lm_context_window_dataset.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import numpy as np
import torch
from typing import Dict

from fairseq.data.monolingual_dataset import MonolingualDataset

from . import FairseqDataset


class LMContextWindowDataset(FairseqDataset):
    """
    Wraps a MonolingualDataset and provides more context for evaluation.

    Each item in the new dataset will have a maximum size of
    ``tokens_per_sample + context_window``.

    Args:
        dataset: dataset to wrap
        tokens_per_sample (int): the max number of tokens in each dataset item
        context_window (int): the number of accumulated tokens to add to each
            dataset item
        pad_idx (int): padding symbol
    """

    def __init__(
        self,
        dataset: MonolingualDataset,
        tokens_per_sample: int,
        context_window: int,
        pad_idx: int,
    ):
        assert context_window > 0
        self.dataset = dataset
        self.tokens_per_sample = tokens_per_sample
        self.context_window = context_window
        self.pad_idx = pad_idx
        self.prev_tokens = np.empty([0])

    def __getitem__(self, index):
        return self.dataset[index]

    def __len__(self):
        return len(self.dataset)

    def collater(self, samples) -> Dict:
        sample = self.dataset.collater(samples)

        pad = self.pad_idx
        max_sample_len = self.tokens_per_sample + self.context_window

        bsz, tsz = sample["net_input"]["src_tokens"].shape
        start_idxs = [0] * bsz
        toks = sample["net_input"]["src_tokens"]
        lengths = sample["net_input"]["src_lengths"]
        tgt = sample["target"]
        new_toks = np.empty([bsz, tsz + self.context_window], dtype=np.int64)
        new_tgt = np.full([bsz, tsz + self.context_window], pad, dtype=np.int64)
        sample_lens = toks.ne(pad).long().sum(dim=1).cpu()
        for i in range(bsz):
            sample_len = sample_lens[i]
            extra = len(self.prev_tokens) + sample_len - max_sample_len
            if extra > 0:
                self.prev_tokens = self.prev_tokens[extra:]
            pads = np.full(self.context_window - len(self.prev_tokens), pad)
            new_toks[i] = np.concatenate([self.prev_tokens, toks[i].numpy(), pads])
            new_tgt[
                i, len(self.prev_tokens) : len(self.prev_tokens) + len(tgt[i])
            ] = tgt[i]
            start_idxs[i] = len(self.prev_tokens)
            lengths[i] += len(self.prev_tokens)
            self.prev_tokens = new_toks[i][new_toks[i] != pad][-self.context_window :]
        sample["net_input"]["src_tokens"] = torch.from_numpy(new_toks)
        sample["target"] = torch.from_numpy(new_tgt)
        sample["start_indices"] = start_idxs
        return sample

    def num_tokens(self, index):
        return self.dataset.num_tokens(index)

    def size(self, index):
        return self.dataset.size(index)

    def ordered_indices(self):
        # NOTE we don't shuffle the data to retain access to the previous dataset elements
        return np.arange(len(self.dataset))

    @property
    def supports_prefetch(self):
        return getattr(self.dataset, "supports_prefetch", False)

    def prefetch(self, indices):
        return self.dataset.prefetch(indices)