Merge pull request #5810 from brkirch/fix-training-mps

    return orig_layer_norm(*args, **kwargs)

# MPS workaround for https://github.com/pytorch/pytorch/issues/90532

orig_tensor_numpy = torch.Tensor.numpy

def numpy_fix(self, *args, **kwargs):

    if self.requires_grad:

        self = self.detach()

    return orig_tensor_numpy(self, *args, **kwargs)

# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working

if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):

    torch.Tensor.to = tensor_to_fix

    torch.nn.functional.layer_norm = layer_norm_fix

    torch.Tensor.numpy = numpy_fix

        if module == 'collections' and name == 'OrderedDict':

            return getattr(collections, name)

        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:

        if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']:

            return getattr(torch._utils, name)

        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:

        if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']:

            return getattr(torch, name)

        if module == 'torch.nn.modules.container' and name in ['ParameterDict']:

            return getattr(torch.nn.modules.container, name)

        if module == 'numpy.core.multiarray' and name == 'scalar':

            return numpy.core.multiarray.scalar

        if module == 'numpy' and name == 'dtype':

            return numpy.dtype

        if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']:

            return getattr(numpy.core.multiarray, name)

        if module == 'numpy' and name in ['dtype', 'ndarray']:

            return getattr(numpy, name)

        if module == '_codecs' and name == 'encode':

            return encode

        if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':