from colossalai.utils.memory_tracer.model_data_memtracer import ModelDataTracer from .async_memtracer import get_cuda_memory_used from colossalai.utils import get_current_device import torch class SamplingCounter: def __init__(self) -> None: self._samplint_cnt = 0 def advance(self): self._samplint_cnt += 1 @property def sampling_cnt(self): return self._samplint_cnt def reset(self): self._samplint_cnt = 0 class MemStatsCollector: def __init__(self) -> None: """ Collecting Memory Statistics. It has two phases. 1. Collection Phase: collect memory usage statistics 2. Runtime Phase: do not collect statistics. """ self._sampling_cnter = SamplingCounter() self._model_data_cuda = [] self._overall_cuda = [] # TODO(jiaruifang) Now no cpu mem stats collecting self._model_data_cpu = [] self._overall_cpu = [] self._start_flag = False def start_collection(self): self._start_flag = True def finish_collection(self): self._start_flag = False def sample_memstats(self) -> None: """ Sampling memory statistics. Record the current model data CUDA memory usage as well as system CUDA memory usage. """ if self._start_flag: sampling_cnt = self._sampling_cnter.sampling_cnt assert sampling_cnt == len(self._overall_cuda) self._model_data_cuda.append(ModelDataTracer().cuda_usage) self._overall_cuda.append(get_cuda_memory_used(torch.device(f'cuda:{get_current_device()}'))) self._sampling_cnter.advance() def fetch_memstats(self) -> (int, int): """ returns cuda usage of model data and overall cuda usage. """ sampling_cnt = self._sampling_cnter.sampling_cnt if len(self._model_data_cuda) < sampling_cnt: raise RuntimeError return (self._model_data_cuda[sampling_cnt], self._overall_cuda[sampling_cnt]) def reset_sampling_cnter(self) -> None: self._sampling_cnter.reset() def clear(self) -> None: self._model_data_cuda = [] self._overall_cuda = [] self._model_data_cpu = [] self._overall_cpu = [] self._start_flag = False self._sampling_cnter.reset()