91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
import logging
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from dataclasses import dataclass
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from typing import Dict, Optional, List, Union
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import torch
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from torch import nn, Tensor
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from transformers import AutoTokenizer
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from transformers.file_utils import ModelOutput
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logger = logging.getLogger(__name__)
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@dataclass
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class RerankerOutput(ModelOutput):
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loss: Optional[Tensor] = None
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scores: Optional[Tensor] = None
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class BiEncoderModel(nn.Module):
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def __init__(self,
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model: None,
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tokenizer: AutoTokenizer = None,
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train_batch_size: int = 4,
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):
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super().__init__()
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self.model = model
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self.tokenizer = tokenizer
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self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')
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if self.model.config.pad_token_id is None:
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self.model.config.pad_token_id = self.tokenizer.pad_token_id
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self.config = self.model.config
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self.train_batch_size = train_batch_size
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self.yes_loc = self.tokenizer('Yes', add_special_tokens=False)['input_ids'][-1]
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def gradient_checkpointing_enable(self, **kwargs):
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self.model.gradient_checkpointing_enable(**kwargs)
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def enable_input_require_grads(self, **kwargs):
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self.model.enable_input_require_grads(**kwargs)
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def encode(self, features):
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# input('continue?')
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if features is None:
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return None
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outputs = self.model(input_ids=features['input_ids'],
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attention_mask=features['attention_mask'],
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position_ids=features['position_ids'] if 'position_ids' in features.keys() else None,
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output_hidden_states=True)
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_, max_indices = torch.max(features['labels'], dim=1)
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predict_indices = max_indices - 1
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logits = [outputs.logits[i, predict_indices[i], :] for i in range(outputs.logits.shape[0])]
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logits = torch.stack(logits, dim=0)
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scores = logits[:, self.yes_loc]
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return scores.contiguous()
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def forward(self, pair: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None):
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ranker_logits = self.encode(pair) # (batch_size * num, dim)
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if self.training:
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grouped_logits = ranker_logits.view(self.train_batch_size, -1)
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target = torch.zeros(self.train_batch_size, device=grouped_logits.device, dtype=torch.long)
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loss = self.compute_loss(grouped_logits, target)
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else:
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loss = None
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# print(loss)
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return RerankerOutput(
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loss=loss,
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scores=ranker_logits,
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)
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def compute_loss(self, scores, target):
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return self.cross_entropy(scores, target)
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def save(self, output_dir: str):
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# self.model.save_pretrained(output_dir)
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state_dict = self.model.state_dict()
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state_dict = type(state_dict)(
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{k: v.clone().cpu()
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for k,
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v in state_dict.items()})
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self.model.save_pretrained(output_dir, state_dict=state_dict)
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def save_pretrained(self, **kwargs):
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self.tokenizer.save_pretrained(**kwargs)
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return self.model.save_pretrained(**kwargs)
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