I see some github comments saying the output of the model() call's loss is in the form of perplexity: https://github.com/huggingface/transformers/issues/473
But when I look at the relevant code... https://huggingface.co/transformers/_modules/transformers/modeling_openai.html#OpenAIGPTLMHeadModel.forward
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), lm_logits, (all hidden states), (all attentions)
I see cross entropy being calculated, but no transformation into perplexity. Where does the loss finally get transformed? Or is there a transformation already there that I'm not understanding?
Ah ok, I found the answer. The code is actually returning cross entropy. In the github comment where they say it is perplexity...they are saying that because the OP does
return math.exp(loss)
which transforms entropy to perplexity :)