787010a637
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>
520 lines
18 KiB
Python
520 lines
18 KiB
Python
#
|
|
# 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 os
|
|
from typing import Any, List, Optional, Tuple, TypeVar, Union
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
from modelscope import snapshot_download # type: ignore[import-untyped]
|
|
from PIL import Image
|
|
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 import TaskOption, _get_and_verify_dtype
|
|
from vllm.inputs import ExplicitEncoderDecoderPrompt, TextPrompt, TokensPrompt
|
|
from vllm.outputs import RequestOutput
|
|
from vllm.sampling_params import BeamSearchParams
|
|
from vllm.transformers_utils.utils import maybe_model_redirect
|
|
from vllm.utils import is_list_of
|
|
|
|
from tests.e2e.model_utils import (PROMPT_TEMPLATES, TokensTextLogprobs,
|
|
TokensTextLogprobsPromptLogprobs)
|
|
# 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]
|
|
|
|
_TEST_DIR = os.path.dirname(__file__)
|
|
_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
|
|
|
|
|
|
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 VllmRunner:
|
|
|
|
def __init__(
|
|
self,
|
|
model_name: str,
|
|
task: TaskOption = "auto",
|
|
tokenizer_name: Optional[str] = None,
|
|
tokenizer_mode: str = "auto",
|
|
# Use smaller max model length, otherwise bigger model cannot run due
|
|
# to kv cache size limit.
|
|
max_model_len: int = 1024,
|
|
dtype: str = "half",
|
|
disable_log_stats: bool = True,
|
|
tensor_parallel_size: int = 1,
|
|
block_size: int = 16,
|
|
enable_chunked_prefill: bool = False,
|
|
swap_space: int = 4,
|
|
enforce_eager: Optional[bool] = True,
|
|
quantization: Optional[str] = None,
|
|
**kwargs,
|
|
) -> None:
|
|
self.model = LLM(
|
|
model=model_name,
|
|
task=task,
|
|
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: List[str],
|
|
images: Optional[PromptImageInput] = None,
|
|
videos: Optional[PromptVideoInput] = None,
|
|
audios: Optional[PromptAudioInput] = None,
|
|
) -> List[TextPrompt]:
|
|
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)
|
|
|
|
inputs = [TextPrompt(prompt=prompt) for prompt in prompts]
|
|
if images is not None:
|
|
for i, image in enumerate(images):
|
|
if image is not None:
|
|
inputs[i]["multi_modal_data"] = {"image": image}
|
|
|
|
if videos is not None:
|
|
for i, video in enumerate(videos):
|
|
if video is not None:
|
|
inputs[i]["multi_modal_data"] = {"video": video}
|
|
|
|
if audios is not None:
|
|
for i, audio in enumerate(audios):
|
|
if audio is not None:
|
|
inputs[i]["multi_modal_data"] = {"audio": audio}
|
|
|
|
return inputs
|
|
|
|
def generate(
|
|
self,
|
|
prompts: List[str],
|
|
sampling_params: SamplingParams,
|
|
images: Optional[PromptImageInput] = None,
|
|
videos: Optional[PromptVideoInput] = None,
|
|
audios: Optional[PromptAudioInput] = None,
|
|
) -> 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)
|
|
|
|
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 + 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,
|
|
) -> 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)
|
|
|
|
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_encoder_decoder_w_logprobs(
|
|
self,
|
|
encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]],
|
|
sampling_params: SamplingParams,
|
|
) -> Union[List[TokensTextLogprobs],
|
|
List[TokensTextLogprobsPromptLogprobs]]:
|
|
'''
|
|
Logprobs generation for vLLM encoder/decoder models
|
|
'''
|
|
|
|
assert sampling_params.logprobs is not None
|
|
req_outputs = self.model.generate(encoder_decoder_prompts,
|
|
sampling_params=sampling_params)
|
|
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: List[str],
|
|
max_tokens: int,
|
|
images: Optional[PromptImageInput] = None,
|
|
videos: Optional[PromptVideoInput] = None,
|
|
audios: Optional[PromptAudioInput] = None,
|
|
) -> 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)
|
|
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: 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,
|
|
) -> 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)
|
|
|
|
def generate_encoder_decoder_greedy_logprobs(
|
|
self,
|
|
encoder_decoder_prompts: List[ExplicitEncoderDecoderPrompt[str, str]],
|
|
max_tokens: int,
|
|
num_logprobs: int,
|
|
num_prompt_logprobs: Optional[int] = None,
|
|
) -> Union[List[TokensTextLogprobs],
|
|
List[TokensTextLogprobsPromptLogprobs]]:
|
|
greedy_logprobs_params = SamplingParams(
|
|
temperature=0.0,
|
|
max_tokens=max_tokens,
|
|
logprobs=num_logprobs,
|
|
prompt_logprobs=(num_prompt_logprobs),
|
|
)
|
|
'''
|
|
Greedy logprobs generation for vLLM encoder/decoder models
|
|
'''
|
|
|
|
return self.generate_encoder_decoder_w_logprobs(
|
|
encoder_decoder_prompts, greedy_logprobs_params)
|
|
|
|
def generate_beam_search(
|
|
self,
|
|
prompts: Union[List[str], List[List[int]]],
|
|
beam_width: int,
|
|
max_tokens: int,
|
|
) -> List[Tuple[List[List[int]], List[str]]]:
|
|
if is_list_of(prompts, str, check="all"):
|
|
prompts = [TextPrompt(prompt=prompt) for prompt in prompts]
|
|
else:
|
|
prompts = [
|
|
TokensPrompt(prompt_token_ids=tokens) for tokens in prompts
|
|
]
|
|
outputs = self.model.beam_search(
|
|
prompts,
|
|
BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens))
|
|
returned_outputs = []
|
|
for output in outputs:
|
|
token_ids = [x.tokens for x in output.sequences]
|
|
texts = [x.text for x in output.sequences]
|
|
returned_outputs.append((token_ids, texts))
|
|
return returned_outputs
|
|
|
|
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 encode(
|
|
self,
|
|
prompts: List[str],
|
|
images: Optional[PromptImageInput] = None,
|
|
videos: Optional[PromptVideoInput] = None,
|
|
audios: Optional[PromptAudioInput] = None,
|
|
) -> List[List[float]]:
|
|
inputs = self.get_inputs(prompts,
|
|
images=images,
|
|
videos=videos,
|
|
audios=audios)
|
|
|
|
req_outputs = self.model.embed(inputs)
|
|
return [req_output.outputs.embedding for req_output in req_outputs]
|
|
|
|
def score(
|
|
self,
|
|
text_1: Union[str, List[str]],
|
|
text_2: Union[str, List[str]],
|
|
) -> List[float]:
|
|
req_outputs = self.model.score(text_1, text_2)
|
|
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
|
|
cleanup_dist_env_and_memory()
|
|
|
|
|
|
@pytest.fixture(scope="session")
|
|
def vllm_runner():
|
|
return VllmRunner
|
|
|
|
|
|
@pytest.fixture(params=list(PROMPT_TEMPLATES.keys()))
|
|
def prompt_template(request):
|
|
return PROMPT_TEMPLATES[request.param]
|
|
|
|
|
|
def _read_prompts(filename: str) -> list[str]:
|
|
with open(filename) as f:
|
|
prompts = f.readlines()
|
|
return prompts
|
|
|
|
|
|
@pytest.fixture
|
|
def example_prompts() -> list[str]:
|
|
prompts = []
|
|
for filename in _TEST_PROMPTS:
|
|
prompts += _read_prompts(filename)
|
|
return prompts
|
|
|
|
|
|
@pytest.fixture(scope="session")
|
|
def ilama_lora_files():
|
|
return snapshot_download(repo_id="vllm-ascend/ilama-text2sql-spider")
|
|
|
|
|
|
class HfRunner:
|
|
|
|
def get_default_device(self):
|
|
from vllm.platforms import current_platform
|
|
|
|
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 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
|