mirror of https://github.com/hpcaitech/ColossalAI
[booster] added the plugin base and torch ddp plugin (#3180)
* [booster] added the plugin base and torch ddp plugin * polish code * polish code * polish codepull/3197/head
Frank Lee
GitHub
parent
e5f668f280
commit
e7f3bed2d3
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@ -1,9 +1,9 @@
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import warnings
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from contextlib import contextmanager
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from typing import Callable, Iterable, Iterator, List, Optional, Tuple, Union
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from typing import Callable, Iterator, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from torch import Tensor
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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@ -55,27 +55,43 @@ class Booster:
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device: str = 'cuda',
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mixed_precision: Union[MixedPrecision, str] = None,
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plugin: Optional[Plugin] = None) -> None:
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# TODO(FrankLeeeee): add plugin control logic
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# if self.plugin is not None and self.plugin.control_accelerator:
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# ...
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# create acclerator
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self.acceleartor = Accelerator(device)
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self.acceleartor.set_default_device()
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if plugin is not None:
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assert isinstance(
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plugin, Plugin), f'Expected the argument plugin to be an instance of Plugin, but got {type(plugin)}.'
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self.plugin = plugin
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# validate and set precision
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if isinstance(MixedPrecision, str):
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# the user will take the default arguments for amp training
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self.mixed_precision = mixed_precision_factory(mixed_precision)
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elif isinstance(mixed_precision, MixedPrecision):
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# the user can customize the arguments by passing the precision object
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self.mixed_precision = mixed_precision
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# set accelerator
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if self.plugin and self.plugin.control_device:
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self.accelerator = None
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warnings.warn('The plugin will control the accelerator, so the device argument will be ignored.')
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else:
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raise ValueError(
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f'Expected the argument mixed_precision to be a string or an instance of Precision, but got {type(mixed_precision)}.'
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)
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self.accelerator = Accelerator(device)
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def boost(self, model: nn.Module, optimizer: Optimizer, criterion: Callable, lr_scheduler: LRScheduler,
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dataloader: DataLoader) -> List[Union[nn.Module, Optimizer, LRScheduler, DataLoader]]:
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# set precision
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if mixed_precision is None or (self.plugin and self.plugin.control_precision):
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self.mixed_precision = None
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warnings.warn('The plugin will control the precision, so the mixed_precision argument will be ignored.')
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else:
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# validate and set precision
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if isinstance(MixedPrecision, str):
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# the user will take the default arguments for amp training
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self.mixed_precision = mixed_precision_factory(mixed_precision)
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elif isinstance(mixed_precision, MixedPrecision):
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# the user can customize the arguments by passing the precision object
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self.mixed_precision = mixed_precision
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else:
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raise ValueError(
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f'Expected the argument mixed_precision to be a string or an instance of Precision, but got {type(mixed_precision)}.'
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)
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def boost(
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self,
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model: nn.Module,
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optimizer: Optimizer,
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criterion: Callable = None,
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dataloader: DataLoader = None,
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lr_scheduler: LRScheduler = None,
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) -> List[Union[nn.Module, Optimizer, LRScheduler, DataLoader]]:
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"""
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Boost the model, optimizer, criterion, lr_scheduler, and dataloader.
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@ -83,22 +99,25 @@ class Booster:
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model (nn.Module): The model to be boosted.
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optimizer (Optimizer): The optimizer to be boosted.
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criterion (Callable): The criterion to be boosted.
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lr_scheduler (LRScheduler): The lr_scheduler to be boosted.
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dataloader (DataLoader): The dataloader to be boosted.
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lr_scheduler (LRScheduler): The lr_scheduler to be boosted.
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"""
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# TODO(FrankLeeeee): add plugin control logic
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# if self.plugin is not None and self.plugin.control_accelerator:
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# ...
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model = self.acceleartor.configure_model(model)
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# TODO(FrankLeeeee): consider multi-model and multi-optimizer case
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# TODO(lsg): Add plugin control logic
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# e.g.
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# if self.plugin is not None and self.plugin.control_boost:
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# ...
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# TODO(FrankLeeeee): consider multi-dataloader case
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# transform model for mixed precision
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model, optimizer, criterion = self.mixed_precision.configure(model, optimizer, criterion)
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return model, optimizer, criterion, lr_scheduler, dataloader
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if self.plugin:
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model, optimizer, criterion, dataloader, lr_scheduler = self.plugin.configure(
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model, optimizer, criterion, dataloader, lr_scheduler)
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if self.plugin and not self.plugin.control_device:
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# transform model for accelerator
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model = self.accelerator.configure(model)
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if self.mixed_precision and self.plugin and not self.plugin.control_precision:
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# transform model for mixed precision
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model, optimizer, criterion = self.mixed_precision.configure(model, optimizer, criterion)
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return model, optimizer, criterion, dataloader, lr_scheduler
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def backward(self, loss: torch.Tensor, optimizer: Optimizer) -> None:
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# TODO: implement this method with plugin
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@ -117,8 +136,9 @@ class Booster:
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pass
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def no_sync(self, model: nn.Module) -> contextmanager:
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# TODO: implement this method
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pass
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assert self.plugin is not None, f'no_sync is only enabled when a plugin is provided and the plugin supports no_sync.'
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assert self.plugin.support_no_sync, f'The plugin {self.plugin.__class__.__name__} does not support no_sync.'
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return self.plugin.no_sync(model)
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def save(self,
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obj: Union[nn.Module, Optimizer, LRScheduler],
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@ -1,46 +0,0 @@
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from typing import List, Tuple
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import torch
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import torch.nn as nn
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader
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from colossalai.device.device_mesh import DeviceMesh
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__all__ = ['Plugin']
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class Plugin:
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@property
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def supported_devices(self) -> List[torch.device]:
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pass
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@property
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def supported_precisions(self) -> List[str]:
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pass
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@property
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def control_precision(self) -> bool:
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pass
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@property
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def control_device(self) -> bool:
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pass
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@property
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def support_no_sync(self) -> bool:
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pass
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def setup_model(self, model: nn.Module, device_mesh_pool: DeviceMesh) -> nn.Module:
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pass
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def setup_optimizer(self, optimizer: Optimizer) -> Optimizer:
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pass
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def setup_dataloader(self, dataloader: DataLoader) -> DataLoader:
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pass
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@property
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def device_mesh_shape(self) -> List[Tuple[int, ...]]:
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pass
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@ -0,0 +1,4 @@
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from .plugin_base import Plugin
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from .torch_ddp_plugin import TorchDDPPlugin
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__all__ = ['Plugin', 'TorchDDPPlugin']
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@ -0,0 +1,51 @@
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from abc import ABC, abstractmethod
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from typing import Callable, List, Tuple, Union
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import torch.nn as nn
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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from colossalai.booster.interface import OptimizerWrapper
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__all__ = ['Plugin']
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class Plugin(ABC):
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@property
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@abstractmethod
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def supported_devices(self) -> List[str]:
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pass
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@property
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@abstractmethod
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def supported_precisions(self) -> List[str]:
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pass
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@property
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@abstractmethod
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def control_precision(self) -> bool:
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pass
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@property
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@abstractmethod
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def control_device(self) -> bool:
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pass
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@property
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@abstractmethod
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def support_no_sync(self) -> bool:
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pass
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@abstractmethod
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def configure(
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self,
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model: nn.Module,
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optimizer: Optimizer,
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criterion: Callable = None,
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dataloader: DataLoader = None,
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lr_scheduler: LRScheduler = None,
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) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]:
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# implement this method
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pass
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@ -0,0 +1,147 @@
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import random
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from typing import Callable, List, Tuple, Union
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
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from torch.utils.data import DataLoader
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from torch.utils.data.distributed import DistributedSampler
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from colossalai.booster.interface import OptimizerWrapper
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from .plugin_base import Plugin
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__all__ = ['TorchDDPPlugin']
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class TorchDDPPlugin(Plugin):
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"""
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Plugin for PyTorch DDP.
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Example:
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>>> from colossalai.booster import Booster
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>>> from colossalai.booster.plugin import TorchDDPPlugin
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>>>
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>>> model, train_dataset, optimizer, criterion = ...
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>>> plugin = TorchDDPPlugin()
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>>> train_dataloader = plugin.prepare_train_dataloader(train_dataset, batch_size=8)
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>>> booster = Booster(plugin=plugin)
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>>> model, optimizer, train_dataloader, criterion = booster.boost(model, optimizer, train_dataloader, criterion)
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Args:
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broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training. Defaults to True.
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bucket_cap_mb (int, optional): The bucket size in MB. Defaults to 25.
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find_unused_parameters (bool, optional): Whether to find unused parameters. Defaults to False.
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check_reduction (bool, optional): Whether to check reduction. Defaults to False.
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gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view. Defaults to False.
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static_graph (bool, optional): Whether to use static graph. Defaults to False.
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"""
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def __init__(self,
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broadcast_buffers: bool = True,
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bucket_cap_mb: int = 25,
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find_unused_parameters: bool = False,
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check_reduction: bool = False,
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gradient_as_bucket_view: bool = False,
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static_graph: bool = False) -> None:
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assert dist.is_initialized(
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), 'torch.distributed is not initialized, please use colossalai.launch to create the distributed environment'
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self.rank = dist.get_rank()
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self.world_size = dist.get_world_size()
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self.ddp_kwargs = dict(broadcast_buffers=broadcast_buffers,
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bucket_cap_mb=bucket_cap_mb,
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find_unused_parameters=find_unused_parameters,
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check_reduction=check_reduction,
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gradient_as_bucket_view=gradient_as_bucket_view,
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static_graph=static_graph)
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def support_no_sync(self) -> bool:
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return True
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def control_precision(self) -> bool:
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return False
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def supported_precisions(self) -> List[str]:
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return ['fp16', 'fp16_apex', 'bf16', 'fp8']
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def control_device(self) -> bool:
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return True
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def supported_devices(self) -> List[str]:
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return ['cuda']
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def prepare_train_dataloader(self,
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dataset,
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batch_size,
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shuffle=False,
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seed=1024,
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drop_last=False,
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pin_memory=False,
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num_workers=0,
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**kwargs):
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r"""
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Prepare a dataloader for distributed training. The dataloader will be wrapped by
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`torch.utils.data.DataLoader` and `torch.utils.data.DistributedSampler`.
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Note:
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1. Evaluation datasets should not be passed to this function.
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Args:
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dataset (`torch.utils.data.Dataset`): The dataset to be loaded.
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shuffle (bool, optional): Whether to shuffle the dataset. Defaults to False.
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seed (int, optional): Random worker seed for sampling, defaults to 1024.
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add_sampler: Whether to add ``DistributedDataParallelSampler`` to the dataset. Defaults to True.
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drop_last (bool, optional): Set to True to drop the last incomplete batch, if the dataset size
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is not divisible by the batch size. If False and the size of dataset is not divisible by
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the batch size, then the last batch will be smaller, defaults to False.
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pin_memory (bool, optional): Whether to pin memory address in CPU memory. Defaults to False.
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num_workers (int, optional): Number of worker threads for this dataloader. Defaults to 0.
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kwargs (dict): optional parameters for ``torch.utils.data.DataLoader``, more details could be found in
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`DataLoader <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataloader.html#DataLoader>`_.
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Returns:
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:class:`torch.utils.data.DataLoader`: A DataLoader used for training or testing.
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"""
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_kwargs = kwargs.copy()
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sampler = DistributedSampler(dataset, num_replicas=self.world_size, rank=self.rank, shuffle=shuffle)
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# Deterministic dataloader
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def seed_worker(worker_id):
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worker_seed = seed
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np.random.seed(worker_seed)
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torch.manual_seed(worker_seed)
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random.seed(worker_seed)
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return DataLoader(dataset,
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batch_size=batch_size,
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sampler=sampler,
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worker_init_fn=seed_worker,
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drop_last=drop_last,
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pin_memory=pin_memory,
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num_workers=num_workers,
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**_kwargs)
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def configure(
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self,
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model: nn.Module,
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optimizer: Optimizer,
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criterion: Callable = None,
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dataloader: DataLoader = None,
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lr_scheduler: LRScheduler = None,
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) -> Tuple[Union[nn.Module, OptimizerWrapper, LRScheduler, DataLoader]]:
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# cast model to cuda
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model = model.cuda()
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# wrap the model with PyTorch DDP
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model = DDP(model, **self.ddp_kwargs)
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if not isinstance(optimizer, OptimizerWrapper):
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optimizer = OptimizerWrapper(optimizer)
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return model, optimizer, criterion, dataloader, lr_scheduler
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@ -1,13 +1,27 @@
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import pytest
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from functools import partial
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import torch.multiprocessing as mp
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import torch.nn as nn
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from torchvision.models import resnet18
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from colossalai.booster.accelerator import Accelerator
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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@pytest.mark.parametrize('device', ['cpu', 'cuda'])
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def test_accelerator(device):
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@parameterize('device', ['cpu', 'cuda'])
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def run_accelerator(device):
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acceleartor = Accelerator(device)
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model = nn.Linear(8, 8)
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model = acceleartor.configure_model(model)
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assert next(model.parameters()).device.type == device
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del model, acceleartor
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def run_dist(rank):
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run_accelerator()
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@rerun_if_address_is_in_use()
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def test_accelerator():
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world_size = 1
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run_func = partial(run_dist)
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mp.spawn(run_func, nprocs=world_size)
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@ -1,12 +1,21 @@
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from functools import partial
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import torch
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import torch.multiprocessing as mp
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from torch.optim import Adam
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import colossalai
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from colossalai.booster.mixed_precision import FP16TorchMixedPrecision
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from tests.kit.model_zoo import model_zoo
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def test_torch_amp():
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for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items():
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def run_torch_amp(rank, world_size, port):
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# init dist env
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colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
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sub_model_zoo = model_zoo.get_sub_registry('timm')
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for name, (model_fn, data_gen_fn, output_transform_fn, _) in sub_model_zoo.items():
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# dlrm_interactionarch has not parameters, so skip
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if name == 'dlrm_interactionarch':
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continue
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@ -27,3 +36,11 @@ def test_torch_amp():
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optimizer.backward(loss)
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optimizer.clip_grad_by_norm(1.0)
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optimizer.step()
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del model, optimizer, criterion, data, output, mixed_precision
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@rerun_if_address_is_in_use()
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def test_torch_ddp_plugin():
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world_size = 1
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run_func = partial(run_torch_amp, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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@ -0,0 +1,85 @@
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from functools import partial
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import SGD
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import colossalai
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from colossalai.booster import Booster
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from colossalai.booster.interface import OptimizerWrapper
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from colossalai.booster.plugin import TorchDDPPlugin
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from tests.kit.model_zoo import model_zoo
|
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|
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def check_torch_ddp_plugin():
|
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plugin = TorchDDPPlugin()
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booster = Booster(plugin=plugin)
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|
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for name, (model_fn, data_gen_fn, output_transform_fn, _) in model_zoo.items():
|
||||
if name == 'dlrm_interactionarch':
|
||||
continue
|
||||
|
||||
model = model_fn()
|
||||
optimizer = SGD(model.parameters(), lr=1e-3)
|
||||
criterion = lambda x: x.mean()
|
||||
data = data_gen_fn()
|
||||
|
||||
data = {
|
||||
k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
|
||||
}
|
||||
|
||||
model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
|
||||
|
||||
assert isinstance(model, DDP)
|
||||
assert isinstance(optimizer, OptimizerWrapper)
|
||||
|
||||
output = model(**data)
|
||||
output = output_transform_fn(output)
|
||||
output_key = list(output.keys())[0]
|
||||
loss = criterion(output[output_key])
|
||||
|
||||
booster.backward(loss, optimizer)
|
||||
optimizer.clip_grad_by_norm(1.0)
|
||||
optimizer.step()
|
||||
|
||||
|
||||
def check_dataloader_sharding():
|
||||
plugin = TorchDDPPlugin()
|
||||
|
||||
# create a custom dasetset with 0 to 10
|
||||
dataset = torch.utils.data.TensorDataset(torch.arange(0, 10))
|
||||
train_dataloader = plugin.prepare_train_dataloader(dataset, batch_size=2)
|
||||
|
||||
# get the first batch of data
|
||||
batch = next(iter(train_dataloader))[0].cuda()
|
||||
is_rank_0 = dist.get_rank() == 0
|
||||
|
||||
if is_rank_0:
|
||||
batch_to_compare = batch.clone()
|
||||
else:
|
||||
batch_to_compare = batch
|
||||
# pass to the rank 1 value to rank 0
|
||||
dist.broadcast(batch_to_compare, src=1)
|
||||
|
||||
# compare on rank 0
|
||||
if is_rank_0:
|
||||
assert not torch.equal(batch,
|
||||
batch_to_compare), 'Same number was found across ranks but expected it to be different'
|
||||
|
||||
|
||||
def run_dist(rank, world_size, port):
|
||||
# init dist env
|
||||
colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
|
||||
check_dataloader_sharding()
|
||||
check_torch_ddp_plugin()
|
||||
|
||||
|
||||
@rerun_if_address_is_in_use()
|
||||
def test_torch_ddp_plugin():
|
||||
world_size = 2
|
||||
run_func = partial(run_dist, world_size=world_size, port=free_port())
|
||||
mp.spawn(run_func, nprocs=world_size)
|
||||
Loading…
Reference in New Issue