修复extern报错、精度问题，支持radix cache和chunk prefill

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

@@ -432,11 +432,18 @@ class DCUMLABackend(AttentionBackend):
         layer: "RadixAttention",
         forward_batch: ForwardBatch,
         save_kv_cache: bool = True,
+
+        # For multi-head latent attention
+        q_rope: Optional[torch.Tensor] = None,
+        k_rope: Optional[torch.Tensor] = None,
         sinks=None,
     ):
-        if (
+        if save_kv_cache:
+            return self.forward_decode(q,k,v,layer,forward_batch, save_kv_cache)
+    
+        if ((
             forward_batch.forward_mode == ForwardMode.EXTEND
-            or forward_batch.forward_mode == ForwardMode.DRAFT_EXTEND
+            or forward_batch.forward_mode == ForwardMode.DRAFT_EXTEND)
         ):
             # flash_attn不支持fp8，fp8无法正常执行extend
             if not self.skip_prefill:
@@ -444,7 +451,7 @@ class DCUMLABackend(AttentionBackend):
                 #                 q, k, v, layer, forward_batch, save_kv_cache, sinks
                 #             )
                 return self.flashattn_backend.forward_extend(
-                            q, k, v, layer, forward_batch, save_kv_cache, sinks
+                            q, k, v, layer, forward_batch, save_kv_cache, q_rope, k_rope, sinks
                         )
             else:
                 raise RuntimeError("skip prefill but use forward_extend")

python/sglang/srt/managers/schedule_batch.py

@@ -1618,7 +1618,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         self.seq_lens_cpu = self.seq_lens_cpu[keep_indices]
         self.orig_seq_lens = self.orig_seq_lens[keep_indices_device]
         self.out_cache_loc = None
-        self.seq_lens_sum = self.seq_lens.sum()
+        self.seq_lens_sum = self.seq_lens.sum().item()
         self.output_ids = self.output_ids[keep_indices_device]
         self.return_logprob = any(req.return_logprob for req in self.reqs)
         if self.return_logprob:

python/sglang/srt/model_executor/model_runner.py

@@ -174,6 +174,7 @@ MLA_ATTENTION_BACKENDS = [
 CHUNKED_PREFIX_CACHE_SUPPORTED_ATTENTION_BACKENDS = [
     "flashinfer",
     "fa3",
+    "dcu_mla",
     "fa4",
     "flashmla",
     "cutlass_mla",
@@ -2238,7 +2239,6 @@ class ModelRunner:
             and self.graph_runner
             and self.graph_runner.can_run(forward_batch)
         )
-
         if can_run_graph:
             ret = self.graph_runner.replay(
                 forward_batch,