mirror of https://github.com/hpcaitech/ColossalAI
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
from typing import Optional
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from transformers.models.gpt2.configuration_gpt2 import GPT2Config
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from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
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from ..base import Actor
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class GPTActor(Actor):
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"""
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GPT Actor model.
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Args:
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pretrained (str): Pretrained model name or path.
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config (GPT2Config): Model config.
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checkpoint (bool): Enable gradient checkpointing.
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lora_rank (int): Rank of the LoRa layer.
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lora_train_bias (str): Bias training strategy for the LoRa layer.
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"""
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def __init__(self,
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pretrained: Optional[str] = None,
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config: Optional[GPT2Config] = None,
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checkpoint: bool = False,
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lora_rank: int = 0,
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lora_train_bias: str = 'none',
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**kwargs) -> None:
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if pretrained is not None:
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model = GPT2LMHeadModel.from_pretrained(pretrained)
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elif config is not None:
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model = GPT2LMHeadModel(config)
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else:
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model = GPT2LMHeadModel(GPT2Config())
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if checkpoint:
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model.gradient_checkpointing_enable()
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super().__init__(model, lora_rank, lora_train_bias, **kwargs)
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