Source code for nemo.collections.common.losses.spanning_loss
# Copyright (c) 2020, NVIDIA CORPORATION. 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from torch import nn
from nemo.core.classes import Loss, typecheck
from nemo.core.neural_types import ChannelType, LogitsType, LossType, NeuralType
__all__ = ['SpanningLoss']
[docs]
class SpanningLoss(Loss):
"""
implements start and end loss of a span e.g. for Question Answering.
"""
@property
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"logits": NeuralType(('B', 'T', 'D'), LogitsType()),
"start_positions": NeuralType(tuple('B'), ChannelType()),
"end_positions": NeuralType(tuple('B'), ChannelType()),
}
@property
def output_types(self):
"""Returns definitions of module output ports.
"""
return {
"loss": NeuralType(elements_type=LossType()),
"start_logits": NeuralType(('B', 'T'), LogitsType()),
"end_logits": NeuralType(('B', 'T'), LogitsType()),
}
[docs]
def __init__(self,):
super().__init__()
[docs]
@typecheck()
def forward(self, logits, start_positions, end_positions):
"""
Args:
logits: Output of question answering head, which is a token classfier.
start_positions: Ground truth start positions of the answer w.r.t.
input sequence. If question is unanswerable, this will be
pointing to start token, e.g. [CLS], of the input sequence.
end_positions: Ground truth end positions of the answer w.r.t.
input sequence. If question is unanswerable, this will be
pointing to start token, e.g. [CLS], of the input sequence.
"""
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
return total_loss, start_logits, end_logits