[v0.11.0][Feat] Prefetching Attention QKV Linear Weight With AddRmsNormQuant Custom Op (vllm-project#3649)

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

- `qkv_proj.weight` prefetching has been implemented with `Quant` op,
when `AddRmsNormQuant` is enabled (vllm-project#3465) `qkv_proj.weight` prefetching
won't work
- Implement `qkv_proj.weight` prefetching with `AddRmsNormQuant`, which
has been merged on `main` branch (vllm-project#3517)

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

None.

### How was this patch tested?

Tested on `Qwen3-235B-A22B-W8A8`
<img width="1868" height="109" alt="image"

src="https://github.com/user-attachments/assets/0bc28082-0287-4d5c-b8f6-f907c3134d36"
/>


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

---------

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Signed-off-by: zhoux77899 <[email protected]>

Author: zhoux77899

tests/ut/ops/test_layernorm.py

@@ -117,6 +117,7 @@ def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
         mock_forward_context.layer_idx = 0
         mock_forward_context.num_hidden_layers = num_hidden_layers
         mock_forward_context.fusion_linear = "gate_up_dense"
+        mock_forward_context.weight_prefetch_method = None
 
         # Ensure fusion and layer_idx increment are handled correctly
         x = torch.randn(4, 8, dtype=torch.float16)
@@ -125,28 +126,28 @@ def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
 
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 1
+        assert mock_get_forward_context.call_count == 2
         assert mock_forward_context.fusion_linear == "qkv_dense"
         assert mock_forward_context.layer_idx == 1
 
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 2
+        assert mock_get_forward_context.call_count == 4
         assert mock_forward_context.fusion_linear == "gate_up_dense"
         assert mock_forward_context.layer_idx == 1
 
         if torch_npu_check:
             mock_forward_context.fusion_linear = "gate_moe"
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 3
+        assert mock_get_forward_context.call_count == 6
         fusion_linear_expected = "qkv_moe" if torch_npu_check else "qkv_dense"
         assert mock_forward_context.fusion_linear == fusion_linear_expected
         assert mock_forward_context.layer_idx == 2
 
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 4
+        assert mock_get_forward_context.call_count == 7
         fusion_linear_expected = "gate_moe" if torch_npu_check else "qkv_dense"
         assert mock_forward_context.fusion_linear == fusion_linear_expected
         assert mock_forward_context.layer_idx == 2
@@ -156,13 +157,13 @@ def test_forward_oot_with_quant_fusion(self, mocker: MockerFixture):
         # last layer returned directly
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 5
+        assert mock_get_forward_context.call_count == 8
         assert mock_forward_context.fusion_linear == "qkv_moe"
         assert mock_forward_context.layer_idx == 3
 
         x_out, residual_out = layer.forward_oot(x, residual)
 
-        assert mock_get_forward_context.call_count == 6
+        assert mock_get_forward_context.call_count == 9
         assert mock_forward_context.fusion_linear == "qkv_moe"
         assert mock_forward_context.layer_idx == 3
 

vllm_ascend/ops/layernorm.py

@@ -38,6 +38,21 @@ def _addrmsnorm_forward_oot(
 
     torch_npu_check = version_check()
     if layer is not None and not is_310p():
+        layer_cls_name = layer.__class__.__name__
+        try:
+            weight_prefetch_method = get_forward_context(
+            ).weight_prefetch_method
+        except AssertionError:
+            weight_prefetch_method = None
+
+        # 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,
+            )
+        # add_rms_norm_quant
         if torch_npu_check:
             x, _, residual = torch_npu.npu_add_rms_norm_quant(
                 x,
@@ -55,6 +70,13 @@ def _addrmsnorm_forward_oot(
                 layer.aclnn_input_scale,
                 layer.aclnn_input_offset,
                 epsilon=self.variance_epsilon)
+        # prefetch qkvo_proj.weight postprocess
+        if weight_prefetch_method:
+            weight_prefetch_method.maybe_prefetch_attn_weight_postprocess(
+                layer_cls_name=layer_cls_name,
+                stop_flag=x,
+            )
+
     else:
         if is_310p():
             orig_dtype = residual.dtype