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# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 tempfile
import unittest
import torch
from peft import (
AutoPeftModel,
AutoPeftModelForCausalLM,
AutoPeftModelForFeatureExtraction,
AutoPeftModelForQuestionAnswering,
AutoPeftModelForSeq2SeqLM,
AutoPeftModelForSequenceClassification,
AutoPeftModelForTokenClassification,
PeftModel,
PeftModelForCausalLM,
PeftModelForFeatureExtraction,
PeftModelForQuestionAnswering,
PeftModelForSeq2SeqLM,
PeftModelForSequenceClassification,
PeftModelForTokenClassification,
)
from peft.utils import infer_device
class PeftAutoModelTester(unittest.TestCase):
dtype = torch.float16 if infer_device() == "mps" else torch.bfloat16
def test_peft_causal_lm(self):
model_id = "peft-internal-testing/tiny-OPTForCausalLM-lora"
model = AutoPeftModelForCausalLM.from_pretrained(model_id)
assert isinstance(model, PeftModelForCausalLM)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForCausalLM.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForCausalLM)
# check if kwargs are passed correctly
model = AutoPeftModelForCausalLM.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForCausalLM)
assert model.base_model.lm_head.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForCausalLM.from_pretrained(model_id, adapter_name, is_trainable, torch_dtype=self.dtype)
def test_peft_causal_lm_extended_vocab(self):
model_id = "peft-internal-testing/tiny-random-OPTForCausalLM-extended-vocab"
model = AutoPeftModelForCausalLM.from_pretrained(model_id)
assert isinstance(model, PeftModelForCausalLM)
# check if kwargs are passed correctly
model = AutoPeftModelForCausalLM.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForCausalLM)
assert model.base_model.lm_head.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForCausalLM.from_pretrained(model_id, adapter_name, is_trainable, torch_dtype=self.dtype)
def test_peft_seq2seq_lm(self):
model_id = "peft-internal-testing/tiny_T5ForSeq2SeqLM-lora"
model = AutoPeftModelForSeq2SeqLM.from_pretrained(model_id)
assert isinstance(model, PeftModelForSeq2SeqLM)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForSeq2SeqLM.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForSeq2SeqLM)
# check if kwargs are passed correctly
model = AutoPeftModelForSeq2SeqLM.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForSeq2SeqLM)
assert model.base_model.lm_head.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForSeq2SeqLM.from_pretrained(model_id, adapter_name, is_trainable, torch_dtype=self.dtype)
def test_peft_sequence_cls(self):
model_id = "peft-internal-testing/tiny_OPTForSequenceClassification-lora"
model = AutoPeftModelForSequenceClassification.from_pretrained(model_id)
assert isinstance(model, PeftModelForSequenceClassification)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForSequenceClassification.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForSequenceClassification)
# check if kwargs are passed correctly
model = AutoPeftModelForSequenceClassification.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForSequenceClassification)
assert model.score.original_module.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForSequenceClassification.from_pretrained(
model_id, adapter_name, is_trainable, torch_dtype=self.dtype
)
def test_peft_token_classification(self):
model_id = "peft-internal-testing/tiny_GPT2ForTokenClassification-lora"
model = AutoPeftModelForTokenClassification.from_pretrained(model_id)
assert isinstance(model, PeftModelForTokenClassification)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForTokenClassification.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForTokenClassification)
# check if kwargs are passed correctly
model = AutoPeftModelForTokenClassification.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForTokenClassification)
assert model.base_model.classifier.original_module.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForTokenClassification.from_pretrained(
model_id, adapter_name, is_trainable, torch_dtype=self.dtype
)
def test_peft_question_answering(self):
model_id = "peft-internal-testing/tiny_OPTForQuestionAnswering-lora"
model = AutoPeftModelForQuestionAnswering.from_pretrained(model_id)
assert isinstance(model, PeftModelForQuestionAnswering)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForQuestionAnswering.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForQuestionAnswering)
# check if kwargs are passed correctly
model = AutoPeftModelForQuestionAnswering.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForQuestionAnswering)
assert model.base_model.qa_outputs.original_module.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForQuestionAnswering.from_pretrained(
model_id, adapter_name, is_trainable, torch_dtype=self.dtype
)
def test_peft_feature_extraction(self):
model_id = "peft-internal-testing/tiny_OPTForFeatureExtraction-lora"
model = AutoPeftModelForFeatureExtraction.from_pretrained(model_id)
assert isinstance(model, PeftModelForFeatureExtraction)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModelForFeatureExtraction.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModelForFeatureExtraction)
# check if kwargs are passed correctly
model = AutoPeftModelForFeatureExtraction.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModelForFeatureExtraction)
assert model.base_model.model.decoder.embed_tokens.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModelForFeatureExtraction.from_pretrained(
model_id, adapter_name, is_trainable, torch_dtype=self.dtype
)
def test_peft_whisper(self):
model_id = "peft-internal-testing/tiny_WhisperForConditionalGeneration-lora"
model = AutoPeftModel.from_pretrained(model_id)
assert isinstance(model, PeftModel)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
model = AutoPeftModel.from_pretrained(tmp_dirname)
assert isinstance(model, PeftModel)
# check if kwargs are passed correctly
model = AutoPeftModel.from_pretrained(model_id, torch_dtype=self.dtype)
assert isinstance(model, PeftModel)
assert model.base_model.model.model.encoder.embed_positions.weight.dtype == self.dtype
adapter_name = "default"
is_trainable = False
# This should work
_ = AutoPeftModel.from_pretrained(model_id, adapter_name, is_trainable, torch_dtype=self.dtype)
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