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https://github.com/SillyTavern/SillyTavern-Extras.git
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304 lines
12 KiB
Python
304 lines
12 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 math
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from typing import Dict, List, Tuple
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import numpy as np
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import torch
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from fairseq.data import Dictionary, FairseqDataset, data_utils
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from fairseq.data.concat_dataset import ConcatDataset
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from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
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from fairseq.data.token_block_dataset import TokenBlockDataset
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class MaskedLMDataset(FairseqDataset):
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"""
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A wrapper Dataset for masked language modelling. The dataset
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wraps around TokenBlockDataset or BlockedPairDataset and creates a batch
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where the input blocks are masked according to the specified masking
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probability. Additionally the batch can also contain sentence level targets
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if this is specified.
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Args:
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dataset: Dataset which generates blocks of data. Only BlockPairDataset
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and TokenBlockDataset are supported.
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sizes: Sentence lengths
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vocab: Dictionary with the vocabulary and special tokens.
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pad_idx: Id of padding token in dictionary
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mask_idx: Id of mask token in dictionary
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classif_token_idx: Id of classification token in dictionary. This is the
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token associated with the sentence embedding (Eg: CLS for BERT)
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sep_token_idx: Id of separator token in dictionary
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(Eg: SEP in BERT)
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seed: Seed for random number generator for reproducibility.
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shuffle: Shuffle the elements before batching.
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has_pairs: Specifies whether the underlying dataset
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generates a pair of blocks along with a sentence_target or not.
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Setting it to True assumes that the underlying dataset generates a
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label for the pair of sentences which is surfaced as
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sentence_target. The default value assumes a single block with no
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sentence target.
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segment_id: An optional segment id for filling in the segment labels
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when we are in the single block setting (Eg: XLM). Default is 0.
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masking_ratio: specifies what percentage of the blocks should be masked.
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masking_prob: specifies the probability of a given token being
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replaced with the "MASK" token.
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random_token_prob: specifies the probability of a given token being
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replaced by a random token from the vocabulary.
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"""
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def __init__(
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self,
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dataset: FairseqDataset,
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sizes: np.ndarray,
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vocab: Dictionary,
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pad_idx: int,
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mask_idx: int,
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classif_token_idx: int,
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sep_token_idx: int,
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seed: int = 1,
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shuffle: bool = True,
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has_pairs: bool = True,
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segment_id: int = 0,
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masking_ratio: float = 0.15,
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masking_prob: float = 0.8,
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random_token_prob: float = 0.1,
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):
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# Make sure the input datasets are the ones supported
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assert (
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isinstance(dataset, TokenBlockDataset)
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or isinstance(dataset, BlockPairDataset)
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or isinstance(dataset, ConcatDataset)
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), (
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"MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or "
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"ConcatDataset"
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)
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self.dataset = dataset
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self.sizes = np.array(sizes)
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self.vocab = vocab
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self.pad_idx = pad_idx
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self.mask_idx = mask_idx
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self.classif_token_idx = classif_token_idx
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self.sep_token_idx = sep_token_idx
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self.shuffle = shuffle
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self.seed = seed
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self.has_pairs = has_pairs
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self.segment_id = segment_id
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self.masking_ratio = masking_ratio
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self.masking_prob = masking_prob
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self.random_token_prob = random_token_prob
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# If we have only one block then sizes needs to be updated to include
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# the classification token
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if not has_pairs:
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self.sizes = self.sizes + 1
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def __getitem__(self, index: int):
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# if has_pairs, then expect 2 blocks and a sentence target
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if self.has_pairs:
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(block_one, block_two, sentence_target) = self.dataset[index]
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else:
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block_one = self.dataset[index]
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return {
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"id": index,
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"block_one": block_one,
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"block_two": block_two if self.has_pairs else None,
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"sentence_target": sentence_target if self.has_pairs else None,
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}
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def __len__(self):
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return len(self.dataset)
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def _mask_block(
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self,
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sentence: np.ndarray,
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mask_idx: int,
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pad_idx: int,
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dictionary_token_range: Tuple,
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):
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"""
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Mask tokens for Masked Language Model training
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Samples mask_ratio tokens that will be predicted by LM.
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Note:This function may not be efficient enough since we had multiple
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conversions between np and torch, we can replace them with torch
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operators later.
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Args:
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sentence: 1d tensor to be masked
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mask_idx: index to use for masking the sentence
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pad_idx: index to use for masking the target for tokens we aren't
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predicting
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dictionary_token_range: range of indices in dictionary which can
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be used for random word replacement
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(e.g. without special characters)
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Return:
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masked_sent: masked sentence
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target: target with words which we are not predicting replaced
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by pad_idx
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"""
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masked_sent = np.copy(sentence)
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sent_length = len(sentence)
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mask_num = math.ceil(sent_length * self.masking_ratio)
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mask = np.random.choice(sent_length, mask_num, replace=False)
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target = np.copy(sentence)
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for i in range(sent_length):
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if i in mask:
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rand = np.random.random()
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# replace with mask if probability is less than masking_prob
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# (Eg: 0.8)
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if rand < self.masking_prob:
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masked_sent[i] = mask_idx
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# replace with random token if probability is less than
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# masking_prob + random_token_prob (Eg: 0.9)
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elif rand < (self.masking_prob + self.random_token_prob):
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# sample random token from dictionary
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masked_sent[i] = np.random.randint(
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dictionary_token_range[0], dictionary_token_range[1]
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)
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else:
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target[i] = pad_idx
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return masked_sent, target
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def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int):
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"""
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Does the heavy lifting for creating a batch from the input list of
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examples. The logic is as follows:
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1. Mask the input blocks. In case has_pair is True then we have 2
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blocks to mask.
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2. Prepend the first masked block tensor with the special token
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used as sentence embedding. Eg: CLS in BERT. This happens
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irrespective of the value of has_pair.
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3. If has_pair is True, then append the first masked block with the
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special separator token (eg: SEP for BERT) and compute segment
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label accordingly. In this case, also append the second masked
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block with this special separator token and compute its segment
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label.
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4. For the targets tensor, prepend and append with padding index
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accordingly.
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5. Concatenate all tensors.
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"""
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if len(samples) == 0:
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return {}
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# To ensure determinism, we reset the state of the PRNG after every
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# batch based on the seed and the first id of the batch. This ensures
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# that across epochs we get the same mask for the same example. This
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# is needed for reproducibility and is how BERT does masking
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# TODO: Can we add deteminism without this constraint?
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with data_utils.numpy_seed(self.seed + samples[0]["id"]):
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for s in samples:
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# token range is needed for replacing with random token during
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# masking
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token_range = (self.vocab.nspecial, len(self.vocab))
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# mask according to specified probabilities.
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masked_blk_one, masked_tgt_one = self._mask_block(
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s["block_one"],
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self.mask_idx,
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self.pad_idx,
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token_range,
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)
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tokens = np.concatenate([[self.classif_token_idx], masked_blk_one])
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targets = np.concatenate([[self.pad_idx], masked_tgt_one])
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segments = np.ones(len(tokens)) * self.segment_id
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# if has_pairs is True then we need to add the SEP token to both
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# the blocks after masking and re-compute segments based on the new
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# lengths.
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if self.has_pairs:
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tokens_one = np.concatenate([tokens, [self.sep_token_idx]])
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targets_one = np.concatenate([targets, [self.pad_idx]])
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masked_blk_two, masked_tgt_two = self._mask_block(
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s["block_two"], self.mask_idx, self.pad_idx, token_range
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)
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tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]])
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targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]])
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# block + 1 sep + 1 special (CLS)
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segments_one = np.zeros(len(tokens_one))
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# block + 1 sep
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segments_two = np.ones(len(tokens_two))
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tokens = np.concatenate([tokens_one, tokens_two])
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targets = np.concatenate([targets_one, targets_two])
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segments = np.concatenate([segments_one, segments_two])
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s["source"] = torch.LongTensor(tokens)
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s["segment_labels"] = torch.LongTensor(segments)
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s["lm_target"] = torch.LongTensor(targets)
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def merge(key):
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return data_utils.collate_tokens(
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[s[key] for s in samples], pad_idx, eos_idx, left_pad=False
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)
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return {
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"id": torch.LongTensor([s["id"] for s in samples]),
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"ntokens": sum(len(s["source"]) for s in samples),
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"net_input": {
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"src_tokens": merge("source"),
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"segment_labels": merge("segment_labels"),
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},
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"lm_target": merge("lm_target"),
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"sentence_target": torch.LongTensor([s["sentence_target"] for s in samples])
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if self.has_pairs
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else None,
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"nsentences": len(samples),
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}
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def collater(self, samples: List[Dict]):
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"""Merge a list of samples to form a mini-batch.
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Args:
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samples (List[dict]): samples to collate
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Returns:
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dict: a mini-batch of data
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"""
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return self._collate(samples, self.vocab.pad(), self.vocab.eos())
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def num_tokens(self, index: int):
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"""
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Return the number of tokens in a sample. This value is used to
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enforce max-tokens during batching.
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"""
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return self.sizes[index]
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def size(self, index: int):
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"""
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Return an example's size as a float or tuple. This value is used when
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filtering a dataset with max-positions.
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"""
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return self.sizes[index]
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def ordered_indices(self):
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"""
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Return an ordered list of indices. Batches will be constructed based
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on this order.
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"""
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if self.shuffle:
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return np.random.permutation(len(self))
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else:
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order = [np.arange(len(self))]
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order.append(self.sizes)
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return np.lexsort(order)
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@property
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def supports_prefetch(self):
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return getattr(self.dataset, "supports_prefetch", False)
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def prefetch(self, indices):
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self.dataset.prefetch(indices)
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