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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 tempfile
import unittest
import torch
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.testing_utils import require_liger_kernel, require_peft
from trl import KTOConfig, KTOTrainer
from trl.trainer.kto_trainer import _get_kl_dataset, _process_tokens, _tokenize
from .testing_utils import require_no_wandb
class KTOTrainerTester(unittest.TestCase):
def setUp(self):
self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
self.model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.tokenizer.pad_token = self.tokenizer.eos_token
# get t5 as seq2seq example:
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration"
self.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
self.t5_ref_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
self.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
@parameterized.expand(
[
("qwen", "standard_preference", "kto", True, True),
# ("t5", "standard_implicit_prompt_preference", "kto", True, False), # KTO broken for enc-dec
("qwen", "standard_unpaired_preference", "kto", False, True),
# ("t5", "conversational_preference", "kto", False, False),
("qwen", "conversational_implicit_prompt_preference", "apo_zero_unpaired", True, True),
# ("t5", "conversational_unpaired_preference", "apo_zero_unpaired", True, False),
("qwen", "standard_unpaired_preference", "apo_zero_unpaired", False, True),
# ("t5", "conversational_unpaired_preference", "apo_zero_unpaired", False, False),
]
)
def test_kto_trainer(self, name, config_name, loss_type, pre_compute, eval_dataset):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=1,
learning_rate=9e-1,
eval_strategy="steps" if eval_dataset else "no",
beta=0.1,
precompute_ref_log_probs=pre_compute,
loss_type=loss_type,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", config_name)
if name == "qwen":
model = self.model
ref_model = self.ref_model
tokenizer = self.tokenizer
elif name == "t5":
model = self.t5_model
ref_model = self.t5_ref_model
tokenizer = self.t5_tokenizer
trainer = KTOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"] if eval_dataset else None,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# Check that the parameters have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
if param.sum() != 0: # ignore 0 biases
self.assertFalse(torch.equal(param, new_param))
def test_kto_trainer_with_ref_model_is_model(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
with self.assertRaises(ValueError):
KTOTrainer(
model=self.model,
ref_model=self.model, # ref_model can't be the same as model
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
)
def test_tokenize_and_process_tokens(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=1,
learning_rate=9e-1,
eval_strategy="steps",
beta=0.1,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
trainer = KTOTrainer(
model=self.model,
ref_model=self.ref_model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
)
train_dataset = dummy_dataset["train"]
tokenized_dataset = train_dataset.map(
_tokenize,
fn_kwargs={"tokenizer": trainer.tokenizer},
batched=True,
batch_size=2,
)
self.assertListEqual(tokenized_dataset["prompt"], train_dataset["prompt"])
self.assertListEqual(tokenized_dataset["completion"], train_dataset["completion"])
self.assertListEqual(tokenized_dataset["label"], train_dataset["label"])
self.assertListEqual(tokenized_dataset["prompt_input_ids"][0], [46518, 374, 2664, 1091])
self.assertListEqual(tokenized_dataset["prompt_attention_mask"][0], [1, 1, 1, 1])
self.assertListEqual(tokenized_dataset["answer_input_ids"][0], [27261, 13])
self.assertListEqual(tokenized_dataset["answer_attention_mask"][0], [1, 1])
# Test corruption of (prompt, completion) pairs for KL dataset
for batch_size in [2, 3]:
tokenized_kl_dataset = tokenized_dataset.map(_get_kl_dataset, batched=True, batch_size=batch_size)
# Verify that the "answer_input_ids" have been modified, meaning the new "answer_input_ids" differ
# from the original ones. However, when the length of the dataset modulo batch_size equals 1,
# the last batch remains unaltered. This is a rare scenario that does not impact the training
# process, so we exclude it from testing by iterating only up to len - 1.
for i in range(len(tokenized_kl_dataset["answer_input_ids"]) - 1):
self.assertListEqual(
tokenized_dataset["prompt_input_ids"][i],
tokenized_kl_dataset["prompt_input_ids"][i],
)
self.assertListEqual(
tokenized_dataset["prompt_attention_mask"][i],
tokenized_kl_dataset["prompt_attention_mask"][i],
)
self.assertNotEqual(
tokenized_dataset["answer_input_ids"][i],
tokenized_kl_dataset["answer_input_ids"][i],
)
fn_kwargs = {
"prefix": "",
"is_encoder_decoder": trainer.is_encoder_decoder,
"tokenizer": trainer.tokenizer,
"max_length": trainer.max_length,
"truncation_mode": trainer.truncation_mode,
"label_pad_token_id": trainer.label_pad_token_id,
"max_prompt_length": trainer.max_prompt_length,
}
processed_dataset = tokenized_dataset.map(_process_tokens, fn_kwargs=fn_kwargs, num_proc=2)
self.assertListEqual(processed_dataset["prompt"], train_dataset["prompt"])
self.assertListEqual(processed_dataset["completion"], train_dataset["completion"])
self.assertListEqual(processed_dataset["label"], train_dataset["label"])
self.assertListEqual(processed_dataset["prompt_input_ids"][0], [46518, 374, 2664, 1091])
self.assertListEqual(processed_dataset["prompt_attention_mask"][0], [1, 1, 1, 1])
self.assertListEqual(
processed_dataset["completion_input_ids"][0], [46518, 374, 2664, 1091, 27261, 13, 151645]
)
self.assertListEqual(processed_dataset["completion_attention_mask"][0], [1, 1, 1, 1, 1, 1, 1])
self.assertListEqual(
processed_dataset["completion_labels"][0], [-100, -100, -100, -100, 27261, 13, 151645]
)
def test_kto_trainer_without_providing_ref_model(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
eval_strategy="steps",
beta=0.1,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
trainer = KTOTrainer(
model=self.model,
ref_model=None,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# Check that the parameters have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
if param.sum() != 0: # ignore 0 biases
self.assertFalse(torch.equal(param, new_param))
@require_peft
def test_kto_trainer_without_providing_ref_model_with_lora(self):
from peft import LoraConfig
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
eval_strategy="steps",
beta=0.1,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
trainer = KTOTrainer(
model=self.model,
ref_model=None,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
peft_config=lora_config,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# Check that the parameters have changed
for n, param in previous_trainable_params.items():
if "lora" in n:
new_param = trainer.model.get_parameter(n)
if param.sum() != 0: # ignore 0 biases
self.assertFalse(torch.equal(param, new_param))
@require_no_wandb
def test_kto_trainer_generate_during_eval_no_wandb(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=1,
learning_rate=9e-1,
eval_strategy="steps",
beta=0.1,
generate_during_eval=True,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
with self.assertRaisesRegex(
ValueError,
expected_regex="`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
" Please install `wandb` or `comet-ml` to resolve.",
):
KTOTrainer(
model=self.model,
ref_model=None,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
)
@require_peft
def test_kto_lora_save(self):
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
# lora model
model = AutoModelForCausalLM.from_pretrained(self.model_id)
model_peft = get_peft_model(model, lora_config)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
eval_strategy="steps",
beta=0.1,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
# kto train lora model with a lora config
trainer = KTOTrainer(
model=model_peft,
ref_model=None,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
peft_config=lora_config,
)
# train the model
trainer.train()
# save peft adapter
trainer.save_model()
# assert that the model is loaded without giving OSError
try:
AutoModelForCausalLM.from_pretrained(tmp_dir)
except OSError:
self.fail("Loading the saved peft adapter failed")
@require_liger_kernel
def test_kto_trainer_with_liger(self):
"""Test KTO trainer with Liger loss enabled."""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
report_to="none",
use_liger_loss=True, # Enable Liger loss
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
trainer = KTOTrainer(
model=self.model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
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)
# check the params have changed - ignore 0 biases
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))
def test_compute_metrics(self):
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
ref_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
tokenizer.pad_token = tokenizer.eos_token
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
def dummy_compute_metrics(*args, **kwargs):
return {"test": 0.0}
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = KTOConfig(
output_dir=tmp_dir,
remove_unused_columns=False,
per_device_train_batch_size=2,
do_eval=True,
eval_strategy="steps",
eval_steps=1,
per_device_eval_batch_size=2,
report_to="none",
)
trainer = KTOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
compute_metrics=dummy_compute_metrics,
)
trainer.train()
self.assertEqual(trainer.state.log_history[-2]["eval_test"], 0.0)
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