diff options
| -rw-r--r-- | modules/mac_specific.py | 9 | ||||
| -rw-r--r-- | modules/sd_hijack_optimizations.py | 3 |
2 files changed, 10 insertions, 2 deletions
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 6fe8dea0..40ce2101 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -54,6 +54,11 @@ if has_mps: CondFunc('torch.cumsum', cumsum_fix_func, None) CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None) CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None) - if version.parse(torch.__version__) == version.parse("2.0"): + # MPS workaround for https://github.com/pytorch/pytorch/issues/96113 - CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6) + CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps') + + # MPS workaround for https://github.com/pytorch/pytorch/issues/92311 + if platform.processor() == 'i386': + for funcName in ['torch.argmax', 'torch.Tensor.argmax']: + CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
\ No newline at end of file diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 372555ff..f10865cd 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -256,6 +256,9 @@ def sub_quad_attention_forward(self, x, context=None, mask=None): k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+ if q.device.type == 'mps':
+ q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
+
dtype = q.dtype
if shared.opts.upcast_attn:
q, k = q.float(), k.float()
|