test_tensorizer.py

import gc
import json
import os
import pathlib
import subprocess
from unittest.mock import MagicMock, patch

import openai
import pytest
import torch
from huggingface_hub import snapshot_download
from tensorizer import EncryptionParams

from vllm import EngineArgs, LLMEngine, RequestOutput, SamplingParams
from vllm.engine.arg_utils import EngineArgs
# yapf conflicts with isort for this docstring
# yapf: disable
from vllm.model_executor.model_loader.tensorizer import (TensorizerConfig,
                                                         TensorSerializer,
                                                         is_vllm_tensorized,
                                                         load_with_tensorizer,
                                                         open_stream,
                                                         serialize_vllm_model,
                                                         tensorize_vllm_model)
# yapf: enable
from vllm.utils import import_from_path

from ..conftest import VllmRunner
from ..utils import VLLM_PATH, RemoteOpenAIServer
from .conftest import retry_until_skip
from ..utils import RemoteOpenAIServer, models_path_prefix

EXAMPLES_PATH = VLLM_PATH / "examples"

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95, seed=0)

model_ref = os.path.join(models_path_prefix, "facebook/opt-125m")
tensorize_model_for_testing_script = os.path.join(
    os.path.dirname(__file__), "tensorize_vllm_model_for_testing.py")


def is_curl_installed():
    try:
        subprocess.check_call(['curl', '--version'])
        return True
    except (subprocess.CalledProcessError, FileNotFoundError):
        return False


def get_torch_model(vllm_runner: VllmRunner):
    return vllm_runner \
        .model \
        .llm_engine \
        .model_executor \
        .driver_worker \
        .model_runner \
        .model


def write_keyfile(keyfile_path: str):
    encryption_params = EncryptionParams.random()
    pathlib.Path(keyfile_path).parent.mkdir(parents=True, exist_ok=True)
    with open(keyfile_path, 'wb') as f:
        f.write(encryption_params.key)


@patch('vllm.model_executor.model_loader.tensorizer.TensorizerAgent')
def test_load_with_tensorizer(mock_agent, tensorizer_config):
    mock_linear_method = MagicMock()
    mock_agent_instance = mock_agent.return_value
    mock_agent_instance.deserialize.return_value = MagicMock()

    result = load_with_tensorizer(tensorizer_config,
                                  quant_method=mock_linear_method)

    mock_agent.assert_called_once_with(tensorizer_config,
                                       quant_method=mock_linear_method)
    mock_agent_instance.deserialize.assert_called_once()
    assert result == mock_agent_instance.deserialize.return_value


@pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed")
def test_can_deserialize_s3(vllm_runner):
    model_ref = os.path.join(models_path_prefix, "EleutherAI/pythia-1.4b")
    tensorized_path = f"{model_ref}/fp16/model.tensors"

    with vllm_runner(model_ref,
                     load_format="tensorizer",
                     model_loader_extra_config=TensorizerConfig(
                         tensorizer_uri=tensorized_path,
                         num_readers=1,
                         s3_endpoint="object.ord1.coreweave.com",
                     )) as loaded_hf_model:
        deserialized_outputs = loaded_hf_model.generate(
            prompts, sampling_params)
        # noqa: E501

        assert deserialized_outputs


@pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed")
def test_deserialized_encrypted_vllm_model_has_same_outputs(
        vllm_runner, tmp_path):
    with vllm_runner(model_ref) as vllm_model:
        model_path = tmp_path / (model_ref + ".tensors")
        key_path = tmp_path / (model_ref + ".key")
        write_keyfile(key_path)

        outputs = vllm_model.generate(prompts, sampling_params)

        config_for_serializing = TensorizerConfig(tensorizer_uri=model_path,
                                                  encryption_keyfile=key_path)
        serialize_vllm_model(get_torch_model(vllm_model),
                             config_for_serializing)

    config_for_deserializing = TensorizerConfig(tensorizer_uri=model_path,
                                                encryption_keyfile=key_path)

    with vllm_runner(model_ref,
                     load_format="tensorizer",
                     model_loader_extra_config=config_for_deserializing
                     ) as loaded_vllm_model:  # noqa: E501

        deserialized_outputs = loaded_vllm_model.generate(
            prompts, sampling_params)
        # noqa: E501

        assert outputs == deserialized_outputs


def test_deserialized_hf_model_has_same_outputs(hf_runner, vllm_runner,
                                                tmp_path):
    with hf_runner(model_ref) as hf_model:
        model_path = tmp_path / (model_ref + ".tensors")
        max_tokens = 50
        outputs = hf_model.generate_greedy(prompts, max_tokens=max_tokens)
        with open_stream(model_path, "wb+") as stream:
            serializer = TensorSerializer(stream)
            serializer.write_module(hf_model.model)

    with vllm_runner(model_ref,
                     load_format="tensorizer",
                     model_loader_extra_config=TensorizerConfig(
                         tensorizer_uri=model_path,
                         num_readers=1,
                     )) as loaded_hf_model:
        deserialized_outputs = loaded_hf_model.generate_greedy(
            prompts, max_tokens=max_tokens)

        assert outputs == deserialized_outputs


def create_test_prompts(
        lora_path: str
) -> List[Tuple[str, SamplingParams, Optional[LoRARequest]]]:
    """Create a list of test prompts with their sampling parameters.

    2 requests for base model, 4 requests for the LoRA. We define 2
    different LoRA adapters (using the same model for demo purposes).
    Since we also set `max_loras=1`, the expectation is that the requests
    with the second LoRA adapter will be ran after all requests with the
    first adapter have finished.
    """
    return [
        ("A robot may not injure a human being",
         SamplingParams(temperature=0.0,
                        logprobs=1,
                        prompt_logprobs=1,
                        max_tokens=128), None),
        ("To be or not to be,",
         SamplingParams(temperature=0.8,
                        top_k=5,
                        presence_penalty=0.2,
                        max_tokens=128), None),
        (
            "[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
            SamplingParams(temperature=0.0,
                           logprobs=1,
                           prompt_logprobs=1,
                           max_tokens=128,
                           stop_token_ids=[32003]),
            LoRARequest("sql-lora", 1, lora_path)),
        (
            "[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
            SamplingParams(n=3,
                           best_of=3,
                           use_beam_search=True,
                           temperature=0,
                           max_tokens=128,
                           stop_token_ids=[32003]),
            LoRARequest("sql-lora", 1, lora_path)),
        (
            "[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
            SamplingParams(temperature=0.0,
                           logprobs=1,
                           prompt_logprobs=1,
                           max_tokens=128,
                           stop_token_ids=[32003]),
            LoRARequest("sql-lora2", 2, lora_path)),
        (
            "[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
            SamplingParams(n=3,
                           best_of=3,
                           use_beam_search=True,
                           temperature=0,
                           max_tokens=128,
                           stop_token_ids=[32003]),
            LoRARequest("sql-lora", 1, lora_path)),
    ]


def process_requests(engine: LLMEngine,
                     test_prompts: List[Tuple[str, SamplingParams,
                                              Optional[LoRARequest]]]):
    """Continuously process a list of prompts and handle the outputs."""
    request_id = 0

    while test_prompts or engine.has_unfinished_requests():
        if test_prompts:
            prompt, sampling_params, lora_request = test_prompts.pop(0)
            engine.add_request(str(request_id),
                               prompt,
                               sampling_params,
                               lora_request=lora_request)
            request_id += 1

        request_outputs: List[RequestOutput] = engine.step()

        for request_output in request_outputs:
            if request_output.finished:
                print(request_output)


def test_vllm_model_can_load_with_lora(vllm_runner, tmp_path):
    multilora_inference = import_from_path(
        "examples.multilora_inference",
        EXAMPLES_PATH / "multilora_inference.py",
    )

    model_ref = "meta-llama/Llama-2-7b-hf"
    # lora_path = snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
    lora_path = os.path.join(models_path_prefix, "yard1/llama-2-7b-sql-lora-test")
    test_prompts = multilora_inference.create_test_prompts(lora_path)

    # Serialize model before deserializing and binding LoRA adapters
    with vllm_runner(model_ref, ) as vllm_model:
        model_path = tmp_path / (model_ref + ".tensors")

        serialize_vllm_model(get_torch_model(vllm_model),
                             TensorizerConfig(tensorizer_uri=model_path))

    with vllm_runner(
            model_ref,
            load_format="tensorizer",
            model_loader_extra_config=TensorizerConfig(
                tensorizer_uri=model_path,
                num_readers=1,
            ),
            enable_lora=True,
            max_loras=1,
            max_lora_rank=8,
            max_cpu_loras=2,
            max_num_seqs=50,
            max_model_len=1000,
    ) as loaded_vllm_model:
        multilora_inference.process_requests(
            loaded_vllm_model.model.llm_engine, test_prompts)

        assert loaded_vllm_model


def test_load_without_tensorizer_load_format(vllm_runner):
    model = None
    with pytest.raises(ValueError):
        model = vllm_runner(
            model_ref,
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))
    del model
    gc.collect()
    torch.cuda.empty_cache()


@pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed")
def test_openai_apiserver_with_tensorizer(vllm_runner, tmp_path):
    ## Serialize model
    with vllm_runner(model_ref, ) as vllm_model:
        model_path = tmp_path / (model_ref + ".tensors")

        serialize_vllm_model(get_torch_model(vllm_model),
                             TensorizerConfig(tensorizer_uri=model_path))

        model_loader_extra_config = {
            "tensorizer_uri": str(model_path),
        }

    ## Start OpenAI API server
    openai_args = [
        "--dtype",
        "float16",
        "--load-format",
        "tensorizer",
        "--model-loader-extra-config",
        json.dumps(model_loader_extra_config),
    ]

    with RemoteOpenAIServer(model_ref, openai_args) as server:
        print("Server ready.")

        client = server.get_client()
        completion = client.completions.create(model=model_ref,
                                               prompt="Hello, my name is",
                                               max_tokens=5,
                                               temperature=0.0)

        assert completion.id is not None
        assert len(completion.choices) == 1
        assert len(completion.choices[0].text) >= 5
        assert completion.choices[0].finish_reason == "length"
        assert completion.usage == openai.types.CompletionUsage(
            completion_tokens=5, prompt_tokens=6, total_tokens=11)


def test_raise_value_error_on_invalid_load_format(vllm_runner):
    model = None
    with pytest.raises(ValueError):
        model = vllm_runner(
            model_ref,
            load_format="safetensors",
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))
    del model
    gc.collect()
    torch.cuda.empty_cache()


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 GPUs")
def test_tensorizer_with_tp_path_without_template(vllm_runner):
    with pytest.raises(ValueError):
        model_ref = os.path.join(models_path_prefix, "EleutherAI/pythia-1.4b")
        tensorized_path = f"{model_ref}/fp16/model.tensors"

        vllm_runner(
            model_ref,
            load_format="tensorizer",
            model_loader_extra_config=TensorizerConfig(
                tensorizer_uri=tensorized_path,
                num_readers=1,
                s3_endpoint="object.ord1.coreweave.com",
            ),
            tensor_parallel_size=2,
            disable_custom_all_reduce=True,
        )


@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 GPUs")
def test_deserialized_encrypted_vllm_model_with_tp_has_same_outputs(
        vllm_runner, tmp_path):
    model_ref = os.path.join(models_path_prefix, "EleutherAI/pythia-1.4b")
    # record outputs from un-sharded un-tensorized model
    with vllm_runner(
            model_ref,
            disable_custom_all_reduce=True,
            enforce_eager=True,
    ) as base_model:
        outputs = base_model.generate(prompts, sampling_params)
        base_model.model.llm_engine.model_executor.shutdown()

    # load model with two shards and serialize with encryption
    model_path = str(tmp_path / (model_ref + "-%02d.tensors"))
    key_path = tmp_path / (model_ref + ".key")

    tensorizer_config = TensorizerConfig(
        tensorizer_uri=model_path,
        encryption_keyfile=key_path,
    )

    tensorize_vllm_model(
        engine_args=EngineArgs(
            model=model_ref,
            tensor_parallel_size=2,
            disable_custom_all_reduce=True,
            enforce_eager=True,
        ),
        tensorizer_config=tensorizer_config,
    )
    assert os.path.isfile(model_path % 0), "Serialization subprocess failed"
    assert os.path.isfile(model_path % 1), "Serialization subprocess failed"

    with vllm_runner(
            model_ref,
            tensor_parallel_size=2,
            load_format="tensorizer",
            disable_custom_all_reduce=True,
            enforce_eager=True,
            model_loader_extra_config=tensorizer_config) as loaded_vllm_model:
        deserialized_outputs = loaded_vllm_model.generate(
            prompts, sampling_params)

    assert outputs == deserialized_outputs


@retry_until_skip(3)
def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path):
    gc.collect()
    torch.cuda.empty_cache()
    model_ref = os.path.join(models_path_prefix, "facebook/opt-125m")
    model_path = tmp_path / (model_ref + ".tensors")
    config = TensorizerConfig(tensorizer_uri=str(model_path))

    with vllm_runner(model_ref) as vllm_model:
        outputs = vllm_model.generate(prompts, sampling_params)
        serialize_vllm_model(get_torch_model(vllm_model), config)

        assert is_vllm_tensorized(config)

    with vllm_runner(model_ref,
                     load_format="tensorizer",
                     model_loader_extra_config=config) as loaded_vllm_model:
        deserialized_outputs = loaded_vllm_model.generate(
            prompts, sampling_params)
        # noqa: E501

        assert outputs == deserialized_outputs