Fix the incompatibility issue caused by top_p=0 when using vllm to inference (#1265)

lmms_eval/models/chat/vllm.py

@@ -69,12 +69,7 @@ def make_one_request(self, request: Instance) -> Tuple[list[dict], dict]:
         _gen["max_new_tokens"] = self._select_max_new_tokens(_gen.get("max_new_tokens"))
         _gen.setdefault("temperature", 0)
         _gen.setdefault("top_p", 0.95)
-
-        params = {
-            "temperature": _gen["temperature"],
-            "max_tokens": _gen["max_new_tokens"],
-            "top_p": _gen["top_p"],
-        }
+        params = self._build_sampling_params_dict(_gen)
 
         video_kwargs = {
             "max_pixels": self.max_pixels,

lmms_eval/models/chat/vllm_generate.py

@@ -90,12 +90,7 @@ def make_one_request(self, request: Instance) -> Tuple[list[dict], dict]:
         _gen["max_new_tokens"] = self._select_max_new_tokens(_gen.get("max_new_tokens"))
         _gen.setdefault("temperature", 0)
         _gen.setdefault("top_p", 0.95)
-
-        params = {
-            "temperature": _gen["temperature"],
-            "max_tokens": _gen["max_new_tokens"],
-            "top_p": _gen["top_p"],
-        }
+        params = self._build_sampling_params_dict(_gen)
 
         video_kwargs = {
             "max_pixels": self.max_pixels,

lmms_eval/models/simple/vllm.py

@@ -337,6 +337,25 @@ def _select_max_new_tokens(self, request_max_new_tokens: Any) -> int:
             return self.max_new_tokens
         return max(request_max_new_tokens, self.max_new_tokens)
 
+    @staticmethod
+    def _normalize_top_p_for_vllm(top_p: Any) -> Any:
+        if isinstance(top_p, bool):
+            return top_p
+        try:
+            numeric_top_p = float(top_p)
+        except (TypeError, ValueError):
+            return top_p
+        if numeric_top_p == 0.0:
+            return 1.0
+        return top_p
+
+    def _build_sampling_params_dict(self, gen_kwargs: dict[str, Any]) -> dict[str, Any]:
+        return {
+            "max_tokens": gen_kwargs["max_new_tokens"],
+            "temperature": gen_kwargs["temperature"],
+            "top_p": self._normalize_top_p_for_vllm(gen_kwargs["top_p"]),
+        }
+
     def _run_tp_synced(
         self,
         local_inputs: list[Any],
@@ -456,13 +475,7 @@ def generate_until(self, requests) -> List[str]:
                     gen_kwargs["max_new_tokens"] = self._select_max_new_tokens(gen_kwargs.get("max_new_tokens"))
                     gen_kwargs.setdefault("temperature", 0)
                     gen_kwargs.setdefault("top_p", 0.95)
-
-                    params = {
-                        "max_tokens": gen_kwargs["max_new_tokens"],
-                        "temperature": gen_kwargs["temperature"],
-                        "top_p": gen_kwargs["top_p"],
-                    }
-                    sampling_params = SamplingParams(**params)
+                    sampling_params = SamplingParams(**self._build_sampling_params_dict(gen_kwargs))
 
                     visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
                     if None in visuals: