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import importlib
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import logging
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import platform
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from contextlib import contextmanager, nullcontext
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from functools import lru_cache
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from typing import Any, List, Optional, Union
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import torch
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from torch import Tensor, autocast
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from torch.nn import functional
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from torch.overrides import handle_torch_function, has_torch_function_variadic
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logger = logging.getLogger(__name__)
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@lru_cache()
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def get_device() -> str:
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"""Return the best torch backend available"""
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if torch.cuda.is_available():
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return "cuda"
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if torch.backends.mps.is_available():
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return "mps:0"
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return "cpu"
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@lru_cache()
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def get_hardware_description(device_type: str) -> str:
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"""Description of the hardware being used"""
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desc = platform.platform()
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if device_type == "cuda":
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desc += "-" + torch.cuda.get_device_name(0)
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return desc
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def get_obj_from_str(import_path: str, reload=False) -> Any:
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"""
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Gets a python object from a string reference if it's location
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Example: "functools.lru_cache"
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"""
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module_path, obj_name = import_path.rsplit(".", 1)
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if reload:
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module_imp = importlib.import_module(module_path)
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importlib.reload(module_imp)
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module = importlib.import_module(module_path, package=None)
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return getattr(module, obj_name)
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def instantiate_from_config(config: Union[dict, str]) -> Any:
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"""Instantiate an object from a config dict"""
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if "target" not in config:
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if config == "__is_first_stage__":
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return None
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if config == "__is_unconditional__":
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return None
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raise KeyError("Expected key `target` to instantiate.")
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params = config.get("params", {})
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_cls = get_obj_from_str(config["target"])
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return _cls(**params)
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@contextmanager
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def platform_appropriate_autocast(precision="autocast"):
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"""
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Allow calculations to run in mixed precision, which can be faster
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"""
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precision_scope = nullcontext
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# autocast not supported on CPU
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# https://github.com/pytorch/pytorch/issues/55374
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# https://github.com/invoke-ai/InvokeAI/pull/518
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if precision == "autocast" and get_device() in ("cuda",):
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precision_scope = autocast
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with precision_scope(get_device()):
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yield
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def _fixed_layer_norm(
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input: Tensor, # noqa
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normalized_shape: List[int],
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weight: Optional[Tensor] = None,
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bias: Optional[Tensor] = None,
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eps: float = 1e-5,
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) -> Tensor:
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"""
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Applies Layer Normalization for last certain number of dimensions.
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See :class:`~torch.nn.LayerNorm` for details.
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"""
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if has_torch_function_variadic(input, weight, bias):
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return handle_torch_function(
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_fixed_layer_norm,
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(input, weight, bias),
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input,
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normalized_shape,
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weight=weight,
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bias=bias,
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eps=eps,
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)
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return torch.layer_norm(
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input.contiguous(),
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normalized_shape,
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weight,
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bias,
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eps,
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torch.backends.cudnn.enabled,
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)
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@contextmanager
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def fix_torch_nn_layer_norm():
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"""https://github.com/CompVis/stable-diffusion/issues/25#issuecomment-1221416526"""
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orig_function = functional.layer_norm
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functional.layer_norm = _fixed_layer_norm
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try:
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yield
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finally:
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functional.layer_norm = orig_function
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@contextmanager
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def fix_torch_group_norm():
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"""
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Patch group_norm to cast the weights to the same type as the inputs
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From what I can understand all the other repos just switch to full precision instead
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of addressing this. I think this would make things slower but I'm not sure.
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https://github.com/pytorch/pytorch/pull/81852
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"""
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orig_group_norm = functional.group_norm
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def _group_norm_wrapper(
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input: Tensor, # noqa
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num_groups: int,
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weight: Optional[Tensor] = None,
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bias: Optional[Tensor] = None,
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eps: float = 1e-5,
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) -> Tensor:
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if weight is not None and weight.dtype != input.dtype:
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weight = weight.to(input.dtype)
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if bias is not None and bias.dtype != input.dtype:
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bias = bias.to(input.dtype)
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return orig_group_norm(
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input=input, num_groups=num_groups, weight=weight, bias=bias, eps=eps
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)
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functional.group_norm = _group_norm_wrapper
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try:
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yield
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finally:
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functional.group_norm = orig_group_norm
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def randn_seeded(seed: int, size: List[int]) -> Tensor:
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"""Generate a random tensor with a given seed"""
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g_cpu = torch.Generator()
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g_cpu.manual_seed(seed)
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noise = torch.randn(
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size,
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device="cpu",
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generator=g_cpu,
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)
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return noise
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def check_torch_working():
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"""Check that torch is working"""
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try:
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torch.randn(1, device=get_device())
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except RuntimeError as e:
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if "CUDA" in str(e):
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raise RuntimeError(
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"CUDA is not working. Make sure you have a GPU and CUDA installed."
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) from e
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raise e
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