mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-22 08:16:08 +00:00
Brandon Thomas
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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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 torch import nn
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from nemo.core.classes import Loss, typecheck
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from nemo.core.neural_types import ChannelType, LogitsType, LossType, NeuralType
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__all__ = ['SpanningLoss']
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class SpanningLoss(Loss):
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"""
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implements start and end loss of a span e.g. for Question Answering.
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"""
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@property
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def input_types(self):
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"""Returns definitions of module input ports.
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"""
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return {
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"logits": NeuralType(('B', 'T', 'D'), LogitsType()),
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"start_positions": NeuralType(tuple('B'), ChannelType()),
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"end_positions": NeuralType(tuple('B'), ChannelType()),
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}
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@property
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def output_types(self):
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"""Returns definitions of module output ports.
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"""
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return {
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"loss": NeuralType(elements_type=LossType()),
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"start_logits": NeuralType(('B', 'T'), LogitsType()),
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"end_logits": NeuralType(('B', 'T'), LogitsType()),
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}
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def __init__(self,):
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super().__init__()
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@typecheck()
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def forward(self, logits, start_positions, end_positions):
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"""
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Args:
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logits: Output of question answering head, which is a token classfier.
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start_positions: Ground truth start positions of the answer w.r.t.
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input sequence. If question is unanswerable, this will be
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pointing to start token, e.g. [CLS], of the input sequence.
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end_positions: Ground truth end positions of the answer w.r.t.
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input sequence. If question is unanswerable, this will be
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pointing to start token, e.g. [CLS], of the input sequence.
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"""
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start_logits, end_logits = logits.split(1, dim=-1)
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start_logits = start_logits.squeeze(-1)
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end_logits = end_logits.squeeze(-1)
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# If we are on multi-GPU, split add a dimension
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if len(start_positions.size()) > 1:
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start_positions = start_positions.squeeze(-1)
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if len(end_positions.size()) > 1:
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end_positions = end_positions.squeeze(-1)
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ignored_index = start_logits.size(1)
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start_positions.clamp_(0, ignored_index)
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end_positions.clamp_(0, ignored_index)
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loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
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start_loss = loss_fct(start_logits, start_positions)
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end_loss = loss_fct(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2
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return total_loss, start_logits, end_logits
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