convert : add convert-hf-to-gguf-update.py
ggml-ci
This commit is contained in:
Georgi Gerganov
parent
ee6d1b3fb4
commit
7642973616
5 changed files with 215 additions and 26 deletions
2
.github/workflows/python-lint.yml
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2
.github/workflows/python-lint.yml
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@ -20,5 +20,5 @@ jobs:
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- name: flake8 Lint
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uses: py-actions/flake8@v2
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with:
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ignore: "E203,E211,E221,E222,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
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ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
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exclude: "examples/*,examples/*/**,*/**/__init__.py"
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162
convert-hf-to-gguf-update.py
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162
convert-hf-to-gguf-update.py
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@ -0,0 +1,162 @@
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# Instructions:
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#
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# - Add a new model to the "models" list
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# - Run the script with your huggingface token:
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#
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# python3 convert-hf-to-gguf-update.py <huggingface_token>
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#
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# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
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#
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# TODO: generate tokenizer tests for llama.cpp
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#
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import os
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import requests
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import sys
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import json
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from hashlib import sha256
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from enum import IntEnum, auto
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class TOKENIZER_TYPE(IntEnum):
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SPM = auto()
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BPE = auto()
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WPM = auto()
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# TODO: this string has to exercise as much pre-tokenizer functionality as possible
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# will be updated with time - contributions welcome
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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 what\'s \'\'\'\'\'\'```````\"\"\"\"......!!!!!!??????'
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if len(sys.argv) == 2:
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token = sys.argv[1]
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else:
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print("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
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sys.exit(1)
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# TODO: add models here
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models = [
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{ "name": "llama-v2", "tokenizer_type": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
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{ "name": "llama-v3", "tokenizer_type": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
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{ "name": "deepseek-llm", "tokenizer_type": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat", },
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{ "name": "deepseek-coder", "tokenizer_type": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
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{ "name": "bert-bge", "tokenizer_type": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
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]
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# make directory "models/tokenizers" if it doesn't exist
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if not os.path.exists("models/tokenizers"):
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os.makedirs("models/tokenizers")
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def download_file_with_auth(url, token, save_path):
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headers = {"Authorization": f"Bearer {token}"}
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response = requests.get(url, headers=headers)
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if response.status_code == 200:
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with open(save_path, 'wb') as f:
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f.write(response.content)
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print("File downloaded successfully.")
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else:
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print(f"Failed to download file. Status code: {response.status_code}")
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for model in models:
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name = model["name"]
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repo = model["repo"]
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tokenizer_type = model["tokenizer_type"]
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if not os.path.exists(f"models/tokenizers/{name}"):
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os.makedirs(f"models/tokenizers/{name}")
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else:
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print(f"Directory models/tokenizers/{name} already exists - skipping")
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continue
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print(f"Downloading {name} to models/tokenizers/{name}")
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url = f"{repo}/raw/main/tokenizer.json"
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save_path = f"models/tokenizers/{name}/tokenizer.json"
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download_file_with_auth(url, token, save_path)
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if tokenizer_type == TOKENIZER_TYPE.SPM:
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url = f"{repo}/resolve/main/tokenizer.model"
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save_path = f"models/tokenizers/{name}/tokenizer.model"
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download_file_with_auth(url, token, save_path)
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url = f"{repo}/raw/main/tokenizer_config.json"
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save_path = f"models/tokenizers/{name}/tokenizer_config.json"
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download_file_with_auth(url, token, save_path)
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# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
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# TODO: auto-update convert-hf-to-gguf.py with the generated function
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src_ifs = ""
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for model in models:
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name = model["name"]
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tokenizer_type = model["tokenizer_type"]
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if tokenizer_type == TOKENIZER_TYPE.SPM:
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continue
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# create the tokenizer
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
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chktok = tokenizer.encode(chktxt)
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chkhsh = sha256(str(chktok).encode()).hexdigest()
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print(f"model: {name}")
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print(f"tokenizer_type: {tokenizer_type}")
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print(f"repo: {model['repo']}")
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print(f"chktok: {chktok}")
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print(f"chkhsh: {chkhsh}")
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# print the "pre_tokenizer" content from the tokenizer.json
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with open(f"models/tokenizers/{name}/tokenizer.json", "r") as f:
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cfg = json.load(f)
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pre_tokenizer = cfg["pre_tokenizer"]
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print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
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print(f"\n")
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src_ifs += f" if chkhsh == \"{chkhsh}\":\n"
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src_ifs += f" # ref: {model['repo']}\n"
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src_ifs += f" res = \"{name}\"\n"
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src_func = ""
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src_func += " def get_vocab_base_pre(self, tokenizer) -> str:\n"
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src_func += " # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n"
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src_func += " # is specific for the BPE pre-tokenizer used by the model\n"
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src_func += " # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n"
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src_func += " # use in llama.cpp to implement the same pre-tokenizer\n"
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src_func += "\n"
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src_func += f" chktxt = {repr(chktxt)}\n"
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src_func += "\n"
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src_func += " chktok = tokenizer.encode(chktxt)\n"
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src_func += " chkhsh = sha256(str(chktok).encode()).hexdigest()\n"
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src_func += "\n"
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src_func += " print(f\"chktok: {chktok}\")\n"
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src_func += " print(f\"chkhsh: {chkhsh}\")\n"
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src_func += "\n"
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src_func += " res = None\n"
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src_func += "\n"
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src_func += " # NOTE: if you get an error here, you need to add the model to the if-elif chain below\n"
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src_func += f"{src_ifs}\n"
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src_func += " if res is None:\n"
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src_func += " print(f\"\\n\")\n"
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src_func += " print(f\"**************************************************************************************\")\n"
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src_func += " print(f\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n"
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src_func += " print(f\"** This means that it was not added yet or you are using an older version.\")\n"
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src_func += " print(f\"** Check convert-hf-to-gguf-update.py and update it accordingly.\")\n"
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src_func += " print(f\"**\")\n"
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src_func += " print(f\"** chkhsh: {chkhsh}\")\n"
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src_func += " print(f\"**************************************************************************************\")\n"
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src_func += " print(f\"\\n\")\n"
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src_func += " raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n"
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src_func += "\n"
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src_func += " print(f\"tokenizer.ggml.pre: {res}\")\n"
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src_func += " print(f\"chkhsh: {chkhsh}\")\n"
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src_func += "\n"
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src_func += " return res\n"
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print(src_func)
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print("\n")
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print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
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print("\n")
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@ -11,6 +11,7 @@ import sys
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from abc import ABC, abstractmethod
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from enum import IntEnum
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from pathlib import Path
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from hashlib import sha256
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from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast
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import numpy as np
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@ -376,16 +377,19 @@ class Model(ABC):
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return tokens, toktypes, tokpre
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# NOTE: this function is generated by convert-hf-to-gguf-update.py
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# do not modify it manually!
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# ref: https://github.com/ggerganov/llama.cpp/pull/6920
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def get_vocab_base_pre(self, tokenizer) -> str:
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# encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
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# is specific for the BPE pre-tokenizer used by the model
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# we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
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# use in llama.cpp to implement the same pre-tokenizer
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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 what's ''''''```````\"\"\"\"......!!!!!!??????"
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chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶\u200d🌫️ (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 what\'s \'\'\'\'\'\'```````""""......!!!!!!??????'
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chktok = tokenizer.encode(chktxt)
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chkhsh = hash(tuple(chktok))
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chkhsh = sha256(str(chktok).encode()).hexdigest()
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print(f"chktok: {chktok}")
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print(f"chkhsh: {chkhsh}")
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@ -393,21 +397,34 @@ class Model(ABC):
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res = None
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# NOTE: if you get an error here, you need to add the model to the if-elif chain below
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# observe the stdout for the chkhsh value and add it to the chain
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if self.model_arch == gguf.MODEL_ARCH.LLAMA:
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if chkhsh == -3290901550109860290:
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# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/tokenizer.json
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res = "llama3"
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if chkhsh == 5332289095291046364:
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# ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat/blob/main/tokenizer.json
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res = "deepseek-llm"
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if chkhsh == 4190561703949727616:
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# ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct/blob/main/tokenizer.json
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res = "deepseek-coder"
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if chkhsh == "0fc850edd52197e357970116fbf58f6c2567f259cdc1bfc3df081d7e4bc658c1":
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# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
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res = "llama-v3"
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if chkhsh == "58c3d0e812ae7fa6a20931006d2398274732c105a9a964c148c43cf898c5fb7a":
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# ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat
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res = "deepseek-llm"
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if chkhsh == "0438d2a948d7fb26c7a662705ac68374f3138ee29e44f133b1f059203500fb4d":
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# ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
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res = "deepseek-coder"
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if chkhsh == "406f3f61e1c67d7b0456c5df2fce5cbb30c77dd3671a436b07a6c510303f721e":
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# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
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res = "bert-bge"
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if res is None:
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print(f"\n")
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print(f"**************************************************************************************")
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print(f"** WARNING: The BPE pre-tokenizer was not recognized!")
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print(f"** This means that it was not added yet or you are using an older version.")
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print(f"** Check convert-hf-to-gguf-update.py and update it accordingly.")
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print(f"**")
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print(f"** chkhsh: {chkhsh}")
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print(f"**************************************************************************************")
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print(f"\n")
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raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
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print(f"tokenizer.ggml.pre: {res}")
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print(f"chkhsh: {chkhsh}")
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return res
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def _set_vocab_gpt2(self) -> None:
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34
llama.cpp
34
llama.cpp
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@ -4330,19 +4330,29 @@ static void llm_load_vocab(
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vocab.special_mask_id = -1;
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}
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if (tokenizer_pre.empty()) {
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LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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} else if (tokenizer_pre == "default") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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} else if (tokenizer_pre == "llama3") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
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} else if (tokenizer_pre == "deepseek-llm") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
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} else if (tokenizer_pre == "deepseek-coder") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
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// for now, only BPE models have pre-tokenizers
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if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
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if (tokenizer_pre.empty()) {
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LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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} else if (
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tokenizer_pre == "default") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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} else if (
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tokenizer_pre == "llama3" ||
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tokenizer_pre == "llama-v3") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
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} else if (
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tokenizer_pre == "deepseek-llm") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
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} else if (
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tokenizer_pre == "deepseek-coder") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
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} else {
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throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
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}
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} else {
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throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
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}
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}
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