272 lines
7.7 KiB
Python
272 lines
7.7 KiB
Python
import sys
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import contextlib
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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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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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def cond_cast_unet(input):
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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):
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def forward_wrapper(self, *args, **kwargs):
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if any(
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isinstance(arg, torch.Tensor) and arg.dtype != target_dtype
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for arg in args
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):
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args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
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kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
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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 org_dtype != target_dtype:
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self.to(target_dtype)
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result = self.org_forward(*args, **kwargs)
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if org_dtype != target_dtype:
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self.to(org_dtype)
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if target_dtype != dtype_inference:
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if isinstance(result, tuple):
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result = tuple(
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i.to(dtype_inference)
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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(dtype_inference)
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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):
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applied = False
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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)
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else:
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module_type.forward = manual_cast_forward(target_dtype)
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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):
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if disable:
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return contextlib.nullcontext()
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if fp8 and 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(dtype)
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if 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(dtype)
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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.all(torch.isnan(x)).item():
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return
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if where == "unet":
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message = "A tensor with all 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 all 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 all 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 allocaltes about 700MB of memory and
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spends about 2.7 seconds doing that, at least wih 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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