| import os |
|
|
| os.environ['SWIFT_DEBUG'] = '1' |
|
|
| system = 'You are a helpful assistant.' |
|
|
| tools = [{ |
| 'type': 'function', |
| 'function': { |
| 'name': 'get_current_weather', |
| 'description': 'Get the current weather in a given location', |
| 'parameters': { |
| 'type': 'object', |
| 'properties': { |
| 'location': { |
| 'type': 'string', |
| 'description': 'The city and state, e.g. San Francisco, CA' |
| }, |
| 'unit': { |
| 'type': 'string', |
| 'enum': ['celsius', 'fahrenheit'] |
| } |
| }, |
| 'required': ['location'] |
| } |
| } |
| }, { |
| 'name_for_model': 'tool2', |
| 'name_for_human': '工具2', |
| 'description': 'Tool2的描述', |
| }] |
|
|
| glm4_tools = [{ |
| 'type': 'function', |
| 'function': { |
| 'name': 'realtime_aqi', |
| 'description': '天气预报。获取实时空气质量。当前空气质量，PM2.5，PM10信息', |
| 'parameters': { |
| 'type': 'object', |
| 'properties': { |
| 'city': { |
| 'description': '城市名' |
| } |
| }, |
| 'required': ['city'] |
| } |
| } |
| }] |
| glm4_tool_messasges = [ |
| { |
| 'role': 'tool', |
| 'content': '{"city": "北京", "aqi": "10", "unit": "celsius"}' |
| }, |
| { |
| 'role': 'tool', |
| 'content': '{"city": "上海", "aqi": "72", "unit": "fahrenheit"}' |
| }, |
| ] |
| glm4_query = '北京和上海今天的天气情况' |
|
|
|
|
| def _infer(engine, num_tools: int = 1, agent_tools=None, tool_messages=None, query=None): |
| if agent_tools is None: |
| agent_tools = tools |
| if tool_messages is None: |
| tool_messages = [] |
| for _ in range(num_tools): |
| tool_messages.append({ |
| 'role': 'tool', |
| 'content': '{"temperature": 32, "condition": "Sunny", "humidity": 50}' |
| }) |
| stop = [engine.default_template.agent_template.keyword.observation] |
| query = query or "How's the weather in Beijing today?" |
| infer_request = InferRequest([{'role': 'user', 'content': query}], tools=agent_tools) |
| request_config = RequestConfig(max_tokens=512, stop=stop, temperature=0) |
| 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[0].function |
| print(f'response: {response}') |
| print(f'toolcall: {toolcall}') |
| assert toolcall is not None |
| infer_request.messages.append({'role': 'assistant', 'content': response}) |
| infer_request.messages += tool_messages |
| 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}) |
| return infer_request.messages |
|
|
|
|
| def test_react_en(): |
| agent_template = agent_templates['react_en']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 1144 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine) |
| assert messages[-1]['content'] == ( |
| 'Thought: The current temperature in Beijing is 32 degrees Celsius, and the condition is sunny ' |
| 'with a humidity of 50%.\nFinal Answer: The current temperature in Beijing is 32 degrees Celsius,' |
| ' and the condition is sunny with a humidity of 50%.') |
| 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"])}') |
|
|
|
|
| def test_react_zh(): |
| agent_template = agent_templates['react_zh']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 712 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| _infer(engine) |
|
|
|
|
| def test_qwen_en(): |
| agent_template = agent_templates['qwen_en']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 879 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine) |
| assert messages[-1]['content'] == ( |
| '✿RETURN✿: Today in Beijing, the temperature is 32°C with sunny conditions and the humidity ' |
| 'is at 50%. Enjoy the nice weather!') |
| 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"])}') |
|
|
|
|
| def test_qwen_zh(): |
| agent_template = agent_templates['qwen_zh']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 577 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| _infer(engine) |
|
|
|
|
| def test_qwen_en_parallel(): |
| agent_template = agent_templates['qwen_en_parallel']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 1012 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine, num_tools=2) |
| assert messages[-1]['content'] == ( |
| '✿RETURN✿: Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions ' |
| 'and the humidity is at 50%. Enjoy the nice weather!') |
| 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"])}') |
|
|
|
|
| def test_qwen_zh_parallel(): |
| agent_template = agent_templates['qwen_zh_parallel']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 688 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| _infer(engine, num_tools=2) |
|
|
|
|
| def test_hermes(): |
| agent_template = agent_templates['hermes']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 875 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine, num_tools=2) |
| template.template_backend = 'jinja' |
| messages2 = _infer(engine, num_tools=2) |
| assert messages[-1]['content'] == messages2[-1]['content'] == ( |
| 'Today in Beijing, the temperature is 32 degrees Celsius with sunny conditions ' |
| 'and the humidity is at 50%. Enjoy the nice weather!') |
| 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'][:-1] |
|
|
|
|
| def test_toolbench(): |
| agent_template = agent_templates['toolbench']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 1833 |
| engine = PtEngine('Qwen/Qwen2.5-7B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| _infer(engine) |
|
|
|
|
| def test_glm4(): |
| agent_template = agent_templates['glm4']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 846 |
| engine = PtEngine('ZhipuAI/glm-4-9b-chat') |
| template = engine.default_template |
| template.agent_template = agent_template |
| _infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query) |
|
|
|
|
| def test_glm4_0414(): |
| agent_template = agent_templates['glm4_0414']() |
| new_system = agent_template._format_tools(tools, system) |
| assert len(new_system) == 769 |
| engine = PtEngine('ZhipuAI/GLM-4-9B-0414') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine, agent_tools=glm4_tools, tool_messages=glm4_tool_messasges, query=glm4_query) |
| 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。' |
| 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"])}') |
|
|
|
|
| def test_llama3(): |
| agent_template = agent_templates['llama3']() |
| engine = PtEngine('LLM-Research/Llama-3.2-3B-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine) |
|
|
| 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"])}') |
|
|
|
|
| def test_llama4(): |
| agent_template = agent_templates['llama4']() |
| engine = PtEngine('LLM-Research/Llama-4-Scout-17B-16E-Instruct') |
| template = engine.default_template |
| template.agent_template = agent_template |
| messages = _infer(engine) |
| 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"])}') |
|
|
|
|
| if __name__ == '__main__': |
| from swift.plugin import agent_templates |
| from swift.llm import PtEngine, InferRequest, RequestConfig, load_dataset |
| |
| |
| |
| |
| |
| |
| test_hermes() |
| |
| |
| |
| |
| |
|
|
