[PEFT] Allow PEFT model dict to be loaded (#25721)
* Allow PEFT model dict to be loaded * make style * make style * Apply suggestions from code review * address comments * fixup * final change * added tests * fix test * better logic for handling if adapter has been loaded * Update tests/peft_integration/test_peft_integration.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> --------- Co-authored-by: younesbelkada <younesbelkada@gmail.com> Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Patrick von Platen
GitHub
parent
8b13471494
commit
0a55d9f737
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@ -12,13 +12,14 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Optional
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from typing import TYPE_CHECKING, Any, Dict, Optional
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from ..utils import (
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check_peft_version,
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find_adapter_config_file,
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is_accelerate_available,
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is_peft_available,
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is_torch_available,
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logging,
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)
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@ -30,6 +31,11 @@ if is_accelerate_available():
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# Minimum PEFT version supported for the integration
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MIN_PEFT_VERSION = "0.5.0"
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if TYPE_CHECKING:
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if is_torch_available():
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import torch
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logger = logging.get_logger(__name__)
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@ -61,7 +67,7 @@ class PeftAdapterMixin:
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def load_adapter(
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self,
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peft_model_id: str,
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peft_model_id: Optional[str] = None,
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adapter_name: Optional[str] = None,
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revision: Optional[str] = None,
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token: Optional[str] = None,
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@ -69,6 +75,8 @@ class PeftAdapterMixin:
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max_memory: Optional[str] = None,
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offload_folder: Optional[str] = None,
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offload_index: Optional[int] = None,
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peft_config: Dict[str, Any] = None,
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adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
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) -> None:
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"""
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Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we
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@ -77,7 +85,7 @@ class PeftAdapterMixin:
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Requires peft as a backend to load the adapter weights.
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Args:
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peft_model_id (`str`):
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peft_model_id (`str`, *optional*):
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The identifier of the model to look for on the Hub, or a local path to the saved adapter config file
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and adapter weights.
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adapter_name (`str`, *optional*):
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@ -114,6 +122,12 @@ class PeftAdapterMixin:
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If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
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offload_index (`int`, `optional`):
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`offload_index` argument to be passed to `accelerate.dispatch_model` method.
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peft_config (`Dict[str, Any]`, *optional*):
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The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
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methods. This argument is used in case users directly pass PEFT state dicts
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adapter_state_dict (`Dict[str, torch.Tensor]`, *optional*):
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The state dict of the adapter to load. This argument is used in case users directly pass PEFT state
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dicts
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"""
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check_peft_version(min_version=MIN_PEFT_VERSION)
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@ -122,11 +136,15 @@ class PeftAdapterMixin:
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from peft import PeftConfig, inject_adapter_in_model, load_peft_weights
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from peft.utils import set_peft_model_state_dict
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if not self._hf_peft_config_loaded:
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self._hf_peft_config_loaded = True
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elif adapter_name in self.peft_config:
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if self._hf_peft_config_loaded and adapter_name in self.peft_config:
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raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
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if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
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raise ValueError(
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"You should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter."
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)
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if peft_config is None:
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adapter_config_file = find_adapter_config_file(
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peft_model_id,
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revision=revision,
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@ -139,15 +157,19 @@ class PeftAdapterMixin:
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"adapter model."
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)
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loaded_peft_config = PeftConfig.from_pretrained(
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peft_config = PeftConfig.from_pretrained(
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peft_model_id,
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revision=revision,
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use_auth_token=token,
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)
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# Create and add fresh new adapters into the model.
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inject_adapter_in_model(loaded_peft_config, self, adapter_name)
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inject_adapter_in_model(peft_config, self, adapter_name)
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if not self._hf_peft_config_loaded:
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self._hf_peft_config_loaded = True
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if peft_model_id is not None:
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adapter_state_dict = load_peft_weights(peft_model_id, revision=revision, use_auth_token=token)
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# We need to pre-process the state dict to remove unneeded prefixes - for backward compatibility
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@ -16,6 +16,8 @@ import os
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import tempfile
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import unittest
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from huggingface_hub import hf_hub_download
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from transformers import AutoModelForCausalLM, OPTForCausalLM
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from transformers.testing_utils import require_peft, require_torch, require_torch_gpu, slow, torch_device
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from transformers.utils import is_torch_available
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@ -300,3 +302,33 @@ class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
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for model_id in self.peft_test_model_ids:
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pipe = pipeline("text-generation", model_id)
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_ = pipe("Hello")
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def test_peft_add_adapter_with_state_dict(self):
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"""
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Simple test that tests the basic usage of PEFT model through `from_pretrained`. This test tests if
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add_adapter works as expected with a state_dict being passed.
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"""
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from peft import LoraConfig
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dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
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for model_id, peft_model_id in zip(self.transformers_test_model_ids, self.peft_test_model_ids):
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for transformers_class in self.transformers_test_model_classes:
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model = transformers_class.from_pretrained(model_id).to(torch_device)
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peft_config = LoraConfig(init_lora_weights=False)
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with self.assertRaises(ValueError):
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model.load_adapter(peft_model_id=None)
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state_dict_path = hf_hub_download(peft_model_id, "adapter_model.bin")
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dummy_state_dict = torch.load(state_dict_path)
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model.load_adapter(adapter_state_dict=dummy_state_dict, peft_config=peft_config)
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with self.assertRaises(ValueError):
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model.load_adapter(model.load_adapter(adapter_state_dict=dummy_state_dict, peft_config=None))
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self.assertTrue(self._check_lora_correctly_converted(model))
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# dummy generation
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_ = model.generate(input_ids=dummy_input)
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