[2/N][Feat] Attention and MoE weight prefetch in Qwen3MoE models (#3203)

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

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `gate_up_proj.weight` in quantized Attention modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency

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

Add a new config in `--additional-config` for configuration:
```json
{
    "weight_prefetch_config": {
        "enabled": True,
        "prefetch_ratio": {
            "moe": {
                "gate_up": 0.8
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


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

---------

Signed-off-by: yuzhup <15705211260@163.com>

tests/e2e/singlecard/ops/test_fused_moe.py

@@ -291,7 +291,9 @@ def test_select_experts(
         custom_routing_function.return_value = (mock_weights, mock_ids)
 
     with patch("vllm_ascend.ops.moe.experts_selector._native_grouped_topk"
-               ) as mock_native_grouped_topk:
+               ) as mock_native_grouped_topk, \
+            patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
+                  return_value=MagicMock(weight_prefetch_method=MagicMock())):
         mock_native_grouped_topk.side_effect = lambda x, num_groups, k: torch.randn_like(
             x)
 
@@ -325,7 +327,9 @@ def test_select_experts(
 
 @pytest.mark.parametrize("device", DEVICE)
 def test_select_experts_invalid_scoring_func(device: str):
-    with pytest.raises(ValueError,
+    with patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
+                  return_value=MagicMock(weight_prefetch_method=MagicMock())), \
+            pytest.raises(ValueError,
                        match="Unsupported scoring function: invalid"):
         select_experts(hidden_states=torch.randn(1, 128, device=device),
                        router_logits=torch.randn(1, 8, device=device),

tests/ut/ops/test_fused_ops.py

@@ -92,14 +92,16 @@ def mock_dist_env(mocker: MockerFixture):
 
     mock_moe_comm_method.finalize.side_effect = mock_finalize
     dp_metadata = MagicMock(num_tokens_across_dp_cpu=[5, 5])
-    mock_forward_context_obj = MagicMock(moe_comm_method=mock_moe_comm_method,
-                                         moe_comm_type=MoECommType.MC2,
-                                         max_tokens_across_dp=10,
-                                         dp_metadata=dp_metadata,
-                                         mc2_mask=torch.zeros(
-                                             16, dtype=torch.bool),
-                                         padded_num_tokens=16,
-                                         with_quant=False)
+    mock_weight_prefetch_method = MagicMock()
+    mock_forward_context_obj = MagicMock(
+        moe_comm_method=mock_moe_comm_method,
+        moe_comm_type=MoECommType.MC2,
+        max_tokens_across_dp=10,
+        dp_metadata=dp_metadata,
+        mc2_mask=torch.zeros(16, dtype=torch.bool),
+        padded_num_tokens=16,
+        with_quant=False,
+        weight_prefetch_method=mock_weight_prefetch_method)
 
     with patch('torch.distributed.get_rank', return_value=0), \
         patch('torch.distributed.get_world_size', return_value=4), \
@@ -132,7 +134,9 @@ def mock_dist_env(mocker: MockerFixture):
         patch('vllm_ascend.ops.moe.moe_comm_method.AlltoAllCommImpl._get_token_dispatcher',
               return_value=None), \
         patch('vllm_ascend.ops.moe.moe_comm_method.AllGatherCommImpl._get_token_dispatcher',
-              return_value=None):
+              return_value=None), \
+        patch('vllm_ascend.ops.moe.experts_selector.get_forward_context',
+              return_value=mock_forward_context_obj):
 
         yield {
             'mock_forward_context_obj': mock_forward_context_obj,

tests/ut/quantization/test_w8a8.py

@@ -755,6 +755,14 @@ class TestSelectExperts(TestBase):
         self.hidden_states = torch.randn(self.num_tokens, self.hidden_size)
         self.router_logits = torch.randn(self.num_tokens, self.num_experts)
 
+        self.mock_ctx = MagicMock()
+        self.mock_ctx.weight_prefetch_method = MagicMock()
+        patcher = patch(
+            'vllm_ascend.ops.moe.experts_selector.get_forward_context',
+            return_value=self.mock_ctx)
+        self.addCleanup(patcher.stop)
+        patcher.start()
+
     @patch('torch_npu.npu_moe_gating_top_k_softmax')
     def test_softmax_scoring(self, mock_topk):
         """Test softmax scoring function"""