ColossalAI/colossalai/utils/gradient_accumulation/_gradient_accumulation.py

155 lines
5.1 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import torch.nn as nn
from torch import Tensor
from typing import Iterable, Any
from colossalai.nn.optimizer import ColossalaiOptimizer
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from torch.utils.data import DataLoader
from colossalai.utils import conditional_context
from colossalai.engine import BaseGradientHandler
class GradAccumOptimizer(ColossalaiOptimizer):
def __init__(self, optim: Optimizer, accumulate_size: int, model: nn.Module = None):
super().__init__(optim)
self.accumulate_size = accumulate_size
self.accumulate_step = 0
# handle pytorch ddp auto all reduce
self.model = model
self.is_torch_ddp = isinstance(self.model, DistributedDataParallel)
def zero_grad(self, *args, **kwargs):
if self.accumulate_step == 0:
self.optim.zero_grad(*args, **kwargs)
def step(self, *args, **kwargs):
if self.accumulate_step < self.accumulate_size:
return None
else:
self.accumulate_step = 0
return self.optim.step(*args, **kwargs)
def clip_grad_norm(self, model: nn.Module, max_norm: float):
if self.accumulate_step < self.accumulate_size:
pass
else:
self.optim.clip_grad_norm(model, max_norm)
def backward(self, loss: Tensor):
self.accumulate_step += 1
if self.is_torch_ddp:
no_sync = self.accumulate_step < self.accumulate_size
with conditional_context(self.model.no_sync(), enable=no_sync):
scaled_loss = loss / self.accumulate_size
self.optim.backward(scaled_loss)
else:
scaled_loss = loss / self.accumulate_size
self.optim.backward(scaled_loss)
def backward_by_grad(self, tensor: Tensor, grad: Tensor):
no_sync = self.is_torch_ddp and self.accumulate_step < self.accumulate_size
if no_sync:
with self.model.no_sync():
self.optim.backward_by_grad(tensor, grad)
else:
self.optim.backward_by_grad(tensor, grad)
class GradAccumDataloader():
def __init__(self, dataloader: Iterable, accumulate_size: int) -> None:
self.dataloader = dataloader
self.consume_remain_data = not isinstance(dataloader, DataLoader)
self.steps_per_epoch = len(dataloader) - len(dataloader) % accumulate_size
def __getattr__(self, __name: str) -> Any:
return getattr(self.dataloader, __name)
def __len__(self):
return self.steps_per_epoch
def __iter__(self):
self._cur_step = 0
self._dataiter = iter(self.dataloader)
return self
def __next__(self) -> Any:
if self._cur_step < self.steps_per_epoch:
self._cur_step += 1
if self._cur_step == self.steps_per_epoch and self.consume_remain_data:
# this is to handle non standard pytorch dataloader
# such as dali dataloader
while True:
try:
_ = next(self._dataiter)
except StopIteration:
break
return next(self._dataiter)
else:
raise StopIteration
class GradAccumLrSchedulerByStep(_LRScheduler):
def __init__(self, lr_scheduler: _LRScheduler, accumulate_size: int) -> None:
self.lr_scheduler = lr_scheduler
self.accumulate_size = accumulate_size
self.accumulate_step = 0
@staticmethod
def compute_effective_steps_per_epoch(dataloader: Iterable, accumulate_size: int):
return len(dataloader) // accumulate_size
def __getattr__(self, __name: str) -> Any:
return getattr(self.lr_scheduler, __name)
def step(self, *args, **kwargs):
self.accumulate_step += 1
if self.accumulate_step < self.accumulate_size:
pass
else:
self.accumulate_step = 0
self.lr_scheduler.step(*args, **kwargs)
def get_lr(self):
return self.lr_scheduler.get_lr()
def get_last_lr(self):
return self.lr_scheduler.get_last_lr()
def print_lr(self, *args, **kwargs):
self.lr_scheduler.print_lr(*args, **kwargs)
def state_dict(self) -> dict:
return self.lr_scheduler.state_dict()
def load_state_dict(self, state_dict: dict) -> None:
self.lr_scheduler.load_state_dict(state_dict)
class GradAccumGradientHandler():
def __init__(self, grad_handler: BaseGradientHandler, accumulate_size: int) -> None:
assert isinstance(grad_handler, BaseGradientHandler), \
f'expected grad_handler to be type BaseGradientHandler, but got {type(grad_handler)}'
self.grad_handler = grad_handler
self.accumulate_size = accumulate_size
self.accumulate_step = 0
def handle_gradient(self):
self.accumulate_step += 1
if self.accumulate_step < self.accumulate_size:
pass
else:
self.accumulate_step = 0
self.grad_handler.handle_gradient()