# 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 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: list[list[int]], batch_text: list[str] ) -> None: r"""Check 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"""Check 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 = ( "user\nHow are you\n" "model\nI am fine!\n" "user\n你好\n" "model\n" ) answer_str = "很高兴认识你！\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."