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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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 gc
import itertools
import tempfile
import unittest
import pytest
import torch
from accelerate.utils.memory import release_memory
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from transformers.testing_utils import backend_empty_cache, require_peft, require_torch_accelerator, torch_device
from transformers.utils import is_peft_available
from trl import DPOConfig, DPOTrainer
from ..testing_utils import require_bitsandbytes
from .testing_constants import DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST
if is_peft_available():
from peft import LoraConfig, PeftModel
@pytest.mark.slow
@require_torch_accelerator
@require_peft
class DPOTrainerSlowTester(unittest.TestCase):
def setUp(self):
self.dataset = load_dataset("trl-internal-testing/zen", "standard_preference")
self.peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=8,
bias="none",
task_type="CAUSAL_LM",
)
self.max_length = 128
def tearDown(self):
gc.collect()
backend_empty_cache(torch_device)
gc.collect()
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, DPO_LOSS_TYPES, DPO_PRECOMPUTE_LOGITS)))
def test_dpo_bare_model(self, model_id, loss_type, pre_compute_logits):
"""
A test that tests the simple usage of `DPOTrainer` using a bare model in full precision.
"""
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
eval_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
beta=0.1,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
max_length=self.max_length,
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
)
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
@parameterized.expand(
list(
itertools.product(
MODELS_TO_TEST,
DPO_LOSS_TYPES,
DPO_PRECOMPUTE_LOGITS,
GRADIENT_CHECKPOINTING_KWARGS,
)
)
)
@require_peft
def test_dpo_peft_model(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
"""
A test that tests the simple usage of `DPOTrainer` using a peft model in full precision + different scenarios of gradient checkpointing.
"""
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
eval_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
generate_during_eval=False,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
beta=0.1,
max_length=self.max_length,
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
self.assertIsNone(trainer.ref_model)
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
@parameterized.expand(
list(
itertools.product(
MODELS_TO_TEST,
DPO_LOSS_TYPES,
DPO_PRECOMPUTE_LOGITS,
GRADIENT_CHECKPOINTING_KWARGS,
)
)
)
@require_bitsandbytes
@require_peft
def test_dpo_peft_model_qlora(self, model_id, loss_type, pre_compute_logits, gradient_checkpointing_kwargs):
"""
A test that tests the simple usage of `DPOTrainer` using QLoRA + different scenarios of gradient checkpointing.
"""
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token if tokenizer.pad_token is None else tokenizer.pad_token
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = DPOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
eval_strategy="steps",
fp16=True,
logging_strategy="no",
report_to="none",
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
beta=0.1,
generate_during_eval=False,
loss_type=loss_type,
precompute_ref_log_probs=pre_compute_logits,
max_length=self.max_length,
)
# dpo train lora model
trainer = DPOTrainer(
model=model,
ref_model=None,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=tokenizer,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
self.assertIsNone(trainer.ref_model)
# train the model
trainer.train()
# save trained model or adapter
trainer.save_model()
release_memory(model, trainer)
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