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(task_name,
                       insts, 
                       max_len, 
                       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):
    """
    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

    if isinstance(insts[0][3], list): 
        if task_name == "atis_slot": 
            labels_list = [inst[3] + [0] * (max_len - len(inst[3])) for inst in insts]
            labels_list = [np.array(labels_list).astype("int64").reshape([-1, max_len])]
        elif task_name == "dstc2": 
            labels_list = [inst[3] for inst in insts]
            labels_list = [np.array(labels_list).astype("int64")]
    else: 
        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, self_input_mask = pad_batch_data(
        out, 
        max_len, 
        pad_idx=pad_id, 
        return_input_mask=True)
    pos_id = pad_batch_data(
        batch_pos_ids, 
        max_len, 
        pad_idx=pad_id, 
        return_pos=False, 
        return_input_mask=False)
    sent_id = pad_batch_data(
        batch_sent_ids, 
        max_len, 
        pad_idx=pad_id, 
        return_pos=False, 
        return_input_mask=False)

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

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


def pad_batch_data(insts,
                   max_len_in,
                   pad_idx=0,
                   return_pos=False,
                   return_input_mask=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_in if max_len_in != -1 else 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])]

    # 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])]

    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]

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


if __name__ == "__main__":
    pass