55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
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# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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class UniMERNetLoss(nn.Layer):
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def __init__(self, length_aware=True, vocab_size=50000):
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super(UniMERNetLoss, self).__init__()
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self.ignore_index = -100
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self.vocab_size = vocab_size
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self.pad_token_id = 1
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self.length_aware = length_aware
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self.cross = nn.CrossEntropyLoss(
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reduction="mean", ignore_index=self.ignore_index
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)
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self.counting_loss_fct = nn.SmoothL1Loss()
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def _get_count_gt(self, labels):
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mask = (labels != self.pad_token_id).cast("float32")
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one_hot_labels = F.one_hot(
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labels, num_classes=self.vocab_size
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) * mask.unsqueeze(-1)
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count_gt = paddle.sum(one_hot_labels, axis=1)
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return count_gt
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def forward(self, preds, batch):
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logits, count_pred, masked_label = preds
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labels = batch[1][:, 1:]
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word_loss = self.cross(
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paddle.reshape(logits, [-1, logits.shape[-1]]),
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paddle.reshape(masked_label[:, 1:], [-1]),
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)
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loss = word_loss
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if self.length_aware:
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count_gt = self._get_count_gt(labels)
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count_gt = paddle.log(count_gt.cast(paddle.float32) + 1)
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count_loss = self.counting_loss_fct(count_pred, count_gt)
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loss += 0.5 * count_loss
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return {"loss": loss, "word_loss": word_loss, "count_loss": count_loss}
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