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Merge commit '60780769b1c9c19bbbdaae02edb0d7d9dfeb8da5' into feat/whoami

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* commit '60780769b1c9c19bbbdaae02edb0d7d9dfeb8da5':
  Fix/daily (#1155)
  fix user-specified cache path (#1154)
  fix pipeline builder when model is not supported (#1125)
  Fix python push model no disk space (#1148)
  use streaming hash validation (#1127)
  fix daily fail case 1210yk (#1142)
  fix ut (#1143)
  Fix ollama template (#1141)
  downgrade moviepy to 1.0.3 (#1133)
  Skip obsolete sd pipeline (#1131)
This commit is contained in:
yuze.zyz
2024-12-25 20:31:16 +08:00
parent 6e68cb92c1 60780769b1
commit 8509b31729
30 changed files with 190 additions and 89 deletions
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4
.dev_scripts/ci_container_test.sh
View File

@@ -28,6 +28,10 @@ if [ "$MODELSCOPE_SDK_DEBUG" == "True" ]; then
pip install -r requirements/nlp.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
pip install -r requirements/science.txt -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
python -m spacy download en_core_web_sm
pip install faiss-gpu
pip install healpy
pip install huggingface-hub==0.25.2
# test with install
pip install .
else

10
modelscope/hub/api.py
View File

@@ -8,7 +8,6 @@ import pickle
import platform
import re
import shutil
import tempfile
import uuid
from collections import defaultdict
from http import HTTPStatus
@@ -34,8 +33,8 @@ from modelscope.hub.constants import (API_HTTP_CLIENT_MAX_RETRIES,
MODELSCOPE_CLOUD_USERNAME,
MODELSCOPE_REQUEST_ID, ONE_YEAR_SECONDS,
REQUESTS_API_HTTP_METHOD,
DatasetVisibility, Licenses,
ModelVisibility)
TEMPORARY_FOLDER_NAME, DatasetVisibility,
Licenses, ModelVisibility)
from modelscope.hub.errors import (InvalidParameter, NotExistError,
NotLoginException, NoValidRevisionError,
RequestError, datahub_raise_on_error,
@@ -391,7 +390,7 @@ class HubApi:
license=license,
chinese_name=chinese_name,
original_model_id=original_model_id)
tmp_dir = tempfile.mkdtemp()
tmp_dir = os.path.join(model_dir, TEMPORARY_FOLDER_NAME) # make temporary folder
git_wrapper = GitCommandWrapper()
logger.info(f'Pushing folder {model_dir} as model {model_id}.')
logger.info(f'Total folder size {folder_size}, this may take a while depending on actual pushing size...')
@@ -433,6 +432,7 @@ class HubApi:
remote_branch=revision)
if tag is not None:
repo.tag_and_push(tag, tag)
logger.info(f'Successfully push folder {model_dir} to remote repo [{model_id}].')
except Exception:
raise
finally:
@@ -563,7 +563,7 @@ class HubApi:
if revision is None:
revision = MASTER_MODEL_BRANCH
logger.info(
'Model revision not specified, use default: %s in development mode'
'Model revision not specified, using default: [%s] version.'
% revision)
if revision not in all_branches and revision not in all_tags:
raise NotExistError('The model: %s has no revision : %s .' % (model_id, revision))

30
modelscope/hub/check_model.py
View File

@@ -14,6 +14,22 @@ from modelscope.utils.logger import get_logger
logger = get_logger()
def get_model_id_from_cache(model_root_path: str, ) -> str:
model_cache = None
# download with git
if os.path.exists(os.path.join(model_root_path, '.git')):
git_cmd_wrapper = GitCommandWrapper()
git_url = git_cmd_wrapper.get_repo_remote_url(model_root_path)
if git_url.endswith('.git'):
git_url = git_url[:-4]
u_parse = urlparse(git_url)
model_id = u_parse.path[1:]
else: # snapshot_download
model_cache = ModelFileSystemCache(model_root_path)
model_id = model_cache.get_model_id()
return model_id
def check_local_model_is_latest(
model_root_path: str,
user_agent: Optional[Union[Dict, str]] = None,
@@ -22,19 +38,7 @@ def check_local_model_is_latest(
Check local model repo is same as hub latest version.
"""
try:
model_cache = None
# download with git
if os.path.exists(os.path.join(model_root_path, '.git')):
git_cmd_wrapper = GitCommandWrapper()
git_url = git_cmd_wrapper.get_repo_remote_url(model_root_path)
if git_url.endswith('.git'):
git_url = git_url[:-4]
u_parse = urlparse(git_url)
model_id = u_parse.path[1:]
else: # snapshot_download
model_cache = ModelFileSystemCache(model_root_path)
model_id = model_cache.get_model_id()
model_id = get_model_id_from_cache(model_root_path)
# make headers
headers = {
'user-agent':

2
modelscope/hub/constants.py
View File

@@ -9,7 +9,7 @@ DEFAULT_MODELSCOPE_DATA_ENDPOINT = MODELSCOPE_URL_SCHEME + DEFAULT_MODELSCOPE_DO
MODELSCOPE_PARALLEL_DOWNLOAD_THRESHOLD_MB = int(
os.environ.get('MODELSCOPE_PARALLEL_DOWNLOAD_THRESHOLD_MB', 500))
MODELSCOPE_DOWNLOAD_PARALLELS = int(
os.environ.get('MODELSCOPE_DOWNLOAD_PARALLELS', 4))
os.environ.get('MODELSCOPE_DOWNLOAD_PARALLELS', 1))
DEFAULT_MODELSCOPE_GROUP = 'damo'
MODEL_ID_SEPARATOR = '/'
FILE_HASH = 'Sha256'

42
modelscope/hub/file_download.py
View File

@@ -1,6 +1,7 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
import copy
import hashlib
import io
import os
import tempfile
@@ -213,8 +214,9 @@ def _repo_file_download(
if repo_file['Path'] == file_path:
if cache.exists(repo_file):
file_name = repo_file['Name']
logger.debug(
f'File {repo_file["Name"]} already in cache with identical hash, skip downloading!'
f'File {file_name} already in cache with identical hash, skip downloading!'
)
return cache.get_file_by_info(repo_file)
else:
@@ -250,8 +252,9 @@ def _repo_file_download(
if repo_file['Path'] == file_path:
if cache.exists(repo_file):
file_name = repo_file['Name']
logger.debug(
f'File {repo_file["Name"]} already in cache with identical hash, skip downloading!'
f'File {file_name} already in cache with identical hash, skip downloading!'
)
return cache.get_file_by_info(repo_file)
else:
@@ -410,12 +413,19 @@ def parallel_download(
list(executor.map(download_part_with_retry, tasks))
# merge parts.
hash_sha256 = hashlib.sha256()
with open(os.path.join(local_dir, file_name), 'wb') as output_file:
for task in tasks:
part_file_name = task[0] + '_%s_%s' % (task[2], task[3])
with open(part_file_name, 'rb') as part_file:
output_file.write(part_file.read())
while True:
chunk = part_file.read(16 * API_FILE_DOWNLOAD_CHUNK_SIZE)
if not chunk:
break
output_file.write(chunk)
hash_sha256.update(chunk)
os.remove(part_file_name)
return hash_sha256.hexdigest()
def http_get_model_file(
@@ -452,6 +462,8 @@ def http_get_model_file(
os.makedirs(os.path.dirname(temp_file_path), exist_ok=True)
logger.debug('downloading %s to %s', url, temp_file_path)
# retry sleep 0.5s, 1s, 2s, 4s
has_retry = False
hash_sha256 = hashlib.sha256()
retry = Retry(
total=API_FILE_DOWNLOAD_RETRY_TIMES,
backoff_factor=1,
@@ -499,11 +511,14 @@ def http_get_model_file(
if chunk: # filter out keep-alive new chunks
progress.update(len(chunk))
f.write(chunk)
hash_sha256.update(chunk)
break
except (Exception) as e: # no matter what happen, we will retry.
except Exception as e: # no matter what happen, we will retry.
has_retry = True
retry = retry.increment('GET', url, error=e)
retry.sleep()
# if anything went wrong, we would discard the real-time computed hash and return None
return None if has_retry else hash_sha256.hexdigest()
logger.debug('storing %s in cache at %s', url, local_dir)
@@ -591,9 +606,10 @@ def http_get_file(
def download_file(url, file_meta, temporary_cache_dir, cache, headers,
cookies):
file_digest = None
if MODELSCOPE_PARALLEL_DOWNLOAD_THRESHOLD_MB * 1000 * 1000 < file_meta[
'Size'] and MODELSCOPE_DOWNLOAD_PARALLELS > 1: # parallel download large file.
parallel_download(
file_digest = parallel_download(
url,
temporary_cache_dir,
file_meta['Path'],
@@ -601,7 +617,7 @@ def download_file(url, file_meta, temporary_cache_dir, cache, headers,
cookies=None if cookies is None else cookies.get_dict(),
file_size=file_meta['Size'])
else:
http_get_model_file(
file_digest = http_get_model_file(
url,
temporary_cache_dir,
file_meta['Path'],
@@ -612,6 +628,16 @@ def download_file(url, file_meta, temporary_cache_dir, cache, headers,
# check file integrity
temp_file = os.path.join(temporary_cache_dir, file_meta['Path'])
if FILE_HASH in file_meta:
file_integrity_validation(temp_file, file_meta[FILE_HASH])
expected_hash = file_meta[FILE_HASH]
# if a real-time hash has been computed
if file_digest is not None:
# if real-time hash mismatched, try to compute it again
if file_digest != expected_hash:
print(
'Mismatched real-time digest found, falling back to lump-sum hash computation'
)
file_integrity_validation(temp_file, expected_hash)
else:
file_integrity_validation(temp_file, expected_hash)
# put file into to cache
return cache.put_file(file_meta, temp_file)

11
modelscope/hub/snapshot_download.py
View File

@@ -206,7 +206,7 @@ def _snapshot_download(
repo_id, local_dir=local_dir, cache_dir=cache_dir, repo_type=repo_type)
system_cache = cache_dir if cache_dir is not None else os.getenv(
'MODELSCOPE_CACHE',
Path.home().joinpath('.cache', 'modelscope'))
Path.home().joinpath('.cache', 'modelscope', 'hub'))
if local_files_only:
if len(cache.cached_files) == 0:
raise ValueError(
@@ -233,7 +233,7 @@ def _snapshot_download(
if repo_type == REPO_TYPE_MODEL:
directory = os.path.abspath(
local_dir) if local_dir is not None else os.path.join(
system_cache, 'hub', repo_id)
system_cache, repo_id)
print(f'Downloading Model to directory: {directory}')
revision_detail = _api.get_valid_revision_detail(
repo_id, revision=revision, cookies=cookies)
@@ -283,10 +283,13 @@ def _snapshot_download(
logger.info(f'Creating symbolic link [{directory}].')
try:
os.symlink(
os.path.abspath(masked_directory), directory)
os.path.abspath(masked_directory),
directory,
target_is_directory=True)
except OSError:
logger.warning(
f'Failed to create symbolic link {directory}.')
f'Failed to create symbolic link {directory} for {os.path.abspath(masked_directory)}.'
)
elif repo_type == REPO_TYPE_DATASET:
directory = os.path.abspath(

3
modelscope/models/cv/image_super_resolution_pasd/unet_2d_blocks.py
View File

@@ -8,10 +8,11 @@ import torch.nn.functional as F
from diffusers.models.attention_processor import (Attention,
AttnAddedKVProcessor,
AttnAddedKVProcessor2_0)
from diffusers.models.dual_transformer_2d import DualTransformer2DModel
from diffusers.models.resnet import (Downsample2D, FirDownsample2D,
FirUpsample2D, KDownsample2D, KUpsample2D,
ResnetBlock2D, Upsample2D)
from diffusers.models.transformers.dual_transformer_2d import \
DualTransformer2DModel
from torch import nn
from .attention import AdaGroupNorm

5
modelscope/models/cv/image_super_resolution_pasd_v2/unet_2d_blocks.py
View File

@@ -20,12 +20,13 @@ from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import (Attention,
AttnAddedKVProcessor,
AttnAddedKVProcessor2_0)
from diffusers.models.dual_transformer_2d import DualTransformer2DModel
from diffusers.models.normalization import AdaLayerNorm
from diffusers.models.resnet import (Downsample2D, FirDownsample2D,
FirUpsample2D, KDownsample2D, KUpsample2D,
ResnetBlock2D, Upsample2D)
from diffusers.models.transformer_2d import Transformer2DModel
from diffusers.models.transformers.dual_transformer_2d import \
DualTransformer2DModel
from diffusers.models.transformers.transformer_2d import Transformer2DModel
from diffusers.utils import is_torch_version, logging
from einops import rearrange
from torch import nn

2
modelscope/models/cv/image_view_transform/ldm/autoencoder.py
View File

@@ -3,7 +3,7 @@ from contextlib import contextmanager
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from taming.modules.vqvae.quantize import VectorQuantizer
from ..util import instantiate_from_config
from .distributions import DiagonalGaussianDistribution

2
modelscope/models/cv/image_view_transform/ldm/ddpm.py
View File

@@ -16,7 +16,7 @@ import torch
import torch.nn as nn
from einops import rearrange, repeat
from omegaconf import ListConfig
from pytorch_lightning.utilities.distributed import rank_zero_only
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from torch.optim.lr_scheduler import LambdaLR
from torchvision.utils import make_grid
from tqdm import tqdm

22
modelscope/pipelines/builder.py
View File

@@ -125,7 +125,7 @@ def pipeline(task: str = None,
if pipeline_name is None and prefer_llm_pipeline:
pipeline_name = external_engine_for_llm_checker(
model, model_revision, kwargs)
else:
if pipeline_name is None:
model = normalize_model_input(
model,
model_revision,
@@ -170,7 +170,7 @@ def pipeline(task: str = None,
pipeline_props['device'] = device
cfg = ConfigDict(pipeline_props)
clear_llm_info(kwargs)
clear_llm_info(kwargs, pipeline_name)
if kwargs:
cfg.update(kwargs)
@@ -223,23 +223,33 @@ def external_engine_for_llm_checker(model: Union[str, List[str], Model,
List[Model]],
revision: Optional[str],
kwargs: Dict[str, Any]) -> Optional[str]:
from .nlp.llm_pipeline import ModelTypeHelper, LLMAdapterRegistry
from .nlp.llm_pipeline import SWIFT_MODEL_ID_MAPPING, init_swift_model_mapping, ModelTypeHelper, LLMAdapterRegistry
from ..hub.check_model import get_model_id_from_cache
if isinstance(model, list):
model = model[0]
if not isinstance(model, str):
model = model.model_dir
if kwargs.get('llm_framework') == 'swift':
return 'llm'
# check if swift supports
if os.path.exists(model):
model_id = get_model_id_from_cache(model)
else:
model_id = model
init_swift_model_mapping()
if model_id.lower() in SWIFT_MODEL_ID_MAPPING:
return 'llm'
model_type = ModelTypeHelper.get(
model, revision, with_adapter=True, split='-', use_cache=True)
if LLMAdapterRegistry.contains(model_type):
return 'llm'
def clear_llm_info(kwargs: Dict):
def clear_llm_info(kwargs: Dict, pipeline_name: str):
from modelscope.utils.model_type_helper import ModelTypeHelper
kwargs.pop('external_engine_for_llm', None)
if pipeline_name != 'llm':
kwargs.pop('llm_framework', None)
ModelTypeHelper.clear_cache()

4
modelscope/pipelines/multi_modal/efficient_diffusion_tuning_pipeline.py
View File

@@ -36,8 +36,10 @@ class EfficientDiffusionTuningPipeline(Pipeline):
'data/test/images/vision_efficient_tuning_test_1.png')
>>> print(f'Output: {result}.')
"""
logger.warn(
'[NOTE]Do not use this pipeline because the dependencies are too old, '
'use https://github.com/modelscope/DiffSynth-Studio instead')
super().__init__(model=model, **kwargs)
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = self.model.to(self.device)
self.model.eval()

27
modelscope/pipelines/nlp/llm_pipeline.py
View File

@@ -33,6 +33,17 @@ SWIFT_MODEL_ID_MAPPING = {}
SWIFT_FRAMEWORK = 'swift'
def init_swift_model_mapping():
from swift.llm.utils import MODEL_MAPPING
global SWIFT_MODEL_ID_MAPPING
if not SWIFT_MODEL_ID_MAPPING:
SWIFT_MODEL_ID_MAPPING = {
v['model_id_or_path'].lower(): k
for k, v in MODEL_MAPPING.items()
}
class LLMAdapterRegistry:
llm_format_map = {'qwen': [None, None, None]}
@@ -216,14 +227,7 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
def _init_swift(self, model_id, device) -> None:
from swift.llm import prepare_model_template
from swift.llm.utils import MODEL_MAPPING, InferArguments
global SWIFT_MODEL_ID_MAPPING
if not SWIFT_MODEL_ID_MAPPING:
SWIFT_MODEL_ID_MAPPING = {
v['model_id_or_path']: k
for k, v in MODEL_MAPPING.items()
}
from swift.llm.utils import InferArguments
def format_messages(messages: Dict[str, List[Dict[str, str]]],
tokenizer: PreTrainedTokenizer,
@@ -261,9 +265,12 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
else:
return dict(system=system, prompt=prompt, history=history)
assert model_id in SWIFT_MODEL_ID_MAPPING,\
init_swift_model_mapping()
assert model_id.lower() in SWIFT_MODEL_ID_MAPPING,\
f'Invalid model id {model_id} or Swift framework does not support this model.'
args = InferArguments(model_type=SWIFT_MODEL_ID_MAPPING[model_id])
args = InferArguments(
model_type=SWIFT_MODEL_ID_MAPPING[model_id.lower()])
model, template = prepare_model_template(
args, device_map=self.device_map)
self.model = add_stream_generate(model)

9
modelscope/preprocessors/nlp/fill_mask_preprocessor.py
View File

@@ -213,9 +213,14 @@ class FillMaskPoNetPreprocessor(FillMaskPreprocessorBase):
osp.join(model_dir, ModelFile.CONFIGURATION))
self.language = self.cfg.model.get('language', 'en')
if self.language == 'en':
import nltk
from nltk.tokenize import sent_tokenize
import_external_nltk_data(
osp.join(model_dir, 'nltk_data'), 'tokenizers/punkt')
from packaging import version
if version.parse(nltk.__version__) >= version.parse('3.8.2'):
nltk.download('punkt_tab')
else:
import_external_nltk_data(
osp.join(model_dir, 'nltk_data'), 'tokenizers/punkt_tab')
elif self.language in ['zh', 'cn']:
def sent_tokenize(para):

22
modelscope/preprocessors/templates/loader.py
View File

@@ -30,6 +30,8 @@ def cases(*names):
else:
regex += letter
ret.append(regex)
if '-' in regex:
ret.append(regex.replace('-', ' '))
if len(ret) > 1:
ret = '|'.join(ret)
ret = '(' + ret + ')'
@@ -53,6 +55,12 @@ def no_multi_modal():
template_info = [
# llama
## "llama3"
TemplateInfo(
template_regex=
f'.*{cases("llama3.3", "llama-3.3")}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/llama3.3',
),
TemplateInfo(
template_regex=
f'.*{cases("llama3.2", "llama-3.2")}.*{cases("vision")}.*',
@@ -291,7 +299,7 @@ template_info = [
TemplateInfo(
template=TemplateType.llama,
template_regex=
f'.*{cases("mistral")}{no_multi_modal()}.*{chat_suffix}.*',
f'.*{cases("mistral", "ministral")}{no_multi_modal()}.*{chat_suffix}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/mistral',
),
@@ -771,6 +779,18 @@ template_info = [
template_regex=f'.*{cases("paraphrase-multilingual")}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/paraphrase-multilingual'),
TemplateInfo(
template_regex=f'.*{cases("marco")}.*{cases("o1")}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/marco-o1'),
TemplateInfo(
template_regex=f'.*{cases("qwq")}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/qwq'),
TemplateInfo(
template_regex=f'.*{cases("exaone")}.*',
modelfile_prefix=
'https://modelscope.oss-cn-beijing.aliyuncs.com/llm_template/ollama/exaone3.5'),
]

8
modelscope/utils/streaming_output.py
View File

@@ -175,7 +175,11 @@ class PretrainedModelStreamingOutputMixin(StreamingOutputMixin):
@contextmanager
def _replace_generate(self, model: PreTrainedModel) -> Generator:
if version.parse(transformers.__version__) >= version.parse('4.39.0'):
if version.parse(transformers.__version__) >= version.parse('4.43.0'):
greedy_search_name = 'stream_greedy_search'
sample_name = '_sample'
elif version.parse(
transformers.__version__) >= version.parse('4.39.0'):
greedy_search_name = '_greedy_search'
sample_name = '_sample'
else:
@@ -449,6 +453,8 @@ class PretrainedModelStreamingOutputMixin(StreamingOutputMixin):
break
# prepare model inputs
model_kwargs = self._get_initial_cache_position(
input_ids, model_kwargs)
model_inputs = self.prepare_inputs_for_generation(
input_ids, **model_kwargs)

2
requirements/cv.txt
View File

@@ -32,7 +32,7 @@ mmdet>=2.25.0,<=2.28.2
# mmdet3d-1.0.0rc6 remove networkx and numba version restriction
mmdet3d==1.0.0a1
mmsegmentation<=0.30.0
moviepy>=1.0.3
moviepy==1.0.3
nerfacc==0.2.2
networkx
numba

10
tests/pipelines/test_efficient_diffusion_tuning.py
View File

@@ -11,10 +11,10 @@ from modelscope.utils.test_utils import test_level
class EfficientDiffusionTuningTest(unittest.TestCase):
def setUp(self) -> None:
os.system('pip install ms-swift -U')
# os.system('pip install ms-swift -U')
self.task = Tasks.efficient_diffusion_tuning
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_efficient_diffusion_tuning_lora_run_pipeline(self):
model_id = 'damo/multi-modal_efficient-diffusion-tuning-lora'
model_revision = 'v1.0.2'
@@ -24,7 +24,7 @@ class EfficientDiffusionTuningTest(unittest.TestCase):
result = edt_pipeline(inputs)
print(f'Efficient-diffusion-tuning-lora output: {result}.')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_efficient_diffusion_tuning_lora_load_model_from_pretrained(self):
model_id = 'damo/multi-modal_efficient-diffusion-tuning-lora'
model_revision = 'v1.0.2'
@@ -32,7 +32,7 @@ class EfficientDiffusionTuningTest(unittest.TestCase):
from modelscope.models.multi_modal import EfficientStableDiffusion
self.assertTrue(model.__class__ == EfficientStableDiffusion)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_efficient_diffusion_tuning_control_lora_run_pipeline(self):
# TODO: to be fixed in the future
model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'
@@ -48,7 +48,7 @@ class EfficientDiffusionTuningTest(unittest.TestCase):
result = edt_pipeline(inputs)
print(f'Efficient-diffusion-tuning-control-lora output: {result}.')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_efficient_diffusion_tuning_control_lora_load_model_from_pretrained(
self):
model_id = 'damo/multi-modal_efficient-diffusion-tuning-control-lora'

9
tests/pipelines/test_fill_mask.py
View File

@@ -125,13 +125,8 @@ class FillMaskTest(unittest.TestCase):
for language in ['zh', 'en']:
ori_text = self.ori_texts[language]
test_input = self.test_inputs[language].replace('[MASK]', '<mask>')
with self.regress_tool.monitor_module_single_forward(
pipeline_ins.model,
f'fill_mask_veco_{language}',
compare_fn=IgnoreKeyFn('.*intermediate_act_fn')):
print(
f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
f'{pipeline_ins(test_input)}\n')
print(f'\nori_text: {ori_text}\ninput: {test_input}\npipeline: '
f'{pipeline_ins(test_input)}\n')
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_run_with_model_name(self):

6
tests/pipelines/test_mplug_owl_multimodal_dialogue.py
View File

@@ -39,7 +39,7 @@ class MplugOwlMultimodalDialogueTest(unittest.TestCase):
},
]
}
result = pipeline_multimodal_dialogue(messages, max_length=5)
result = pipeline_multimodal_dialogue(messages)
print(result[OutputKeys.TEXT])
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@@ -68,7 +68,7 @@ class MplugOwlMultimodalDialogueTest(unittest.TestCase):
},
]
}
result = pipeline_multimodal_dialogue(messages, max_length=120)
result = pipeline_multimodal_dialogue(messages, max_new_tokens=512)
print(result[OutputKeys.TEXT])
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@@ -90,7 +90,7 @@ class MplugOwlMultimodalDialogueTest(unittest.TestCase):
},
]
}
result = pipeline_multimodal_dialogue(messages)
result = pipeline_multimodal_dialogue(messages, max_new_tokens=512)
print(result[OutputKeys.TEXT])

2
tests/pipelines/test_nerf_recon_4k.py
View File

@@ -50,7 +50,7 @@ class NeRFRecon4KTest(unittest.TestCase):
# nerf_recon_4k(
# dict(data_cfg=self.data_dic, render_dir=self.render_dir))
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
@unittest.skipIf(not torch.cuda.is_available(), 'cuda unittest only')
def test_run_modelhub(self):
nerf_recon_4k = pipeline(

4
tests/pipelines/test_text_to_video_synthesis.py
View File

@@ -24,7 +24,7 @@ class TextToVideoSynthesisTest(unittest.TestCase):
'out_width': 256,
}
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_run_with_model_from_modelhub(self):
pipe_line_text_to_video_synthesis = pipeline(
task=self.task, model=self.model_id)
@@ -32,7 +32,7 @@ class TextToVideoSynthesisTest(unittest.TestCase):
self.test_text)[OutputKeys.OUTPUT_VIDEO]
print(output_video_path)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_run_modelhub_user_control(self):
pipe_line_text_to_video_synthesis = pipeline(
task=self.task, model=self.model_id)

17
tests/tools/test_to_ollama.py
View File

@@ -7,8 +7,9 @@ from modelscope.preprocessors.templates.loader import TemplateLoader
from modelscope.utils.test_utils import test_level
def _test_check_tmpl_type(model, tmpl_type):
ollama, info = TemplateLoader.to_ollama(model, debug=True)
def _test_check_tmpl_type(model, tmpl_type, gguf_meta={}):
ollama, info = TemplateLoader.to_ollama(
model, gguf_meta=gguf_meta, debug=True)
assert info.__dict__.get('modelfile_prefix').split(
'/')[-1] == tmpl_type, info
@@ -121,6 +122,10 @@ class TestToOllama(unittest.TestCase):
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_check_template_type(self):
_test_check_tmpl_type(
'LLM-Research/Llama-3.3-70B-Instruct',
'llama3.3',
gguf_meta={'general.name': 'Llama 3.3 70B Instruct'})
_test_check_tmpl_type(
'AI-ModelScope/Llama-3.2-11B-Vision-Instruct-GGUF',
'llama3.2-vision')
@@ -190,6 +195,8 @@ class TestToOllama(unittest.TestCase):
'mistral-openorca')
_test_check_tmpl_type('QuantFactory/Mistral-7B-Instruct-v0.1-GGUF',
'mistral')
_test_check_tmpl_type('QuantFactory/Ministral-8B-Instruct-2410-GGUF',
'mistral')
_test_check_tmpl_type(
'second-state/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF',
'nous-hermes2-mixtral')
@@ -298,6 +305,12 @@ class TestToOllama(unittest.TestCase):
_test_check_tmpl_type(
'Ceceliachenen/paraphrase-multilingual-MiniLM-L12-v2',
'paraphrase-multilingual')
_test_check_tmpl_type('bartowski/Marco-o1-GGUF', 'marco-o1')
_test_check_tmpl_type('Qwen/QwQ-32B-Preview', 'qwq')
_test_check_tmpl_type('LLM-Research/Llama-3.3-70B-Instruct',
'llama3.3')
_test_check_tmpl_type('bartowski/EXAONE-3.5-7.8B-Instruct-GGUF',
'exaone3.5')
if __name__ == '__main__':

4
tests/trainers/audio/test_separation_trainer.py
View File

@@ -50,7 +50,7 @@ class TestSeparationTrainer(unittest.TestCase):
shutil.rmtree(self.tmp_dir, ignore_errors=True)
super().tearDown()
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_trainer(self):
kwargs = dict(
model=self.model_id,
@@ -73,7 +73,7 @@ class TestSeparationTrainer(unittest.TestCase):
self.assertEqual(
len(checkpoint_dirs), 2, f'Cannot find checkpoint in {save_dir}!')
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
@unittest.skip
def test_eval(self):
kwargs = dict(
model=self.model_id,

2
tests/trainers/test_clip_trainer.py
View File

@@ -52,7 +52,7 @@ class TestClipTrainer(unittest.TestCase):
'metrics': [{'type': 'inbatch_recall'}]},
'preprocessor': []}
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_trainer_std(self):
WORKSPACE = './workspace/ckpts/clip'
os.makedirs(WORKSPACE, exist_ok=True)

4
tests/trainers/test_document_grounded_dialog_generate_trainer.py
View File

@@ -16,12 +16,12 @@ class DocumentGroundedDialogGenerateTest(unittest.TestCase):
def setUp(self) -> None:
self.model_id = 'DAMO_ConvAI/nlp_convai_generation_pretrain'
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_trainer_with_model_name(self):
# load data
train_dataset = MsDataset.load(
'DAMO_ConvAI/FrDoc2BotGeneration',
download_mode=DownloadMode.FORCE_REDOWNLOAD)
download_mode=DownloadMode.FORCE_REDOWNLOAD)['train']
test_len = 1
sub_train_dataset = [x for x in train_dataset][:1]
sub_train_dataset = [{

2
tests/trainers/test_document_grounded_dialog_retrieval_trainer.py
View File

@@ -21,7 +21,7 @@ class DocumentGroundedDialogRetrievalTest(unittest.TestCase):
# load data
train_dataset = MsDataset.load(
'DAMO_ConvAI/FrDoc2BotRetrieval',
download_mode=DownloadMode.FORCE_REDOWNLOAD)
download_mode=DownloadMode.FORCE_REDOWNLOAD)['train']
sub_train_dataset = [x for x in train_dataset][:10]
all_passages = ['阑尾炎', '肠胃炎', '肚脐开始', '肚脐为止']

6
tests/trainers/test_lora_diffusion_trainer.py
View File

@@ -35,7 +35,8 @@ class TestLoraDiffusionTrainer(unittest.TestCase):
shutil.rmtree(self.tmp_dir)
super().tearDown()
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
# need diffusers==0.24.0, skip in ci
@unittest.skip
def test_lora_diffusion_train(self):
model_id = 'AI-ModelScope/stable-diffusion-v1-5'
model_revision = 'v1.0.9'
@@ -67,7 +68,8 @@ class TestLoraDiffusionTrainer(unittest.TestCase):
results_files = os.listdir(self.tmp_dir)
self.assertIn(f'{trainer.timestamp}.log.json', results_files)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
# need diffusers==0.24.0, skip in ci
@unittest.skip
def test_lora_diffusion_eval(self):
model_id = 'AI-ModelScope/stable-diffusion-v1-5'
model_revision = 'v1.0.9'

6
tests/trainers/test_lora_diffusion_xl_trainer.py
View File

@@ -35,7 +35,8 @@ class TestLoraDiffusionXLTrainer(unittest.TestCase):
shutil.rmtree(self.tmp_dir)
super().tearDown()
@unittest.skipUnless(test_level() >= 1, 'skip test for oom')
# need diffusers==0.24.0, skip in ci
@unittest.skip
def test_lora_diffusion_xl_train(self):
model_id = 'AI-ModelScope/stable-diffusion-xl-base-1.0'
model_revision = 'v1.0.2'
@@ -67,7 +68,8 @@ class TestLoraDiffusionXLTrainer(unittest.TestCase):
results_files = os.listdir(self.tmp_dir)
self.assertIn(f'{trainer.timestamp}.log.json', results_files)
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
# need diffusers==0.24.0, skip in ci
@unittest.skip
def test_lora_diffusion_xl_eval(self):
model_id = 'AI-ModelScope/stable-diffusion-xl-base-1.0'
model_revision = 'v1.0.2'

2
tests/trainers/test_ofa_trainer.py
View File

@@ -76,7 +76,7 @@ class TestOfaTrainer(unittest.TestCase):
shutil.rmtree(self.WORKSPACE, ignore_errors=True)
super().tearDown()
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@unittest.skip
def test_trainer_std(self):
os.makedirs(self.WORKSPACE, exist_ok=True)
config_file = os.path.join(self.WORKSPACE, ModelFile.CONFIGURATION)