[Bugfix]: Fix issues related to prefix caching example (#5177) (#5180)

examples/offline_inference_with_prefix.py

+from time import time
+
 from vllm import LLM, SamplingParams
 
+# 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 "
@@ -18,36 +21,60 @@ prompts = [
     "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)
 
 # Create an LLM.
-llm = LLM(model="facebook/opt-125m", enable_prefix_caching=True)
+regular_llm = LLM(model="facebook/opt-125m", gpu_memory_utilization=0.4)
 
-generating_prompts = [prefix + prompt for prompt in prompts]
+prefix_cached_llm = LLM(model="facebook/opt-125m",
+                        enable_prefix_caching=True,
+                        gpu_memory_utilization=0.4)
+print("Results without `enable_prefix_caching`")
 
 # 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(generating_prompts, sampling_params)
+start_time_regular = time()
+outputs = regular_llm.generate(generating_prompts, sampling_params)
+duration_regular = time() - start_time_regular
+
+regular_generated_texts = []
 # Print the outputs.
 for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs[0].text
+    regular_generated_texts.append(generated_text)
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 
 print("-" * 80)
 
 # The llm.generate call will batch all prompts and send the batch at once
-# if resources allow. The prefix will only be cached after the first batch
-# is processed, so we need to call generate once to calculate the prefix
-# and cache it.
-outputs = llm.generate(generating_prompts[0], sampling_params)
+# if resources allow.
+start_time_cached = time()
+outputs = prefix_cached_llm.generate(generating_prompts, sampling_params)
+duration_cached = time() - start_time_cached
 
-# Subsequent batches can leverage the cached prefix
-outputs = llm.generate(generating_prompts, sampling_params)
+print("Results with `enable_prefix_caching`")
 
-# Print the outputs. You should see the same outputs as before
+cached_generated_texts = []
+# Print the outputs. You should see the same outputs as before.
 for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs[0].text
+    cached_generated_texts.append(generated_text)
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+print("-" * 80)
+
+# 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}")
+
+speedup = round(duration_regular / duration_cached, 2)
+print(f"Speed up of cached generation compared to the regular is: {speedup}")