feat: /v1/query/execute endpoint for span queries

requirements/common.txt

@@ -52,4 +52,5 @@ anthropic >= 0.71.0
 model-hosting-container-standards >= 0.1.13, < 1.0.0
 mcp
 grpcio
-grpcio-reflection
+grpcio-reflection
+spnl >= 0.21.0

vllm/entrypoints/openai/api_server.py

@@ -263,6 +263,127 @@ def load_log_config(log_config_file: str | None) -> dict | None:
         )
         return None
 
+if envs.VLLM_V1_SPANS_ENABLED:
+    import spnl
+    import time
+    from fastapi import Body
+    from vllm import SamplingParams
+    from vllm.inputs import TokensPrompt
+    from vllm.outputs import RequestOutput
+    from vllm.entrypoints.openai.protocol import (ChatMessage,ChatCompletionStreamResponse,ChatCompletionResponseStreamChoice,ChatCompletionResponseChoice,DeltaMessage,UsageInfo)
+    spnl_state = spnl.init(10)
+    PAD_TOKEN = 27
+    PLUS_TOKEN = envs.VLLM_V1_SPANS_TOKEN_PLUS if envs.VLLM_V1_SPANS_TOKEN_PLUS >= 0 else None
+    CROSS_TOKEN = envs.VLLM_V1_SPANS_TOKEN_CROSS if envs.VLLM_V1_SPANS_TOKEN_CROSS >= 0 else None
+    def wrap(prompt: str | list[str]) -> TokensPrompt:
+        if isinstance(prompt[0], list):
+            return [TokensPrompt(prompt_token_ids=p) for p in prompt]
+        return TokensPrompt(prompt_token_ids=prompt)
+    @router.post("/v1/query/execute")
+    @with_cancellation
+    @load_aware_call
+    async def execute_query(raw_request: Request,
+                            query: str = Body(..., media_type="text/plain"),
+                            stream: bool = False):
+        req = spnl.tokenize_query(
+            spnl_state,
+            query,
+            PAD_TOKEN,
+            CROSS_TOKEN,
+            PLUS_TOKEN,
+            raw_request.app.state.vllm_config.cache_config.block_size
+        )
+
+        match req:
+            case spnl.TokenizedQuery.TokenizedChatCompletionQuery(q):
+                req = q # intentional fall-through
+            case spnl.TokenizedQuery.CompletionRequest(q):
+                request = CompletionRequest(model=q.model, max_tokens=q.max_tokens, temperature=q.temperature, prompt=q.inputs, stream=stream)
+                # what we want to do, but this is a fastapi endpoint... return create_completion(request, raw_request)
+                handler = completion(raw_request)
+                if handler is None:
+                    return base(raw_request).create_error_response(
+                        message="The model does not support Completions API")
+
+                try:
+                    generator = await handler.create_completion(request, raw_request)
+                except OverflowError as e:
+                    raise HTTPException(status_code=HTTPStatus.BAD_REQUEST.value,
+                                        detail=str(e)) from e
+                except Exception as e:
+                    raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
+                                        detail=str(e)) from e
+
+                if isinstance(generator, ErrorResponse):
+                    return JSONResponse(content=generator.model_dump(),
+                                        status_code=generator.error.code)
+                elif isinstance(generator, CompletionResponse):
+                    return JSONResponse(content=generator.model_dump())
+
+                return StreamingResponse(content=generator, media_type="text/event-stream")
+
+        request_id = raw_request.headers.get(
+            "X-Request-Id") or uuid.uuid4().hex
+        client = engine_client(raw_request)
+        generator = client.generate(wrap(req.messages), SamplingParams(n=1 if req.n <= 0 else req.n,temperature=req.temperature if req.temperature is not None else 0,max_tokens=req.max_tokens if req.max_tokens is not None and req.max_tokens != 0 else 2048), request_id)
+
+        if stream:
+            async def sgen():
+                output_idx: List[int] = [0 for _ in range(req.n)]
+                async for res in generator:
+                    yield ChatCompletionStreamResponse(
+                        id=request_id,
+                        object="chat.completion.chunk",
+                        created=int(time.time()),
+                        model=req.model,
+                        choices=[
+                            ChatCompletionResponseStreamChoice(
+                                index=index,
+                                delta=DeltaMessage(role="assistant", content=output.text[output_idx[index]:]),
+                                logprobs=output.logprobs,
+                                finish_reason=output.finish_reason,
+                                stop_reason=output.stop_reason,
+                            )
+                            for index, output in enumerate(res.outputs)
+                        ]
+                    ).model_dump_json(exclude_unset=True)
+                    for index, output in enumerate(res.outputs):
+                        output_idx[index] = len(output.text)
+            return StreamingResponse(content=sgen(), media_type="text/event-stream")
+
+        outputs: List[Optional[CompletionOutput]] = [None for _ in range(req.n)]
+        async for res in generator:
+            for output in res.outputs:
+                outputs[output.index] = output
+        choices = [
+            ChatCompletionResponseChoice(
+                index=index,
+                message=ChatMessage(role="assistant", content=output.text),
+                logprobs=output.logprobs,
+                finish_reason=output.finish_reason,
+                stop_reason=output.stop_reason,
+            )
+            for index, output in enumerate(outputs)
+        ]
+        num_prompt_tokens=0 # TODO
+        num_generated_tokens=0 # TODO
+        usage = UsageInfo(prompt_tokens=num_prompt_tokens,
+                          completion_tokens=num_generated_tokens,
+                          total_tokens=num_prompt_tokens +
+                          num_generated_tokens)
+        response = ChatCompletionResponse(
+            id=request_id,
+            created=int(time.time()),
+            model=req.model,
+            choices=choices,
+            usage=usage
+        )
+
+        if isinstance(generator, ErrorResponse):
+            return JSONResponse(content=generator.model_dump(),
+                                status_code=generator.error.code)
+        return JSONResponse(content=response.model_dump())
+
 
 class AuthenticationMiddleware:
     """

vllm/v1/core/kv_cache_manager.py

@@ -116,6 +116,7 @@ def __init__(
         # potential configs we could expose in the future.
         self.prefix_cache_stats = PrefixCacheStats() if log_stats else None
 
+        self.hash_block_size = hash_block_size
         self.coordinator = get_kv_cache_coordinator(
             kv_cache_config=kv_cache_config,
             max_model_len=self.max_model_len,
@@ -192,6 +193,9 @@ def get_computed_blocks(self, request: Request) -> tuple[KVCacheBlocks, int]:
                 request.block_hashes, max_cache_hit_length
             )
         )
+        if len(request.block_hashes) > 0:
+            bs = self.hash_block_size  # spnl: cache hit rate logging
+            print(f"vLLMCacheHitRate {100*(num_new_computed_tokens/(len(request.block_hashes)*bs)):.2f}% computed={num_new_computed_tokens} requested={len(request.block_hashes)*bs}", flush=True)
 
         if self.log_stats:
             assert self.prefix_cache_stats is not None