Source code for nemo.collections.common.losses.spanning_loss

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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