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# -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@File : utils.py
@Time : 2022/10/28 18:27
@Author : Qi Yang
@Version : 1.0
@Contact : [email protected]
@License : (C)Copyright 2022-2023, CCNL-IDEA
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn.functional as F
class LabelSmoothingCrossEntropy(torch.nn.Module):
def __init__(self, smoothing=0.1):
super(LabelSmoothingCrossEntropy, self).__init__()
self.smoothing = smoothing
self.ignore_index = -100
def forward(self, x, target):
confidence = 1. - self.smoothing
logprobs = F.log_softmax(x, dim=-1)
targets_ignore = torch.where(target != self.ignore_index, target, 0)
nll_loss = -logprobs.gather(dim=-1, index=targets_ignore.unsqueeze(1))
nll_loss = nll_loss.squeeze(1)
smooth_loss = -logprobs.mean(dim=-1)
loss = confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
def truncate_sequence(document: str, max_num_tokens: int, reverse=False):
total_length = len(document)
if total_length <= max_num_tokens:
return document
else:
if reverse:
return document[-1*max_num_tokens:]
else:
return document[:max_num_tokens]
def padding_to_maxlength(ids, max_length, pad_id):
cur_len = len(ids)
len_diff = max_length - len(ids)
return ids + [pad_id] * len_diff, [1] * cur_len + [0] * len_diff
def white_space_fix(text):
return "".join(text.split(" "))
def remove_prompt(text):
if ":" in text:
return text.split(":")[1]
return text
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