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102 lines
4.3 KiB
Python
102 lines
4.3 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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"""
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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 huggingface_hub import snapshot_download
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from datasets import load_dataset
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from tqdm import tqdm
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import argparse
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from data_common import write_datafile
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# ------------------------------------------
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parser = argparse.ArgumentParser(description="FineWeb dataset preprocessing")
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parser.add_argument("-v", "--version", type=str, default="10B", help="Which version of fineweb to use 10B|100B")
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parser.add_argument("-s", "--shard_size", type=int, default=10**8, help="Size of each shard 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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if args.version == "10B":
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local_dir = "fineweb10B"
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remote_name = "sample-10BT"
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elif args.version == "100B":
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local_dir = "fineweb100B"
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remote_name = "sample-100BT"
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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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fw = load_dataset("HuggingFaceFW/fineweb", name=remote_name, split="train")
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# init the tokenizer
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enc = tiktoken.get_encoding("gpt2")
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eot = enc._special_tokens['<|endoftext|>'] # end of text token
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def tokenize(doc):
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# tokenizes a single document and returns a numpy array of uint16 tokens
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tokens = [eot] # the special <|endoftext|> token delimits all documents
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tokens.extend(enc.encode_ordinary(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_uint16 = tokens_np.astype(np.uint16)
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return tokens_np_uint16
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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=np.uint16)
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token_count = 0
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progress_bar = None
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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"fineweb_{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)
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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"fineweb_{split}_{shard_index:06d}.bin")
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write_datafile(filename, all_tokens_np[:token_count])
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