mirror of https://github.com/hpcaitech/ColossalAI
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from typing import Optional
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import torch
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import torch.nn as nn
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from ..lora import LoRAModule
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class RewardModel(LoRAModule):
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"""
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Reward model base class.
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Args:
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model (nn.Module): Reward model.
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value_head (nn.Module): Value head to get reward score.
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lora_rank (int): LoRA rank.
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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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model: nn.Module,
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value_head: Optional[nn.Module] = None,
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lora_rank: int = 0,
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lora_train_bias: str = 'none') -> None:
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super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)
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self.model = model
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self.convert_to_lora()
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if value_head is not None:
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if value_head.out_features != 1:
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raise ValueError("The value head of reward model's output dim should be 1!")
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self.value_head = value_head
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else:
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self.value_head = nn.Linear(model.config.n_embd, 1)
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def forward(self, sequences: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
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outputs = self.model(sequences, attention_mask=attention_mask)
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last_hidden_states = outputs['last_hidden_state']
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values = self.value_head(last_hidden_states)[:, :-1]
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value = values.mean(dim=1).squeeze(1) # ensure shape is (B)
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return value
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