Add special case for 1-D matrix multiplication.
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@ -318,12 +318,28 @@ void ONNXMatMulOp::inferShapes() {
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if (!getOperand(0).getType().isa<RankedTensorType>() ||
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!getOperand(1).getType().isa<RankedTensorType>())
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return;
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auto lhsTy = getOperand(0).getType().cast<RankedTensorType>();
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auto rhsTy = getOperand(1).getType().cast<RankedTensorType>();
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auto lhsTy = getOperand(0)->getType().cast<RankedTensorType>();
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auto rhsTy = getOperand(1)->getType().cast<RankedTensorType>();
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SmallVector<int64_t, 2> dims;
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dims.emplace_back(lhsTy.getShape()[0]);
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dims.emplace_back(rhsTy.getShape()[1]);
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getResult().setType(RankedTensorType::get(dims, lhsTy.getElementType()));
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auto lhsShape = lhsTy.getShape();
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auto rhsShape = rhsTy.getShape();
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if (lhsShape.size() == 1 && rhsShape.size() == 1) {
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// Special case when both arrays are 1-dimensional and according to
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// numpy rules the types need to be extended to 1xN and Nx1. Helper sizes
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// need to be removed after the multiplication but cannot be removed if all
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// remaining sizes are 1.
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if (lhsShape[0] != -1 && rhsShape[0] != -1 &&
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lhsShape[0] != rhsShape[0])
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emitError("Attempt to multiply incompatible matrices.");
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dims.emplace_back(1);
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} else {
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dims.emplace_back(lhsShape.size() == 1 ? 1 : lhsShape[0]);
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dims.emplace_back(rhsShape.size() == 1 ? 1 : rhsShape[1]);
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}
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getResult()->setType(RankedTensorType::get(dims, lhsTy.getElementType()));
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}
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// TODO:
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