add better validation

examples/offline_inference_fakehpu.py

@@ -2,21 +2,32 @@
 
 # Sample prompts.
 prompts = [
-    "Hello, my name is",
-    "The president of the United States is",
-    "The capital of France is",
-    "The future of AI is",
+    "Berlin is the capital city of ",
+    "Louvre is located in the city called ",
+    "Barack Obama was the 44th president of ",
+    "Warsaw is the capital city of ",
+    "Gniezno is a city in ",
+    "Hebrew is an official state language of ",
+    "San Francisco is located in the state of ",
+    "Llanfairpwllgwyngyll is located in country of ",
+]
+ref_answers = [
+    "Germany", "Paris", "United States", "Poland", "Poland", "Israel",
+    "California", "Wales"
 ]
 # Create a sampling params object.
-sampling_params = SamplingParams()
+sampling_params = SamplingParams(temperature=0, n=1, use_beam_search=False)
 
 # Create an LLM.
 llm = LLM(model="facebook/opt-125m", max_model_len=32, max_num_seqs=4)
 # 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:
+for output, answer in zip(outputs, ref_answers):
     prompt = output.prompt
     generated_text = output.outputs[0].text
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    assert answer in generated_text, (
+        f"The generated text does not contain the correct answer: {answer}")
+print('PASSED')

vllm/utils.py

@@ -212,7 +212,8 @@ def is_hpu() -> bool:
 
 @lru_cache(maxsize=None)
 def is_fake_hpu() -> bool:
-    return not _is_habana_frameworks_installed() and _is_built_for_hpu()
+    return os.environ.get('VLLM_USE_FAKE_HPU', '0') != '0' or (
+        not _is_habana_frameworks_installed() and _is_built_for_hpu())
 
 
 @lru_cache(maxsize=None)