[Bugfix] Fix weight prefetching AssertionError in W8A8 MTP scene (#3361)

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

- Fix `AssertionError` of `weight_prefetch_method` in W8A8 MTP scene
- Remove hard-code key
(https://github.com/vllm-project/vllm-ascend/pull/3146#discussion_r2416644010)

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

None

### How was this patch tested?
`weight_prefetch_method is None` (tested on DeepSeek-R1-w8a8mix_MTP)

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>

vllm_ascend/ops/weight_prefetch.py

@@ -4,6 +4,8 @@ import torch
 import torch_npu
 
 from vllm_ascend.ascend_config import WeightPrefetchConfig
+from vllm_ascend.ops.linear import (AscendQKVParallelLinear,
+                                    AscendRowParallelLinear)
 
 SUPPORTED_MODULES = ["attn", "mlp", "moe"]
 
@@ -13,6 +15,7 @@ class ModuleWeightPrefetchConfig:
     module_name: str
     enable: bool = False
     prefetch_ratio: dict = field(default_factory=dict)
+    linear_prefix_map: dict = field(default_factory=dict)
 
     def __post_init__(self) -> None:
         self.prefetch_ratio = {
@@ -38,14 +41,19 @@ class WeightPrefetchMethod:
             module_name="attn",
             enable=weight_prefetch_config.enabled,
             prefetch_ratio=weight_prefetch_config.prefetch_ratio.get(
-                "attn", {}))
+                "attn", {}),
+            linear_prefix_map={
+                AscendQKVParallelLinear.__name__: "qkv",
+                AscendRowParallelLinear.__name__: "o",
+            })
 
     def maybe_prefetch_attn_weight_preprocess(
-            self, prefix: str, weight: torch.Tensor,
+            self, layer_cls_name: str, weight: torch.Tensor,
             start_flag: torch.Tensor) -> None:
-        if not self.attn.enable:
+        if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
             return
 
+        prefix = self.attn.linear_prefix_map.get(layer_cls_name, "")
         weight_size = weight.data.element_size() * weight.data.numel(
         ) * self.attn.prefetch_ratio.get(prefix, 0)
 
@@ -54,8 +62,8 @@ class WeightPrefetchMethod:
                                            max_weight_size=int(weight_size))
 
     def maybe_prefetch_attn_weight_postprocess(
-            self, stop_flag: torch.Tensor) -> None:
-        if not self.attn.enable:
+            self, layer_cls_name: str, stop_flag: torch.Tensor) -> None:
+        if not self.attn.enable or layer_cls_name not in self.attn.linear_prefix_map:
             return
 
         torch.ops.vllm.prefetch_postprocess(stop_flag)

vllm_ascend/quantization/w8a8.py

@@ -98,31 +98,29 @@ class AscendW8A8LinearMethod:
         tp_rank: Optional[int] = 0,
     ) -> torch.Tensor:
         if x.dtype != torch.int8:
-            attn_weight_map = {
-                "AscendQKVParallelLinear": "qkv",
-                "AscendRowParallelLinear": "o",
-            }
             layer_cls_name = layer.__class__.__name__
             weight_prefetch_method = get_forward_context(
             ).weight_prefetch_method
-            assert weight_prefetch_method is not None
 
-            # prefetch_qkvo_proj.weight preprocess
-            weight_prefetch_method.maybe_prefetch_attn_weight_preprocess(
-                prefix=attn_weight_map.get(layer_cls_name, ""),
-                weight=layer.weight,
-                start_flag=x,
-            )
+            # prefetch qkvo_proj.weight preprocess
+            if weight_prefetch_method:
+                weight_prefetch_method.maybe_prefetch_attn_weight_preprocess(
+                    layer_cls_name=layer_cls_name,
+                    weight=layer.weight,
+                    start_flag=x,
+                )
             # quant
             x = quant_per_tensor(
                 x,
                 layer.aclnn_input_scale_reciprocal,
                 layer.aclnn_input_offset,
             )
-            # prefetch_qkvo_proj.weight postprocess
-            if layer_cls_name in attn_weight_map.keys():
+            # prefetch qkvo_proj.weight postprocess
+            if weight_prefetch_method:
                 weight_prefetch_method.maybe_prefetch_attn_weight_postprocess(
-                    x)
+                    layer_cls_name=layer_cls_name,
+                    stop_flag=x,
+                )
 
         quant_bias = layer.quant_bias if tp_rank == 0 else None
         if is_310p():