Sync from v0.13

examples/offline_inference/load_sharded_state.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+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/model
+
+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.argparse_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} 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()