wehub-resource-sync
144 lines
6.1 KiB
Python
144 lines
6.1 KiB
Python
"""
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FineWeb dataset (for srs pretraining)
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https://huggingface.co/datasets/HuggingFaceFW/fineweb
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example doc to highlight the structure of the dataset:
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{
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"text": "Posted by mattsmith on 20th April 2012\nStraight from...",
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"id": "<urn:uuid:d853d453-196e-4488-a411-efc2b26c40d2>",
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"dump": "CC-MAIN-2013-20",
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"url": "http://nleastchatter.com/philliesphandom/tag/freddy-galvis/",
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"date": "2013-05-18T07:24:47Z",
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"file_path": "s3://commoncrawl/long.../path.../file.gz",
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"language": "en",
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"language_score": 0.9185474514961243,
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"token_count": 594
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}
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Example of downloading the 100B dataset of FineWebEDU, from root directory:
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python dev/data/fineweb.py -t edu -v 100B
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100B runs for small few hours, depending on your internet and computer.
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"""
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import os
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import argparse
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import multiprocessing as mp
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import numpy as np
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import tiktoken
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from datasets import load_dataset
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from tqdm import tqdm
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from transformers import AutoTokenizer
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from data_common import write_datafile
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# ------------------------------------------
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parser = argparse.ArgumentParser(description="FineWeb and Edu-FineWeb dataset preprocessing")
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parser.add_argument("-t", "--type", type=str, default="classic", help="Fineweb type, edu|classic")
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parser.add_argument("-v", "--version", type=str, default="10B", help="Fineweb data sample size, 10B|100B")
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parser.add_argument("-m", "--model_desc", type=str, default="gpt-2", help="Model descriptor, gpt-2|llama-3")
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parser.add_argument("-s", "--shard_size", type=int, default=10**8, help="Size of each data shard in the output .bin files, in tokens")
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args = parser.parse_args()
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# FineWeb has a few possible subsamples available
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assert args.version in {"10B", "100B"}, "version must be one of: 10B, 100B"
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assert args.type in {"edu", "classic"}, "type must be one of: edu, classic"
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directories = {
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("classic", "10B"): ("fineweb10B", "sample-10BT"),
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("classic", "100B"): ("fineweb100B", "sample-100BT"),
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("edu", "10B"): ("edu_fineweb10B", "sample-10BT"),
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("edu", "100B"): ("edu_fineweb100B", "sample-100BT")
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}
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local_dir, remote_name = directories[(args.type, args.version)]
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# create the cache the local directory if it doesn't exist yet
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DATA_CACHE_DIR = os.path.join(os.path.dirname(__file__), local_dir)
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os.makedirs(DATA_CACHE_DIR, exist_ok=True)
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# download the dataset
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if args.type == "classic":
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fw = load_dataset("HuggingFaceFW/fineweb", name=remote_name, split="train")
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name = "fineweb"
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elif args.type =="edu":
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fw = load_dataset("HuggingFaceFW/fineweb-edu", name=remote_name, split="train")
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name = "edu_fineweb"
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def tokenize_llama(doc):
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# tokenizes a single document and returns a numpy array of uint32 tokens
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B")
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encode = lambda s: tokenizer.encode(s, add_special_tokens=False, verbose=False, split_special_tokens=True)
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eot = tokenizer.encode('')[0] # by default the tokenizer adds the EOT token (128000)
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tokens = [eot] # the special <|endoftext|> token delimits all documents
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tokens.extend(encode(doc["text"]))
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tokens_np = np.array(tokens)
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assert (0 <= tokens_np).all() and (tokens_np < 2**32).all(), "token dictionary too large for uint32"
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tokens_np_uint = tokens_np.astype(np.uint32)
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return tokens_np_uint
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def tokenize_gpt2(doc):
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# tokenizes a single document and returns a numpy array of uint16 tokens
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enc = tiktoken.get_encoding("gpt2")
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encode = lambda s: enc.encode_ordinary(s)
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eot = enc._special_tokens['<|endoftext|>'] # end of text token
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tokens = [eot] # the special <|endoftext|> token delimits all documents
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tokens.extend(encode(doc["text"]))
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tokens_np = np.array(tokens)
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assert (0 <= tokens_np).all() and (tokens_np < 2**16).all(), "token dictionary too large for uint16"
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tokens_np_uint = tokens_np.astype(np.uint16)
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return tokens_np_uint
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token_dtype = {
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"gpt-2": np.uint16,
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"llama-3": np.uint32
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}[args.model_desc]
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# tokenize all documents and write output shards, each of shard_size tokens (last shard has remainder)
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nprocs = max(1, os.cpu_count() - 2) # don't hog the entire system
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with mp.Pool(nprocs) as pool:
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shard_index = 0
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# preallocate buffer to hold current shard
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all_tokens_np = np.empty((args.shard_size,), dtype=token_dtype)
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token_count = 0
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progress_bar = None
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tokenize = lambda x: None
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if args.model_desc == "gpt-2":
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tokenize = tokenize_gpt2
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elif args.model_desc == "llama-3":
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tokenize = tokenize_llama
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else:
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raise ValueError(f"unknown model {args.model_desc}")
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for tokens in pool.imap(tokenize, fw, chunksize=16):
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# is there enough space in the current shard for the new tokens?
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if token_count + len(tokens) < args.shard_size:
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# simply append tokens to current shard
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all_tokens_np[token_count:token_count+len(tokens)] = tokens
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token_count += len(tokens)
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# update progress bar
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if progress_bar is None:
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progress_bar = tqdm(total=args.shard_size, unit="tokens", desc=f"Shard {shard_index}")
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progress_bar.update(len(tokens))
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else:
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# write the current shard and start a new one
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split = "val" if shard_index == 0 else "train"
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filename = os.path.join(DATA_CACHE_DIR, f"{name}_{split}_{shard_index:06d}.bin")
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# split the document into whatever fits in this shard; the remainder goes to next one
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remainder = args.shard_size - token_count
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progress_bar.update(remainder)
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all_tokens_np[token_count:token_count+remainder] = tokens[:remainder]
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write_datafile(filename, all_tokens_np.tolist(), args.model_desc)
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shard_index += 1
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progress_bar = None
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# populate the next shard with the leftovers of the current doc
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all_tokens_np[0:len(tokens)-remainder] = tokens[remainder:]
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token_count = len(tokens)-remainder
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# write any remaining tokens as the last shard
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if token_count != 0:
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split = "val" if shard_index == 0 else "train"
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filename = os.path.join(DATA_CACHE_DIR, f"{name}_{split}_{shard_index:06d}.bin")
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write_datafile(filename, (all_tokens_np[:token_count]).tolist(), args.model_desc)
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