diff options
Diffstat (limited to 'modules/devices.py')
| -rw-r--r-- | modules/devices.py | 88 |
1 files changed, 58 insertions, 30 deletions
diff --git a/modules/devices.py b/modules/devices.py index 058a5e00..1325569c 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -2,30 +2,43 @@ 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), `getattr` for compatibility -has_mps = getattr(torch, 'has_mps', False) -cpu = torch.device("cpu") +# 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): + return False + try: + torch.zeros(1).to(torch.device("mps")) + return True + except Exception: + return False + def extract_device_id(args, name): for x in range(len(args)): - if name in args[x]: return args[x+1] + if name in args[x]: + return args[x + 1] + return None -def get_optimal_device(): - if torch.cuda.is_available(): - from modules import shared - device_id = shared.cmd_opts.device_id +def get_cuda_device_string(): + from modules import shared + + if shared.cmd_opts.device_id is not None: + return f"cuda:{shared.cmd_opts.device_id}" + + return "cuda" + - if device_id is not None: - cuda_device = f"cuda:{device_id}" - return torch.device(cuda_device) - else: - return torch.device("cuda") +def get_optimal_device(): + if torch.cuda.is_available(): + return torch.device(get_cuda_device_string()) - if has_mps: + if has_mps(): return torch.device("mps") return cpu @@ -33,8 +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(): @@ -52,29 +66,22 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") +cpu = torch.device("cpu") device = device_interrogate = device_gfpgan = device_swinir = device_esrgan = device_scunet = device_codeformer = None dtype = torch.float16 dtype_vae = torch.float16 -def randn(seed, shape): - # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. - if device.type == 'mps': - generator = torch.Generator(device=cpu) - generator.manual_seed(seed) - noise = torch.randn(shape, generator=generator, device=cpu).to(device) - return noise +def randn(seed, shape): torch.manual_seed(seed) + if device.type == 'mps': + return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - # Pytorch currently doesn't handle setting randomness correctly when the metal backend is used. if device.type == 'mps': - generator = torch.Generator(device=cpu) - noise = torch.randn(shape, generator=generator, device=cpu).to(device) - return noise - + return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) @@ -89,6 +96,27 @@ def autocast(disable=False): return torch.autocast("cuda") + # 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 |