Merge branch 'master' into master

1 files changed, 37 insertions, 18 deletions

diff --git a/modules/devices.py b/modules/devices.py
index f30b6ebc..e69c1fe3 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -2,9 +2,10 @@ import sys, os, shlex
 import contextlib
 import torch
 from modules import errors
+from packaging import version
 
 
-# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
 # check `getattr` and try it for compatibility
 def has_mps() -> bool:
     if not getattr(torch, 'has_mps', False):
@@ -24,17 +25,18 @@ def extract_device_id(args, name):
     return None
 
 
-def get_optimal_device():
-    if torch.cuda.is_available():
-        from modules import shared
+def get_cuda_device_string():
+    from modules import shared
 
-        device_id = shared.cmd_opts.device_id
+    if shared.cmd_opts.device_id is not None:
+        return f"cuda:{shared.cmd_opts.device_id}"
 
-        if device_id is not None:
-            cuda_device = f"cuda:{device_id}"
-            return torch.device(cuda_device)
-        else:
-            return torch.device("cuda")
+    return "cuda"
+
+
+def get_optimal_device():
+    if torch.cuda.is_available():
+        return torch.device(get_cuda_device_string())
 
     # if has_mps():
     #     return torch.device("mps")
@@ -44,8 +46,9 @@ def get_optimal_device():
 
 def torch_gc():
     if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.ipc_collect()
+        with torch.cuda.device(get_cuda_device_string()):
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
 
 
 def enable_tf32():
@@ -97,9 +100,25 @@ def autocast(disable=False):
 
 
 # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-def mps_contiguous(input_tensor, device):
-    return input_tensor.contiguous() if device.type == 'mps' else input_tensor
-
-
-def mps_contiguous_to(input_tensor, device):
-    return mps_contiguous(input_tensor, device).to(device)
+orig_tensor_to = torch.Tensor.to
+def tensor_to_fix(self, *args, **kwargs):
+    if self.device.type != 'mps' and \
+       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
+       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
+        self = self.contiguous()
+    return orig_tensor_to(self, *args, **kwargs)
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+orig_layer_norm = torch.nn.functional.layer_norm
+def layer_norm_fix(*args, **kwargs):
+    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
+        args = list(args)
+        args[0] = args[0].contiguous()
+    return orig_layer_norm(*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