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

* 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>

src/transformers/modeling_utils.py

@@ -3392,8 +3392,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                         )
                         if resolved_archive_file is not None:
                             is_sharded = True
-
+                    if not local_files_only and not is_offline_mode():
-                    if not local_files_only and resolved_archive_file is not None:
+                        if resolved_archive_file is not None:
                             if filename in [WEIGHTS_NAME, WEIGHTS_INDEX_NAME]:
                                 # If the PyTorch file was found, check if there is a safetensors file on the repository
                                 # If there is no safetensors file on the repositories, start an auto conversion

tests/test_modeling_utils.py

@@ -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):