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added logic for deleting adapters once loaded (#34650)

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* added logic for deleting adapters once loaded

* updated to the latest version of transformers, merged utility function into the source

* updated with missing check

* added peft version check

* Apply suggestions from code review

Co-authored-by: Anton Vlasjuk <73884904+vasqu@users.noreply.github.com>

* changes according to reviewer

* added test for deleting adapter(s)

* styling changes

* styling changes in test

* removed redundant code

* formatted my contributions with ruff

* optimized error handling

* ruff formatted with correct config

* resolved formatting issues

---------

Co-authored-by: Anton Vlasjuk <73884904+vasqu@users.noreply.github.com>
This commit is contained in:
Sarthak Karandikar 2025-01-07 00:06:40 +05:30 committed by GitHub
parent 1650e0e514
commit ca00950057
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2 changed files with 126 additions and 1 deletions
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62
src/transformers/integrations/peft.py
View file

@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import inspect
import warnings
@ -525,3 +526,64 @@ class PeftAdapterMixin:
offload_dir=offload_folder,
**dispatch_model_kwargs,
)
def delete_adapter(self, adapter_names: Union[List[str], str]) -> None:
"""
Delete an adapter's LoRA layers from the underlying model.
Args:
adapter_names (`Union[List[str], str]`):
The name(s) of the adapter(s) to delete.
Example:
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
)
pipeline.delete_adapters("cinematic")
```
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tuners.tuners_utils import BaseTunerLayer
if isinstance(adapter_names, str):
adapter_names = [adapter_names]
# Check that all adapter names are present in the config
missing_adapters = [name for name in adapter_names if name not in self.peft_config]
if missing_adapters:
raise ValueError(
f"The following adapter(s) are not present and cannot be deleted: {', '.join(missing_adapters)}"
)
for adapter_name in adapter_names:
for module in self.modules():
if isinstance(module, BaseTunerLayer):
if hasattr(module, "delete_adapter"):
module.delete_adapter(adapter_name)
else:
raise ValueError(
"The version of PEFT you are using is not compatible, please use a version that is greater than 0.6.1"
)
# For transformers integration - we need to pop the adapter from the config
if getattr(self, "_hf_peft_config_loaded", False) and hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None)
# In case all adapters are deleted, we need to delete the config
# and make sure to set the flag to False
if len(self.peft_config) == 0:
del self.peft_config
self._hf_peft_config_loaded = False

65
tests/peft_integration/test_peft_integration.py
View file

@ -350,7 +350,6 @@ class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
self.assertFalse(
torch.allclose(logits_adapter_1.logits, logits_adapter_mixed.logits, atol=1e-6, rtol=1e-6)
)
self.assertFalse(
torch.allclose(logits_adapter_2.logits, logits_adapter_mixed.logits, atol=1e-6, rtol=1e-6)
)
@ -359,6 +358,70 @@ class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
with self.assertRaises(ValueError), tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
def test_delete_adapter(self):
"""
Enhanced test for `delete_adapter` to handle multiple adapters,
edge cases, and proper error handling.
"""
from peft import LoraConfig
for model_id in self.transformers_test_model_ids:
for transformers_class in self.transformers_test_model_classes:
model = transformers_class.from_pretrained(model_id).to(torch_device)
# Add multiple adapters
peft_config_1 = LoraConfig(init_lora_weights=False)
peft_config_2 = LoraConfig(init_lora_weights=False)
model.add_adapter(peft_config_1, adapter_name="adapter_1")
model.add_adapter(peft_config_2, adapter_name="adapter_2")
# Ensure adapters were added
self.assertIn("adapter_1", model.peft_config)
self.assertIn("adapter_2", model.peft_config)
# Delete a single adapter
model.delete_adapter("adapter_1")
self.assertNotIn("adapter_1", model.peft_config)
self.assertIn("adapter_2", model.peft_config)
# Delete remaining adapter
model.delete_adapter("adapter_2")
self.assertNotIn("adapter_2", model.peft_config)
self.assertFalse(model._hf_peft_config_loaded)
# Re-add adapters for edge case tests
model.add_adapter(peft_config_1, adapter_name="adapter_1")
model.add_adapter(peft_config_2, adapter_name="adapter_2")
# Attempt to delete multiple adapters at once
model.delete_adapter(["adapter_1", "adapter_2"])
self.assertNotIn("adapter_1", model.peft_config)
self.assertNotIn("adapter_2", model.peft_config)
self.assertFalse(model._hf_peft_config_loaded)
# Test edge cases
with self.assertRaisesRegex(ValueError, "The following adapter\\(s\\) are not present"):
model.delete_adapter("nonexistent_adapter")
with self.assertRaisesRegex(ValueError, "The following adapter\\(s\\) are not present"):
model.delete_adapter(["adapter_1", "nonexistent_adapter"])
# Deleting with an empty list or None should not raise errors
model.add_adapter(peft_config_1, adapter_name="adapter_1")
model.add_adapter(peft_config_2, adapter_name="adapter_2")
model.delete_adapter([]) # No-op
self.assertIn("adapter_1", model.peft_config)
self.assertIn("adapter_2", model.peft_config)
model.delete_adapter(None) # No-op
self.assertIn("adapter_1", model.peft_config)
self.assertIn("adapter_2", model.peft_config)
# Deleting duplicate adapter names in the list
model.delete_adapter(["adapter_1", "adapter_1"])
self.assertNotIn("adapter_1", model.peft_config)
self.assertIn("adapter_2", model.peft_config)
@require_torch_gpu
@require_bitsandbytes
def test_peft_from_pretrained_kwargs(self):