[main] adjust the position of warm_up_atb (#2823)

### What this PR does / why we need it?
Adjust the position of warm_up_atb.

### Does this PR introduce _any_ user-facing change?
not involved

### How was this patch tested?
CI passed with existing test.

- vLLM version: main
- vLLM main:
b23fb78623

Signed-off-by: huangxialu <huangxialu1@huawei.com>

vllm_ascend/models/qwen3_moe.py

@@ -20,7 +20,6 @@
 from typing import Optional, Union
 
 import torch
-import torch_npu
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.compilation.decorators import support_torch_compile
@@ -277,11 +276,6 @@ class CustomQwen3MoeModel(Qwen3MoeModel):
         self.make_empty_intermediate_tensors = (
             make_empty_intermediate_tensors_factory(
                 ["hidden_states", "residual"], config.hidden_size))
-        # Call ATB matmul to warm up; otherwise, the first operation (ReshapeAndCache) may cause performance degradation at runtime.
-        x = torch.rand((2, 4), dtype=torch.float16).npu()
-        weight = torch.rand((2, 4), dtype=torch.float16).npu()
-        c = torch.rand((4, 4), dtype=torch.float32).npu()
-        torch_npu._npu_matmul_add_fp32(x, weight, c)
 
     def forward(
         self,

vllm_ascend/worker/worker_v1.py

@@ -250,10 +250,19 @@ class NPUWorker(WorkerBase):
             self.model_runner._dummy_run(size)
         if not self.model_config.enforce_eager:
             self.model_runner.capture_model()
+        # Call ATB matmul to warm up; otherwise, the first operation (ReshapeAndCache)
+        # may cause performance degradation at runtime.
+        self._warm_up_atb()
         # Reset the seed to ensure that the random state is not affected by
         # the model initialization and profiling.
         NPUPlatform.seed_everything(self.model_config.seed)
 
+    def _warm_up_atb(self):
+        x = torch.rand((2, 4), dtype=torch.float16).npu()
+        weight = torch.rand((2, 4), dtype=torch.float16).npu()
+        c = torch.rand((4, 4), dtype=torch.float32).npu()
+        torch_npu._npu_matmul_add_fp32(x, weight, c)
+
     def get_model(self) -> nn.Module:
         return self.model_runner.get_model()