Add m2m 100 multilingual translation model from FAIR
Weights, code are available.

+ Fairseq Code: https://github.com/pytorch/fairseq/tree/master/examples/m2m_100?fbclid=IwAR304kICXsffdDMogK4MWf4D7Xeu_3Cbmgu8pBCU_jKcjijCuJfLK7CY9_I
+ Paper: https://arxiv.org/abs/2010.11125

+ This model will not run on 1 V100 GPU, so model parallelism will be needed. 
+ I would expect the state dict to be very similar to mBART, but not sure yet.
+ All I've done is download the state dict, run their command, and asked for help https://github.com/pytorch/fairseq/issues/2772#issuecomment-716152453 when it broke.


Leaving this unassigned in case somebody else wants to take over.


If it helps, I managed to load the weights from M2M100 - 418M param model to Mbart
```
from transformers import MBartForConditionalGeneration, MBartConfig, AutoTokenizer, AutoModelForSeq2SeqLM
from fairseq import checkpoint_utils, options, tasks, utils
import torch

with open('418M_last_checkpoint.pt', 'rb') as f:
    state = torch.load(f, map_location=torch.device("cpu"))
state = checkpoint_utils._upgrade_state_dict(state)
args = state['args']
args.fixed_dictionary = "model_dict.128k.txt"
args.source_lang = 'en'
args.target_lang = 'hi'

weights = state['model']
keys = [k for k in weights.keys()]
for key in keys:
    if key.startswith('encoder.') or key.startswith('decoder.'):
        new_key = 'model.' + key
        weights[new_key] = weights[key]
        del weights[key]
weights['model.shared.weight'] = weights['model.encoder.embed_tokens.weight']

config1 = MBartConfig(
    activation_function='relu',
    vocab_size=128112,
    encoder_layerdrop=0.05,
    decoder_layerdrop=0.05,
    attention_dropout=0.1,
    add_final_layer_norm=True,
    normalize_before=True,
    scale_embedding=True,
    static_position_embeddings=True,
    pad_token_id=1,
    bos_token_id=0,
    eos_token_id=2,
    normalize_embedding=True,
    use_cache=False
)
mbart1 = MBartForConditionalGeneration(config1)
mbart1.load_state_dict(weights, strict=False)
```
This is based on the checkpoint and dictionary provided [here](https://github.com/pytorch/fairseq/tree/master/examples/m2m_100#418m-and-12b-model).

I also had to replace the position embeddings in `modeling_bart` with the [code from fairseq](https://github.com/pytorch/fairseq/blob/master/fairseq/modules/sinusoidal_positional_embedding.py), because the fairseq implementation of the embeddings seems to be different form the one present in `modeling_bart`.

Although the weights load successfully it generates random tokens, albeit in the correct language. I have a feeling that there's something going on in fairseq's generate function that is not accounted for here, though I may be wrong.

Would greatly appreciate any ideas you might have to debug the generation aspect.

Hope this helps! Thanks!<|||||>This issue has been stale for 1 month.<|||||>`M2M100` is now integrated! 

doc: https://huggingface.co/transformers/master/model_doc/m2m_100.html
models:  https://huggingface.co/models?filter=m2m_100<|||||>> `M2M100` is now integrated!
> 
> doc: https://huggingface.co/transformers/master/model_doc/m2m_100.html
> models: https://huggingface.co/models?filter=m2m_100

There is a problem with loading the model `model = M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_418M')`
produces OSError: Unable to load weights from pytorch checkpoint file for 'facebook/m2m100_418M' at '/root/.cache/huggingface/transformers/f9eabc2ccf1b4ddafac5c7f6dc837130ab7122d75ee98a64ed0a446a20b84871.53192defd013a2942c1d27b5842eba64b84d0e49943b0892c8f71967bf053029'

A manual download of pytorch_model.bin leads to a similar exception, as it produces a zip.<|||||>Hi @ciortanmadalina 

I just tried this and can load the model successfully. This seems to be the issue with the cache, can you delete the cache and try again?<|||||>> Hi @ciortanmadalina
> 
> I just tried this and can load the model successfully. This seems to be the issue with the cache, can you delete the cache and try again?

I soved it: the problem was not the cache but the pytorch version (1.4), which strangely enough, didn't raise a problem for the other transformer models I used (e.g. T5, Bert). Once I upgraded to 1.7, the issue was gone. Thanks for your answer!