docker/bloom13b/Megatron-DeepSpeed/megatron/data/orqa_wiki_dataset.py

# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.

"""Wikipedia dataset from DPR code for ORQA."""

from abc import ABC
import csv
import numpy as np
import random
import torch
from torch.utils.data import Dataset

from megatron import print_rank_0, get_args, get_tokenizer
from megatron.core import tensor_parallel
from megatron.data.biencoder_dataset_utils import make_attention_mask

def get_open_retrieval_wiki_dataset():
    args = get_args()
    tokenizer = get_tokenizer()

    dataset = OpenRetrievalEvidenceDataset('2018 Wikipedia from DPR codebase',
                                           'evidence',
                                           args.evidence_data_path,
                                           tokenizer,
                                           args.retriever_seq_length)
    return dataset


def get_open_retrieval_batch(data_iterator):
    # Items and their type.
    keys = ['row_id', 'context', 'context_mask', 'context_types', 
        'context_pad_mask']
    datatype = torch.int64

    # Broadcast data.
    data = None if data_iterator is None else next(data_iterator)
    data_b = tensor_parallel.broadcast_data(keys, data, datatype)

    # Unpack.
    row_id = data_b['row_id'].long()
    context = data_b['context'].long()

    # TODO: make the context mask a binary one
    context_mask = (data_b['context_mask'] < 0.5)

    context_types = data_b['context_types'].long()
    context_pad_mask = data_b['context_pad_mask'].long()

    return row_id, context, context_mask, context_types, context_pad_mask


def build_tokens_types_paddings_from_text(row, tokenizer, max_seq_length):
    """Build token types and paddings, trim if needed, and pad if needed."""

    title_ids = tokenizer.tokenize(row['title'])
    context_ids = tokenizer.tokenize(row['text'])

    # Appending the title of the context at front
    extended_context_ids = title_ids + [tokenizer.sep_id] + context_ids

    context_ids, context_types, context_pad_mask = \
        build_tokens_types_paddings_from_ids(extended_context_ids, 
            max_seq_length, tokenizer.cls, tokenizer.sep, tokenizer.pad)

    return context_ids, context_types, context_pad_mask


# noinspection DuplicatedCode
def build_tokens_types_paddings_from_ids(text_ids, max_seq_length,
                                         cls_id, sep_id, pad_id):
    """Build token types and paddings, trim if needed, and pad if needed."""
    enc_ids = []
    tokentypes_enc = []

    # [CLS].
    enc_ids.append(cls_id)
    tokentypes_enc.append(0)

    # A.
    len_src = len(text_ids)
    enc_ids.extend(text_ids)
    tokentypes_enc.extend([0] * len_src)

    # Cap the size.
    if len(enc_ids) > max_seq_length - 1:
        enc_ids = enc_ids[0: max_seq_length - 1]
        tokentypes_enc = tokentypes_enc[0: max_seq_length - 1]

    # [SEP].
    enc_ids.append(sep_id)
    tokentypes_enc.append(0)

    num_tokens_enc = len(enc_ids)
    # Padding.
    padding_length = max_seq_length - len(enc_ids)
    if padding_length > 0:
        enc_ids.extend([pad_id] * padding_length)
        tokentypes_enc.extend([pad_id] * padding_length)

    pad_mask = ([1] * num_tokens_enc) + ([0] * padding_length)
    pad_mask = np.array(pad_mask, dtype=np.int64)

    return enc_ids, tokentypes_enc, pad_mask


def build_sample(row_id, context_ids, context_types, context_pad_mask):
    """Convert to numpy and return a sample consumed by the batch producer."""

    context_ids = np.array(context_ids, dtype=np.int64)
    context_types = np.array(context_types, dtype=np.int64)
    context_mask = make_attention_mask(context_ids, context_ids)

    sample = ({
        'row_id': row_id,
        'context': context_ids,
        'context_mask': context_mask,
        'context_types': context_types,
        'context_pad_mask': context_pad_mask
    })
    return sample


class OpenRetrievalEvidenceDataset(ABC, Dataset):
    """Open Retrieval Evidence dataset class."""

    def __init__(self, task_name, dataset_name, datapath, tokenizer,
            max_seq_length):
        # Store inputs.
        self.task_name = task_name
        self.dataset_name = dataset_name
        self.tokenizer = tokenizer
        self.max_seq_length = max_seq_length
        print_rank_0(' > building {} dataset for {}:'.format(self.task_name,
                                                            self.dataset_name))
        # Process the files.
        print_rank_0(datapath)
        self.samples, self.id2text = self.process_samples_from_single_path(
                                        datapath)

        args = get_args()
        if args.sample_rate < 1:  # subsample
            k = int(len(self.samples) * args.sample_rate)
            self.samples = random.sample(self.samples, k)

        print_rank_0('  >> total number of samples: {}'.format(
            len(self.samples)))

    def __len__(self):
        return len(self.samples)

    def __getitem__(self, idx):
        row = self.samples[idx]

        context_ids, context_types, context_pad_mask = \
            build_tokens_types_paddings_from_text(row, self.tokenizer, 
                self.max_seq_length)

        sample = build_sample(row['doc_id'],
                              context_ids,
                              context_types,
                              context_pad_mask)
        return sample

    @staticmethod
    def process_samples_from_single_path(filename):
        print_rank_0(' > Processing {} ...'.format(filename))
        total = 0

        rows = []
        id2text = {}

        with open(filename) as tsvfile:
            reader = csv.reader(tsvfile, delimiter='\t')
            next(reader, None)  # skip the headers
            for row in reader:
                # file format: doc_id, doc_text, title
                doc_id = int(row[0])
                text = row[1]
                title = row[2]

                rows.append({'doc_id': doc_id,
                             'text': text,
                             'title': title})

                assert doc_id not in id2text
                id2text[doc_id] = (text, title)

                total += 1
                if total % 100000 == 0:
                    print_rank_0('  > processed {} rows so far ...'.format(
                        total))

        print_rank_0(' >> processed {} samples.'.format(len(rows)))
        return rows, id2text