Sync from v0.13

examples/offline_inference/reproducibility.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Demonstrates how to achieve reproducibility in vLLM.
+
+Main article: https://docs.vllm.ai/en/latest/usage/reproducibility.html
+"""
+
+import os
+import random
+
+from vllm import LLM, SamplingParams
+
+# Either:
+## Turn off multiprocessing to make the scheduling deterministic, or
+os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
+## Enable batch invariance to get consistent results regardless of scheduling.
+os.environ["VLLM_BATCH_INVARIANT"] = "1"
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+
+def main():
+    llm = LLM(model="facebook/opt-125m")
+    outputs = llm.generate(prompts, sampling_params)
+    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)
+
+    # Try generating random numbers outside vLLM
+    # The same number is output across runs, meaning that the random state
+    # in the user code has been updated by vLLM
+    print(random.randint(0, 100))
+
+
+if __name__ == "__main__":
+    main()