mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
143 lines
3.9 KiB
143 lines
3.9 KiB
from abc import abstractmethod
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
from time import sleep, time
|
|
import json
|
|
|
|
import torch
|
|
|
|
from colossalai.utils import colo_device_memory_used
|
|
from colossalai.utils import get_current_device
|
|
|
|
|
|
class MemoryMonitor:
|
|
"""Base class for all types of memory monitor.
|
|
All monitors should have a list called `time_stamps` and a list called `mem_stats`.
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.time_stamps = []
|
|
self.mem_stats = []
|
|
|
|
def __len__(self):
|
|
return len(self.mem_stats)
|
|
|
|
@abstractmethod
|
|
def start(self):
|
|
pass
|
|
|
|
@abstractmethod
|
|
def finish(self):
|
|
pass
|
|
|
|
def state_dict(self):
|
|
return {
|
|
"time_stamps": self.time_stamps,
|
|
"mem_stats": self.mem_stats,
|
|
}
|
|
|
|
def save(self, filename):
|
|
with open(filename, "w") as f:
|
|
json.dump(self.state_dict(), f)
|
|
|
|
def clear(self):
|
|
self.mem_stats.clear()
|
|
self.time_stamps.clear()
|
|
|
|
|
|
class AsyncMemoryMonitor(MemoryMonitor):
|
|
"""
|
|
An Async Memory Monitor runing during computing. Sampling memory usage of the current GPU
|
|
at interval of `1/(10**power)` sec.
|
|
|
|
The idea comes from Runtime Memory Tracer of PatrickStar
|
|
`PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management`_
|
|
|
|
Usage::
|
|
|
|
async_mem_monitor = AsyncMemoryMonitor()
|
|
input = torch.randn(2, 20).cuda()
|
|
OP1 = torch.nn.Linear(20, 30).cuda()
|
|
OP2 = torch.nn.Linear(30, 40).cuda()
|
|
|
|
async_mem_monitor.start()
|
|
output = OP1(input)
|
|
async_mem_monitor.finish()
|
|
async_mem_monitor.start()
|
|
output = OP2(output)
|
|
async_mem_monitor.finish()
|
|
async_mem_monitor.save('log.pkl')
|
|
|
|
Args:
|
|
power (int, optional): the power of time interva. Defaults to 10.
|
|
|
|
.. _PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management:
|
|
https://arxiv.org/abs/2108.05818
|
|
"""
|
|
|
|
def __init__(self, power: int = 10):
|
|
super().__init__()
|
|
self.keep_measuring = False
|
|
|
|
current_device = get_current_device()
|
|
|
|
def _set_cuda_device():
|
|
torch.cuda.set_device(current_device)
|
|
|
|
self.executor = ThreadPoolExecutor(max_workers=1, initializer=_set_cuda_device)
|
|
self.monitor_thread = None
|
|
self.interval = 1 / (10**power)
|
|
|
|
def set_interval(self, power: int):
|
|
self.clear()
|
|
self.interval = 1 / (10**power)
|
|
|
|
def is_measuring(self):
|
|
return self.keep_measuring
|
|
|
|
def start(self):
|
|
self.keep_measuring = True
|
|
self.monitor_thread = self.executor.submit(self._measure_usage)
|
|
|
|
def finish(self):
|
|
if self.keep_measuring is False:
|
|
return 0
|
|
|
|
self.keep_measuring = False
|
|
max_usage = self.monitor_thread.result()
|
|
|
|
self.monitor_thread = None
|
|
self.time_stamps.append(time())
|
|
self.mem_stats.append(max_usage)
|
|
return max_usage
|
|
|
|
def _measure_usage(self):
|
|
max_usage = 0
|
|
while self.keep_measuring:
|
|
max_usage = max(
|
|
max_usage,
|
|
colo_device_memory_used(get_current_device()),
|
|
)
|
|
sleep(self.interval)
|
|
return max_usage
|
|
|
|
|
|
class SyncCudaMemoryMonitor(MemoryMonitor):
|
|
"""
|
|
A synchronized cuda memory monitor.
|
|
It only record the maximum allocated cuda memory from start point to finish point.
|
|
"""
|
|
|
|
def __init__(self, power: int = 10):
|
|
super().__init__()
|
|
|
|
def start(self):
|
|
torch.cuda.synchronize()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
|
|
def finish(self):
|
|
torch.cuda.synchronize()
|
|
self.time_stamps.append(time())
|
|
max_usage = torch.cuda.max_memory_allocated()
|
|
self.mem_stats.append(max_usage)
|
|
return max_usage
|