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import torch
def standard_loss(self, model, inputs, return_outputs=False):
outputs = model(**inputs)
if self.args.past_index >= 0:
self._past = outputs[self.args.past_index]
loss = outputs[0].mean()
print("loss : ", loss)
return (loss,outputs) if return_outputs else loss
def hinge_cost(m, a, b):
dist = m - torch.sqrt(torch.sum((a - b)**2, axis=1))
return torch.mean(torch.clamp(dist,0,float('inf'))**2)
def sim_metric_loss(self, model, inputs, return_outputs=False):
# single pass version
batch_size = len(inputs["input_ids"])//4
outputs = model(**inputs)
x_p = outputs[:batch_size]
x_n = outputs[batch_size:2*batch_size]
y_p = outputs[2*batch_size:3*batch_size]
y_n = outputs[3*batch_size:]
model_attr = model
if isinstance(model, torch.nn.DataParallel):
model_attr = model.module
cost_p = torch.mean(torch.sum((x_p - y_p)**2, axis=1))
cost_n = model_attr.negative_importance*hinge_cost(
model_attr.negative_threshold, x_n, y_n)
cost_e = model_attr.entropy_importance*torch.mean(
torch.sum(x_p**2, axis=1) + torch.sum(y_p**2, axis=1))
loss = cost_p + cost_n + cost_e
print(loss)
return (loss,None) if return_outputs else loss
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