5550c82189
The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ ``` Differential Revision: https://reviews.llvm.org/D150123
111 lines
4.3 KiB
C++
111 lines
4.3 KiB
C++
//===- TensorToSPIRV.cpp - Tensor to SPIR-V Patterns ----------------------===//
|
|
//
|
|
// 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 file implements patterns to convert Tensor dialect to SPIR-V dialect.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "mlir/Conversion/TensorToSPIRV/TensorToSPIRV.h"
|
|
#include "../SPIRVCommon/Pattern.h"
|
|
#include "mlir/Dialect/SPIRV/IR/SPIRVDialect.h"
|
|
#include "mlir/Dialect/SPIRV/IR/SPIRVOps.h"
|
|
#include "mlir/Dialect/SPIRV/Transforms/SPIRVConversion.h"
|
|
#include "mlir/Dialect/SPIRV/Utils/LayoutUtils.h"
|
|
#include "mlir/Dialect/Tensor/IR/Tensor.h"
|
|
#include "mlir/IR/AffineMap.h"
|
|
#include "mlir/Support/LogicalResult.h"
|
|
#include "llvm/Support/Debug.h"
|
|
|
|
#define DEBUG_TYPE "tensor-to-spirv-pattern"
|
|
|
|
using namespace mlir;
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Operation conversion
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
namespace {
|
|
|
|
/// Converts tensor.extract into loading using access chains from SPIR-V local
|
|
/// variables.
|
|
class TensorExtractPattern final
|
|
: public OpConversionPattern<tensor::ExtractOp> {
|
|
public:
|
|
TensorExtractPattern(TypeConverter &typeConverter, MLIRContext *context,
|
|
int64_t threshold, PatternBenefit benefit = 1)
|
|
: OpConversionPattern(typeConverter, context, benefit),
|
|
byteCountThreshold(threshold) {}
|
|
|
|
LogicalResult
|
|
matchAndRewrite(tensor::ExtractOp extractOp, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
auto tensorType = cast<RankedTensorType>(extractOp.getTensor().getType());
|
|
|
|
if (!tensorType.hasStaticShape())
|
|
return rewriter.notifyMatchFailure(extractOp, "non-static tensor");
|
|
|
|
if (tensorType.getNumElements() * tensorType.getElementTypeBitWidth() >
|
|
byteCountThreshold * 8)
|
|
return rewriter.notifyMatchFailure(extractOp,
|
|
"exceeding byte count threshold");
|
|
|
|
Location loc = extractOp.getLoc();
|
|
|
|
int64_t rank = tensorType.getRank();
|
|
SmallVector<int64_t, 4> strides(rank, 1);
|
|
for (int i = rank - 2; i >= 0; --i) {
|
|
strides[i] = strides[i + 1] * tensorType.getDimSize(i + 1);
|
|
}
|
|
|
|
Type varType = spirv::PointerType::get(adaptor.getTensor().getType(),
|
|
spirv::StorageClass::Function);
|
|
|
|
spirv::VariableOp varOp;
|
|
if (adaptor.getTensor().getDefiningOp<spirv::ConstantOp>()) {
|
|
// We could use the initializer directly; but certain driver compilers
|
|
// have bugs dealing with that. So for now, use spirv.Store for
|
|
// initialization.
|
|
varOp = rewriter.create<spirv::VariableOp>(loc, varType,
|
|
spirv::StorageClass::Function,
|
|
/*initializer=*/nullptr);
|
|
rewriter.create<spirv::StoreOp>(loc, varOp, adaptor.getTensor());
|
|
} else {
|
|
// Need to store the value to the local variable. It's questionable
|
|
// whether we want to support such case though.
|
|
return failure();
|
|
}
|
|
|
|
auto &typeConverter = *getTypeConverter<SPIRVTypeConverter>();
|
|
auto indexType = typeConverter.getIndexType();
|
|
|
|
Value index = spirv::linearizeIndex(adaptor.getIndices(), strides,
|
|
/*offset=*/0, indexType, loc, rewriter);
|
|
auto acOp = rewriter.create<spirv::AccessChainOp>(loc, varOp, index);
|
|
|
|
rewriter.replaceOpWithNewOp<spirv::LoadOp>(extractOp, acOp);
|
|
|
|
return success();
|
|
}
|
|
|
|
private:
|
|
int64_t byteCountThreshold;
|
|
};
|
|
|
|
} // namespace
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Pattern population
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
void mlir::populateTensorToSPIRVPatterns(SPIRVTypeConverter &typeConverter,
|
|
int64_t byteCountThreshold,
|
|
RewritePatternSet &patterns) {
|
|
patterns.add<TensorExtractPattern>(typeConverter, patterns.getContext(),
|
|
byteCountThreshold);
|
|
}
|