llama.cpp/convert_hf_to_gguf_update.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import logging
import os
import pathlib
import re

import requests
import sys
import json
import shutil
import argparse

from hashlib import sha256
from enum import IntEnum, auto
from transformers import AutoTokenizer

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger("convert_hf_to_gguf_update")
sess = requests.Session()

convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
convert_py = convert_py_pth.read_text(encoding="utf-8")
hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token"
hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None


class TOKENIZER_TYPE(IntEnum):
    SPM = auto()
    BPE = auto()
    WPM = auto()
    UGM = auto()


DOC_STRING = """
This script downloads the tokenizer models of the specified models from Huggingface and
generates the get_vocab_base_pre() function for convert_hf_to_gguf.py

/!\\ It is intended to be used by contributors and is not meant to be run by end users

This is necessary in order to analyze the type of pre-tokenizer used by the model and
provide the necessary information to llama.cpp via the GGUF header in order to implement
the same pre-tokenizer.

ref: https://github.com/ggml-org/llama.cpp/pull/6920

Instructions:

- Add a new model to the "models" list
- Run the script with your huggingface token
    By default, token will be read from ~/.cache/huggingface/token
- The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
- Update llama.cpp with the new pre-tokenizer if necessary
"""
# TODO: generate tokenizer tests for llama.cpp

parser = argparse.ArgumentParser(description=DOC_STRING, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument(
    "--full", action="store_true",
    help="download full list of models - make sure you have access to all of them",
)
parser.add_argument(
    "hf_token",
    help="optional HF token",
    nargs="?",
)
args = parser.parse_args()
hf_token = args.hf_token if args.hf_token is not None else hf_token

if hf_token is None:
    logger.error("HF token is required. Please provide it as an argument or set it in ~/.cache/huggingface/token")
    sys.exit(1)

# TODO: this string has to exercise as much pre-tokenizer functionality as possible
#       will be updated with time - contributions welcome
CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'

# TODO: add models here, base models preferred
models = [
    {"name": "llama-spm",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
    {"name": "llama-bpe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
    {"name": "phi-3",            "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
    {"name": "deepseek-llm",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
    {"name": "deepseek-coder",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
    {"name": "falcon",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
    {"name": "bert-bge",         "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
    {"name": "falcon3",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
    {"name": "bert-bge-large",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
    {"name": "mpt",              "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
    {"name": "starcoder",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
    {"name": "gpt-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
    {"name": "stablelm2",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
    {"name": "refact",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
    {"name": "command-r",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
    {"name": "qwen2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
    {"name": "olmo",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
    {"name": "dbrx",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
    {"name": "jina-v1-en",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
    {"name": "jina-v2-en",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
    {"name": "jina-v2-es",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
    {"name": "jina-v2-de",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
    {"name": "smaug-bpe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
    {"name": "poro-chat",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
    {"name": "jina-v2-code",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
    {"name": "viking",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
    {"name": "gemma",            "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
    {"name": "gemma-2",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
    {"name": "jais",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
    {"name": "t5",               "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
    {"name": "codeshell",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
    {"name": "tekken",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
    {"name": "smollm",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
    {'name': "bloom",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
    {'name': "gpt3-finnish",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
    {"name": "exaone",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
    {"name": "phi-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
    {"name": "chameleon",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
    {"name": "roberta-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
    {"name": "gigachat",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
    {"name": "megrez",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
    {"name": "deepseek-v3",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
    {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
    {"name": "gpt-4o",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Xenova/gpt-4o", },
    {"name": "superbpe",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k", },
    {"name": "trillion",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
    {"name": "bailingmoe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
    {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
    {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
    {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
]

# some models are known to be broken upstream, so we will skip them as exceptions
pre_computed_hashes = [
    # chatglm-bpe has 2 hashes, why?
    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
    {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
]


def download_file_with_auth(url, token, save_path):
    headers = {"Authorization": f"Bearer {token}"}
    response = sess.get(url, headers=headers)
    response.raise_for_status()
    os.makedirs(os.path.dirname(save_path), exist_ok=True)
    with open(save_path, 'wb') as downloaded_file:
        downloaded_file.write(response.content)
    logger.info(f"File {save_path} downloaded successfully")


def download_model(model):
    name = model["name"]
    repo = model["repo"]
    tokt = model["tokt"]

    os.makedirs(f"models/tokenizers/{name}", exist_ok=True)

    files = ["config.json", "tokenizer.json", "tokenizer_config.json"]

    if name == "gpt-4o":
        # Xenova/gpt-4o is tokenizer-only, it does not contain config.json
        files = ["tokenizer.json", "tokenizer_config.json"]

    if tokt == TOKENIZER_TYPE.SPM:
        files.append("tokenizer.model")

    if tokt == TOKENIZER_TYPE.UGM:
        files.append("spiece.model")

    if os.path.isdir(repo):
        # If repo is a path on the file system, copy the directory
        for file in files:
            src_path = os.path.join(repo, file)
            dst_path = f"models/tokenizers/{name}/{file}"
            if os.path.isfile(dst_path):
                logger.info(f"{name}: File {dst_path} already exists - skipping")
                continue
            if os.path.isfile(src_path):
                shutil.copy2(src_path, dst_path)
                logger.info(f"{name}: Copied {src_path} to {dst_path}")
            else:
                logger.warning(f"{name}: Source file {src_path} does not exist")
    else:
        # If repo is a URL, download the files
        for file in files:
            save_path = f"models/tokenizers/{name}/{file}"
            if os.path.isfile(save_path):
                logger.info(f"{name}: File {save_path} already exists - skipping")
                continue
            download_file_with_auth(f"{repo}/resolve/main/{file}", hf_token, save_path)


# get list of existing models and chkhsh from the convert_hf_to_gguf.py file
# returns mapping res --> chkhsh
def get_existing_models(convert_py):
    pattern = r'if chkhsh == "([a-f0-9]{64})":\s*\n\s*.*\s*res = "([^"]+)"'
    matches = re.findall(pattern, convert_py)
    output = {}
    for chkhsh, res in matches:
        output[res] = chkhsh
    return output


existing_models = {}
all_models = models.copy()
if not args.full:
    # Filter out models that already exist in convert_hf_to_gguf.py
    existing_models = get_existing_models(convert_py)
    all_models = models.copy()
    models = [model for model in all_models if model["name"] not in existing_models]

logging.info(f"Downloading {len(models)} models...")
for model in models:
    try:
        download_model(model)
    except Exception as e:
        logger.error(f"Failed to download model {model['name']}. Error: {e}")


# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:

src_ifs = ""
for model in [*all_models, *pre_computed_hashes]:
    name = model["name"]
    tokt = model["tokt"]
    chkhsh = model.get("chkhsh")

    if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
        continue

    # Skip if the tokenizer folder does not exist or there are other download issues previously
    if not os.path.exists(f"models/tokenizers/{name}"):
        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
        continue

    # create the tokenizer
    if chkhsh is not None:
        # if the model has a pre-computed hash, use it
        logger.info(f"Using pre-computed hash for model {name}: {chkhsh}")
    elif name in existing_models:
        # if the model already exists in convert_hf_to_gguf.py, skip compute hash
        chkhsh = existing_models[name]
    else:
        # otherwise, compute the hash of the tokenizer
        try:
            logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...")
            if name == "t5":
                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
            else:
                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
        except OSError as e:
            logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
            continue  # Skip to the next model if the tokenizer can't be loaded

        chktok = tokenizer.encode(CHK_TXT)
        chkhsh = sha256(str(chktok).encode()).hexdigest()

        logger.info(f"model: {name}")
        logger.info(f"tokt: {tokt}")
        logger.info(f"repo: {model['repo']}")
        logger.info(f"chktok: {chktok}")
        logger.info(f"chkhsh: {chkhsh}")

        # print the "pre_tokenizer" content from the tokenizer.json
        with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
            cfg = json.load(f)
            normalizer = cfg["normalizer"]
            logger.info("normalizer: " + json.dumps(normalizer, indent=4))
            pre_tokenizer = cfg["pre_tokenizer"]
            logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
            if "ignore_merges" in cfg["model"]:
                logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))

        logger.info("")

    src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
    src_ifs += f"            # ref: {model['repo']}\n"
    src_ifs += f"            res = \"{name}\"\n"

src_func = f"""
    def get_vocab_base_pre(self, tokenizer) -> str:
        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
        # is specific for the BPE pre-tokenizer used by the model
        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
        # use in llama.cpp to implement the same pre-tokenizer

        chktxt = {repr(CHK_TXT)}

        chktok = tokenizer.encode(chktxt)
        chkhsh = sha256(str(chktok).encode()).hexdigest()

        logger.debug(f"chktok: {{chktok}}")
        logger.debug(f"chkhsh: {{chkhsh}}")

        res = None

        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
        #       or pull the latest version of the model from Huggingface
        #       don't edit the hashes manually!
{src_ifs}
        if res is None:
            logger.warning("\\n")
            logger.warning("**************************************************************************************")
            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
            logger.warning("**          There are 2 possible reasons for this:")
            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
            logger.warning("**          - the pre-tokenization config has changed upstream")
            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
            logger.warning("** ref:     https://github.com/ggml-org/llama.cpp/pull/6920")
            logger.warning("**")
            logger.warning(f"** chkhsh:  {{chkhsh}}")
            logger.warning("**************************************************************************************")
            logger.warning("\\n")
            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")

        logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
        logger.debug(f"chkhsh: {{chkhsh}}")

        return res
"""

convert_py = re.sub(
    r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
    lambda m: m.group(1) + src_func + m.group(3),
    convert_py,
    flags=re.DOTALL | re.MULTILINE,
)

convert_py_pth.write_text(convert_py, encoding="utf-8")

logger.info("+++ convert_hf_to_gguf.py was updated")

# generate tests for each tokenizer model

tests = [
    "ied 4 ½ months",
    "Äpfel",
    "",
    " ",
    "  ",
    "   ",
    "\t",
    "\n",
    "\n\n",
    "\n\n\n",
    "\t\n",
    "Hello world",
    " Hello world",
    "Hello World",
    " Hello World",
    " Hello World!",
    "Hello, world!",
    " Hello, world!",
    " this is 🦙.cpp",
    "w048 7tuijk dsdfhu",
    "нещо на Български",
    "កាន់តែពិសេសអាចខលចេញ",
    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
    "Hello",
    " Hello",
    "  Hello",
    "   Hello",
    "    Hello",
    "    Hello\n    Hello",
    " (",
    "\n =",
    "' era",
    "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
    "!!!!!!",
    "3",
    "33",
    "333",
    "3333",
    "33333",
    "333333",
    "3333333",
    "33333333",
    "333333333",
    "Cửa Việt", # llama-bpe fails on this
    " discards",
    CHK_TXT,
]

# write the tests to ./models/ggml-vocab-{name}.gguf.inp
# the format is:
#
# test0
# __ggml_vocab_test__
# test1
# __ggml_vocab_test__
# ...
#

# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out
# for each test, write the resulting tokens on a separate line

for model in models:
    name = model["name"]
    tokt = model["tokt"]

    # Skip if the tokenizer folder does not exist or there are other download issues previously
    if not os.path.exists(f"models/tokenizers/{name}"):
        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
        continue

    # create the tokenizer
    try:
        if name == "t5":
            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
        else:
            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
    except OSError as e:
        logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
        continue  # Skip this model and continue with the next one in the loop

    if not os.path.exists(f"models/ggml-vocab-{name}.gguf"):
        logger.info(f"Skip vocab files for model {name}, no GGUF file found")
        continue

    with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
        for text in tests:
            f.write(f"{text}")
            f.write("\n__ggml_vocab_test__\n")

    with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
        for text in tests:
            res = tokenizer.encode(text, add_special_tokens=False)
            for r in res:
                f.write(f" {r}")
            f.write("\n")

    logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")

# generate commands for creating vocab files

logger.info("\nRun the following commands to generate the vocab files for testing:\n")

for model in models:
    name = model["name"]

    print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100

logger.info("\n")