mem_profiler.py

from cgitb import enable
import torch
from .device import *


class MemProfiler:

    enable = False

    @staticmethod
    def print_memory_stats(prefix, last_allocated=None, device=None, enable_once=False):
        if not enable_once and not MemProfiler.enable:
            return
        if device is None:
            device = default()
        allocated = torch.cuda.memory_allocated(device)
        if last_allocated is not None:
            delta = (allocated - last_allocated) / 1024 / 1024
            delta_str = f'{-delta:.2f}MB is freed, ' if delta < 0 else f'{delta:.2f}MB is allocated, '
        else:
            delta_str = ''
        print(f'{prefix}: {delta_str}currently PyTorch allocates {torch.cuda.memory_allocated(device)/1024/1024:.2f}MB and '
            f'reserves {torch.cuda.memory_reserved(device)/1024/1024:.2f}MB memory')


    def __init__(self, name, device=None) -> None:
        self.name = name
        self.device = device or default()
        self.alloc0 = 0

    def __enter__(self):
        self.alloc0 = torch.cuda.memory_allocated(self.device)
        return self

    def __exit__(self, exc_type, exc_val, exc_traceback):
        MemProfiler.print_memory_stats(self.name, self.alloc0, self.device)