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"""Benchmarking and measurement utilities"""
import functools
import pynvml
import torch
from pynvml.nvml import NVMLError
def check_cuda_device(default_value):
"""
wraps a function and returns the default value instead of running the
wrapped function if cuda isn't available or the device is auto
:param default_value:
:return:
"""
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
device = kwargs.get("device", args[0] if args else None)
if (
not torch.cuda.is_available()
or device == "auto"
or torch.device(device).type == "cpu"
):
return default_value
return func(*args, **kwargs)
return wrapper
return deco
@check_cuda_device(0.0)
def gpu_memory_usage(device=0):
return torch.cuda.memory_allocated(device) / 1024.0**3
@check_cuda_device((0.0, 0.0, 0.0))
def gpu_memory_usage_all(device=0):
usage = torch.cuda.memory_allocated(device) / 1024.0**3
reserved = torch.cuda.memory_reserved(device) / 1024.0**3
smi = gpu_memory_usage_smi(device)
return usage, reserved - usage, max(0, smi - reserved)
@check_cuda_device(0.0)
def gpu_memory_usage_smi(device=0):
if isinstance(device, torch.device):
device = device.index
if isinstance(device, str) and device.startswith("cuda:"):
device = int(device[5:])
try:
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
return info.used / 1024.0**3
except NVMLError:
return 0.0
def log_gpu_memory_usage(log, msg, device):
usage, cache, misc = gpu_memory_usage_all(device)
extras = []
if cache > 0:
extras.append(f"+{cache:.03f}GB cache")
if misc > 0:
extras.append(f"+{misc:.03f}GB misc")
log.info(
f"GPU memory usage {msg}: {usage:.03f}GB ({', '.join(extras)})", stacklevel=2
)
return usage, cache, misc
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