[Test] Remove VLLM_USE_V1 in example and tests (#1733)
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests
- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
519
tests/e2e/conftest.py
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519
tests/e2e/conftest.py
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@@ -0,0 +1,519 @@
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#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/blob/main/tests/conftest.py
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#
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import contextlib
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import gc
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import os
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from typing import Any, List, Optional, Tuple, TypeVar, Union
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import numpy as np
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import pytest
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import torch
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from modelscope import snapshot_download # type: ignore[import-untyped]
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from PIL import Image
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from torch import nn
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from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
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BatchEncoding, BatchFeature)
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from transformers.models.auto.auto_factory import _BaseAutoModelClass
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from vllm import LLM, SamplingParams
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from vllm.config import TaskOption, _get_and_verify_dtype
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from vllm.inputs import ExplicitEncoderDecoderPrompt, TextPrompt, TokensPrompt
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import BeamSearchParams
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from vllm.transformers_utils.utils import maybe_model_redirect
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from vllm.utils import is_list_of
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from tests.e2e.model_utils import (PROMPT_TEMPLATES, TokensTextLogprobs,
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TokensTextLogprobsPromptLogprobs)
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# TODO: remove this part after the patch merged into vllm, if
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# we not explicitly patch here, some of them might be effectiveless
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# in pytest scenario
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from vllm_ascend.utils import adapt_patch # noqa E402
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adapt_patch(True)
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adapt_patch(False)
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from vllm.distributed.parallel_state import ( # noqa E402
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destroy_distributed_environment, destroy_model_parallel)
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_T = TypeVar("_T", nn.Module, torch.Tensor, BatchEncoding, BatchFeature, dict)
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_M = TypeVar("_M")
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_PromptMultiModalInput = Union[List[_M], List[List[_M]]]
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PromptImageInput = _PromptMultiModalInput[Image.Image]
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PromptAudioInput = _PromptMultiModalInput[Tuple[np.ndarray, int]]
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PromptVideoInput = _PromptMultiModalInput[np.ndarray]
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_TEST_DIR = os.path.dirname(__file__)
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_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
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def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
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destroy_model_parallel()
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destroy_distributed_environment()
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with contextlib.suppress(AssertionError):
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torch.distributed.destroy_process_group()
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if shutdown_ray:
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import ray # Lazy import Ray
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ray.shutdown()
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gc.collect()
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torch.npu.empty_cache()
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torch.npu.reset_peak_memory_stats()
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class VllmRunner:
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def __init__(
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self,
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model_name: str,
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task: TaskOption = "auto",
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tokenizer_name: Optional[str] = None,
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tokenizer_mode: str = "auto",
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# Use smaller max model length, otherwise bigger model cannot run due
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# to kv cache size limit.
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max_model_len: int = 1024,
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dtype: str = "half",
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disable_log_stats: bool = True,
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tensor_parallel_size: int = 1,
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block_size: int = 16,
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enable_chunked_prefill: bool = False,
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swap_space: int = 4,
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enforce_eager: Optional[bool] = True,
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quantization: Optional[str] = None,
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**kwargs,
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) -> None:
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self.model = LLM(
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model=model_name,
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task=task,
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tokenizer=tokenizer_name,
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tokenizer_mode=tokenizer_mode,
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trust_remote_code=True,
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dtype=dtype,
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swap_space=swap_space,
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enforce_eager=enforce_eager,
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disable_log_stats=disable_log_stats,
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tensor_parallel_size=tensor_parallel_size,
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max_model_len=max_model_len,
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block_size=block_size,
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enable_chunked_prefill=enable_chunked_prefill,
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quantization=quantization,
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**kwargs,
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)
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def get_inputs(
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self,
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prompts: List[str],
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images: Optional[PromptImageInput] = None,
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videos: Optional[PromptVideoInput] = None,
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audios: Optional[PromptAudioInput] = None,
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) -> List[TextPrompt]:
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if images is not None:
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assert len(prompts) == len(images)
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if videos is not None:
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assert len(prompts) == len(videos)
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if audios is not None:
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assert len(prompts) == len(audios)
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inputs = [TextPrompt(prompt=prompt) for prompt in prompts]
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if images is not None:
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for i, image in enumerate(images):
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if image is not None:
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inputs[i]["multi_modal_data"] = {"image": image}
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if videos is not None:
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for i, video in enumerate(videos):
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if video is not None:
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inputs[i]["multi_modal_data"] = {"video": video}
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if audios is not None:
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for i, audio in enumerate(audios):
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if audio is not None:
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inputs[i]["multi_modal_data"] = {"audio": audio}
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return inputs
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def generate(
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self,
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prompts: List[str],
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sampling_params: SamplingParams,
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images: Optional[PromptImageInput] = None,
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videos: Optional[PromptVideoInput] = None,
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audios: Optional[PromptAudioInput] = None,
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) -> List[Tuple[List[List[int]], List[str]]]:
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inputs = self.get_inputs(prompts,
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images=images,
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videos=videos,
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audios=audios)
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req_outputs = self.model.generate(inputs,
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sampling_params=sampling_params)
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outputs: List[Tuple[List[List[int]], List[str]]] = []
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for req_output in req_outputs:
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prompt_str = req_output.prompt
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prompt_ids = req_output.prompt_token_ids
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req_sample_output_ids: List[List[int]] = []
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req_sample_output_strs: List[str] = []
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for sample in req_output.outputs:
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output_str = sample.text
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output_ids = list(sample.token_ids)
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req_sample_output_ids.append(prompt_ids + output_ids)
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req_sample_output_strs.append(prompt_str + output_str)
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outputs.append((req_sample_output_ids, req_sample_output_strs))
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return outputs
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@staticmethod
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def _final_steps_generate_w_logprobs(
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req_outputs: List[RequestOutput],
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) -> List[TokensTextLogprobsPromptLogprobs]:
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outputs: List[TokensTextLogprobsPromptLogprobs] = []
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for req_output in req_outputs:
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assert len(req_output.outputs) > 0
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for sample in req_output.outputs:
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output_str = sample.text
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output_ids = list(sample.token_ids)
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output_logprobs = sample.logprobs
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outputs.append((output_ids, output_str, output_logprobs,
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req_output.prompt_logprobs))
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return outputs
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def generate_w_logprobs(
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self,
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prompts: List[str],
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sampling_params: SamplingParams,
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images: Optional[PromptImageInput] = None,
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audios: Optional[PromptAudioInput] = None,
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videos: Optional[PromptVideoInput] = None,
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) -> Union[List[TokensTextLogprobs],
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List[TokensTextLogprobsPromptLogprobs]]:
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inputs = self.get_inputs(prompts,
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images=images,
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videos=videos,
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audios=audios)
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req_outputs = self.model.generate(inputs,
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sampling_params=sampling_params)
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toks_str_logsprobs_prompt_logprobs = (
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self._final_steps_generate_w_logprobs(req_outputs))
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# Omit prompt logprobs if not required by sampling params
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return ([x[0:-1] for x in toks_str_logsprobs_prompt_logprobs]
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if sampling_params.prompt_logprobs is None else
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toks_str_logsprobs_prompt_logprobs)
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def generate_encoder_decoder_w_logprobs(
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self,
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encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]],
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sampling_params: SamplingParams,
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) -> Union[List[TokensTextLogprobs],
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List[TokensTextLogprobsPromptLogprobs]]:
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'''
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Logprobs generation for vLLM encoder/decoder models
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'''
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assert sampling_params.logprobs is not None
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req_outputs = self.model.generate(encoder_decoder_prompts,
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sampling_params=sampling_params)
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toks_str_logsprobs_prompt_logprobs = (
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self._final_steps_generate_w_logprobs(req_outputs))
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# Omit prompt logprobs if not required by sampling params
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return ([x[0:-1] for x in toks_str_logsprobs_prompt_logprobs]
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if sampling_params.prompt_logprobs is None else
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toks_str_logsprobs_prompt_logprobs)
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def generate_greedy(
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self,
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prompts: List[str],
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max_tokens: int,
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images: Optional[PromptImageInput] = None,
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videos: Optional[PromptVideoInput] = None,
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audios: Optional[PromptAudioInput] = None,
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) -> List[Tuple[List[int], str]]:
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greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
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outputs = self.generate(prompts,
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greedy_params,
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images=images,
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videos=videos,
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audios=audios)
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return [(output_ids[0], output_str[0])
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for output_ids, output_str in outputs]
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def generate_greedy_logprobs(
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self,
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prompts: List[str],
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max_tokens: int,
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num_logprobs: int,
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num_prompt_logprobs: Optional[int] = None,
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images: Optional[PromptImageInput] = None,
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audios: Optional[PromptAudioInput] = None,
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videos: Optional[PromptVideoInput] = None,
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stop_token_ids: Optional[List[int]] = None,
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stop: Optional[List[str]] = None,
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) -> Union[List[TokensTextLogprobs],
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List[TokensTextLogprobsPromptLogprobs]]:
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greedy_logprobs_params = SamplingParams(
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temperature=0.0,
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max_tokens=max_tokens,
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logprobs=num_logprobs,
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prompt_logprobs=num_prompt_logprobs,
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stop_token_ids=stop_token_ids,
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stop=stop)
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return self.generate_w_logprobs(prompts,
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greedy_logprobs_params,
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images=images,
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audios=audios,
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videos=videos)
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def generate_encoder_decoder_greedy_logprobs(
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self,
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encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]],
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max_tokens: int,
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num_logprobs: int,
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num_prompt_logprobs: Optional[int] = None,
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) -> Union[List[TokensTextLogprobs],
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List[TokensTextLogprobsPromptLogprobs]]:
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greedy_logprobs_params = SamplingParams(
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temperature=0.0,
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max_tokens=max_tokens,
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logprobs=num_logprobs,
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prompt_logprobs=(num_prompt_logprobs),
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)
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'''
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Greedy logprobs generation for vLLM encoder/decoder models
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'''
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return self.generate_encoder_decoder_w_logprobs(
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encoder_decoder_prompts, greedy_logprobs_params)
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def generate_beam_search(
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self,
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prompts: Union[List[str], List[List[int]]],
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beam_width: int,
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max_tokens: int,
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) -> List[Tuple[List[List[int]], List[str]]]:
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if is_list_of(prompts, str, check="all"):
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prompts = [TextPrompt(prompt=prompt) for prompt in prompts]
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else:
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prompts = [
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TokensPrompt(prompt_token_ids=tokens) for tokens in prompts
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]
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outputs = self.model.beam_search(
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prompts,
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BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens))
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returned_outputs = []
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for output in outputs:
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token_ids = [x.tokens for x in output.sequences]
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texts = [x.text for x in output.sequences]
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returned_outputs.append((token_ids, texts))
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return returned_outputs
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def classify(self, prompts: List[str]) -> List[List[float]]:
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req_outputs = self.model.classify(prompts)
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return [req_output.outputs.probs for req_output in req_outputs]
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def encode(
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self,
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prompts: List[str],
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images: Optional[PromptImageInput] = None,
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videos: Optional[PromptVideoInput] = None,
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audios: Optional[PromptAudioInput] = None,
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) -> List[List[float]]:
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inputs = self.get_inputs(prompts,
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images=images,
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videos=videos,
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audios=audios)
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req_outputs = self.model.embed(inputs)
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return [req_output.outputs.embedding for req_output in req_outputs]
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def score(
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self,
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text_1: Union[str, List[str]],
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text_2: Union[str, List[str]],
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) -> List[float]:
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req_outputs = self.model.score(text_1, text_2)
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return [req_output.outputs.score for req_output in req_outputs]
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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del self.model
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cleanup_dist_env_and_memory()
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@pytest.fixture(scope="session")
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def vllm_runner():
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return VllmRunner
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@pytest.fixture(params=list(PROMPT_TEMPLATES.keys()))
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def prompt_template(request):
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return PROMPT_TEMPLATES[request.param]
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def _read_prompts(filename: str) -> list[str]:
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with open(filename) as f:
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prompts = f.readlines()
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return prompts
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@pytest.fixture
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def example_prompts() -> list[str]:
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prompts = []
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for filename in _TEST_PROMPTS:
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prompts += _read_prompts(filename)
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return prompts
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@pytest.fixture(scope="session")
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def ilama_lora_files():
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return snapshot_download(repo_id="vllm-ascend/ilama-text2sql-spider")
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|
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class HfRunner:
|
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def get_default_device(self):
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from vllm.platforms import current_platform
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return ("cpu"
|
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if current_platform.is_cpu() else current_platform.device_type)
|
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|
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def wrap_device(self, x: _T, device: Optional[str] = None) -> _T:
|
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if x is None or isinstance(x, (bool, )):
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return x
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|
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if device is None:
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device = self.device
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|
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if isinstance(x, dict):
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return {k: self.wrap_device(v, device) for k, v in x.items()}
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|
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if hasattr(x, "device") and x.device.type == device:
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return x
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return x.to(device)
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|
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def __init__(
|
||||
self,
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model_name: str,
|
||||
dtype: str = "auto",
|
||||
*,
|
||||
model_kwargs: Optional[dict[str, Any]] = None,
|
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trust_remote_code: bool = True,
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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 encode(self, prompts: list[str], *args,
|
||||
**kwargs) -> list[list[torch.Tensor]]:
|
||||
return self.model.encode(prompts, *args, **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 hf_runner():
|
||||
return HfRunner
|
||||
Reference in New Issue
Block a user