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[Encoding][NFC] Move non attribute implementation to EncodingTypes.cpp (
#20045) It follows the convention, which only put the attribute implementation and local functions to EncodingAttr.cpp. The implementation of other utilities is moved to EncodingTypes.cpp. Additionally, it spells out the types for `auto`. --------- Signed-off-by: hanhanW <[email protected]>
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compiler/src/iree/compiler/Dialect/Encoding/IR/EncodingTypes.cpp
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| Original file line number | Diff line number | Diff line change |
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| // Copyright 2025 The IREE Authors | ||
| // | ||
| // Licensed 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 | ||
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| #include "iree/compiler/Dialect/Encoding/IR/EncodingTypes.h" | ||
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| #include "llvm/ADT/SmallVector.h" | ||
| #include "mlir/Dialect/Affine/Utils.h" | ||
| #include "mlir/Dialect/Linalg/Utils/Utils.h" | ||
| #include "mlir/IR/AffineMap.h" | ||
| #include "mlir/IR/BuiltinAttributes.h" | ||
| #include "mlir/IR/BuiltinTypeInterfaces.h" | ||
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| #include <cassert> | ||
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| namespace mlir::iree_compiler::IREE::Encoding { | ||
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| SerializableEncodingAttrInterface | ||
| getSerializableEncodingAttrInterface(RankedTensorType type) { | ||
| return dyn_cast_or_null<SerializableEncodingAttrInterface>( | ||
| type.getEncoding()); | ||
| } | ||
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| EncodingAttr getEncodingAttr(RankedTensorType type) { | ||
| return dyn_cast_or_null<EncodingAttr>(type.getEncoding()); | ||
| } | ||
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| bool hasPackedStorageAttr(RankedTensorType type) { | ||
| return dyn_cast_or_null<PackedStorageAttr>(type.getEncoding()) != nullptr; | ||
| } | ||
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| FailureOr<linalg::ContractionDimensions> | ||
| getEncodingContractionDims(EncodingAttr encoding) { | ||
| ArrayAttr indexingMapsAttr = encoding.getUserIndexingMaps(); | ||
| if (!indexingMapsAttr) { | ||
| return failure(); | ||
| } | ||
| SmallVector<AffineMap> indexingMaps = llvm::map_to_vector( | ||
| indexingMapsAttr.getValue(), [](Attribute m) -> AffineMap { | ||
| return cast<AffineMapAttr>(m).getAffineMap(); | ||
| }); | ||
| return linalg::inferContractionDims(indexingMaps); | ||
| } | ||
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| std::string stringifyOperandIndex(IntegerAttr valueAttr) { | ||
| uint64_t value = valueAttr.getValue().getZExtValue(); | ||
| switch (value) { | ||
| case MATMUL_LHS: | ||
| return "LHS"; | ||
| case MATMUL_RHS: | ||
| return "RHS"; | ||
| case MATMUL_RESULT: | ||
| return "RESULT"; | ||
| default: | ||
| assert(false && "invalid index"); | ||
| return ""; | ||
| } | ||
| } | ||
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| MatmulNarrowDim getMatmulNarrowDim(linalg::LinalgOp linalgOp, | ||
| int narrowThreshold) { | ||
| linalg::ContractionDimensions cDims = | ||
| linalg::inferContractionDims(linalgOp).value(); | ||
| AffineMap map = linalgOp.getIndexingMapsArray().back(); | ||
| auto outType = llvm::cast<ShapedType>(linalgOp.getDpsInits()[0].getType()); | ||
| auto getOutputSizeAtDimPos = [=](unsigned dimPos) -> int64_t { | ||
| return outType.getDimSize( | ||
| map.getResultPosition(getAffineDimExpr(dimPos, linalgOp->getContext())) | ||
| .value()); | ||
| }; | ||
| // M or N can be empty instead of having an explicit dim size of 1 for matvec | ||
| // and vecmat, so set to 1 if empty. | ||
| int64_t mSize = cDims.m.empty() ? 1 : getOutputSizeAtDimPos(cDims.m[0]); | ||
| int64_t nSize = cDims.n.empty() ? 1 : getOutputSizeAtDimPos(cDims.n[0]); | ||
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| MatmulNarrowDim narrowM, narrowN; | ||
| if (!ShapedType::isDynamic(mSize) && mSize < narrowThreshold) { | ||
| narrowM = {/*dim=*/MatmulNarrowDim::Dim::M, /*size=*/mSize}; | ||
| } | ||
| if (!ShapedType::isDynamic(nSize) && nSize < narrowThreshold) { | ||
| narrowN = {/*dim=*/MatmulNarrowDim::Dim::N, /*size=*/nSize}; | ||
| } | ||
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| return (narrowM && (!narrowN || mSize <= nSize)) ? narrowM : narrowN; | ||
| } | ||
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| MatmulNarrowDim getMatmulNarrowDim(EncodingAttr encoding) { | ||
| if (encoding.getOpType().getValue() != EncodingOpType::matmul) { | ||
| return {}; | ||
| } | ||
| ArrayRef<int64_t> roundDimsTo = encoding.getRoundDimsToArray(); | ||
| if (roundDimsTo.empty()) { | ||
| return {}; | ||
| } | ||
| int m = roundDimsTo[0]; | ||
| int n = roundDimsTo[1]; | ||
| if (m < n) { | ||
| return {MatmulNarrowDim::Dim::M, m}; | ||
| } | ||
| if (n < m) { | ||
| return {MatmulNarrowDim::Dim::N, n}; | ||
| } | ||
| return {}; | ||
| } | ||
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| bool isNarrowNResult(EncodingAttr encoding) { | ||
| if (encoding.getOperandIndex().getValue() != IREE::Encoding::MATMUL_RESULT) { | ||
| return false; | ||
| } | ||
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| return IREE::Encoding::getMatmulNarrowDim(encoding).isN(); | ||
| } | ||
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| RankedTensorType dropEncoding(RankedTensorType type) { | ||
| return RankedTensorType::get(type.getShape(), type.getElementType()); | ||
| } | ||
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| } // namespace mlir::iree_compiler::IREE::Encoding |