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fix lint

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This commit is contained in:
suluyan
2025-01-07 21:16:44 +08:00
parent 0a643276ca
commit 496b103abe
11 changed files with 85 additions and 55 deletions
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7
modelscope/models/nlp/hf_transformers/backbone.py
View File

@@ -99,9 +99,10 @@ class TransformersModel(TorchModel, PreTrainedModel):
return model
# return the model only
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
config, kwargs = AutoConfig.from_pretrained(
model_dir,
return_unused_kwargs=True,

7
modelscope/models/nlp/polylm/text_generation.py
View File

@@ -27,9 +27,10 @@ class PolyLMForTextGeneration(TorchModel, StreamingOutputMixin):
super().__init__(model_dir, *args, **kwargs)
self.tokenizer = AutoTokenizer.from_pretrained(
model_dir, legacy=False, use_fast=False)
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
self.model = AutoModelForCausalLM.from_pretrained(
model_dir, device_map='auto', trust_remote_code=True)
self.model.eval()

8
modelscope/msdatasets/data_loader/data_loader.py
View File

@@ -134,9 +134,11 @@ class OssDownloader(BaseDownloader):
if dataset_py_script and dataset_formation == DatasetFormations.hf_compatible:
if trust_remote_code:
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.'
)
self.dataset = hf_load_dataset(
dataset_py_script,

16
modelscope/msdatasets/data_loader/data_loader_manager.py
View File

@@ -72,9 +72,11 @@ class LocalDataLoaderManager(DataLoaderManager):
# TODO: more loaders to be supported.
if data_loader_type == LocalDataLoaderType.HF_DATA_LOADER:
if trust_remote_code:
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.'
)
# Build huggingface data loader and return dataset.
return hf_data_loader(
@@ -116,9 +118,11 @@ class RemoteDataLoaderManager(DataLoaderManager):
# To use the huggingface data loader
if data_loader_type == RemoteDataLoaderType.HF_DATA_LOADER:
if trust_remote_code:
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.'
)
dataset_ret = hf_data_loader(
dataset_name,
name=subset_name,

7
modelscope/msdatasets/ms_dataset.py
View File

@@ -238,9 +238,10 @@ class MsDataset:
raise 'The dataset_name should be in the form of `namespace/dataset_name` or `dataset_name`.'
if trust_remote_code:
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {dataset_name}.')
# Init context config
dataset_context_config = DatasetContextConfig(

11
modelscope/pipelines/accelerate/vllm.py
View File

@@ -1,10 +1,12 @@
from typing import List, Union
from modelscope import get_logger
from modelscope.pipelines.accelerate.base import InferFramework
from modelscope.utils.import_utils import is_vllm_available
from modelscope import get_logger
logger = get_logger()
class Vllm(InferFramework):
def __init__(self,
@@ -28,9 +30,10 @@ class Vllm(InferFramework):
if not Vllm.check_gpu_compatibility(8) and (dtype
in ('bfloat16', 'auto')):
dtype = 'float16'
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {self.model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {self.model_dir}.')
self.model = LLM(
self.model_dir,
dtype=dtype,

9
modelscope/pipelines/multi_modal/ovis_vl_pipeline.py
View File

@@ -12,8 +12,10 @@ from modelscope.pipelines.multi_modal.visual_question_answering_pipeline import
VisualQuestionAnsweringPipeline
from modelscope.preprocessors import Preprocessor, load_image
from modelscope.utils.constant import Fields, Frameworks, Tasks
logger = get_logger()
@PIPELINES.register_module(
Tasks.visual_question_answering, module_name='ovis-vl')
class VisionChatPipeline(VisualQuestionAnsweringPipeline):
@@ -35,9 +37,10 @@ class VisionChatPipeline(VisualQuestionAnsweringPipeline):
torch_dtype = kwargs.get('torch_dtype', torch.float16)
multimodal_max_length = kwargs.get('multimodal_max_length', 8192)
self.device = 'cuda' if device == 'gpu' else device
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
self.model = AutoModelForCausalLM.from_pretrained(
model,
torch_dtype=torch_dtype,

29
modelscope/pipelines/nlp/llm_pipeline.py
View File

@@ -97,9 +97,10 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
assert base_model is not None, 'Cannot get adapter_cfg.model_id_or_path from configuration.json file.'
revision = self.cfg.safe_get('adapter_cfg.model_revision',
'master')
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {base_model}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {base_model}.')
base_model = Model.from_pretrained(
base_model,
revision,
@@ -137,9 +138,10 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
model) else snapshot_download(model)
# TODO: Temporary use of AutoModelForCausalLM
# Need to be updated into a universal solution
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
model = AutoModelForCausalLM.from_pretrained(
model_dir,
device_map=self.device_map,
@@ -179,9 +181,11 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
self.llm_framework = llm_framework
if os.path.exists(kwargs['model']):
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {kwargs['model']}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {kwargs["model"]}.'
)
config = AutoConfig.from_pretrained(
kwargs['model'], trust_remote_code=True)
q_config = config.__dict__.get('quantization_config', None)
@@ -432,9 +436,10 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
model_dir = self.model.model_dir
if tokenizer_class is None:
tokenizer_class = AutoTokenizer
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
return tokenizer_class.from_pretrained(
model_dir, trust_remote_code=True)

29
modelscope/pipelines/nlp/text_generation_pipeline.py
View File

@@ -269,9 +269,10 @@ class ChatGLM6bV2TextGenerationPipeline(Pipeline):
if use_bf16:
default_torch_dtype = torch.bfloat16
torch_dtype = kwargs.get('torch_dtype', default_torch_dtype)
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
model = Model.from_pretrained(
model_dir,
trust_remote_code=True,
@@ -288,9 +289,11 @@ class ChatGLM6bV2TextGenerationPipeline(Pipeline):
self.model = model
self.model.eval()
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {self.model.model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {self.model.model_dir}.'
)
self.tokenizer = AutoTokenizer.from_pretrained(
self.model.model_dir, trust_remote_code=True)
@@ -334,9 +337,10 @@ class QWenChatPipeline(Pipeline):
bf16 = False
if isinstance(model, str):
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
self.tokenizer = AutoTokenizer.from_pretrained(
model, revision=revision, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(
@@ -401,9 +405,10 @@ class QWenTextGenerationPipeline(Pipeline):
bf16 = False
if isinstance(model, str):
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model}.')
self.model = AutoModelForCausalLM.from_pretrained(
model,
device_map=device_map,

11
modelscope/utils/automodel_utils.py
View File

@@ -3,14 +3,16 @@ import os
from types import MethodType
from typing import Any, Optional
from modelscope import get_logger
from modelscope.metainfo import Tasks
from modelscope.utils.ast_utils import INDEX_KEY
from modelscope.utils.import_utils import (LazyImportModule,
is_torch_available,
is_transformers_available)
from modelscope import get_logger
logger = get_logger()
def can_load_by_ms(model_dir: str, task_name: Optional[str],
model_type: Optional[str]) -> bool:
if model_type is None or task_name is None:
@@ -92,9 +94,10 @@ def get_hf_automodel_class(model_dir: str,
if not os.path.exists(config_path):
return None
try:
logger.warning('Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
logger.warning(
'Use trust_remote_code=True. The code will be downloaded'
' and used from the remote repo. Please make sure that'
f' the remote code content is what you need {model_dir}.')
config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
if task_name is None:
automodel_class = get_default_automodel(config)

6
modelscope/utils/plugins.py
View File

@@ -451,8 +451,10 @@ def register_plugins_repo(plugins: List[str]) -> None:
def register_modelhub_repo(model_dir, allow_remote=False) -> None:
""" Try to install and import remote model from modelhub"""
if allow_remote:
logger.warning('Use allow_remote=True. The code will be downloaded and used from the remote repo.'
f' Please make sure that the remote code content is what you need {model_dir}.')
logger.warning(
'Use allow_remote=True. The code will be downloaded and used from the remote repo.'
f' Please make sure that the remote code content is what you need {model_dir}.'
)
try:
import_module_from_model_dir(model_dir)
except KeyError: