mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-01-20 13:20:11 +08:00
352 lines
10 KiB
Python
352 lines
10 KiB
Python
import sys
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import contextlib
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from copy import deepcopy
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from functools import lru_cache
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import torch
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from modules import errors, shared, npu_specific
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if sys.platform == "darwin":
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from modules import mac_specific
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if shared.cmd_opts.use_ipex:
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from modules import xpu_specific
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def has_xpu() -> bool:
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return shared.cmd_opts.use_ipex and xpu_specific.has_xpu
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def has_mps() -> bool:
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if sys.platform != "darwin":
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return False
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else:
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return mac_specific.has_mps
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def cuda_no_autocast(device_id=None) -> bool:
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if device_id is None:
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device_id = get_cuda_device_id()
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return (
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torch.cuda.get_device_capability(device_id) == (7, 5)
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and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16")
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)
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def get_cuda_device_id():
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return (
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int(shared.cmd_opts.device_id)
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if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit()
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else 0
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) or torch.cuda.current_device()
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def get_cuda_device_string():
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if shared.cmd_opts.device_id is not None:
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return f"cuda:{shared.cmd_opts.device_id}"
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return "cuda"
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def get_optimal_device_name():
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if torch.cuda.is_available():
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return get_cuda_device_string()
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if has_mps():
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return "mps"
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if has_xpu():
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return xpu_specific.get_xpu_device_string()
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if npu_specific.has_npu:
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return npu_specific.get_npu_device_string()
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return "cpu"
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def get_optimal_device():
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return torch.device(get_optimal_device_name())
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def get_device_for(task):
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if task in shared.cmd_opts.use_cpu or "all" in shared.cmd_opts.use_cpu:
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return cpu
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return get_optimal_device()
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(get_cuda_device_string()):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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if has_mps():
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mac_specific.torch_mps_gc()
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if has_xpu():
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xpu_specific.torch_xpu_gc()
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if npu_specific.has_npu:
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torch_npu_set_device()
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npu_specific.torch_npu_gc()
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def torch_npu_set_device():
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# Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue
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if npu_specific.has_npu:
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torch.npu.set_device(0)
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def enable_tf32():
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if torch.cuda.is_available():
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# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
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# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
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if cuda_no_autocast():
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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errors.run(enable_tf32, "Enabling TF32")
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cpu: torch.device = torch.device("cpu")
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fp8: bool = False
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# Force fp16 for all models in inference. No casting during inference.
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# This flag is controlled by "--precision half" command line arg.
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force_fp16: bool = False
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device: torch.device = None
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device_interrogate: torch.device = None
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device_gfpgan: torch.device = None
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device_esrgan: torch.device = None
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device_codeformer: torch.device = None
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dtype: torch.dtype = torch.float16
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dtype_vae: torch.dtype = torch.float16
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dtype_unet: torch.dtype = torch.float16
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dtype_inference: torch.dtype = torch.float16
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unet_needs_upcast = False
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supported_vae_dtypes = [torch.float16, torch.float32]
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# prepare available dtypes
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if torch.version.cuda:
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if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
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supported_vae_dtypes = [torch.bfloat16] + supported_vae_dtypes
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if has_xpu():
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supported_vae_dtypes = [torch.bfloat16] + supported_vae_dtypes
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def supports_non_blocking():
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if has_mps() or has_xpu():
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return False
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if npu_specific.has_npu:
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return False
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return True
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def cond_cast_unet(input):
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if force_fp16:
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return input.to(torch.float16)
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return input.to(dtype_unet) if unet_needs_upcast else input
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def cond_cast_float(input):
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return input.float() if unet_needs_upcast else input
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nv_rng = None
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patch_module_list = [
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torch.nn.Linear,
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torch.nn.Conv2d,
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torch.nn.MultiheadAttention,
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torch.nn.GroupNorm,
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torch.nn.LayerNorm,
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]
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def manual_cast_forward(target_dtype, target_device=None, copy=False):
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params = dict()
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if supports_non_blocking():
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params['non_blocking'] = True
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def forward_wrapper(self, *args, **kwargs):
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if target_device is not None:
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params['device'] = target_device
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params['dtype'] = target_dtype
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args = list(args)
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for j in (i for i, arg in enumerate(args) if isinstance(arg, torch.Tensor) and arg.dtype != target_dtype):
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args[j] = args[j].to(**params)
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args = tuple(args)
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for key in (k for k, v in kwargs.items() if isinstance(v, torch.Tensor) and v.dtype != target_dtype):
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kwargs[key] = kwargs[key].to(**params)
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org_dtype = target_dtype
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for param in self.parameters():
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if param.dtype != target_dtype:
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org_dtype = param.dtype
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break
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if copy:
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copied = deepcopy(self)
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if org_dtype != target_dtype:
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copied.to(**params)
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result = copied.org_forward(*args, **kwargs)
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del copied
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else:
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if org_dtype != target_dtype:
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self.to(**params)
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result = self.org_forward(*args, **kwargs)
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if org_dtype != target_dtype:
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params['dtype'] = org_dtype
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self.to(**params)
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if target_dtype != dtype_inference:
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params['dtype'] = dtype_inference
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if isinstance(result, tuple):
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result = tuple(
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i.to(**params)
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if isinstance(i, torch.Tensor)
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else i
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for i in result
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)
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elif isinstance(result, torch.Tensor):
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result = result.to(**params)
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return result
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return forward_wrapper
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@contextlib.contextmanager
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def manual_cast(target_dtype, target_device=None):
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applied = False
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copy = shared.opts.lora_without_backup_weight
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for module_type in patch_module_list:
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if hasattr(module_type, "org_forward"):
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continue
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applied = True
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org_forward = module_type.forward
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if module_type == torch.nn.MultiheadAttention:
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module_type.forward = manual_cast_forward(torch.float32, target_device, copy)
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else:
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module_type.forward = manual_cast_forward(target_dtype, target_device, copy)
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module_type.org_forward = org_forward
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try:
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yield None
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finally:
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if applied:
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for module_type in patch_module_list:
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if hasattr(module_type, "org_forward"):
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module_type.forward = module_type.org_forward
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delattr(module_type, "org_forward")
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def autocast(disable=False, current_dtype=None, target_dtype=None, target_device=None):
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if disable:
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return contextlib.nullcontext()
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if target_dtype is None:
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target_dtype = dtype
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if target_device is None:
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target_device = device
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if force_fp16:
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# No casting during inference if force_fp16 is enabled.
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# All tensor dtype conversion happens before inference.
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return contextlib.nullcontext()
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if fp8 and target_device==cpu:
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return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
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if fp8 and dtype_inference == torch.float32:
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return manual_cast(target_dtype, target_device)
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if target_dtype != dtype_inference:
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return manual_cast(target_dtype, target_device)
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if current_dtype is not None and current_dtype != target_dtype:
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return manual_cast(target_dtype, target_device)
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if target_dtype == torch.float32 or dtype_inference == torch.float32:
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return contextlib.nullcontext()
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if has_xpu() or has_mps() or cuda_no_autocast():
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return manual_cast(target_dtype, target_device)
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return torch.autocast("cuda")
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def without_autocast(disable=False):
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return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
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class NansException(Exception):
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pass
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def test_for_nans(x, where):
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if shared.cmd_opts.disable_nan_check:
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return
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if not torch.isnan(x[(0, ) * len(x.shape)]):
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return
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if where == "unet":
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message = "A tensor with NaNs was produced in Unet."
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if not shared.cmd_opts.no_half:
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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."
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elif where == "vae":
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message = "A tensor with NaNs was produced in VAE."
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if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
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message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
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else:
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message = "A tensor with NaNs was produced."
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message += " Use --disable-nan-check commandline argument to disable this check."
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raise NansException(message)
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@lru_cache
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def first_time_calculation():
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"""
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just do any calculation with pytorch layers - the first time this is done it allocates about 700MB of memory and
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spends about 2.7 seconds doing that, at least with NVidia.
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"""
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x = torch.zeros((1, 1)).to(device, dtype)
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linear = torch.nn.Linear(1, 1).to(device, dtype)
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linear(x)
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x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
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conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
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conv2d(x)
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def force_model_fp16():
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"""
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ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
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force conversion of input to float32. If force_fp16 is enabled, we need to
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prevent this casting.
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"""
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assert force_fp16
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import sgm.modules.diffusionmodules.util as sgm_util
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import ldm.modules.diffusionmodules.util as ldm_util
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sgm_util.GroupNorm32 = torch.nn.GroupNorm
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ldm_util.GroupNorm32 = torch.nn.GroupNorm
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print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")
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