[Minor] Revert change in offline inference example (#10545)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

examples/offline_inference.py

-from dataclasses import asdict
-
 from vllm import LLM, SamplingParams
-from vllm.engine.arg_utils import EngineArgs
-from vllm.utils import FlexibleArgumentParser
-
 
-def get_prompts(num_prompts: int):
-    # The default sample prompts.
-    prompts = [
+# Sample prompts.
+prompts = [
     "Hello, my name is",
     "The president of the United States is",
     "The capital of France is",
     "The future of AI is",
-    ]
-
-    if num_prompts != len(prompts):
-        prompts = (prompts * ((num_prompts // len(prompts)) + 1))[:num_prompts]
-
-    return prompts
-
-
-def main(args):
-    # Create prompts
-    prompts = get_prompts(args.num_prompts)
-
-    # Create a sampling params object.
-    sampling_params = SamplingParams(n=args.n,
-                                     temperature=args.temperature,
-                                     top_p=args.top_p,
-                                     top_k=args.top_k,
-                                     max_tokens=args.max_tokens)
-
-    # Create an LLM.
-    # The default model is 'facebook/opt-125m'
-    engine_args = EngineArgs.from_cli_args(args)
-    llm = LLM(**asdict(engine_args))
-
-    # Generate texts from the prompts.
-    # The output is a list of RequestOutput objects
-    # that contain the prompt, generated text, and other information.
-    outputs = llm.generate(prompts, sampling_params)
-    # Print the outputs.
-    for output in outputs:
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Create an LLM.
+llm = LLM(model="facebook/opt-125m")
+# Generate texts from the prompts. The output is a list of RequestOutput objects
+# that contain the prompt, generated text, and other information.
+outputs = llm.generate(prompts, sampling_params)
+# Print the outputs.
+for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs[0].text
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-
-
-if __name__ == '__main__':
-    parser = FlexibleArgumentParser()
-    parser = EngineArgs.add_cli_args(parser)
-    group = parser.add_argument_group("SamplingParams options")
-    group.add_argument("--num-prompts",
-                       type=int,
-                       default=4,
-                       help="Number of prompts used for inference")
-    group.add_argument("--max-tokens",
-                       type=int,
-                       default=16,
-                       help="Generated output length for sampling")
-    group.add_argument('--n',
-                       type=int,
-                       default=1,
-                       help='Number of generated sequences per prompt')
-    group.add_argument('--temperature',
-                       type=float,
-                       default=0.8,
-                       help='Temperature for text generation')
-    group.add_argument('--top-p',
-                       type=float,
-                       default=0.95,
-                       help='top_p for text generation')
-    group.add_argument('--top-k',
-                       type=int,
-                       default=-1,
-                       help='top_k for text generation')
-
-    args = parser.parse_args()
-    main(args)

examples/offline_inference_cli.py

+from dataclasses import asdict
+
+from vllm import LLM, SamplingParams
+from vllm.engine.arg_utils import EngineArgs
+from vllm.utils import FlexibleArgumentParser
+
+
+def get_prompts(num_prompts: int):
+    # The default sample prompts.
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+
+    if num_prompts != len(prompts):
+        prompts = (prompts * ((num_prompts // len(prompts)) + 1))[:num_prompts]
+
+    return prompts
+
+
+def main(args):
+    # Create prompts
+    prompts = get_prompts(args.num_prompts)
+
+    # Create a sampling params object.
+    sampling_params = SamplingParams(n=args.n,
+                                     temperature=args.temperature,
+                                     top_p=args.top_p,
+                                     top_k=args.top_k,
+                                     max_tokens=args.max_tokens)
+
+    # Create an LLM.
+    # The default model is 'facebook/opt-125m'
+    engine_args = EngineArgs.from_cli_args(args)
+    llm = LLM(**asdict(engine_args))
+
+    # Generate texts from the prompts.
+    # The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = llm.generate(prompts, sampling_params)
+    # Print the outputs.
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+if __name__ == '__main__':
+    parser = FlexibleArgumentParser()
+    parser = EngineArgs.add_cli_args(parser)
+    group = parser.add_argument_group("SamplingParams options")
+    group.add_argument("--num-prompts",
+                       type=int,
+                       default=4,
+                       help="Number of prompts used for inference")
+    group.add_argument("--max-tokens",
+                       type=int,
+                       default=16,
+                       help="Generated output length for sampling")
+    group.add_argument('--n',
+                       type=int,
+                       default=1,
+                       help='Number of generated sequences per prompt')
+    group.add_argument('--temperature',
+                       type=float,
+                       default=0.8,
+                       help='Temperature for text generation')
+    group.add_argument('--top-p',
+                       type=float,
+                       default=0.95,
+                       help='top_p for text generation')
+    group.add_argument('--top-k',
+                       type=int,
+                       default=-1,
+                       help='top_k for text generation')
+
+    args = parser.parse_args()
+    main(args)