[Fix] Refactor dummy attention metadata creation (#3497)

### What this PR does / why we need it?
The `force_attention` parameter is designed for flash infer kernel
warmup, we don't actually need it on Ascend device (at least for
now).And it tends to make things more complicated. So we replace the
`force_attention` parameter with `aclgraph_runtime_mode` in the
attention metadata creation logic.

This change makes the control flow more explicit by directly using the
graph runtime mode to determine how to build attention metadata, rather
than relying on an intermediate boolean flag. This simplification
removes redundant logic and clarifies the conditions for building
attention metadata for full decode graph mode.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
DP + `FULL_DECODE_ONLY` + online serving.

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/worker/model_runner_v1.py

@@ -2250,18 +2250,24 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 scheduler_output.finished_req_ids)
         return None, None
 
-    def _build_attention_metadata(self, create_mixed_batch, num_reqs,
-                                  num_tokens, max_query_len, force_attention):
+    def _build_dummy_attn_metadata(
+        self,
+        with_prefill: bool,
+        num_reqs: int,
+        num_tokens: int,
+        max_query_len: int,
+        aclgraph_runtime_mode: Optional[CUDAGraphMode] = None,
+        force_attention: bool = False,
+    ) -> Optional[dict[str, Any]]:
         attn_metadata: Optional[dict[str, Any]] = None
 
-        if force_attention:
+        if force_attention or aclgraph_runtime_mode == CUDAGraphMode.FULL:
+            assert with_prefill is False, \
+                "Full decode graph only supports uniform batch now."
+
             attn_metadata = {}
 
-            if create_mixed_batch:
-                raise NotImplementedError(
-                    "force_attention=True is not supported for mixed batches.")
-            else:
-                seq_lens = self.model_config.max_model_len
+            seq_lens = self.model_config.max_model_len
             self.seq_lens_np[:num_reqs] = seq_lens
             self.seq_lens_np[num_reqs:] = 0
 
@@ -2321,7 +2327,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         forward_context = get_forward_context()
         assert forward_context is not None
         if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL and \
-            not forward_context.capturing:
+            not forward_context.capturing and forward_context.attn_metadata is not None:
             if self.vllm_config.model_config.use_mla:
                 # FIXME: Try using `auto_dispatch_capture=True`
                 update_mla_attn_params(self.update_stream, forward_context,
@@ -2409,12 +2415,14 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         if self.is_kv_producer and not self.is_kv_consumer:
             with_prefill = True
 
-        # TODO(cmq): check if with_prefill is reasonable
-        attn_metadata = self._build_attention_metadata(
+        # TODO(Mengqing): Set create_mixed_batch to False since it's only used in FI warmup
+        # and not supported in ASCEND now. We could remove it in the future.
+        attn_metadata = self._build_dummy_attn_metadata(
             False,
             num_reqs=num_reqs,
             num_tokens=num_tokens,
             max_query_len=max_query_len,
+            aclgraph_runtime_mode=aclgraph_runtime_mode,
             force_attention=force_attention,
         )