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[legacy] move engine to legacy (#4560)
* [legacy] move engine to legacy * [example] fix seq parallel example * [example] fix seq parallel example * [test] test gemini pluging hang * [test] test gemini pluging hang * [test] test gemini pluging hang * [test] test gemini pluging hang * [test] test gemini pluging hang * [example] update seq parallel requirements
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Hongxin Liu
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Any, Iterable, Tuple, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn.parallel.distributed import DistributedDataParallel
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.legacy.engine import BaseGradientHandler
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from colossalai.nn.optimizer import ColossalaiOptimizer
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from colossalai.utils import conditional_context
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class GradAccumOptimizer(ColossalaiOptimizer):
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"""A wrapper for the optimizer to enable gradient accumulation by skipping the steps
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before accumulation size is reached.
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Args:
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optim (:class:`torch.optim.Optimizer`): Your optimizer object for gradient accumulation.
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accumulate_size (int): The number of steps to accumulate gradients.
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model (:class:`torch.nn.Module`):
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Your model object to check if it is DistributedDataParallel for special handling of no_sync() context.
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"""
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def __init__(self, optim: Optimizer, accumulate_size: int, model: nn.Module = None):
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super().__init__(optim)
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self.accumulate_size = accumulate_size
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self.accumulate_step = 0
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# handle pytorch ddp auto all reduce
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self.model = model
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self.is_torch_ddp = isinstance(self.model, DistributedDataParallel)
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def zero_grad(self, *args, **kwargs) -> None:
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"""
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Set all gradients to zero.
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Args:
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*args: positional arguments for the optimizer wrapped
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**kwargs: keyword arguments for the optimizer wrapped
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"""
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if self.accumulate_step == 0:
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self.optim.zero_grad(*args, **kwargs)
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def step(self, *args, **kwargs) -> None:
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"""
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Update the model parameters.
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Args:
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*args: positional arguments for the optimizer wrapped
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**kwargs: keyword arguments for the optimizer wrapped
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"""
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if self.accumulate_step < self.accumulate_size:
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return None
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else:
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self.accumulate_step = 0
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return self.optim.step(*args, **kwargs)
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> None:
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"""
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Clip gradients by norm.
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Args:
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model (:class:`torch.nn.Module`): a torch module instance
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max_norm (float): the max norm for gradient clipping
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"""
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if self.accumulate_step < self.accumulate_size:
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pass
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else:
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self.optim.clip_grad_norm(model, max_norm)
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def backward(self, loss: Tensor) -> None:
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"""Execute backward pass.
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Args:
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loss (:class:`torch.Tensor`): the loss value.
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"""
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self.accumulate_step += 1
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if self.is_torch_ddp:
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no_sync = self.accumulate_step < self.accumulate_size
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with conditional_context(self.model.no_sync(), enable=no_sync):
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scaled_loss = loss / self.accumulate_size
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self.optim.backward(scaled_loss)
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else:
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scaled_loss = loss / self.accumulate_size
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self.optim.backward(scaled_loss)
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def backward_by_grad(self, tensor: Tensor, grad: Tensor) -> None:
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"""Execute backward pass given the gradients of the output.
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Args:
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loss (:class:`torch.Tensor`): the loss value.
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grad (:class:`torch.Tensor`): the output gradient.
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"""
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self.accumulate_step += 1
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no_sync = self.is_torch_ddp and self.accumulate_step < self.accumulate_size
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if no_sync:
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with self.model.no_sync():
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self.optim.backward_by_grad(tensor, grad)
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else:
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self.optim.backward_by_grad(tensor, grad)
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class GradAccumDataloader:
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"""A wrapper for dataloader to enable gradient accumulation by dropping the last incomplete steps.
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Note:
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The dataloader would drop the last incomplete steps for gradient accumulation.
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For example, if a dataloader has 10 batches of data and accumulate size is 4. The model parameters will
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be updated only twice at step 4 and step 8. The last two batches of data do not form a complete 4-step cycle.
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Thus, they will be automatically skipped by this class. If the dataloader is not standard PyTorch dataloader,
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(e.g. Dali dataloader), this class will automatically consume (load data for nothing) the remaining 2 batches.
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Args:
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dataloader (``Iterable``): Your dataloader object for gradient accumulation.
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accumulate_size (int): The number of steps to accumulate gradients.
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"""
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def __init__(self, dataloader: Iterable, accumulate_size: int) -> None:
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self.dataloader = dataloader
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self.consume_remain_data = not isinstance(dataloader, DataLoader)
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self.steps_per_epoch = len(dataloader) - len(dataloader) % accumulate_size
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def __getattr__(self, __name: str) -> Any:
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return getattr(self.dataloader, __name)
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def __len__(self) -> int:
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return self.steps_per_epoch
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def __iter__(self) -> Iterable:
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self._cur_step = 0
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self._dataiter = iter(self.dataloader)
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return self
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def __next__(self) -> Union[Tensor, Tuple[Tensor]]:
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if self._cur_step < self.steps_per_epoch:
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self._cur_step += 1
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data = next(self._dataiter)
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if self._cur_step == self.steps_per_epoch and self.consume_remain_data:
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# this is to handle non standard pytorch dataloader
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# such as dali dataloader
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while True:
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try:
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_ = next(self._dataiter)
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except StopIteration:
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break
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return data
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else:
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raise StopIteration
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class GradAccumLrSchedulerByStep(_LRScheduler):
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"""A wrapper for the LR scheduler to enable gradient accumulation by skipping the steps
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before accumulation size is reached.
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Args:
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lr_scheduler (:class:`torch.optim.lr_scheduler._LRScheduler`):
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Your ``lr_scheduler`` object for gradient accumulation.
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accumulate_size (int): The number of steps to accumulate gradients.
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"""
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def __init__(self, lr_scheduler: _LRScheduler, accumulate_size: int) -> None:
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self.lr_scheduler = lr_scheduler
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self.accumulate_size = accumulate_size
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self.accumulate_step = 0
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@staticmethod
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def compute_effective_steps_per_epoch(dataloader: Iterable, accumulate_size: int) -> int:
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"""
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Computes the number of effective training iterations. An effective iteration is defined
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as the the aggregation of <accumulate_size> iterations. For examples, if accumulate_size = 4,
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then 4 iterations are considered as one effective iteration.
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Args:
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dataloader (``Iterable``): Your dataloader object for gradient accumulation.
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accumulate_size (int): The number of steps to accumulate gradients.
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"""
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return len(dataloader) // accumulate_size
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def __getattr__(self, __name: str) -> Any:
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return getattr(self.lr_scheduler, __name)
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def step(self, *args, **kwargs) -> None:
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"""
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Update the learning rate.
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Args:
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*args: positional arguments for the lr scheduler wrapped.
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**kwargs: keyword arguments for the lr scheduler wrapped.
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"""
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self.accumulate_step += 1
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if self.accumulate_step < self.accumulate_size:
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pass
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else:
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self.accumulate_step = 0
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self.lr_scheduler.step(*args, **kwargs)
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def get_lr(self) -> Tensor:
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"""
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Compute the next learning rate.
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Returns:
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Tensor: the upcoming learning rate.
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"""
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return self.lr_scheduler.get_lr()
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def get_last_lr(self) -> Tensor:
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"""
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Returns the current learning rate.
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Returns:
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Tensor: the current learning rate.
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"""
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return self.lr_scheduler.get_last_lr()
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def print_lr(self, *args, **kwargs) -> None:
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"""
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Print he learning rate.
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Args:
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*args: positional arguments for the lr scheduler wrapped.
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**kwargs: keyword arguments for the lr scheduler wrapped.
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"""
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self.lr_scheduler.print_lr(*args, **kwargs)
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def state_dict(self) -> dict:
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"""
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Returns the states of the lr scheduler as dictionary.
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Returns:
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dict: the states of the lr scheduler.
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"""
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return self.lr_scheduler.state_dict()
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def load_state_dict(self, state_dict: dict) -> None:
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"""
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Load the states of the lr scheduler from a dictionary object.
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Returns:
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dict: the states of the lr scheduler.
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"""
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self.lr_scheduler.load_state_dict(state_dict)
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class GradAccumGradientHandler:
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r"""A wrapper for the gradient handler to enable gradient accumulation by skipping the steps
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before accumulation size is reached.
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Args:
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grad_handler (:class:`colossalai.legacy.engine.BaseGradientHandler`):
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Your ``gradient_handler`` object for gradient accumulation, would be called when achieving `accumulate_size`.
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accumulate_size (int): The number of steps to accumulate gradients.
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More details about ``gradient_handlers`` could be found in
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`Gradient_handler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/engine/gradient_handler>`_.
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"""
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def __init__(self, grad_handler: BaseGradientHandler, accumulate_size: int) -> None:
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assert isinstance(grad_handler, BaseGradientHandler), \
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f'expected grad_handler to be type BaseGradientHandler, but got {type(grad_handler)}'
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self.grad_handler = grad_handler
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self.accumulate_size = accumulate_size
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self.accumulate_step = 0
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def handle_gradient(self) -> None:
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"""
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Handle gradients reduction only in the last gradient accumulation step.
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"""
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self.accumulate_step += 1
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if self.accumulate_step < self.accumulate_size:
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pass
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else:
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self.accumulate_step = 0
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self.grad_handler.handle_gradient()
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