64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""Compare the outputs of HF and distributed vLLM when using greedy sampling.
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vLLM will allocate all the available memory, so we need to run the tests one
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by one. The solution is to pass arguments (model name) by environment
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variables.
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Run:
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```sh
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cd $VLLM_PATH/tests
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TEST_DIST_MODEL=facebook/opt-125m pytest \
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distributed/test_basic_distributed_correctness.py
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TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf \
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distributed/test_basic_distributed_correctness.py
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```
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"""
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import os
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import pytest
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from vllm.utils import cuda_device_count_stateless
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from ..models.utils import check_outputs_equal
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MODELS = [
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os.environ["TEST_DIST_MODEL"],
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]
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DISTRIBUTED_EXECUTOR_BACKEND = "DISTRIBUTED_EXECUTOR_BACKEND"
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@pytest.mark.skipif(cuda_device_count_stateless() < 2,
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reason="Need at least 2 GPUs to run the test.")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [5])
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def test_models(
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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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dtype: str,
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max_tokens: int,
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) -> None:
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distributed_executor_backend = os.getenv(DISTRIBUTED_EXECUTOR_BACKEND)
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# NOTE: take care of the order. run vLLM first, and then run HF.
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# vLLM needs a fresh new process without cuda initialization.
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# if we run HF first, the cuda initialization will be done and it
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# will hurt multiprocessing backend with fork method (the default method).
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with vllm_runner(model,
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dtype=dtype,
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tensor_parallel_size=2,
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distributed_executor_backend=distributed_executor_backend
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) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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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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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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