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46 lines
1.5 KiB
46 lines
1.5 KiB
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.logging import get_dist_logger
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from .init_ctx import ZeroInitContext, no_shard_zero_context, no_shard_zero_decrator
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from .shard_utils import BucketTensorShardStrategy, TensorShardStrategy
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from .sharded_model import ShardedModelV2
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from .sharded_optim import ShardedOptimizerV2
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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}', ranks=[0])
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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}', ranks=[0])
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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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__all__ = [
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'convert_to_zero_v2', 'ShardedModelV2', 'ShardedOptimizerV2', 'ZeroInitContext', 'no_shard_zero_context',
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'no_shard_zero_decrator', 'TensorShardStrategy', 'BucketTensorShardStrategy'
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]
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