mirror of https://github.com/hpcaitech/ColossalAI
39 lines
1.2 KiB
Python
39 lines
1.2 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 import OPTConfig, OPTModel
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from ..base import RewardModel
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class OPTRM(RewardModel):
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"""
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OPT Reward model.
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Args:
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pretrained (str): Pretrained model name or path.
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config (OPTConfig): 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[OPTConfig] = 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') -> None:
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if pretrained is not None:
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model = OPTModel.from_pretrained(pretrained)
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elif config is not None:
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model = OPTModel(config)
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else:
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model = OPTModel(OPTConfig())
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if checkpoint:
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model.gradient_checkpointing_enable()
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value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
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value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.word_embed_proj_dim + 1))
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super().__init__(model, value_head, lora_rank, lora_train_bias)
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