149 lines
10 KiB
Python
149 lines
10 KiB
Python
import pytest
|
||
import ray
|
||
|
||
import vllm
|
||
from vllm.lora.request import LoRARequest
|
||
|
||
from .conftest import cleanup
|
||
|
||
MODEL_PATH = "meta-llama/Llama-2-7b-hf"
|
||
|
||
|
||
def do_sample(llm, lora_path: str, lora_id: int):
|
||
prompts = [
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]", # noqa: E501
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? [/user] [assistant]", # noqa: E501
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_95 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a low tone mora with a gloss of /˩okiru/ [òkìɽɯ́]? [/user] [assistant]", # noqa: E501
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]", # noqa: E501
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? [/user] [assistant]", # noqa: E501
|
||
"[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]" # noqa: E501
|
||
]
|
||
sampling_params = vllm.SamplingParams(temperature=0,
|
||
max_tokens=256,
|
||
stop=["[/assistant]"])
|
||
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 = []
|
||
for output in outputs:
|
||
prompt = output.prompt
|
||
generated_text = output.outputs[0].text
|
||
generated_texts.append(generated_text)
|
||
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||
return generated_texts
|
||
|
||
|
||
@pytest.mark.parametrize("tp_size", [1])
|
||
def test_llama_lora(sql_lora_files, tp_size):
|
||
# Cannot use as it will initialize torch.cuda too early...
|
||
# if torch.cuda.device_count() < tp_size:
|
||
# pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
|
||
|
||
llm = vllm.LLM(MODEL_PATH,
|
||
enable_lora=True,
|
||
max_num_seqs=16,
|
||
max_loras=4,
|
||
tensor_parallel_size=tp_size)
|
||
|
||
expected_no_lora_output = [
|
||
"\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_75 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_76 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_77 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_78 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user]", # noqa: E501
|
||
" Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_11 (nationality VARCHAR, elector VARCHAR)\n\n question: When Anchero Pantaleone was the elector what is under nationality? ", # noqa: E501
|
||
"\n\n answer: 1\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_96 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_97 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one mora for a high tone mora with a gloss of /˧kot/ [kòt]? [/user] [assistant]\n\n answer: 2\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_98 (one_mora VARCHAR, gloss VARCHAR, accented_mora VARCHAR)\n\n question: What is the one m", # noqa: E501
|
||
" Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. ", # noqa: E501
|
||
" Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_60 (pick INTEGER, former_wnba_team VARCHAR)\n\n question: What pick was a player that previously played for the Minnesota Lynx? ", # noqa: E501
|
||
"\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]\n\n [user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE", # noqa: E501
|
||
]
|
||
expected_lora_output = [
|
||
" SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ", # noqa: E501
|
||
" SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ", # noqa: E501
|
||
" SELECT one_mora FROM table_name_95 WHERE gloss = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] AND accented_mora = 'low tone mora with a gloss of /˩okiru/' [òkìɽɯ́] ", # noqa: E501
|
||
" SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ", # noqa: E501
|
||
" SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ", # noqa: E501
|
||
" SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' " # noqa: E501
|
||
]
|
||
|
||
print("lora adapter created")
|
||
assert do_sample(llm, sql_lora_files, lora_id=0) == expected_no_lora_output
|
||
|
||
print("lora 1")
|
||
assert do_sample(llm, sql_lora_files, lora_id=1) == expected_lora_output
|
||
|
||
print("no lora")
|
||
assert do_sample(llm, sql_lora_files, lora_id=0) == expected_no_lora_output
|
||
|
||
print("lora 2")
|
||
assert do_sample(llm, sql_lora_files, lora_id=2) == expected_lora_output
|
||
|
||
print("removing lora")
|
||
|
||
|
||
@pytest.mark.skip("Requires multiple GPUs")
|
||
def test_llama_tensor_parallel_equality(sql_lora_files):
|
||
# Cannot use as it will initialize torch.cuda too early...
|
||
# if torch.cuda.device_count() < 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,
|
||
tensor_parallel_size=1)
|
||
output_tp1 = do_sample(llm_tp1, sql_lora_files, lora_id=1)
|
||
|
||
del llm_tp1
|
||
cleanup()
|
||
|
||
llm_tp2 = vllm.LLM(MODEL_PATH,
|
||
enable_lora=True,
|
||
max_num_seqs=16,
|
||
max_loras=4,
|
||
tensor_parallel_size=2)
|
||
output_tp2 = do_sample(llm_tp2, sql_lora_files, lora_id=1)
|
||
|
||
del llm_tp2
|
||
cleanup()
|
||
|
||
assert output_tp1 == output_tp2
|
||
|
||
llm_tp4 = vllm.LLM(MODEL_PATH,
|
||
enable_lora=True,
|
||
max_num_seqs=16,
|
||
max_loras=4,
|
||
tensor_parallel_size=4)
|
||
output_tp4 = do_sample(llm_tp4, sql_lora_files, lora_id=1)
|
||
|
||
del llm_tp4
|
||
cleanup()
|
||
|
||
assert output_tp1 == output_tp4
|
||
|
||
|
||
def test_llama_lora_warmup(sql_lora_files):
|
||
"""Test that the LLM initialization works with a warmup LORA path and
|
||
is more conservative"""
|
||
|
||
@ray.remote(num_gpus=1)
|
||
def get_num_gpu_blocks_lora():
|
||
llm = vllm.LLM(MODEL_PATH, enable_lora=True, max_num_seqs=16)
|
||
num_gpu_blocks_lora_warmup = llm.llm_engine.cache_config.num_gpu_blocks
|
||
return num_gpu_blocks_lora_warmup
|
||
|
||
@ray.remote(num_gpus=1)
|
||
def get_num_gpu_blocks_no_lora():
|
||
llm = vllm.LLM(MODEL_PATH, max_num_seqs=16)
|
||
num_gpu_blocks_no_lora_warmup = (
|
||
llm.llm_engine.cache_config.num_gpu_blocks)
|
||
return num_gpu_blocks_no_lora_warmup
|
||
|
||
num_gpu_blocks_lora_warmup = ray.get(get_num_gpu_blocks_lora.remote())
|
||
num_gpu_blocks_no_lora_warmup = ray.get(
|
||
get_num_gpu_blocks_no_lora.remote())
|
||
assert num_gpu_blocks_lora_warmup < num_gpu_blocks_no_lora_warmup, (
|
||
"The warmup with lora should be more "
|
||
"conservative than without lora, therefore the number of "
|
||
"memory blocks for the KV cache should be "
|
||
"less when using lora than when not using lora")
|