206fad0e21
This commit marks the type converter in `populate...` functions as `const`. This is useful for debugging. Patterns already take a `const` type converter. However, some `populate...` functions do not only add new patterns, but also add additional type conversion rules. That makes it difficult to find the place where a type conversion was added in the code base. With this change, all `populate...` functions that only populate pattern now have a `const` type converter. Programmers can then conclude from the function signature that these functions do not register any new type conversion rules. Also some minor cleanups around the 1:N dialect conversion infrastructure, which did not always pass the type converter as a `const` object internally.
112 lines
4.4 KiB
C++
112 lines
4.4 KiB
C++
//===- TensorToSPIRV.cpp - Tensor to SPIR-V Patterns ----------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements patterns to convert Tensor dialect to SPIR-V dialect.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Conversion/TensorToSPIRV/TensorToSPIRV.h"
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#include "../SPIRVCommon/Pattern.h"
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#include "mlir/Dialect/SPIRV/IR/SPIRVDialect.h"
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#include "mlir/Dialect/SPIRV/IR/SPIRVOps.h"
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#include "mlir/Dialect/SPIRV/Transforms/SPIRVConversion.h"
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#include "mlir/Dialect/SPIRV/Utils/LayoutUtils.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/AffineMap.h"
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#include "llvm/Support/Debug.h"
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#define DEBUG_TYPE "tensor-to-spirv-pattern"
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using namespace mlir;
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//===----------------------------------------------------------------------===//
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// Operation conversion
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//===----------------------------------------------------------------------===//
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namespace {
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/// Converts tensor.extract into loading using access chains from SPIR-V local
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/// variables.
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class TensorExtractPattern final
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: public OpConversionPattern<tensor::ExtractOp> {
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public:
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TensorExtractPattern(const TypeConverter &typeConverter, MLIRContext *context,
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int64_t threshold, PatternBenefit benefit = 1)
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: OpConversionPattern(typeConverter, context, benefit),
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byteCountThreshold(threshold) {}
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LogicalResult
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matchAndRewrite(tensor::ExtractOp extractOp, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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auto tensorType = cast<RankedTensorType>(extractOp.getTensor().getType());
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if (!isa<spirv::ScalarType>(tensorType.getElementType()))
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return rewriter.notifyMatchFailure(extractOp, "unsupported type");
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if (!tensorType.hasStaticShape())
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return rewriter.notifyMatchFailure(extractOp, "non-static tensor");
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if (tensorType.getNumElements() * tensorType.getElementTypeBitWidth() >
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byteCountThreshold * 8)
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return rewriter.notifyMatchFailure(extractOp,
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"exceeding byte count threshold");
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Location loc = extractOp.getLoc();
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int64_t rank = tensorType.getRank();
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SmallVector<int64_t, 4> strides(rank, 1);
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for (int i = rank - 2; i >= 0; --i) {
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strides[i] = strides[i + 1] * tensorType.getDimSize(i + 1);
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}
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Type varType = spirv::PointerType::get(adaptor.getTensor().getType(),
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spirv::StorageClass::Function);
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spirv::VariableOp varOp;
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if (adaptor.getTensor().getDefiningOp<spirv::ConstantOp>()) {
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// We could use the initializer directly; but certain driver compilers
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// have bugs dealing with that. So for now, use spirv.Store for
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// initialization.
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varOp = rewriter.create<spirv::VariableOp>(loc, varType,
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spirv::StorageClass::Function,
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/*initializer=*/nullptr);
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rewriter.create<spirv::StoreOp>(loc, varOp, adaptor.getTensor());
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} else {
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// Need to store the value to the local variable. It's questionable
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// whether we want to support such case though.
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return failure();
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}
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auto &typeConverter = *getTypeConverter<SPIRVTypeConverter>();
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auto indexType = typeConverter.getIndexType();
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Value index = spirv::linearizeIndex(adaptor.getIndices(), strides,
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/*offset=*/0, indexType, loc, rewriter);
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auto acOp = rewriter.create<spirv::AccessChainOp>(loc, varOp, index);
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rewriter.replaceOpWithNewOp<spirv::LoadOp>(extractOp, acOp);
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return success();
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}
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private:
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int64_t byteCountThreshold;
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};
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} // namespace
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//===----------------------------------------------------------------------===//
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// Pattern population
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//===----------------------------------------------------------------------===//
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void mlir::populateTensorToSPIRVPatterns(
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const SPIRVTypeConverter &typeConverter, int64_t byteCountThreshold,
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RewritePatternSet &patterns) {
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patterns.add<TensorExtractPattern>(typeConverter, patterns.getContext(),
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byteCountThreshold);
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}
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