51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
# copyright (c) 2020 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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class ParseQLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(ParseQLoss, self).__init__()
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def forward(self, predicts, targets):
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label = targets[1] # label
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label_len = targets[2]
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max_step = paddle.max(label_len).cpu().numpy()[0] + 2
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tgt = label[:, :max_step]
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logits_list = predicts['logits_list']
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pad_id = predicts['pad_id']
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eos_id = predicts['eos_id']
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tgt_out = tgt[:, 1:]
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loss = 0
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loss_numel = 0
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n = (tgt_out != pad_id).sum().item()
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for i, logits in enumerate(logits_list):
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loss += n * paddle.nn.functional.cross_entropy(input=logits, label=tgt_out.flatten(), ignore_index=pad_id)
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loss_numel += n
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if i == 1:
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tgt_out = paddle.where(condition=tgt_out == eos_id, x=pad_id, y=tgt_out)
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n = (tgt_out != pad_id).sum().item()
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loss /= loss_numel
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return {'loss': loss}
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