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-rw-r--r--modules/mac_specific.py9
1 files changed, 8 insertions, 1 deletions
diff --git a/modules/mac_specific.py b/modules/mac_specific.py
index 18e6ff72..6fe8dea0 100644
--- a/modules/mac_specific.py
+++ b/modules/mac_specific.py
@@ -1,4 +1,5 @@
import torch
+import platform
from modules import paths
from modules.sd_hijack_utils import CondFunc
from packaging import version
@@ -32,6 +33,10 @@ if has_mps:
# MPS fix for randn in torchsde
CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
+ if platform.mac_ver()[0].startswith("13.2."):
+ # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
+ CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
+
if version.parse(torch.__version__) < version.parse("1.13"):
# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
@@ -49,4 +54,6 @@ 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)