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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 unittest
from typing import Callable
from datasets import Dataset, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl.extras.dataset_formatting import get_formatting_func_from_dataset
from trl.models.utils import ChatMlSpecialTokens, clone_chat_template, setup_chat_format
class DatasetFormattingTestCase(unittest.TestCase):
def setUp(self):
self.llama_tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-MistralForCausalLM-0.1")
self.chatml_tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
def test_get_formatting_func_from_dataset_with_chatml_messages(self):
dataset = Dataset.from_dict(
{
"messages": [
[
{"role": "system", "content": "You are helpful"},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi, how can I help you?"},
]
]
}
)
# Llama tokenizer
formatting_func = get_formatting_func_from_dataset(dataset, self.llama_tokenizer)
self.assertIsInstance(formatting_func, Callable)
formatted_text = formatting_func(dataset[0])
expected = "<s> [INST] You are helpful\n\nHello [/INST] Hi, how can I help you?</s>"
self.assertEqual(formatted_text, expected)
formatted_text = formatting_func(dataset[0:1])
self.assertListEqual(formatted_text, [expected])
# ChatML tokenizer
formatting_func = get_formatting_func_from_dataset(dataset, self.chatml_tokenizer)
formatted_text = formatting_func(dataset[0])
expected = "<|im_start|>system\nYou are helpful<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi, how can I help you?<|im_end|>\n"
self.assertEqual(formatted_text, expected)
formatted_text = formatting_func(dataset[0:1])
self.assertListEqual(formatted_text, [expected])
def test_get_formatting_func_from_dataset_with_chatml_conversations(self):
dataset = Dataset.from_dict(
{
"conversations": [
[
{"role": "system", "content": "You are helpful"},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi, how can I help you?"},
]
]
}
)
# Llama tokenizer
formatting_func = get_formatting_func_from_dataset(dataset, self.llama_tokenizer)
self.assertIsInstance(formatting_func, Callable)
formatted_text = formatting_func(dataset[0])
expected = "<s> [INST] You are helpful\n\nHello [/INST] Hi, how can I help you?</s>"
self.assertEqual(formatted_text, expected)
formatted_text = formatting_func(dataset[0:1])
self.assertListEqual(formatted_text, [expected])
# ChatML tokenizer
formatting_func = get_formatting_func_from_dataset(dataset, self.chatml_tokenizer)
formatted_text = formatting_func(dataset[0])
expected = "<|im_start|>system\nYou are helpful<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi, how can I help you?<|im_end|>\n"
self.assertEqual(formatted_text, expected)
formatted_text = formatting_func(dataset[0:1])
self.assertListEqual(formatted_text, [expected])
def test_get_formatting_func_from_dataset_with_instruction(self):
dataset = Dataset.from_list(
[{"prompt": "What is 2+2?", "completion": "4"}, {"prompt": "What is 3+3?", "completion": "6"}]
)
formatting_func = get_formatting_func_from_dataset(dataset, self.llama_tokenizer)
self.assertIsNotNone(formatting_func)
self.assertIsInstance(formatting_func, Callable)
formatted_text = formatting_func(dataset[0])
self.assertEqual(formatted_text, "<s> [INST] What is 2+2? [/INST] 4</s>")
formatted_text = formatting_func(dataset[0:1])
self.assertListEqual(formatted_text, ["<s> [INST] What is 2+2? [/INST] 4</s>"])
def test_get_formatting_func_from_dataset_from_hub(self):
ds_1 = load_dataset("philschmid/trl-test-instruction", split="train")
ds_2 = load_dataset("philschmid/dolly-15k-oai-style", split="train")
for ds in [ds_1, ds_2]:
formatting_func = get_formatting_func_from_dataset(ds, self.llama_tokenizer)
self.assertIsNotNone(formatting_func)
self.assertIsInstance(formatting_func, Callable)
ds_3 = load_dataset("philschmid/guanaco-sharegpt-style", split="train")
formatting_func = get_formatting_func_from_dataset(ds_3, self.llama_tokenizer)
self.assertIsNone(formatting_func)
def test_get_formatting_func_from_dataset_with_unknown_format(self):
dataset = Dataset.from_dict({"text": "test"})
formatting_func = get_formatting_func_from_dataset(dataset, self.llama_tokenizer)
self.assertIsNone(formatting_func)
class SetupChatFormatTestCase(unittest.TestCase):
def setUp(self):
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
# remove built-in chat_template to simulate a model having no chat_template
self.tokenizer.chat_template = None
def test_setup_chat_format(self):
modified_model, modified_tokenizer = setup_chat_format(
self.model, self.tokenizer, format="chatml", resize_to_multiple_of=123
)
_chatml = ChatMlSpecialTokens()
# Check if special tokens are correctly set
self.assertEqual(modified_tokenizer.eos_token, "<|im_end|>")
self.assertEqual(modified_tokenizer.pad_token, "<|im_end|>")
self.assertEqual(modified_tokenizer.bos_token, "<|im_start|>")
self.assertEqual(modified_tokenizer.eos_token, _chatml.eos_token)
self.assertEqual(modified_tokenizer.pad_token, _chatml.pad_token)
self.assertEqual(modified_tokenizer.bos_token, _chatml.bos_token)
self.assertEqual((modified_model.vocab_size % 123), 0)
def test_example_with_setup_model(self):
modified_model, modified_tokenizer = setup_chat_format(
self.model,
self.tokenizer,
)
messages = [
{"role": "system", "content": "You are helpful"},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi, how can I help you?"},
]
prompt = modified_tokenizer.apply_chat_template(messages, tokenize=False)
self.assertEqual(
prompt,
"<|im_start|>system\nYou are helpful<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\nHi, how can I help you?<|im_end|>\n",
)
class CloneChatTemplateTestCase(unittest.TestCase):
def setUp(self):
# This tokenizer doesn't have a chat_template by default
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-BloomForCausalLM")
self.model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-BloomForCausalLM")
# This one has a chat_template by default
self.source = "trl-internal-testing/tiny-Qwen3ForCausalLM"
def test_clone(self):
_, modified_tokenizer, _ = clone_chat_template(self.model, self.tokenizer, self.source)
# Check if special tokens are correctly set
self.assertEqual(modified_tokenizer.eos_token, "<|im_end|>")
def test_clone_with_resize(self):
modified_model, modified_tokenizer, _ = clone_chat_template(
self.model, self.tokenizer, self.source, resize_to_multiple_of=123
)
# Check that the input embeddings have been resized to a multiple of 123
self.assertEqual((modified_model.vocab_size % 123), 0)
# Check that the input embeddings size matches the tokenizer vocabulary size
self.assertEqual(self.model.vocab_size, len(modified_tokenizer.vocab))
def test_clone_with_resize_and_extra_tokens_already_in_vocab(self):
# This will add <extra_id_0>, <extra_id_1>, ... to the tokenizer
modified_model, modified_tokenizer, _ = clone_chat_template(
self.model, self.tokenizer, self.source, resize_to_multiple_of=123
)
# Try if we can resize a tokenizer that already has extra these extra tokens
modified_model, modified_tokenizer, _ = clone_chat_template(
modified_model, modified_tokenizer, self.source, resize_to_multiple_of=124
)
# Check that the input embeddings have been resized to a multiple of 123
self.assertEqual((modified_model.vocab_size % 124), 0)
# Check that the input embeddings size matches the tokenizer vocabulary size
self.assertEqual(self.model.vocab_size, len(modified_tokenizer.vocab))
def test_apply_new_chat_template(self):
_, modified_tokenizer, _ = clone_chat_template(self.model, self.tokenizer, self.source)
messages = [
{"role": "system", "content": "You are helpful"},
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi, how can I help you?"},
]
prompt = modified_tokenizer.apply_chat_template(messages, tokenize=False)
self.assertEqual(
prompt,
"<|im_start|>system\nYou are helpful<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\nHi, how can I help you?<|im_end|>\n",
)
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