[Frontend] OpenAI server: propagate usage accounting to FastAPI middl…

…eware layer (vllm-project#8672)

vllm/entrypoints/openai/protocol.py

@@ -107,6 +107,11 @@ class UsageInfo(OpenAIBaseModel):
     completion_tokens: Optional[int] = 0
 
 
+class RequestResponseMetadata(BaseModel):
+    request_id: str
+    final_usage_info: Optional[UsageInfo] = None
+
+
 class JsonSchemaResponseFormat(OpenAIBaseModel):
     name: str
     description: Optional[str] = None

vllm/entrypoints/openai/serving_chat.py

@@ -22,7 +22,8 @@
     ChatCompletionRequest, ChatCompletionResponse,
     ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
     ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
-    DeltaToolCall, ErrorResponse, FunctionCall, ToolCall, UsageInfo)
+    DeltaToolCall, ErrorResponse, FunctionCall, RequestResponseMetadata,
+    ToolCall, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
                                                     LoRAModulePath,
                                                     OpenAIServing,
@@ -175,6 +176,11 @@ async def create_chat_completion(
                 "--enable-auto-tool-choice and --tool-call-parser to be set")
 
         request_id = f"chat-{random_uuid()}"
+
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
         try:
             guided_decode_logits_processor = (
                 await self._guided_decode_logits_processor(request, tokenizer))
@@ -241,11 +247,13 @@ async def create_chat_completion(
         # Streaming response
         if request.stream:
             return self.chat_completion_stream_generator(
-                request, result_generator, request_id, conversation, tokenizer)
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
 
         try:
             return await self.chat_completion_full_generator(
-                request, result_generator, request_id, conversation, tokenizer)
+                request, result_generator, request_id, conversation, tokenizer,
+                request_metadata)
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))
@@ -262,6 +270,7 @@ async def chat_completion_stream_generator(
         request_id: str,
         conversation: List[ConversationMessage],
         tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
     ) -> AsyncGenerator[str, None]:
         model_name = self.base_model_paths[0].name
         created_time = int(time.time())
@@ -580,6 +589,13 @@ async def chat_completion_stream_generator(
                     exclude_unset=True, exclude_none=True))
                 yield f"data: {final_usage_data}\n\n"
 
+            # report to FastAPI middleware aggregate usage across all choices
+            num_completion_tokens = sum(previous_num_tokens)
+            request_metadata.final_usage_info = UsageInfo(
+                prompt_tokens=num_prompt_tokens,
+                completion_tokens=num_completion_tokens,
+                total_tokens=num_prompt_tokens + num_completion_tokens)
+
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             logger.error("error in chat completion stream generator: %s", e)
@@ -595,6 +611,7 @@ async def chat_completion_full_generator(
         request_id: str,
         conversation: List[ConversationMessage],
         tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
     ) -> Union[ErrorResponse, ChatCompletionResponse]:
 
         model_name = self.base_model_paths[0].name
@@ -714,6 +731,9 @@ async def chat_completion_full_generator(
             completion_tokens=num_generated_tokens,
             total_tokens=num_prompt_tokens + num_generated_tokens,
         )
+
+        request_metadata.final_usage_info = usage
+
         response = ChatCompletionResponse(
             id=request_id,
             created=created_time,

vllm/entrypoints/openai/serving_completion.py

@@ -18,7 +18,9 @@
                                               CompletionResponseChoice,
                                               CompletionResponseStreamChoice,
                                               CompletionStreamResponse,
-                                              ErrorResponse, UsageInfo)
+                                              ErrorResponse,
+                                              RequestResponseMetadata,
+                                              UsageInfo)
 # yapf: enable
 from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
                                                     LoRAModulePath,
@@ -94,6 +96,10 @@ async def create_completion(
         request_id = f"cmpl-{random_uuid()}"
         created_time = int(time.time())
 
+        request_metadata = RequestResponseMetadata(request_id=request_id)
+        if raw_request:
+            raw_request.state.request_metadata = request_metadata
+
         # Schedule the request and get the result generator.
         generators: List[AsyncGenerator[RequestOutput, None]] = []
         try:
@@ -165,13 +171,15 @@ async def create_completion(
 
         # Streaming response
         if stream:
-            return self.completion_stream_generator(request,
-                                                    result_generator,
-                                                    request_id,
-                                                    created_time,
-                                                    model_name,
-                                                    num_prompts=len(prompts),
-                                                    tokenizer=tokenizer)
+            return self.completion_stream_generator(
+                request,
+                result_generator,
+                request_id,
+                created_time,
+                model_name,
+                num_prompts=len(prompts),
+                tokenizer=tokenizer,
+                request_metadata=request_metadata)
 
         # Non-streaming response
         final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
@@ -198,6 +206,7 @@ async def create_completion(
                 created_time,
                 model_name,
                 tokenizer,
+                request_metadata,
             )
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
@@ -227,6 +236,7 @@ async def completion_stream_generator(
         model_name: str,
         num_prompts: int,
         tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
     ) -> AsyncGenerator[str, None]:
         num_choices = 1 if request.n is None else request.n
         previous_text_lens = [0] * num_choices * num_prompts
@@ -346,6 +356,14 @@ async def completion_stream_generator(
                     exclude_unset=False, exclude_none=True))
                 yield f"data: {final_usage_data}\n\n"
 
+            # report to FastAPI middleware aggregate usage across all choices
+            total_prompt_tokens = sum(num_prompt_tokens)
+            total_completion_tokens = sum(previous_num_tokens)
+            request_metadata.final_usage_info = UsageInfo(
+                prompt_tokens=total_prompt_tokens,
+                completion_tokens=total_completion_tokens,
+                total_tokens=total_prompt_tokens + total_completion_tokens)
+
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             data = self.create_streaming_error_response(str(e))
@@ -360,6 +378,7 @@ def request_output_to_completion_response(
         created_time: int,
         model_name: str,
         tokenizer: AnyTokenizer,
+        request_metadata: RequestResponseMetadata,
     ) -> CompletionResponse:
         choices: List[CompletionResponseChoice] = []
         num_prompt_tokens = 0
@@ -433,6 +452,8 @@ def request_output_to_completion_response(
             total_tokens=num_prompt_tokens + num_generated_tokens,
         )
 
+        request_metadata.final_usage_info = usage
+
         return CompletionResponse(
             id=request_id,
             created=created_time,