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refactor: add function to handleTranspose #7

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refactor: add function to handleTranspose #7

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202 changes: 108 additions & 94 deletions lib/Conversion/TorchToLinalg/Linear.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -813,6 +813,110 @@ Value preprocessPadding(ConversionPatternRewriter &rewriter, Value pad,
return pad;
}

void handleTranspose(ConversionPatternRewriter &rewriter, Location loc,
Value &weight, SmallVectorImpl<Value> &outDims,
Type weightDTy, size_t inRank, Value inputBatch,
Value inputChannels, size_t numSpatialDims,
SmallVectorImpl<Value> &inputSpatialDimensions,
SmallVectorImpl<Value> &weightDims,
ConvolutionAttributes &convolutionAttributes, Value pad,
Value input, Type inputDTy, Value &paddedInput) {
Value c0 = rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(0));
Value c1 = rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(1));
Value c2 = rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(2));

// Transpose and flip weight
SmallVector<Value> weightInitDims = getTensorSizes(rewriter, loc, weight);
std::iter_swap(weightInitDims.begin(), weightInitDims.begin() + 1);
outDims[1] = weightInitDims[0];
Value weightInitTensor =
createZeroInitTensor(rewriter, loc, weightInitDims, weightDTy);
SmallVector<utils::IteratorType> iteratorTypes(inRank,
utils::IteratorType::parallel);
SmallVector<AffineMap> indexingMaps{
AffineMap::getMultiDimIdentityMap(inRank, rewriter.getContext())};
weight =
rewriter
.create<linalg::GenericOp>(
loc, weightInitTensor.getType(), ValueRange{}, weightInitTensor,
indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
SmallVector<Value> indices;
for (size_t i = 0; i < inRank; i++)
indices.push_back(b.create<linalg::IndexOp>(loc, i));
std::iter_swap(indices.begin(), indices.begin() + 1);
// Flip only the spatial dimensions (from 2 to inRank)
for (size_t flipDim = 2; flipDim < inRank; flipDim++) {
indices[flipDim] = b.create<arith::SubIOp>(
loc,
b.create<arith::SubIOp>(loc, weightInitDims[flipDim], c1),
indices[flipDim]);
}
Value res = b.create<tensor::ExtractOp>(loc, weight, indices)
.getResult();
b.create<linalg::YieldOp>(loc, res);
})
.getResult(0);

// Calculate padded input size, allocate tensor
SmallVector<Value> outerSizes{inputBatch, inputChannels};
SmallVector<Value> innerSizes{inputBatch, inputChannels};
SmallVector<Value> offsets{c0, c0};
for (size_t i = 0; i < numSpatialDims; i++) {
Value innerSize =
rewriter.create<arith::SubIOp>(loc, inputSpatialDimensions[i], c1);
innerSize = rewriter.create<arith::MulIOp>(
loc, innerSize,
castIntToIndex(rewriter, loc, convolutionAttributes.strideValues[i]));
innerSize = rewriter.create<arith::AddIOp>(loc, innerSize, c1);

Value offset = rewriter.create<arith::SubIOp>(loc, weightDims[i], c1);
offset = rewriter.create<arith::MulIOp>(
loc, offset,
castIntToIndex(rewriter, loc, convolutionAttributes.dilationValues[i]));
offset = rewriter.create<arith::SubIOp>(
loc, offset,
castIntToIndex(rewriter, loc, convolutionAttributes.padding[i]));

Value outerSize = rewriter.create<arith::MulIOp>(loc, offset, c2);
outerSize = rewriter.create<arith::AddIOp>(loc, outerSize, innerSize);
outerSize = rewriter.create<arith::AddIOp>(
loc, outerSize,
castIntToIndex(rewriter, loc, convolutionAttributes.outputPadding[i]));

outerSizes.push_back(outerSize);
offsets.push_back(offset);
}

// Allocate padded input tensor
Value initTensor = createInitTensor(rewriter, loc, outerSizes, inputDTy, pad);

// Insert input into allocated tensor
SmallVector<Value> strideIndexValues{c1, c1};
for (auto stride : convolutionAttributes.strideValues)
strideIndexValues.push_back(castIntToIndex(rewriter, loc, stride));
SmallVector<Value> insertSizes = getTensorSizes(rewriter, loc, input);

paddedInput = rewriter.create<tensor::InsertSliceOp>(
loc, torch_to_linalg::removeSizeInformation(rewriter, loc, input),
initTensor, offsets, insertSizes, strideIndexValues);

// Calculate output dims
for (size_t i = 0; i < numSpatialDims; i++)
outDims.push_back(torch_to_linalg::getOutputDimForConvTransposeOps(
rewriter, loc, inputSpatialDimensions[i],
convolutionAttributes.padding[i],
convolutionAttributes.dilationValues[i],
rewriter.createOrFold<arith::IndexCastOp>(
loc, rewriter.getIntegerType(64), weightDims[i]),
convolutionAttributes.strideValues[i],
convolutionAttributes.outputPadding[i]));

// Set stride to 1
convolutionAttributes.stride.clear();
convolutionAttributes.stride.append(numSpatialDims, 1);
}

class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
public:
using OpConversionPattern::OpConversionPattern;
Expand Down Expand Up @@ -947,100 +1051,10 @@ class ConvertAtenConvolutionOp : public OpConversionPattern<AtenConvolutionOp> {
Value pad = inputZp;
pad = preprocessPadding(rewriter, pad, op, inputDTy);
if (transposed) {
Value c0 =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(0));
Value c1 =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(1));
Value c2 =
rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(2));

// Transpose and flip weight
SmallVector<Value> weightInitDims = getTensorSizes(rewriter, loc, weight);
std::iter_swap(weightInitDims.begin(), weightInitDims.begin() + 1);
outDims[1] = weightInitDims[0];
Value weightInitTensor =
createZeroInitTensor(rewriter, loc, weightInitDims, weightDTy);
SmallVector<utils::IteratorType> iteratorTypes(
inRank, utils::IteratorType::parallel);
SmallVector<AffineMap> indexingMaps{
AffineMap::getMultiDimIdentityMap(inRank, context)};
weight = rewriter
.create<linalg::GenericOp>(
loc, weightInitTensor.getType(), ValueRange{},
weightInitTensor, indexingMaps, iteratorTypes,
[&](OpBuilder &b, Location loc, ValueRange args) {
SmallVector<Value> indices;
for (size_t i = 0; i < inRank; i++)
indices.push_back(b.create<linalg::IndexOp>(loc, i));
std::iter_swap(indices.begin(), indices.begin() + 1);
// Flip only the spatial dimensions (from 2 to inRank)
for (size_t flipDim = 2; flipDim < inRank; flipDim++) {
indices[flipDim] = b.create<arith::SubIOp>(
loc,
b.create<arith::SubIOp>(
loc, weightInitDims[flipDim], c1),
indices[flipDim]);
}
Value res =
b.create<tensor::ExtractOp>(loc, weight, indices)
.getResult();
b.create<linalg::YieldOp>(loc, res);
})
.getResult(0);

// Calculate padded input size, allocate tensor
SmallVector<Value> outerSizes{inputBatch, inputChannels};
SmallVector<Value> innerSizes{inputBatch, inputChannels};
SmallVector<Value> offsets{c0, c0};
for (size_t i = 0; i < numSpatialDims; i++) {
Value innerSize = rewriter.create<arith::SubIOp>(loc, inputSpatialDimensions[i], c1);
innerSize = rewriter.create<arith::MulIOp>(
loc, innerSize, castIntToIndex(rewriter, loc, convolutionAttributes->strideValues[i]));
innerSize = rewriter.create<arith::AddIOp>(loc, innerSize, c1);

Value offset = rewriter.create<arith::SubIOp>(loc, weightDims[i], c1);
offset = rewriter.create<arith::MulIOp>(
loc, offset, castIntToIndex(rewriter, loc, convolutionAttributes->dilationValues[i]));
offset = rewriter.create<arith::SubIOp>(
loc, offset, castIntToIndex(rewriter, loc, convolutionAttributes->padding[i]));

Value outerSize = rewriter.create<arith::MulIOp>(loc, offset, c2);
outerSize = rewriter.create<arith::AddIOp>(loc, outerSize, innerSize);
outerSize = rewriter.create<arith::AddIOp>(
loc, outerSize,
castIntToIndex(rewriter, loc, convolutionAttributes->outputPadding[i]));

outerSizes.push_back(outerSize);
offsets.push_back(offset);
}

// Allocate padded input tensor
Value initTensor =
createInitTensor(rewriter, loc, outerSizes, inputDTy, pad);

// Insert input into allocated tensor
SmallVector<Value> strideIndexValues{c1, c1};
for (auto stride : convolutionAttributes->strideValues)
strideIndexValues.push_back(castIntToIndex(rewriter, loc, stride));
SmallVector<Value> insertSizes = getTensorSizes(rewriter, loc, input);

paddedInput = rewriter.create<tensor::InsertSliceOp>(
loc, torch_to_linalg::removeSizeInformation(rewriter, loc, input),
initTensor, offsets, insertSizes, strideIndexValues);

// Calculate output dims
for (size_t i = 0; i < numSpatialDims; i++)
outDims.push_back(torch_to_linalg::getOutputDimForConvTransposeOps(
rewriter, loc, inputSpatialDimensions[i],
convolutionAttributes->padding[i],
convolutionAttributes->dilationValues[i],
castIndexToInt(weightDims[i]),
convolutionAttributes->strideValues[i],
convolutionAttributes->outputPadding[i]));

// Set stride to 1
convolutionAttributes->stride.clear();
convolutionAttributes->stride.append(numSpatialDims, 1);
handleTranspose(
rewriter, loc, weight, outDims, weightDTy, inRank, inputBatch,
inputChannels, numSpatialDims, inputSpatialDimensions, weightDims,
convolutionAttributes.value(), pad, input, inputDTy, paddedInput);
} else {
// Pad input
paddedInput = torch_to_linalg::getDynamicZeroPaddedTensor(
Expand Down