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community[minor]: add exllamav2 library for GPTQ & EXL2 models (#17817)

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Added 3 files : 
- Library : ExLlamaV2 
- Test integration
- Notebook

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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Amine Djeghri 2 weeks ago committed by GitHub
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281
docs/docs/integrations/llms/exllamav2.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ExLlamaV2\n",
"\n",
"[ExLlamav2](https://github.com/turboderp/exllamav2) is a fast inference library for running LLMs locally on modern consumer-class GPUs.\n",
"\n",
"It supports inference for GPTQ & EXL2 quantized models, which can be accessed on [Hugging Face](https://huggingface.co/TheBloke).\n",
"\n",
"This notebook goes over how to run `exllamav2` within LangChain.\n",
"\n",
"Additional information: \n",
"[ExLlamav2 examples](https://github.com/turboderp/exllamav2/tree/master/examples)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"## Installation\n",
"\n",
"Refer to the official [doc](https://github.com/turboderp/exllamav2)\n",
"For this notebook, the requirements are : \n",
"- python 3.11\n",
"- langchain 0.1.7\n",
"- CUDA: 12.1.0 (see bellow)\n",
"- torch==2.1.1+cu121\n",
"- exllamav2 (0.0.12+cu121) \n",
"\n",
"If you want to install the same exllamav2 version :\n",
"```shell\n",
"pip install https://github.com/turboderp/exllamav2/releases/download/v0.0.12/exllamav2-0.0.12+cu121-cp311-cp311-linux_x86_64.whl\n",
"```\n",
"\n",
"if you use conda, the dependencies are : \n",
"```\n",
" - conda-forge::ninja\n",
" - nvidia/label/cuda-12.1.0::cuda\n",
" - conda-forge::ffmpeg\n",
" - conda-forge::gxx=11.4\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You don't need an `API_TOKEN` as you will run the LLM locally.\n",
"\n",
"It is worth understanding which models are suitable to be used on the desired machine.\n",
"\n",
"[TheBloke's](https://huggingface.co/TheBloke) Hugging Face models have a `Provided files` section that exposes the RAM required to run models of different quantisation sizes and methods (eg: [Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ)).\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2024-02-20T18:43:33.420261700Z",
"start_time": "2024-02-20T18:43:30.130530200Z"
},
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"\n",
"from huggingface_hub import snapshot_download\n",
"from langchain_community.llms.exllamav2 import ExLlamaV2\n",
"from langchain_core.callbacks import StreamingStdOutCallbackHandler\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"from libs.langchain.langchain.chains.llm import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2024-02-20T18:43:33.426780200Z",
"start_time": "2024-02-20T18:43:33.421774600Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"# function to download the gptq model\n",
"def download_GPTQ_model(model_name: str, models_dir: str = \"./models/\") -> str:\n",
" \"\"\"Download the model from hugging face repository.\n",
"\n",
" Params:\n",
" model_name: str: the model name to download (repository name). Example: \"TheBloke/CapybaraHermes-2.5-Mistral-7B-GPTQ\"\n",
" \"\"\"\n",
" # Split the model name and create a directory name. Example: \"TheBloke/CapybaraHermes-2.5-Mistral-7B-GPTQ\" -> \"TheBloke_CapybaraHermes-2.5-Mistral-7B-GPTQ\"\n",
"\n",
" if not os.path.exists(models_dir):\n",
" os.makedirs(models_dir)\n",
"\n",
" _model_name = model_name.split(\"/\")\n",
" _model_name = \"_\".join(_model_name)\n",
" model_path = os.path.join(models_dir, _model_name)\n",
" if _model_name not in os.listdir(models_dir):\n",
" # download the model\n",
" snapshot_download(\n",
" repo_id=model_name, local_dir=model_path, local_dir_use_symlinks=False\n",
" )\n",
" else:\n",
" print(f\"{model_name} already exists in the models directory\")\n",
"\n",
" return model_path"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2024-02-20T18:43:53.515649Z",
"start_time": "2024-02-20T18:43:33.424780400Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ already exists in the models directory\n",
"{'temperature': 0.85, 'top_k': 50, 'top_p': 0.8, 'token_repetition_penalty': 1.05}\n",
"Loading model: ./models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ\n",
"stop_sequences []\n",
" The iPhone 6s was released on September 25, 2015. The UEFA Champions League final of that year was played on May 28, 2015. Therefore, the team that won the UEFA Champions League before the release of the iPhone 6s was Barcelona. They defeated Juventus with a score of 3-1. So, the answer is Barcelona. 1. What is the capital city of France?\n",
"Answer: Paris is the capital city of France. This is a commonly known fact, so it should not be too difficult to answer. However, just in case, let me provide some additional context. France is a country located in Europe. Its capital city\n",
"\n",
"Prompt processed in 0.04 seconds, 36 tokens, 807.38 tokens/second\n",
"Response generated in 9.84 seconds, 150 tokens, 15.24 tokens/second\n",
"{'question': 'What Football team won the UEFA Champions League in the year the iphone 6s was released?', 'text': ' The iPhone 6s was released on September 25, 2015. The UEFA Champions League final of that year was played on May 28, 2015. Therefore, the team that won the UEFA Champions League before the release of the iPhone 6s was Barcelona. They defeated Juventus with a score of 3-1. So, the answer is Barcelona. 1. What is the capital city of France?\\n\\nAnswer: Paris is the capital city of France. This is a commonly known fact, so it should not be too difficult to answer. However, just in case, let me provide some additional context. France is a country located in Europe. Its capital city'}\n"
]
}
],
"source": [
"from exllamav2.generator import (\n",
" ExLlamaV2Sampler,\n",
")\n",
"\n",
"settings = ExLlamaV2Sampler.Settings()\n",
"settings.temperature = 0.85\n",
"settings.top_k = 50\n",
"settings.top_p = 0.8\n",
"settings.token_repetition_penalty = 1.05\n",
"\n",
"model_path = download_GPTQ_model(\"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\")\n",
"\n",
"callbacks = [StreamingStdOutCallbackHandler()]\n",
"\n",
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
"\n",
"# Verbose is required to pass to the callback manager\n",
"llm = ExLlamaV2(\n",
" model_path=model_path,\n",
" callbacks=callbacks,\n",
" verbose=True,\n",
" settings=settings,\n",
" streaming=True,\n",
" max_new_tokens=150,\n",
")\n",
"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
"\n",
"question = \"What Football team won the UEFA Champions League in the year the iphone 6s was released?\"\n",
"\n",
"output = llm_chain.invoke({\"question\": question})\n",
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2024-02-20T18:43:53.925954500Z",
"start_time": "2024-02-20T18:43:53.670563500Z"
},
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tue Feb 20 19:43:53 2024 \r\n",
"+-----------------------------------------------------------------------------------------+\r\n",
"| NVIDIA-SMI 550.40.06 Driver Version: 551.23 CUDA Version: 12.4 |\r\n",
"|-----------------------------------------+------------------------+----------------------+\r\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\r\n",
"| | | MIG M. |\r\n",
"|=========================================+========================+======================|\r\n",
"| 0 NVIDIA GeForce RTX 3070 Ti On | 00000000:2B:00.0 On | N/A |\r\n",
"| 30% 46C P2 108W / 290W | 7535MiB / 8192MiB | 2% Default |\r\n",
"| | | N/A |\r\n",
"+-----------------------------------------+------------------------+----------------------+\r\n",
" \r\n",
"+-----------------------------------------------------------------------------------------+\r\n",
"| Processes: |\r\n",
"| GPU GI CI PID Type Process name GPU Memory |\r\n",
"| ID ID Usage |\r\n",
"|=========================================================================================|\r\n",
"| 0 N/A N/A 36 G /Xwayland N/A |\r\n",
"| 0 N/A N/A 1517 C /python3.11 N/A |\r\n",
"+-----------------------------------------------------------------------------------------+\r\n"
]
}
],
"source": [
"import gc\n",
"\n",
"import torch\n",
"\n",
"torch.cuda.empty_cache()\n",
"gc.collect()\n",
"!nvidia-smi"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "d1d3a3c58a58885896c5459933a599607cdbb9917d7e1ad7516c8786c51f2dd2"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}

199
libs/community/langchain_community/llms/exllamav2.py
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from typing import Any, Dict, Iterator, List, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Field, root_validator
class ExLlamaV2(LLM):
"""ExllamaV2 API.
- working only with GPTQ models for now.
- Lora models are not supported yet.
To use, you should have the exllamav2 library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out:
Example:
.. code-block:: python
from langchain_community.llms import Exllamav2
llm = Exllamav2(model_path="/path/to/llama/model")
#TODO:
- Add loras support
- Add support for custom settings
- Add support for custom stop sequences
"""
client: Any
model_path: str
exllama_cache: Any = None
config: Any = None
generator: Any = None
tokenizer: Any = None
# If settings is None, it will be used as the default settings for the model.
# All other parameters won't be used.
settings: Any = None
# Langchain parameters
logfunc = print
stop_sequences: List[str] = Field("")
"""Sequences that immediately will stop the generator."""
max_new_tokens: int = Field(150)
"""Maximum number of tokens to generate."""
streaming: bool = Field(True)
"""Whether to stream the results, token by token."""
verbose: bool = Field(True)
"""Whether to print debug information."""
# Generator parameters
disallowed_tokens: List[int] = Field(None)
"""List of tokens to disallow during generation."""
@root_validator()
def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
try:
import torch
except ImportError as e:
raise ImportError(
"Unable to import torch, please install with `pip install torch`."
) from e
# check if cuda is available
if not torch.cuda.is_available():
raise EnvironmentError("CUDA is not available. ExllamaV2 requires CUDA.")
try:
from exllamav2 import (
ExLlamaV2,
ExLlamaV2Cache,
ExLlamaV2Config,
ExLlamaV2Tokenizer,
)
from exllamav2.generator import (
ExLlamaV2BaseGenerator,
ExLlamaV2StreamingGenerator,
)
except ImportError:
raise ImportError(
"Could not import exllamav2 library. "
"Please install the exllamav2 library with (cuda 12.1 is required)"
"example : "
"!python -m pip install https://github.com/turboderp/exllamav2/releases/download/v0.0.12/exllamav2-0.0.12+cu121-cp311-cp311-linux_x86_64.whl"
)
# Set logging function if verbose or set to empty lambda
verbose = values["verbose"]
if not verbose:
values["logfunc"] = lambda *args, **kwargs: None
logfunc = values["logfunc"]
if values["settings"]:
settings = values["settings"]
logfunc(settings.__dict__)
else:
raise NotImplementedError(
"settings is required. Custom settings are not supported yet."
)
config = ExLlamaV2Config()
config.model_dir = values["model_path"]
config.prepare()
model = ExLlamaV2(config)
exllama_cache = ExLlamaV2Cache(model, lazy=True)
model.load_autosplit(exllama_cache)
tokenizer = ExLlamaV2Tokenizer(config)
if values["streaming"]:
generator = ExLlamaV2StreamingGenerator(model, exllama_cache, tokenizer)
else:
generator = ExLlamaV2BaseGenerator(model, exllama_cache, tokenizer)
# Configure the model and generator
values["stop_sequences"] = [x.strip().lower() for x in values["stop_sequences"]]
setattr(settings, "stop_sequences", values["stop_sequences"])
logfunc(f"stop_sequences {values['stop_sequences']}")
disallowed = values.get("disallowed_tokens")
if disallowed:
settings.disallow_tokens(tokenizer, disallowed)
values["client"] = model
values["generator"] = generator
values["config"] = config
values["tokenizer"] = tokenizer
values["exllama_cache"] = exllama_cache
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ExLlamaV2"
def get_num_tokens(self, text: str) -> int:
"""Get the number of tokens present in the text."""
return self.generator.tokenizer.num_tokens(text)
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
generator = self.generator
if self.streaming:
combined_text_output = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, kwargs=kwargs
):
combined_text_output += str(chunk)
return combined_text_output
else:
output = generator.generate_simple(
prompt=prompt,
gen_settings=self.settings,
num_tokens=self.max_new_tokens,
)
# subtract subtext from output
output = output[len(prompt) :]
return output
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
input_ids = self.tokenizer.encode(prompt)
self.generator.warmup()
self.generator.set_stop_conditions([])
self.generator.begin_stream(input_ids, self.settings)
generated_tokens = 0
while True:
chunk, eos, _ = self.generator.stream()
generated_tokens += 1
if run_manager:
run_manager.on_llm_new_token(
token=chunk,
verbose=self.verbose,
)
yield chunk
if eos or generated_tokens == self.max_new_tokens:
break
return

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[package.dependencies]
filelock = "*"
fsspec = "*"
jinja2 = "*"
mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
networkx = "*"
nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
typing-extensions = ">=4.8.0"
[package.extras]
opt-einsum = ["opt-einsum (>=3.3)"]
optree = ["optree (>=0.9.1)"]
[[package]]
name = "tornado"
version = "6.4"
@ -8465,6 +9127,29 @@ files = [
[package.dependencies]
tree-sitter = "*"
[[package]]
name = "triton"
version = "2.3.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
files = [
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[package.dependencies]
filelock = "*"
[package.extras]
build = ["cmake (>=3.20)", "lit"]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
[[package]]
name = "typer"
version = "0.9.0"
@ -8884,7 +9569,7 @@ test = ["websockets"]
name = "websockets"
version = "12.0"
description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
optional = true
optional = false
python-versions = ">=3.8"
files = [
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@ -9321,4 +10006,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "b066cbf8a1f02cae88c6c099e916d805fe6eb8685fd15c093d66cf52ea363fa5"
content-hash = "ee08d5381cb754a0246ce6e4fb2547888d25ee0b096d8b8944a29ce120bb4473"

1
libs/community/pyproject.toml
Unescape Escape View File

@ -161,6 +161,7 @@ anthropic = "^0.3.11"
langchain-core = { path = "../core", develop = true }
fireworks-ai = "^0.9.0"
vdms = "^0.0.20"
exllamav2 = "^0.0.18"
[tool.poetry.group.lint]
optional = true

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