更新vllm==0.6.6.post1

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

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 ----

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

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
 
 

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",

tests/test_openai_completion_response_format.py

@@ -3,18 +3,17 @@
 
 # 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"},
 )
 print(output.choices[0].message.content)
 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,
 )