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| # -*- coding: utf-8 -*- | |
| # @Time : 2023/5/6 4:12 p.m. | |
| # @Author : JianingWang | |
| # @File : critic.py | |
| from typing import Optional, Tuple, Union | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from transformers import AutoModel, AutoConfig | |
| from models.basic_modules.generation import generate | |
| def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor: | |
| tensor = tensor * mask | |
| tensor = tensor.sum(dim=dim) | |
| mask_sum = mask.sum(dim=dim) | |
| mean = tensor / (mask_sum + 1e-8) | |
| return mean | |
| """ | |
| Critic model. | |
| """ | |
| class Critic(nn.Module): | |
| """ | |
| Critic model base class. | |
| Args: | |
| model (nn.Module): Critic model. | |
| value_head (nn.Module): Value head to get value. | |
| """ | |
| def __init__( | |
| self, | |
| model: nn.Module, | |
| value_head: nn.Module, | |
| use_action_mask: bool = False, | |
| ) -> None: | |
| self.model = model | |
| self.value_head = value_head # critic layer for predict value function | |
| self.use_action_mask = use_action_mask | |
| def forward(self, | |
| sequences: torch.LongTensor, | |
| action_mask: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: | |
| outputs = self.model(sequences, attention_mask=attention_mask) | |
| last_hidden_states = outputs['last_hidden_state'] | |
| values = self.value_head(last_hidden_states).squeeze(-1) | |
| if action_mask is not None and self.use_action_mask: | |
| num_actions = action_mask.size(1) | |
| prompt_mask = attention_mask[:, :-num_actions] | |
| values = values[:, :-num_actions] | |
| value = masked_mean(values, prompt_mask, dim=1) | |
| return value | |
| values = values[:, :-1] | |
| value = values.mean(dim=1) | |
| return value | |
| """ | |
| Auto Model for Critic | |
| """ | |
| class AutoModelCritic(Critic): | |
| """ | |
| AutoModel Critic model. | |
| Args: | |
| pretrained (str): Pretrained model name or path. | |
| config (AutoConfig): Model config. | |
| checkpoint (bool): Enable gradient checkpointing. | |
| """ | |
| def __init__(self, | |
| pretrained: Optional[str] = None, | |
| config: Optional[AutoConfig] = None, | |
| checkpoint: bool = False, | |
| lora_rank: int = 0, | |
| lora_train_bias: str = 'none', | |
| **kwargs) -> None: | |
| if pretrained is not None: | |
| model = AutoModel.from_pretrained(pretrained) | |
| elif config is not None: | |
| model = AutoModel(config) | |
| else: | |
| model = AutoModel(AutoConfig()) | |
| if checkpoint: | |
| model.gradient_checkpointing_enable() | |
| value_head = nn.Linear(model.config.word_embed_proj_dim, 1) | |
| super().__init__(model, value_head, **kwargs) |