[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>

examples/offline_weight_load.py

@@ -70,6 +70,9 @@ from safetensors.torch import load_file
 from vllm.utils.mem_constants import GiB_bytes
 from vllm.utils.network_utils import get_open_port
 
+from vllm.model_executor.model_loader.utils import \
+    process_weights_after_loading
+
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
@@ -219,15 +222,6 @@ def main(
         gpu_memory_utilization = 0.95,
         enable_sleep_mode=enable_sleep_mode,
     )
-    model_path = model
-    runmodel = llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
-    patch_vllm_moe_model_weight_loader(runmodel)
-    sd = load_and_merge_safetensors(model_path)
-    runmodel.load_weights(sd.items())
-    print('load state dict done')
-    tp_ranks = get_tp_group().ranks
-    print(f'TP RANKS: {tp_ranks}')
-
     outputs = llm.generate(prompts, sampling_params)
 
     if enable_sleep_mode:
@@ -242,6 +236,20 @@ def main(
             assert freed_bytes >= model_weight_gib / tensor_parallel_size * GiB_bytes
 
         llm.wake_up()
+
+        model_path = model
+        runmodel = llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
+        patch_vllm_moe_model_weight_loader(runmodel)
+        sd = load_and_merge_safetensors(model_path)
+        runmodel.load_weights(sd.items())
+        print('load state dict done')
+        tp_ranks = get_tp_group().ranks
+        print(f'TP RANKS: {tp_ranks}')
+
+        vllm_config = llm.llm_engine.vllm_config.model_config
+        device = next(runmodel.parameters()).device
+        process_weights_after_loading(runmodel, vllm_config, device)
+
         outputs_after_wakeup = llm.generate(prompts, sampling_params)
         if rank == 0:
             # cmp output