[2/N][refactor] torchair deepseek mla backend refactor (#2459)

### What this PR does / why we need it?
This PR move current unified mla backend to torchair folder and remove
torchair-related code in attention/mla_v1.py (1.3k -> 0.9k).

 
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Running eager mode with mla backend, and torchair mode with code before
[2445](https://github.com/vllm-project/vllm-ascend/pull/2445)


- vLLM version: v0.10.0
- vLLM main:
f571ff8eb6

Signed-off-by: linfeng-yuan <1102311262@qq.com>

vllm_ascend/worker/model_runner_v1.py

@@ -92,6 +92,7 @@ from vllm_ascend.multistream.ms_split import compute_split_seq_index
 from vllm_ascend.platform import NPUPlatform
 from vllm_ascend.sample.rejection_sampler import AscendRejectionSampler
 from vllm_ascend.torchair.torchair_attention import AscendTorchairMetadata
+from vllm_ascend.torchair.torchair_mla import AscendMLATorchairMetadata
 from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_ND, ACL_FORMAT_FRACTAL_NZ,
                                ProfileExecuteDuration, is_310p,
                                maybe_converting_weight_acl_format)
@@ -624,7 +625,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self,
         scheduler_output: "SchedulerOutput",
     ) -> dict[str, Union[AscendMetadata, AscendMLAMetadata,
-                         AscendTorchairMetadata]]:
+                         AscendTorchairMetadata, AscendMLATorchairMetadata]]:
         total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
         assert total_num_scheduled_tokens > 0
         num_reqs = self.input_batch.num_reqs
@@ -736,7 +737,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self.query_start_loc[num_reqs + 1:].fill_(-1)
 
         attn_metadata: dict[str, Union[AscendMetadata, AscendMLAMetadata,
-                                       AscendTorchairMetadata]] = {}
+                                       AscendTorchairMetadata,
+                                       AscendMLATorchairMetadata]] = {}
         # Prepare the attention metadata for each KV cache group and make layers
         # in the same group share the same metadata.
         for kv_cache_group_id, kv_cache_group_spec in enumerate(
@@ -1000,8 +1002,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self,
         scheduler_output: "SchedulerOutput",
         intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> tuple[Union[AscendMetadata, AscendMLAMetadata,
-                     AscendTorchairMetadata], torch.Tensor, np.ndarray, int,
+    ) -> tuple[Union[AscendMetadata, AscendMLAMetadata, AscendTorchairMetadata,
+                     AscendMLATorchairMetadata], torch.Tensor, np.ndarray, int,
                torch.Tensor, int, torch.Tensor, SpecDecodeMetadata,
                Optional[torch.Tensor], Optional[torch.Tensor],
                Optional[torch.Tensor]]:
@@ -1466,7 +1468,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         num_scheduled_tokens: int,
         hidden_states: torch.Tensor,
         attn_metadata: Union[AscendMetadata, AscendMLAMetadata,
-                             AscendTorchairMetadata],
+                             AscendTorchairMetadata,
+                             AscendMLATorchairMetadata],
         aux_hidden_states: torch.Tensor = None,
     ) -> Optional[list[list[int]]]:
         if not self.use_spec_decode:
@@ -2540,7 +2543,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         num_scheduled_tokens: int,
         hidden_states: torch.Tensor,
         attn_metadata: Union[AscendMetadata, AscendMLAMetadata,
-                             AscendTorchairMetadata],
+                             AscendTorchairMetadata,
+                             AscendMLATorchairMetadata],
     ):
         assert isinstance(self.drafter, MtpProposer)
         next_token_ids: list[int] = []