23 lines
842 B
Python
23 lines
842 B
Python
|
from vllm import LLM, SamplingParams
|
||
|
|
|
||
|
|
|
||
|
|
def test_cascade_attention(example_system_message, monkeypatch):
|
||
|
|
prompt = "\n<User>: Implement fibonacci sequence in Python.\n<Claude>:"
|
||
|
|
|
||
|
|
with monkeypatch.context() as m:
|
||
|
|
m.setenv("VLLM_USE_V1", "1")
|
||
|
|
|
||
|
|
llm = LLM(model="Qwen/Qwen2-1.5B-Instruct")
|
||
|
|
sampling_params = SamplingParams(temperature=0.0, max_tokens=100)
|
||
|
|
|
||
|
|
# No cascade attention.
|
||
|
|
single_prompt = [example_system_message + prompt]
|
||
|
|
responses = llm.generate(single_prompt, sampling_params)
|
||
|
|
ref_output = responses[0].outputs[0].text
|
||
|
|
|
||
|
|
# (Probably) Use cascade attention.
|
||
|
|
prompts = [example_system_message + prompt] * 64
|
||
|
|
responses = llm.generate(prompts, sampling_params)
|
||
|
|
for response in responses:
|
||
|
|
assert response.outputs[0].text == ref_output
|