[Refactor] Adapt deepseek-v3.2 to vllm 0.11.0 (#3432)

### What this PR does / why we need it?
Adapt deepseek-v3.2 to vllm 0.11.0, removing the useless patch.

The final goal is to remove all the patches and align the code arch to
vllm, thus we need to do the following work in next prs.
TODO:
- [x] remove patch on attention spec
- [ ] refactor the kvcache creation logic

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
1. CI passed with existing test.
2. Test pass with deepseek-v3.2-exp


- 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

@@ -309,13 +309,14 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 dtype=self.dtype,
                 device=self.device)
         # Set up Attention
-        self.attn_backend = get_attn_backend(
-            0,
-            self.dtype,
-            None,
-            self.block_size,
-            use_mla=self.model_config.use_mla,
-            use_sfa=self.ascend_config.use_sfa)
+        self.use_sparse = hasattr(self.vllm_config.model_config.hf_config,
+                                  "index_topk")
+        self.attn_backend = get_attn_backend(0,
+                                             self.dtype,
+                                             None,
+                                             self.block_size,
+                                             use_mla=self.model_config.use_mla,
+                                             use_sparse=self.use_sparse)
         if torch.version.cann.startswith("8.3"):
             self.attn_mask_builder = AttentionMaskBuilder(
                 self.scheduler_config.max_num_batched_tokens, self.dtype,
@@ -871,7 +872,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         if self.model_config.runner_type == "pooling" and self.model_config.pooler_config.pooling_type == "CLS":
             return self.attn_mask_builder.get_pooling_mask(self.device)
         # Chunk Prefill situation.
-        elif attn_state == AscendAttentionState.ChunkedPrefill and not self.vllm_config.model_config.use_mla and not self.ascend_config.use_sfa:
+        elif attn_state == AscendAttentionState.ChunkedPrefill and not self.vllm_config.model_config.use_mla and not self.use_sparse:
             if torch.version.cann.startswith("8.3"):
                 return self.attn_mask_builder.get_splitfuse_attn_mask()
             else:
@@ -1507,7 +1508,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                         model=self.get_model(),
                         **extra_attn_metadata_args)
 
-                if self.vllm_config.model_config.use_mla or self.ascend_config.use_sfa:
+                if self.vllm_config.model_config.use_mla or self.use_sparse:
                     attn_metadata_i.num_input_tokens = num_input_tokens
                 for layer_name in attn_group.layer_names:
                     attn_metadata[layer_name] = attn_metadata_i
@@ -2655,7 +2656,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self.may_add_encoder_only_layers_to_kv_cache_config()
         self.initialize_attn_backend(kv_cache_config)
 
-        if self.ascend_config.is_deepseek_sfa:
+        if self.use_sparse:
             kv_caches = self.initialize_kv_cache_tensors_deepseek_sfa(
                 kv_cache_config)
         elif self.model_config.is_deepseek_mla:
@@ -2699,7 +2700,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                         kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
                 elif hasattr(
                         attn_backend, "get_supported_block_size"
-                ) and not self.model_config.is_deepseek_mla and not self.ascend_config.is_deepseek_sfa:
+                ) and not self.model_config.is_deepseek_mla and not self.use_sparse:
                     block_size = attn_backend.get_supported_block_size()[0]
                     block_size_chunk = kv_cache_spec.block_size // block_size
                     kv_cache_shape = attn_backend.get_kv_cache_shape(
@@ -3245,7 +3246,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
 
         block_size = self.vllm_config.cache_config.block_size
         use_mla = self.vllm_config.model_config.use_mla
-        use_sfa = self.ascend_config.use_sfa
+        use_sparse = self.use_sparse
         kv_cache_spec: dict[str, KVCacheSpec] = {}
         attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention)
         for layer_name, attn_module in attn_layers.items():
@@ -3267,7 +3268,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
             # TODO(lucas): move the attention specs into the model layers like
             # the attention backends
             if attn_module.attn_type == AttentionType.DECODER:
-                if use_mla and not use_sfa:
+                if use_mla and not use_sparse:
                     kv_cache_spec[layer_name] = MLAAttentionSpec(
                         block_size=block_size,
                         num_kv_heads=attn_module.num_kv_heads,