wehub-resource-sync
119 lines
3.4 KiB
Python
119 lines
3.4 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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from dataclasses import dataclass, field
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from typing import Optional
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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.dataclass import FairseqDataclass
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from fairseq.tasks import FairseqTask, register_task
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from omegaconf import II
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logger = logging.getLogger(__name__)
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@dataclass
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class DummyLMConfig(FairseqDataclass):
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dict_size: int = 49996
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dataset_size: int = 100000
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tokens_per_sample: int = field(
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default=512, metadata={"help": "max sequence length"}
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)
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add_bos_token: bool = False
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batch_size: Optional[int] = II("dataset.batch_size")
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max_tokens: Optional[int] = II("dataset.max_tokens")
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max_target_positions: int = II("task.tokens_per_sample")
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@register_task("dummy_lm", dataclass=DummyLMConfig)
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class DummyLMTask(FairseqTask):
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def __init__(self, cfg: DummyLMConfig):
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super().__init__(cfg)
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# load dictionary
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self.dictionary = Dictionary()
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for i in range(cfg.dict_size):
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self.dictionary.add_symbol("word{}".format(i))
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self.dictionary.pad_to_multiple_(8) # often faster if divisible by 8
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logger.info("dictionary: {} types".format(len(self.dictionary)))
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seq = torch.arange(cfg.tokens_per_sample + 1) + self.dictionary.pad() + 1
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self.dummy_src = seq[:-1]
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self.dummy_tgt = seq[1:]
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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.cfg.batch_size is not None:
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bsz = self.cfg.batch_size
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else:
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bsz = max(1, self.cfg.max_tokens // self.cfg.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.cfg.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.cfg.tokens_per_sample,
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},
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num_items=self.cfg.dataset_size,
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item_size=self.cfg.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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