test_tensorizer.py

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

import openai
import pytest
import ray
import torch
from tensorizer import EncryptionParams

from vllm import SamplingParams
from vllm.engine.arg_utils import EngineArgs
# 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)

from ..conftest import VllmRunner, cleanup
from ..utils import RemoteOpenAIServer

# yapf conflicts with isort for this docstring



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 = "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)

@pytest.fixture(autouse=True)
def tensorizer_config():
    config = TensorizerConfig(tensorizer_uri="vllm")
    return config


@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 = "EleutherAI/pythia-1.4b"
    tensorized_path = f"s3://tensorized/{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 test_vllm_model_can_load_with_lora(vllm_runner, tmp_path):
    from huggingface_hub import snapshot_download

    from examples.multilora_inference import (create_test_prompts,
                                              process_requests)

    model_ref = "meta-llama/Llama-2-7b-hf"
    lora_path = snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test")
    test_prompts = 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:
        process_requests(loaded_vllm_model.model.llm_engine, test_prompts)

        assert loaded_vllm_model


def test_load_without_tensorizer_load_format(vllm_runner):
    with pytest.raises(ValueError):
        vllm_runner(
            model_ref,
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))


@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 = [
        "--model", model_ref, "--dtype", "float16", "--load-format",
        "tensorizer", "--model-loader-extra-config",
        json.dumps(model_loader_extra_config),
    ]

    server = RemoteOpenAIServer(openai_args)
    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):
    with pytest.raises(ValueError):
        vllm_runner(
            model_ref,
            load_format="safetensors",
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))


@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 = "EleutherAI/pythia-1.4b"
        tensorized_path = f"s3://tensorized/{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 = "EleutherAI/pythia-1.4b"
    # record outputs from un-sharded un-tensorized model
    base_model = vllm_runner(
        model_ref,
        disable_custom_all_reduce=True,
        enforce_eager=True,
    )
    outputs = base_model.generate(prompts, sampling_params)

    base_model.model.llm_engine.model_executor.shutdown()
    del base_model
    cleanup()
    ray.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"
    cleanup()
    ray.shutdown()

    loaded_vllm_model = 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)

    deserialized_outputs = loaded_vllm_model.generate(prompts, sampling_params)

    assert outputs == deserialized_outputs


def test_vllm_tensorized_model_has_same_outputs(vllm_runner, tmp_path):
    model_ref = "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