Merge branch 'master' into 6866-fix-hires-prompt-matrix

1 files changed, 53 insertions, 0 deletions

diff --git a/modules/mac_specific.py b/modules/mac_specific.py
new file mode 100644
index 00000000..ddcea53b
--- /dev/null
+++ b/modules/mac_specific.py
@@ -0,0 +1,53 @@
+import torch
+from modules import paths
+from modules.sd_hijack_utils import CondFunc
+from packaging import version
+
+
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
+# check `getattr` and try it for compatibility
+def check_for_mps() -> bool:
+    if not getattr(torch, 'has_mps', False):
+        return False
+    try:
+        torch.zeros(1).to(torch.device("mps"))
+        return True
+    except Exception:
+        return False
+has_mps = check_for_mps()
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+    if input.device.type == 'mps':
+        output_dtype = kwargs.get('dtype', input.dtype)
+        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)
+
+
+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 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
+
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+        CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
+                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
+                                                                                        lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+        CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
+    elif version.parse(torch.__version__) > version.parse("1.13.1"):
+        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))
+        cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
+        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)
+