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[QNN, ONNX] Extension of QLinearMatMul in ONNX front-end for all ranks of input tensors #13322
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vvchernov
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WIP: [QNN, ONNX] Extension of QLinearMatMul in ONNX front-end for all ranks of input tensors
[QNN, ONNX] Extension of QLinearMatMul in ONNX front-end for all ranks of input tensors
Nov 9, 2022
masahi
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Nov 10, 2022
xinetzone
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Nov 10, 2022
…s of input tensors (apache#13322) * QLinearMatMul was extended for all ranks of a and b * CI test for QLinearMatMul was implemented (onnx front-end) * fix after black check * numpy type fix * fix weight scale and zero point, output type * fix after pylint * resolve different input types in tests * skip resolved TODO * update covering of QLinearMatMul by tests * pylint fixes * skip test of QLinearMatMul on CUDA Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
xinetzone
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Nov 25, 2022
…s of input tensors (apache#13322) * QLinearMatMul was extended for all ranks of a and b * CI test for QLinearMatMul was implemented (onnx front-end) * fix after black check * numpy type fix * fix weight scale and zero point, output type * fix after pylint * resolve different input types in tests * skip resolved TODO * update covering of QLinearMatMul by tests * pylint fixes * skip test of QLinearMatMul on CUDA Co-authored-by: Valery Chernov <valery.chernov@deelvin.com>
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QLinearMatMul has supported rank =2 only for both input tensors.
It was extended using _qnn.op.dense and _qnn.op.batch_matmul for all ranks
Y = X*W
Works:
Note: Different types of input tensors (int8 and uint8) does not work currently