Add NLLB (M2M100) support

[vrmer]

I implemented AdapterHub support for the Facebook NLLB model and its underlying M2M100 architecture. I've carried out and ran all the relevant tests, auto formatting and quality checks.

The code passes 124 tests, skipping 7, and failing 11. The 11 failed tests are all connected to Parallel composition blocks (that I did not implement) and flex heads, which I also did not implement. As the model is a machine translation model, it does not need to have classification heads on top of it, but I didn't find how to disable the irrelevant head_types in the ADAPTER_MODEL_MAPPING dictionary to be able to skip these tests.

Any advice on this is greatly appreciated!

Key addition:

• A new M2M100AdapterModel class with the relevant WithAdapters and AdaptersMixin classes implemented.

[calpt]

Thanks a lot for working on this! Already looks pretty good overall, I left some comments regarding the open issues that are hopefully helpful.

Once that's done, please also add this new model type to the docs as described in the contributing guide, thanks!

[calpt]

this defines the range of supported heads. Since I believe we'd only want to support sequence generation, you can remove everything except for "seq2seq_lm" here.

[calpt]

In case we don't want to support Parallel composition (which is totally fine), please remove these two mixins to disable the tests.

Otherwise, by adding the model type here:

adapters/src/adapters/composition.py

Line 121 in f0ca962

SUPPORTED_MODELS = {

, you can declare it as supported (since I believe the implementation is already mostly there from looking at your code)

[calpt]

Since this model doesn't support text classification tasks (and we test text classification training by default in the test runs), we'd have to override the add_heead() and dataset() methods here. That would roughly look like here:

adapters/tests/test_bert_generation.py

Lines 29 to 70 in f0ca962

	def add_head(self, model, name, **kwargs):
	model.add_masked_lm_head(name)
	return self.default_input_samples_shape[-1]
	def dataset(self, tokenizer=None):
	# setup tokenizer
	if tokenizer is None:
	tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name, use_fast=False)
	if tokenizer.pad_token is None:
	tokenizer.pad_token = tokenizer.eos_token
	def preprocess_function(examples):
	inputs = examples["document"]
	targets = examples["summary"]
	inputs = ["Summarize: " + inp for inp in inputs]
	model_inputs = tokenizer(inputs, padding="max_length", truncation=True, max_length=128)
	# Setup the tokenizer for targets
	with tokenizer.as_target_tokenizer():
	labels = tokenizer(targets, padding="max_length", truncation=True, max_length=128)
	# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
	# padding in the loss.
	labels["input_ids"] = [
	[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
	]
	model_inputs["labels"] = labels["input_ids"]
	return model_inputs
	data_args = {
	"task_name": "xsum",
	"path": "./tests/fixtures/samples/xsum/sample.json",
	}
	dataset = load_dataset("json", data_files=data_args["path"])
	train_dataset = dataset["train"]
	train_dataset = train_dataset.map(
	preprocess_function,
	batched=True,
	desc="Running tokenizer on train dataset",
	)
	return train_dataset