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


def mask(batch_tokens, 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
        prob_index += pre_sent_len
        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)

        # ensure at least mask one word in a sentence
        while not mask_flag:
            token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
            if sent[token_index] != SEP and sent[token_index] != CLS:
                mask_label.append(sent[token_index])
                sent[token_index] = MASK
                mask_flag = True
                mask_pos.append(sent_index * max_len + token_index)
    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_attn_bias=True,
                       return_max_len=True,
                       return_num_token=False):
    """
    1. generate Tensor of data
    2. generate Tensor of position
    3. generate self attention mask, [shape: batch_size *  max_len * max_len]
    """

    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_list = []
    # compatible with squad, whose example includes start/end positions, 
    # or unique id

    for i in range(3, len(insts[0]), 1):
        labels = [inst[i] for inst in insts]
        labels = np.array(labels).astype("int64").reshape([-1, 1])
        labels_list.append(labels)

    # First step: do mask without padding
    if mask_id >= 0:
        out, mask_label, mask_pos = mask(
            batch_src_ids,
            total_token_num,
            vocab_size=voc_size,
            CLS=cls_id,
            SEP=sep_id,
            MASK=mask_id)
    else:
        out = batch_src_ids
    # Second step: padding
    src_id, next_sent_index, self_attn_bias = pad_batch_data(
        out, pad_idx=pad_id, return_next_sent_pos=True, return_attn_bias=True)
    pos_id = pad_batch_data(
        batch_pos_ids, pad_idx=pad_id, return_pos=False, return_attn_bias=False)
    sent_id = pad_batch_data(
        batch_sent_ids,
        pad_idx=pad_id,
        return_pos=False,
        return_attn_bias=False)

    if mask_id >= 0:
        return_list = [src_id, pos_id, sent_id, self_attn_bias, mask_label, mask_pos] \
                      + labels_list + [next_sent_index]
    else:
        return_list = [src_id, pos_id, sent_id, self_attn_bias] + labels_list \
                     + [next_sent_index]

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


def pad_batch_data(insts,
                   pad_idx=0,
                   return_pos=False,
                   return_next_sent_pos=False,
                   return_attn_bias=False,
                   return_max_len=False,
                   return_num_token=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])]

    # next_sent_pos for extract first token embedding of each sentence
    if return_next_sent_pos:
        batch_size = inst_data.shape[0]
        max_seq_len = inst_data.shape[1]
        next_sent_index = np.array(
            range(0, batch_size * max_seq_len, max_seq_len)).astype(
                "int64").reshape(-1, 1)
        return_list += [next_sent_index]

    # 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_attn_bias:
        # This is used to avoid attention on paddings.
        slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
                                       (max_len - len(inst)) for inst in insts])
        slf_attn_bias_data = np.tile(
            slf_attn_bias_data.reshape([-1, 1, max_len]), [1, max_len, 1])
        return_list += [slf_attn_bias_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]

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


if __name__ == "__main__":
    pass