batching.py

#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Mask, padding and batching."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from six.moves import xrange


def mask(batch_tokens,
         seg_labels,
         mask_word_tags,
         total_token_num,
         vocab_size,
         CLS=1,
         SEP=2,
         MASK=3):
    """
    Add mask for batch_tokens, return out, mask_label, mask_pos;
    Note: mask_pos responding the batch_tokens after padded;
    """
    max_len = max([len(sent) for sent in batch_tokens])
    mask_label = []
    mask_pos = []
    prob_mask = np.random.rand(total_token_num)
    # Note: the first token is [CLS], so [low=1]
    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
    pre_sent_len = 0
    prob_index = 0
    for sent_index, sent in enumerate(batch_tokens):
        mask_flag = False
        mask_word = mask_word_tags[sent_index]
        prob_index += pre_sent_len
        if mask_word:
            beg = 0
            for token_index, token in enumerate(sent):
                seg_label = seg_labels[sent_index][token_index]
                if seg_label == 1:
                    continue
                if beg == 0:
                    if seg_label != -1:
                        beg = token_index
                    continue

                prob = prob_mask[prob_index + beg]
                if prob > 0.15:
                    pass
                else:
                    for index in xrange(beg, token_index):
                        prob = prob_mask[prob_index + index]
                        base_prob = 1.0
                        if index == beg:
                            base_prob = 0.15
                        if base_prob * 0.2 < prob <= base_prob:
                            mask_label.append(sent[index])
                            sent[index] = MASK
                            mask_flag = True
                            mask_pos.append(sent_index * max_len + index)
                        elif base_prob * 0.1 < prob <= base_prob * 0.2:
                            mask_label.append(sent[index])
                            sent[index] = replace_ids[prob_index + index]
                            mask_flag = True
                            mask_pos.append(sent_index * max_len + index)
                        else:
                            mask_label.append(sent[index])
                            mask_pos.append(sent_index * max_len + index)

                if seg_label == -1:
                    beg = 0
                else:
                    beg = token_index
        else:
            for token_index, token in enumerate(sent):
                prob = prob_mask[prob_index + token_index]
                if prob > 0.15:
                    continue
                elif 0.03 < prob <= 0.15:
                    # mask
                    if token != SEP and token != CLS:
                        mask_label.append(sent[token_index])
                        sent[token_index] = MASK
                        mask_flag = True
                        mask_pos.append(sent_index * max_len + token_index)
                elif 0.015 < prob <= 0.03:
                    # random replace
                    if token != SEP and token != CLS:
                        mask_label.append(sent[token_index])
                        sent[token_index] = replace_ids[prob_index +
                                                        token_index]
                        mask_flag = True
                        mask_pos.append(sent_index * max_len + token_index)
                else:
                    # keep the original token
                    if token != SEP and token != CLS:
                        mask_label.append(sent[token_index])
                        mask_pos.append(sent_index * max_len + token_index)

        pre_sent_len = len(sent)

    mask_label = np.array(mask_label).astype("int64").reshape([-1, 1])
    mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1])
    return batch_tokens, mask_label, mask_pos


def prepare_batch_data(insts,
                       total_token_num,
                       voc_size=0,
                       pad_id=None,
                       cls_id=None,
                       sep_id=None,
                       mask_id=None,
                       return_input_mask=True,
                       return_max_len=True,
                       return_num_token=False):

    batch_src_ids = [inst[0] for inst in insts]
    batch_sent_ids = [inst[1] for inst in insts]
    batch_pos_ids = [inst[2] for inst in insts]
    labels = [inst[3] for inst in insts]
    labels = np.array(labels).astype("int64").reshape([-1, 1])
    seg_labels = [inst[4] for inst in insts]
    mask_word_tags = [inst[5] for inst in insts]

    # First step: do mask without padding
    assert mask_id >= 0, "[FATAL] mask_id must >= 0"
    out, mask_label, mask_pos = mask(
        batch_src_ids,
        seg_labels,
        mask_word_tags,
        total_token_num,
        vocab_size=voc_size,
        CLS=cls_id,
        SEP=sep_id,
        MASK=mask_id)

    # Second step: padding
    src_id, self_input_mask = pad_batch_data(
        out, pad_idx=pad_id, return_input_mask=True)
    pos_id = pad_batch_data(batch_pos_ids, pad_idx=pad_id)
    sent_id = pad_batch_data(batch_sent_ids, pad_idx=pad_id)

    return_list = [
        src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos, labels
    ]

    return return_list


def pad_batch_data(insts,
                   pad_idx=0,
                   return_pos=False,
                   return_input_mask=False,
                   return_max_len=False,
                   return_num_token=False,
                   return_seq_lens=False):
    """
    Pad the instances to the max sequence length in batch, and generate the
    corresponding position data and attention bias.
    """
    return_list = []
    max_len = max(len(inst) for inst in insts)
    # Any token included in dict can be used to pad, since the paddings' loss
    # will be masked out by weights and make no effect on parameter gradients.

    inst_data = np.array(
        [inst + list([pad_idx] * (max_len - len(inst))) for inst in insts])
    return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])]

    # position data
    if return_pos:
        inst_pos = np.array([
            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
            for inst in insts
        ])

        return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])]

    if return_input_mask:
        # This is used to avoid attention on paddings.
        input_mask_data = np.array([[1] * len(inst) + [0] *
                                    (max_len - len(inst)) for inst in insts])
        input_mask_data = np.expand_dims(input_mask_data, axis=-1)
        return_list += [input_mask_data.astype("float32")]

    if return_max_len:
        return_list += [max_len]

    if return_num_token:
        num_token = 0
        for inst in insts:
            num_token += len(inst)
        return_list += [num_token]

    if return_seq_lens:
        seq_lens = np.array([len(inst) for inst in insts])
        return_list += [seq_lens.astype("int64").reshape([-1])]

    return return_list if len(return_list) > 1 else return_list[0]


if __name__ == "__main__":

    pass