84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import pytest
|
|
|
|
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
|
|
|
|
|
|
@pytest.fixture(autouse=True)
|
|
def v1(run_with_both_engines_lora):
|
|
# Simple autouse wrapper to run both engines for each test
|
|
# This can be promoted up to conftest.py to run for every
|
|
# test in a package
|
|
pass
|
|
|
|
|
|
# Skipping for V1 for now as we are hitting,
|
|
# "Head size 80 is not supported by FlashAttention." error.
|
|
@pytest.mark.skip_v1
|
|
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])
|