mirror of https://github.com/hpcaitech/ColossalAI
55 lines
1.6 KiB
Python
55 lines
1.6 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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from ..utils import masked_mean
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class Critic(LoRAModule):
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"""
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Critic model base class.
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Args:
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model (nn.Module): Critic model.
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value_head (nn.Module): Value head to get value.
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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__(
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self,
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model: nn.Module,
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value_head: nn.Module,
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lora_rank: int = 0,
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lora_train_bias: str = 'none',
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use_action_mask: bool = False,
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) -> 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.value_head = value_head
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self.use_action_mask = use_action_mask
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self.convert_to_lora()
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def forward(self,
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sequences: torch.LongTensor,
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action_mask: Optional[torch.Tensor] = None,
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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).squeeze(-1)
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if action_mask is not None and self.use_action_mask:
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num_actions = action_mask.size(1)
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prompt_mask = attention_mask[:, :-num_actions]
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values = values[:, :-num_actions]
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value = masked_mean(values, prompt_mask, dim=1)
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return value
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values = values[:, :-1]
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value = values.mean(dim=1)
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return value
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