mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
291 lines
10 KiB
291 lines
10 KiB
#!/usr/bin/env python
|
|
# -*- encoding: utf-8 -*-
|
|
|
|
from typing import Union
|
|
import torch.nn as nn
|
|
from torch import Tensor
|
|
from typing import Iterable, Any, Tuple
|
|
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):
|
|
"""A wrapper for the optimizer to enable gradient accumulation by skipping the steps
|
|
before accumulation size is reached.
|
|
|
|
Args:
|
|
optim (:class:`torch.optim.Optimizer`): Your optimizer object for gradient accumulation.
|
|
accumulate_size (int): The number of steps to accumulate gradients.
|
|
model (:class:`torch.nn.Module`):
|
|
Your model object to check if it is DistributedDataParallel for special handling of no_sync() context.
|
|
"""
|
|
|
|
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) -> None:
|
|
"""
|
|
Set all gradients to zero.
|
|
|
|
Args:
|
|
*args: positional arguments for the optimizer wrapped
|
|
**kwargs: keyword arguments for the optimizer wrapped
|
|
"""
|
|
|
|
if self.accumulate_step == 0:
|
|
self.optim.zero_grad(*args, **kwargs)
|
|
|
|
def step(self, *args, **kwargs) -> None:
|
|
"""
|
|
Update the model parameters.
|
|
|
|
Args:
|
|
*args: positional arguments for the optimizer wrapped
|
|
**kwargs: keyword arguments for the optimizer wrapped
|
|
"""
|
|
|
|
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) -> None:
|
|
"""
|
|
Clip gradients by norm.
|
|
|
|
Args:
|
|
model (:class:`torch.nn.Module`): a torch module instance
|
|
max_norm (float): the max norm for gradient clipping
|
|
"""
|
|
|
|
if self.accumulate_step < self.accumulate_size:
|
|
pass
|
|
else:
|
|
self.optim.clip_grad_norm(model, max_norm)
|
|
|
|
def backward(self, loss: Tensor) -> None:
|
|
"""Execute backward pass.
|
|
|
|
Args:
|
|
loss (:class:`torch.Tensor`): the loss value.
|
|
"""
|
|
|
|
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) -> None:
|
|
"""Execute backward pass given the gradients of the output.
|
|
|
|
Args:
|
|
loss (:class:`torch.Tensor`): the loss value.
|
|
grad (:class:`torch.Tensor`): the output gradient.
|
|
"""
|
|
|
|
self.accumulate_step += 1
|
|
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:
|
|
"""A wrapper for dataloader to enable gradient accumulation by dropping the last incomplete steps.
|
|
|
|
Note:
|
|
The dataloader would drop the last incomplete steps for gradient accumulation.
|
|
For example, if a dataloader has 10 batches of data and accumulate size is 4. The model parameters will
|
|
be updated only twice at step 4 and step 8. The last two batches of data do not form a complete 4-step cycle.
|
|
Thus, they will be automatically skipped by this class. If the dataloader is not standard PyTorch dataloader,
|
|
(e.g. Dali dataloader), this class will automatically consume (load data for nothing) the remaining 2 batches.
|
|
|
|
Args:
|
|
dataloader (``Iterable``): Your dataloader object for gradient accumulation.
|
|
accumulate_size (int): The number of steps to accumulate gradients.
|
|
"""
|
|
|
|
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) -> int:
|
|
return self.steps_per_epoch
|
|
|
|
def __iter__(self) -> Iterable:
|
|
self._cur_step = 0
|
|
self._dataiter = iter(self.dataloader)
|
|
return self
|
|
|
|
def __next__(self) -> Union[Tensor, Tuple[Tensor]]:
|
|
if self._cur_step < self.steps_per_epoch:
|
|
self._cur_step += 1
|
|
data = next(self._dataiter)
|
|
|
|
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 data
|
|
else:
|
|
raise StopIteration
|
|
|
|
|
|
class GradAccumLrSchedulerByStep(_LRScheduler):
|
|
"""A wrapper for the LR scheduler to enable gradient accumulation by skipping the steps
|
|
before accumulation size is reached.
|
|
|
|
Args:
|
|
lr_scheduler (:class:`torch.optim.lr_scheduler._LRScheduler`):
|
|
Your ``lr_scheduler`` object for gradient accumulation.
|
|
accumulate_size (int): The number of steps to accumulate gradients.
|
|
"""
|
|
|
|
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) -> int:
|
|
"""
|
|
Computes the number of effective training iterations. An effective iteration is defined
|
|
as the the aggregation of <accumulate_size> iterations. For examples, if accumulate_size = 4,
|
|
then 4 iterations are considered as one effective iteration.
|
|
|
|
Args:
|
|
dataloader (``Iterable``): Your dataloader object for gradient accumulation.
|
|
accumulate_size (int): The number of steps to accumulate gradients.
|
|
|
|
"""
|
|
return len(dataloader) // accumulate_size
|
|
|
|
def __getattr__(self, __name: str) -> Any:
|
|
return getattr(self.lr_scheduler, __name)
|
|
|
|
def step(self, *args, **kwargs) -> None:
|
|
"""
|
|
Update the learning rate.
|
|
|
|
Args:
|
|
*args: positional arguments for the lr scheduler wrapped.
|
|
**kwargs: keyword arguments for the lr scheduler wrapped.
|
|
"""
|
|
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) -> Tensor:
|
|
"""
|
|
Compute the next learning rate.
|
|
|
|
Returns:
|
|
Tensor: the upcoming learning rate.
|
|
"""
|
|
|
|
return self.lr_scheduler.get_lr()
|
|
|
|
def get_last_lr(self) -> Tensor:
|
|
"""
|
|
Returns the current learning rate.
|
|
|
|
Returns:
|
|
Tensor: the current learning rate.
|
|
"""
|
|
|
|
return self.lr_scheduler.get_last_lr()
|
|
|
|
def print_lr(self, *args, **kwargs) -> None:
|
|
"""
|
|
Print he learning rate.
|
|
|
|
Args:
|
|
*args: positional arguments for the lr scheduler wrapped.
|
|
**kwargs: keyword arguments for the lr scheduler wrapped.
|
|
"""
|
|
self.lr_scheduler.print_lr(*args, **kwargs)
|
|
|
|
def state_dict(self) -> dict:
|
|
"""
|
|
Returns the states of the lr scheduler as dictionary.
|
|
|
|
Returns:
|
|
dict: the states of the lr scheduler.
|
|
"""
|
|
return self.lr_scheduler.state_dict()
|
|
|
|
def load_state_dict(self, state_dict: dict) -> None:
|
|
"""
|
|
Load the states of the lr scheduler from a dictionary object.
|
|
|
|
Returns:
|
|
dict: the states of the lr scheduler.
|
|
"""
|
|
self.lr_scheduler.load_state_dict(state_dict)
|
|
|
|
|
|
class GradAccumGradientHandler:
|
|
r"""A wrapper for the gradient handler to enable gradient accumulation by skipping the steps
|
|
before accumulation size is reached.
|
|
|
|
Args:
|
|
grad_handler (:class:`colossalai.engine.BaseGradientHandler`):
|
|
Your ``gradient_handler`` object for gradient accumulation, would be called when achieving `accumulate_size`.
|
|
accumulate_size (int): The number of steps to accumulate gradients.
|
|
|
|
More details about ``gradient_handlers`` could be found in
|
|
`Gradient_handler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/engine/gradient_handler>`_.
|
|
|
|
"""
|
|
|
|
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) -> None:
|
|
"""
|
|
Handle gradients reduction only in the last gradient accumulation step.
|
|
"""
|
|
|
|
self.accumulate_step += 1
|
|
if self.accumulate_step < self.accumulate_size:
|
|
pass
|
|
else:
|
|
self.accumulate_step = 0
|
|
self.grad_handler.handle_gradient()
|