Merge mainline llama.cpp (#3)

* Merging mainline - WIP

* Merging mainline - WIP

AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.

* Merging mainline - fix Metal

* Remove check

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

1 files changed, 0 insertions, 330 deletions

diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py
deleted file mode 100755
index 67598b56..00000000
--- a/convert-hf-to-gguf-update.py
+++ /dev/null
@@ -1,330 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# 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
-#
-
-import logging
-import os
-import pathlib
-import re
-
-import requests
-import sys
-import json
-
-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()
-
-
-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]
-    if not token.startswith("hf_"):
-        logger.info("Huggingface token seems invalid")
-        logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
-        sys.exit(1)
-else:
-    logger.info("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", },
-    {"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-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", },
-]
-
-
-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 f:
-        f.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 tokt == TOKENIZER_TYPE.SPM:
-        files.append("tokenizer.model")
-
-    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}", token, save_path)
-
-
-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 models:
-    name = model["name"]
-    tokt = model["tokt"]
-
-    if tokt == TOKENIZER_TYPE.SPM:
-        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
-    try:
-        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(chktxt)
-    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(chktxt)}
-
-        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/ggerganov/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_pth = pathlib.Path("convert-hf-to-gguf.py")
-convert_py = convert_py_pth.read_text(encoding="utf-8")
-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",
-    "Führer",
-    "",
-    " ",
-    "  ",
-    "   ",
-    "\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
-    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"]
-
-    # 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:
-        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
-
-    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")