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Add workaround for broken nn.Linear on macOS 13.2

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Credit to danieldk (https://github.com/explosion/curated-transformers/pull/124) for the workaround this is based on.
This commit is contained in:
brkirch
2023-03-24 02:58:18 -04:00
parent a9fed7c364
commit c5142e2fbe
2 changed files with 31 additions and 0 deletions
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5
modules/mac_specific.py
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@@ -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