[Feature] support min_p_sampling (#2872)

* Fastdeploy support min_p

* add test_min_p

* fix

* min_p_sampling

* update

* delete vl_gpu_model_runner.py

* fix

* Align usage of min_p with vLLM

* fix

* modified unit test

* fix test_min_sampling

* pre-commit all files

* fix

* fix

* fix

* fix xpu_model_runner.py

fastdeploy/engine/sampling_params.py

@@ -53,6 +53,9 @@ class SamplingParams:
         top_p: Float that controls the cumulative probability of the top tokens
             to consider. Must be in [0, 1]. Set to 1 to consider all tokens.
         top_k: Int that controls the number of top tokens to consider. Must be a positive integer.
+        min_p: Float that represents the minimum probability for a token to be
+            considered, relative to the probability of the most likely token.
+            Must be in [0, 1]. Set to 0 to disable this.
         seed: Random seed to use for the generation.
         stop: list of strings that stop the generation when they are generated.
             The returned output will not contain the stop strings.
@@ -84,6 +87,7 @@ class SamplingParams:
     temperature: float = None
     top_p: float = None
     top_k: int = 0
+    min_p: float = 0.0
     seed: Optional[int] = None
     stop: Optional[Union[str, List[str]]] = None
     stop_token_ids: Optional[Union[List[List[int]], List[int]]] = None
@@ -114,6 +118,7 @@ class SamplingParams:
         temperature,
         top_p,
         top_k,
+        min_p,
         seed=None,
         stop=None,
         stop_token_ids=None,
@@ -133,6 +138,7 @@ class SamplingParams:
             temperature=temperature if temperature is not None else 1.0,
             top_p=top_p,
             top_k=top_k if top_k is not None else 0,
+            min_p=min_p if min_p is not None else 0.0,
             seed=seed,
             stop=stop,
             stop_token_ids=stop_token_ids,
@@ -170,6 +176,8 @@ class SamplingParams:
             raise ValueError(f"top_k must be 0 (disable), or at least 1, " f"got {self.top_k}.")
         if not isinstance(self.top_k, int):
             raise TypeError(f"top_k must be an integer, got {type(self.top_k).__name__}")
+        if not 0.0 <= self.min_p <= 1.0:
+            raise ValueError("min_p must be in [0,1],got f{self.min_p}")
 
         if self.max_tokens is not None and self.max_tokens < 1:
             raise ValueError(f"max_tokens must be at least 1, got {self.max_tokens}.")