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[PEFT] Allow PEFT model dict to be loaded (#25721)

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* 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
2023-09-15 18:22:01 +02:00
committed by GitHub
parent 8b13471494
commit 0a55d9f737
2 changed files with 76 additions and 22 deletions
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32
tests/peft_integration/test_peft_integration.py
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@@ -16,6 +16,8 @@ import os
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, OPTForCausalLM
from transformers.testing_utils import require_peft, require_torch, require_torch_gpu, slow, torch_device
from transformers.utils import is_torch_available
@@ -300,3 +302,33 @@ class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
for model_id in self.peft_test_model_ids:
pipe = pipeline("text-generation", model_id)
_ = pipe("Hello")
def test_peft_add_adapter_with_state_dict(self):
"""
Simple test that tests the basic usage of PEFT model through `from_pretrained`. This test tests if
add_adapter works as expected with a state_dict being passed.
"""
from peft import LoraConfig
dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
for model_id, peft_model_id in zip(self.transformers_test_model_ids, self.peft_test_model_ids):
for transformers_class in self.transformers_test_model_classes:
model = transformers_class.from_pretrained(model_id).to(torch_device)
peft_config = LoraConfig(init_lora_weights=False)
with self.assertRaises(ValueError):
model.load_adapter(peft_model_id=None)
state_dict_path = hf_hub_download(peft_model_id, "adapter_model.bin")
dummy_state_dict = torch.load(state_dict_path)
model.load_adapter(adapter_state_dict=dummy_state_dict, peft_config=peft_config)
with self.assertRaises(ValueError):
model.load_adapter(model.load_adapter(adapter_state_dict=dummy_state_dict, peft_config=None))
self.assertTrue(self._check_lora_correctly_converted(model))
# dummy generation
_ = model.generate(input_ids=dummy_input)