mirror of https://github.com/hpcaitech/ColossalAI
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
from typing import Tuple
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import torch
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import torch.nn as nn
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from colossalai.amp.naive_amp import NaiveAMPModel
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from colossalai.logging import get_dist_logger
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from colossalai.zero.sharded_model.sharded_model_v2 import ShardedModelV2
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from colossalai.zero.sharded_optim.sharded_optim_v2 import ShardedOptimizerV2
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from torch.optim import Optimizer
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from .sharded_model import ShardedModel
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from .sharded_optim import ShardedOptimizer
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def convert_to_zero_v2(model: nn.Module, optimizer: torch.optim.Optimizer, model_config,
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optimizer_config) -> Tuple[ShardedModelV2, ShardedOptimizerV2]:
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"""
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A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading
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:param model: Your model object
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:type model: :class:`torch.nn.Module`
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:param optimizer_config: Your optimizer object
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:type optimizer_config: :class:`dict`
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:return: (model, optimizer)
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:rtype: Tuple
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"""
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logger = get_dist_logger('convert_to_zero_v2')
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logger.info(f'optimizer_config is {optimizer_config}')
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if optimizer_config is None:
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optimizer_config = dict()
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logger.info(f'model_config is {model_config}')
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if model_config is None:
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model_config = dict()
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zero_model = ShardedModelV2(model, **model_config)
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zero_optimizer = ShardedOptimizerV2(zero_model, optimizer, **optimizer_config)
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return zero_model, zero_optimizer
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def convert_to_zero(model: nn.Module, optimizer: Optimizer, level: int, zero_config: dict):
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"""
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A helper function to integrate the model and optimizer with ZeRO optimizer and off-loading
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:param model: Your model object
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:type model: :class:`torch.nn.Module`
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:param optimizer: Your optimizer object
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:type optimizer: :class:`torch.optim.Optimizer`
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:param level: Optimizer level, can be 2 or 3
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:type level: int
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:param zero_config: Configuration for zero
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:type zero_config: dict
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:return: (model, optimizer)
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:rtype: Tuple
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"""
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assert 1 <= level <= 3, 'Only ZERO Optimizer Level 1-3 are provided'
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if level in [1, 2]:
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if level == 2:
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if 'partition_grad' in zero_config:
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assert zero_config['partition_grad'], \
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'Sharded Optimizer requires partition_grad to be True'
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else:
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zero_config['partiton_grad'] = True
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model = NaiveAMPModel(model, output_to_fp32=True)
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optimizer = ShardedOptimizer(optimizer, **zero_config)
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else:
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model = ShardedModel(module=model, **zero_config)
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return model, optimizer
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__all__ = ['convert_to_zero', 'ShardedModel', 'ShardedOptimizer']
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