_store_load_4x4(%buffer: memref<4x4xf32>) {
vector.store %0, %buffer[%c0, %c0] : memref<4x4xf32>, vector<4x4xf32>
return
}

// -----
// CHECK-LABEL: @test_linearize_index
// CHECK-SAME: (%[[ARG0:.*]]: vector<2x2xindex>, %[[ARG1:.*]]: vector<2x2xi32>) -> vector<2x2xindex> {
// CHECK: %[[T0:.*]] = vector.shape_cast %[[ARG1]] : vector<2x2xi32> to vector<4xi32>
// CHECK: %[[T1:.*]] = vector.shape_cast %[[ARG0]] : vector<2x2xindex> to vector<4xindex>
// CHECK: %[[CST:.*]] = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex>
// CHECK: %[[T2:.*]] = arith.addi %[[T1]], %[[CST]] : vector<4xindex>
// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : vector<4xindex> to vector<4xi32>
// CHECK: %[[T4:.*]] = arith.muli %[[T3]], %[[T0]] : vector<4xi32>
// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : vector<4xi32> to vector<4xindex>
// CHECK: %[[T6:.*]] = vector.shape_cast %[[T5]] : vector<4xindex> to vector<2x2xindex>
// CHECK: return %[[T6]] : vector<2x2xindex>
func.func @test_linearize_index(%arg0: vector<2x2xindex>, %arg1: vector<2x2xi32>) -> vector<2x2xindex> {
%0 = arith.constant dense<[[0, 1], [2, 3]]> : vector<2x2xindex>
// Arith and math ops are handled in generic way, check some of them
%1 = arith.addi %arg0, %0 : vector<2x2xindex>
%2 = arith.index_cast %1 : vector<2x2xindex> to vector<2x2xi32>
%3 = arith.muli %2, %arg1 : vector<2x2xi32>
%4 = arith.index_cast %3 : vector<2x2xi32> to vector<2x2xindex>
return %4 : vector<2x2xindex>
}

// -----
// CHECK-LABEL: @add_kernel_f32
// CHECK: %[[CST0:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]> : vector<16xindex>
// CHECK: %[[CST1:.*]] = arith.constant dense<[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]> : vector<16xindex>
// CHECK: %[[T0:.*]] = vector.splat %{{.*}} : vector<16xindex>
// CHECK: %[[T1:.*]] = arith.addi %[[T0]], %[[CST0]] : vector<16xindex>
// CHECK: %[[T2:.*]] = arith.addi %[[T0]], %[[CST1]] : vector<16xindex>
// CHECK: %[[T3:.*]] = arith.index_cast %[[T1]] : vector<16xindex> to vector<16xi32>
// CHECK: %[[T4:.*]] = arith.index_cast %[[T2]] : vector<16xindex> to vector<16xi32>
// CHECK: %[[T5:.*]] = vector.splat %{{.*}} : vector<16xi32>
// CHECK: %[[T6:.*]] = arith.addi %[[T5]], %[[T3]] : vector<16xi32>
// CHECK: %[[T7:.*]] = arith.addi %[[T5]], %[[T4]] : vector<16xi32>
// CHECK: %[[T8:.*]] = arith.index_cast %[[T6]] : vector<16xi32> to vector<16xindex>
// CHECK: %[[T9:.*]] = arith.index_cast %[[T7]] : vector<16xi32> to vector<16xindex>
gpu.module @add_kernel_f32 attributes {spirv.target_env = #spirv.target_env<#spirv.vce<v1.0, [Addresses, Bfloat16ConversionINTEL, BFloat16TypeKHR, Float16Buffer, Int64, Int16, Int8, Kernel, Linkage, Vector16, GenericPointer, Groups, Float16, Float64, AtomicFloat32AddEXT, ExpectAssumeKHR, VectorAnyINTEL, VectorComputeINTEL], [SPV_EXT_shader_atomic_float_add, SPV_KHR_bfloat16, SPV_KHR_expect_assume, SPV_INTEL_bfloat16_conversion, SPV_INTEL_vector_compute]>, api=OpenCL, #spirv.resource_limits<>>} {
gpu.func @add_kernel_f32(%arg0: memref<*xf32>, %arg1: memref<*xf32>, %arg2: memref<*xf32>) kernel attributes {VectorComputeFunctionINTEL, known_block_size = array<i32: 1, 32, 1>, known_grid_size = array<i32: 1, 1, 1>, spirv.entry_point_abi = #spirv.entry_point_abi<>} {
%cst = arith.constant dense<true> : vector<16xi1>
%c32 = arith.constant 32 : index
%c1024_i32 = arith.constant 1024 : i32
%cst_0 = arith.constant dense<[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]]> : vector<1x16xindex>
%cst_1 = arith.constant dense<[[16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]]> : vector<1x16xindex>
%thread_id_x = gpu.thread_id x
%thread_id_y = gpu.thread_id y
%block_dim_y = gpu.block_dim y
%0 = arith.muli %thread_id_x, %block_dim_y : index
%1 = arith.addi %0, %thread_id_y : index
%cast = memref.cast %arg0 : memref<*xf32> to memref<?xf32>
%cast_2 = memref.cast %arg1 : memref<*xf32> to memref<?xf32>
%cast_3 = memref.cast %arg2 : memref<*xf32> to memref<?xf32>
%2 = arith.remsi %1, %c32 : index
%3 = arith.muli %2, %c32 : index
%4 = vector.splat %3 : vector<1x16xindex>
%5 = arith.addi %4, %cst_0 : vector<1x16xindex>
%6 = arith.addi %4, %cst_1 : vector<1x16xindex>
%7 = arith.index_cast %5 : vector<1x16xindex> to vector<1x16xi32>
%8 = arith.index_cast %6 : vector<1x16xindex> to vector<1x16xi32>
%block_id_x = gpu.block_id x
%9 = arith.index_cast %block_id_x : index to i32
%10 = arith.muli %9, %c1024_i32 : i32
%11 = vector.splat %10 : vector<1x16xi32>
%12 = arith.addi %11, %7 : vector<1x16xi32>
%13 = arith.addi %11, %8 : vector<1x16xi32>
%14 = arith.index_cast %12 : vector<1x16xi32> to vector<1x16xindex>
%15 = arith.index_cast %13 : vector<1x16xi32> to vector<1x16xindex>
%16 = vector.shape_cast %14 : vector<1x16xindex> to vector<16xindex>
%17 = xegpu.create_tdesc %cast, %16 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%18 = vector.shape_cast %15 : vector<1x16xindex> to vector<16xindex>
%19 = xegpu.create_tdesc %cast, %18 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%20 = xegpu.load %17, %cst <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<cached>, l3_hint = #xegpu.cache_hint<cached>}> : !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1> -> vector<16xf32>
%21 = vector.shape_cast %20 : vector<16xf32> to vector<1x16xf32>
%22 = xegpu.load %19, %cst <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<cached>, l3_hint = #xegpu.cache_hint<cached>}> : !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1> -> vector<16xf32>
%23 = vector.shape_cast %22 : vector<16xf32> to vector<1x16xf32>
%24 = xegpu.create_tdesc %cast_2, %16 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%25 = xegpu.create_tdesc %cast_2, %18 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%26 = xegpu.load %24, %cst <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<cached>, l3_hint = #xegpu.cache_hint<cached>}> : !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1> -> vector<16xf32>
%27 = vector.shape_cast %26 : vector<16xf32> to vector<1x16xf32>
%28 = xegpu.load %25, %cst <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<cached>, l3_hint = #xegpu.cache_hint<cached>}> : !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1> -> vector<16xf32>
%29 = vector.shape_cast %28 : vector<16xf32> to vector<1x16xf32>
%30 = arith.addf %21, %27 : vector<1x16xf32>
%31 = arith.addf %23, %29 : vector<1x16xf32>
%32 = xegpu.create_tdesc %cast_3, %16 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%33 = xegpu.create_tdesc %cast_3, %18 : memref<?xf32>, vector<16xindex> -> !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>
%34 = vector.shape_cast %30 : vector<1x16xf32> to vector<16xf32>
xegpu.store %34, %32, %cst <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<write_back>, l3_hint = #xegpu.cache_hint<write_back>}> : vector<16xf32>, !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1>
%35 = vector.shape_cast %31 : vector<1x16xf32> to vector<16xf32>
xegpu.store %35, %33, %cst <{l1_hint = #xegpu.cache_hint<write_back>, l2_hint = #xegpu.cache_hint<write_back>, l3_hint = #xegpu.cache_hint<write_back>}> : vector<16xf32>, !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<memory_space = global, chunk_size = 1 : i64>>, vector<16xi1>
gpu.return
}
}
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