utils.py

import os
import subprocess
import sys
import time
import warnings
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Dict, List

import openai
import ray
import requests
from transformers import AutoTokenizer

from vllm.distributed import (ensure_model_parallel_initialized,
                              init_distributed_environment)
from vllm.entrypoints.openai.cli_args import make_arg_parser
from vllm.utils import FlexibleArgumentParser, get_open_port, is_hip

if is_hip():
    from amdsmi import (amdsmi_get_gpu_vram_usage,
                        amdsmi_get_processor_handles, amdsmi_init,
                        amdsmi_shut_down)

    @contextmanager
    def _nvml():
        try:
            amdsmi_init()
            yield
        finally:
            amdsmi_shut_down()
else:
    from pynvml import (nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo,
                        nvmlInit, nvmlShutdown)

    @contextmanager
    def _nvml():
        try:
            nvmlInit()
            yield
        finally:
            nvmlShutdown()


VLLM_PATH = Path(__file__).parent.parent
"""Path to root of the vLLM repository."""


class RemoteOpenAIServer:
    DUMMY_API_KEY = "token-abc123"  # vLLM's OpenAI server does not need API key
    MAX_SERVER_START_WAIT_S = 600  # wait for server to start for 60 seconds

    def __init__(
        self,
        model: str,
        cli_args: List[str],
        *,
        auto_port: bool = True,
    ) -> None:
        if auto_port:
            if "-p" in cli_args or "--port" in cli_args:
                raise ValueError("You have manually specified the port"
                                 "when `auto_port=True`.")

            cli_args = cli_args + ["--port", str(get_open_port())]

        parser = FlexibleArgumentParser(
            description="vLLM's remote OpenAI server.")
        parser = make_arg_parser(parser)
        args = parser.parse_args(cli_args)
        self.host = str(args.host or 'localhost')
        self.port = int(args.port)

        env = os.environ.copy()
        # the current process might initialize cuda,
        # to be safe, we should use spawn method
        env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
        self.proc = subprocess.Popen(["vllm", "serve"] + [model] + cli_args,
                                     env=env,
                                     stdout=sys.stdout,
                                     stderr=sys.stderr)
        self._wait_for_server(url=self.url_for("health"),
                              timeout=self.MAX_SERVER_START_WAIT_S)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.proc.terminate()

    def _wait_for_server(self, *, url: str, timeout: float):
        # run health check
        start = time.time()
        while True:
            try:
                if requests.get(url).status_code == 200:
                    break
            except Exception as err:
                result = self.proc.poll()
                if result is not None and result != 0:
                    raise RuntimeError("Server exited unexpectedly.") from err

                time.sleep(0.5)
                if time.time() - start > timeout:
                    raise RuntimeError(
                        "Server failed to start in time.") from err

    @property
    def url_root(self) -> str:
        return f"http://{self.host}:{self.port}"

    def url_for(self, *parts: str) -> str:
        return self.url_root + "/" + "/".join(parts)

    def get_client(self):
        return openai.OpenAI(
            base_url=self.url_for("v1"),
            api_key=self.DUMMY_API_KEY,
        )

    def get_async_client(self):
        return openai.AsyncOpenAI(
            base_url=self.url_for("v1"),
            api_key=self.DUMMY_API_KEY,
        )


def compare_two_settings(model: str, arg1: List[str], arg2: List[str]):
    """
    Launch API server with two different sets of arguments and compare the
    results of the API calls. The arguments are after the model name.
    """

    tokenizer = AutoTokenizer.from_pretrained(model)

    prompt = "Hello, my name is"
    token_ids = tokenizer(prompt)["input_ids"]
    results = []
    for args in (arg1, arg2):
        with RemoteOpenAIServer(model, args) as server:
            client = server.get_client()

            # test models list
            models = client.models.list()
            models = models.data
            served_model = models[0]
            results.append({
                "test": "models_list",
                "id": served_model.id,
                "root": served_model.root,
            })

            # test with text prompt
            completion = client.completions.create(model=model,
                                                   prompt=prompt,
                                                   max_tokens=5,
                                                   temperature=0.0)

            results.append({
                "test": "single_completion",
                "text": completion.choices[0].text,
                "finish_reason": completion.choices[0].finish_reason,
                "usage": completion.usage,
            })

            # test using token IDs
            completion = client.completions.create(
                model=model,
                prompt=token_ids,
                max_tokens=5,
                temperature=0.0,
            )

            results.append({
                "test": "token_ids",
                "text": completion.choices[0].text,
                "finish_reason": completion.choices[0].finish_reason,
                "usage": completion.usage,
            })

            # test simple list
            batch = client.completions.create(
                model=model,
                prompt=[prompt, prompt],
                max_tokens=5,
                temperature=0.0,
            )

            results.append({
                "test": "simple_list",
                "text0": batch.choices[0].text,
                "text1": batch.choices[1].text,
            })

            # test streaming
            batch = client.completions.create(
                model=model,
                prompt=[prompt, prompt],
                max_tokens=5,
                temperature=0.0,
                stream=True,
            )
            texts = [""] * 2
            for chunk in batch:
                assert len(chunk.choices) == 1
                choice = chunk.choices[0]
                texts[choice.index] += choice.text
            results.append({
                "test": "streaming",
                "texts": texts,
            })

    n = len(results) // 2
    arg1_results = results[:n]
    arg2_results = results[n:]
    for arg1_result, arg2_result in zip(arg1_results, arg2_results):
        assert arg1_result == arg2_result, \
            f"Results for {model=} are not the same with {arg1=} and {arg2=}"


def init_test_distributed_environment(
    tp_size: int,
    pp_size: int,
    rank: int,
    distributed_init_port: str,
    local_rank: int = -1,
) -> None:
    distributed_init_method = f"tcp://localhost:{distributed_init_port}"
    init_distributed_environment(
        world_size=pp_size * tp_size,
        rank=rank,
        distributed_init_method=distributed_init_method,
        local_rank=local_rank)
    ensure_model_parallel_initialized(tp_size, pp_size)


def multi_process_parallel(
    tp_size: int,
    pp_size: int,
    test_target: Any,
) -> None:
    # Using ray helps debugging the error when it failed
    # as compared to multiprocessing.
    # NOTE: We need to set working_dir for distributed tests,
    # otherwise we may get import errors on ray workers
    ray.init(runtime_env={"working_dir": VLLM_PATH})

    distributed_init_port = get_open_port()
    refs = []
    for rank in range(tp_size * pp_size):
        refs.append(
            test_target.remote(tp_size, pp_size, rank, distributed_init_port))
    ray.get(refs)

    ray.shutdown()


@contextmanager
def error_on_warning():
    """
    Within the scope of this context manager, tests will fail if any warning
    is emitted.
    """
    with warnings.catch_warnings():
        warnings.simplefilter("error")

        yield


@_nvml()
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.
    start_time = time.time()
    while True:
        output: Dict[int, str] = {}
        output_raw: Dict[int, float] = {}
        for device in devices:
            if is_hip():
                dev_handle = amdsmi_get_processor_handles()[device]
                mem_info = amdsmi_get_gpu_vram_usage(dev_handle)
                gb_used = mem_info["vram_used"] / 2**10
            else:
                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)