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finish helper refactoring
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use uniqued constant helpers everywhere
materialize transposed constants directly
This commit is contained in:
NiccoloN
2026-05-29 17:05:45 +02:00
parent 819d8af0f7
commit 8bb0babf1b
32 changed files with 300 additions and 467 deletions
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src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Conv.cpp
+15 -14
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@@ -61,9 +61,9 @@ static Value createPaddedRows(Value tensorValue,
padBlock->addArgument(rewriter.getIndexType(), loc);
padOp.getRegion().push_back(padBlock);
rewriter.setInsertionPointToStart(padBlock);
auto zero = arith::ConstantOp::create(
rewriter, loc, tensorType.getElementType(), rewriter.getZeroAttr(tensorType.getElementType()));
tensor::YieldOp::create(rewriter, loc, zero.getResult());
auto zero = getOrCreateConstant(rewriter, padOp.getOperation(), rewriter.getZeroAttr(tensorType.getElementType()),
tensorType.getElementType());
tensor::YieldOp::create(rewriter, loc, zero);
rewriter.setInsertionPointAfter(padOp);
return padOp.getResult();
}
@@ -106,7 +106,7 @@ static Value buildPackedWeight(DenseElementsAttr wDenseAttr,
}
auto packedAttr = DenseElementsAttr::get(packedWeightType, packedValues);
return arith::ConstantOp::create(rewriter, loc, packedWeightType, packedAttr);
return getOrCreateConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), packedAttr, packedWeightType);
}
static Value createConvWeightMatrix(Value w,
@@ -158,7 +158,7 @@ static Value buildPackedBias(bool hasBias,
auto packedBiasType = RankedTensorType::get({1, packFactor * numChannelsOut}, outType.getElementType());
auto packedBiasAttr = DenseElementsAttr::get(packedBiasType, packedValues);
return arith::ConstantOp::create(rewriter, loc, packedBiasType, packedBiasAttr).getResult();
return getOrCreateConstant(rewriter, rewriter.getInsertionBlock()->getParentOp(), packedBiasAttr, packedBiasType);
}
static Value createIm2colRowComputes(Value x,
@@ -214,8 +214,8 @@ static Value createIm2colRowComputes(Value x,
padBlock->addArgument(rewriter.getIndexType(), loc);
padOp.getRegion().push_back(padBlock);
rewriter.setInsertionPointToStart(padBlock);
auto zero = arith::ConstantOp::create(rewriter, loc, elemType, rewriter.getFloatAttr(elemType, 0.0));
tensor::YieldOp::create(rewriter, loc, zero.getResult());
auto zero = getOrCreateConstant(rewriter, padOp.getOperation(), rewriter.getFloatAttr(elemType, 0.0), elemType);
tensor::YieldOp::create(rewriter, loc, zero);
rewriter.setInsertionPointAfter(padOp);
paddedInput = padOp.getResult();
}
@@ -223,13 +223,14 @@ static Value createIm2colRowComputes(Value x,
// Build im2col [numPatches, patchSize] incrementally to keep the IR small
// until the late PIM unrolling step.
Value im2colInit = tensor::EmptyOp::create(rewriter, loc, im2colType.getShape(), elemType);
auto c0 = arith::ConstantIndexOp::create(rewriter, loc, 0);
auto c1 = arith::ConstantIndexOp::create(rewriter, loc, 1);
auto cNumPatches = arith::ConstantIndexOp::create(rewriter, loc, numPatches);
auto cNumPatchesPerBatch = arith::ConstantIndexOp::create(rewriter, loc, numPatchesPerBatch);
auto cOutWidth = arith::ConstantIndexOp::create(rewriter, loc, outWidth);
auto cStrideHeight = arith::ConstantIndexOp::create(rewriter, loc, strideHeight);
auto cStrideWidth = arith::ConstantIndexOp::create(rewriter, loc, strideWidth);
Operation* anchorOp = rewriter.getInsertionBlock()->getParentOp();
auto c0 = getOrCreateIndexConstant(rewriter, anchorOp, 0);
auto c1 = getOrCreateIndexConstant(rewriter, anchorOp, 1);
auto cNumPatches = getOrCreateIndexConstant(rewriter, anchorOp, numPatches);
auto cNumPatchesPerBatch = getOrCreateIndexConstant(rewriter, anchorOp, numPatchesPerBatch);
auto cOutWidth = getOrCreateIndexConstant(rewriter, anchorOp, outWidth);
auto cStrideHeight = getOrCreateIndexConstant(rewriter, anchorOp, strideHeight);
auto cStrideWidth = getOrCreateIndexConstant(rewriter, anchorOp, strideWidth);
auto im2colLoop = scf::ForOp::create(rewriter, loc, c0, cNumPatches, c1, ValueRange {im2colInit});
rewriter.setInsertionPointToStart(im2colLoop.getBody());