test_tensorizer.py

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

import gc
import os
import pathlib
import subprocess

import pytest
import torch

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,
                                                         open_stream,
                                                         tensorize_vllm_model)
from vllm.lora.request import LoRARequest

# yapf: enable
from vllm.utils import PlaceholderModule

from ..utils import VLLM_PATH, models_path_prefix

try:
    from tensorizer import EncryptionParams
except ImportError:
    tensorizer = PlaceholderModule("tensorizer")  # type: ignore[assignment]
    EncryptionParams = tensorizer.placeholder_attr("EncryptionParams")

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 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.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):
    args = EngineArgs(model=model_ref)
    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=str(model_path),
                                              encryption_keyfile=str(key_path))

    tensorize_vllm_model(args, config_for_serializing)

    config_for_deserializing = TensorizerConfig(
        tensorizer_uri=str(model_path), encryption_keyfile=str(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_load_without_tensorizer_load_format(vllm_runner, capfd):
    model = None
    try:
        model = vllm_runner(
            model_ref,
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))
    except RuntimeError:
        out, err = capfd.readouterr()
        combined_output = out + err
        assert ("ValueError: Model loader extra config "
                "is not supported for load "
                "format LoadFormat.AUTO") in combined_output
    finally:
        del model
        gc.collect()
        torch.cuda.empty_cache()


def test_raise_value_error_on_invalid_load_format(vllm_runner, capfd):
    model = None
    try:
        model = vllm_runner(
            model_ref,
            load_format="safetensors",
            model_loader_extra_config=TensorizerConfig(tensorizer_uri="test"))
    except RuntimeError:
        out, err = capfd.readouterr()

        combined_output = out + err
        assert ("ValueError: Model loader extra config is not supported "
                "for load format LoadFormat.SAFETENSORS") in combined_output
    finally:
        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, capfd):
    try:
        model_ref = os.path.join(models_path_prefix, "EleutherAI/pythia-1.4b")
        # tensorized_path = f"s3://tensorized/{model_ref}/fp16/model.tensors"
        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,
        )
    except RuntimeError:
        out, err = capfd.readouterr()
        combined_output = out + err
        assert ("ValueError: For a sharded model, tensorizer_uri "
                "should include a string format template like '%04d' "
                "to be formatted with the rank "
                "of the shard") in combined_output


@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)

    # 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=str(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


@pytest.mark.flaky(reruns=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))
    args = EngineArgs(model=model_ref, device="cuda")

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

    tensorize_vllm_model(args, 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