ut: add example and e2e test for sleepmode in external_launcher (#2152)

### What this PR does / why we need it?
This pr add e2e testcase to make sure sleep mode in external_launcher is
ok.

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

### How was this patch tested?
not involved


- vLLM version: v0.10.0
- vLLM main:
74333ae2f6

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

examples/offline_external_launcher.py

@@ -28,7 +28,7 @@ Single node:
                 --proc-per-node=2
     MOE models:
         python examples/offline_external_launcher.py \
-                --model="Qwen/Qwen3-0.6B" \
+                --model="Qwen/Qwen3-30B-A3B" \
                 --tp-size=2 \
                 --proc-per-node=2 \
                 --enable-expert-parallel
@@ -36,7 +36,7 @@ Single node:
 Multi-node:
     Node 0 (assume the node has ip of 10.99.48.128):
             python examples/offline_external_launcher.py \
-                    --model="Qwen/Qwen3-0.6B" \
+                    --model="Qwen/Qwen3-30B-A3B" \
                     --tp-size=2 \
                     --node-size=2 \
                     --node-rank=0 \
@@ -46,7 +46,7 @@ Multi-node:
                     --master-port=13345
     Node 1:
             python examples/offline_external_launcher.py \
-                    --model="Qwen/Qwen3-0.6B" \
+                    --model="Qwen/Qwen3-30B-A3B" \
                     --tp-size=2 \
                     --node-size=2 \
                     --node-rank=1 \
@@ -66,7 +66,7 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import (  # noqa E402
     destroy_distributed_environment, destroy_model_parallel, get_tp_group)
-from vllm.utils import get_open_port
+from vllm.utils import get_open_port, GiB_bytes
 
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
@@ -114,7 +114,28 @@ def parse_args():
     parser.add_argument("--enable-expert-parallel",
                         action="store_true",
                         help="Enable expert parallel, used in MOE models.")
-    return parser.parse_args()
+    parser.add_argument("--enable-sleep-mode",
+                        action="store_true",
+                        help="Enable sleep mode for the engine.")
+    parser.add_argument("--temperature",
+                        type=float,
+                        default=0.8,
+                        help="Float that controls the randomness of the sampling.")
+    parser.add_argument("--model-weight-gib",
+                        type=float,
+                        default=None,
+                        help="Model weight memory usage in GiB (e.g., 1.0 for 0.5B model).")
+
+    args = parser.parse_args()
+    if args.enable_sleep_mode:
+        if args.model_weight_gib is None or args.temperature != 0:
+            parser.error("model-weight-gib must be provided, and temperature must be zero when enable-sleep-mode is set.")
+        if args.model_weight_gib <= 0:
+            parser.error("model-weight-gib must be greater than 0 when enable-sleep-mode is set.")
+        if args.model == parser.get_default("model") and args.model_weight_gib is None:
+            parser.error("model-weight-gib must be provided for default model when enable-sleep-mode is set.")
+
+    return args
 
 
 def main(
@@ -122,12 +143,15 @@ def main(
     rank: int,
     master_addr: str,
     master_port: int,
+    model_weight_gib: float,
     model: str = "Qwen/Qwen3-0.6B",
     world_size: int = 4,
     tensor_parallel_size: int = 2,
     enable_expert_parallel: bool = False,
     enforce_eager: bool = False,
     trust_remote_code: bool = True,
+    enable_sleep_mode: bool = False,
+    temperature: float = 0.8,
 ):
     os.environ["MASTER_ADDR"] = master_addr
     os.environ["MASTER_PORT"] = str(master_port)
@@ -147,7 +171,7 @@ def main(
         "The future of AI is",
     ] * 10
     sampling_params = SamplingParams(
-        temperature=0.8,
+        temperature=temperature,
         top_p=0.95,
         max_tokens=10,
     )
@@ -159,10 +183,31 @@ def main(
         trust_remote_code=trust_remote_code,
         distributed_executor_backend="external_launcher",
         seed=0,
+        enable_sleep_mode=enable_sleep_mode,
     )
     tp_ranks = get_tp_group().ranks
     print(f'TP RANKS: {tp_ranks}')
+
     outputs = llm.generate(prompts, sampling_params)
+
+    if enable_sleep_mode:
+        if rank == 0:
+            free_bytes_before_sleep, total = torch.npu.mem_get_info()
+        llm.sleep(level=1)
+        if rank == 0:
+            free_bytes_after_sleep, total = torch.npu.mem_get_info()
+            freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
+            print(f"Freed memory: {freed_bytes / 1024 ** 3:.2f} GiB")
+            # now the freed memory should be larger than the model weights
+            assert freed_bytes >= model_weight_gib / tensor_parallel_size * GiB_bytes
+
+        llm.wake_up()
+        outputs_after_wakeup = llm.generate(prompts, sampling_params)
+        if rank == 0:
+            # cmp output
+            assert outputs[0].outputs[0].text == outputs_after_wakeup[0].outputs[0].text
+            print("Sleep and wake up successfully!!")
+
     for i, output in enumerate(outputs):
         if i >= 5:
             # print only 5 outputs
@@ -214,12 +259,15 @@ if __name__ == "__main__":
                            rank,
                            master_addr,
                            master_port,
+                           args.model_weight_gib,
                            args.model,
                            world_size,
                            tp_size,
                            args.enable_expert_parallel,
                            args.enforce_eager,
                            args.trust_remote_code,
+                           args.enable_sleep_mode,
+                           args.temperature,
                        ))
 
         proc.start()