[Model][3/N] Refactor sfa into mla and remove deepseek_v3_2.py (#3769)

This is the follow-up PR to PR #3189, which continues to refactor sfa
into mla and finally remove deepseek_v3_2.py. This is the last PR of
deepseek modeling refactoring. After this, all deepseek-related model
codes are removed from vllm_ascend.

FurtherMore, after this PR deepseek v3.2 can run chunk-prefill with
correct accuracy.

- vLLM version: v0.11.0rc3
- vLLM main:
83f478bb19

---------

Signed-off-by: whx-sjtu <2952154980@qq.com>

vllm_ascend/models/layers/mla.py

@@ -52,6 +52,35 @@ else:
     from vllm.model_executor.layers.mla import MultiHeadLatentAttentionWrapper
 
 
+class IndexerWrapper(nn.Module):
+    ''' 
+    A wrapper of Indexer for Deepseek v3.2.
+    This wrapper is currently used to solve the fp8 hard code issue of vllm's deepseek_v2.py.
+    It wraps the original Indexer, inherits its module weights
+    (including wq_b, wk, weights_proj, k_norm)
+    while deletes the unused topk_indices_buffer and k_cache to save memory. 
+    TODO: Will be removed once original Indexer supports different quantization methods.
+    '''
+
+    def __init__(self, vllm_indexer: nn.Module) -> None:
+        super().__init__()
+
+        self.n_head: int = vllm_indexer.n_head  # 64
+        self.head_dim: int = vllm_indexer.head_dim  # 128
+        self.topk_tokens: int = vllm_indexer.topk_tokens  # 2048
+        self.q_lora_rank: int = vllm_indexer.q_lora_rank  # 1536
+        self.wq_b = vllm_indexer.wq_b
+        self.wk = vllm_indexer.wk
+        self.weights_proj = vllm_indexer.weights_proj
+        self.k_norm = vllm_indexer.k_norm
+        self.softmax_scale = vllm_indexer.softmax_scale
+        vllm_indexer.topk_indices_buffer = None  # delete topk_indices_buffer
+        vllm_indexer.k_cache = None  # delete k_cache
+
+    def forward(self):
+        return
+
+
 # TODO(whx): adapt v0.11.0 and DSA
 class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
 
@@ -86,6 +115,10 @@ class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
         self.first_k_dense_replace = hf_config.first_k_dense_replace
         self.tp_size = get_tensor_model_parallel_world_size()
         self.layers = hf_config.num_hidden_layers
+        if mla_modules.indexer is not None:
+            ascend_indexer = IndexerWrapper(mla_modules.indexer)
+        else:
+            ascend_indexer = None
 
         if vllm_version_is("0.11.0"):
             self.mla_attn = Attention(
@@ -97,6 +130,8 @@ class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
                 quant_config=quant_config,
                 prefix=f"{prefix}.attn",
                 use_mla=True,
+                indexer=ascend_indexer,
+                use_sparse=mla_modules.is_sparse,
                 # MLA Args
                 q_lora_rank=self.q_lora_rank,
                 kv_lora_rank=self.kv_lora_rank,
@@ -128,7 +163,7 @@ class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
                 quant_config=quant_config,
                 prefix=f"{prefix}.attn",
                 use_sparse=mla_modules.is_sparse,
-                indexer=mla_modules.indexer,
+                indexer=ascend_indexer,
                 # extra args
                 rotary_emb=mla_modules.rotary_emb,
                 fused_qkv_a_proj=mla_modules.fused_qkv_a_proj,