Gradient checkpointing with GPT2DoubleHeadsModel
## ❓ Questions & Help

I've been trying to fine-tune `GPT2DoubleHeadsModel` using `gpt2-large` and `gpt2-xl` on the [Topical-Chat](https://github.com/alexa/alexa-prize-topical-chat-dataset) dataset.

I'm finding that loading even a single example into memory is difficult with the larger versions of GPT-2. I found [this](https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255) Medium post by @thomwolf which suggests that gradient checkpointing would be effective at handling this situation.

Is there a gradient-checkpointed version of the code in `GPT2DoubleHeadsModel` or the underlying `GPT2Model` that could be used as-is? I'm trying to do this myself by editing `modeling_gpt2.py`, but I'm facing issues.

https://github.com/huggingface/transformers/blob/babd41e7fa07bdd764f8fe91c33469046ab7dbd1/src/transformers/modeling_gpt2.py#L478-L480

Specifically, I added a checkpoint in the above line like this:

`outputs = checkpoint(block, hidden_states, layer_past, attention_mask, head_mask[i])`

NOTE: I had to remove the key names since it looks like checkpoint does not support key-value arguments, only positional. This might lead to compatibility issues, I'd love to know thoughts on this as well.

This is using the official PyTorch [checkpoint](https://pytorch.org/docs/stable/checkpoint.html). I'm also considering trying [this](https://github.com/csrhddlam/pytorch-checkpoint/blob/master/checkpoint.py) other implementation for checkpoint since I read somewhere that it is supposed to be faster than the official implementation.

With the official PyTorch implementation, I'm getting the following error:
`CheckpointFunctionBackward.forward: expected Variable (got list) for return value 0.`

[This](https://discuss.pytorch.org/t/checkpoint-didnt-support-list-output/16957/3) thread on the PyTorch forums seems to suggest that this error arises when attempting to use `torch.utils.checkpoint` with modules that return a variable number of tensors, which is the case with `Block` within `GPT2Model`.

Could @thomwolf, @LysandreJik or anyone else in the Hugging Face team please help with this? Thanks!
I think I figured this out, it looks like I'll have to change the outputs returned by `Block` to be tuples instead of lists:

https://github.com/huggingface/transformers/blob/babd41e7fa07bdd764f8fe91c33469046ab7dbd1/src/transformers/modeling_gpt2.py#L238

i.e., change the above to `return tuple(outputs)` for checkpointing of the blocks inside `GPT2Model` to work.

@thomwolf @LysandreJik Would this explicit type-casting of the outputs to tuple lead to any unexpected, downstream effects? If not, I think this update should be reflected in the repo as well, given that the README says that every model's forward() method always outputs a `tuple`.

I am also finding that checkpointing the blocks doesn't seem to help fit a single example into memory with `gpt2-xl`. A check-pointed version of these classes would be really helpful!<|||||>Bumping this, I'm training a TensorFlow ALBERT model and with long sequence lengths (512) it's tough to get a large enough batch size - currently I'm constrained to 8 or 16 per GPU. Adding automatic gradient checkpointing support for `tf.recompute_grad()` would be a godsend :)<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>I am using `GPT2Model` and would also find this very useful. 