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[data] qwen3 fixes (#8109)

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hoshi-hiyouga
2025-05-20 02:00:30 +08:00
committed by GitHub
parent 45030ff803
commit 9b5baa97f0
13 changed files with 197 additions and 160 deletions
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119
tests/data/test_template.py
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@@ -126,29 +126,50 @@ def test_encode_multiturn(use_fast: bool):
@pytest.mark.parametrize("use_fast", [True, False])
def test_reasoning_encode_oneturn(use_fast: bool):
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False])
def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="qwen3"))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, messages)
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<think>\n\n</think>\n\n"
f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
)
answer_str = "很高兴认识你！<|im_end|>\n"
answer_str = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages:
if enable_thinking:
answer_str = "<think>\n\n</think>\n\n" + answer_str
else:
prompt_str = prompt_str + "<think>\n\n</think>\n\n"
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.parametrize("use_fast", [True, False])
def test_reasoning_encode_multiturn(use_fast: bool):
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False])
def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="qwen3"))
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
prompt_str_1 = "<|im_start|>user\nHow are you<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
answer_str_1 = "I am fine!<|im_end|>\n"
prompt_str_2 = "<|im_start|>user\n你好<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
answer_str_2 = "很高兴认识你！<|im_end|>\n"
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
encoded_pairs = template.encode_multiturn(tokenizer, messages)
prompt_str_1 = f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_1 = f"{messages[1]['content']}<|im_end|>\n"
prompt_str_2 = f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_2 = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages:
if enable_thinking:
answer_str_1 = "<think>\n\n</think>\n\n" + answer_str_1
answer_str_2 = "<think>\n\n</think>\n\n" + answer_str_2
else:
prompt_str_1 = prompt_str_1 + "<think>\n\n</think>\n\n"
prompt_str_2 = prompt_str_2 + "<think>\n\n</think>\n\n"
_check_tokenization(
tokenizer,
(encoded_pairs[0][0], encoded_pairs[0][1], encoded_pairs[1][0], encoded_pairs[1][1]),
@@ -193,12 +214,12 @@ def test_get_stop_token_ids():
@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"
f"<bos><start_of_turn>user\n{MESSAGES[0]['content']}<end_of_turn>\n"
f"<start_of_turn>model\n{MESSAGES[1]['content']}<end_of_turn>\n"
f"<start_of_turn>user\n{MESSAGES[2]['content']}<end_of_turn>\n"
"<start_of_turn>model\n"
)
answer_str = "很高兴认识你！<end_of_turn>\n"
answer_str = f"{MESSAGES[3]['content']}<end_of_turn>\n"
_check_template("google/gemma-3-4b-it", "gemma", prompt_str, answer_str, use_fast)
@@ -206,12 +227,12 @@ def test_gemma_template(use_fast: bool):
@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|>"
f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{MESSAGES[0]['content']}<|eot_id|>"
f"<|start_header_id|>assistant<|end_header_id|>\n\n{MESSAGES[1]['content']}<|eot_id|>"
f"<|start_header_id|>user<|end_header_id|>\n\n{MESSAGES[2]['content']}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = "很高兴认识你！<|eot_id|>"
answer_str = f"{MESSAGES[3]['content']}<|eot_id|>"
_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
@@ -220,12 +241,12 @@ def test_llama3_template(use_fast: bool):
)
def test_llama4_template(use_fast: bool):
prompt_str = (
"<|begin_of_text|><|header_start|>user<|header_end|>\n\nHow are you<|eot|>"
"<|header_start|>assistant<|header_end|>\n\nI am fine!<|eot|>"
"<|header_start|>user<|header_end|>\n\n你好<|eot|>"
f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{MESSAGES[0]['content']}<|eot|>"
f"<|header_start|>assistant<|header_end|>\n\n{MESSAGES[1]['content']}<|eot|>"
f"<|header_start|>user<|header_end|>\n\n{MESSAGES[2]['content']}<|eot|>"
"<|header_start|>assistant<|header_end|>\n\n"
)
answer_str = "很高兴认识你！<|eot|>"
answer_str = f"{MESSAGES[3]['content']}<|eot|>"
_check_template(TINY_LLAMA4, "llama4", prompt_str, answer_str, use_fast)
@@ -234,12 +255,12 @@ def test_llama4_template(use_fast: bool):
)
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|>"
f"<|im_start|>user<|im_sep|>{MESSAGES[0]['content']}<|im_end|>"
f"<|im_start|>assistant<|im_sep|>{MESSAGES[1]['content']}<|im_end|>"
f"<|im_start|>user<|im_sep|>{MESSAGES[2]['content']}<|im_end|>"
"<|im_start|>assistant<|im_sep|>"
)
answer_str = "很高兴认识你！<|im_end|>"
answer_str = f"{MESSAGES[3]['content']}<|im_end|>"
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
@@ -247,34 +268,30 @@ def test_phi4_template(use_fast: bool):
def test_qwen2_5_template(use_fast: bool):
prompt_str = (
"<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You 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"
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = "很高兴认识你！<|im_end|>\n"
answer_str = f"{MESSAGES[3]['content']}<|im_end|>\n"
_check_template("Qwen/Qwen2.5-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
@pytest.mark.parametrize("use_fast", [True, False])
def test_qwen3_template(use_fast: bool):
@pytest.mark.parametrize("cot_messages", [True, False])
def test_qwen3_template(use_fast: bool, cot_messages: bool):
messages = MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES
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<think>\n\n</think>\n\n"
)
answer_str = "很高兴认识你！<|im_end|>\n"
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, use_fast)
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"
f"<|im_start|>user\n{messages[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{messages[2]['content']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = "<think>\n模型思考内容\n</think>\n\n很高兴认识你！<|im_end|>\n"
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, use_fast, messages=MESSAGES_WITH_THOUGHT)
answer_str = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages:
answer_str = "<think>\n\n</think>\n\n" + answer_str
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, use_fast, messages=messages)
def test_parse_llama3_template():
@@ -293,6 +310,7 @@ def test_parse_llama3_template():
def test_parse_qwen_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.__class__.__name__ == "Template"
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"]
@@ -303,6 +321,7 @@ def test_parse_qwen_template():
def test_parse_qwen3_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.__class__.__name__ == "ReasoningTemplate"
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"]