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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 json
import os
import tempfile
import unittest
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, Trainer, TrainingArguments
from transformers.testing_utils import require_peft, require_wandb
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import is_peft_available
from tests.testing_utils import require_comet, require_mergekit
from trl import BasePairwiseJudge, DPOConfig, DPOTrainer, LogCompletionsCallback, MergeModelCallback, WinRateCallback
from trl.mergekit_utils import MergeConfig
if is_peft_available():
from peft import LoraConfig
class HalfPairwiseJudge(BasePairwiseJudge):
"""Naive pairwise judge that always returns [1, 0] for two prompts"""
def judge(self, prompts, completions, shuffle_order=True, return_scores=False):
# just check that the batch size is 2
assert len(prompts) == 2
if return_scores:
return [0.3, 0.9]
return [1, 0]
class TrainerWithRefModel(Trainer):
# This is a dummy class to test the callback. Compared to the Trainer class, it only has an additional
# ref_model attribute
def __init__(self, model, ref_model, args, train_dataset, eval_dataset, processing_class):
super().__init__(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=processing_class,
)
self.ref_model = ref_model
class WinRateCallbackTester(unittest.TestCase):
def setUp(self):
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.ref_model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer.pad_token = self.tokenizer.eos_token
dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
dataset["train"] = dataset["train"].select(range(8))
self.expected_winrates = [
{"eval_win_rate": 0.5, "epoch": 0.0, "step": 0},
{"eval_win_rate": 0.5, "epoch": 0.5, "step": 2},
{"eval_win_rate": 0.5, "epoch": 1.0, "step": 4},
{"eval_win_rate": 0.5, "epoch": 1.5, "step": 6},
{"eval_win_rate": 0.5, "epoch": 2.0, "step": 8},
{"eval_win_rate": 0.5, "epoch": 2.5, "step": 10},
{"eval_win_rate": 0.5, "epoch": 3.0, "step": 12},
]
def tokenize_function(examples):
out = self.tokenizer(examples["prompt"], padding="max_length", max_length=16, truncation=True)
out["labels"] = out["input_ids"].copy()
return out
self.dataset = dataset.map(tokenize_function, batched=True)
self.generation_config = GenerationConfig(max_length=32)
self.judge = HalfPairwiseJudge()
def test_basic(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="none",
)
trainer = TrainerWithRefModel(
model=self.model,
ref_model=self.ref_model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
win_rate_callback = WinRateCallback(
judge=self.judge, trainer=trainer, generation_config=self.generation_config
)
trainer.add_callback(win_rate_callback)
trainer.train()
winrate_history = [h for h in trainer.state.log_history if "eval_win_rate" in h]
self.assertListEqual(winrate_history, self.expected_winrates)
def test_without_ref_model(self):
# Same as before, but without the ref_model attribute. It should use the model attribute instead
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="none",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
win_rate_callback = WinRateCallback(
judge=self.judge, trainer=trainer, generation_config=self.generation_config
)
trainer.add_callback(win_rate_callback)
trainer.train()
winrate_history = [h for h in trainer.state.log_history if "eval_win_rate" in h]
self.assertListEqual(winrate_history, self.expected_winrates)
def test_soft_judge(self):
"""Test that the soft judge functionality works correctly"""
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="none",
)
trainer = TrainerWithRefModel(
model=self.model,
ref_model=self.ref_model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
win_rate_callback = WinRateCallback(
judge=self.judge, trainer=trainer, generation_config=self.generation_config, use_soft_judge=True
)
trainer.add_callback(win_rate_callback)
trainer.train()
# Expected values based on judge returning [0.3, 0.9] for each pair
expected_soft_winrates = [
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 0.0, "step": 0},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 0.5, "step": 2},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 1.0, "step": 4},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 1.5, "step": 6},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 2.0, "step": 8},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 2.5, "step": 10},
{"eval_avg_win_prob": 0.4, "eval_win_rate": 0.5, "epoch": 3.0, "step": 12},
]
winrate_history = [
{k: h[k] for k in ["eval_avg_win_prob", "eval_win_rate", "epoch", "step"]}
for h in trainer.state.log_history
if "eval_avg_win_prob" in h
]
self.assertListEqual(winrate_history, expected_soft_winrates)
@require_peft
def test_lora(self):
with tempfile.TemporaryDirectory() as tmp_dir:
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
self.model.add_adapter(peft_config)
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="none",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
win_rate_callback = WinRateCallback(
judge=self.judge, trainer=trainer, generation_config=self.generation_config
)
trainer.add_callback(win_rate_callback)
trainer.train()
winrate_history = [h for h in trainer.state.log_history if "eval_win_rate" in h]
self.assertListEqual(winrate_history, self.expected_winrates)
class LogCompletionsCallbackTester(unittest.TestCase):
def setUp(self):
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer.pad_token = self.tokenizer.eos_token
dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
dataset["train"] = dataset["train"].select(range(8))
def tokenize_function(examples):
out = self.tokenizer(examples["prompt"], padding="max_length", max_length=16, truncation=True)
out["labels"] = out["input_ids"].copy()
return out
self.dataset = dataset.map(tokenize_function, batched=True)
self.generation_config = GenerationConfig(max_length=32)
@require_wandb
def test_basic_wandb(self):
import wandb
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="wandb",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
completions_callback = LogCompletionsCallback(trainer, self.generation_config, num_prompts=2)
trainer.add_callback(completions_callback)
trainer.train()
# Get the current run
completions_path = wandb.run.summary.completions["path"]
json_path = os.path.join(wandb.run.dir, completions_path)
with open(json_path) as f:
completions = json.load(f)
# Check that the columns are correct
self.assertIn("step", completions["columns"])
self.assertIn("prompt", completions["columns"])
self.assertIn("completion", completions["columns"])
# Check that the prompt is in the log
self.assertIn(self.dataset["test"][0]["prompt"], completions["data"][0])
@require_comet
def test_basic_comet(self):
import comet_ml
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
eval_strategy="steps",
eval_steps=2, # evaluate every 2 steps
per_device_train_batch_size=2, # 8 samples in total so 4 batches of 2 per epoch
per_device_eval_batch_size=2,
report_to="comet_ml",
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=self.dataset["train"],
eval_dataset=self.dataset["test"],
processing_class=self.tokenizer,
)
completions_callback = LogCompletionsCallback(trainer, self.generation_config, num_prompts=2)
trainer.add_callback(completions_callback)
trainer.train()
# close experiment to make sure all pending data are flushed
experiment = comet_ml.get_running_experiment()
assert experiment is not None
experiment.end()
# get experiment assets and check that all required tables was logged
steps = len(self.dataset["train"]) + len(self.dataset["test"])
tables_logged = int(steps / 2) + 1 # +1 to include zero step
api_experiment = comet_ml.APIExperiment(previous_experiment=experiment.id)
tables = api_experiment.get_asset_list("dataframe")
assert tables is not None
assert len(tables) == tables_logged
assert all(table["fileName"] == "completions.csv" for table in tables)
@require_mergekit
class MergeModelCallbackTester(unittest.TestCase):
def setUp(self):
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.dataset = load_dataset("trl-internal-testing/zen", "standard_preference", split="train")
def test_callback(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = DPOConfig(
output_dir=tmp_dir,
num_train_epochs=1,
report_to="none",
save_strategy="steps",
save_steps=1,
)
config = MergeConfig()
merge_callback = MergeModelCallback(config)
trainer = DPOTrainer(
model=self.model,
args=training_args,
train_dataset=self.dataset,
processing_class=self.tokenizer,
callbacks=[merge_callback],
)
trainer.train()
last_checkpoint = get_last_checkpoint(tmp_dir)
merged_path = os.path.join(last_checkpoint, "merged")
self.assertTrue(os.path.isdir(merged_path), "Merged folder does not exist in the last checkpoint.")
def test_every_checkpoint(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = DPOConfig(
output_dir=tmp_dir,
num_train_epochs=1,
report_to="none",
save_strategy="steps",
save_steps=1,
)
config = MergeConfig()
merge_callback = MergeModelCallback(config, merge_at_every_checkpoint=True)
trainer = DPOTrainer(
model=self.model,
args=training_args,
train_dataset=self.dataset,
processing_class=self.tokenizer,
callbacks=[merge_callback],
)
trainer.train()
checkpoints = sorted(
[os.path.join(tmp_dir, cp) for cp in os.listdir(tmp_dir) if cp.startswith("checkpoint-")]
)
for checkpoint in checkpoints:
merged_path = os.path.join(checkpoint, "merged")
self.assertTrue(
os.path.isdir(merged_path), f"Merged folder does not exist in checkpoint {checkpoint}."
)
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