vllm/examples/offline_inference/simple_profiling.py
Benjamin Chislett e858bfe051
[Cleanup] Refactor profiling env vars into a CLI config (#29912)
Signed-off-by: Benjamin Chislett <bchislett@nvidia.com>
Signed-off-by: Benjamin Chislett <chislett.ben@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
2025-12-09 13:29:33 -05:00

53 lines
1.3 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import time
from vllm import LLM, SamplingParams
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
def main():
# Create an LLM.
llm = LLM(
model="facebook/opt-125m",
tensor_parallel_size=1,
profiler_config={
"profiler": "torch",
"torch_profiler_dir": "./vllm_profile",
},
)
llm.start_profile()
# 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)
llm.stop_profile()
# Print the outputs.
print("-" * 50)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
print("-" * 50)
# Add a buffer to wait for profiler in the background process
# (in case MP is on) to finish writing profiling output.
time.sleep(10)
if __name__ == "__main__":
main()