test_llama.py

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]",
        "[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]",
        "[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]",
        "[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]",
        "[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]",
        "[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 womens doubles for werner schlager [/user] [assistant]"
    ]
    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]",
        " 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? ",
        "\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",
        " 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. ",
        " 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? ",
        "\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 womens 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 womens 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 womens 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",
    ]
    expected_lora_output = [
        "  SELECT icao FROM table_name_74 WHERE airport = 'lilongwe international airport' ",
        "  SELECT nationality FROM table_name_11 WHERE elector = 'anchero pantaleone' ",
        "  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] ",
        "  SELECT sex FROM people WHERE people_id IN (SELECT people_id FROM candidate GROUP BY sex ORDER BY COUNT(people_id) DESC LIMIT 1) ",
        "  SELECT pick FROM table_name_60 WHERE former_wnba_team = 'Minnesota Lynx' ",
        "  SELECT womens_doubles FROM table_28138035_4 WHERE mens_singles = 'Werner Schlager' "
    ]

    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")