Optimize conflicts between CUDA graph and vocab mask tensors (#1392)

python/sglang/srt/layers/sampler.py

@@ -35,21 +35,6 @@ class Sampler(CustomOp):
         self.forward_native = self.forward_cuda
         self.is_torch_compile = False
 
-    def _apply_penalties(self, logits: torch.Tensor, sampling_info: SamplingBatchInfo):
-        # min-token, presence, frequency
-        if sampling_info.linear_penalties is not None:
-            logits += sampling_info.linear_penalties
-
-        # repetition
-        if sampling_info.scaling_penalties is not None:
-            logits = torch.where(
-                logits > 0,
-                logits / sampling_info.scaling_penalties,
-                logits * sampling_info.scaling_penalties,
-            )
-
-        return logits
-
     def _get_probs(self, logits: torch.Tensor, sampling_info: SamplingBatchInfo):
         # Post process logits
         logits = logits.contiguous()
@@ -58,14 +43,6 @@ class Sampler(CustomOp):
             # FIXME: Temporary workaround for unknown bugs in torch.compile
             logits.add_(0)
 
-        if sampling_info.logit_bias is not None:
-            logits.add_(sampling_info.logit_bias)
-
-        if sampling_info.vocab_mask is not None:
-            logits = logits.masked_fill(sampling_info.vocab_mask, float("-inf"))
-
-        logits = self._apply_penalties(logits, sampling_info)
-
         return torch.softmax(logits, dim=-1)
 
     def forward_cuda(