File size: 1,354 Bytes
e303d64 2414673 e303d64 7b55fe6 e303d64 2414673 e303d64 f6060a6 15f6e57 7b55fe6 e303d64 7b55fe6 e303d64 7b55fe6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 |
"""Benchmarking and measurement utilities"""
import pynvml
import torch
from pynvml.nvml import NVMLError
def gpu_memory_usage(device=0):
return torch.cuda.memory_allocated(device) / 1024.0**3
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)
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):
if not torch.cuda.is_available() or device == "auto":
return (0, 0, 0)
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
|