"""Compare the with and without prefix caching. Run `pytest tests/prefix_caching/test_prefix_caching.py`. """ import pytest from tests.kernels.utils import override_backend_env_variable from vllm import SamplingParams, TokensPrompt from ..models.utils import check_outputs_equal MODELS = [ "facebook/opt-125m", ] UNSTABLE_PROMPT_SEQUENCE = [ ([0] * 588) + ([1] * 1332) + ([2] * 30) + ([3] * 1), ([0] * 588) + ([1] * 1332) + ([4] * 3) + ([5] * 50), ([0] * 588) + ([1] * 1332) + ([2] * 30) + ([6] * 95), ([0] * 588) + ([1] * 1332) + ([4] * 3) + ([7] * 174), ([0] * 588) + ([8] * 1539), ] @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"]) @pytest.mark.parametrize("dtype", ["half"]) @pytest.mark.parametrize("max_tokens", [5]) @pytest.mark.parametrize("cached_position", [0, 1]) def test_mixed_requests( hf_runner, vllm_runner, example_prompts, model: str, backend: str, dtype: str, max_tokens: int, cached_position: int, monkeypatch, ) -> None: """ Test the case when some sequences have the prefix cache hit and the others don't. The cached position determines where the sequence is at among the batch of prefills. """ override_backend_env_variable(monkeypatch, backend) with hf_runner(model, dtype=dtype) as hf_model: hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) cached_prompt = example_prompts[cached_position] with vllm_runner( model, dtype=dtype, enable_prefix_caching=True, ) as vllm_model: # Run the first prompt so the cache is populated vllm_outputs = vllm_model.generate_greedy([cached_prompt], max_tokens) # Run all the promopts vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) check_outputs_equal( outputs_0_lst=hf_outputs, outputs_1_lst=vllm_outputs, name_0="hf", name_1="vllm", ) @pytest.mark.parametrize("backend", ["FLASH_ATTN", "FLASHINFER", "XFORMERS"]) def test_unstable_prompt_sequence( vllm_runner, backend: str, monkeypatch, ) -> None: override_backend_env_variable(monkeypatch, backend) with vllm_runner( "Qwen/Qwen2.5-0.5B-Instruct", enable_chunked_prefill=True, enable_prefix_caching=True, max_model_len=4096, ) as vllm_model: for prompt in UNSTABLE_PROMPT_SEQUENCE: vllm_model.generate(TokensPrompt(prompt_token_ids=prompt), SamplingParams(max_tokens=1))