[V1] AsyncLLM Implementation (#9826)

Signed-off-by: Nick Hill <nickhill@us.ibm.com>
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Signed-off-by: Nick Hill <nhill@redhat.com>
Co-authored-by: Nick Hill <nickhill@us.ibm.com>
Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>
Co-authored-by: Nick Hill <nhill@redhat.com>
Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
This commit is contained in:
Robert Shaw
2024-11-11 18:05:38 -05:00
committed by GitHub
parent 08f93e7439
commit 6ace6fba2c
29 changed files with 2409 additions and 724 deletions

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"""
This file test accuracy of the vLLM server via LMEval.
It uses local-completions, which interacts with vLLM
through the OAI API with N concurrent connections.
This simulates real work usage of the API and makes
sure that the zmq frontend mp RPC message passing and
AsyncLLMEngine are working correctly.
"""
import lm_eval
import pytest
from vllm.platforms import current_platform
MODEL_NAME = "Qwen/Qwen2-1.5B-Instruct"
NUM_CONCURRENT = 500
TASK = "gsm8k"
FILTER = "exact_match,strict-match"
RTOL = 0.03
EXPECTED_VALUE = 0.58
def run_test():
"""Run the end to end accuracy test."""
model_args = f"pretrained={MODEL_NAME},max_model_len=2048"
results = lm_eval.simple_evaluate(
model="vllm",
model_args=model_args,
tasks="gsm8k",
batch_size="auto",
)
measured_value = results["results"][TASK][FILTER]
assert (measured_value - RTOL < EXPECTED_VALUE
and measured_value + RTOL > EXPECTED_VALUE
), f"Expected: {EXPECTED_VALUE} | Measured: {measured_value}"
@pytest.mark.skipif(not current_platform.is_cuda(),
reason="V1 is currently only supported on CUDA.")
def test_lm_eval_accuracy_v1_engine(monkeypatch):
"""Run with the V1 Engine."""
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "1")
run_test()
def test_lm_eval_accuracy_v0_engine(monkeypatch):
"""Run with the V0 Engine."""
with monkeypatch.context() as m:
m.setenv("VLLM_USE_V1", "0")
run_test()