multilinguality_megatron/new_monolingual_data.py

import gzip
import json
import sys

import pandas as pd
from pathlib import Path

l = sys.argv[-1]


def _close_when_exhausted(file):
    with file:
        for line in file:
            yield json.loads(line)


def open_read_cleaned(filename):
    file: TextIO = gzip.open(filename, "rt")  # type: ignore
    return _close_when_exhausted(file)


def write_json_lines_to_gzip(filename: str, data):
    try:
        with gzip.open(filename, "wt") as f:
            for item in data:
                json_line = json.dumps(item)
                f.write(json_line + "\n")
    finally:
        f.close()  # Ensure file is closed even if an exception occurs


def write_json_lines(filename: str, data):
    try:
        with open(filename, "w") as f:
            for item in data:
                json_line = json.dumps(item)
                f.write(json_line + "\n")
    finally:
        f.close()  # Ensure file is closed even if an exception occurs


TEST_SIZE = 10000
TRAIN_LEN = 2_000_000  # 2 million instances is likely enough, since 3.8M yields 9.6G italian tokens

# red pajama (en, de, es, fr, it)

root_dir = "/mnt/data/shared/tower_llm_data/redpajama_v2_heuristic_filtered"
# l_datasets = {
#     "it": {
#         "train": [
#             "filtered_it_2023-06_head_documents.jsonl.gz",
#             "filtered_it_2022-49_head_documents.jsonl.gz",
#             "filtered_it_2022-40_head_documents.jsonl.gz",
#         ],
#         "test": "filtered_it_2023-14_head_documents.jsonl.gz",
#     },
#     "es": {
#         "train": [
#             "filtered_es_2023-06_head_documents.jsonl.gz",
#             "filtered_es_2022-49_head_documents.jsonl.gz",
#         ],
#         "test": "filtered_es_2023-14_head_documents.jsonl.gz",
#     },
#     "de": {
#         "train": [
#             "filtered_de_2023-06_head_documents.jsonl.gz",
#             "filtered_de_2022-49_head_documents.jsonl.gz",
#         ],
#         "test": "filtered_de_2023-14_head_documents.jsonl.gz",
#     },
#     "fr": {
#         "train": [
#             "filtered_fr_2023-06_head_documents.jsonl.gz",
#             "filtered_fr_2022-49_head_documents.jsonl.gz",
#         ],
#         "test": "filtered_fr_2023-14_head_documents.jsonl.gz",
#     },
#     "en": {
#         "train": [
#             "filtered_en_2023-06_head_documents.jsonl.gz",
#         ],
#         "test": "filtered_en_2023-14_head_documents.jsonl.gz",
#     },
# }

obs = []
# train
# append = True
# for d in l_datasets[l]["train"]:
#     if append:
#         for o in open_read_cleaned(f"{root_dir}/{l}/{d}"):
#             obs.append(o)
#             print(f"Selected {len(obs)} instances...")
#             if len(obs) == TRAIN_LEN:
#                 append = False
#                 break

# print("Saving")
# write_json_lines_to_gzip(f"{root_dir}/{l}/train.jsonl.gz", obs)
# test
# obs = []
# for o in open_read_cleaned(f'{root_dir}/{l}/{l_datasets[l]["test"]}'):
#     obs.append(o)
# test = pd.DataFrame(obs)
# test = test.sample(n=TEST_SIZE, random_state=42).reset_index(drop=True)
# test.to_json(
#     f"/mnt/data/jpombal/tower-results/raw_data/monolingual/red_pajama_filtered.{l}/test.jsonl",
#     orient="records",
#     lines=True,
# )

# number of words that exceeds by far the number of words for the training data;
# this way we ensure the test data does not overlap
n_words_dict = {
    "nl": 933333330,
    "pt": 933333330,
    "ru": 600000000,
    "zh": 33888888,
    "ko": 350000000,
}

corpus = open_read_cleaned(
    f"/mnt/data/shared/tower_llm_data/webcorpus/{l}/0000.json.gz"
)

n_words = 0
rows = 0
data = []
for doc in corpus:
    if l == "zh":
        n_words += len(doc["text"])
    else:
        n_words += len(doc["text"].split(" "))
    if n_words >= n_words_dict[l]:
        data.append({"text": doc["text"]})
        rows += 1
        if rows == TEST_SIZE:
            break

Path(f"/mnt/data/jpombal/tower-results/raw_data/monolingual/webcorpus.{l}").mkdir(
    exist_ok=True, parents=True
)

write_json_lines(
    f"/mnt/data/jpombal/tower-results/raw_data/monolingual/webcorpus.{l}/test.jsonl",
    data,
)

print("done")