From 771128e42c1e28987563e817fe86c988e09252a3 Mon Sep 17 00:00:00 2001 From: NeilJohnson0930 Date: Fri, 20 Sep 2024 17:21:56 +0800 Subject: [PATCH] Add Phi-3.5-vision-instruct VLM model --- camel/configs/vllm_config.py | 91 +----- camel/models/model_factory.py | 4 +- camel/models/vllm_model.py | 194 ++++++------- camel/types/enums.py | 15 + examples/models/vllm_model_example.py | 121 +++++++- poetry.lock | 395 ++++++++++++-------------- test/models/test_vllm_model.py | 27 +- 7 files changed, 426 insertions(+), 421 deletions(-) diff --git a/camel/configs/vllm_config.py b/camel/configs/vllm_config.py index 8efbeca63..a01261eaa 100644 --- a/camel/configs/vllm_config.py +++ b/camel/configs/vllm_config.py @@ -13,90 +13,27 @@ # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. =========== from __future__ import annotations -from typing import Sequence, Union +from typing import List, Optional -from openai._types import NOT_GIVEN, NotGiven from pydantic import Field from camel.configs.base_config import BaseConfig -# flake8: noqa: E501 class VLLMConfig(BaseConfig): - r"""Defines the parameters for generating chat completions using the - OpenAI API. - - Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html - - Args: - temperature (float, optional): Sampling temperature to use, between - :obj:`0` and :obj:`2`. Higher values make the output more random, - while lower values make it more focused and deterministic. - (default: :obj:`0.2`) - top_p (float, optional): An alternative to sampling with temperature, - called nucleus sampling, where the model considers the results of - the tokens with top_p probability mass. So :obj:`0.1` means only - the tokens comprising the top 10% probability mass are considered. - (default: :obj:`1.0`) - n (int, optional): How many chat completion choices to generate for - each input message. (default: :obj:`1`) - response_format (object, optional): An object specifying the format - that the model must output. Compatible with GPT-4 Turbo and all - GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to - {"type": "json_object"} enables JSON mode, which guarantees the - message the model generates is valid JSON. Important: when using - JSON mode, you must also instruct the model to produce JSON - yourself via a system or user message. Without this, the model - may generate an unending stream of whitespace until the generation - reaches the token limit, resulting in a long-running and seemingly - "stuck" request. Also note that the message content may be - partially cut off if finish_reason="length", which indicates the - generation exceeded max_tokens or the conversation exceeded the - max context length. - stream (bool, optional): If True, partial message deltas will be sent - as data-only server-sent events as they become available. - (default: :obj:`False`) - stop (str or list, optional): Up to :obj:`4` sequences where the API - will stop generating further tokens. (default: :obj:`None`) - max_tokens (int, optional): The maximum number of tokens to generate - in the chat completion. The total length of input tokens and - generated tokens is limited by the model's context length. - (default: :obj:`None`) - presence_penalty (float, optional): Number between :obj:`-2.0` and - :obj:`2.0`. Positive values penalize new tokens based on whether - they appear in the text so far, increasing the model's likelihood - to talk about new topics. See more information about frequency and - presence penalties. (default: :obj:`0.0`) - frequency_penalty (float, optional): Number between :obj:`-2.0` and - :obj:`2.0`. Positive values penalize new tokens based on their - existing frequency in the text so far, decreasing the model's - likelihood to repeat the same line verbatim. See more information - about frequency and presence penalties. (default: :obj:`0.0`) - logit_bias (dict, optional): Modify the likelihood of specified tokens - appearing in the completion. Accepts a json object that maps tokens - (specified by their token ID in the tokenizer) to an associated - bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias - is added to the logits generated by the model prior to sampling. - The exact effect will vary per model, but values between:obj:` -1` - and :obj:`1` should decrease or increase likelihood of selection; - values like :obj:`-100` or :obj:`100` should result in a ban or - exclusive selection of the relevant token. (default: :obj:`{}`) - user (str, optional): A unique identifier representing your end-user, - which can help OpenAI to monitor and detect abuse. - (default: :obj:`""`) - """ - - temperature: float = 0.2 # openai default: 1.0 - top_p: float = 1.0 - n: int = 1 - stream: bool = False - stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN - max_tokens: Union[int, NotGiven] = NOT_GIVEN - presence_penalty: float = 0.0 - response_format: Union[dict, NotGiven] = NOT_GIVEN - frequency_penalty: float = 0.0 - logit_bias: dict = Field(default_factory=dict) - user: str = "" + model: str = "microsoft/Phi-3.5-vision-instruct" + trust_remote_code: bool = True + max_model_len: int = 4096 + limit_mm_per_prompt: dict = Field(default_factory=lambda: {"image": 2}) + temperature: float = 0.0 + max_tokens: int = 128 + stop_token_ids: Optional[List[int]] = None + method: str = "generate" + image_urls: List[str] = Field(default_factory=list) + question: str = "" + + class Config: + arbitrary_types_allowed = True VLLM_API_PARAMS = {param for param in VLLMConfig.model_fields.keys()} diff --git a/camel/models/model_factory.py b/camel/models/model_factory.py index 34a0635ad..f5fbbf460 100644 --- a/camel/models/model_factory.py +++ b/camel/models/model_factory.py @@ -96,6 +96,8 @@ def create( model_class = MistralModel elif model_platform.is_reka and model_type.is_reka: model_class = RekaModel + elif model_platform.is_vllm and model_type.is_vllm: + model_class = VLLMModel elif model_type == ModelType.STUB: model_class = StubModel else: @@ -109,8 +111,6 @@ def create( return model_class( model_type, model_config_dict, url, token_counter ) - elif model_platform.is_vllm: - model_class = VLLMModel elif model_platform.is_litellm: model_class = LiteLLMModel elif model_platform.is_openai_compatibility_model: diff --git a/camel/models/vllm_model.py b/camel/models/vllm_model.py index 0fb32d25f..215e57377 100644 --- a/camel/models/vllm_model.py +++ b/camel/models/vllm_model.py @@ -11,79 +11,41 @@ # See the License for the specific language governing permissions and # limitations under the License. # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. =========== -import os -import subprocess from typing import Any, Dict, List, Optional, Union -from openai import OpenAI, Stream +from vllm import LLM, SamplingParams +from vllm.multimodal.utils import fetch_image -from camel.configs import VLLM_API_PARAMS +from camel.configs import VLLM_API_PARAMS, VLLMConfig from camel.messages import OpenAIMessage +from camel.models import BaseModelBackend from camel.types import ChatCompletion, ChatCompletionChunk, ModelType from camel.utils import BaseTokenCounter, OpenAITokenCounter -# flake8: noqa: E501 -class VLLMModel: - r"""vLLM service interface.""" - +class VLLMModel(BaseModelBackend): def __init__( self, - model_type: str, + model_type: ModelType, model_config_dict: Dict[str, Any], - url: Optional[str] = None, api_key: Optional[str] = None, + url: Optional[str] = None, token_counter: Optional[BaseTokenCounter] = None, ) -> None: - r"""Constructor for vLLM backend with OpenAI compatibility. - - # Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html - - Args: - model_type (str): Model for which a backend is created. - model_config_dict (Dict[str, Any]): A dictionary that will - be fed into openai.ChatCompletion.create(). - url (Optional[str]): The url to the model service. (default: - :obj:`"http://localhost:8000/v1"`) - api_key (Optional[str]): The API key for authenticating with the - model service. - token_counter (Optional[BaseTokenCounter]): Token counter to use - for the model. If not provided, `OpenAITokenCounter(ModelType. - GPT_4O_MINI)` will be used. - """ - self.model_type = model_type - self.model_config_dict = model_config_dict - self._url = ( - url - or os.environ.get("VLLM_BASE_URL") - or "http://localhost:8000/v1" - ) - if not url and not os.environ.get("VLLM_BASE_URL"): - self._start_server() - # Use OpenAI cilent as interface call vLLM - self._client = OpenAI( - timeout=60, - max_retries=3, - base_url=self._url, + super().__init__( + model_type=model_type, + model_config_dict=model_config_dict, api_key=api_key, + url=url, ) self._token_counter = token_counter - self.check_model_config() - - def _start_server(self) -> None: - r"""Starts the vllm server in a subprocess.""" - try: - subprocess.Popen( - ["vllm", "server", "--port", "8000"], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - print( - f"vllm server started on http://localhost:8000/v1 " - f"for {self.model_type} model." - ) - except Exception as e: - print(f"Failed to start vllm server: {e}.") + self.config = VLLMConfig(**model_config_dict) + self.llm = LLM( + model=self.config.model, + trust_remote_code=self.config.trust_remote_code, + max_model_len=self.config.max_model_len, + limit_mm_per_prompt=self.config.limit_mm_per_prompt, + ) @property def token_counter(self) -> BaseTokenCounter: @@ -97,66 +59,84 @@ def token_counter(self) -> BaseTokenCounter: self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI) return self._token_counter - def check_model_config(self): - r"""Check whether the model configuration contains any - unexpected arguments to vLLM API. - - Raises: - ValueError: If the model configuration dictionary contains any - unexpected arguments to OpenAI API. - """ - for param in self.model_config_dict: - if param not in VLLM_API_PARAMS: - raise ValueError( - f"Unexpected argument `{param}` is " - "input into vLLM model backend." - ) - def run( self, messages: List[OpenAIMessage], - ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]: - r"""Runs inference of OpenAI chat completion. - - Args: - messages (List[OpenAIMessage]): Message list with the chat history - in OpenAI API format. - - Returns: - Union[ChatCompletion, Stream[ChatCompletionChunk]]: - `ChatCompletion` in the non-stream mode, or - `Stream[ChatCompletionChunk]` in the stream mode. - """ + ) -> Union[ChatCompletion, ChatCompletionChunk]: + question = messages[-1]['content'] + image_urls = self.config.image_urls + image_data = [fetch_image(url) for url in image_urls] + + sampling_params = SamplingParams( + temperature=self.config.temperature, + max_tokens=self.config.max_tokens, + stop_token_ids=self.config.stop_token_ids, + ) - response = self._client.chat.completions.create( - messages=messages, - model=self.model_type, - **self.model_config_dict, + if self.config.method == "generate": + placeholders = "\n".join( + f"<|image_{i}|>" for i, _ in enumerate(image_urls, start=1) + ) + prompt = ( + f"<|user|>\n{placeholders}\n{question}<|end|>\n<|assistant|>\n" + ) + outputs = self.llm.generate( + {"prompt": prompt, "multi_modal_data": {"image": image_data}}, + sampling_params=sampling_params, + ) + elif self.config.method == "chat": + outputs = self.llm.chat( + [ + { + "role": "user", + "content": [{"type": "text", "text": question}] + + [ + {"type": "image_url", "image_url": {"url": url}} + for url in image_urls + ], + } + ], + sampling_params=sampling_params, + ) + else: + raise ValueError(f"Invalid method: {self.config.method}") + + # Convert vLLM output to OpenAI-like format + response = ChatCompletion( + id="vllm_response", + object="chat.completion", + created=0, + model=self.config.model, + choices=[ + { + "index": 0, + "message": { + "role": "assistant", + "content": outputs[0].outputs[0].text, + }, + "finish_reason": "stop", + } + ], + usage={ + "prompt_tokens": 0, + "completion_tokens": 0, + "total_tokens": 0, + }, ) return response + def check_model_config(self): + for param in self.model_config_dict: + if param not in VLLM_API_PARAMS: + raise ValueError( + f"Unexpected argument `{param}` is " + "input into VLLM model backend." + ) + @property def token_limit(self) -> int: - r"""Returns the maximum token limit for the given model. - - Returns: - int: The maximum token limit for the given model. - """ - max_tokens = self.model_config_dict.get("max_tokens") - if isinstance(max_tokens, int): - return max_tokens - print( - "Must set `max_tokens` as an integer in `model_config_dict` when" - " setting up the model. Using 4096 as default value." - ) - return 4096 + return self.config.max_model_len @property def stream(self) -> bool: - r"""Returns whether the model is in stream mode, which sends partial - results each time. - - Returns: - bool: Whether the model is in stream mode. - """ - return self.model_config_dict.get('stream', False) + return False # VLLM doesn't support streaming in this implementation diff --git a/camel/types/enums.py b/camel/types/enums.py index f9d8eff8e..f2678c6f3 100644 --- a/camel/types/enums.py +++ b/camel/types/enums.py @@ -88,6 +88,9 @@ class ModelType(Enum): REKA_FLASH = "reka-flash" REKA_EDGE = "reka-edge" + # VLLM models + PHI_3_5_VISION = "microsoft/Phi-3.5-vision-instruct" + @property def value_for_tiktoken(self) -> str: if self.is_openai: @@ -221,6 +224,17 @@ def is_reka(self) -> bool: ModelType.REKA_FLASH, } + @property + def is_vllm(self) -> bool: + r"""Returns whether this type of models is a VLLM model. + + Returns: + bool: Whether this type of models is VLLM. + """ + return self in { + ModelType.PHI_3_5_VISION, + } + @property def token_limit(self) -> int: r"""Returns the maximum token limit for a given model. @@ -240,6 +254,7 @@ def token_limit(self) -> int: ModelType.REKA_CORE, ModelType.REKA_EDGE, ModelType.REKA_FLASH, + ModelType.PHI_3_5_VISION, }: return 4_096 elif self in { diff --git a/examples/models/vllm_model_example.py b/examples/models/vllm_model_example.py index ea499d0cd..173c26398 100644 --- a/examples/models/vllm_model_example.py +++ b/examples/models/vllm_model_example.py @@ -11,37 +11,128 @@ # See the License for the specific language governing permissions and # limitations under the License. # =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. =========== - from camel.agents import ChatAgent +from camel.configs import VLLMConfig from camel.messages import BaseMessage from camel.models import ModelFactory -from camel.types import ModelPlatformType +from camel.types import ModelPlatformType, ModelType + +# Example image URLs +IMAGE_URLS = [ + "https://upload.wikimedia.org/wikipedia/commons/d/da/2015_Kaczka_krzy%C5%BCowka_w_wodzie_%28samiec%29.jpg", + "https://upload.wikimedia.org/wikipedia/commons/7/77/002_The_lion_king_Snyggve_in_the_Serengeti_National_Park_Photo_by_Giles_Laurent.jpg", +] + +# Create VLLMConfig +vllm_config = VLLMConfig( + model="microsoft/Phi-3.5-vision-instruct", + image_urls=IMAGE_URLS, + question="What is the content of each image?", +) +# Create VLLMModel vllm_model = ModelFactory.create( model_platform=ModelPlatformType.VLLM, - model_type="microsoft/Phi-3-mini-4k-instruct", - model_config_dict={"temperature": 0.0}, - api_key="vllm", + model_type=ModelType.PHI_3_5_VISION, + model_config_dict=vllm_config.dict(), ) -assistant_sys_msg = BaseMessage.make_assistant_message( - role_name="Assistant", - content="You are a helpful assistant.", +# Define system message +sys_msg = BaseMessage.make_assistant_message( + role_name="Assistant", content="You are a helpful assistant." ) -agent = ChatAgent(assistant_sys_msg, model=vllm_model, token_limit=4096) + +# Set agent +camel_agent = ChatAgent(system_message=sys_msg, model=vllm_model) user_msg = BaseMessage.make_user_message( role_name="User", - content="Say hi to CAMEL AI", + content="""Say hi to CAMEL AI, one open-source community dedicated to the + study of autonomous and communicative agents.""", ) -assistant_response = agent.step(user_msg) -print(assistant_response.msg.content) + +# Get response information +response = camel_agent.step(user_msg) +print(response.msgs[0].content) """ =============================================================================== -vllm server started on http://localhost:8000/v1 for microsoft/ -Phi-3-mini-4k-instruct model +INFO 09-20 15:54:42 llm_engine.py:223] + Initializing an LLM engine (v0.6.1.post2) + with config: model='microsoft/Phi-3.5-vision-instruct', + speculative_config=None, tokenizer='microsoft/Phi-3.5-vision-instruct', + skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, + override_neuron_config=None, rope_scaling=None, rope_theta=None, + tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, + max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, + tensor_parallel_size=1, pipeline_parallel_size=1, + disable_custom_all_reduce=False, + quantization=None, enforce_eager=False, kv_cache_dtype=auto, + quantization_param_path=None, device_config=cuda, + decoding_config=DecodingConfig(guided_decoding_backend='outlines'), + observability_config=ObservabilityConfig(otlp_traces_endpoint=None, + collect_model_forward_time=False, collect_model_execute_time=False), + seed=0, served_model_name=microsoft/Phi-3.5-vision-instruct, + use_v2_block_manager=False, num_scheduler_steps=1, + enable_prefix_caching=False, use_async_output_proc=True) +INFO 09-20 15:54:42 selector.py:240] + Cannot use FlashAttention-2 backend due to sliding window. +INFO 09-20 15:54:42 selector.py:116] Using XFormers backend. + /home/mi/anaconda3/envs/camel/lib/python3.10/site-packages/xformers/ops/fmha/flash.py:211: + FutureWarning: `torch.library.impl_abstract` + was renamed to `torch.library.register_fake`. + Please use that instead; we will remove + `torch.library.impl_abstract` in a future version of PyTorch. + @torch.library.impl_abstract("xformers_flash::flash_fwd") + /home/mi/anaconda3/envs/camel/lib/python3.10/site-packages/xformers/ops/fmha/flash.py:344: + FutureWarning: `torch.library.impl_abstract` + was renamed to `torch.library.register_fake`. + Please use that instead; we will remove + `torch.library.impl_abstract` in a future version of PyTorch. + @torch.library.impl_abstract("xformers_flash::flash_bwd") +INFO 09-20 15:54:43 model_runner.py:997] + Starting to load model microsoft/Phi-3.5-vision-instruct... +INFO 09-20 15:54:43 selector.py:240] + Cannot use FlashAttention-2 backend due to sliding window. +INFO 09-20 15:54:43 selector.py:116] Using XFormers backend. +INFO 09-20 15:54:44 weight_utils.py:242] + Using model weights format ['*.safetensors'] +Loading safetensors checkpoint shards: 0% Completed | 0/2 [00:00=3.8" files = [ - {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, - {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, + {file = "anyio-4.5.0-py3-none-any.whl", hash = "sha256:fdeb095b7cc5a5563175eedd926ec4ae55413bb4be5770c424af0ba46ccb4a78"}, + {file = "anyio-4.5.0.tar.gz", hash = "sha256:c5a275fe5ca0afd788001f58fca1e69e29ce706d746e317d660e21f70c530ef9"}, ] [package.dependencies] @@ -316,9 +316,9 @@ sniffio = ">=1.1" typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] -doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] -trio = ["trio (>=0.23)"] +doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"] +trio = ["trio (>=0.26.1)"] [[package]] name = "appnope" @@ -360,19 +360,6 @@ files = [ {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"}, ] -[[package]] -name = "asyncio" -version = 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"model_type", [ - ModelType.GPT_4, - ModelType.GPT_4_TURBO, - ModelType.GPT_4O, - ModelType.GPT_4O_MINI, + "microsoft/Phi-3.5-vision-instruct", ], ) -def test_vllm_model(model_type: ModelType): +def test_vllm_model(model_type): model_config_dict = VLLMConfig().as_dict() - model = VLLMModel(model_type.value, model_config_dict, api_key="vllm") - assert model.model_type == model_type.value + model = VLLMModel(model_type, model_config_dict) + assert model.model_type == model_type assert model.model_config_dict == model_config_dict assert isinstance(model.token_counter, OpenAITokenCounter) - assert isinstance(model.model_type, str) - assert isinstance(model.token_limit, int) @pytest.mark.model_backend def test_vllm_model_unexpected_argument(): - model_type = ModelType.GPT_4 - model_config_dict = {"model_path": "vicuna-7b-v1.5"} + model_type = ModelType.PHI_3_5_VISION + model_config = OpenSourceConfig( + model_path="microsoft/Phi-3.5-vision-instruct", + server_url="http://localhost:8000/v1", + ) + model_config_dict = model_config.as_dict() with pytest.raises( ValueError, match=re.escape( ( "Unexpected argument `model_path` is " - "input into vLLM model backend." + "input into VLLM model backend." ) ), ): - _ = VLLMModel(model_type, model_config_dict, api_key="vllm") + _ = VLLMModel(model_type, model_config_dict)