Support min-p sampling

docs/en/sampling_params.md

@@ -45,6 +45,8 @@ temperature: float = 1.0,
 top_p: float = 1.0,
 # Top-k sampling
 top_k: int = -1,
+# Min-p sampling
+min_p: float = 0.0,
 # Whether to ignore EOS token.
 ignore_eos: bool = False,
 # Whether to skip the special tokens during detokenization.

python/sglang/api.py

@@ -66,6 +66,7 @@ def gen(
     temperature: Optional[float] = None,
     top_p: Optional[float] = None,
     top_k: Optional[int] = None,
+    min_p: Optional[float] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
     ignore_eos: Optional[bool] = None,
@@ -103,6 +104,7 @@ def gen(
         temperature,
         top_p,
         top_k,
+        min_p,
         frequency_penalty,
         presence_penalty,
         ignore_eos,
@@ -123,6 +125,7 @@ def gen_int(
     temperature: Optional[float] = None,
     top_p: Optional[float] = None,
     top_k: Optional[int] = None,
+    min_p: Optional[float] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
     ignore_eos: Optional[bool] = None,
@@ -139,6 +142,7 @@ def gen_int(
         temperature,
         top_p,
         top_k,
+        min_p,
         frequency_penalty,
         presence_penalty,
         ignore_eos,
@@ -159,6 +163,7 @@ def gen_string(
     temperature: Optional[float] = None,
     top_p: Optional[float] = None,
     top_k: Optional[int] = None,
+    min_p: Optional[float] = None,
     frequency_penalty: Optional[float] = None,
     presence_penalty: Optional[float] = None,
     ignore_eos: Optional[bool] = None,
@@ -175,6 +180,7 @@ def gen_string(
         temperature,
         top_p,
         top_k,
+        min_p,
         frequency_penalty,
         presence_penalty,
         ignore_eos,

python/sglang/lang/compiler.py

@@ -130,6 +130,7 @@ def run(
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: float = 0.0,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         backend=None,
@@ -145,6 +146,7 @@ def run(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
         )
@@ -160,6 +162,7 @@ def run_batch(
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: float = 0.0,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         backend=None,
@@ -178,6 +181,7 @@ def run_batch(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
         )

python/sglang/lang/interpreter.py

@@ -663,6 +663,7 @@ def _resolve_sampling_params(self, sampling_params):
             "temperature",
             "top_p",
             "top_k",
+            "min_p",
             "frequency_penalty",
             "presence_penalty",
             "ignore_eos",

python/sglang/lang/ir.py

@@ -22,6 +22,7 @@ class SglSamplingParams:
     temperature: float = 1.0
     top_p: float = 1.0
     top_k: int = -1  # -1 means disable
+    min_p: float = 0.0
     frequency_penalty: float = 0.0
     presence_penalty: float = 0.0
     ignore_eos: bool = False
@@ -42,6 +43,7 @@ def clone(self):
             self.temperature,
             self.top_p,
             self.top_k,
+            self.min_p,
             self.frequency_penalty,
             self.presence_penalty,
             self.ignore_eos,
@@ -114,6 +116,7 @@ def to_srt_kwargs(self):
             "temperature": self.temperature,
             "top_p": self.top_p,
             "top_k": self.top_k,
+            "min_p": self.min_p,
             "frequency_penalty": self.frequency_penalty,
             "presence_penalty": self.presence_penalty,
             "ignore_eos": self.ignore_eos,
@@ -149,6 +152,7 @@ def run(
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: float = 0.0,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         ignore_eos: bool = False,
@@ -169,6 +173,7 @@ def run(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
             ignore_eos=ignore_eos,
@@ -190,6 +195,7 @@ def run_batch(
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: float = 0.0,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         ignore_eos: bool = False,
@@ -228,6 +234,7 @@ def run_batch(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
             ignore_eos=ignore_eos,
@@ -408,6 +415,7 @@ def __init__(
         temperature: Optional[float] = None,
         top_p: Optional[float] = None,
         top_k: Optional[int] = None,
+        min_p: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         presence_penalty: Optional[float] = None,
         ignore_eos: Optional[bool] = None,
@@ -428,6 +436,7 @@ def __init__(
             temperature=temperature,
             top_p=top_p,
             top_k=top_k,
+            min_p=min_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
             ignore_eos=ignore_eos,

python/sglang/srt/managers/schedule_batch.py

@@ -21,7 +21,12 @@
 
 import torch
 import torch.distributed as dist
-from flashinfer.sampling import top_k_top_p_sampling_from_probs
+from flashinfer.sampling import (
+    min_p_sampling_from_probs,
+    top_k_renorm_prob,
+    top_k_top_p_sampling_from_probs,
+    top_p_renorm_prob,
+)
 from vllm.distributed import get_tensor_model_parallel_group
 
 import sglang.srt.sampling.penaltylib as penaltylib
@@ -339,6 +344,7 @@ class ScheduleBatch:
     temperatures: torch.Tensor = None
     top_ps: torch.Tensor = None
     top_ks: torch.Tensor = None
+    min_ps: torch.Tensor = None
     penalizer_orchestrator: penaltylib.BatchedPenalizerOrchestrator = None
     logit_bias: torch.Tensor = None
 
@@ -403,6 +409,9 @@ def batch_sampling_params(self, vocab_size):
         self.top_ks = torch.tensor(
             [r.sampling_params.top_k for r in reqs], dtype=torch.int, device=device
         )
+        self.min_ps = torch.tensor(
+            [r.sampling_params.min_p for r in reqs], dtype=torch.float, device=device
+        )
 
         # Each penalizers will do nothing if they evaluate themselves as not required by looking at
         # the sampling_params of the requests (See {_is_required()} of each penalizers). So this
@@ -701,6 +710,7 @@ def filter_batch(self, unfinished_indices: List[int]):
             "temperatures",
             "top_ps",
             "top_ks",
+            "min_ps",
             "logit_bias",
         ]:
             self_val = getattr(self, item, None)
@@ -730,6 +740,7 @@ def merge(self, other: "ScheduleBatch"):
             "temperatures",
             "top_ps",
             "top_ks",
+            "min_ps",
         ]:
             self_val = getattr(self, item, None)
             other_val = getattr(other, item, None)
@@ -780,13 +791,20 @@ def sample(self, logits: torch.Tensor):
             uniform_samples = torch.rand(
                 (max_top_k_round, batch_size), device=probs.device
             )
-            batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
-                probs, uniform_samples, self.top_ks, self.top_ps
-            )
+            if self.min_ps.any():
+                probs = top_k_renorm_prob(probs, self.top_ks)
+                probs = top_p_renorm_prob(probs, self.top_ps)
+                batch_next_token_ids, success = min_p_sampling_from_probs(
+                    probs, uniform_samples, self.min_ps
+                )
+            else:
+                batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
+                    probs, uniform_samples, self.top_ks, self.top_ps
+                )
         else:
             # Here we provide a slower fallback implementation.
-            batch_next_token_ids, success = top_k_top_p_sampling_from_probs_torch(
-                probs, self.top_ks, self.top_ps
+            batch_next_token_ids, success = top_k_top_p_min_p_sampling_from_probs_torch(
+                probs, self.top_ks, self.top_ps, self.min_ps
             )
 
         if not torch.all(success):
@@ -810,17 +828,22 @@ def sample(self, logits: torch.Tensor):
         return batch_next_token_ids
 
 
-def top_k_top_p_sampling_from_probs_torch(
-    probs: torch.Tensor, top_ks: torch.Tensor, top_ps: torch.Tensor
+def top_k_top_p_min_p_sampling_from_probs_torch(
+    probs: torch.Tensor,
+    top_ks: torch.Tensor,
+    top_ps: torch.Tensor,
+    min_ps: torch.Tensor,
 ):
-    """A top-k and top-k sampling implementation with native pytorch operations."""
+    """A top-k, top-p and min-p sampling implementation with native pytorch operations."""
     probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
     probs_sum = torch.cumsum(probs_sort, dim=-1)
+    min_p_thresholds = probs_sort[:, 0] * min_ps
     probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
     probs_sort[
         torch.arange(0, probs.shape[-1], device=probs.device).view(1, -1)
         >= top_ks.view(-1, 1)
     ] = 0.0
+    probs_sort[probs_sort < min_p_thresholds.view(-1, 1)] = 0.0
     probs_sort.div_(probs_sort.max(dim=-1, keepdim=True)[0])
     try:
         sampled_index = torch.multinomial(probs_sort, num_samples=1)

python/sglang/srt/sampling_params.py

@@ -30,6 +30,7 @@ def __init__(
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
+        min_p: float = 0.0,
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         repetition_penalty: float = 1.0,
@@ -42,6 +43,7 @@ def __init__(
         self.temperature = temperature
         self.top_p = top_p
         self.top_k = top_k
+        self.min_p = min_p
         self.frequency_penalty = frequency_penalty
         self.presence_penalty = presence_penalty
         self.repetition_penalty = repetition_penalty
@@ -69,6 +71,8 @@ def verify(self):
             )
         if not 0.0 < self.top_p <= 1.0:
             raise ValueError(f"top_p must be in (0, 1], got {self.top_p}.")
+        if not 0.0 <= self.min_p <= 1.0:
+            raise ValueError(f"min_p must be in [0, 1], got {self.min_p}.")
         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}."