import sys import contextlib from functools import lru_cache import torch from modules import errors, shared if sys.platform == "darwin": from modules import mac_specific if shared.cmd_opts.use_ipex: from modules import xpu_specific def has_xpu() -> bool: return shared.cmd_opts.use_ipex and xpu_specific.has_xpu def has_mps() -> bool: if sys.platform != "darwin": return False else: return mac_specific.has_mps def cuda_no_autocast(device_id=None) -> bool: if device_id is None: device_id = get_cuda_device_id() return ( torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16") ) def get_cuda_device_id(): return ( int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0 ) or torch.cuda.current_device() def get_cuda_device_string(): if shared.cmd_opts.device_id is not None: return f"cuda:{shared.cmd_opts.device_id}" return "cuda" def get_optimal_device_name(): if torch.cuda.is_available(): return get_cuda_device_string() if has_mps(): return "mps" if has_xpu(): return xpu_specific.get_xpu_device_string() return "cpu" def get_optimal_device(): return torch.device(get_optimal_device_name()) def get_device_for(task): if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu: return cpu return get_optimal_device() def torch_gc(): if torch.cuda.is_available(): with torch.cuda.device(get_cuda_device_string()): torch.cuda.empty_cache() torch.cuda.ipc_collect() if has_mps(): mac_specific.torch_mps_gc() if has_xpu(): xpu_specific.torch_xpu_gc() def enable_tf32(): if torch.cuda.is_available(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 if cuda_no_autocast(): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True errors.run(enable_tf32, "Enabling TF32") cpu: torch.device = torch.device("cpu") fp8: bool = False device: torch.device = None device_interrogate: torch.device = None device_gfpgan: torch.device = None device_esrgan: torch.device = None device_codeformer: torch.device = None dtype: torch.dtype = torch.float16 dtype_vae: torch.dtype = torch.float16 dtype_unet: torch.dtype = torch.float16 dtype_inference: torch.dtype = torch.float16 unet_needs_upcast = False def cond_cast_unet(input): return input.to(dtype_unet) if unet_needs_upcast else input def cond_cast_float(input): return input.float() if unet_needs_upcast else input nv_rng = None patch_module_list = [ torch.nn.Linear, torch.nn.Conv2d, torch.nn.MultiheadAttention, torch.nn.GroupNorm, torch.nn.LayerNorm, ] def manual_cast_forward(target_dtype): def forward_wrapper(self, *args, **kwargs): if any( isinstance(arg, torch.Tensor) and arg.dtype != target_dtype for arg in args ): args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args] kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()} org_dtype = target_dtype for param in self.parameters(): if param.dtype != target_dtype: org_dtype = param.dtype break if org_dtype != target_dtype: self.to(target_dtype) result = self.org_forward(*args, **kwargs) if org_dtype != target_dtype: self.to(org_dtype) if target_dtype != dtype_inference: if isinstance(result, tuple): result = tuple( i.to(dtype_inference) if isinstance(i, torch.Tensor) else i for i in result ) elif isinstance(result, torch.Tensor): result = result.to(dtype_inference) return result return forward_wrapper @contextlib.contextmanager def manual_cast(target_dtype): applied = False for module_type in patch_module_list: if hasattr(module_type, "org_forward"): continue applied = True org_forward = module_type.forward if module_type == torch.nn.MultiheadAttention: module_type.forward = manual_cast_forward(torch.float32) else: module_type.forward = manual_cast_forward(target_dtype) module_type.org_forward = org_forward try: yield None finally: if applied: for module_type in patch_module_list: if hasattr(module_type, "org_forward"): module_type.forward = module_type.org_forward delattr(module_type, "org_forward") def autocast(disable=False): if disable: return contextlib.nullcontext() if fp8 and device==cpu: return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True) if fp8 and dtype_inference == torch.float32: return manual_cast(dtype) if dtype == torch.float32 or dtype_inference == torch.float32: return contextlib.nullcontext() if has_xpu() or has_mps() or cuda_no_autocast(): return manual_cast(dtype) return torch.autocast("cuda") def without_autocast(disable=False): return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext() class NansException(Exception): pass def test_for_nans(x, where): if shared.cmd_opts.disable_nan_check: return if not torch.all(torch.isnan(x)).item(): return if where == "unet": message = "A tensor with all NaNs was produced in Unet." if not shared.cmd_opts.no_half: message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this." elif where == "vae": message = "A tensor with all NaNs was produced in VAE." if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae: message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this." else: message = "A tensor with all NaNs was produced." message += " Use --disable-nan-check commandline argument to disable this check." raise NansException(message) @lru_cache def first_time_calculation(): """ just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and spends about 2.7 seconds doing that, at least wih NVidia. """ x = torch.zeros((1, 1)).to(device, dtype) linear = torch.nn.Linear(1, 1).to(device, dtype) linear(x) x = torch.zeros((1, 1, 3, 3)).to(device, dtype) conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) conv2d(x)