ernie_dataset.py

# Copyright (c) 2021, PadddlePaddle authors. All Rights Reserved.
# Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT Style dataset."""

import copy

import numpy as np
import paddle

from .dataset_utils import (
    create_masked_lm_predictions,
    create_tokens_and_tokentypes,
    get_a_and_b_segments,
    get_samples_mapping,
    truncate_segments,
)


class ErnieDataset(paddle.io.Dataset):
    def __init__(
        self,
        name,
        tokenizer,
        indexed_dataset,
        data_prefix,
        num_epochs,
        max_num_samples,
        masked_lm_prob,
        max_seq_length,
        short_seq_prob,
        seed,
        binary_head,
        share_folder=False,
        args=None,
    ):

        # Params to store.
        self.name = name
        self.seed = seed
        self.masked_lm_prob = masked_lm_prob
        self.max_seq_length = max_seq_length
        self.binary_head = binary_head
        self.share_folder = share_folder
        self.args = args

        # Dataset.
        self.indexed_dataset = indexed_dataset

        # Build the samples mapping.
        self.samples_mapping = get_samples_mapping(
            self.indexed_dataset,
            data_prefix,
            num_epochs,
            max_num_samples,
            self.max_seq_length - 3,  # account for added tokens
            short_seq_prob,
            self.seed,
            self.name,
            self.binary_head,
            self.share_folder,
        )

        # Vocab stuff.
        # tokenizer = get_tokenizer()
        # self.vocab_id_list = list(tokenizer.inv_vocab.keys())
        # self.vocab_id_to_token_dict = tokenizer.inv_vocab
        self.vocab_id_list = list(tokenizer.vocab.idx_to_token.keys())
        self.vocab_id_to_token_dict = copy.deepcopy(tokenizer.vocab.idx_to_token)
        self.vocab_token_to_id_dict = copy.deepcopy(tokenizer.vocab.token_to_idx)

        # ERNIE is chinse char level model, sometime is need
        # add ## chinse char to encode and decode.
        # Here we extend the vocab dict.
        self.vocab_id_to_token_dict.update(tokenizer.added_tokens_decoder)
        self.vocab_token_to_id_dict.update(tokenizer.added_tokens_encoder)

        self.cls_id = tokenizer.cls_token_id
        self.sep_id = tokenizer.sep_token_id
        self.mask_id = tokenizer.mask_token_id
        self.pad_id = tokenizer.pad_token_id

    def __len__(self):
        return self.samples_mapping.shape[0]

    def __getitem__(self, idx):
        start_idx, end_idx, seq_length = self.samples_mapping[idx]
        sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)]

        # Note that this rng state should be numpy and not python since
        # python randint is inclusive whereas the numpy one is exclusive.
        # We % 2**32 since numpy requres the seed to be between 0 and 2**32 - 1
        np_rng = np.random.RandomState(seed=((self.seed + idx) % 2**32))
        return build_training_sample(
            sample,
            seq_length,
            self.max_seq_length,  # needed for padding
            self.vocab_id_list,
            self.vocab_id_to_token_dict,
            self.vocab_token_to_id_dict,
            self.cls_id,
            self.sep_id,
            self.mask_id,
            self.pad_id,
            self.masked_lm_prob,
            np_rng,
            self.binary_head,
            self.args,
        )


def build_training_sample(
    sample,
    target_seq_length,
    max_seq_length,
    vocab_id_list,
    vocab_id_to_token_dict,
    vocab_token_to_id_dict,
    cls_id,
    sep_id,
    mask_id,
    pad_id,
    masked_lm_prob,
    np_rng,
    binary_head,
    args=None,
):
    """Biuld training sample.

    Arguments:
        sample: A list of sentences in which each sentence is a list token ids.
        target_seq_length: Desired sequence length.
        max_seq_length: Maximum length of the sequence. All values are padded to
            this length.
        vocab_id_list: List of vocabulary ids. Used to pick a random id.
        vocab_id_to_token_dict: A dictionary from vocab ids to text tokens.
        vocab_token_to_id_dict: A dictionary from text tokens to vocab ids.
        cls_id: Start of example id.
        sep_id: Separator id.
        mask_id: Mask token id.
        pad_id: Padding token id.
        masked_lm_prob: Probability to mask tokens.
        np_rng: Random number genenrator. Note that this rng state should be
              numpy and not python since python randint is inclusive for
              the opper bound whereas the numpy one is exclusive.
    """

    if binary_head:
        # We assume that we have at least two sentences in the sample
        assert len(sample) > 1, "The sentence num should be large than 1."
    assert target_seq_length <= max_seq_length

    # Divide sample into two segments (A and B).
    if binary_head:
        tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng)
    else:
        tokens_a = []
        for j in range(len(sample)):
            tokens_a.extend(sample[j])
        tokens_b = []
        is_next_random = False

    # Truncate to `target_sequence_length`.
    max_num_tokens = target_seq_length
    truncate_segments(tokens_a, tokens_b, len(tokens_a), len(tokens_b), max_num_tokens, np_rng)

    # Build tokens and toketypes.
    tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id)

    # Masking.
    max_predictions_per_seq = masked_lm_prob * max_num_tokens
    (tokens, masked_positions, masked_labels, _, _) = create_masked_lm_predictions(
        tokens,
        vocab_id_list,
        vocab_id_to_token_dict,
        masked_lm_prob,
        cls_id,
        sep_id,
        mask_id,
        max_predictions_per_seq,
        np_rng,
        vocab_token_to_id_dict=vocab_token_to_id_dict,
        to_chinese_char=True,
        inplace_random_mask=False,
        favor_longer_ngram=False if args is None else args.favor_longer_ngram,
        max_ngrams=3 if args is None else args.max_ngrams,
    )

    # Padding.
    tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np = pad_and_convert_to_numpy(
        tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length
    )

    return tokens_np, tokentypes_np, padding_mask_np, masked_positions, masked_labels, int(is_next_random)


def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length):
    """Pad sequences and convert them to numpy."""

    # Some checks.
    num_tokens = len(tokens)
    print(num_tokens, max_seq_length)
    padding_length = max_seq_length - num_tokens
    assert padding_length >= 0
    assert len(tokentypes) == num_tokens
    assert len(masked_positions) == len(masked_labels)

    # Tokens and token types.
    filler = [pad_id] * padding_length
    tokens_np = np.array(tokens + filler, dtype=np.int64)
    tokentypes_np = np.array(tokentypes + filler, dtype=np.int64)

    # Padding mask.
    padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.float32)
    padding_mask_np = (1 - padding_mask_np) * -1e4

    padding_mask_np = padding_mask_np.reshape([1, 1, -1])
    # Lables and loss mask.
    labels = [-1] * max_seq_length
    loss_mask = [0] * max_seq_length
    for i in range(len(masked_positions)):
        assert masked_positions[i] < num_tokens
        labels[masked_positions[i]] = masked_labels[i]
        loss_mask[masked_positions[i]] = 1
    labels_np = np.array(labels, dtype=np.int64)
    loss_mask_np = np.array(loss_mask, dtype=np.int64)

    return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np