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[CI/Build] Remove redundant LoRA model tests (#23706)

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Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
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Jee Jee Li 2025-08-27 13:51:50 +08:00 committed by GitHub
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5
tests/lora/conftest.py
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@ -216,11 +216,6 @@ def tinyllama_lora_files():
return snapshot_download(repo_id="jashing/tinyllama-colorist-lora") return snapshot_download(repo_id="jashing/tinyllama-colorist-lora")
@pytest.fixture(scope="session")
def phi2_lora_files():
return snapshot_download(repo_id="isotr0py/phi-2-test-sql-lora")
@pytest.fixture @pytest.fixture
def reset_default_device(): def reset_default_device():
""" """

112
tests/lora/test_baichuan.py
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@ -1,112 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import vllm
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.lora.request import LoRARequest
MODEL_PATH = "baichuan-inc/Baichuan-7B"
PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:""" # noqa: E501
def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
prompts = [
PROMPT_TEMPLATE.format(query="How many singers do we have?"),
PROMPT_TEMPLATE.format(
query=
"What is the average, minimum, and maximum age of all singers from France?" # noqa: E501
),
PROMPT_TEMPLATE.format(
query=
"Show name, country, age for all singers ordered by age from the oldest to the youngest." # noqa: E501
),
]
print(prompts)
sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
outputs = llm.generate(
prompts,
sampling_params,
lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
if lora_id else None)
# Print the outputs.
generated_texts: list[str] = []
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text.strip()
generated_texts.append(generated_text)
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
return generated_texts
def test_baichuan_lora(baichuan_lora_files):
llm = vllm.LLM(MODEL_PATH,
max_model_len=1024,
enable_lora=True,
max_loras=4,
max_lora_rank=64,
trust_remote_code=True)
expected_lora_output = [
"SELECT count(*) FROM singer",
"SELECT avg(age) , min(age) , max(age) FROM singer WHERE Country = 'France'", # noqa: E501
"SELECT name , country , age FROM singer ORDER BY age ASC",
]
output1 = do_sample(llm, baichuan_lora_files, lora_id=1)
for i in range(len(expected_lora_output)):
assert output1[i] == expected_lora_output[i]
output2 = do_sample(llm, baichuan_lora_files, lora_id=2)
for i in range(len(expected_lora_output)):
assert output2[i] == expected_lora_output[i]
@pytest.mark.parametrize("fully_sharded", [True, False])
def test_baichuan_tensor_parallel_equality(baichuan_lora_files,
num_gpus_available, fully_sharded):
if num_gpus_available < 4:
pytest.skip(f"Not enough GPUs for tensor parallelism {4}")
llm_tp1 = vllm.LLM(MODEL_PATH,
enable_lora=True,
max_num_seqs=16,
max_loras=4,
max_lora_rank=64,
trust_remote_code=True,
fully_sharded_loras=fully_sharded)
output_tp1 = do_sample(llm_tp1, baichuan_lora_files, lora_id=1)
del llm_tp1
cleanup_dist_env_and_memory()
llm_tp2 = vllm.LLM(MODEL_PATH,
enable_lora=True,
max_num_seqs=16,
max_loras=4,
max_lora_rank=64,
tensor_parallel_size=2,
trust_remote_code=True,
fully_sharded_loras=fully_sharded)
output_tp2 = do_sample(llm_tp2, baichuan_lora_files, lora_id=2)
del llm_tp2
cleanup_dist_env_and_memory()
assert output_tp1 == output_tp2
llm_tp4 = vllm.LLM(MODEL_PATH,
enable_lora=True,
max_num_seqs=16,
max_loras=4,
max_lora_rank=64,
tensor_parallel_size=4,
trust_remote_code=True,
fully_sharded_loras=fully_sharded)
output_tp4 = do_sample(llm_tp4, baichuan_lora_files, lora_id=2)
del llm_tp4
cleanup_dist_env_and_memory()
assert output_tp1 == output_tp4

71
tests/lora/test_phi.py
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@ -1,71 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import vllm
from vllm.lora.request import LoRARequest
MODEL_PATH = "microsoft/phi-2"
PROMPT_TEMPLATE = "### Instruct: {sql_prompt}\n\n### Context: {context}\n\n### Output:" # noqa: E501
def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
prompts = [
PROMPT_TEMPLATE.format(
sql_prompt=
"Which catalog publisher has published the most catalogs?",
context="CREATE TABLE catalogs (catalog_publisher VARCHAR);"),
PROMPT_TEMPLATE.format(
sql_prompt=
"Which trip started from the station with the largest dock count? Give me the trip id.", # noqa: E501
context=
"CREATE TABLE trip (id VARCHAR, start_station_id VARCHAR); CREATE TABLE station (id VARCHAR, dock_count VARCHAR);" # noqa: E501
),
PROMPT_TEMPLATE.format(
sql_prompt=
"How many marine species are found in the Southern Ocean?", # noqa: E501
context=
"CREATE TABLE marine_species (name VARCHAR(50), common_name VARCHAR(50), location VARCHAR(50));" # noqa: E501
),
]
sampling_params = vllm.SamplingParams(temperature=0,
max_tokens=64,
stop="### End")
outputs = llm.generate(
prompts,
sampling_params,
lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
if lora_id else None,
)
# Print the outputs.
generated_texts: list[str] = []
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text.strip()
generated_texts.append(generated_text)
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
return generated_texts
def test_phi2_lora(phi2_lora_files):
# We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
# Otherwise, the lora-test will fail due to CUDA OOM.
llm = vllm.LLM(MODEL_PATH,
max_model_len=1024,
enable_lora=True,
max_loras=2,
enforce_eager=True,
enable_chunked_prefill=True)
expected_lora_output = [
"SELECT catalog_publisher, COUNT(*) as num_catalogs FROM catalogs GROUP BY catalog_publisher ORDER BY num_catalogs DESC LIMIT 1;", # noqa: E501
"SELECT trip.id FROM trip JOIN station ON trip.start_station_id = station.id WHERE station.dock_count = (SELECT MAX(dock_count) FROM station);", # noqa: E501
"SELECT COUNT(*) FROM marine_species WHERE location = 'Southern Ocean';", # noqa: E501
]
output1 = do_sample(llm, phi2_lora_files, lora_id=1)
for i in range(len(expected_lora_output)):
assert output1[i].startswith(expected_lora_output[i])
output2 = do_sample(llm, phi2_lora_files, lora_id=2)
for i in range(len(expected_lora_output)):
assert output2[i].startswith(expected_lora_output[i])