mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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235 lines
10 KiB
Python
235 lines
10 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import TYPE_CHECKING
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import pytest
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from transformers import AutoTokenizer
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from llamafactory.data import get_template_and_fix_tokenizer
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from llamafactory.data.template import parse_template
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from llamafactory.hparams import DataArguments
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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer
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HF_TOKEN = os.getenv("HF_TOKEN")
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TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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MESSAGES = [
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{"role": "user", "content": "How are you"},
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{"role": "assistant", "content": "I am fine!"},
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{"role": "user", "content": "你好"},
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{"role": "assistant", "content": "很高兴认识你！"},
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]
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def _check_tokenization(
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tokenizer: "PreTrainedTokenizer", batch_input_ids: list[list[int]], batch_text: list[str]
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) -> None:
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r"""Check token ids and texts.
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encode(text) == token_ids
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decode(token_ids) == text
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"""
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for input_ids, text in zip(batch_input_ids, batch_text):
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assert tokenizer.encode(text, add_special_tokens=False) == input_ids
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assert tokenizer.decode(input_ids) == text
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def _check_template(model_id: str, template_name: str, prompt_str: str, answer_str: str, use_fast: bool) -> None:
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r"""Check template.
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Args:
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model_id: the model id on hugging face hub.
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template_name: the template name.
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prompt_str: the string corresponding to the prompt part.
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answer_str: the string corresponding to the answer part.
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use_fast: whether to use fast tokenizer.
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"""
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tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
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content_str = tokenizer.apply_chat_template(MESSAGES, tokenize=False)
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content_ids = tokenizer.apply_chat_template(MESSAGES, tokenize=True)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
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prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
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assert content_str == prompt_str + answer_str
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assert content_ids == prompt_ids + answer_ids
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_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_encode_oneturn(use_fast: bool):
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
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prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
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prompt_str = (
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"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
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"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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answer_str = "很高兴认识你！<|eot_id|>"
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_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_encode_multiturn(use_fast: bool):
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
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encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
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prompt_str_1 = (
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"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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answer_str_1 = "I am fine!<|eot_id|>"
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prompt_str_2 = (
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"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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answer_str_2 = "很高兴认识你！<|eot_id|>"
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_check_tokenization(
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tokenizer,
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(encoded_pairs[0][0], encoded_pairs[0][1], encoded_pairs[1][0], encoded_pairs[1][1]),
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(prompt_str_1, answer_str_1, prompt_str_2, answer_str_2),
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)
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_jinja_template(use_fast: bool):
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
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ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, use_fast=use_fast)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
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tokenizer.chat_template = template._get_jinja_template(tokenizer) # llama3 template no replace
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assert tokenizer.chat_template != ref_tokenizer.chat_template
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assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
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def test_ollama_modelfile():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
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assert template.get_ollama_modelfile(tokenizer) == (
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"# ollama modelfile auto-generated by llamafactory\n\n"
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"FROM .\n\n"
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'TEMPLATE """<|begin_of_text|>'
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"{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}"
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'{{ range .Messages }}{{ if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Content }}'
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"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
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'{{ else if eq .Role "assistant" }}{{ .Content }}<|eot_id|>{{ end }}{{ end }}"""\n\n'
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'PARAMETER stop "<|eom_id|>"\n'
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'PARAMETER stop "<|eot_id|>"\n'
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"PARAMETER num_ctx 4096\n"
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)
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def test_get_stop_token_ids():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
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assert set(template.get_stop_token_ids(tokenizer)) == {128008, 128009}
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_gemma_template(use_fast: bool):
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prompt_str = (
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"<bos><start_of_turn>user\nHow are you<end_of_turn>\n"
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"<start_of_turn>model\nI am fine!<end_of_turn>\n"
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"<start_of_turn>user\n你好<end_of_turn>\n"
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"<start_of_turn>model\n"
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)
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answer_str = "很高兴认识你！<end_of_turn>\n"
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_check_template("google/gemma-2-9b-it", "gemma", prompt_str, answer_str, use_fast)
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_llama3_template(use_fast: bool):
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prompt_str = (
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"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
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"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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answer_str = "很高兴认识你！<|eot_id|>"
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_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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@pytest.mark.parametrize(
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"use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken."))]
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)
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def test_phi4_template(use_fast: bool):
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prompt_str = (
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"<|im_start|>user<|im_sep|>How are you<|im_end|>"
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"<|im_start|>assistant<|im_sep|>I am fine!<|im_end|>"
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"<|im_start|>user<|im_sep|>你好<|im_end|>"
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"<|im_start|>assistant<|im_sep|>"
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)
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answer_str = "很高兴认识你！<|im_end|>"
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_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.") # TODO: why it is gated?
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@pytest.mark.parametrize("use_fast", [True, False])
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def test_qwen_template(use_fast: bool):
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prompt_str = (
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"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\nHow are you<|im_end|>\n"
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"<|im_start|>assistant\nI am fine!<|im_end|>\n"
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"<|im_start|>user\n你好<|im_end|>\n"
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"<|im_start|>assistant\n"
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)
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answer_str = "很高兴认识你！<|im_end|>\n"
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_check_template("Qwen/Qwen2-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
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@pytest.mark.parametrize("use_fast", [True, False])
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@pytest.mark.xfail(reason="Yi tokenizer is broken.")
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def test_yi_template(use_fast: bool):
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prompt_str = (
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"<|im_start|>user\nHow are you<|im_end|>\n"
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"<|im_start|>assistant\nI am fine!<|im_end|>\n"
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"<|im_start|>user\n你好<|im_end|>\n"
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"<|im_start|>assistant\n"
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)
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answer_str = "很高兴认识你！<|im_end|>\n"
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_check_template("01-ai/Yi-1.5-6B-Chat", "yi", prompt_str, answer_str, use_fast)
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def test_parse_template():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA, token=HF_TOKEN)
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template = parse_template(tokenizer)
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assert template.format_user.slots == [
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"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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]
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assert template.format_assistant.slots == ["{{content}}<|eot_id|>"]
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assert template.format_system.slots == ["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
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assert template.format_prefix.slots == ["<|begin_of_text|>"]
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assert template.default_system == ""
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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def test_parse_qwen_template():
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct", token=HF_TOKEN)
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template = parse_template(tokenizer)
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assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]
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assert template.format_assistant.slots == ["{{content}}<|im_end|>\n"]
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assert template.format_system.slots == ["<|im_start|>system\n{{content}}<|im_end|>\n"]
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assert template.format_prefix.slots == []
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assert template.default_system == "You are a helpful assistant."
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