perf(triton): avoid per-step D2H .item() sync in cuda-graph loc translate (#29921)

Co-authored-by: lch1475369 <lch1475369@gmail.com>

python/sglang/srt/layers/attention/triton_backend.py

@@ -636,15 +636,31 @@ class TritonAttnBackend(AttentionBackend):
         """
         if self._translate_kv_loc is None:
             return None
-        # Full-attention read path.
-        n_kv = int(self.kv_indptr[bs].item())
+        # seq_lens_sum is the reliable "mirror present" signal: it is
+        # None-preserving into the replay view, unlike seq_lens_cpu (always a
+        # non-None but stale slice for gpu_only batches). None -> fall back to a
+        # per-step D2H `.item()` on the indptr.
+        have_cpu_mirror = forward_batch.seq_lens_sum is not None
+        # Full-attention read path. kv_indptr[bs] == seq_lens_sum.
+        n_kv = (
+            forward_batch.seq_lens_sum
+            if have_cpu_mirror
+            else int(self.kv_indptr[bs].item())
+        )
         if n_kv > 0:
             self.cuda_graph_kv_indices[:n_kv] = self._translate_kv_loc(
                 self.cuda_graph_kv_indices[:n_kv]
             )
-        # SWA window read path (hybrid-SWA unified pools only).
+        # SWA window read path. window_kv_indptr[bs] == sum(min(seq_len, window)).
         if self.sliding_window_size is not None and self.sliding_window_size > 0:
-            n_win = int(self.window_kv_indptr[bs].item())
+            if have_cpu_mirror:
+                n_win = int(
+                    forward_batch.seq_lens_cpu[:bs]
+                    .clamp(max=self.sliding_window_size)
+                    .sum()
+                )
+            else:
+                n_win = int(self.window_kv_indptr[bs].item())
             if n_win > 0:
                 self.cuda_graph_window_kv_indices[:n_win] = (
                     self.token_to_kv_pool.translate_loc_from_full_to_swa(