Auto-sync from demo at Fri Jan 9 03:05:08 UTC 2026

graphgen/bases/base_llm_wrapper.py

@@ -26,11 +26,11 @@ class BaseLLMWrapper(abc.ABC):
         **kwargs: Any,
     ):
         self.system_prompt = system_prompt
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-        self.repetition_penalty = repetition_penalty
-        self.top_p = top_p
-        self.top_k = top_k
+        self.temperature = float(temperature)
+        self.max_tokens = int(max_tokens)
+        self.repetition_penalty = float(repetition_penalty)
+        self.top_p = float(top_p)
+        self.top_k = int(top_k)
         self.tokenizer = tokenizer
 
         for k, v in kwargs.items():

graphgen/models/llm/local/vllm_wrapper.py

@@ -1,6 +1,7 @@
 import math
 import uuid
 from typing import Any, List, Optional
+import asyncio
 
 from graphgen.bases.base_llm_wrapper import BaseLLMWrapper
 from graphgen.bases.datatypes import Token
@@ -19,12 +20,9 @@ class VLLMWrapper(BaseLLMWrapper):
         temperature: float = 0.6,
         top_p: float = 1.0,
         top_k: int = 5,
+        timeout: float = 300,
         **kwargs: Any,
     ):
-        temperature = float(temperature)
-        top_p = float(top_p)
-        top_k = int(top_k)
-
         super().__init__(temperature=temperature, top_p=top_p, top_k=top_k, **kwargs)
         try:
             from vllm import AsyncEngineArgs, AsyncLLMEngine, SamplingParams
@@ -43,6 +41,7 @@ class VLLMWrapper(BaseLLMWrapper):
             disable_log_stats=False,
         )
         self.engine = AsyncLLMEngine.from_engine_args(engine_args)
+        self.timeout = float(timeout)
 
     @staticmethod
     def _build_inputs(prompt: str, history: Optional[List[str]] = None) -> str:
@@ -58,6 +57,12 @@ class VLLMWrapper(BaseLLMWrapper):
         lines.append(prompt)
         return "\n".join(lines)
 
+    async def _consume_generator(self, generator):
+        final_output = None
+        async for request_output in generator:
+            final_output = request_output
+        return final_output
+
     async def generate_answer(
         self, text: str, history: Optional[List[str]] = None, **extra: Any
     ) -> str:
@@ -72,14 +77,21 @@ class VLLMWrapper(BaseLLMWrapper):
 
         result_generator = self.engine.generate(full_prompt, sp, request_id=request_id)
 
-        final_output = None
-        async for request_output in result_generator:
-            final_output = request_output
+        try:
+            final_output = await asyncio.wait_for(
+                self._consume_generator(result_generator),
+                timeout=self.timeout
+            )
+
+            if not final_output or not final_output.outputs:
+                return ""
 
-        if not final_output or not final_output.outputs:
-            return ""
+            result_text = final_output.outputs[0].text
+            return result_text
 
-        return final_output.outputs[0].text
+        except (Exception, asyncio.CancelledError):
+            await self.engine.abort(request_id)
+            raise
 
     async def generate_topk_per_token(
         self, text: str, history: Optional[List[str]] = None, **extra: Any
@@ -91,42 +103,47 @@ class VLLMWrapper(BaseLLMWrapper):
             temperature=0,
             max_tokens=1,
             logprobs=self.top_k,
-            prompt_logprobs=1,
         )
 
         result_generator = self.engine.generate(full_prompt, sp, request_id=request_id)
 
-        final_output = None
-        async for request_output in result_generator:
-            final_output = request_output
-
-        if (
-            not final_output
-            or not final_output.outputs
-            or not final_output.outputs[0].logprobs
-        ):
-            return []
-
-        top_logprobs = final_output.outputs[0].logprobs[0]
-
-        candidate_tokens = []
-        for _, logprob_obj in top_logprobs.items():
-            tok_str = (
-                logprob_obj.decoded_token.strip() if logprob_obj.decoded_token else ""
+        try:
+            final_output = await asyncio.wait_for(
+                self._consume_generator(result_generator),
+                timeout=self.timeout
             )
-            prob = float(math.exp(logprob_obj.logprob))
-            candidate_tokens.append(Token(tok_str, prob))
 
-        candidate_tokens.sort(key=lambda x: -x.prob)
+            if (
+                not final_output
+                or not final_output.outputs
+                or not final_output.outputs[0].logprobs
+            ):
+                return []
+
+            top_logprobs = final_output.outputs[0].logprobs[0]
+
+            candidate_tokens = []
+            for _, logprob_obj in top_logprobs.items():
+                tok_str = (
+                    logprob_obj.decoded_token.strip() if logprob_obj.decoded_token else ""
+                )
+                prob = float(math.exp(logprob_obj.logprob))
+                candidate_tokens.append(Token(tok_str, prob))
+
+            candidate_tokens.sort(key=lambda x: -x.prob)
+
+            if candidate_tokens:
+                main_token = Token(
+                    text=candidate_tokens[0].text,
+                    prob=candidate_tokens[0].prob,
+                    top_candidates=candidate_tokens,
+                )
+                return [main_token]
+            return []
 
-        if candidate_tokens:
-            main_token = Token(
-                text=candidate_tokens[0].text,
-                prob=candidate_tokens[0].prob,
-                top_candidates=candidate_tokens,
-            )
-            return [main_token]
-        return []
+        except (Exception, asyncio.CancelledError):
+            await self.engine.abort(request_id)
+            raise
 
     async def generate_inputs_prob(
         self, text: str, history: Optional[List[str]] = None, **extra: Any