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# Copyright 2025 the LlamaFactory team.
#
# 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
from typing import TYPE_CHECKING, Sequence
import pytest
from transformers import AutoTokenizer
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.template import parse_template
from llamafactory.hparams import DataArguments
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer
HF_TOKEN = os.getenv("HF_TOKEN")
TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
MESSAGES = [
{"role": "user", "content": "How are you"},
{"role": "assistant", "content": "I am fine!"},
{"role": "user", "content": "你好"},
{"role": "assistant", "content": "很高兴认识你！"},
]
def _check_tokenization(
tokenizer: "PreTrainedTokenizer", batch_input_ids: Sequence[Sequence[int]], batch_text: Sequence[str]
) -> None:
r"""
Checks token ids and texts.
encode(text) == token_ids
decode(token_ids) == text
"""
for input_ids, text in zip(batch_input_ids, batch_text):
assert tokenizer.encode(text, add_special_tokens=False) == input_ids
assert tokenizer.decode(input_ids) == text
def _check_template(model_id: str, template_name: str, prompt_str: str, answer_str: str, use_fast: bool) -> None:
r"""
Checks template.
Args:
model_id: the model id on hugging face hub.
template_name: the template name.
prompt_str: the string corresponding to the prompt part.
answer_str: the string corresponding to the answer part.
use_fast: whether to use fast tokenizer.
"""
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
assert content_str == prompt_str + answer_str
assert content_ids == prompt_ids + answer_ids
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_oneturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
prompt_str = (
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = "很高兴认识你！<|eot_id|>"
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_multiturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
prompt_str_1 = (
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str_1 = "I am fine!<|eot_id|>"
prompt_str_2 = (
"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str_2 = "很高兴认识你！<|eot_id|>"
_check_tokenization(
tokenizer,
(encoded_pairs[0][0], encoded_pairs[0][1], encoded_pairs[1][0], encoded_pairs[1][1]),
(prompt_str_1, answer_str_1, prompt_str_2, answer_str_2),
)
@pytest.mark.parametrize("use_fast", [True, False])
def test_jinja_template(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
tokenizer.chat_template = template._get_jinja_template(tokenizer) # llama3 template no replace
assert tokenizer.chat_template != ref_tokenizer.chat_template
assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
def test_ollama_modelfile():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
assert template.get_ollama_modelfile(tokenizer) == (
"# ollama modelfile auto-generated by llamafactory\n\n"
"FROM .\n\n"
'TEMPLATE """<|begin_of_text|>'
"{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}"
'{{ range .Messages }}{{ if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Content }}'
"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
'{{ else if eq .Role "assistant" }}{{ .Content }}<|eot_id|>{{ end }}{{ end }}"""\n\n'
'PARAMETER stop "<|eom_id|>"\n'
'PARAMETER stop "<|eot_id|>"\n'
"PARAMETER num_ctx 4096\n"
)
def test_get_stop_token_ids():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
assert set(template.get_stop_token_ids(tokenizer)) == {128008, 128009}
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_gemma_template(use_fast: bool):
prompt_str = (
"<bos><start_of_turn>user\nHow are you<end_of_turn>\n"
"<start_of_turn>model\nI am fine!<end_of_turn>\n"
"<start_of_turn>user\n你好<end_of_turn>\n"
"<start_of_turn>model\n"
)
answer_str = "很高兴认识你！<end_of_turn>\n"
_check_template("google/gemma-2-9b-it", "gemma", prompt_str, answer_str, use_fast)
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_llama3_template(use_fast: bool):
prompt_str = (
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = "很高兴认识你！<|eot_id|>"
_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize(
"use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken."))]
)
def test_phi4_template(use_fast: bool):
prompt_str = (
"<|im_start|>user<|im_sep|>How are you<|im_end|>"
"<|im_start|>assistant<|im_sep|>I am fine!<|im_end|>"
"<|im_start|>user<|im_sep|>你好<|im_end|>"
"<|im_start|>assistant<|im_sep|>"
)
answer_str = "很高兴认识你！<|im_end|>"
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.") # TODO: why it is gated?
@pytest.mark.parametrize("use_fast", [True, False])
def test_qwen_template(use_fast: bool):
prompt_str = (
"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
"<|im_start|>user\nHow are you<|im_end|>\n"
"<|im_start|>assistant\nI am fine!<|im_end|>\n"
"<|im_start|>user\n你好<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = "很高兴认识你！<|im_end|>\n"
_check_template("Qwen/Qwen2-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.xfail(reason="Yi tokenizer is broken.")
def test_yi_template(use_fast: bool):
prompt_str = (
"<|im_start|>user\nHow are you<|im_end|>\n"
"<|im_start|>assistant\nI am fine!<|im_end|>\n"
"<|im_start|>user\n你好<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = "很高兴认识你！<|im_end|>\n"
_check_template("01-ai/Yi-1.5-6B-Chat", "yi", prompt_str, answer_str, use_fast)
def test_parse_template():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.format_user.slots == [
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
]
assert template.format_assistant.slots == ["{{content}}<|eot_id|>"]
assert template.format_system.slots == ["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
assert template.format_prefix.slots == ["<|begin_of_text|>"]
assert template.default_system == ""
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
def test_parse_qwen_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct", token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]
assert template.format_assistant.slots == ["{{content}}<|im_end|>\n"]
assert template.format_system.slots == ["<|im_start|>system\n{{content}}<|im_end|>\n"]
assert template.format_prefix.slots == []
assert template.default_system == "You are a helpful assistant."
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