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59 lines
2.0 KiB
59 lines
2.0 KiB
8 months ago
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"""
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Base class for critic and reward model
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"""
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from typing import Optional
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import torch
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import torch.nn as nn
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from transformers import AutoModel, PretrainedConfig
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class BaseModel(nn.Module):
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"""
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Actor model base class.
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Args:
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pretrained (str): path to pretrained model.
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config (PretrainedConfig): PretrainedConfig used to initiate the base model.
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**kwargs: all other kwargs as in AutoModel.from_pretrained
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"""
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def __init__(self, pretrained: str = None, config: Optional[PretrainedConfig] = None, **kwargs) -> None:
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super().__init__()
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if pretrained is not None:
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if config is not None:
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# initialize with config and load weights from pretrained
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self.model = AutoModel.from_pretrained(pretrained, config=config, **kwargs)
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else:
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# initialize with pretrained
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self.model = AutoModel.from_pretrained(pretrained, **kwargs)
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elif config is not None:
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# initialize with config
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self.model = AutoModel.from_config(config, **kwargs)
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else:
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raise ValueError("Either pretrained or config must be provided.")
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self.config = self.model.config
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# create dummy input to get the size of the last hidden state
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if "use_flash_attention_2" in kwargs:
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self.model = self.model.cuda()
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dummy_input = torch.zeros((1, 1), dtype=torch.long).to(self.model.device)
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out = self.model(dummy_input)
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self.last_hidden_state_size = out.last_hidden_state.shape[-1]
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self.model = self.model.cpu()
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# print("self.last_hidden_state_size: ",self.last_hidden_state_size)
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def resize_token_embeddings(self, *args, **kwargs):
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"""
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Resize the token embeddings of the model.
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Args:
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*args: Variable length argument list.
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**kwargs: Arbitrary keyword arguments.
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Returns:
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The resized token embeddings.
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"""
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return self.model.resize_token_embeddings(*args, **kwargs)
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