xc-llm-ascend/tests/e2e/singlecard/test_llama32_lora.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from unittest.mock import patch

import vllm
import vllm.config
from vllm.lora.request import LoRARequest

from tests.e2e.conftest import VllmRunner
from vllm_ascend.utils import enable_custom_op

enable_custom_op()

PROMPT_TEMPLATE = """<|eot_id|><|start_header_id|>user<|end_header_id|>
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.
"
##Instruction:
candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
The People_ID of candidate is the foreign key of People_ID of people.
###Input:
{context}
###Response:<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""  # noqa: E501

EXPECTED_LORA_OUTPUT = [
    "SELECT count(*) FROM candidate",
    "SELECT count(*) FROM candidate",
    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
]

EXPECTED_BASE_MODEL_OUTPUT = [
    "SELECT COUNT(*) FROM candidate",
    "`SELECT COUNT(*) FROM candidate;`",
    "SELECT Poll_Source FROM candidate GROUP BY Poll_Source ORDER BY COUNT(*) DESC LIMIT 1;",
    "SELECT * FROM candidate ORDER BY Candidate_ID DESC LIMIT 1",
]

# For hk region, we need to use the model from hf to avoid the network issue
MODEL_PATH = "meta-llama/Llama-3.2-3B-Instruct"


def do_sample(
    llm: vllm.LLM,
    lora_path: str,
    lora_id: int,
    tensorizer_config_dict: dict | None = None,
) -> list[str]:
    prompts = [
        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
        PROMPT_TEMPLATE.format(
            context="Which poll resource provided the most number of candidate information?"  # noqa: E501
        ),
        PROMPT_TEMPLATE.format(context="Return the poll resource associated with the most candidates."),
    ]

    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64, stop=["<|im_end|>"])
    if tensorizer_config_dict is not None:
        outputs = llm.generate(
            prompts,
            sampling_params,
            lora_request=LoRARequest(
                str(lora_id),
                lora_id,
                lora_path,
                tensorizer_config_dict=tensorizer_config_dict,
            )
            if lora_id
            else None,
        )
    else:
        outputs = llm.generate(
            prompts,
            sampling_params,
            lora_request=LoRARequest(str(lora_id), lora_id, lora_path) if lora_id else None,
        )

    generated_texts: list[str] = []
    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


def generate_and_test(llm, llama32_lora_files, tensorizer_config_dict: dict | None = None):
    print("lora adapter created")
    print("lora 1")
    assert (
        do_sample(
            llm,
            llama32_lora_files,
            tensorizer_config_dict=tensorizer_config_dict,
            lora_id=1,
        )
        == EXPECTED_LORA_OUTPUT
    )

    print("lora 2")
    assert (
        do_sample(
            llm,
            llama32_lora_files,
            tensorizer_config_dict=tensorizer_config_dict,
            lora_id=2,
        )
        == EXPECTED_LORA_OUTPUT
    )

    print("base model")
    assert (
        do_sample(
            llm,
            llama32_lora_files,
            tensorizer_config_dict=tensorizer_config_dict,
            lora_id=0,
        )
        == EXPECTED_BASE_MODEL_OUTPUT
    )

    print("removing lora")


@patch.dict("os.environ", {"VLLM_USE_MODELSCOPE": "False"})
def test_llama_lora(llama32_lora_files):
    vllm_model = VllmRunner(
        MODEL_PATH,
        enable_lora=True,
        # also test odd max_num_seqs
        max_num_seqs=7,
        max_model_len=1024,
        max_loras=4,
    )
    llm = vllm_model.model
    generate_and_test(llm, llama32_lora_files)