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fix from_pretrained in offline mode when model is preloaded in cache (#31010)

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* Unit test to verify fix

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

* fix from_pretrained in offline mode when model is preloaded in cache

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

* minor: fmt

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

---------

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>
Co-authored-by: Raphael Glon <oOraph@users.noreply.github.com>
This commit is contained in:
oOraph
2024-05-28 11:56:05 +02:00
committed by GitHub
parent 537deb7869
commit 936ab7bae5
2 changed files with 154 additions and 60 deletions
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96
tests/test_modeling_utils.py
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@@ -33,6 +33,7 @@ from requests.exceptions import HTTPError
from transformers import (
AutoConfig,
AutoModel,
AutoModelForImageClassification,
AutoModelForSequenceClassification,
OwlViTForObjectDetection,
PretrainedConfig,
@@ -76,7 +77,6 @@ sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig, NoSuperInitConfig # noqa E402
if is_torch_available():
import torch
from safetensors.torch import save_file as safe_save_file
@@ -194,6 +194,97 @@ if is_torch_available():
attention_mask = _prepare_4d_attention_mask(mask, dtype=inputs_embeds.dtype)
return attention_mask
class TestOffline(unittest.TestCase):
def test_offline(self):
# Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
from huggingface_hub import constants
from transformers.utils import hub
offlfine_env = hub._is_offline_mode
hub_cache_env = constants.HF_HUB_CACHE
hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
default_cache = constants.default_cache_path
transformers_cache = hub.TRANSFORMERS_CACHE
try:
hub._is_offline_mode = True
with tempfile.TemporaryDirectory() as tmpdir:
LOG.info("Temporary cache dir %s", tmpdir)
constants.HF_HUB_CACHE = tmpdir
constants.HUGGINGFACE_HUB_CACHE = tmpdir
constants.default_cache_path = tmpdir
hub.TRANSFORMERS_CACHE = tmpdir
# First offline load should fail
try:
AutoModelForImageClassification.from_pretrained(
TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None
)
except OSError:
LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
else:
self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
# Download model -> Huggingface Hub not concerned by our offline mode
LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
hub_api = HfApi()
local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
AutoModelForImageClassification.from_pretrained(
TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None
)
finally:
# Tear down: reset env as it was before calling this test
hub._is_offline_mode = offlfine_env
constants.HF_HUB_CACHE = hub_cache_env
constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
constants.default_cache_path = default_cache
hub.TRANSFORMERS_CACHE = transformers_cache
def test_local_files_only(self):
# Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
from huggingface_hub import constants
from transformers.utils import hub
hub_cache_env = constants.HF_HUB_CACHE
hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
default_cache = constants.default_cache_path
transformers_cache = hub.TRANSFORMERS_CACHE
try:
with tempfile.TemporaryDirectory() as tmpdir:
LOG.info("Temporary cache dir %s", tmpdir)
constants.HF_HUB_CACHE = tmpdir
constants.HUGGINGFACE_HUB_CACHE = tmpdir
constants.default_cache_path = tmpdir
hub.TRANSFORMERS_CACHE = tmpdir
try:
AutoModelForImageClassification.from_pretrained(
TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True
)
except OSError:
LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
else:
self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
hub_api = HfApi()
local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
AutoModelForImageClassification.from_pretrained(
TINY_IMAGE_CLASSIF, revision="main", use_auth_token=None, local_files_only=True
)
finally:
# Tear down: reset env as it was before calling this test
constants.HF_HUB_CACHE = hub_cache_env
constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
constants.default_cache_path = default_cache
hub.TRANSFORMERS_CACHE = transformers_cache
if is_flax_available():
from transformers import FlaxBertModel
@@ -205,6 +296,9 @@ if is_tf_available():
TINY_T5 = "patrickvonplaten/t5-tiny-random"
TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
TINY_IMAGE_CLASSIF = "hf-internal-testing/tiny-random-SiglipForImageClassification"
LOG = logging.get_logger(__name__)
def check_models_equal(model1, model2):