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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_liger_kernel,
require_peft,
require_torch_accelerator,
require_torch_multi_accelerator,
torch_device,
)
from transformers.utils import is_peft_available
from trl import SFTConfig, SFTTrainer
from trl.models.utils import setup_chat_format
from ..testing_utils import require_bitsandbytes
from .testing_constants import DEVICE_MAP_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS, MODELS_TO_TEST, PACKING_OPTIONS
if is_peft_available():
from peft import LoraConfig, PeftModel
@pytest.mark.slow
@require_torch_accelerator
@require_peft
class SFTTrainerSlowTester(unittest.TestCase):
def setUp(self):
self.train_dataset = load_dataset("stanfordnlp/imdb", split="train[:10%]")
self.eval_dataset = load_dataset("stanfordnlp/imdb", split="test[:10%]")
self.max_length = 128
self.peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=8,
bias="none",
task_type="CAUSAL_LM",
)
def tearDown(self):
gc.collect()
backend_empty_cache(torch_device)
gc.collect()
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
def test_sft_trainer_str(self, model_name, packing):
"""
Simply tests if passing a simple str to `SFTTrainer` loads and runs the trainer
as expected.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
)
trainer = SFTTrainer(
model_name,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
def test_sft_trainer_transformers(self, model_name, packing):
"""
Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
as expected.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
@require_peft
def test_sft_trainer_peft(self, model_name, packing):
"""
Simply tests if passing a transformers model + peft config to `SFTTrainer` loads and runs the trainer
as expected.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
fp16=True,
packing=packing,
max_length=self.max_length,
)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
def test_sft_trainer_transformers_mp(self, model_name, packing):
"""
Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
as expected in mixed precision.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
fp16=True, # this is sufficient to enable amp
packing=packing,
max_length=self.max_length,
)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS)))
def test_sft_trainer_transformers_mp_gc(self, model_name, packing, gradient_checkpointing_kwargs):
"""
Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
as expected in mixed precision + different scenarios of gradient_checkpointing.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
fp16=True, # this is sufficient to enable amp
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS)))
@require_peft
def test_sft_trainer_transformers_mp_gc_peft(self, model_name, packing, gradient_checkpointing_kwargs):
"""
Simply tests if passing a transformers model + PEFT to `SFTTrainer` loads and runs the trainer
as expected in mixed precision + different scenarios of gradient_checkpointing.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
fp16=True, # this is sufficient to enable amp
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(
list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS, DEVICE_MAP_OPTIONS))
)
@require_torch_multi_accelerator
def test_sft_trainer_transformers_mp_gc_device_map(
self, model_name, packing, gradient_checkpointing_kwargs, device_map
):
"""
Simply tests if passing a transformers model to `SFTTrainer` loads and runs the trainer
as expected in mixed precision + different scenarios of gradient_checkpointing (single, multi-gpu, etc).
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
fp16=True, # this is sufficient to enable amp
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS, GRADIENT_CHECKPOINTING_KWARGS)))
@require_peft
@require_bitsandbytes
def test_sft_trainer_transformers_mp_gc_peft_qlora(self, model_name, packing, gradient_checkpointing_kwargs):
"""
Simply tests if passing a transformers model + PEFT + bnb to `SFTTrainer` loads and runs the trainer
as expected in mixed precision + different scenarios of gradient_checkpointing.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
packing=packing,
max_length=self.max_length,
fp16=True, # this is sufficient to enable amp
gradient_checkpointing=True,
gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
)
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
@require_peft
@require_bitsandbytes
def test_sft_trainer_with_chat_format_qlora(self, model_name, packing):
"""
Simply tests if using setup_chat_format with a transformers model + peft + bnb config to `SFTTrainer` loads and runs the trainer
as expected.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
train_dataset = load_dataset("trl-internal-testing/dolly-chatml-sft", split="train")
training_args = SFTConfig(
packing=packing,
max_length=self.max_length,
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=10,
fp16=True,
)
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
if tokenizer.chat_template is None:
model, tokenizer = setup_chat_format(model, tokenizer)
trainer = SFTTrainer(
model,
args=training_args,
processing_class=tokenizer,
train_dataset=train_dataset,
peft_config=self.peft_config,
)
self.assertIsInstance(trainer.model, PeftModel)
trainer.train()
release_memory(model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
@require_liger_kernel
def test_sft_trainer_with_liger(self, model_name, packing):
"""
Tests if passing use_liger=True to SFTConfig loads and runs the trainer
with AutoLigerKernelForCausalLM as expected.
"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = SFTConfig(
output_dir=tmp_dir,
logging_strategy="no",
report_to="none",
per_device_train_batch_size=2,
max_steps=2,
packing=packing,
max_length=self.max_length,
use_liger_kernel=True,
)
trainer = SFTTrainer(
model_name,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
)
trainer.train()
release_memory(trainer.model, trainer)
@parameterized.expand(list(itertools.product(MODELS_TO_TEST, PACKING_OPTIONS)))
@require_torch_accelerator
def test_train_offloading(self, model_name, packing):
"""Test that activation offloading works with SFTTrainer."""
with tempfile.TemporaryDirectory() as tmp_dir:
# Initialize the trainer
training_args = SFTConfig(
output_dir=tmp_dir,
activation_offloading=True,
report_to="none",
per_device_train_batch_size=2,
max_steps=2,
packing=packing,
max_length=self.max_length,
)
trainer = SFTTrainer(
model=model_name, args=training_args, train_dataset=self.train_dataset, eval_dataset=self.eval_dataset
)
# Save the initial parameters to compare them later
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
# Train the model
trainer.train()
# Check that the training loss is not None
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# Check the params have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
self.assertFalse(torch.allclose(param, new_param), f"Parameter {n} has not changed")
release_memory(trainer.model, trainer)
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