wehub-resource-sync
749 lines
32 KiB
Python
749 lines
32 KiB
Python
import os
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os.environ['SWIFT_DEBUG'] = '1'
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os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0,1,2,3'
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system = 'You are a helpful assistant.'
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tools = [{
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'type': 'function',
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'function': {
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'name': 'get_current_weather',
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'description': 'Get the current weather in a given location',
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'parameters': {
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'type': 'object',
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'properties': {
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'location': {
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'type': 'string',
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'description': 'The city and state, e.g. San Francisco, CA'
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},
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'unit': {
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'type': 'string',
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'enum': ['celsius', 'fahrenheit']
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}
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},
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'required': ['location']
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}
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}
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}, {
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'name_for_model': 'tool2',
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'name_for_human': '工具2',
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'description': 'Tool2的描述',
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}]
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glm4_tools = [{
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'type': 'function',
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'function': {
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'name': 'realtime_aqi',
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'description': '天气预报。获取实时空气质量。当前空气质量，PM2.5，PM10信息',
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'parameters': {
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'type': 'object',
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'properties': {
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'city': {
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'description': '城市名'
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}
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},
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'required': ['city']
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}
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}
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}]
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glm4_tool_messasges = [
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{
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'role': 'tool',
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'content': '{"city": "北京", "aqi": "10", "unit": "celsius"}'
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},
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{
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'role': 'tool',
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'content': '{"city": "上海", "aqi": "72", "unit": "fahrenheit"}'
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},
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]
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glm4_query = '北京和上海今天的天气情况'
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def _infer(engine, num_tools: int = 1, agent_tools=None, tool_messages=None, query=None):
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if agent_tools is None:
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agent_tools = tools
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if tool_messages is None:
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tool_messages = []
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for _ in range(num_tools):
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tool_messages.append({
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'role': 'tool',
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'content': '{"temperature": 32, "condition": "Sunny", "humidity": 50}'
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})
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stop = [engine.template.agent_template.keyword.observation]
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query = query or "How's the weather in Beijing today?"
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infer_request = InferRequest([{'role': 'user', 'content': query}], tools=agent_tools)
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request_config = RequestConfig(max_tokens=512, stop=stop, temperature=0)
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resp_list = engine.infer([infer_request], request_config=request_config)
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response = resp_list[0].choices[0].message.content
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toolcall = resp_list[0].choices[0].message.tool_calls[0].function
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print(f'response: {response}')
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print(f'toolcall: {toolcall}')
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assert toolcall is not None
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infer_request.messages.append({'role': 'assistant', 'content': response})
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infer_request.messages += tool_messages
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resp_list = engine.infer([infer_request], request_config=request_config)
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response2 = resp_list[0].choices[0].message.content
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print(f'response2: {response2}')
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infer_request.messages.append({'role': 'assistant', 'content': response2})
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return infer_request.messages
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def test_react_en():
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agent_template = agent_template_map['react_en']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 1144
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'react_en'
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messages = _infer(engine)
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assert messages[-1]['content'] == (
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'Thought: The current temperature in Beijing is 32 degrees Celsius, and the condition is sunny '
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'with a humidity of 50%.\nFinal Answer: The current temperature in Beijing is 32 degrees Celsius,'
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' and the condition is sunny with a humidity of 50%.')
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_react_zh():
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agent_template = agent_template_map['react_zh']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 712
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'react_zh'
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_infer(engine)
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def test_qwen_en():
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agent_template = agent_template_map['qwen_en']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 879
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'qwen_en'
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messages = _infer(engine)
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assert messages[-1]['content'] == (
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'✿RETURN✿: Today in Beijing, the temperature is 32°C with sunny conditions and the humidity '
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'is at 50%. Enjoy the nice weather!')
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_qwen_zh():
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agent_template = agent_template_map['qwen_zh']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 577
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'qwen_zh'
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_infer(engine)
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def test_qwen_en_parallel():
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agent_template = agent_template_map['qwen_en_parallel']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 1012
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'qwen_en_parallel'
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messages = _infer(engine, num_tools=2)
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assert messages[-1]['content'] == (
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'✿RETURN✿: Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions '
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'and the humidity is at 50%. Enjoy the nice weather!')
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_qwen_zh_parallel():
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agent_template = agent_template_map['qwen_zh_parallel']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 688
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'qwen_zh_parallel'
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_infer(engine, num_tools=2)
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def test_hermes():
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agent_template = agent_template_map['hermes']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 875
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'hermes'
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messages = _infer(engine, num_tools=2)
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template.template_backend = 'jinja'
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messages2 = _infer(engine, num_tools=2)
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assert messages[-1]['content'] == messages2[-1]['content'] == (
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'Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions '
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'and the humidity is at 50%. Enjoy the nice weather!')
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'] == encoded2['input_ids']
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def test_toolbench():
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agent_template = agent_template_map['toolbench']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 1833
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engine = TransformersEngine('Qwen/Qwen2.5-7B-Instruct')
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template = engine.template
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template._agent_template = 'toolbench'
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_infer(engine)
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def test_chatglm4():
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agent_template = agent_template_map['chatglm4']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 846
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engine = TransformersEngine('ZhipuAI/glm-4-9b-chat')
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template = engine.template
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template._agent_template = 'chatglm4'
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_infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query)
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def test_glm4():
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agent_template = agent_template_map['glm4']()
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new_system = agent_template._format_tools(tools, system)
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assert len(new_system) == 769
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engine = TransformersEngine('ZhipuAI/GLM-4-9B-0414')
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template = engine.template
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template._agent_template = 'glm4'
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messages = _infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query)
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assert messages[-1]['content'] == '根据天气预报工具，北京今天的空气质量指数为10，属于良好水平；上海今天的空气质量指数为72，属于轻度污染水平。'
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_llama3():
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engine = TransformersEngine('LLM-Research/Llama-3.2-3B-Instruct')
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template = engine.template
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template._agent_template = 'llama3'
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messages = _infer(engine)
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_llama4():
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engine = TransformersEngine('LLM-Research/Llama-4-Scout-17B-16E-Instruct')
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template = engine.template
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messages = _infer(engine)
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template.set_mode('train')
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encoded = template.encode({'messages': messages})
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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def test_hunyuan():
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engine = TransformersEngine('Tencent-Hunyuan/Hunyuan-1.8B-Instruct')
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template = engine.template
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template.template_backend = 'jinja'
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_infer(engine, num_tools=2)
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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template.set_mode('train')
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'][:-1] == encoded2['input_ids']
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def test_glm4_5():
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engine = TransformersEngine('ZhipuAI/GLM-4.5-Air')
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template = engine.template
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template.template_backend = 'jinja'
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_infer(engine, num_tools=2)
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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template.set_mode('train')
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'][:-1] == encoded2['input_ids']
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def test_glm4_7():
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engine = TransformersEngine('ZhipuAI/GLM-4.7-FP8', load_model=False)
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template = engine.template
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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template.set_mode('train')
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'][:-1] == encoded2['input_ids']
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def test_qwen3_coder():
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engine = TransformersEngine('Qwen/Qwen3-Coder-30B-A3B-Instruct')
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template = engine.template
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template.template_backend = 'jinja'
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_infer(engine, num_tools=2)
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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template.template_backend = 'swift'
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template.set_mode('train')
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'] == encoded2['input_ids']
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def test_qwen3_5():
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engine = TransformersEngine('Qwen/Qwen3.5-35B-A3B')
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template = engine.template
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template.template_backend = 'jinja'
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_infer(engine, num_tools=2)
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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data['messages'].insert(1, data['messages'][1])
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data['messages'].insert(3, data['messages'][3])
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data['messages'].insert(0, {'role': 'system', 'content': 'You are a helpful assistant.'})
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template.template_backend = 'swift'
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template.set_mode('train')
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encoded = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded["labels"])}')
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template.template_backend = 'jinja'
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encoded2 = template.encode(data)
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print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
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print(f'labels: {template.safe_decode(encoded2["labels"])}')
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assert encoded['input_ids'] == encoded2['input_ids']
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def test_deepseek_v3_1():
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engine = TransformersEngine('deepseek-ai/DeepSeek-V3.1', load_model=False)
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template = engine.template
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dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
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data = dataset[6]
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||
# To test multiple tool calls and responses, we duplicate some messages.
|
||
data['messages'].insert(1, data['messages'][1])
|
||
data['messages'].insert(3, data['messages'][3])
|
||
template.template_backend = 'swift'
|
||
template.set_mode('train')
|
||
encoded = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded["labels"])}')
|
||
template.template_backend = 'jinja'
|
||
encoded2 = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded2["labels"])}')
|
||
|
||
expected_input_ids = (
|
||
'<｜begin▁of▁sentence｜>\n\n## Tools\n'
|
||
'You have access to the following tools:\n\n'
|
||
'### convert_temperature\n'
|
||
'Description: Convert temperature from one unit to another\n\n'
|
||
"Parameters: {\"type\": \"object\", \"properties\": {\"temperature\": {\"type\": \"number\", "
|
||
"\"description\": \"The temperature value\"}, \"from_unit\": {\"type\": \"string\", \"description\": "
|
||
"\"The unit to convert from\"}, \"to_unit\": {\"type\": \"string\", \"description\": \"The unit "
|
||
"to convert to\"}}, \"required\": [\"temperature\", \"from_unit\", \"to_unit\"]}\n\n"
|
||
'### get_current_date\n'
|
||
'Description: Get the current date\n\n'
|
||
'Parameters: {}\n\n'
|
||
'IMPORTANT: ALWAYS adhere to this exact format for tool use:\n'
|
||
'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>tool_call_name<｜tool▁sep｜>tool_call_arguments<｜tool▁call▁end｜>'
|
||
'{additional_tool_calls}<｜tool▁calls▁end｜>\n\n'
|
||
'Where:\n'
|
||
'- `tool_call_name` must be an exact match to one of the available tools\n'
|
||
"- `tool_call_arguments` must be valid JSON that strictly follows the tool's Parameters Schema\n"
|
||
'- For multiple tool calls, chain them directly without separators or spaces<｜User｜>'
|
||
'Hi, I need to convert a temperature from Celsius to Fahrenheit. The temperature is 30 degrees Celsius.'
|
||
'<｜Assistant｜></think><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>convert_temperature<｜tool▁sep｜>'
|
||
"{\"temperature\": 30, \"from_unit\": \"Celsius\", \"to_unit\": \"Fahrenheit\"}<｜tool▁call▁end｜>"
|
||
'<｜tool▁call▁begin｜>convert_temperature<｜tool▁sep｜>'
|
||
"{\"temperature\": 30, \"from_unit\": \"Celsius\", \"to_unit\": \"Fahrenheit\"}<｜tool▁call▁end｜>"
|
||
'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'
|
||
"<｜tool▁output▁begin｜>{\"converted_temperature\": 86}<｜tool▁output▁end｜>"
|
||
"<｜tool▁output▁begin｜>{\"converted_temperature\": 86}<｜tool▁output▁end｜>"
|
||
'The converted temperature from 30 degrees Celsius to Fahrenheit is 86 degrees Fahrenheit.<｜end▁of▁sentence｜>')
|
||
|
||
# Expected labels string
|
||
expected_labels = (
|
||
'[-100 * 239]</think><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>convert_temperature<｜tool▁sep｜>'
|
||
"{\"temperature\": 30, \"from_unit\": \"Celsius\", \"to_unit\": \"Fahrenheit\"}<｜tool▁call▁end｜>"
|
||
'<｜tool▁call▁begin｜>convert_temperature<｜tool▁sep｜>'
|
||
"{\"temperature\": 30, \"from_unit\": \"Celsius\", \"to_unit\": \"Fahrenheit\"}<｜tool▁call▁end｜>"
|
||
'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>[-100 * 22]'
|
||
'The converted temperature from 30 degrees Celsius to Fahrenheit is 86 degrees Fahrenheit.<｜end▁of▁sentence｜>')
|
||
|
||
assert template.safe_decode(encoded['input_ids']) == expected_input_ids
|
||
assert template.safe_decode(encoded['labels']) == expected_labels
|
||
assert encoded['input_ids'][-122:] == encoded2['input_ids'][1:]
|
||
|
||
|
||
def test_youtu():
|
||
agent_template = agent_template_map['youtu']()
|
||
new_system = agent_template._format_tools(tools, system)
|
||
assert len(new_system) == 883
|
||
engine = TransformersEngine('Tencent-YouTu-Research/Youtu-LLM-2B')
|
||
template = engine.template
|
||
template._agent_template = 'youtu'
|
||
|
||
stop = [template.agent_template.keyword.observation]
|
||
query = "How's the weather in Beijing today?"
|
||
tool_messages = [{'role': 'tool', 'content': '{"temperature": 32, "condition": "Sunny", "humidity": 50}'}]
|
||
infer_request = InferRequest([{'role': 'user', 'content': query}], tools=tools)
|
||
request_config = RequestConfig(max_tokens=2048, stop=stop, temperature=0)
|
||
|
||
# First inference: get tool call
|
||
resp_list = engine.infer([infer_request], request_config=request_config)
|
||
response = resp_list[0].choices[0].message.content
|
||
toolcall = resp_list[0].choices[0].message.tool_calls
|
||
print(f'response: {response}')
|
||
print(f'toolcall: {toolcall}')
|
||
assert toolcall is not None, 'No tool_call generated'
|
||
infer_request.messages.append({'role': 'assistant', 'content': response})
|
||
infer_request.messages += tool_messages
|
||
|
||
# Second inference: get final response
|
||
resp_list = engine.infer([infer_request], request_config=request_config)
|
||
response2 = resp_list[0].choices[0].message.content
|
||
print(f'response2: {response2}')
|
||
infer_request.messages.append({'role': 'assistant', 'content': response2})
|
||
messages = infer_request.messages
|
||
|
||
template.set_mode('train')
|
||
encoded = template.encode({'messages': messages})
|
||
print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded["labels"])}')
|
||
|
||
dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
|
||
data = dataset[6]
|
||
data['messages'].insert(1, data['messages'][1])
|
||
data['messages'].insert(3, data['messages'][3])
|
||
template.template_backend = 'swift'
|
||
encoded = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded["labels"])}')
|
||
template.template_backend = 'jinja'
|
||
encoded2 = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded2["labels"])}')
|
||
assert encoded['input_ids'] == encoded2['input_ids']
|
||
|
||
|
||
def test_deepseek_v4():
|
||
engine = TransformersEngine('deepseek-ai/DeepSeek-V4-Flash', load_model=False)
|
||
template = engine.template
|
||
|
||
tools = [{
|
||
'type': 'function',
|
||
'function': {
|
||
'name': 'get_weather',
|
||
'description': 'Get the weather for a specific location',
|
||
'parameters': {
|
||
'type': 'object',
|
||
'properties': {
|
||
'location': {
|
||
'type': 'string',
|
||
'description': 'The city name'
|
||
},
|
||
'unit': {
|
||
'type': 'string',
|
||
'enum': ['celsius', 'fahrenheit'],
|
||
'description': 'Temperature unit'
|
||
}
|
||
},
|
||
'required': ['location']
|
||
}
|
||
}
|
||
}, {
|
||
'type': 'function',
|
||
'function': {
|
||
'name': 'search',
|
||
'description': 'Search the web for information',
|
||
'parameters': {
|
||
'type': 'object',
|
||
'properties': {
|
||
'query': {
|
||
'type': 'string',
|
||
'description': 'Search query'
|
||
},
|
||
'num_results': {
|
||
'type': 'integer',
|
||
'description': 'Number of results to return'
|
||
}
|
||
},
|
||
'required': ['query']
|
||
}
|
||
}
|
||
}]
|
||
data = {
|
||
'tools':
|
||
tools,
|
||
'messages': [{
|
||
'role': 'system',
|
||
'content': 'You are a helpful assistant.'
|
||
}, {
|
||
'role': 'user',
|
||
'content': "What's the weather in Beijing?"
|
||
}, {
|
||
'role':
|
||
'assistant',
|
||
'content':
|
||
'<think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think>\n\n'
|
||
}, {
|
||
'role':
|
||
'tool_call',
|
||
'content':
|
||
'{"name": "get_weather", "arguments": "{\\"location\\": \\"Beijing\\", \\"unit\\": \\"celsius\\"}"}'
|
||
}, {
|
||
'role': 'tool_response',
|
||
'content': '{"temperature": 22, "condition": "sunny", "humidity": 45}'
|
||
}, {
|
||
'role':
|
||
'assistant',
|
||
'content': ('<think>Got the weather data. Let me format a nice response.</think>'
|
||
'The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.')
|
||
}]
|
||
}
|
||
|
||
template.template_backend = 'swift'
|
||
template.set_mode('train')
|
||
encoded = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded["labels"])}')
|
||
|
||
expected_input_ids = (
|
||
'<｜begin▁of▁sentence｜>You are a helpful assistant.\n\n## Tools\n\n'
|
||
'You have access to a set of tools to help answer the user\'s question. '
|
||
'You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:\n\n'
|
||
'<｜DSML｜tool_calls>\n'
|
||
'<｜DSML｜invoke name="$TOOL_NAME">\n'
|
||
'<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>\n'
|
||
'...\n'
|
||
'</｜DSML｜invoke>\n'
|
||
'<｜DSML｜invoke name="$TOOL_NAME2">\n'
|
||
'...\n'
|
||
'</｜DSML｜invoke>\n'
|
||
'</｜DSML｜tool_calls>\n\n'
|
||
'String parameters should be specified as is and set `string="true"`. '
|
||
'For all other types (numbers, booleans, arrays, objects), '
|
||
'pass the value in JSON format and set `string="false"`.\n\n'
|
||
'If thinking_mode is enabled (triggered by <think>), '
|
||
'you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.'
|
||
'\n\nOtherwise, output directly after </think> with tool calls or final response.\n\n'
|
||
'### Available Tool Schemas\n\n'
|
||
'{"name": "get_weather", "description": "Get the weather for a specific location", '
|
||
'"parameters": {"type": "object", "properties": {"location": {"type": "string", '
|
||
'"description": "The city name"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], '
|
||
'"description": "Temperature unit"}}, "required": ["location"]}}\n'
|
||
'{"name": "search", "description": "Search the web for information", '
|
||
'"parameters": {"type": "object", "properties": {"query": {"type": "string", '
|
||
'"description": "Search query"}, "num_results": {"type": "integer", '
|
||
'"description": "Number of results to return"}}, "required": ["query"]}}\n\n'
|
||
'You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n'
|
||
'<｜User｜>What\'s the weather in Beijing?<｜Assistant｜>'
|
||
'<think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think>\n\n'
|
||
'<｜DSML｜tool_calls>\n'
|
||
'<｜DSML｜invoke name="get_weather">\n'
|
||
'<｜DSML｜parameter name="location" string="true">Beijing</｜DSML｜parameter>\n'
|
||
'<｜DSML｜parameter name="unit" string="true">celsius</｜DSML｜parameter>\n'
|
||
'</｜DSML｜invoke>\n'
|
||
'</｜DSML｜tool_calls>'
|
||
'<｜end▁of▁sentence｜>'
|
||
'<｜User｜><tool_result>{"temperature": 22, "condition": "sunny", "humidity": 45}</tool_result>'
|
||
'<｜Assistant｜>'
|
||
'<think>Got the weather data. Let me format a nice response.</think>'
|
||
'The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.'
|
||
'<｜end▁of▁sentence｜>')
|
||
|
||
assert template.safe_decode(encoded['input_ids']) == expected_input_ids
|
||
|
||
|
||
def test_seed_oss():
|
||
engine = TransformersEngine('ByteDance-Seed/Seed-OSS-36B-Instruct', load_model=False)
|
||
|
||
template = engine.template
|
||
dataset = load_dataset('AI-ModelScope/function-calling-chatml')[0]
|
||
data = dataset[6]
|
||
# To test multiple tool calls and responses, we duplicate some messages.
|
||
data['messages'].insert(1, data['messages'][1])
|
||
data['messages'].insert(3, data['messages'][3])
|
||
|
||
# Incomplete tool function will cause seed template to throw an error.
|
||
data['tools'] = [('{\n'
|
||
' "name": "convert_temperature",\n'
|
||
' "description": "Convert temperature from one unit to another",\n'
|
||
' "parameters": {\n'
|
||
' "type": "object",\n'
|
||
' "properties": {\n'
|
||
' "temperature": {\n'
|
||
' "type": "number",\n'
|
||
' "description": "The temperature value"\n'
|
||
' },\n'
|
||
' "from_unit": {\n'
|
||
' "type": "string",\n'
|
||
' "description": "The unit to convert from"\n'
|
||
' },\n'
|
||
' "to_unit": {\n'
|
||
' "type": "string",\n'
|
||
' "description": "The unit to convert to"\n'
|
||
' }\n'
|
||
' },\n'
|
||
' "required": [\n'
|
||
' "temperature",\n'
|
||
' "from_unit",\n'
|
||
' "to_unit"\n'
|
||
' ]\n'
|
||
' }\n'
|
||
'}'),
|
||
('{\n'
|
||
' "name": "get_current_date",\n'
|
||
' "description": "Get the current date",\n'
|
||
' "parameters": {\n'
|
||
' "type": "object",\n'
|
||
' "properties": {\n'
|
||
' "date": {\n'
|
||
' "type": "number",\n'
|
||
' "description": "The date value"}}}\n'
|
||
'}')]
|
||
|
||
data['thinking_budget'] = 0
|
||
|
||
template.template_backend = 'swift'
|
||
template.set_mode('train')
|
||
encoded = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded["labels"])}')
|
||
import re
|
||
expected_input_ids = re.sub(
|
||
r'<seed:think>.*?</seed:think>', '', template.safe_decode(encoded['input_ids']), flags=re.DOTALL)
|
||
template.template_backend = 'jinja'
|
||
encoded2 = template.encode(data)
|
||
print(f'input_ids: {template.safe_decode(encoded2["input_ids"])}')
|
||
print(f'labels: {template.safe_decode(encoded2["labels"])}')
|
||
assert template.safe_decode(encoded2['input_ids']) == expected_input_ids
|
||
|
||
|
||
if __name__ == '__main__':
|
||
from swift import InferRequest, RequestConfig, TransformersEngine, agent_template_map, load_dataset
|
||
|
||
# test_react_en()
|
||
# test_react_zh()
|
||
# test_qwen_en()
|
||
# test_qwen_zh()
|
||
# test_qwen_en_parallel()
|
||
# test_qwen_zh_parallel()
|
||
# test_hermes()
|
||
# test_toolbench()
|
||
# test_chatglm4()
|
||
# test_glm4()
|
||
# test_llama3()
|
||
# test_llama4()
|
||
# test_hunyuan()
|
||
# test_glm4_5()
|
||
# test_glm4_7()
|
||
# test_qwen3_coder()
|
||
# test_qwen3_5()
|
||
# test_deepseek_v3_1()
|
||
test_deepseek_v4()
|
||
# test_seed_oss()
|
||
# test_youtu()
|