80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
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import gc
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import datetime
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import pynvml
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import torch
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import numpy as np
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class MemTracker(object):
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"""
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Class used to track pytorch memory usage
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Arguments:
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frame: a frame to detect current py-file runtime
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detail(bool, default True): whether the function shows the detail gpu memory usage
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path(str): where to save log file
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verbose(bool, default False): whether show the trivial exception
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device(int): GPU number, default is 0
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"""
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def __init__(self, frame, detail=True, path='', verbose=False, device=0):
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self.frame = frame
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self.print_detail = detail
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self.last_tensor_sizes = set()
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self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H.%M.%S}-gpu_mem_track.txt'
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self.verbose = verbose
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self.begin = True
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self.device = device
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self.func_name = frame.f_code.co_name
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self.filename = frame.f_globals["__file__"]
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if (self.filename.endswith(".pyc") or
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self.filename.endswith(".pyo")):
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self.filename = self.filename[:-1]
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self.module_name = self.frame.f_globals["__name__"]
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self.curr_line = self.frame.f_lineno
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def get_tensors(self):
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for obj in gc.get_objects():
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try:
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if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
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tensor = obj
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else:
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continue
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if tensor.is_cuda:
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yield tensor
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except Exception as e:
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if self.verbose:
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print('A trivial exception occured: {}'.format(e))
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def track(self):
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"""
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Track the GPU memory usage
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"""
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pynvml.nvmlInit()
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handle = pynvml.nvmlDeviceGetHandleByIndex(self.device)
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meminfo = pynvml.nvmlDeviceGetMemoryInfo(handle)
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self.curr_line = self.frame.f_lineno
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where_str = self.module_name + ' ' + self.func_name + ':' + ' line ' + str(self.curr_line)
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with open(self.gpu_profile_fn, 'a+') as f:
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if self.begin:
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f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |"
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f" Total Used Memory:{meminfo.used/1000**2:<7.1f}Mb\n\n")
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self.begin = False
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if self.print_detail is True:
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ts_list = [tensor.size() for tensor in self.get_tensors()]
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new_tensor_sizes = {(type(x), tuple(x.size()), ts_list.count(x.size()), np.prod(np.array(x.size()))*4/1000**2)
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for x in self.get_tensors()}
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for t, s, n, m in new_tensor_sizes - self.last_tensor_sizes:
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f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20}\n')
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for t, s, n, m in self.last_tensor_sizes - new_tensor_sizes:
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f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} \n')
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self.last_tensor_sizes = new_tensor_sizes
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f.write(f"\nAt {where_str:<50}"
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f"Total Used Memory:{meminfo.used/1000**2:<7.1f}Mb\n\n")
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pynvml.nvmlShutdown()
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