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This pull request was exported from Phabricator. Differential Revision: D78526966

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@haowhsu-quic @shewu-quic @DannyYuyang-quic @winskuo-quic I ran into some issues to lower an internal model to QNN and this is one of the missing ops. I think the output are two nodes, while the regular min output is one node. Do you think we should re-use the op_min or have a different file to handle this case?

This pull request was exported from Phabricator. Differential Revision: D78526966

Actually, seems like the min operator in HTP doesn't support returning index, we may need to decompose min.dim, is it correct?

Actually, seems like the min operator in HTP doesn't support returning index, we may need to decompose min.dim, is it correct?

Yes, looks like the most similar one is ReduceMin. But it cannot return index.
Maybe we can compose it with ReduceMin + ArgMin.

Actually, seems like the min operator in HTP doesn't support returning index, we may need to decompose min.dim, is it correct?

Thanks for catching that, I’ll handle the min.dim op support

Actually, seems like the min operator in HTP doesn't support returning index, we may need to decompose min.dim, is it correct?

Thanks for catching that, I’ll handle the min.dim op support

Thanks! That will be super helpful. I find that we need to have 2 more ops...can we have the following ops supported?

                "aten.min.dim",
                "aten.logical_and.default",
                "aten.max.dim",

Actually, seems like the min operator in HTP doesn't support returning index, we may need to decompose min.dim, is it correct?

Thanks for catching that, I’ll handle the min.dim op support

Thanks! That will be super helpful. I find that we need to have 2 more ops...can we have the following ops supported?

                "aten.min.dim",
                "aten.logical_and.default",
                "aten.max.dim",

Yes sure, could you please also share the source(testcase) for aten.logical_and.default~?

Something like this will be helpful

class LogicalAnd(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        return torch.logical_and(x, y)

a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
model = LogicalAnd()
example_inputs = (a, b)
model(*example_inputs)

Something like this will be helpful

class LogicalAnd(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        return torch.logical_and(x, y)

a = torch.tensor([0, 1, 10, 0], dtype=torch.int8)
b = torch.tensor([4, 0, 1, 0], dtype=torch.int8)
model = LogicalAnd()
example_inputs = (a, b)
model(*example_inputs)

aten.min.dim and aten.max.dim are almost ready, I’ll push the PR once internal CI passes.

Regarding logical_and, it seems that int8 inputs are not supported, the ElementWiseAnd only accepts UFIXED_POINT_8, SFIXED_POINT_8, or BOOL_8. Is int8 part of your internal model in FP mode?

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merged with a different PR