Merge pull request #6922 from brkirch/cumsum-fix

def cumsum_fix(input, cumsum_func, *args, **kwargs):

    if input.device.type == 'mps':

        output_dtype = kwargs.get('dtype', input.dtype)

        if any(output_dtype == broken_dtype for broken_dtype in [torch.bool, torch.int8, torch.int16, torch.int64]):

        if output_dtype == torch.int64:

            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)

        elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):

            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)

    return cumsum_func(input, *args, **kwargs)

        torch.nn.functional.layer_norm = layer_norm_fix

        torch.Tensor.numpy = numpy_fix

    elif version.parse(torch.__version__) > version.parse("1.13.1"):

        if not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.Tensor([1,1]).to(torch.device("mps")).cumsum(0, dtype=torch.int16)):

        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))

        cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))

        torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )

        torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )

        orig_narrow = torch.narrow