Simplify the nan detection and greedy check in sampler (#1709)

python/sglang/srt/sampling/sampling_batch_info.py

@@ -20,6 +20,9 @@ class SamplingBatchInfo:
     top_ks: torch.Tensor
     min_ps: torch.Tensor
 
+    # All requests use greedy sampling
+    is_all_greedy: bool
+
     # Dispatch in CUDA graph
     need_min_p_sampling: bool
 
@@ -73,6 +76,7 @@ class SamplingBatchInfo:
             top_ks=top_ks,
             min_ps=min_ps,
             need_min_p_sampling=any(r.sampling_params.min_p > 0 for r in reqs),
+            is_all_greedy=top_ks.max().item() <= 1,
             vocab_size=vocab_size,
             device=batch.input_ids.device,
         )
@@ -204,6 +208,7 @@ class SamplingBatchInfo:
             other_val = getattr(other, item, None)
             setattr(self, item, torch.concat([self_val, other_val]))
 
+        self.is_all_greedy = self.is_all_greedy and other.is_all_greedy
         self.logit_bias = SamplingBatchInfo.merge_bias_tensor(
             self.logit_bias, other.logit_bias, len(self), len(other), self.device
         )