[MOE]move weight transpose to wakeup for RL secnarios (#4626)

### What this PR does / why we need it?
In reinforcement learning scenarios, the current inference applies a
transpose operation to the weights. For a cleaner architecture, the
weight transpose module was moved to wakeup.

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

### How was this patch tested?

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

Signed-off-by: lhp-deep <liuhaopeng1@huawei.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>

vllm_ascend/worker/worker_v1.py

@@ -176,9 +176,28 @@ class NPUWorker(WorkerBase):
         allocator = CaMemAllocator.get_instance()
         allocator.wake_up(tags=tags)
 
+        hidden_size = self.vllm_config.model_config.hf_config.hidden_size
+        model = self.model_runner.model
+        for name, param in model.named_parameters():
+            if 'w2_weight' in name and param.shape[2] == hidden_size:
+                parts = name.split('.')
+                param_name = parts[-1]
+                parent_module = model.get_submodule(".".join(parts[:-1]))
+
+                w2_data = param.transpose(1, 2)
+                w2_data = torch.nn.Parameter(w2_data, requires_grad=False)
+                setattr(parent_module, param_name, w2_data)
+            elif 'w13_weight' in name and param.shape[1] == hidden_size:
+                parts = name.split('.')
+                param_name = parts[-1]
+                parent_module = model.get_submodule(".".join(parts[:-1]))
+
+                w13_data = param.transpose(1, 2)
+                w13_data = torch.nn.Parameter(w13_data, requires_grad=False)
+                setattr(parent_module, param_name, w13_data)
+
         # Restore the buffers after level 2 sleep
         if len(self._sleep_saved_buffers):
-            model = self.model_runner.model
             for name, buffer in model.named_buffers():
                 if name in self._sleep_saved_buffers:
                     buffer.data.copy_(self._sleep_saved_buffers[name].data)