170 lines
7.1 KiB
Python
170 lines
7.1 KiB
Python
# Copyright 2024 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, List, Sequence
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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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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer
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HF_TOKEN = os.environ.get("HF_TOKEN", None)
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TINY_LLAMA = os.environ.get("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: Sequence[Sequence[int]], batch_text: Sequence[str]
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) -> None:
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for input_ids, text in zip(batch_input_ids, batch_text):
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assert input_ids == tokenizer.encode(text, add_special_tokens=False)
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assert tokenizer.decode(input_ids) == text
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def _check_single_template(
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model_id: str, template_name: str, prompt_str: str, answer_str: str, extra_str: str, use_fast: bool
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) -> List[str]:
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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, name=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 + extra_str
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assert content_ids == prompt_ids + answer_ids + tokenizer.encode(extra_str, add_special_tokens=False)
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_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
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return content_ids
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def _check_template(model_id: str, template_name: str, prompt_str: str, answer_str: str, extra_str: str = "") -> None:
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"""
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Checks template for both the slow tokenizer and the fast tokenizer.
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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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extra_str: the extra string in the jinja template of the original tokenizer.
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"""
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slow_ids = _check_single_template(model_id, template_name, prompt_str, answer_str, extra_str, use_fast=False)
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fast_ids = _check_single_template(model_id, template_name, prompt_str, answer_str, extra_str, use_fast=True)
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assert slow_ids == fast_ids
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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, name="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, name="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|>"
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"<|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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get_template_and_fix_tokenizer(tokenizer, name="llama3")
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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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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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def test_gemma_template():
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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！"
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_check_template("google/gemma-2-9b-it", "gemma", prompt_str, answer_str, extra_str="<end_of_turn>\n")
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@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
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def test_llama3_template():
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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)
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def test_qwen_template():
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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|>"
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_check_template("Qwen/Qwen2-7B-Instruct", "qwen", prompt_str, answer_str, extra_str="\n")
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@pytest.mark.skip(reason="The fast tokenizer of Yi model is corrupted.")
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def test_yi_template():
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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|>"
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_check_template("01-ai/Yi-1.5-6B-Chat", "yi", prompt_str, answer_str)
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