mirror of https://github.com/hpcaitech/ColossalAI
parent
1b17859328
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bcab249565
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@ -4,9 +4,7 @@
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import os
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import os.path as osp
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import torch
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from typing import List
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from decimal import Decimal
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.registry import HOOKS
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@ -15,6 +13,7 @@ from colossalai.utils import report_memory_usage, is_dp_rank_0, \
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is_tp_rank_0, is_no_pp_or_last_stage, MultiTimer
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from ._base_hook import BaseHook
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from ._commons_ import _format_number
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from colossalai.trainer.hooks._metric_hook import ThroughputMetric
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class LogByEpochHook(BaseHook):
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@ -53,12 +52,18 @@ class LogMetricByStepHook(BaseHook):
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def after_train_iter(self, trainer, *args):
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trainer.states['step_metrics'] = dict()
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for metric_name, metric_calculator in trainer.states['metrics']['train'].items():
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_value()
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if isinstance(metric_calculator, ThroughputMetric):
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_info()
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else:
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_value()
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def after_test_iter(self, trainer, *args):
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trainer.states['step_metrics'] = dict()
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for metric_name, metric_calculator in trainer.states['metrics']['test'].items():
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_value()
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if isinstance(metric_calculator, ThroughputMetric):
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_info()
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else:
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trainer.states['step_metrics'][metric_name.lower()] = metric_calculator.get_last_step_value()
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@HOOKS.register_module
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@ -52,7 +52,7 @@ class Metric(ABC):
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pass
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@abstractmethod
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def get_last_step_value(self) -> str:
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def get_last_step_value(self) -> float:
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"""Returns the metric value in the last iteration.
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"""
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pass
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@ -121,10 +121,10 @@ class LossMetric(Metric):
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self.accum_loss.div_(self.count)
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return self.accum_loss.item()
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def get_last_step_value(self) -> str:
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def get_last_step_value(self) -> float:
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"""Returns :attr:`last_step_loss`.
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"""
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return str(self.last_step_loss.cpu().item())
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return self.last_step_loss.cpu().item()
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@staticmethod
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def is_better(a, b):
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@ -149,8 +149,8 @@ class LearningRateMetric(Metric):
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def update(self, lr) -> None:
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self.lr = lr
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def get_last_step_value(self) -> str:
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return str(self.lr)
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def get_last_step_value(self) -> float:
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return self.lr
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def get_accumulated_value(self):
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return self.lr
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@ -204,10 +204,10 @@ class AccuracyMetric(Metric):
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self.accumulated_sum += self.last_step_sum
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self.accumulated_correct += self.last_step_correct
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def get_last_step_value(self) -> str:
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def get_last_step_value(self) -> float:
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self.last_step_sum = all_reduce(self.last_step_sum, ParallelMode.DATA)
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self.last_step_correct = all_reduce(self.last_step_correct, ParallelMode.DATA)
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return str(_format_number((self.last_step_correct / self.last_step_sum).cpu().item()))
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return _format_number((self.last_step_correct / self.last_step_sum).cpu().item())
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def get_accumulated_value(self):
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self.accumulated_sum = all_reduce(self.accumulated_sum, ParallelMode.DATA)
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@ -350,7 +350,18 @@ class ThroughputMetric(Metric):
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self.accumulated_num_samples += self.last_step_num_samples
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self.accumulated_used_time += self.last_step_used_time
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def get_last_step_value(self) -> str:
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def get_last_step_value(self) -> float:
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if self._use_local:
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self.last_step_num_samples *= gpc.get_world_size(ParallelMode.DATA)
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else:
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self.last_step_used_time = all_reduce(self.last_step_used_time, ParallelMode.DATA) / \
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gpc.get_world_size(ParallelMode.DATA)
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self.last_step_num_samples = all_reduce(self.last_step_num_samples, ParallelMode.DATA)
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sample_per_sec = _format_number(self.last_step_num_samples / (self.last_step_used_time + 1e-12).item())
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return sample_per_sec
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def get_last_step_info(self) -> str:
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if self._use_local:
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self.last_step_num_samples *= gpc.get_world_size(ParallelMode.DATA)
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else:
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