Refactor AscendMultiHeadLatentAttention (#2826)

### What this PR does / why we need it?
Register AscendMultiHeadLatentAttention as CustomOP, following vllm changes

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

### How was this patch tested?
CI passed with new added/existing test.


- vLLM version: main
- vLLM main:
b23fb78623

---------

Signed-off-by: Icey <1790571317@qq.com>

vllm_ascend/models/deepseek_v2.py

@@ -31,7 +31,7 @@ import torch
 import torch_npu
 from torch import nn
 from transformers import PretrainedConfig
-from vllm.attention import Attention, AttentionMetadata
+from vllm.attention import AttentionMetadata
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (get_pp_group, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
@@ -48,6 +48,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear,
                                                UnquantizedLinearMethod)
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.mla import MultiHeadLatentAttention
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import get_sampler
@@ -68,6 +69,7 @@ from vllm.model_executor.models.utils import (
 from vllm.sequence import IntermediateTensors
 
 from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.models.layers.mla import AscendMLAModules
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
 from vllm_ascend.quantization.quant_config import AscendLinearMethod
 from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
@@ -529,29 +531,7 @@ class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):
             mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
             self.scaling = self.scaling * mscale * mscale
 
-        # In the MLA backend, kv_cache includes both k_c and
-        # pe (i.e. decoupled position embeddings). In particular,
-        # the concat_and_cache_mla op requires
-        #     k_c.size(1) + k_pe.size(1) == kv_cache.size(2)
-        # i.e.
-        #     kv_lora_rank + qk_rope_head_dim == head_size
-        self.mla_attn = Attention(
-            num_heads=self.num_local_heads,
-            head_size=self.kv_lora_rank + self.qk_rope_head_dim,
-            scale=self.scaling,
-            num_kv_heads=1,
-            cache_config=cache_config,
-            quant_config=quant_config,
-            prefix=f"{prefix}.attn",
-            use_mla=True,
-            # MLA Args
-            q_lora_rank=self.q_lora_rank,
-            kv_lora_rank=self.kv_lora_rank,
-            qk_nope_head_dim=self.qk_nope_head_dim,
-            qk_rope_head_dim=self.qk_rope_head_dim,
-            qk_head_dim=self.qk_head_dim,
-            v_head_dim=self.v_head_dim,
-            rotary_emb=self.rotary_emb,
+        mla_modules = AscendMLAModules(
             q_a_proj=self.q_a_proj if self.q_lora_rank is not None else None,
             q_a_layernorm=self.q_a_layernorm
             if self.q_lora_rank is not None else None,
@@ -560,6 +540,28 @@ class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):
             kv_a_layernorm=self.kv_a_layernorm,
             kv_b_proj=self.kv_b_proj,
             o_proj=self.o_proj,
+            rotary_emb=self.rotary_emb,
+        )
+
+        self.mla_attn = MultiHeadLatentAttention(
+            self.hidden_size,
+            self.enable_shared_expert_dp,
+            self.debug_layer_idx,
+            self.first_k_dense_replace,
+            self.tp_size,
+            mla_modules,
+            self.num_local_heads,
+            self.scaling,
+            self.layers,
+            self.kv_lora_rank,
+            self.qk_rope_head_dim,
+            self.q_lora_rank,
+            self.qk_nope_head_dim,
+            self.qk_head_dim,
+            self.v_head_dim,
+            cache_config,
+            quant_config,
+            prefix,
         )
 
     def forward(
@@ -568,30 +570,7 @@ class CustomDeepseekV2MLAAttention(DeepseekV2MLAAttention):
             hidden_states: torch.Tensor,
             kv_cache: Optional[torch.Tensor] = None,
             attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
-        forward_context = get_forward_context()
-        if kv_cache is None:
-            kv_cache = self.mla_attn.kv_cache[forward_context.virtual_engine]
-        num_tokens = hidden_states.shape[0]
-        need_gather_q_kv = False
-        if self.enable_shared_expert_dp and self.debug_layer_idx > self.first_k_dense_replace and self.debug_layer_idx < self.layers:
-            # Simulate all gather to calculate output shape
-            num_tokens = num_tokens * self.tp_size
-            need_gather_q_kv = True
-        if not self.enable_shared_expert_dp or self.debug_layer_idx < self.first_k_dense_replace:
-            output_shape = hidden_states.shape
-        else:
-            rows = num_tokens // self.tp_size
-            if num_tokens % self.tp_size:
-                rows += 1
-            output_shape = (rows, hidden_states.shape[1])
-        output = torch.empty(output_shape,
-                             dtype=hidden_states.dtype,
-                             device=hidden_states.device)
-        output = self.mla_attn.impl.forward(hidden_states, kv_cache,
-                                            forward_context.attn_metadata,
-                                            need_gather_q_kv, output)
-        output = output.view(-1, output_shape[-1])
-        return output
+        return self.mla_attn(positions, hidden_states, kv_cache, attn_metadata)
 
 
 class CustomDeepseekV2DecoderLayer(DeepseekV2DecoderLayer):