2024-06-19 12:03:50 +00:00
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# 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 transformers import AutoTokenizer
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from llamafactory.data import get_template_and_fix_tokenizer
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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2024-06-30 17:19:27 +00:00
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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 test_encode_oneturn():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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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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assert tokenizer.decode(prompt_ids) == (
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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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assert tokenizer.decode(answer_ids) == "很高兴认识你!<|eot_id|>"
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def test_encode_multiturn():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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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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assert tokenizer.decode(encoded_pairs[0][0]) == (
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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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assert tokenizer.decode(encoded_pairs[0][1]) == "I am fine!<|eot_id|>"
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assert tokenizer.decode(encoded_pairs[1][0]) == (
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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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assert tokenizer.decode(encoded_pairs[1][1]) == "很高兴认识你!<|eot_id|>"
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2024-06-19 12:03:50 +00:00
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def test_jinja_template():
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tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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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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2024-06-30 17:19:27 +00:00
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assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
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2024-06-19 12:03:50 +00:00
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2024-06-30 17:19:27 +00:00
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def test_qwen_template():
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
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template = get_template_and_fix_tokenizer(tokenizer, name="qwen")
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prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
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assert tokenizer.decode(prompt_ids) == (
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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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assert tokenizer.decode(answer_ids) == "很高兴认识你!<|im_end|>"
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