wehub-resource-sync
128 lines
3.7 KiB
Python
128 lines
3.7 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import logging
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import numpy as np
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import torch
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from fairseq.data import Dictionary, FairseqDataset
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from fairseq.tasks import LegacyFairseqTask, register_task
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logger = logging.getLogger(__name__)
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@register_task("dummy_masked_lm")
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class DummyMaskedLMTask(LegacyFairseqTask):
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@staticmethod
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def add_args(parser):
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"""Add task-specific arguments to the parser."""
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parser.add_argument("--dict-size", default=49995, type=int)
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parser.add_argument("--dataset-size", default=100000, type=int)
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parser.add_argument(
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"--tokens-per-sample",
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default=512,
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type=int,
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help="max number of total tokens over all segments "
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"per sample for BERT dataset",
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)
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def __init__(self, args, dictionary):
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super().__init__(args)
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self.dictionary = dictionary
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# add mask token
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self.mask_idx = dictionary.add_symbol("<mask>")
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dictionary.pad_to_multiple_(8) # often faster if divisible by 8
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mask_idx = 0
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pad_idx = 1
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seq = torch.arange(args.tokens_per_sample) + pad_idx + 1
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mask = torch.arange(2, args.tokens_per_sample, 7) # ~15%
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src = seq.clone()
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src[mask] = mask_idx
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tgt = torch.full_like(seq, pad_idx)
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tgt[mask] = seq[mask]
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self.dummy_src = src
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self.dummy_tgt = tgt
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@classmethod
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def setup_task(cls, args, **kwargs):
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"""Setup the task. """
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dictionary = Dictionary()
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for i in range(args.dict_size):
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dictionary.add_symbol("word{}".format(i))
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logger.info("dictionary: {} types".format(len(dictionary)))
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return cls(args, dictionary)
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def load_dataset(self, split, epoch=1, combine=False, **kwargs):
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"""Load a given dataset split.
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Args:
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split (str): name of the split (e.g., train, valid, test)
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"""
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if self.args.batch_size is not None:
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bsz = self.args.batch_size
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else:
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bsz = max(1, self.args.max_tokens // self.args.tokens_per_sample)
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self.datasets[split] = DummyDataset(
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{
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"id": 1,
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"net_input": {
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"src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]),
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"src_lengths": torch.full(
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(bsz,), self.args.tokens_per_sample, dtype=torch.long
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),
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},
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"target": torch.stack([self.dummy_tgt for _ in range(bsz)]),
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"nsentences": bsz,
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"ntokens": bsz * self.args.tokens_per_sample,
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},
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num_items=self.args.dataset_size,
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item_size=self.args.tokens_per_sample,
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)
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@property
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def source_dictionary(self):
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return self.dictionary
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@property
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def target_dictionary(self):
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return self.dictionary
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class DummyDataset(FairseqDataset):
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def __init__(self, batch, num_items, item_size):
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super().__init__()
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self.batch = batch
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self.num_items = num_items
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self.item_size = item_size
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def __getitem__(self, index):
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return index
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def __len__(self):
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return self.num_items
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def collater(self, samples):
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return self.batch
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@property
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def sizes(self):
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return np.array([self.item_size] * self.num_items)
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def num_tokens(self, index):
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return self.item_size
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def size(self, index):
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return self.item_size
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def ordered_indices(self):
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return np.arange(self.num_items)
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@property
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def supports_prefetch(self):
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return False
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