Support Min P Sampler (#1642)

vllm/sampling_params.py

@@ -52,6 +52,9 @@ class SamplingParams:
             to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
         top_k: Integer that controls the number of top tokens to consider. Set
             to -1 to consider all tokens.
+        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.
         use_beam_search: Whether to use beam search instead of sampling.
         length_penalty: Float that penalizes sequences based on their length.
             Used in beam search.
@@ -94,6 +97,7 @@ class SamplingParams:
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: int = 0.0,
         use_beam_search: bool = False,
         length_penalty: float = 1.0,
         early_stopping: Union[bool, str] = False,
@@ -115,6 +119,7 @@ class SamplingParams:
         self.temperature = temperature
         self.top_p = top_p
         self.top_k = top_k
+        self.min_p = min_p
         self.use_beam_search = use_beam_search
         self.length_penalty = length_penalty
         self.early_stopping = early_stopping
@@ -167,6 +172,9 @@ class SamplingParams:
         if self.top_k < -1 or self.top_k == 0:
             raise ValueError(f"top_k must be -1 (disable), or at least 1, "
                              f"got {self.top_k}.")
+        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 < 1:
             raise ValueError(
                 f"max_tokens must be at least 1, got {self.max_tokens}.")
@@ -228,6 +236,7 @@ class SamplingParams:
                 f"temperature={self.temperature}, "
                 f"top_p={self.top_p}, "
                 f"top_k={self.top_k}, "
+                f"min_p={self.min_p}, "
                 f"use_beam_search={self.use_beam_search}, "
                 f"length_penalty={self.length_penalty}, "
                 f"early_stopping={self.early_stopping}, "