mirror of https://github.com/hpcaitech/ColossalAI
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import torch
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import torch.nn as nn
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from ..lora import LoRAModule
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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, model: nn.Module, value_head: nn.Module, lora_rank: int = 0, lora_train_bias: str = "none"
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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.convert_to_lora()
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def forward(self, sequences: torch.LongTensor, attention_mask: torch.Tensor) -> 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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sequence_lengths = torch.max(attention_mask * torch.arange(sequences.size(1), device=sequences.device), dim=1)[
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0
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]
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sequence_hidden_states = last_hidden_states[torch.arange(last_hidden_states.size(0)), sequence_lengths]
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values = self.value_head(sequence_hidden_states).squeeze(1) # ensure shape is (B, )
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return values
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