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import torch
from transformers import Trainer
class ContrastiveTrainer(Trainer):
def __init__(self, loss_func, is_vision_model, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_func = loss_func
self.is_vision_model = is_vision_model
def compute_loss(self, model, inputs, return_outputs=False):
query_outputs = model(input_ids=inputs["query_input_ids"], attention_mask=inputs["query_attention_mask"])
if self.is_vision_model:
if "doc_pixel_attention_mask" not in inputs:
doc_outputs = model(
input_ids=inputs["doc_input_ids"],
attention_mask=inputs["doc_attention_mask"],
pixel_values=inputs["doc_pixel_values"],
)
else:
doc_outputs = model(
input_ids=inputs["doc_input_ids"],
attention_mask=inputs["doc_attention_mask"],
pixel_values=inputs["doc_pixel_values"],
pixel_attention_mask=inputs["doc_pixel_attention_mask"],
)
else:
doc_outputs = model(input_ids=inputs["doc_input_ids"], attention_mask=inputs["doc_attention_mask"])
loss = self.loss_func(query_outputs, doc_outputs)
return (loss, (query_outputs, doc_outputs)) if return_outputs else loss
def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=True):
"""This function is used to generate predictions and return the loss for the given inputs."""
if not prediction_loss_only:
raise ValueError("prediction_step is only called with prediction_loss_only=True")
with torch.no_grad():
if self.is_vision_model:
if "doc_pixel_attention_mask" not in inputs:
doc_outputs = model(
input_ids=inputs["doc_input_ids"],
attention_mask=inputs["doc_attention_mask"],
pixel_values=inputs["doc_pixel_values"],
)
else:
doc_outputs = model(
input_ids=inputs["doc_input_ids"],
attention_mask=inputs["doc_attention_mask"],
pixel_values=inputs["doc_pixel_values"],
pixel_attention_mask=inputs["doc_pixel_attention_mask"],
)
query_outputs = model(
input_ids=inputs["query_input_ids"], attention_mask=inputs["query_attention_mask"]
)
else:
query_outputs = model(
input_ids=inputs["query_input_ids"], attention_mask=inputs["query_attention_mask"]
)
doc_outputs = model(input_ids=inputs["doc_input_ids"], attention_mask=inputs["doc_attention_mask"])
loss = self.loss_func(query_outputs, doc_outputs)
return loss, None, None
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