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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 os
import tempfile
import unittest
import torch
import torch.nn.functional as F
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from trl import GKDConfig, GKDTrainer
from trl.trainer.utils import SIMPLE_CHAT_TEMPLATE
class TestGKDTrainer(unittest.TestCase):
@classmethod
def setUpClass(cls):
model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
cls.tokenizer = AutoTokenizer.from_pretrained(model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token
cls.model = AutoModelForCausalLM.from_pretrained(model_id)
cls.generation_config = GenerationConfig(
max_new_tokens=20,
num_return_sequences=1,
pad_token_id=cls.tokenizer.pad_token_id,
eos_token_id=cls.tokenizer.eos_token_id,
)
def test_generate_on_policy_outputs_deterministic(self):
prompts = ["Hello, how are you?", "What's the weather like today?"]
tokenized_prompts = self.tokenizer(prompts, return_tensors="pt", padding=True)
inputs = {
"prompts": tokenized_prompts["input_ids"],
"prompt_attention_mask": tokenized_prompts["attention_mask"],
}
# Set temperature to 0 for deterministic output
deterministic_generation_config = GenerationConfig(
max_new_tokens=30,
num_return_sequences=1,
pad_token_id=self.tokenizer.pad_token_id,
eos_token_id=self.tokenizer.eos_token_id,
temperature=0.0,
)
outputs = GKDTrainer.generate_on_policy_outputs(
self.model, inputs, deterministic_generation_config, self.tokenizer.pad_token_id
)
new_input_ids, new_attention_mask, new_labels = outputs
# Decode the generated outputs
generated_texts = self.tokenizer.batch_decode(new_input_ids, skip_special_tokens=True)
# Check if the generated texts start with the original prompts
for prompt, generated_text in zip(prompts, generated_texts):
self.assertTrue(
generated_text.startswith(prompt),
f"Generated text '{generated_text}' does not start with prompt '{prompt}'",
)
# Run the generation twice and check if the outputs are identical
outputs2 = GKDTrainer.generate_on_policy_outputs(
self.model, inputs, deterministic_generation_config, self.tokenizer.pad_token_id
)
new_input_ids2, new_attention_mask2, new_labels2 = outputs2
# Check if the two generations are identical
self.assertTrue(torch.all(new_input_ids.eq(new_input_ids2)), "Deterministic generations are not identical")
self.assertTrue(
torch.all(new_attention_mask.eq(new_attention_mask2)),
"Attention masks for deterministic generations are not identical",
)
self.assertTrue(
torch.all(new_labels.eq(new_labels2)),
"Labels for deterministic generations are not identical",
)
def test_generate_on_policy_outputs(self):
prompts = ["Hello, how are you?", "What's the weather like today?"]
tokenized_prompts = self.tokenizer(prompts, return_tensors="pt", padding=True)
inputs = {
"prompts": tokenized_prompts["input_ids"],
"attention_mask": tokenized_prompts["attention_mask"],
}
outputs = GKDTrainer.generate_on_policy_outputs(
self.model, inputs, self.generation_config, self.tokenizer.pad_token_id
)
# Check that outputs is a tuple of three tensors
self.assertIsInstance(outputs, tuple)
self.assertEqual(len(outputs), 3)
new_input_ids, new_attention_mask, new_labels = outputs
# Check shapes
batch_size = len(prompts)
self.assertEqual(new_input_ids.shape[0], batch_size)
self.assertEqual(new_attention_mask.shape[0], batch_size)
self.assertEqual(new_labels.shape[0], batch_size)
# Check types
self.assertIsInstance(new_input_ids, torch.Tensor)
self.assertIsInstance(new_attention_mask, torch.Tensor)
self.assertIsInstance(new_labels, torch.Tensor)
# Check that new_input_ids and new_attention_mask have the same shape
self.assertEqual(new_input_ids.shape, new_attention_mask.shape)
self.assertEqual(new_labels.shape, new_attention_mask.shape)
class TestGeneralizedJSDLoss(unittest.TestCase):
def setUp(self):
self.batch_size = 2
self.seq_length = 3
self.vocab_size = 5
self.student_logits = torch.randn(self.batch_size, self.seq_length, self.vocab_size)
self.teacher_logits = torch.randn(self.batch_size, self.seq_length, self.vocab_size)
def test_uniform_distribution(self):
logits = torch.ones(1, 1, self.vocab_size)
loss = GKDTrainer.generalized_jsd_loss(logits, logits)
self.assertAlmostEqual(loss.item(), 0, places=5)
def test_generalized_jsd_loss_edge_cases(self):
# Setup
student_logits = torch.log(torch.tensor([[0.1, 0.9]])).unsqueeze(0)
teacher_logits = torch.log(torch.tensor([[0.9, 0.1]])).unsqueeze(0)
# Case 1: beta = 1 (should be equivalent to KL(student || teacher))
loss_beta_1 = GKDTrainer.generalized_jsd_loss(student_logits, teacher_logits, beta=1)
expected_loss_beta_1 = F.kl_div(
F.log_softmax(teacher_logits, dim=-1), F.softmax(student_logits, dim=-1), reduction="batchmean"
)
self.assertAlmostEqual(loss_beta_1.item(), expected_loss_beta_1.item(), places=5)
# Case 2: beta = 0 (should be equivalent to KL(teacher || student))
loss_beta_0 = GKDTrainer.generalized_jsd_loss(student_logits, teacher_logits, beta=0)
expected_loss_beta_0 = F.kl_div(
F.log_softmax(student_logits, dim=-1), F.softmax(teacher_logits, dim=-1), reduction="batchmean"
)
self.assertAlmostEqual(loss_beta_0.item(), expected_loss_beta_0.item(), places=5)
def test_output_shape(self):
loss = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits)
self.assertTrue(torch.is_tensor(loss))
self.assertEqual(loss.shape, torch.Size([]))
def test_beta_values(self):
loss_beta_0 = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, beta=0)
loss_beta_1 = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, beta=1)
self.assertNotEqual(loss_beta_0, loss_beta_1)
def test_temperature_scaling(self):
loss_temp_1 = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, temperature=1)
loss_temp_2 = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, temperature=2)
self.assertNotEqual(loss_temp_1, loss_temp_2)
def test_reduction_methods(self):
loss_batchmean = GKDTrainer.generalized_jsd_loss(
self.student_logits, self.teacher_logits, reduction="batchmean"
)
loss_sum = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, reduction="sum")
loss_mean = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, reduction="mean")
loss_none = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, reduction="none")
self.assertEqual(loss_batchmean.shape, torch.Size([]))
self.assertEqual(loss_sum.shape, torch.Size([]))
self.assertEqual(loss_mean.shape, torch.Size([]))
self.assertEqual(loss_none.shape, self.student_logits.shape)
def test_symmetry(self):
student_teacher = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, beta=0.1)
teacher_student = GKDTrainer.generalized_jsd_loss(self.teacher_logits, self.student_logits, beta=0.1)
self.assertNotEqual(student_teacher, teacher_student)
student_teacher = GKDTrainer.generalized_jsd_loss(self.student_logits, self.teacher_logits, beta=0.5)
teacher_student = GKDTrainer.generalized_jsd_loss(self.teacher_logits, self.student_logits, beta=0.5)
self.assertEqual(student_teacher, teacher_student)
def test_zero_loss_for_identical_inputs(self):
identical_logits = torch.randn(self.batch_size, self.seq_length, self.vocab_size)
loss = GKDTrainer.generalized_jsd_loss(identical_logits, identical_logits)
self.assertAlmostEqual(loss.item(), 0, places=6)
class GKDTrainerTester(unittest.TestCase):
def setUp(self):
self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
self.model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.teacher_model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self.tokenizer.pad_token = self.tokenizer.eos_token
# Ensure the tokenizer has a chat template
if not hasattr(self.tokenizer, "chat_template") or self.tokenizer.chat_template is None:
self.tokenizer.chat_template = SIMPLE_CHAT_TEMPLATE
def test_gkd_trainer(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = GKDConfig(
output_dir=tmp_dir,
dataloader_drop_last=True,
eval_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
report_to="none",
)
dummy_dataset = load_dataset("trl-internal-testing/zen", "conversational_language_modeling")
trainer = GKDTrainer(
model=self.model_id,
teacher_model=self.model_id,
args=training_args,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
processing_class=self.tokenizer,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[(-1)]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertIn("model.safetensors", os.listdir(tmp_dir + "/checkpoint-2"))
def test_generation_config_init(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = GKDConfig(output_dir=tmp_dir)
dummy_dataset = load_dataset("trl-internal-testing/zen", "conversational_language_modeling")
trainer = GKDTrainer(
model=self.model_id,
teacher_model=self.model_id,
args=training_args,
train_dataset=dummy_dataset["train"],
eval_dataset=dummy_dataset["test"],
processing_class=self.tokenizer,
)
self.assertEqual(trainer.generation_config.pad_token_id, self.tokenizer.eos_token_id)
self.assertEqual(trainer.generation_config.eos_token_id, self.model.generation_config.eos_token_id)
self.assertEqual(trainer.generation_config.max_new_tokens, training_args.max_new_tokens)
self.assertEqual(trainer.generation_config.temperature, training_args.temperature)
self.assertEqual(trainer.generation_config.top_k, 0)
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