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Merge branch 'master' into master-merge-internal20231007

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This commit is contained in:
liuyhwangyh
2023-10-11 10:23:52 +08:00
committed by GitHub
parent 4d099aa603 362872d6bb
commit 7e12fe96f9
5 changed files with 58 additions and 99 deletions
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12
modelscope/models/base/base_model.py
View File

@@ -134,12 +134,13 @@ class Model(ABC):
ignore_file_pattern=ignore_file_pattern)
logger.info(f'initialize model from {local_model_dir}')
configuration_path = osp.join(local_model_dir, ModelFile.CONFIGURATION)
cfg = None
if cfg_dict is not None:
cfg = cfg_dict
else:
cfg = Config.from_file(
osp.join(local_model_dir, ModelFile.CONFIGURATION))
task_name = cfg.task
elif os.path.exists(configuration_path):
cfg = Config.from_file(configuration_path)
task_name = getattr(cfg, 'task', None)
if 'task' in kwargs:
task_name = kwargs.pop('task')
try:
@@ -167,6 +168,9 @@ class Model(ABC):
model = model.to(device)
return model
# use ms
if cfg is None:
raise FileNotFoundError(
f'`{ModelFile.CONFIGURATION}` file not found.')
model_cfg.model_dir = local_model_dir
# install and import remote repos before build

14
modelscope/trainers/trainer.py
View File

@@ -181,8 +181,20 @@ class EpochBasedTrainer(BaseTrainer):
compile_options = {}
self.model = compile_model(self.model, **compile_options)
if 'work_dir' in kwargs:
if kwargs.get('work_dir', None) is not None:
self.work_dir = kwargs['work_dir']
if 'train' not in self.cfg:
self.cfg['train'] = ConfigDict()
self.cfg['train']['work_dir'] = self.work_dir
if 'checkpoint' in self.cfg['train']:
if 'period' in self.cfg['train']['checkpoint']:
self.cfg['train']['checkpoint']['period'][
'save_dir'] = self.work_dir
if 'best' in self.cfg['train']['checkpoint']:
self.cfg['train']['checkpoint']['best'][
'save_dir'] = self.work_dir
if 'logging' in self.cfg['train']:
self.cfg['train']['logging']['out_dir'] = self.work_dir
else:
self.work_dir = self.cfg.train.get('work_dir', './work_dir')

59
modelscope/utils/automodel_utils.py
View File

@@ -6,8 +6,11 @@ from modelscope.utils.ast_utils import INDEX_KEY
from modelscope.utils.import_utils import LazyImportModule
def can_load_by_ms(model_dir: str, tast_name: str, model_type: str) -> bool:
if ('MODELS', tast_name,
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:
return False
if ('MODELS', task_name,
model_type) in LazyImportModule.AST_INDEX[INDEX_KEY]:
return True
ms_wrapper_path = os.path.join(model_dir, 'ms_wrapper.py')
@@ -25,11 +28,27 @@ def _can_load_by_hf_automodel(automodel_class: type, config) -> bool:
return False
def get_hf_automodel_class(model_dir: str, task_name: str) -> Optional[type]:
from modelscope import (AutoConfig, AutoModel, AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForTokenClassification,
AutoModelForSequenceClassification)
def get_default_automodel(config) -> Optional[type]:
import modelscope.utils.hf_util as hf_util
if not hasattr(config, 'auto_map'):
return None
auto_map = config.auto_map
automodel_list = [k for k in auto_map.keys() if k.startswith('AutoModel')]
if len(automodel_list) == 1:
return getattr(hf_util, automodel_list[0])
if len(automodel_list) > 1 and len(
set([auto_map[k] for k in automodel_list])) == 1:
return getattr(hf_util, automodel_list[0])
return None
def get_hf_automodel_class(model_dir: str,
task_name: Optional[str]) -> Optional[type]:
from modelscope.utils.hf_util import (AutoConfig, AutoModel,
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoModelForTokenClassification,
AutoModelForSequenceClassification)
automodel_mapping = {
Tasks.backbone: AutoModel,
Tasks.chat: AutoModelForCausalLM,
@@ -37,19 +56,18 @@ def get_hf_automodel_class(model_dir: str, task_name: str) -> Optional[type]:
Tasks.text_classification: AutoModelForSequenceClassification,
Tasks.token_classification: AutoModelForTokenClassification,
}
automodel_class = automodel_mapping.get(task_name, None)
if automodel_class is None:
return None
config_path = os.path.join(model_dir, 'config.json')
if not os.path.exists(config_path):
return None
try:
try:
config = AutoConfig.from_pretrained(
model_dir, trust_remote_code=True)
except (FileNotFoundError, ValueError):
return None
config = AutoConfig.from_pretrained(model_dir, trust_remote_code=True)
if task_name is None:
automodel_class = get_default_automodel(config)
else:
automodel_class = automodel_mapping.get(task_name, None)
if automodel_class is None:
return None
if _can_load_by_hf_automodel(automodel_class, config):
return automodel_class
if (automodel_class is AutoModelForCausalLM
@@ -71,14 +89,5 @@ def try_to_load_hf_model(model_dir: str, task_name: str,
model = None
if automodel_class is not None:
# use hf
device_map = kwargs.get('device_map', None)
torch_dtype = kwargs.get('torch_dtype', None)
config = kwargs.get('config', None)
model = automodel_class.from_pretrained(
model_dir,
device_map=device_map,
torch_dtype=torch_dtype,
config=config,
trust_remote_code=True)
model = automodel_class.from_pretrained(model_dir, **kwargs)
return model

70
modelscope/utils/hf_util.py
View File

@@ -21,7 +21,7 @@ from transformers.models.auto.tokenization_auto import (
TOKENIZER_MAPPING_NAMES, get_tokenizer_config)
from modelscope import snapshot_download
from modelscope.utils.constant import Invoke
from modelscope.utils.constant import DEFAULT_MODEL_REVISION, Invoke
try:
from transformers import GPTQConfig as GPTQConfigHF
@@ -84,69 +84,6 @@ patch_tokenizer_base()
patch_model_base()
def check_hf_code(model_dir: str, auto_class: type,
trust_remote_code: bool) -> None:
config_path = os.path.join(model_dir, 'config.json')
if not os.path.exists(config_path):
raise FileNotFoundError(f'{config_path} is not found')
config_dict = PretrainedConfig.get_config_dict(config_path)[0]
auto_class_name = auto_class.__name__
if auto_class is AutoTokenizerHF:
tokenizer_config = get_tokenizer_config(model_dir)
# load from repo
if trust_remote_code:
has_remote_code = False
if auto_class is AutoTokenizerHF:
auto_map = tokenizer_config.get('auto_map', None)
if auto_map is not None:
module_name = auto_map.get(auto_class_name, None)
if module_name is not None:
module_name = module_name[0]
has_remote_code = True
else:
auto_map = config_dict.get('auto_map', None)
if auto_map is not None:
module_name = auto_map.get(auto_class_name, None)
has_remote_code = module_name is not None
if has_remote_code:
module_path = os.path.join(model_dir,
module_name.split('.')[0] + '.py')
if not os.path.exists(module_path):
raise FileNotFoundError(f'{module_path} is not found')
return
# trust_remote_code is False or has_remote_code is False
model_type = config_dict.get('model_type', None)
if model_type is None:
raise ValueError(f'`model_type` key is not found in {config_path}.')
trust_remote_code_info = '.'
if not trust_remote_code:
trust_remote_code_info = ', You can try passing `trust_remote_code=True`.'
if auto_class is AutoConfigHF:
if model_type not in CONFIG_MAPPING:
raise ValueError(
f'{model_type} not found in HF `CONFIG_MAPPING`{trust_remote_code_info}'
)
elif auto_class is AutoTokenizerHF:
tokenizer_class = tokenizer_config.get('tokenizer_class')
if tokenizer_class is not None:
return
if model_type not in TOKENIZER_MAPPING_NAMES:
raise ValueError(
f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}'
)
else:
mapping_names = [
m.model_type for m in auto_class._model_mapping.keys()
]
if model_type not in mapping_names:
raise ValueError(
f'{model_type} not found in HF `auto_class._model_mapping`{trust_remote_code_info}'
)
def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs):
"""Get a custom wrapper class for auto classes to download the models from the ModelScope hub
Args:
@@ -166,7 +103,7 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs):
ignore_file_pattern = kwargs.pop('ignore_file_pattern',
default_ignore_file_pattern)
if not os.path.exists(pretrained_model_name_or_path):
revision = kwargs.pop('revision', None)
revision = kwargs.pop('revision', DEFAULT_MODEL_REVISION)
model_dir = snapshot_download(
pretrained_model_name_or_path,
revision=revision,
@@ -175,9 +112,6 @@ def get_wrapped_class(module_class, ignore_file_pattern=[], **kwargs):
else:
model_dir = pretrained_model_name_or_path
if module_class is not GenerationConfigHF:
trust_remote_code = kwargs.get('trust_remote_code', False)
check_hf_code(model_dir, module_class, trust_remote_code)
module_obj = module_class.from_pretrained(model_dir, *model_args,
**kwargs)

2
modelscope/utils/plugins.py
View File

@@ -9,7 +9,6 @@ import os
import pkgutil
import shutil
import sys
import venv
from contextlib import contextmanager
from fnmatch import fnmatch
from pathlib import Path
@@ -1144,6 +1143,7 @@ class EnvsManager(object):
cfg = read_config(model_dir)
self.plugins = cfg.get('plugins', [])
self.allow_remote = cfg.get('allow_remote', False)
import venv
self.env_builder = venv.EnvBuilder(
system_site_packages=True,
clear=False,