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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
from functools import partial
import torch
from accelerate import Accelerator
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
from transformers.testing_utils import require_peft
from transformers.utils import is_peft_available
from trl import BCOConfig, BCOTrainer
from trl.trainer.bco_trainer import _process_tokens, _tokenize
from .testing_utils import require_no_wandb, require_sklearn
if is_peft_available():
from peft import LoraConfig
class BCOTrainerTester(unittest.TestCase):
@parameterized.expand([("standard_unpaired_preference"), ("conversational_unpaired_preference")])
@require_sklearn
def test_train(self, config_name):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
ref_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", config_name, split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
learning_rate=0.1, # increase the learning rate to speed up the test
report_to="none",
)
trainer = BCOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
)
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.cpu(), new_param.cpu()))
@require_sklearn
def test_train_with_precompute(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
ref_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
learning_rate=0.1, # increase the learning rate to speed up the test
precompute_ref_log_probs=True,
report_to="none",
)
trainer = BCOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
)
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.cpu(), new_param.cpu()))
@require_sklearn
def test_train_eval(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
ref_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
eval_strategy="steps",
eval_steps=3,
report_to="none",
)
trainer = BCOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
)
trainer.train()
@require_sklearn
def test_init_with_ref_model_is_model(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
report_to="none",
)
with self.assertRaises(ValueError):
BCOTrainer(
model=model,
ref_model=model, # ref_model can't be the same as model
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
)
@require_sklearn
def test_tokenize_and_process_tokens(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
ref_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
report_to="none",
)
trainer = BCOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
)
tokenized_dataset = dataset.map(
_tokenize,
fn_kwargs={"tokenizer": trainer.tokenizer},
batched=True,
batch_size=2,
)
self.assertListEqual(tokenized_dataset["prompt"], dataset["prompt"])
self.assertListEqual(tokenized_dataset["completion"], dataset["completion"])
self.assertListEqual(tokenized_dataset["label"], 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])
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)
self.assertListEqual(processed_dataset["prompt"], dataset["prompt"])
self.assertListEqual(processed_dataset["completion"], dataset["completion"])
self.assertListEqual(processed_dataset["label"], 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]
)
@require_sklearn
def test_train_without_providing_ref_model(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
learning_rate=0.1, # increase the learning rate to speed up the test
report_to="none",
)
trainer = BCOTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
)
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.cpu(), new_param.cpu()))
@require_sklearn
def test_train_udm(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Get embedding model
embedding_model_id = "trl-internal-testing/tiny-BartModel"
embedding_model = AutoModel.from_pretrained(embedding_model_id)
embedding_tokenizer = AutoTokenizer.from_pretrained(embedding_model_id)
def embed_prompt(input_ids, attention_mask, model):
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
return outputs.last_hidden_state.mean(dim=1)
embedding_model = Accelerator().prepare_model(embedding_model)
embedding_func = partial(embed_prompt, model=embedding_model)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
learning_rate=0.1, # increase the learning rate to speed up the test
report_to="none",
)
trainer = BCOTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
embedding_func=embedding_func,
embedding_tokenizer=embedding_tokenizer,
)
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.cpu(), new_param.cpu()))
@require_sklearn
@require_peft
def test_train_without_providing_ref_model_with_lora(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05, task_type="CAUSAL_LM")
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference", split="train")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
learning_rate=0.1, # increase the learning rate to speed up the test
report_to="none",
)
trainer = BCOTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset,
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.cpu(), new_param.cpu()))
@require_sklearn
@require_no_wandb
def test_generate_during_eval_no_wandb(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
eval_strategy="steps",
eval_steps=3,
generate_during_eval=True,
report_to="none",
)
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.",
):
BCOTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
)
@require_sklearn
@require_peft
def test_lora_train_and_save(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(r=16, lora_alpha=32, lora_dropout=0.05, task_type="CAUSAL_LM")
tokenizer = AutoTokenizer.from_pretrained(model_id)
dataset = load_dataset("trl-internal-testing/zen", "standard_unpaired_preference")
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
report_to="none",
)
trainer = BCOTrainer(
model=model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset["train"],
peft_config=lora_config,
)
# train the model
trainer.train()
# save peft adapter
trainer.save_model()
# assert that the model is loaded without giving OSError
AutoModelForCausalLM.from_pretrained(tmp_dir)
@require_sklearn
def test_compute_metrics(self):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
model = AutoModelForCausalLM.from_pretrained(model_id)
ref_model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
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 = BCOConfig(
output_dir=tmp_dir,
remove_unused_columns=False, # warning raised if not set to False
eval_strategy="steps",
eval_steps=3,
report_to="none",
)
trainer = BCOTrainer(
model=model,
ref_model=ref_model,
args=training_args,
processing_class=tokenizer,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
compute_metrics=dummy_compute_metrics,
)
trainer.train()
self.assertEqual(trainer.state.log_history[-2]["eval_test"], 0.0)
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