vllm/tests/distributed/test_basic_distributed_correctness.py

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