Sync from v0.13

examples/offline_inference/prefix_caching.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+
+# NOTE: This is just a running example. For benchmarking purpose,
+# please see benchmarks/benchmark_prefix_caching.py
+
+# Common prefix.
+prefix = (
+    "You are an expert school principal, skilled in effectively managing "
+    "faculty and staff. Draft 10-15 questions for a potential first grade "
+    "Head Teacher for my K-12, all-girls', independent school that emphasizes "
+    "community, joyful discovery, and life-long learning. The candidate is "
+    "coming in for a first-round panel interview for a 8th grade Math "
+    "teaching role. They have 5 years of previous teaching experience "
+    "as an assistant teacher at a co-ed, public school with experience "
+    "in middle school math teaching. Based on these information, fulfill "
+    "the following paragraph: "
+)
+
+# Sample prompts.
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+generating_prompts = [prefix + prompt for prompt in prompts]
+
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.0)
+
+
+def main():
+    # Create an LLM without prefix caching as a baseline.
+    regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4)
+
+    print("Results without `enable_prefix_caching`")
+
+    # ruff: noqa: E501
+    # Generate texts from the prompts. The output is a list of RequestOutput objects
+    # that contain the prompt, generated text, and other information.
+    outputs = regular_llm.generate(generating_prompts, sampling_params)
+
+    regular_generated_texts = []
+    # Print the outputs.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        regular_generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+    # Destroy the LLM object and free up the GPU memory.
+    del regular_llm
+    cleanup_dist_env_and_memory()
+
+    # Create an LLM with prefix caching enabled.
+    prefix_cached_llm = LLM(
+        model="facebook/opt-125m",
+        enable_prefix_caching=True,
+        gpu_memory_utilization=0.4,
+    )
+
+    # Warmup so that the shared prompt's KV cache is computed.
+    prefix_cached_llm.generate(generating_prompts[0], sampling_params)
+
+    # Generate with prefix caching.
+    outputs = prefix_cached_llm.generate(generating_prompts, sampling_params)
+
+    print("Results with `enable_prefix_caching`")
+
+    cached_generated_texts = []
+    # Print the outputs. You should see the same outputs as before.
+    print("-" * 50)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        cached_generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
+        print("-" * 50)
+
+    # Compare the results and display the speedup
+    generated_same = all(
+        [
+            regular_generated_texts[i] == cached_generated_texts[i]
+            for i in range(len(prompts))
+        ]
+    )
+    print(f"Generated answers are the same: {generated_same}")
+
+
+if __name__ == "__main__":
+    main()