Why do language modeling heads not have activation functions?
## ❓ Questions & Help

<!-- A clear and concise description of the question. -->

This is more of a question about the transformer architecture in general than anything else. I noticed that, in `modeling_openai.py`, for example, the `self.lm_head()` module is just a linear layer. Why is it sufficient to use a linear transformation here for the language modeling task? Would there by any advantages/disadvantages to throwing a `gelu` or something on the end and then calling the output of that the logits?

```python
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
    r"""
        **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Labels for language modeling.
            Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids``
            Indices are selected in ``[-1, 0, ..., config.vocab_size]``
            All labels set to ``-1`` are ignored (masked), the loss is only
            computed for labels in ``[0, ..., config.vocab_size]``
    Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
        **loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
            Language modeling loss.
        **prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        **hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
            list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
            of shape ``(batch_size, sequence_length, hidden_size)``:
            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        **attentions**: (`optional`, returned when ``config.output_attentions=True``)
            list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
    Examples::
        tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')
        model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
        input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0)  # Batch size 1
        outputs = model(input_ids, labels=input_ids)
        loss, logits = outputs[:2]
    """
    def __init__(self, config):
        super(OpenAIGPTLMHeadModel, self).__init__(config)
        self.transformer = OpenAIGPTModel(config)
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

        self.init_weights()

    def get_output_embeddings(self):
        return self.lm_head

    def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None,
                labels=None):
        transformer_outputs = self.transformer(input_ids,
                                               attention_mask=attention_mask,
                                               token_type_ids=token_type_ids,
                                               position_ids=position_ids,
                                               head_mask=head_mask,
                                               inputs_embeds=inputs_embeds)
        hidden_states = transformer_outputs[0]
        lm_logits = self.lm_head(hidden_states)

        outputs = (lm_logits,) + transformer_outputs[1:]
        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(ignore_index=-1)
            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)
```

Hi, some Transformers have activation in their heads, for instance, Bert.
See here: https://github.com/huggingface/transformers/blob/master/transformers/modeling_bert.py#L421

This is most likely a design choice with a minor effect for deep transformers as they learn to generate the current or next token along several of the output layers.
See the nice blog post and paper of Lena Voita for some intuition on this: https://lena-voita.github.io/posts/emnlp19_evolution.html<|||||>Thank you! That link was very helpful.