wehub-resource-sync
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
|
|
import torch
|
|
|
|
from swift.utils import get_last_valid_indices, get_logger
|
|
from .trainer import Trainer
|
|
from .utils import gather_for_unpadded_tensors
|
|
|
|
logger = get_logger()
|
|
|
|
|
|
class RerankerTrainer(Trainer):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.gather_function = gather_for_unpadded_tensors
|
|
|
|
def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
|
|
# Check if we have a custom loss function
|
|
if self.compute_loss_func is not None:
|
|
# Get labels and compute outputs
|
|
labels = inputs.pop('labels', None)
|
|
outputs = model(**inputs)
|
|
if self.task_type == 'generative_reranker':
|
|
logits = outputs.logits
|
|
attention_mask = inputs.get('attention_mask')
|
|
last_valid_indices = -1 if attention_mask is None else get_last_valid_indices(attention_mask)
|
|
batch_indices = torch.arange(logits.shape[0], device=logits.device)
|
|
outputs.logits = logits[batch_indices, last_valid_indices]
|
|
|
|
if labels is not None:
|
|
# Call custom loss function
|
|
loss = self.compute_loss_func(outputs, labels, num_items_in_batch=num_items_in_batch)
|
|
else:
|
|
# Fallback to model's loss
|
|
loss = outputs.loss
|
|
|
|
if num_items_in_batch is not None and self.model_accepts_loss_kwargs:
|
|
loss = loss / self.args.gradient_accumulation_steps
|
|
|
|
if labels is not None:
|
|
self._compute_acc(outputs, labels)
|
|
|
|
return (loss, outputs) if return_outputs else loss
|
|
else:
|
|
return super().compute_loss(model, inputs, return_outputs, num_items_in_batch)
|
|
|
|
def evaluation_loop(self, *args, **kwargs):
|
|
output = super().evaluation_loop(*args, **kwargs)
|
|
self.gather_function = gather_for_unpadded_tensors
|
|
return output
|