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feature: Add presence_penalty and frequency_penalty parameters to the … (#5637)
Co-authored-by: liuzhenghua-jk <liuzhenghua-jk@360shuke.com>
This commit is contained in:
liuzhenghua
GitHub
parent
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commit
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@ -39,6 +39,7 @@ from core.model_runtime.entities.message_entities import (
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)
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from core.model_runtime.entities.model_entities import (
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AIModelEntity,
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DefaultParameterName,
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FetchFrom,
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ModelFeature,
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ModelPropertyKey,
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@ -67,7 +68,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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def _invoke(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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model_parameters: dict, tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
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-> LLMResult | Generator:
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-> LLMResult | Generator:
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"""
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invoke LLM
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@ -113,7 +114,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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elif 'generate' in extra_param.model_ability:
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credentials['completion_type'] = 'completion'
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else:
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raise ValueError(f'xinference model ability {extra_param.model_ability} is not supported, check if you have the right model type')
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raise ValueError(
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f'xinference model ability {extra_param.model_ability} is not supported, check if you have the right model type')
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if extra_param.support_function_call:
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credentials['support_function_call'] = True
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@ -206,6 +208,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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:param tools: tools for tool calling
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:return: number of tokens
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"""
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def tokens(text: str):
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return self._get_num_tokens_by_gpt2(text)
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@ -339,6 +342,45 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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zh_Hans='最大生成长度',
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en_US='Max Tokens'
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)
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),
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ParameterRule(
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name=DefaultParameterName.PRESENCE_PENALTY,
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use_template=DefaultParameterName.PRESENCE_PENALTY,
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type=ParameterType.FLOAT,
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label=I18nObject(
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en_US='Presence Penalty',
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zh_Hans='存在惩罚',
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),
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required=False,
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help=I18nObject(
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en_US='Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they '
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'appear in the text so far, increasing the model\'s likelihood to talk about new topics.',
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zh_Hans='介于 -2.0 和 2.0 之间的数字。正值会根据新词是否已出现在文本中对其进行惩罚，从而增加模型谈论新话题的可能性。'
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),
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default=0.0,
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min=-2.0,
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max=2.0,
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precision=2
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),
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ParameterRule(
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name=DefaultParameterName.FREQUENCY_PENALTY,
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use_template=DefaultParameterName.FREQUENCY_PENALTY,
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type=ParameterType.FLOAT,
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label=I18nObject(
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en_US='Frequency Penalty',
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zh_Hans='频率惩罚',
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),
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required=False,
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help=I18nObject(
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en_US='Number between -2.0 and 2.0. Positive values penalize new tokens based on their '
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'existing frequency in the text so far, decreasing the model\'s likelihood to repeat the '
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'same line verbatim.',
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zh_Hans='介于 -2.0 和 2.0 之间的数字。正值会根据新词在文本中的现有频率对其进行惩罚，从而降低模型逐字重复相同内容的可能性。'
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),
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default=0.0,
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min=-2.0,
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max=2.0,
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precision=2
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)
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]
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@ -364,7 +406,6 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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else:
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raise ValueError(f'xinference model ability {extra_args.model_ability} is not supported')
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features = []
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support_function_call = credentials.get('support_function_call', False)
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@ -395,9 +436,9 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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return entity
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def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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model_parameters: dict, extra_model_kwargs: XinferenceModelExtraParameter,
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tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
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model_parameters: dict, extra_model_kwargs: XinferenceModelExtraParameter,
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tools: list[PromptMessageTool] | None = None,
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stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
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-> LLMResult | Generator:
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"""
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generate text from LLM
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@ -429,6 +470,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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'temperature': model_parameters.get('temperature', 1.0),
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'top_p': model_parameters.get('top_p', 0.7),
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'max_tokens': model_parameters.get('max_tokens', 512),
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'presence_penalty': model_parameters.get('presence_penalty', 0.0),
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'frequency_penalty': model_parameters.get('frequency_penalty', 0.0),
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}
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if stop:
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@ -453,10 +496,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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if stream:
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if tools and len(tools) > 0:
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raise InvokeBadRequestError('xinference tool calls does not support stream mode')
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return self._handle_chat_stream_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_chat_generate_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_chat_stream_response(model=model, credentials=credentials,
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prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_chat_generate_response(model=model, credentials=credentials,
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prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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elif isinstance(xinference_model, RESTfulGenerateModelHandle):
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resp = client.completions.create(
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model=credentials['model_uid'],
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@ -466,10 +511,12 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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**generate_config,
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)
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if stream:
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return self._handle_completion_stream_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_completion_generate_response(model=model, credentials=credentials, prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_completion_stream_response(model=model, credentials=credentials,
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prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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return self._handle_completion_generate_response(model=model, credentials=credentials,
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prompt_messages=prompt_messages,
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tools=tools, resp=resp)
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else:
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raise NotImplementedError(f'xinference model handle type {type(xinference_model)} is not supported')
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@ -523,8 +570,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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return tool_call
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def _handle_chat_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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tools: list[PromptMessageTool],
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resp: ChatCompletion) -> LLMResult:
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tools: list[PromptMessageTool],
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resp: ChatCompletion) -> LLMResult:
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"""
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handle normal chat generate response
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"""
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@ -549,7 +596,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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prompt_tokens = self._num_tokens_from_messages(messages=prompt_messages, tools=tools)
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completion_tokens = self._num_tokens_from_messages(messages=[assistant_prompt_message], tools=tools)
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usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
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usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens)
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response = LLMResult(
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model=model,
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@ -560,10 +608,10 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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)
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return response
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def _handle_chat_stream_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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tools: list[PromptMessageTool],
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resp: Iterator[ChatCompletionChunk]) -> Generator:
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tools: list[PromptMessageTool],
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resp: Iterator[ChatCompletionChunk]) -> Generator:
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"""
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handle stream chat generate response
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"""
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@ -634,8 +682,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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full_response += delta.delta.content
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def _handle_completion_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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tools: list[PromptMessageTool],
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resp: Completion) -> LLMResult:
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tools: list[PromptMessageTool],
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resp: Completion) -> LLMResult:
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"""
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handle normal completion generate response
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"""
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@ -671,8 +719,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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return response
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def _handle_completion_stream_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
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tools: list[PromptMessageTool],
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resp: Iterator[Completion]) -> Generator:
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tools: list[PromptMessageTool],
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resp: Iterator[Completion]) -> Generator:
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"""
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handle stream completion generate response
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"""
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@ -764,4 +812,4 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
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InvokeBadRequestError: [
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ValueError
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]
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}
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}
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