Change BartLearnedPositionalEmbedding's forward method signature to support Opacus training
As outlined in #18425, this PR changes the signature of `BartLearnedPositionalEmbedding`'s forward method signature to take the `input_ids` tensor (and not just its shape). This is needed to enable private training of BART via DP-SGD in Opacus. PR welcomed by @sgugger in linked issue.

Fixes #18425.
_The documentation is not available anymore as the PR was closed or merged._<|||||>You will also need to apply the same changes to all the models that are touched by the change in embedding (mBART, plBARt etc) to have the tests passing.<|||||>@sgugger I am iterating through. 👍 thanks for the heads up, though!<|||||>Ah, this also looks like it's breaking the conversion to `torch.fx`. Let's see if @michaelbenayoun can think of an easy solution to that.<|||||>Thanks, I was about to ask.. any thoughts? I am not well-versed in torch's symbolic tracer (or FX generally). I'm happy to do the work if you can point me somewhere useful 😄 <|||||>Current offending line is line 985 in `src/transformers/utils/fx.py` (`HFTracer.trace()`):
`self.graph = super().trace(root, concrete_args=concrete_args)` 

where `root` is 
```
PLBartModel(
  (shared): Embedding(99, 16, padding_idx=1)
  (encoder): PLBartEncoder(
    (embed_tokens): Embedding(99, 16, padding_idx=1)
    (embed_positions): PLBartLearnedPositionalEmbedding(102, 16)
    (layers): ModuleList(
      (0): PLBartEncoderLayer(
        (self_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (activation_fn): GELUActivation()
        (fc1): Linear(in_features=16, out_features=4, bias=True)
        (fc2): Linear(in_features=4, out_features=16, bias=True)
        (final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
      (1): PLBartEncoderLayer(
        (self_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (activation_fn): GELUActivation()
        (fc1): Linear(in_features=16, out_features=4, bias=True)
        (fc2): Linear(in_features=4, out_features=16, bias=True)
        (final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layernorm_embedding): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
  )
  (decoder): PLBartDecoder(
    (embed_tokens): Embedding(99, 16, padding_idx=1)
    (embed_positions): PLBartLearnedPositionalEmbedding(102, 16)
    (layers): ModuleList(
      (0): PLBartDecoderLayer(
        (self_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (activation_fn): GELUActivation()
        (self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=16, out_features=4, bias=True)
        (fc2): Linear(in_features=4, out_features=16, bias=True)
        (final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
      (1): PLBartDecoderLayer(
        (self_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (activation_fn): GELUActivation()
        (self_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (encoder_attn): PLBartAttention(
          (k_proj): Linear(in_features=16, out_features=16, bias=True)
          (v_proj): Linear(in_features=16, out_features=16, bias=True)
          (q_proj): Linear(in_features=16, out_features=16, bias=True)
          (out_proj): Linear(in_features=16, out_features=16, bias=True)
        )
        (encoder_attn_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
        (fc1): Linear(in_features=16, out_features=4, bias=True)
        (fc2): Linear(in_features=4, out_features=16, bias=True)
        (final_layer_norm): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
      )
    )
    (layernorm_embedding): LayerNorm((16,), eps=1e-05, elementwise_affine=True)
  )
) 
```
and `concrete_args` is:
```
{'head_mask': None, 'decoder_head_mask': None, 'cross_attn_head_mask': None, 'encoder_outputs': None, 'past_key_values': None, 'inputs_embeds': None, 'decoder_inputs_embeds': None, 'use_cache': None, 'output_attentions': None, 'output_hidden_states': None, 'return_dict': None}
```<|||||>I will check on Monday and come back to you, it should be easily fixable I think.<|||||>Hey @michaelbenayoun, let me know if you have any thoughts to resolve the tracer issue :)<|||||>@sgugger all resolved now. Would you mind giving the PR another look?<|||||>Thanks a lot for working on this!<|||||>No problem, thank you for your support 👍 