[Fearture] Support mm model close prefix cache (#4459)

* [Feature] support prefix cache in DP

* fix

* Update common_engine.py

* Update common_engine.py

* Update common_engine.py

* Update common_engine.py

* [BugFix] fix workers more than 1

* fix

* Update api_server.py

* fix

* Update api_server.py

* fix

* [Fearture] Support mm model close prefix cache

* Update api_server.py

* Update engine_client.py

* Update engine_client.py

* add test

* Update test_chat.py

* fix

* fix

* Update test_chat.py

* Update test_chat.py

---------

Co-authored-by: ltd0924 <luotingdan@baidu.com>
Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>

fastdeploy/cache_manager/cache_data.py

@@ -21,6 +21,18 @@ from fastdeploy.utils import get_logger
 logger = get_logger("prefix_cache_manager", "prefix_cache_manager.log")
 
 
+DISABLE_PREFIX_CACHE_MM_MODEL: set[str] = {
+    "Ernie5ForCausalLM",
+}
+
+
+def is_mm_model_disable_prefix_cache(model_config):
+    """
+    check if the model architecture is in DISABLE_PREFIX_CACHE_MM_MODEL
+    """
+    return model_config._architecture in DISABLE_PREFIX_CACHE_MM_MODEL
+
+
 class CacheStatus(Enum):
     """
     cache status enum class

fastdeploy/entrypoints/engine_client.py

@@ -86,6 +86,13 @@ class EngineClient:
         self.enable_splitwise = splitwise_role != "mixed"
         max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
 
+        if self.enable_mm and self.enable_prefix_caching:
+            from fastdeploy.cache_manager.cache_data import (
+                is_mm_model_disable_prefix_cache,
+            )
+
+            self.disable_prefix_mm = is_mm_model_disable_prefix_cache(model_config)
+
         if tensor_parallel_size <= max_chips_per_node:
             self.is_master = True
         else:
@@ -152,6 +159,16 @@ class EngineClient:
         await self.add_requests(prompts)
         return prompts["prompt_token_ids"]
 
+    def _check_mm_disable_prefix_cache(self, task):
+        is_multimodal_data = False
+        if self.disable_prefix_mm:
+            multimodal_inputs = task.get("multimodal_inputs", [])
+            if multimodal_inputs:
+                token_type_ids = multimodal_inputs.get("token_type_ids", [])
+                if token_type_ids:
+                    is_multimodal_data = np.sum(token_type_ids) > 0
+        return is_multimodal_data
+
     async def add_requests(self, task):
         """
         Add a new request to the queue.
@@ -174,6 +191,16 @@ class EngineClient:
             else:
                 self.data_processor.process_request_dict(task, self.max_model_len)
 
+            if self.enable_mm and self.enable_prefix_caching:
+                if self._check_mm_disable_prefix_cache(task):
+                    api_server_logger.error(
+                        "The current service does not support processing requests containing multimodal data when prefix cache is enabled. Please send only text-based requests or disable prefix cache"
+                    )
+                    raise EngineError(
+                        "The current service does not support processing requests containing multimodal data when prefix cache is enabled. Please send only text-based requests or disable prefix cache",
+                        error_code=400,
+                    )
+
             task["prompt_token_ids_len"] = len(task["prompt_token_ids"])
             input_ids_len = task["prompt_token_ids_len"]
             task["max_tokens"] = min(self.max_model_len - input_ids_len, task.get("max_tokens"))

fastdeploy/output/token_processor.py

@@ -269,7 +269,7 @@ class TokenProcessor:
             if self.tokens_counter[task_id] == 0:
                 if task.messages is not None:
                     result.prompt = task.messages
-                result.num_cached_tokens = task.num_cached_tokens
+            result.num_cached_tokens = task.num_cached_tokens
 
             is_prefill = task.disaggregate_info is not None and task.disaggregate_info["role"] == "prefill"
             result = self._process_per_token(task, i, token_ids, result, is_prefill)

tests/entrypoints/test_chat.py

@@ -27,11 +27,13 @@ MODEL_NAME = os.getenv("MODEL_PATH") + "/ERNIE-4.5-0.3B-Paddle"
 class TestChat(unittest.TestCase):
     """Test case for chat functionality"""
 
+    COMMON_PREFIX = "I am a highly capable, compassionate, and trustworthy AI assistant dedicated to providing you with exceptional support. Whatever questions or challenges you may have, I will utilize my full capabilities to offer thoughtful and comprehensive assistance. As your intelligent companion, I consistently maintain honesty, transparency, and patience to ensure our interactions are both productive and enjoyable."
+
     PROMPTS = [
-        [{"content": "The color of tomato is ", "role": "user"}],
-        [{"content": "The equation 2+3= ", "role": "user"}],
-        [{"content": "The equation 4-1= ", "role": "user"}],
         [{"content": "PaddlePaddle is ", "role": "user"}],
+        [{"content": COMMON_PREFIX + "The color of tomato is ", "role": "user"}],
+        [{"content": COMMON_PREFIX + "The equation 2+3= ", "role": "user"}],
+        [{"content": COMMON_PREFIX + "The equation 4-1= ", "role": "user"}],
     ]
 
     @classmethod
@@ -58,6 +60,8 @@ class TestChat(unittest.TestCase):
     def test_chat(self):
         outputs = self.llm.chat(messages=self.PROMPTS, sampling_params=None)
         self.assertEqual(len(self.PROMPTS), len(outputs))
+        self.assertEqual(outputs[-1].num_cached_tokens, outputs[-2].num_cached_tokens)
+        self.assertEqual(outputs[-1].num_cached_tokens, 64)
 
     def test_chat_with_tools(self):
         """Test chat with tools: