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