# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2023 The vLLM team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # This file is a part of the vllm-ascend project. # Adapted from vllm-project/vllm/blob/main/tests/conftest.py # import contextlib import gc import json import logging import os import shlex import subprocess import sys import time from typing import Any, Optional, Tuple, TypeVar, Union import httpx import numpy as np import openai import pytest import requests import torch from modelscope import snapshot_download # type: ignore[import-untyped] from PIL import Image from requests.exceptions import RequestException from torch import nn from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer, BatchEncoding, BatchFeature) from transformers.models.auto.auto_factory import _BaseAutoModelClass from vllm import LLM, SamplingParams from vllm.config.model import (ConvertOption, RunnerOption, _get_and_verify_dtype) from vllm.inputs import TextPrompt from vllm.outputs import RequestOutput from vllm.platforms import current_platform from vllm.transformers_utils.utils import maybe_model_redirect from vllm.utils.network_utils import get_open_port from tests.e2e.model_utils import (TokensTextLogprobs, TokensTextLogprobsPromptLogprobs) from tests.e2e.nightly.multi_node.config.multi_node_config import NodeInfo from vllm_ascend.ascend_config import clear_ascend_config # TODO: remove this part after the patch merged into vllm, if # we not explicitly patch here, some of them might be effectiveless # in pytest scenario from vllm_ascend.utils import adapt_patch # noqa E402 adapt_patch(True) adapt_patch(False) from vllm.distributed.parallel_state import ( # noqa E402 destroy_distributed_environment, destroy_model_parallel) _T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict) _M = TypeVar("_M") _PromptMultiModalInput = Union[list[_M], list[list[_M]]] PromptImageInput = _PromptMultiModalInput[Image.Image] PromptAudioInput = _PromptMultiModalInput[Tuple[np.ndarray, int]] PromptVideoInput = _PromptMultiModalInput[np.ndarray] logger = logging.getLogger(__name__) _TEST_DIR = os.path.dirname(__file__) def cleanup_dist_env_and_memory(shutdown_ray: bool = False): destroy_model_parallel() destroy_distributed_environment() with contextlib.suppress(AssertionError): torch.distributed.destroy_process_group() if shutdown_ray: import ray # Lazy import Ray ray.shutdown() gc.collect() torch.npu.empty_cache() torch.npu.reset_peak_memory_stats() class RemoteOpenAIServer: DUMMY_API_KEY = "token-abc123" # vLLM's OpenAI server does not need API key def _start_server(self, model: str, server_cmd: list[str], env_dict: Optional[dict[str, str]]) -> None: """Subclasses override this method to customize server process launch """ env = os.environ.copy() # the current process might initialize npu, # to be safe, we should use spawn method env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn' if env_dict is not None: env.update(env_dict) self.proc: subprocess.Popen = subprocess.Popen( server_cmd, env=env, stdout=sys.stdout, stderr=sys.stderr, ) def __init__(self, model: str, vllm_serve_args: Union[list[str], str], *, server_host: str = '0.0.0.0', server_port: int = 8080, env_dict: Optional[dict[str, str]] = None, seed: Optional[int] = None, auto_port: bool = True, nodes_info: Optional[list[NodeInfo]] = None, disaggregated_prefill: Optional[dict] = None, proxy_port: Optional[int] = None, max_wait_seconds: Optional[float] = None, override_hf_configs: Optional[dict[str, Any]] = None) -> None: if isinstance(vllm_serve_args, str): vllm_serve_args = shlex.split(vllm_serve_args) else: vllm_serve_args = ["vllm", "serve", model, *vllm_serve_args] if auto_port: if "-p" in vllm_serve_args or "--port" in vllm_serve_args: raise ValueError("You have manually specified the port " "when `auto_port=True`.") # No need for a port if using unix sockets if "--uds" not in vllm_serve_args: # Don't mutate the input args vllm_serve_args = vllm_serve_args + [ "--port", str(get_open_port()) ] if seed is not None: if "--seed" in vllm_serve_args: raise ValueError("You have manually specified the seed " f"when `seed={seed}`.") vllm_serve_args = vllm_serve_args + ["--seed", str(seed)] if override_hf_configs is not None: vllm_serve_args = vllm_serve_args + [ "--hf-overrides", json.dumps(override_hf_configs) ] self.host = str(server_host) self.port = int(server_port) # for multi-nodes test self.nodes_info = nodes_info self.disaggregated_prefill = disaggregated_prefill self.cur_index = os.getenv("LWS_WORKER_INDEX", 0) self.proxy_port = proxy_port self._start_server(model, vllm_serve_args, env_dict) max_wait_seconds = max_wait_seconds or 2800 if self.disaggregated_prefill: assert proxy_port is not None, "for disaggregated_prefill, proxy port must be provided" self._wait_for_server_pd(timeout=max_wait_seconds) else: self._wait_for_multiple_servers( [(self.host, self.url_for("health"))], timeout=max_wait_seconds) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self._terminate_server() def _poll(self) -> Optional[int]: """Subclasses override this method to customize process polling""" return self.proc.poll() def hang_until_terminated(self, url) -> None: """ Wait until the server process terminates. This is for headless mode, where the api server process only exists in the leader node. """ client = requests try: while True: try: resp = client.get(url, timeout=5) if resp.status_code != 200: break time.sleep(5) except Exception: break finally: if isinstance(client, httpx.Client): client.close() self._terminate_server() def _wait_for_server_pd(self, timeout: float): # Wait for all api_server nodes ready assert self.nodes_info is not None, "cluster info must be provided" proxy_port = self.proxy_port def url_health(ip: str, port: int) -> str: return f"http://{ip}:{port}/health" targets = [(node_info.ip, url_health(node_info.ip, node_info.server_port)) for node_info in self.nodes_info if not node_info.headless] # Wait for proxy ready master_node = self.nodes_info[0] url_proxy = f"http://{master_node.ip}:{proxy_port}/healthcheck" # Wait for master node proxy first self._wait_for_multiple_servers([(master_node.ip, url_proxy)], timeout=timeout) # Then wait for all api_server nodes self._wait_for_multiple_servers(targets=targets, timeout=timeout) def _wait_for_multiple_servers(self, targets, timeout: float, log_interval: float = 30.0): """ targets: List[(node_ip, url)] log_interval """ start = time.time() client = requests ready = {node_ip: False for node_ip, _ in targets} last_log_time = 0.0 while True: now = time.time() all_ready = True should_log = (now - last_log_time) >= log_interval for node_ip, url in targets: if ready[node_ip]: continue try: resp = client.get(url) if resp.status_code == 200: ready[node_ip] = True logger.info(f"[READY] Node {node_ip} is ready.") except RequestException: all_ready = False if should_log: logger.info(f"[WAIT] {url}: connection failed") # check unexpected exit result = self._poll() if result is not None and result != 0: raise RuntimeError( f"Server at {node_ip} exited unexpectedly." ) from None if should_log: last_log_time = now if all_ready: break if now - start > timeout: not_ready_nodes = [n for n, ok in ready.items() if not ok] self._terminate_server() raise RuntimeError( f"Timeout: these nodes did not become ready: {not_ready_nodes} in time: {timeout}s" ) from None time.sleep(5) @property def url_root(self) -> str: return f"http://{self.host}:{self.port}" def _terminate_server(self) -> None: """Subclasses override this method to customize server process termination""" self.proc.terminate() try: self.proc.wait(8) except subprocess.TimeoutExpired: # force kill if needed self.proc.kill() def url_for(self, *parts: str) -> str: return self.url_root + "/" + "/".join(parts) def get_client(self, **kwargs): if "timeout" not in kwargs: kwargs["timeout"] = 600 return openai.OpenAI( base_url=self.url_for("v1"), api_key=self.DUMMY_API_KEY, max_retries=0, **kwargs, ) def get_async_client(self, **kwargs): if "timeout" not in kwargs: kwargs["timeout"] = 600 return openai.AsyncOpenAI(base_url=self.url_for("v1"), api_key=self.DUMMY_API_KEY, max_retries=0, **kwargs) class VllmRunner: def __init__( self, model_name: str, runner: RunnerOption = "auto", convert: ConvertOption = "auto", tokenizer_name: Optional[str] = None, tokenizer_mode: str = "auto", max_model_len: Optional[int] = 1024, dtype: str = "auto", disable_log_stats: bool = True, tensor_parallel_size: int = 1, block_size: int = 16, enable_chunked_prefill: bool = True, swap_space: int = 4, enforce_eager: Optional[bool] = False, quantization: Optional[str] = None, **kwargs, ) -> None: self.model = LLM( model=model_name, runner=runner, convert=convert, tokenizer=tokenizer_name, tokenizer_mode=tokenizer_mode, trust_remote_code=True, dtype=dtype, swap_space=swap_space, enforce_eager=enforce_eager, disable_log_stats=disable_log_stats, tensor_parallel_size=tensor_parallel_size, max_model_len=max_model_len, block_size=block_size, enable_chunked_prefill=enable_chunked_prefill, quantization=quantization, **kwargs, ) def get_inputs( self, prompts: Union[list[str], list[torch.Tensor], list[int]], images: Optional[PromptImageInput] = None, videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, ) -> list[TextPrompt]: if any(x is not None and len(x) != len(prompts) for x in [images, videos, audios]): raise ValueError( "All non-None multimodal inputs must have the same length as " "prompts") inputs = [] for i, prompt in enumerate(prompts): multi_modal_data = {} if images is not None and (image := images[i]) is not None: multi_modal_data["image"] = image if videos is not None and (video := videos[i]) is not None: multi_modal_data["video"] = video if audios is not None and (audio := audios[i]) is not None: multi_modal_data["audio"] = audio text_prompt_kwargs: dict[str, Any] = { "multi_modal_data": multi_modal_data or None } if isinstance(prompt, str): text_prompt_kwargs["prompt"] = prompt elif isinstance(prompt, list): text_prompt_kwargs["prompt_token_ids"] = prompt else: text_prompt_kwargs["prompt_embeds"] = prompt inputs.append(TextPrompt(**text_prompt_kwargs)) return inputs def generate( self, prompts: Union[list[str], list[torch.Tensor]], sampling_params: SamplingParams, images: Optional[PromptImageInput] = None, videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, **kwargs: Any, ) -> list[tuple[list[list[int]], list[str]]]: inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios) req_outputs = self.model.generate(inputs, sampling_params=sampling_params, **kwargs) outputs: list[tuple[list[list[int]], list[str]]] = [] for req_output in req_outputs: prompt_str = req_output.prompt prompt_ids = req_output.prompt_token_ids req_sample_output_ids: list[list[int]] = [] req_sample_output_strs: list[str] = [] for sample in req_output.outputs: output_str = sample.text output_ids = list(sample.token_ids) req_sample_output_ids.append(prompt_ids + output_ids) req_sample_output_strs.append((prompt_str or "") + output_str) outputs.append((req_sample_output_ids, req_sample_output_strs)) return outputs @staticmethod def _final_steps_generate_w_logprobs( req_outputs: list[RequestOutput], ) -> list[TokensTextLogprobsPromptLogprobs]: outputs: list[TokensTextLogprobsPromptLogprobs] = [] for req_output in req_outputs: assert len(req_output.outputs) > 0 for sample in req_output.outputs: output_str = sample.text output_ids = list(sample.token_ids) output_logprobs = sample.logprobs outputs.append((output_ids, output_str, output_logprobs, req_output.prompt_logprobs)) return outputs def generate_w_logprobs( self, prompts: list[str], sampling_params: SamplingParams, images: Optional[PromptImageInput] = None, audios: Optional[PromptAudioInput] = None, videos: Optional[PromptVideoInput] = None, **kwargs: Any, ) -> Union[list[TokensTextLogprobs], list[TokensTextLogprobsPromptLogprobs]]: inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios) req_outputs = self.model.generate(inputs, sampling_params=sampling_params, **kwargs) toks_str_logsprobs_prompt_logprobs = ( self._final_steps_generate_w_logprobs(req_outputs)) # Omit prompt logprobs if not required by sampling params return ([x[0:-1] for x in toks_str_logsprobs_prompt_logprobs] if sampling_params.prompt_logprobs is None else toks_str_logsprobs_prompt_logprobs) def generate_greedy( self, prompts: Union[list[str], list[torch.Tensor]], max_tokens: int, images: Optional[PromptImageInput] = None, videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, **kwargs: Any, ) -> list[tuple[list[int], str]]: greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens) outputs = self.generate(prompts, greedy_params, images=images, videos=videos, audios=audios, **kwargs) return [(output_ids[0], output_str[0]) for output_ids, output_str in outputs] def generate_greedy_logprobs( self, prompts: list[str], max_tokens: int, num_logprobs: Optional[int], num_prompt_logprobs: Optional[int] = None, images: Optional[PromptImageInput] = None, audios: Optional[PromptAudioInput] = None, videos: Optional[PromptVideoInput] = None, stop_token_ids: Optional[list[int]] = None, stop: Optional[list[str]] = None, **kwargs: Any, ) -> Union[list[TokensTextLogprobs], list[TokensTextLogprobsPromptLogprobs]]: greedy_logprobs_params = SamplingParams( temperature=0.0, max_tokens=max_tokens, logprobs=num_logprobs, prompt_logprobs=num_prompt_logprobs, stop_token_ids=stop_token_ids, stop=stop) return self.generate_w_logprobs(prompts, greedy_logprobs_params, images=images, audios=audios, videos=videos, **kwargs) def classify(self, prompts: list[str]) -> list[list[float]]: req_outputs = self.model.classify(prompts) return [req_output.outputs.probs for req_output in req_outputs] def embed(self, prompts: list[str], images: Optional[PromptImageInput] = None, videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, *args, **kwargs) -> list[list[float]]: inputs = self.get_inputs(prompts, images=images, videos=videos, audios=audios) req_outputs = self.model.embed(inputs, *args, **kwargs) return [req_output.outputs.embedding for req_output in req_outputs] def encode(self, prompts: list[str]) -> list[list[float]]: req_outputs = self.model.encode(prompts) return [req_output.outputs.data for req_output in req_outputs] def reward(self, prompts: list[str]) -> list[list[float]]: req_outputs = self.model.reward(prompts) return [req_output.outputs.data for req_output in req_outputs] def score( self, text_1: Union[str, list[str]], text_2: Union[str, list[str]], *args, **kwargs, ) -> list[float]: req_outputs = self.model.score(text_1, text_2, *args, **kwargs) return [req_output.outputs.score for req_output in req_outputs] def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): del self.model clear_ascend_config() cleanup_dist_env_and_memory() class HfRunner: def get_default_device(self): return ("cpu" if current_platform.is_cpu() else current_platform.device_type) def wrap_device(self, x: _T, device: Optional[str] = None) -> _T: if x is None or isinstance(x, (bool, )): return x if device is None: device = self.device if isinstance(x, dict): return {k: self.wrap_device(v, device) for k, v in x.items()} if hasattr(x, "device") and x.device.type == device: return x return x.to(device) def __init__( self, model_name: str, dtype: str = "auto", *, model_kwargs: Optional[dict[str, Any]] = None, trust_remote_code: bool = True, is_sentence_transformer: bool = False, is_cross_encoder: bool = False, skip_tokenizer_init: bool = False, auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM, ) -> None: model_name = maybe_model_redirect(model_name) self.model_name = model_name self.config = AutoConfig.from_pretrained( model_name, trust_remote_code=trust_remote_code, ) self.device = self.get_default_device() self.dtype = torch_dtype = _get_and_verify_dtype( self.model_name, self.config, dtype=dtype, is_pooling_model=is_sentence_transformer or is_cross_encoder, ) model_kwargs = model_kwargs if model_kwargs is not None else {} model_kwargs.setdefault("torch_dtype", torch_dtype) if is_sentence_transformer: # Lazy init required for AMD CI from sentence_transformers import SentenceTransformer self.model = SentenceTransformer( model_name, device=self.device, model_kwargs=model_kwargs, trust_remote_code=trust_remote_code, ) elif is_cross_encoder: # Lazy init required for AMD CI from sentence_transformers import CrossEncoder self.model = CrossEncoder( model_name, device=self.device, automodel_args=model_kwargs, trust_remote_code=trust_remote_code, ) else: model = auto_cls.from_pretrained( model_name, trust_remote_code=trust_remote_code, **model_kwargs, ) # in case some unquantized custom models are not in same dtype if (getattr(model, "quantization_method", None) is None and any(p.dtype != self.dtype for p in model.parameters())): model = model.to(dtype=self.dtype) if (getattr(model, "quantization_method", None) != "bitsandbytes" and len({p.device for p in model.parameters()}) < 2): model = model.to(device=self.device) self.model = model if not skip_tokenizer_init: self.tokenizer = AutoTokenizer.from_pretrained( model_name, torch_dtype=torch_dtype, trust_remote_code=trust_remote_code, ) # don't put this import at the top level # it will call torch.cuda.device_count() from transformers import AutoProcessor # noqa: F401 self.processor = AutoProcessor.from_pretrained( model_name, torch_dtype=torch_dtype, trust_remote_code=trust_remote_code, ) if skip_tokenizer_init: self.tokenizer = self.processor.tokenizer def get_inputs( self, prompts: list[str], images: Optional[PromptImageInput] = None, videos: Optional[PromptVideoInput] = None, audios: Optional[PromptAudioInput] = None, ) -> list[Union[BatchFeature, BatchEncoding]]: if images is not None: assert len(prompts) == len(images) if videos is not None: assert len(prompts) == len(videos) if audios is not None: assert len(prompts) == len(audios) all_inputs: list[Union[BatchFeature, BatchEncoding]] = [] for i, prompt in enumerate(prompts): processor_kwargs: dict[str, Any] = { "text": prompt, "return_tensors": "pt", } if images is not None and (image := images[i]) is not None: processor_kwargs["images"] = image if videos is not None and (video := videos[i]) is not None: processor_kwargs["videos"] = video if audios is not None and (audio_inputs := audios[i]) is not None: # HACK - not all processors take sampling_rate; we should # clean this up in the future. if len(audio_inputs) == 2: audio, sr = audio_inputs processor_kwargs["audio"] = audio processor_kwargs["sampling_rate"] = sr else: processor_kwargs["audio"] = audio_inputs inputs = self.processor(**processor_kwargs) if isinstance(inputs, BatchFeature): inputs = inputs.to(dtype=self.dtype) all_inputs.append(inputs) return all_inputs def classify(self, prompts: list[str]) -> list[str]: # output is final logits all_inputs = self.get_inputs(prompts) outputs = [] problem_type = getattr(self.config, "problem_type", "") for inputs in all_inputs: output = self.model(**self.wrap_device(inputs)) if problem_type == "regression": logits = output.logits[0].tolist() elif problem_type == "multi_label_classification": logits = output.logits.sigmoid()[0].tolist() else: logits = output.logits.softmax(dim=-1)[0].tolist() outputs.append(logits) return outputs def encode(self, prompts: list[str], *args, **kwargs) -> list[list[torch.Tensor]]: return self.model.encode(prompts, *args, **kwargs) def predict(self, prompts: list[list[str]], *args, **kwargs) -> torch.Tensor: return self.model.predict(prompts, *args, convert_to_tensor=True, **kwargs) def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): del self.model cleanup_dist_env_and_memory() @pytest.fixture(scope="session") def ilama_lora_files(): return snapshot_download(repo_id="vllm-ascend/ilama-text2sql-spider") def qwen_prompt(questions: list[str]) -> list[str]: placeholder = "<|image_pad|>" return [("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n" f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>" f"{q}<|im_end|>\n<|im_start|>assistant\n") for q in questions] def hunyuan_prompt(questions: list[str]) -> list[str]: placeholder = "<｜hy_place▁holder▁no▁100｜><｜hy_place▁holder▁no▁102｜><｜hy_place▁holder▁no▁101｜>" # noqa: E501 return [ f"<｜hy_begin▁of▁sentence｜>{placeholder}{question}<｜hy_User｜>" for question in questions ] PROMPT_CONFIGS = { "qwen-vl": { "model": "Qwen/Qwen3-VL-8B-Instruct", "prompt_fn": qwen_prompt, "mm_processor_kwargs": { "min_pixels": 28 * 28, "max_pixels": 1280 * 28 * 28, "fps": 1, }, }, } @pytest.fixture(params=PROMPT_CONFIGS.keys()) def vl_config(request): return PROMPT_CONFIGS[request.param]