fix return_token_log_probs on vLLM > 0.3.3 endpoints

model-engine/model_engine_server/inference/vllm/requirements.txt

@@ -1,2 +1,2 @@
-vllm==0.3.3
+vllm==0.4.0.post1
 pydantic>=2.0

model-engine/model_engine_server/inference/vllm/vllm_server.py

@@ -4,7 +4,7 @@
 import signal
 import subprocess
 import traceback
-from typing import AsyncGenerator
+from typing import AsyncGenerator, Dict, List, Optional
 
 import uvicorn
 from fastapi import BackgroundTasks, FastAPI, HTTPException, Request
@@ -13,7 +13,9 @@
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import CompletionRequest as OpenAICompletionRequest
 from vllm.model_executor.guided_decoding import get_guided_decoding_logits_processor
+from vllm.outputs import CompletionOutput
 from vllm.sampling_params import SamplingParams
+from vllm.sequence import Logprob
 from vllm.utils import random_uuid
 
 TIMEOUT_KEEP_ALIVE = 5  # seconds.
@@ -76,13 +78,12 @@ async def generate(request: Request) -> Response:
     async def stream_results() -> AsyncGenerator[str, None]:
         last_output_text = ""
         async for request_output in results_generator:
+            log_probs = format_logprobs(request_output)
             ret = {
                 "text": request_output.outputs[-1].text[len(last_output_text) :],
                 "count_prompt_tokens": len(request_output.prompt_token_ids),
                 "count_output_tokens": len(request_output.outputs[0].token_ids),
-                "log_probs": (
-                    request_output.outputs[0].logprobs[-1] if sampling_params.logprobs else None
-                ),
+                "log_probs": log_probs[-1] if log_probs and sampling_params.logprobs else None,
                 "finished": request_output.finished,
             }
             last_output_text = request_output.outputs[-1].text
@@ -116,7 +117,7 @@ async def abort_request() -> None:
         "text": final_output.outputs[0].text,
         "count_prompt_tokens": len(final_output.prompt_token_ids),
         "count_output_tokens": len(final_output.outputs[0].token_ids),
-        "log_probs": final_output.outputs[0].logprobs,
+        "log_probs": format_logprobs(final_output),
         "tokens": tokens,
     }
     return Response(content=json.dumps(ret))
@@ -166,6 +167,18 @@ def debug(sig, frame):
     i.interact(message)
 
 
+def format_logprobs(request_output: CompletionOutput) -> Optional[List[Dict[int, float]]]:
+    """Given a request output, format the logprobs if they exist."""
+    output_logprobs = request_output.outputs[0].logprobs
+    if output_logprobs is None:
+        return None
+
+    def extract_logprobs(logprobs: Dict[int, Logprob]) -> Dict[int, float]:
+        return {k: v.logprob for k, v in logprobs.items()}
+
+    return [extract_logprobs(logprobs) for logprobs in output_logprobs]
+
+
 if __name__ == "__main__":
     check_unknown_startup_memory_usage()
     parser = argparse.ArgumentParser()

model-engine/tests/unit/inference/conftest.py

@@ -58,29 +58,37 @@ def create_batch_completions_request_content():
 
 @pytest.fixture
 def create_vllm_request_outputs():
+    class Logprob:
+        """mock, from https://github.com/vllm-project/vllm/blob/v0.4.1/vllm/sequence.py#L18"""
+
+        def __init__(self, logprob: float):
+            self.logprob = logprob
+
     mock_vllm_request_output1 = MagicMock()
     mock_vllm_request_output1.outputs = [
         MagicMock(text="text1"),
     ]
     mock_vllm_request_output1.prompt_token_ids = [1, 2, 3]
     mock_vllm_request_output1.outputs[0].token_ids = [4]
-    mock_vllm_request_output1.outputs[0].logprobs = [{4: 0.1}]
+    mock_vllm_request_output1.outputs[0].logprobs = [{4: Logprob(0.1)}]
 
     mock_vllm_request_output2 = MagicMock()
     mock_vllm_request_output2.outputs = [
         MagicMock(text="text1 text2"),
     ]
     mock_vllm_request_output2.prompt_token_ids = [1, 2, 3]
     mock_vllm_request_output2.outputs[0].token_ids = [4, 5]
-    mock_vllm_request_output2.outputs[0].logprobs = [{4: 0.1, 5: 0.2}]
+    mock_vllm_request_output2.outputs[0].logprobs = [{4: Logprob(0.1), 5: Logprob(0.2)}]
 
     mock_vllm_request_output3 = MagicMock()
     mock_vllm_request_output3.outputs = [
         MagicMock(text="text1 text2 text3"),
     ]
     mock_vllm_request_output3.prompt_token_ids = [1, 2, 3]
     mock_vllm_request_output3.outputs[0].token_ids = [4, 5, 6]
-    mock_vllm_request_output3.outputs[0].logprobs = [{4: 0.1, 5: 0.2, 6: 0.3}]
+    mock_vllm_request_output3.outputs[0].logprobs = [
+        {4: Logprob(0.1), 5: Logprob(0.2), 6: Logprob(0.3)}
+    ]
     return [mock_vllm_request_output1, mock_vllm_request_output2, mock_vllm_request_output3]