mirror of https://github.com/hpcaitech/ColossalAI
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
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from typing import Optional
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import torch.nn as nn
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from transformers.models.roberta.configuration_roberta import RobertaConfig
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from transformers.models.roberta.modeling_roberta import RobertaModel
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from ..base import Critic
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class RoBERTaCritic(Critic):
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"""
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RoBERTa Critic model.
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Args:
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pretrained (str): Pretrained model name or path.
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config (RoBERTa Config): Model config.
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checkpoint (bool): Enable gradient checkpointing.
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lora_rank (int): Rank of the low-rank approximation.
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lora_train_bias (str): LoRA bias training mode.
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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[RobertaConfig] = 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 = RobertaModel.from_pretrained(pretrained, add_pooling_layer=False)
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elif config is not None:
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model = RobertaModel(config)
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else:
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model = RobertaModel(RobertaConfig())
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if checkpoint:
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model.gradient_checkpointing_enable()
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value_head = nn.Linear(model.config.hidden_size, 1)
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super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
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