[perf] Fix MLAPO weight disposal for KV-consumer MLA in PD-mix deploy... (#5192)

### What this PR does / why we need it?

- Problem: In MLA+MLAPO, KV-consumer deployments keep
fused_qkv_a_proj/q_proj weights and quant params even though MLAPO uses
the prepacked buffers, increasing memory footprint on decode nodes.
- Fix: Conditionally drop those tensors only when
`kv_transfer_config.is_kv_consumer` to reclaim memory (consistent with
the SFA behavior #4774 ).

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

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: Chen Chen <0109chenchen@gmail.com>

vllm_ascend/attention/mla_v1.py

@@ -912,6 +912,21 @@ class AscendMLAImpl(MLAAttentionImpl):
         self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
         self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)
 
+        # On KV consumers (decode-only) MLAPO uses the transformed weights built above;
+        # the original fused_qkv_a_proj/q_proj weights and quant params are no longer
+        # referenced, so drop them to save memory.
+        ascend_config = get_ascend_config()
+        if self.vllm_config.kv_transfer_config is not None and \
+                self.vllm_config.kv_transfer_config.is_kv_consumer and \
+                ascend_config.recompute_scheduler_enable:
+            self.fused_qkv_a_proj.weight = None
+            self.fused_qkv_a_proj.deq_scale = None
+            self.fused_qkv_a_proj.quant_bias = None
+            self.q_proj.weight = None
+            self.q_proj.deq_scale = None
+            self.q_proj.quant_bias = None
+            torch.npu.empty_cache()
+
     def get_context_seq_len_npu(self, index: int,
                                 attn_metadata: AscendMLAMetadata):
         prefill_metadata = attn_metadata.prefill

vllm_ascend/attention/sfa_v1.py

@@ -371,7 +371,7 @@ class AscendSFAImpl(MLAAttentionImpl):
         if self.enable_sfa_cp:
             self.local_num_heads = self.num_heads * self.tp_size
 
-            #TODO: Temporarily adapt sfa-cp, remove after adapting near PCP. --clrs97
+            # TODO: Temporarily adapt sfa-cp, remove after adapting near PCP. --clrs97
             self._replace_linear_class_for_sfa_cp()
             from vllm_ascend.distributed.parallel_state import \
                 get_shared_weight_group
@@ -537,7 +537,7 @@ class AscendSFAImpl(MLAAttentionImpl):
                 cache_mode=cache_mode,
                 is_output_kv=True,
             )
-            #TODO: Temporarily adapt SFA-CP and replace it later with PCP. --clrs97
+            # TODO: Temporarily adapt SFA-CP and replace it later with PCP. --clrs97
             k_pe = get_tp_group().all_gather(k_pe, 0)
             k_nope = get_tp_group().all_gather(k_nope, 0)
 
@@ -659,8 +659,13 @@ class AscendSFAImpl(MLAAttentionImpl):
         self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
         self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)
 
+        # On KV consumers (decode-only) MLAPO uses the transformed weights built above;
+        # the original fused_qkv_a_proj/q_proj weights and quant params are no longer
+        # referenced, so drop them to save memory.
+        ascend_config = get_ascend_config()
         if self.vllm_config.kv_transfer_config is not None and \
-            self.vllm_config.kv_transfer_config.is_kv_consumer:
+                self.vllm_config.kv_transfer_config.is_kv_consumer and \
+                ascend_config.recompute_scheduler_enable:
             self.fused_qkv_a_proj.weight = None
             self.fused_qkv_a_proj.deq_scale = None
             self.fused_qkv_a_proj.quant_bias = None