78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
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import threading
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import time
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from collections import defaultdict
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import torch
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class MemUsageMonitor(threading.Thread):
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run_flag = None
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device = None
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disabled = False
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opts = None
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data = None
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def __init__(self, name, device, opts):
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threading.Thread.__init__(self)
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self.name = name
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self.device = device
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self.opts = opts
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self.daemon = True
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self.run_flag = threading.Event()
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self.data = defaultdict(int)
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def run(self):
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if self.disabled:
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return
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while True:
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self.run_flag.wait()
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torch.cuda.reset_peak_memory_stats()
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self.data.clear()
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if self.opts.memmon_poll_rate <= 0:
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self.run_flag.clear()
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continue
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self.data["min_free"] = torch.cuda.mem_get_info()[0]
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while self.run_flag.is_set():
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free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug?
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self.data["min_free"] = min(self.data["min_free"], free)
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time.sleep(1 / self.opts.memmon_poll_rate)
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def dump_debug(self):
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print(self, 'recorded data:')
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for k, v in self.read().items():
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print(k, -(v // -(1024 ** 2)))
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print(self, 'raw torch memory stats:')
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tm = torch.cuda.memory_stats(self.device)
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for k, v in tm.items():
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if 'bytes' not in k:
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continue
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print('\t' if 'peak' in k else '', k, -(v // -(1024 ** 2)))
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print(torch.cuda.memory_summary())
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def monitor(self):
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self.run_flag.set()
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def read(self):
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free, total = torch.cuda.mem_get_info()
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self.data["total"] = total
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torch_stats = torch.cuda.memory_stats(self.device)
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self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
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self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
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self.data["system_peak"] = total - self.data["min_free"]
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return self.data
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def stop(self):
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self.run_flag.clear()
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return self.read()
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