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import functools
import gc
import torch
try:
HAS_CUDA = torch.cuda.is_available()
except Exception:
HAS_CUDA = False
try:
HAS_MPS = torch.backends.mps.is_available()
except Exception:
HAS_MPS = False
try:
import intel_extension_for_pytorch as ipex # noqa
HAS_XPU = torch.xpu.is_available()
except Exception:
HAS_XPU = False
def clean_memory():
gc.collect()
if HAS_CUDA:
torch.cuda.empty_cache()
if HAS_XPU:
torch.xpu.empty_cache()
if HAS_MPS:
torch.mps.empty_cache()
def clean_memory_on_device(device: torch.device):
r"""
Clean memory on the specified device, will be called from training scripts.
"""
gc.collect()
# device may "cuda" or "cuda:0", so we need to check the type of device
if device.type == "cuda":
torch.cuda.empty_cache()
if device.type == "xpu":
torch.xpu.empty_cache()
if device.type == "mps":
torch.mps.empty_cache()
@functools.lru_cache(maxsize=None)
def get_preferred_device() -> torch.device:
r"""
Do not call this function from training scripts. Use accelerator.device instead.
"""
if HAS_CUDA:
device = torch.device("cuda")
elif HAS_XPU:
device = torch.device("xpu")
elif HAS_MPS:
device = torch.device("mps")
else:
device = torch.device("cpu")
print(f"get_preferred_device() -> {device}")
return device
def init_ipex():
"""
Apply IPEX to CUDA hijacks using `library.ipex.ipex_init`.
This function should run right after importing torch and before doing anything else.
If IPEX is not available, this function does nothing.
"""
try:
if HAS_XPU:
from library.ipex import ipex_init
is_initialized, error_message = ipex_init()
if not is_initialized:
print("failed to initialize ipex:", error_message)
else:
return
except Exception as e:
print("failed to initialize ipex:", e)
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