0f53b138f6
### What this PR does / why we need it? Add V1Engine LoRA support. Add LoRA e2e test on single card and multiple cards. ### Does this PR introduce _any_ user-facing change? support lora for V1 ### How was this patch tested? CI passed with new added test --------- Signed-off-by: jesse <szxfml@gmail.com> Signed-off-by: paulyu <paulyu0307@gmail.com> Signed-off-by: paulyu12 <507435917@qq.com> Co-authored-by: jesse <szxfml@gmail.com> Co-authored-by: paulyu <paulyu0307@gmail.com>
61 lines
3.1 KiB
Python
61 lines
3.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import vllm
|
|
from vllm.lora.request import LoRARequest
|
|
|
|
from tests.conftest import VllmRunner
|
|
|
|
MODEL_PATH = "ArthurZ/ilama-3.2-1B"
|
|
|
|
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
|
|
|
|
EXPECTED_LORA_OUTPUT = [
|
|
"SELECT count(*) FROM singer",
|
|
"SELECT avg(age) , min(age) , max(age) FROM singer WHERE country = 'France'", # noqa: E501
|
|
"SELECT DISTINCT Country FROM singer WHERE Age > 20",
|
|
]
|
|
|
|
|
|
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=
|
|
"What are all distinct countries where singers above age 20 are from?" # noqa: E501
|
|
),
|
|
]
|
|
sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
|
|
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_ilama_lora(ilama_lora_files):
|
|
with VllmRunner(model_name=MODEL_PATH,
|
|
enable_lora=True,
|
|
max_loras=4,
|
|
max_model_len=1024,
|
|
max_num_seqs=16) as vllm_model:
|
|
|
|
output1 = do_sample(vllm_model.model, ilama_lora_files, lora_id=1)
|
|
for i in range(len(EXPECTED_LORA_OUTPUT)):
|
|
assert output1[i] == EXPECTED_LORA_OUTPUT[i]
|
|
|
|
output2 = do_sample(vllm_model.model, ilama_lora_files, lora_id=2)
|
|
for i in range(len(EXPECTED_LORA_OUTPUT)):
|
|
assert output2[i] == EXPECTED_LORA_OUTPUT[i]
|