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test_nash_md_trainer.py
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# Copyright 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 datasets import load_dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
from transformers.testing_utils import require_peft
from transformers.utils import is_peft_available
from trl import NashMDConfig, NashMDTrainer
from .testing_utils import RandomPairwiseJudge, require_llm_blender
if is_peft_available():
from peft import LoraConfig, get_peft_model
class TestNashMDTrainer(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.reward_model = AutoModelForSequenceClassification.from_pretrained(self.model_id, num_labels=1)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.tokenizer.pad_token = self.tokenizer.eos_token
@parameterized.expand([("standard_prompt_only",), ("conversational_prompt_only",)])
def test_nash_md_trainer_training(self, config_name):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = NashMDConfig(
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",
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", config_name)
trainer = NashMDTrainer(
model=self.model,
ref_model=self.ref_model,
reward_model=self.reward_model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
)
trainer.train()
# Check if training loss is available
self.assertIn("train_loss", trainer.state.log_history[-1])
@require_peft
def test_training_with_peft(self):
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 = NashMDConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
learning_rate=5.0e-7,
eval_strategy="steps",
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
trainer = NashMDTrainer(
model=self.model,
reward_model=self.reward_model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
peft_config=lora_config,
)
trainer.train()
# Check if training loss is available
self.assertIn("train_loss", trainer.state.log_history[-1])
@require_peft
def test_training_with_peft_and_ref_model(self):
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 = NashMDConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
learning_rate=5.0e-7,
eval_strategy="steps",
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
trainer = NashMDTrainer(
model=self.model,
ref_model=self.ref_model,
reward_model=self.reward_model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
peft_config=lora_config,
)
trainer.train()
# Check if training loss is available
self.assertIn("train_loss", trainer.state.log_history[-1])
@require_peft
def test_training_with_peft_model_and_peft_config(self):
model_lora_config = LoraConfig(r=8, lora_alpha=16, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM")
model = get_peft_model(self.model, model_lora_config)
# we want only the "train adapter" to be trained
lora_train_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 = NashMDConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
learning_rate=5.0e-7,
eval_strategy="steps",
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "standard_prompt_only")
trainer = NashMDTrainer(
model=model,
reward_model=self.reward_model,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
peft_config=lora_train_config,
)
trainer.train()
# Check if training loss is available
self.assertIn("train_loss", trainer.state.log_history[-1])
@parameterized.expand([("standard_prompt_only",), ("conversational_prompt_only",)])
@require_llm_blender
def test_nash_md_trainer_judge_training(self, config_name):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = NashMDConfig(
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",
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", config_name)
judge = RandomPairwiseJudge()
trainer = NashMDTrainer(
model=self.model,
ref_model=self.ref_model,
judge=judge,
args=training_args,
processing_class=self.tokenizer,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
)
trainer.train()
# Check if training loss is available
self.assertIn("train_loss", trainer.state.log_history[-1])