mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 21:39:52 +00:00
280 lines
11 KiB
Python
280 lines
11 KiB
Python
# 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
|
||
#
|
||
# 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/ggerganov/llama.cpp/pull/6920
|
||
#
|
||
# Instructions:
|
||
#
|
||
# - Add a new model to the "models" list
|
||
# - Run the script with your huggingface token:
|
||
#
|
||
# python3 convert-hf-to-gguf-update.py <huggingface_token>
|
||
#
|
||
# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
|
||
# - Update llama.cpp with the new pre-tokenizer if necessary
|
||
#
|
||
# TODO: generate tokenizer tests for llama.cpp
|
||
# TODO: automate the update of convert-hf-to-gguf.py
|
||
#
|
||
|
||
import os
|
||
import requests
|
||
import sys
|
||
import json
|
||
|
||
from hashlib import sha256
|
||
from enum import IntEnum, auto
|
||
|
||
class TOKENIZER_TYPE(IntEnum):
|
||
SPM = auto()
|
||
BPE = auto()
|
||
WPM = auto()
|
||
|
||
# TODO: this string has to exercise as much pre-tokenizer functionality as possible
|
||
# will be updated with time - contributions welcome
|
||
chktxt = '\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'
|
||
|
||
if len(sys.argv) == 2:
|
||
token = sys.argv[1]
|
||
else:
|
||
print("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
|
||
sys.exit(1)
|
||
|
||
# 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": "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", },
|
||
]
|
||
|
||
# make directory "models/tokenizers" if it doesn't exist
|
||
if not os.path.exists("models/tokenizers"):
|
||
os.makedirs("models/tokenizers")
|
||
|
||
def download_file_with_auth(url, token, save_path):
|
||
headers = {"Authorization": f"Bearer {token}"}
|
||
response = requests.get(url, headers=headers)
|
||
if response.status_code == 200:
|
||
with open(save_path, 'wb') as f:
|
||
f.write(response.content)
|
||
print(f"File {save_path} downloaded successfully")
|
||
else:
|
||
print(f"Failed to download file. Status code: {response.status_code}")
|
||
|
||
# download the tokenizer models
|
||
for model in models:
|
||
name = model["name"]
|
||
repo = model["repo"]
|
||
tokt = model["tokt"]
|
||
|
||
if not os.path.exists(f"models/tokenizers/{name}"):
|
||
os.makedirs(f"models/tokenizers/{name}")
|
||
else:
|
||
print(f"Directory models/tokenizers/{name} already exists - skipping")
|
||
continue
|
||
|
||
print(f"Downloading {name} to models/tokenizers/{name}")
|
||
|
||
url = f"{repo}/raw/main/config.json"
|
||
save_path = f"models/tokenizers/{name}/config.json"
|
||
download_file_with_auth(url, token, save_path)
|
||
|
||
url = f"{repo}/raw/main/tokenizer.json"
|
||
save_path = f"models/tokenizers/{name}/tokenizer.json"
|
||
download_file_with_auth(url, token, save_path)
|
||
|
||
if tokt == TOKENIZER_TYPE.SPM:
|
||
url = f"{repo}/resolve/main/tokenizer.model"
|
||
save_path = f"models/tokenizers/{name}/tokenizer.model"
|
||
download_file_with_auth(url, token, save_path)
|
||
|
||
url = f"{repo}/raw/main/tokenizer_config.json"
|
||
save_path = f"models/tokenizers/{name}/tokenizer_config.json"
|
||
download_file_with_auth(url, token, save_path)
|
||
|
||
# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
|
||
# TODO: auto-update convert-hf-to-gguf.py with the generated function
|
||
|
||
src_ifs = ""
|
||
for model in models:
|
||
name = model["name"]
|
||
tokt = model["tokt"]
|
||
|
||
if tokt == TOKENIZER_TYPE.SPM:
|
||
continue
|
||
|
||
# create the tokenizer
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
|
||
|
||
chktok = tokenizer.encode(chktxt)
|
||
chkhsh = sha256(str(chktok).encode()).hexdigest()
|
||
|
||
print(f"model: {name}")
|
||
print(f"tokt: {tokt}")
|
||
print(f"repo: {model['repo']}")
|
||
print(f"chktok: {chktok}")
|
||
print(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)
|
||
pre_tokenizer = cfg["pre_tokenizer"]
|
||
print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
|
||
|
||
print(f"\n")
|
||
|
||
src_ifs += f" if chkhsh == \"{chkhsh}\":\n"
|
||
src_ifs += f" # ref: {model['repo']}\n"
|
||
src_ifs += f" res = \"{name}\"\n"
|
||
|
||
src_func = ""
|
||
src_func += " def get_vocab_base_pre(self, tokenizer) -> str:\n"
|
||
src_func += " # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n"
|
||
src_func += " # is specific for the BPE pre-tokenizer used by the model\n"
|
||
src_func += " # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n"
|
||
src_func += " # use in llama.cpp to implement the same pre-tokenizer\n"
|
||
src_func += "\n"
|
||
src_func += f" chktxt = {repr(chktxt)}\n"
|
||
src_func += "\n"
|
||
src_func += " chktok = tokenizer.encode(chktxt)\n"
|
||
src_func += " chkhsh = sha256(str(chktok).encode()).hexdigest()\n"
|
||
src_func += "\n"
|
||
src_func += " print(f\"chktok: {chktok}\")\n"
|
||
src_func += " print(f\"chkhsh: {chkhsh}\")\n"
|
||
src_func += "\n"
|
||
src_func += " res = None\n"
|
||
src_func += "\n"
|
||
src_func += " # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script\n"
|
||
src_func += " # or pull the latest version of the model from Huggingface\n"
|
||
src_func += " # don't edit the hashes manually!\n"
|
||
src_func += f"{src_ifs}\n"
|
||
src_func += " if res is None:\n"
|
||
src_func += " print(\"\\n\")\n"
|
||
src_func += " print(\"**************************************************************************************\")\n"
|
||
src_func += " print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n"
|
||
src_func += " print(\"** There are 2 possible reasons for this:\")\n"
|
||
src_func += " print(\"** - the model has not been added to convert-hf-to-gguf-update.py yet\")\n"
|
||
src_func += " print(\"** - the pre-tokenization config has changed upstream\")\n"
|
||
src_func += " print(\"** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.\")\n"
|
||
src_func += " print(\"** ref: https://github.com/ggerganov/llama.cpp/pull/6920\")\n"
|
||
src_func += " print(\"**\")\n"
|
||
src_func += " print(f\"** chkhsh: {chkhsh}\")\n"
|
||
src_func += " print(\"**************************************************************************************\")\n"
|
||
src_func += " print(\"\\n\")\n"
|
||
src_func += " raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n"
|
||
src_func += "\n"
|
||
src_func += " print(f\"tokenizer.ggml.pre: {res}\")\n"
|
||
src_func += " print(f\"chkhsh: {chkhsh}\")\n"
|
||
src_func += "\n"
|
||
src_func += " return res\n"
|
||
|
||
print(src_func)
|
||
|
||
print("\n")
|
||
print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
|
||
print("\n")
|
||
|
||
# generate tests for each tokenizer model
|
||
|
||
tests = [
|
||
"",
|
||
" ",
|
||
" ",
|
||
" ",
|
||
"\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",
|
||
chktxt,
|
||
]
|
||
|
||
# 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"]
|
||
|
||
# create the tokenizer
|
||
from transformers import AutoTokenizer
|
||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
|
||
|
||
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")
|
||
|
||
print(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
|
||
|
||
# generate commands for creating vocab files
|
||
|
||
print("\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")
|
||
|
||
print("\n")
|