| |
| |
| |
| |
| """ |
| make a general fairseq task for MM pretraining. |
| """ |
|
|
| import random |
|
|
| from fairseq.tasks import LegacyFairseqTask, register_task |
|
|
| from .task import Task |
| from .retritask import RetriTask |
| from ..datasets import FairseqMMDataset |
| from .. import utils |
|
|
|
|
| @register_task("mmtask") |
| class FairseqMMTask(LegacyFairseqTask): |
| @staticmethod |
| def add_args(parser): |
| |
| |
| parser.add_argument( |
| "taskconfig", |
| metavar="FILE", |
| help=("taskconfig to load all configurations" "outside fairseq parser."), |
| ) |
|
|
| @classmethod |
| def setup_task(cls, args, **kwargs): |
| return FairseqMMTask(args) |
|
|
| def __init__(self, args): |
| super().__init__(args) |
| config = utils.load_config(args) |
| self.mmtask = Task.config_task(config) |
| self.mmtask.build_dataset() |
| self.mmtask.build_model() |
| self.mmtask.build_loss() |
|
|
| def load_dataset(self, split, **kwargs): |
| split_map = { |
| "train": self.mmtask.train_data, |
| "valid": self.mmtask.val_data, |
| "test": self.mmtask.test_data, |
| } |
| if split not in split_map: |
| raise ValueError("unknown split type.") |
| if split_map[split] is not None: |
| self.datasets[split] = FairseqMMDataset(split_map[split]) |
|
|
| def get_batch_iterator( |
| self, |
| dataset, |
| max_tokens=None, |
| max_sentences=None, |
| max_positions=None, |
| ignore_invalid_inputs=False, |
| required_batch_size_multiple=1, |
| seed=1, |
| num_shards=1, |
| shard_id=0, |
| num_workers=0, |
| epoch=1, |
| data_buffer_size=0, |
| disable_iterator_cache=False, |
| skip_remainder_batch=False, |
| grouped_shuffling=False, |
| update_epoch_batch_itr=False, |
| ): |
| random.seed(epoch) |
| if dataset.mmdataset.split == "train" and isinstance(self.mmtask, RetriTask): |
| if epoch >= self.mmtask.config.retri_epoch: |
| if not hasattr(self.mmtask, "retri_dataloader"): |
| self.mmtask.build_dataloader() |
| self.mmtask.retrive_candidates(epoch) |
|
|
| return super().get_batch_iterator( |
| dataset, |
| max_tokens, |
| max_sentences, |
| max_positions, |
| ignore_invalid_inputs, |
| required_batch_size_multiple, |
| seed, |
| num_shards, |
| shard_id, |
| num_workers, |
| epoch, |
| data_buffer_size, |
| disable_iterator_cache, |
| grouped_shuffling, |
| update_epoch_batch_itr, |
| ) |
|
|
| @property |
| def source_dictionary(self): |
| return None |
|
|
| @property |
| def target_dictionary(self): |
| return None |
|
|
