CHORE: Ensure PEFT works with huggingface_hub 1.0.0 (#2808)

The reset_sessions function is removed but it's also no longer necessary
to call it for the purpose we used it.

Moreover, the deprecated use_auth_token argument is fully removed now,
so everywhere we used to pass it, it is now removed, unless a user
passes it explicitly.

Also, remove the deprecated local_dir_use_symlinks argument.

src/peft/mixed_model.py

@@ -422,15 +422,17 @@ class PeftMixedModel(PushToHubMixin, torch.nn.Module):
 
         # load the config
         if config is None:
-            config = PEFT_TYPE_TO_CONFIG_MAPPING[
-                PeftConfig._get_peft_type(
-                    model_id,
-                    subfolder=kwargs.get("subfolder", None),
-                    revision=kwargs.get("revision", None),
-                    cache_dir=kwargs.get("cache_dir", None),
-                    use_auth_token=kwargs.get("use_auth_token", None),
+            hf_kwargs = {
+                "subfolder": kwargs.get("subfolder", None),
+                "revision": kwargs.get("revision", None),
+                "cache_dir": kwargs.get("cache_dir", None),
+                "token": kwargs.get("token", None),
+            }
+            if use_auth_token := kwargs.get("use_auth_token", None):
+                hf_kwargs["use_auth_token"] = use_auth_token
+            config = PEFT_TYPE_TO_CONFIG_MAPPING[PeftConfig._get_peft_type(model_id, **hf_kwargs)].from_pretrained(
+                model_id, **kwargs
             )
-            ].from_pretrained(model_id, **kwargs)
         elif isinstance(config, PeftConfig):
             config.inference_mode = not is_trainable
         else:

src/peft/peft_model.py

@@ -434,16 +434,17 @@ class PeftModel(PushToHubMixin, torch.nn.Module):
 
         # load the config
         if config is None:
-            config = PEFT_TYPE_TO_CONFIG_MAPPING[
-                PeftConfig._get_peft_type(
-                    model_id,
-                    subfolder=kwargs.get("subfolder", None),
-                    revision=kwargs.get("revision", None),
-                    cache_dir=kwargs.get("cache_dir", None),
-                    use_auth_token=kwargs.get("use_auth_token", None),
-                    token=kwargs.get("token", None),
+            hf_kwargs = {
+                "subfolder": kwargs.get("subfolder", None),
+                "revision": kwargs.get("revision", None),
+                "cache_dir": kwargs.get("cache_dir", None),
+                "token": kwargs.get("token", None),
+            }
+            if use_auth_token := kwargs.get("use_auth_token", None):
+                hf_kwargs["use_auth_token"] = use_auth_token
+            config = PEFT_TYPE_TO_CONFIG_MAPPING[PeftConfig._get_peft_type(model_id, **hf_kwargs)].from_pretrained(
+                model_id, **kwargs
             )
-            ].from_pretrained(model_id, **kwargs)
         elif isinstance(config, PeftConfig):
             config.inference_mode = not is_trainable
         else:

src/peft/utils/hotswap.py

@@ -597,17 +597,15 @@ def hotswap_adapter(model, model_name_or_path, adapter_name, torch_device=None,
     ############################
     # LOAD CONFIG AND VALIDATE #
     ############################
-
-    config_cls = PEFT_TYPE_TO_CONFIG_MAPPING[
-        PeftConfig._get_peft_type(
-            model_name_or_path,
-            subfolder=kwargs.get("subfolder", None),
-            revision=kwargs.get("revision", None),
-            cache_dir=kwargs.get("cache_dir", None),
-            use_auth_token=kwargs.get("use_auth_token", None),
-            token=kwargs.get("token", None),
-        )
-    ]
+    hf_kwargs = {
+        "subfolder": kwargs.get("subfolder", None),
+        "revision": kwargs.get("revision", None),
+        "cache_dir": kwargs.get("cache_dir", None),
+        "token": kwargs.get("token", None),
+    }
+    if use_auth_token := kwargs.get("use_auth_token", None):
+        hf_kwargs["use_auth_token"] = use_auth_token
+    config_cls = PEFT_TYPE_TO_CONFIG_MAPPING[PeftConfig._get_peft_type(model_name_or_path, **hf_kwargs)]
     config = config_cls.from_pretrained(model_name_or_path, **kwargs)
     # config keys that could affect the model output besides what is determined by the state_dict
     check_hotswap_configs_compatible(model.active_peft_config, config)

tests/regression/test_regression.py

@@ -95,13 +95,7 @@ def setup_tearndown():
     # provide such a feature
 
     # download regression artifacts from Hugging Face Hub at the start
-    snapshot_download(
-        repo_id=HF_REPO,
-        local_dir=REGRESSION_DIR,
-        # Don't use symlink, because this prevents us from properly cleaning up the files once finished
-        local_dir_use_symlinks=False,
-    )
-
+    snapshot_download(repo_id=HF_REPO, local_dir=REGRESSION_DIR)
     yield
 
     # delete regression artifacts at the end of the test session; optionally, upload them first if in creation mode

tests/test_initialization.py

@@ -27,8 +27,6 @@ import pytest
 import torch
 from datasets import Dataset
 from huggingface_hub import snapshot_download
-from huggingface_hub.errors import HfHubHTTPError, LocalEntryNotFoundError
-from huggingface_hub.utils import reset_sessions
 from safetensors.torch import load_file
 from scipy import stats
 from torch import nn
@@ -73,6 +71,13 @@ from peft.utils.hotswap import hotswap_adapter, prepare_model_for_compiled_hotsw
 from .testing_utils import load_dataset_english_quotes, require_deterministic_for_xpu
 
 
+try:
+    from huggingface_hub.utils import reset_sessions
+except ImportError:
+    # this function was removed in hfh v1.0.0
+    reset_sessions = None
+
+
 class TestLoraInitialization:
     """Test class to check the initialization of LoRA adapters."""
 
@@ -1986,13 +1991,19 @@ class TestLoadAdapterOfflineMode:
     def hub_offline_ctx(self):
         # this is required to simulate offline mode, setting the env var dynamically inside the test does not work
         # because the value is checked only once at the start of the session
+
+        if reset_sessions is None:
+            # this means we're using huggingface_hub >= 1.0.0, there is no need to call reset_sessions() anymore
+            with patch("huggingface_hub.constants.HF_HUB_OFFLINE", True):
+                yield
+        else:
+            # in huggingface_hub < 1.0.0, it's necessary to reset the session
+            # TODO: remove once huggingface_hub < 1.0.0 is no longer supported
             with patch("huggingface_hub.constants.HF_HUB_OFFLINE", True):
                 reset_sessions()
                 yield
             reset_sessions()
 
-    # TODO remove when/if Hub is more stable
-    @pytest.mark.xfail(reason="Test is flaky on CI", raises=HfHubHTTPError)
     def test_load_from_hub_then_offline_model(self):
         # this uses LoRA but it's the same mechanism for other methods
         base_model = AutoModelForCausalLM.from_pretrained(self.base_model)
@@ -2019,8 +2030,6 @@ class TestLoadAdapterOfflineMode:
         snapshot_download(self.base_model, cache_dir=cache_dir)
         snapshot_download(self.peft_model_id, cache_dir=cache_dir)
 
-    # TODO remove when/if Hub is more stable
-    @pytest.mark.xfail(reason="Test is flaky on CI", raises=LocalEntryNotFoundError)
     def test_load_checkpoint_offline_non_default_cache_dir(self, changed_default_cache_dir, tmp_path):
         # See #2373 for context
         self.load_checkpoints(tmp_path)