mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
from __future__ import annotations
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from contextlib import nullcontext
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from packaging import version
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import platform
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import torch
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from torch import autocast
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from typing import Union
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from invokeai.app.services.config import InvokeAIAppConfig
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CPU_DEVICE = torch.device("cpu")
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CUDA_DEVICE = torch.device("cuda")
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MPS_DEVICE = torch.device("mps")
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config = InvokeAIAppConfig.get_config()
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def choose_torch_device() -> torch.device:
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"""Convenience routine for guessing which GPU device to run model on"""
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if config.always_use_cpu:
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return CPU_DEVICE
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if torch.cuda.is_available():
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return torch.device("cuda")
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if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
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return torch.device("mps")
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return CPU_DEVICE
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def choose_precision(device: torch.device) -> str:
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"""Returns an appropriate precision for the given torch device"""
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if device.type == "cuda":
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device_name = torch.cuda.get_device_name(device)
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if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):
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return "float16"
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elif device.type == "mps" and version.parse(platform.mac_ver()[0]) < version.parse("14.0.0"):
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return "float16"
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return "float32"
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def torch_dtype(device: torch.device) -> torch.dtype:
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if config.full_precision:
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return torch.float32
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if choose_precision(device) == "float16":
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return torch.float16
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else:
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return torch.float32
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def choose_autocast(precision):
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"""Returns an autocast context or nullcontext for the given precision string"""
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# float16 currently requires autocast to avoid errors like:
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# 'expected scalar type Half but found Float'
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if precision == "autocast" or precision == "float16":
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return autocast
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return nullcontext
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def normalize_device(device: Union[str, torch.device]) -> torch.device:
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"""Ensure device has a device index defined, if appropriate."""
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device = torch.device(device)
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if device.index is None:
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# cuda might be the only torch backend that currently uses the device index?
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# I don't see anything like `current_device` for cpu or mps.
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if device.type == "cuda":
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device = torch.device(device.type, torch.cuda.current_device())
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return device
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