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"""
Download, preprocess and serve the TinyStories dataset as a DataLoader.
"""
import argparse
import glob
import json
import os
import random
from typing import List
from concurrent.futures import ProcessPoolExecutor
from functools import partial
import numpy as np
import requests
import sentencepiece as spm
import torch
import torch.distributed as dist
from tqdm import tqdm
from tokenizer import Tokenizer
DATA_CACHE_DIR = "data"
def train_vocab(vocab_size, path_to_text):
"""
Trains a custom sentencepiece tokenizer on the TinyStories dataset.
The custom tokenizer files will be saved in DATA_CACHE_DIR/tok{N} directories,
where N is the vocab size. This is also where the pretok .bin files will go.
"""
assert vocab_size > 0, "Vocab size must be positive"
os.makedirs(DATA_CACHE_DIR, exist_ok=True)
# output file prefix path for sentencepiece
prefix = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
# how many shards we'll use for vocab training, kept low for efficiency
num_shards = 10
tiny_file = path_to_text
# train the sentencepiece model
print("Will now train the vocab...")
spm.SentencePieceTrainer.train(input=tiny_file,
model_prefix=prefix,
model_type="bpe",
vocab_size=vocab_size,
self_test_sample_size=0,
input_format="text",
character_coverage=1.0,
num_threads=os.cpu_count(),
split_digits=True,
allow_whitespace_only_pieces=True,
byte_fallback=True,
unk_surface=r" \342\201\207 ",
normalization_rule_name="identity")
def process_shard(args, vocab_size, path_to_text):
shard_id, shard = args
tokenizer_model = get_tokenizer_model_path(vocab_size)
enc = Tokenizer(tokenizer_model)
all_tokens = []
with open(path_to_text, 'r') as file:
# Iterate through each line in the file
for line in file:
# Process the line as needed
print(line.strip())
contents = line.strip()
tokens = enc.encode(contents, bos=True, eos=False)
all_tokens.extend(tokens)
print(all_tokens)
# convert to uint16 nparray
all_tokens = np.array(all_tokens, dtype=np.uint16)
# calculate the output filename
if vocab_size == 0:
# if we're using Llama 2, just save the tokenized file in the same dir
tokenized_filename = shard.replace(".json", ".bin")
else:
# save .bin files into a new tok{N} directory
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
tokenized_filename = os.path.join(bin_dir, 'data00.bin')
#print(tokenized_filename)
# write the bytes
with open(tokenized_filename, "wb") as f:
f.write(all_tokens.tobytes())
# calculate the average sequence length (they are separated by BOS=1)
avg_seq_len = all_tokens.size / ((all_tokens == 1).sum())
print(f"Saved {tokenized_filename}, average seqlen: {avg_seq_len:.2f}")
def pretokenize(vocab_size, path_to_text):
# create a single file since we have only one .txt file
shard_filenames = ["data00.bin"]#sorted(glob.glob(os.path.join(data_dir, "*.json")))
if vocab_size > 0:
# .bin files will be saved into tok{N} directory, create it once here
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
os.makedirs(bin_dir, exist_ok=True)
# process
fun = partial(process_shard, vocab_size=vocab_size, path_to_text=path_to_text)
print(shard_filenames)
with ProcessPoolExecutor() as executor:
executor.map(fun, enumerate(shard_filenames))
print("Done.")
class PretokDataset(torch.utils.data.IterableDataset):
"""Loads pretokenized examples from disk and yields them as PyTorch tensors."""
def __init__(self, split, max_seq_len, vocab_size, vocab_source):
super().__init__()
self.split = split
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.vocab_source = vocab_source
def __iter__(self):
# get worker info within a DataLoader
worker_info = torch.utils.data.get_worker_info()
worker_id = worker_info.id if worker_info else 0
# get DDP rank info
rank = dist.get_rank() if dist.is_initialized() else 0
# combine the worker_id and worker_rank to create a unique seed for rng
seed = 42 + worker_id + 1337 * rank
rng = random.Random(seed)
print(f"Created a PretokDataset with rng seed {seed}")
if self.vocab_source == "llama2":
# the .bin files are right along the .json files
bin_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
shard_filenames = sorted(glob.glob(os.path.join(bin_dir, "*.bin")))
elif self.vocab_source == "custom":
# the .bin files are in tok{N} directory
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{self.vocab_size}")
shard_filenames = sorted(glob.glob(os.path.join(bin_dir, "*.bin")))
# train/test split. let's use only shard 0 for test split, rest train
shard_filenames = shard_filenames[:1] if self.split == "train" else shard_filenames[:1]
assert len(shard_filenames)>0, f"No bin files found in {bin_dir}"
while True:
rng.shuffle(shard_filenames)
for shard in shard_filenames:
# open the dataset for reading but keep it on disk with memmap
m = np.memmap(shard, dtype=np.uint16, mode="r")
num_batches = len(m) // self.max_seq_len
num_batches -= 1 # drop the last partial batch
assert num_batches > 0, "this shard is way too small? investigate."
ixs = list(range(num_batches))
rng.shuffle(ixs)
for ix in ixs:
start = ix * self.max_seq_len
end = start + self.max_seq_len + 1
# calling .astype will copy the data into a new numpy array, now in RAM
chunk = torch.from_numpy((m[start:end]).astype(np.int64))
x = chunk[:-1]
y = chunk[1:]
yield x, y
# -----------------------------------------------------------------------------
# public interface functions
def get_tokenizer_model_path(vocab_size):
"""
Returns path to the sentencepiece tokenizer model for a given vocab size
vocab_size = 0 designates the default Llama 2 tokenizer, in that case
None is returned.
"""
if vocab_size == 0:
return None
else:
return os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}.model")
class Task:
@staticmethod
def iter_batches(batch_size, device, num_workers=0, **dataset_kwargs):
ds = PretokDataset(**dataset_kwargs)
dl = torch.utils.data.DataLoader(
ds, batch_size=batch_size, pin_memory=True, num_workers=num_workers
)
for x, y in dl:
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
yield x, y
# -----------------------------------------------------------------------------
# CLI for constructing the dataset
if __name__ == "__main__":
"""
These stages are designed to be run in order.
To tokenize data with the Llama 2 tokenizer:
python tinystories.py download
python tinystories.py pretokenize
To tokenize data with a custom tokenizer we train ourselves with sentencepiece, e.g.:
python tinystories.py download
python tinystories.py train_vocab --vocab_size=2048
python tinystories.py pretokenize --vocab_size=2048
"""
parser = argparse.ArgumentParser()
parser.add_argument("stage", type=str, choices=["download", "pretokenize", "train_vocab"])
parser.add_argument("--vocab_size", type=int, default=0, help="pretokenization vocab size. 0 = use Llama 2 tokenizer.")
parser.add_argument("--path_to_text", type=str)
args = parser.parse_args()
# depending on the stage call the appropriate function
if args.stage == "download":
download()
elif args.stage == "train_vocab":
train_vocab(vocab_size=args.vocab_size, path_to_text=args.path_to_text)
elif args.stage == "pretokenize":
pretokenize(vocab_size=args.vocab_size, path_to_text=args.path_to_text)
else:
raise ValueError(f"Unknown stage {args.stage}")
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