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30412866e0
* Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit06741d894d. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * add test for reward model training * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * Add RoBERTa for RLHF Stage 2 & 3 (test) RoBERTa for RLHF Stage 2 & 3 (still in testing) * Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)" This reverts commit06741d894d. * Add RoBERTa for RLHF stage 2 & 3 1. add roberta folder under model folder 2. add roberta option in train_reward_model.py 3. add some test in testci * Update test_ci.sh * Revert "Update test_ci.sh" This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a. * update roberta with coati
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from typing import Optional
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import torch.nn as nn
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from transformers import RobertaConfig, RobertaModel
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from ..base import RewardModel
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class RoBERTaRM(RewardModel):
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"""
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RoBERTa Reward model.
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Args:
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pretrained (str): Pretrained model name or path.
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config (RoBERTaConfig): 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') -> 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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value_head.weight.data.normal_(mean=0.0, std=1/(model.config.hidden_size + 1))
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super().__init__(model, value_head, lora_rank, lora_train_bias) |