[Bugfix][V1] Fix bug from putting llm_engine.model_executor in a background process (#15367)

Signed-off-by: wwl2755 <wangwenlong2755@gmail.com>

examples/offline_inference/load_sharded_state.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Validates the loading of a model saved with the sharded_state format.
+This script demonstrates how to load a model that was previously saved
+using save_sharded_state.py and validates it by running inference.
+Example usage:
+(First need to save a sharded_state mode)
+
+python save_sharded_state.py \
+    --model /path/to/load \
+    --quantization deepspeedfp \
+    --tensor-parallel-size 8 \
+    --output /path/to/save/sharded/modele
+
+python load_sharded_state.py \
+    --model /path/to/saved/sharded/model \
+    --load-format sharded_state \
+    --quantization deepspeedfp \
+    --tensor-parallel-size 8 \
+    --prompt "Hello, my name is" \
+    --max-tokens 50
+"""
+
+import dataclasses
+
+from vllm import LLM, EngineArgs, SamplingParams
+from vllm.utils import FlexibleArgumentParser
+
+
+def parse_args():
+    parser = FlexibleArgumentParser()
+    # Add engine arguments
+    EngineArgs.add_cli_args(parser)
+
+    # Override default load_format for clarity
+    parser.set_defaults(load_format="sharded_state")
+
+    # Add validation arguments
+    parser.add_argument("--prompt",
+                        type=str,
+                        default="Hello, world!",
+                        help="Prompt for validation")
+    parser.add_argument("--max-tokens",
+                        type=int,
+                        default=100,
+                        help="Maximum number of tokens to generate")
+    parser.add_argument("--temperature",
+                        type=float,
+                        default=0.7,
+                        help="Sampling temperature")
+    parser.add_argument("--top-p",
+                        type=float,
+                        default=1.0,
+                        help="Top-p sampling parameter")
+
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    engine_args = EngineArgs.from_cli_args(args)
+
+    print(f"Loading model from {engine_args.model} "
+          f"using format {engine_args.load_format}")
+    print(f"Tensor parallel size: {engine_args.tensor_parallel_size}")
+
+    # Load the model using engine args
+    llm = LLM(**dataclasses.asdict(engine_args))
+
+    # Prepare sampling parameters
+    sampling_params = SamplingParams(
+        temperature=args.temperature,
+        top_p=args.top_p,
+        max_tokens=args.max_tokens,
+    )
+
+    print("\nRunning inference:")
+    print(f"Prompt: {args.prompt}")
+
+    # Generate completion
+    outputs = llm.generate(args.prompt, sampling_params)
+
+    # Display generated text
+    print("\nGenerated outputs:")
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        print("-" * 50)
+        print(f"Full output: {args.prompt}{generated_text}")
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    main()

examples/offline_inference/save_sharded_state.py

@@ -57,10 +57,25 @@ def main(args):
     # Prepare output directory
     Path(args.output).mkdir(exist_ok=True)
     # Dump worker states to output directory
+
+    # Check which engine version is being used
+    is_v1_engine = hasattr(llm.llm_engine, "engine_core")
+
+    if is_v1_engine:
+        # For V1 engine, we need to use engine_core.save_sharded_state
+        print("Using V1 engine save path")
+        llm.llm_engine.engine_core.save_sharded_state(
+            path=args.output,
+            pattern=args.file_pattern,
+            max_size=args.max_file_size)
+    else:
+        # For V0 engine
+        print("Using V0 engine save path")
         model_executor = llm.llm_engine.model_executor
         model_executor.save_sharded_state(path=args.output,
                                           pattern=args.file_pattern,
                                           max_size=args.max_file_size)
+
     # Copy metadata files to output directory
     for file in os.listdir(model_path):
         if os.path.splitext(file)[1] not in (".bin", ".pt", ".safetensors"):

vllm/v1/engine/core.py

@@ -285,6 +285,16 @@ class EngineCore:
     def pin_lora(self, lora_id: int) -> bool:
         return self.model_executor.pin_lora(lora_id)
 
+    def save_sharded_state(
+        self,
+        path: str,
+        pattern: Optional[str] = None,
+        max_size: Optional[int] = None,
+    ) -> None:
+        self.model_executor.save_sharded_state(path=path,
+                                               pattern=pattern,
+                                               max_size=max_size)
+
     def collective_rpc(self,
                        method: Union[str, Callable[..., _R]],
                        timeout: Optional[float] = None,

vllm/v1/engine/core_client.py

@@ -119,6 +119,12 @@ class EngineCoreClient(ABC):
     def pin_lora(self, lora_id: int) -> bool:
         raise NotImplementedError
 
+    def save_sharded_state(self,
+                           path: str,
+                           pattern: Optional[str] = None,
+                           max_size: Optional[int] = None) -> None:
+        raise NotImplementedError
+
     def collective_rpc(self,
                        method: Union[str, Callable[..., _R]],
                        timeout: Optional[float] = None,
@@ -162,6 +168,12 @@ class EngineCoreClient(ABC):
     async def pin_lora_async(self, lora_id: int) -> bool:
         raise NotImplementedError
 
+    async def save_sharded_state_async(self,
+                                       path: str,
+                                       pattern: Optional[str] = None,
+                                       max_size: Optional[int] = None) -> None:
+        raise NotImplementedError
+
     async def collective_rpc_async(
             self,
             method: Union[str, Callable[..., _R]],
@@ -227,6 +239,12 @@ class InprocClient(EngineCoreClient):
     def pin_lora(self, lora_id: int) -> bool:
         return self.engine_core.pin_lora(lora_id)
 
+    def save_sharded_state(self,
+                           path: str,
+                           pattern: Optional[str] = None,
+                           max_size: Optional[int] = None) -> None:
+        self.engine_core.save_sharded_state(path, pattern, max_size)
+
     def collective_rpc(self,
                        method: Union[str, Callable[..., _R]],
                        timeout: Optional[float] = None,
@@ -537,6 +555,12 @@ class SyncMPClient(MPClient):
         return self.call_utility("collective_rpc", method, timeout, args,
                                  kwargs)
 
+    def save_sharded_state(self,
+                           path: str,
+                           pattern: Optional[str] = None,
+                           max_size: Optional[int] = None) -> None:
+        self.call_utility("save_sharded_state", path, pattern, max_size)
+
 
 class AsyncMPClient(MPClient):
     """Asyncio-compatible client for multi-proc EngineCore."""
@@ -668,6 +692,13 @@ class AsyncMPClient(MPClient):
     async def pin_lora_async(self, lora_id: int) -> bool:
         return await self.call_utility_async("pin_lora", lora_id)
 
+    async def save_sharded_state_async(self,
+                                       path: str,
+                                       pattern: Optional[str] = None,
+                                       max_size: Optional[int] = None) -> None:
+        await self.call_utility_async("save_sharded_state", path, pattern,
+                                      max_size)
+
     async def collective_rpc_async(
             self,
             method: Union[str, Callable[..., _R]],

vllm/v1/worker/gpu_worker.py

@@ -269,6 +269,20 @@ class Worker(WorkerBase):
         # worker will always be healthy as long as it's running.
         return
 
+    def save_sharded_state(
+        self,
+        path: str,
+        pattern: Optional[str] = None,
+        max_size: Optional[int] = None,
+    ) -> None:
+        from vllm.model_executor.model_loader.loader import ShardedStateLoader
+        ShardedStateLoader.save_model(
+            self.model_runner.model,
+            path,
+            pattern=pattern,
+            max_size=max_size,
+        )
+
 
 def init_worker_distributed_environment(
     parallel_config: ParallelConfig,