flagopen--flagembedding
wehub-resource-sync
112 行
3.8 KiB
Python
112 行
3.8 KiB
Python
import logging
|
|
from dataclasses import dataclass
|
|
from typing import Dict, Optional, List, Union
|
|
|
|
import torch
|
|
from torch import nn, Tensor
|
|
from transformers import AutoTokenizer
|
|
from transformers.file_utils import ModelOutput
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@dataclass
|
|
class RerankerOutput(ModelOutput):
|
|
loss: Optional[Tensor] = None
|
|
scores: Optional[Tensor] = None
|
|
|
|
class BiEncoderModel(nn.Module):
|
|
def __init__(self,
|
|
model: None,
|
|
tokenizer: AutoTokenizer = None,
|
|
train_batch_size: int = 4,
|
|
start_layer: int = 8
|
|
):
|
|
super().__init__()
|
|
self.model = model
|
|
self.tokenizer = tokenizer
|
|
self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')
|
|
|
|
if self.model.config.pad_token_id is None:
|
|
self.model.config.pad_token_id = self.tokenizer.pad_token_id
|
|
self.config = self.model.config
|
|
|
|
self.train_batch_size = train_batch_size
|
|
|
|
|
|
self.start_layer = start_layer
|
|
|
|
|
|
def gradient_checkpointing_enable(self, **kwargs):
|
|
self.model.gradient_checkpointing_enable(**kwargs)
|
|
|
|
def enable_input_require_grads(self, **kwargs):
|
|
self.model.enable_input_require_grads(**kwargs)
|
|
|
|
def encode(self, features):
|
|
if features is None:
|
|
return None
|
|
outputs = self.model(input_ids=features['input_ids'],
|
|
attention_mask=features['attention_mask'],
|
|
position_ids=features['position_ids'] if 'position_ids' in features.keys() else None,
|
|
output_hidden_states=True,
|
|
cutoff_layers=list(range(self.start_layer, self.model.config.num_hidden_layers+1)))
|
|
_, max_indices = torch.max(features['labels'], dim=1)
|
|
predict_indices = max_indices - 1
|
|
all_logits = outputs.logits
|
|
all_scores = []
|
|
for logits in all_logits:
|
|
logits = [logits[i, predict_indices[i]] for i in range(logits.shape[0])]
|
|
scores = torch.stack(logits, dim=0)
|
|
all_scores.append(scores.contiguous())
|
|
return all_scores
|
|
|
|
def forward(self, pair: Union[Dict[str, Tensor], List[Dict[str, Tensor]]] = None):
|
|
ranker_logits = self.encode(pair) # (batch_size * num, dim)
|
|
|
|
if self.training:
|
|
loss = 0
|
|
for logits in ranker_logits:
|
|
grouped_logits = logits.view(self.train_batch_size, -1)
|
|
target = torch.zeros(self.train_batch_size, device=grouped_logits.device, dtype=torch.long)
|
|
loss += self.compute_loss(grouped_logits, target)
|
|
|
|
teacher_scores = ranker_logits[-1].view(
|
|
self.train_batch_size,
|
|
-1
|
|
)
|
|
teacher_targets = torch.softmax(teacher_scores.detach(), dim=-1)
|
|
for logits in ranker_logits[:-1]:
|
|
student_scores = logits.view(
|
|
self.train_batch_size,
|
|
-1
|
|
)
|
|
loss += - torch.mean(torch.sum(torch.log_softmax(student_scores, dim=-1) * teacher_targets, dim=-1))
|
|
else:
|
|
loss = None
|
|
|
|
# print(loss)
|
|
return RerankerOutput(
|
|
loss=loss,
|
|
scores=ranker_logits,
|
|
)
|
|
|
|
def compute_loss(self, scores, target):
|
|
return self.cross_entropy(scores, target)
|
|
|
|
def save(self, output_dir: str):
|
|
# self.model.save_pretrained(output_dir)
|
|
state_dict = self.model.state_dict()
|
|
state_dict = type(state_dict)(
|
|
{k: v.clone().cpu()
|
|
for k,
|
|
v in state_dict.items()})
|
|
self.model.save_pretrained(output_dir, state_dict=state_dict)
|
|
|
|
def save_pretrained(self, **kwargs):
|
|
self.tokenizer.save_pretrained(**kwargs)
|
|
self.model.config.save_pretrained(**kwargs)
|
|
return self.model.save_pretrained(**kwargs)
|
|
|
|
|