conftest.py

import asyncio
import time
from itertools import cycle
from typing import Dict, List, Optional, Tuple, Union

import pytest
import ray
import torch

from vllm.utils import is_hip

if (not is_hip()):
    from pynvml import (nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo,
                        nvmlInit)

from vllm import LLM
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.lora.request import LoRARequest
from vllm.model_executor.utils import set_random_seed
from vllm.multimodal import MultiModalData
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
from vllm.sequence import Logprob
from vllm.usage.usage_lib import UsageContext
from vllm.utils import Counter, random_uuid

from ...conftest import cleanup


class AsyncLLM:
    """AsyncLLM

    Note: Current LLM class in vllm don't support async mode, for test purpose,
    we implement async one in here. Maybe we could move to
    vllm/entrypoints/llm.py in future.

    Below AsyncLLM is directly borrow from vllm/entrypoints/llm.py with changes
    to make to work in async mode.
    """

    def __init__(
        self,
        model: str,
        tokenizer: Optional[str] = None,
        tokenizer_mode: str = "auto",
        skip_tokenizer_init: bool = False,
        trust_remote_code: bool = False,
        tensor_parallel_size: int = 1,
        dtype: str = "auto",
        quantization: Optional[str] = None,
        revision: Optional[str] = None,
        tokenizer_revision: Optional[str] = None,
        seed: int = 0,
        gpu_memory_utilization: float = 0.9,
        swap_space: int = 4,
        enforce_eager: bool = False,
        max_seq_len_to_capture: int = 8192,
        disable_custom_all_reduce: bool = False,
        **kwargs,
    ) -> None:
        if "disable_log_stats" not in kwargs:
            kwargs["disable_log_stats"] = True
        engine_args = AsyncEngineArgs(
            model=model,
            tokenizer=tokenizer,
            tokenizer_mode=tokenizer_mode,
            skip_tokenizer_init=skip_tokenizer_init,
            trust_remote_code=trust_remote_code,
            tensor_parallel_size=tensor_parallel_size,
            dtype=dtype,
            quantization=quantization,
            revision=revision,
            tokenizer_revision=tokenizer_revision,
            seed=seed,
            gpu_memory_utilization=gpu_memory_utilization,
            swap_space=swap_space,
            enforce_eager=enforce_eager,
            max_seq_len_to_capture=max_seq_len_to_capture,
            # For now use ray for the distributed back-end, since
            # we rely on the use of engine_use_ray=True to avoid
            # reinitializing CUDA in the same process (driver worker)
            engine_use_ray=True,
            distributed_executor_backend="ray",
            disable_custom_all_reduce=disable_custom_all_reduce,
            **kwargs,
        )
        self.request_counter = Counter()
        self.llm_engine = AsyncLLMEngine.from_engine_args(
            engine_args, usage_context=UsageContext.LLM_CLASS)

    def generate(
        self,
        prompts: Optional[Union[str, List[str]]] = None,
        sampling_params: Optional[Union[SamplingParams,
                                        List[SamplingParams]]] = None,
        prompt_token_ids: Optional[List[List[int]]] = None,
        use_tqdm: bool = True,
        lora_request: Optional[LoRARequest] = None,
        multi_modal_data: Optional[MultiModalData] = None,
    ) -> List[RequestOutput]:

        if prompts is None:
            raise ValueError("prompts must be provided.")
        if isinstance(prompts, str):
            # Convert a single prompt to a list.
            prompts = [prompts]

        if prompts is not None:
            num_requests = len(prompts)

        if sampling_params is None:
            # Use default sampling params.
            sampling_params = SamplingParams()

        elif isinstance(sampling_params,
                        list) and len(sampling_params) != num_requests:
            raise ValueError("The lengths of prompts and "
                             "sampling_params must be the same.")

        async def get_output(prompt, sampling_param) -> str:
            request_id = random_uuid()
            results_generator = self.llm_engine.generate(
                prompt, sampling_param, request_id)
            final_output = None
            async for request_output in results_generator:
                final_output = request_output
            return final_output

        outputs = []
        try:
            for i in range(num_requests):
                prompt = prompts[i] if prompts is not None else None
                res = asyncio.run(get_output(prompt, sampling_params))
                outputs.append(res)
        finally:
            ray.shutdown()
        return outputs


@pytest.fixture
def baseline_llm_generator(request, common_llm_kwargs,
                           per_test_common_llm_kwargs, baseline_llm_kwargs,
                           seed):
    return create_llm_generator("baseline", request, common_llm_kwargs,
                                per_test_common_llm_kwargs,
                                baseline_llm_kwargs, seed)


@pytest.fixture
def test_llm_generator(request, common_llm_kwargs, per_test_common_llm_kwargs,
                       test_llm_kwargs, seed):
    return create_llm_generator("test", request, common_llm_kwargs,
                                per_test_common_llm_kwargs, test_llm_kwargs,
                                seed)


def create_llm_generator(baseline_or_test, request, common_llm_kwargs,
                         per_test_common_llm_kwargs, distinct_llm_kwargs,
                         seed):
    kwargs = {
        **common_llm_kwargs,
        **per_test_common_llm_kwargs,
        **distinct_llm_kwargs,
    }
    test_name = request.node.name

    def generator_inner():

        wait_for_gpu_memory_to_clear(
            devices=list(range(torch.cuda.device_count())),
            threshold_bytes=2 * 2**30,
            timeout_s=60,
        )

        use_async = False
        if "use_async" in kwargs:
            use_async = kwargs.pop("use_async")
        print(f'{use_async=}')

        print(f'Creating {baseline_or_test=} LLM for {test_name=}. {kwargs=}')
        llm = AsyncLLM(**kwargs) if use_async else LLM(**kwargs)
        set_random_seed(seed)

        yield llm
        del llm
        cleanup()

    def generator_outer():
        for llm in generator_inner():
            yield llm
            del llm

    return generator_outer


def maybe_assert_ngram_worker(llm):
    # Verify the proposer worker is ngram if ngram is specified.
    if (not isinstance(llm, AsyncLLM)
            and llm.llm_engine.speculative_config is not None
            and llm.llm_engine.speculative_config.ngram_prompt_lookup_max > 0):
        from vllm.spec_decode.ngram_worker import NGramWorker
        assert isinstance(
            llm.llm_engine.model_executor.driver_worker.proposer_worker,
            NGramWorker)


def get_output_from_llm_generator(
        llm_generator, prompts,
        sampling_params) -> Tuple[List[str], List[List[int]]]:
    tokens = []
    token_ids = []
    for llm in llm_generator():
        maybe_assert_ngram_worker(llm)

        outputs = llm.generate(prompts, sampling_params, use_tqdm=True)
        token_ids = [output.outputs[0].token_ids for output in outputs]
        tokens = [output.outputs[0].text for output in outputs]
        del llm

    return tokens, token_ids


def get_logprobs_from_llm_generator(
        llm_generator, prompts,
        sampling_params) -> List[List[Dict[int, Logprob]]]:
    """Returns a dict of (token_id: Logprob) for each generated position, for
    each sequence in the batch.
    """
    for llm in llm_generator():
        outputs = llm.generate(prompts, sampling_params, use_tqdm=True)
        logprobs = [output.outputs[0].logprobs[:] for output in outputs]
        del llm

    return logprobs


def run_greedy_equality_correctness_test(baseline_llm_generator,
                                         test_llm_generator,
                                         batch_size,
                                         max_output_len,
                                         force_output_len: bool,
                                         print_tokens: bool = False):
    """Helper method that compares the outputs of both the baseline LLM and
    the test LLM. It asserts greedy equality, e.g. that the outputs are exactly
    the same when temperature is zero.
    """
    temperature = 0.0

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
        "San Francisco is know for its",
        "Facebook was created in 2004 by",
        "Curious George is a",
        "Python 3.11 brings improvements to its",
    ]

    prompts = [prompt for prompt, _ in zip(cycle(prompts), range(batch_size))]

    # If the test requires that we generated max_output_len tokens, then set the
    # sampling params to ignore eos token.
    ignore_eos = force_output_len

    sampling_params = SamplingParams(
        max_tokens=max_output_len,
        ignore_eos=ignore_eos,
        temperature=temperature,
    )

    spec_batch_tokens, spec_batch_token_ids = get_output_from_llm_generator(
        test_llm_generator, prompts, sampling_params)

    (baseline_batch_tokens,
     baseline_batch_token_ids) = get_output_from_llm_generator(
         baseline_llm_generator, prompts, sampling_params)

    assert len(baseline_batch_token_ids) == len(prompts)
    assert len(spec_batch_token_ids) == len(prompts)

    for i, (baseline_token_ids, baseline_tokens, spec_token_ids,
            spec_tokens) in enumerate(
                zip(baseline_batch_token_ids, baseline_batch_tokens,
                    spec_batch_token_ids, spec_batch_tokens)):
        if print_tokens:
            print(f'{i=} {baseline_tokens=}')
            print(f'{i=}     {spec_tokens=}')
        print(f'{i=} {baseline_token_ids=}')
        print(f'{i=}     {spec_token_ids=}')
        assert baseline_token_ids == spec_token_ids


def wait_for_gpu_memory_to_clear(devices: List[int],
                                 threshold_bytes: int,
                                 timeout_s: float = 120) -> None:
    # Use nvml instead of pytorch to reduce measurement error from torch cuda
    # context.
    nvmlInit()
    start_time = time.time()
    while True:
        output = {}
        output_raw = {}
        for device in devices:
            dev_handle = nvmlDeviceGetHandleByIndex(device)
            mem_info = nvmlDeviceGetMemoryInfo(dev_handle)
            gb_used = mem_info.used / 2**30
            output_raw[device] = gb_used
            output[device] = f'{gb_used:.02f}'

        print('gpu memory used (GB): ', end='')
        for k, v in output.items():
            print(f'{k}={v}; ', end='')
        print('')

        dur_s = time.time() - start_time
        if all(v <= (threshold_bytes / 2**30) for v in output_raw.values()):
            print(f'Done waiting for free GPU memory on devices {devices=} '
                  f'({threshold_bytes/2**30=}) {dur_s=:.02f}')
            break

        if dur_s >= timeout_s:
            raise ValueError(f'Memory of devices {devices=} not free after '
                             f'{dur_s=:.02f} ({threshold_bytes/2**30=})')

        time.sleep(5)