[ModelRunner][Qwen3-Next] Fix attn_group initialization timing (#3477)

### What this PR does / why we need it?
Fix attn_group initialization timing so that fix qwen3-next model

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

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

---------

Signed-off-by: MengqingCao <cmq0113@163.com>

vllm_ascend/worker/model_runner_v1.py

@@ -2694,6 +2694,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         """
         kv_cache_config = deepcopy(kv_cache_config)
         self.kv_cache_config = kv_cache_config
+        self.may_add_encoder_only_layers_to_kv_cache_config()
+        # NOTE(cmq): initialize_attn_backend must before using self.attn_groups
+        self.initialize_attn_backend(kv_cache_config)
         self.use_hybrid_blocks = (len(self.attn_groups) > 1)
         # NOTE: Currently, we determine whether we need `num_accepted_tokens` through `MambaSpec`.
         self.need_accepted_tokens = any([
@@ -2702,8 +2705,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         ])
 
         self.may_reinitialize_input_batch(kv_cache_config)
-        self.may_add_encoder_only_layers_to_kv_cache_config()
-        self.initialize_attn_backend(kv_cache_config)
 
         if self.use_sparse:
             kv_caches = self.initialize_kv_cache_tensors_deepseek_sfa(
@@ -3100,6 +3101,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         block_sizes = [
             kv_cache_group.kv_cache_spec.block_size
             for kv_cache_group in kv_cache_config.kv_cache_groups
+            if not isinstance(kv_cache_group.kv_cache_spec,
+                              EncoderOnlyAttentionSpec)
         ]
 
         # Generate kernel_block_sizes that matches each block_size
@@ -3109,7 +3112,11 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         kernel_block_sizes = []
         for kv_cache_group_id, kv_cache_group in enumerate(
                 kv_cache_config.kv_cache_groups):
-            if isinstance(kv_cache_group.kv_cache_spec, AttentionSpec):
+
+            if isinstance(kv_cache_group.kv_cache_spec,
+                          EncoderOnlyAttentionSpec):
+                continue
+            elif isinstance(kv_cache_group.kv_cache_spec, AttentionSpec):
                 # This is an attention backend that supports virtual
                 # block splitting. Get the supported block sizes from
                 # the backend.
@@ -3137,7 +3144,10 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 # of mamba block. In this case, BlockTable.block_size will never equal
                 # to kernel_block_sizes[0]
                 kernel_block_sizes.append([0])
-        if kernel_block_sizes != [[self.cache_config.block_size]]:
+
+        if block_sizes != [
+                self.cache_config.block_size
+        ] or kernel_block_sizes != [self.cache_config.block_size]:
             assert self.cache_config.cpu_offload_gb == 0, (
                 "Cannot re-initialize the input batch when CPU weight "
                 "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 "  # noqa: E501