From 2d4fff927cfdbd769a69f745c3883aae22bed817 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=88=98=E5=AE=87?= <506610466@qq.com> Date: Sun, 29 Dec 2024 16:09:00 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0vllm=3D=3D0.6.6.post1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- gpt_server/model_backend/utils.py | 23 +- gpt_server/model_backend/vllm_backend.py | 2 +- gpt_server/model_worker/embedding_infinity.py | 54 ++- gpt_server/utils.py | 3 +- pyproject.toml | 2 +- .../test_openai_completion_response_format.py | 7 +- uv.lock | 409 +++++++++++++++++- 7 files changed, 461 insertions(+), 39 deletions(-) diff --git a/gpt_server/model_backend/utils.py b/gpt_server/model_backend/utils.py index 1491d3d..3915c3c 100644 --- a/gpt_server/model_backend/utils.py +++ b/gpt_server/model_backend/utils.py @@ -1,15 +1,34 @@ -from typing import List +from typing import List, Type, Union +from pydantic import BaseModel from transformers.generation.logits_process import LogitsProcessor +from transformers import PreTrainedTokenizerBase from transformers.generation.stopping_criteria import ( StoppingCriteria, StoppingCriteriaList, STOPPING_CRITERIA_INPUTS_DOCSTRING, add_start_docstrings, ) - +import xgrammar as xgr import torch +class XgrammarLogitsProcessor(LogitsProcessor): + def __init__(self, tokenizer: PreTrainedTokenizerBase): + tokenizer_info = xgr.TokenizerInfo.from_huggingface(tokenizer) + self.grammar_compiler = xgr.GrammarCompiler(tokenizer_info) + # ----------- + + def get_grammar_compiler(self, schema: Union[str, Type[BaseModel]]): + compiled_grammar = self.grammar_compiler.compile_json_schema(schema) + self.xgr_logits_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar) + return self.xgr_logits_processor + + def __call__( + self, input_ids: torch.LongTensor, scores: torch.FloatTensor + ) -> torch.FloatTensor: + return self.xgr_logits_processor(input_ids=input_ids, scores=scores) + + class InvalidScoreLogitsProcessor(LogitsProcessor): def __call__( self, input_ids: torch.LongTensor, scores: torch.FloatTensor diff --git a/gpt_server/model_backend/vllm_backend.py b/gpt_server/model_backend/vllm_backend.py index 89fd8b2..8e6b222 100644 --- a/gpt_server/model_backend/vllm_backend.py +++ b/gpt_server/model_backend/vllm_backend.py @@ -126,7 +126,7 @@ async def stream_chat(self, params: Dict[str, Any]) -> AsyncGenerator: choice=None, grammar=None, json_object=guided_json_object, - backend="lm-format-enforcer", + backend="xgrammar", whitespace_pattern=None, ) # ---- 支持 response_format,但是官方对BPE分词器的支持仍然太差 ---- diff --git a/gpt_server/model_worker/embedding_infinity.py b/gpt_server/model_worker/embedding_infinity.py index 6dda3ba..6a8a563 100644 --- a/gpt_server/model_worker/embedding_infinity.py +++ b/gpt_server/model_worker/embedding_infinity.py @@ -4,6 +4,7 @@ from loguru import logger from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine +from infinity_emb.inference.select_model import get_engine_type_from_config from gpt_server.model_worker.base.model_worker_base import ModelWorkerBase label_to_category = { @@ -49,30 +50,26 @@ def __init__( bettertransformer = True if model_type is not None and "deberta" in model_type: bettertransformer = False - self.engine: AsyncEmbeddingEngine = AsyncEngineArray.from_args( - [ - EngineArgs( - model_name_or_path=model_path, - engine="torch", - embedding_dtype="float32", - dtype="float32", - device=device, - bettertransformer=bettertransformer, - ) - ] - )[0] + engine_args = EngineArgs( + model_name_or_path=model_path, + engine="torch", + embedding_dtype="float32", + dtype="float32", + device=device, + bettertransformer=bettertransformer, + ) + engine_type = get_engine_type_from_config(engine_args) + engine_type_str = str(engine_type) + if "EmbedderEngine" in engine_type_str: + self.mode = "embedding" + elif "RerankEngine" in engine_type_str: + self.mode = "rerank" + elif "ImageEmbedEngine" in engine_type_str: + self.mode = "image" + self.engine: AsyncEmbeddingEngine = AsyncEngineArray.from_args([engine_args])[0] loop = asyncio.get_running_loop() loop.create_task(self.engine.astart()) - self.mode = "embedding" - # rerank - for model_name in model_names: - if "rerank" in model_name: - self.mode = "rerank" - break - if self.mode == "rerank": - logger.info("正在使用 rerank 模型...") - elif self.mode == "embedding": - logger.info("正在使用 embedding 模型...") + logger.info(f"正在使用 {self.mode} 模型...") logger.info(f"模型:{model_names[0]}") async def astart(self): @@ -83,7 +80,7 @@ async def get_embeddings(self, params): logger.info(f"worker_id: {self.worker_id}") self.call_ct += 1 ret = {"embedding": [], "token_num": 0} - texts = params["input"] + texts: list = params["input"] if self.mode == "embedding": texts = list(map(lambda x: x.replace("\n", " "), texts)) embeddings, usage = await self.engine.embed(sentences=texts) @@ -105,6 +102,17 @@ async def get_embeddings(self, params): embedding = [ [round(float(score["relevance_score"]), 6)] for score in ranking ] + elif self.mode == "image": + if ( + isinstance(texts[0], bytes) + or "http" in texts[0] + or "data:image" in texts[0] + ): + embeddings, usage = await self.engine.image_embed(images=texts) + else: + embeddings, usage = await self.engine.embed(sentences=texts) + + embedding = [embedding.tolist() for embedding in embeddings] ret["embedding"] = embedding ret["token_num"] = usage return ret diff --git a/gpt_server/utils.py b/gpt_server/utils.py index 99d58ec..323bbac 100644 --- a/gpt_server/utils.py +++ b/gpt_server/utils.py @@ -42,7 +42,8 @@ def kill_child_processes(parent_pid, including_parent=False): def signal_handler(signum, frame): print("\nCtrl-C detected! Cleaning up...") - kill_child_processes(parent_pid, including_parent=False) + # kill_child_processes(parent_pid, including_parent=False) + stop_server() exit(0) # 正常退出程序 diff --git a/pyproject.toml b/pyproject.toml index 9fc0e4d..eeab634 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ dependencies = [ "torch==2.5.1", "torchvision==0.20.1", "transformers==4.45.2", - "vllm==0.6.5", + "vllm==0.6.6.post1", "qwen_vl_utils", "evalscope[perf]==0.7.0", "modelscope==1.20.1", diff --git a/tests/test_openai_completion_response_format.py b/tests/test_openai_completion_response_format.py index 24dbb15..9a35546 100644 --- a/tests/test_openai_completion_response_format.py +++ b/tests/test_openai_completion_response_format.py @@ -3,10 +3,9 @@ # 新版本 opnai client = OpenAI(api_key="EMPTY", base_url="http://localhost:8082/v1") - # 方式一 output = client.chat.completions.create( - model="qwen-32b", + model="qwen-3b", messages=[{"role": "user", "content": "南京到北京多远"}], response_format={"type": "text"}, ) @@ -14,7 +13,7 @@ print("-" * 100) # 方式二 output = client.chat.completions.create( - model="qwen-32b", + model="qwen-3b", messages=[ {"role": "system", "content": "用json进行回答"}, {"role": "user", "content": "南京到北京多远"}, @@ -32,7 +31,7 @@ class Distance(BaseModel): output = client.beta.chat.completions.parse( - model="qwen-32b", + model="qwen-3b", messages=[{"role": "user", "content": "南京到北京多远"}], response_format=Distance, ) diff --git a/uv.lock b/uv.lock index ddfa655..7d706f0 100644 --- a/uv.lock +++ b/uv.lock @@ -160,6 +160,18 @@ wheels = [ { url = "https://pypi.tuna.tsinghua.edu.cn/packages/ae/63/3e1aee3e554263f3f1011cca50d78a4894ae16ce99bf78101ac3a2f0ef74/aiohttp-3.10.10-cp313-cp313-win_amd64.whl", hash = "sha256:486f7aabfa292719a2753c016cc3a8f8172965cabb3ea2e7f7436c7f5a22a151", size = 376785 }, ] +[[package]] +name = "aiohttp-cors" +version = "0.7.0" +source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" } +dependencies = [ + { name = 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