Fix sampler nan check when calling top_k_top_p_sampling_from_probs (#5546)

python/sglang/srt/layers/sampler.py

@@ -100,17 +100,16 @@ class Sampler(nn.Module):
                         probs, sampling_info.min_ps
                     )
                 else:
+                    # Check Nan will throw exception, only check when crash_on_warnings is True
+                    check_nan = self.use_nan_detection and crash_on_warnings()
                     batch_next_token_ids = top_k_top_p_sampling_from_probs(
                         probs,
                         sampling_info.top_ks,
                         sampling_info.top_ps,
                         filter_apply_order="joint",
+                        check_nan=check_nan,
                     )
 
-                    if self.use_nan_detection:
-                        logger.warning("Detected errors during sampling!")
-                        batch_next_token_ids = torch.zeros_like(batch_next_token_ids)
-
             elif global_server_args_dict["sampling_backend"] == "pytorch":
                 # A slower fallback implementation with torch native operations.
                 batch_next_token_ids = top_k_top_p_min_p_sampling_from_probs_torch(

sgl-kernel/python/sgl_kernel/sampling.py

-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 
 import torch
 from sgl_kernel.utils import _to_tensor_scalar_tuple, get_cuda_stream
@@ -109,7 +109,7 @@ def _top_p_sampling_from_probs_internal(
     top_p_val: float,
     deterministic: bool,
     generator: Optional[torch.Generator],
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> torch.Tensor:
     with probs.device as device:
         probs = probs.float()
         maybe_top_p_arr = (
@@ -135,7 +135,7 @@ def top_p_sampling_from_probs(
     deterministic: bool = True,
     generator: Optional[torch.Generator] = None,
     check_nan: bool = False,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> torch.Tensor:
     r"""Adapt from https://github.com/flashinfer-ai/flashinfer/flashinfer/sampling.py
     Fused GPU kernel for top-p sampling (nucleus sampling) from probabilities,
     this operator implements GPU-based rejection sampling without explicit sorting.
@@ -194,7 +194,7 @@ def _top_k_top_p_sampling_from_probs_internal(
     top_p_val: float,
     deterministic: bool,
     generator: Optional[torch.Generator],
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> torch.Tensor:
     with probs.device as device:
         probs = probs.float()
         maybe_top_k_arr = maybe_top_k_arr.int() if maybe_top_k_arr is not None else None
@@ -225,7 +225,7 @@ def top_k_top_p_sampling_from_probs(
     deterministic: bool = True,
     generator: Optional[torch.Generator] = None,
     check_nan: bool = False,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> torch.Tensor:
     r"""Adapt from https://github.com/flashinfer-ai/flashinfer/flashinfer/sampling.py
     Fused GPU kernel for top-k and top-p sampling from probabilities,