75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
# 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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class PPFormulaNet_S_Loss(nn.Layer):
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"""
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PP=FormulaNet-S adopt CrossEntropyLoss for network training.
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"""
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def __init__(self, vocab_size=50000, parallel_step=1):
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super(PPFormulaNet_S_Loss, self).__init__()
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self.ignore_index = -100
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self.vocab_size = vocab_size
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self.parallel_step = int(parallel_step)
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self.pad_token_id = 1
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# ignore padding characters during training
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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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def forward(self, preds, batch):
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logits, masked_label = preds
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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[:, self.parallel_step :], [-1]),
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)
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loss = word_loss
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return {
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"loss": loss,
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"word_loss": word_loss,
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}
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class PPFormulaNet_L_Loss(nn.Layer):
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"""
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PPFormulaNet_L adopt CrossEntropyLoss for network training.
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"""
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def __init__(self, vocab_size=50000):
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super(PPFormulaNet_L_Loss, 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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# ignore padding characters during training
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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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def forward(self, preds, batch):
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logits, masked_label = preds
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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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return {
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"loss": loss,
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"word_loss": word_loss,
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}
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