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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 process_shard(args, vocab_size):
shard_id, shard = args
tokenizer_model = get_tokenizer_model_path(vocab_size)
enc = Tokenizer(tokenizer_model)
try:
print(f"Processing shard {shard_id} - {shard}")
with open(shard, "r") as f:
data = json.load(f)
all_tokens = []
for example in tqdm(data, position=shard_id):
text = example["story"]
text = text.strip() # get rid of leading/trailing whitespace
tokens = enc.encode(text, bos=True, eos=False) # encode the text, use BOS
all_tokens.extend(tokens)
# convert to uint16 nparray
all_tokens = np.array(all_tokens, dtype=np.uint16)
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}")
shard_basename = os.path.basename(shard)
bin_basename = shard_basename.replace(".json", ".bin")
tokenized_filename = os.path.join(bin_dir, bin_basename)
# 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}")
except Exception as e:
print(f"Error processing shard {shard_id}: {str(e)}")
def pretokenize(vocab_size):
# iterate the shards and tokenize all of them one by one
data_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
shard_filenames = 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 all the shards in a process pool
fun = partial(process_shard, vocab_size=vocab_size)
with ProcessPoolExecutor() as executor:
executor.map(fun, enumerate(shard_filenames))
print("Done.")
# Call pretokenize with your desired vocab_size
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
if __name__ == '__main__':
pretokenize(vocab_size=0)
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