diff --git a/python/pyproject.toml b/python/pyproject.toml index 649f9415a942..9edbe0177c67 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -73,7 +73,8 @@ hugging_face = [ "torch == 2.4.1" ] mongo = [ - "motor >= 3.3.2,< 3.7.0" + "pymongo >= 4.8.0, < 4.9", + "motor >= 3.3.2,< 3.6.0" ] notebooks = [ "ipykernel ~= 6.29" @@ -88,6 +89,9 @@ mistralai = [ ollama = [ "ollama ~= 0.2" ] +onnx = [ + "onnxruntime-genai ~= 0.4; platform_system != 'Darwin'" +] anthropic = [ "anthropic ~= 0.32" ] diff --git a/python/samples/concepts/README.md b/python/samples/concepts/README.md index 507f2dee5650..96c871f0d7cf 100644 --- a/python/samples/concepts/README.md +++ b/python/samples/concepts/README.md @@ -10,7 +10,7 @@ This section contains code snippets that demonstrate the usage of Semantic Kerne | Filtering | Creating and using Filters | | Functions | Invoking [`Method`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_method.py) or [`Prompt`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/functions/kernel_function_from_prompt.py) functions with [`Kernel`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/kernel.py) | | Grounding | An example of how to perform LLM grounding | -| Local Models | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) to talk to models hosted locally in Ollama and LM Studio | +| Local Models | Using the [`OpenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/open_ai/services/open_ai_chat_completion.py) and [`OnnxGenAI connector`](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py) to talk to models hosted locally in Ollama, OnnxGenAI and LM Studio | | Logging | Showing how to set up logging | | Memory | Using [`Memory`](https://github.com/microsoft/semantic-kernel/tree/main/dotnet/src/SemanticKernel.Abstractions/Memory) AI concepts | | Model-as-a-Service | Using models deployed as [`serverless APIs on Azure AI Studio`](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models-serverless?tabs=azure-ai-studio) to benchmark model performance against open-source datasets | diff --git a/python/samples/concepts/local_models/onnx_chat_completion.py b/python/samples/concepts/local_models/onnx_chat_completion.py new file mode 100644 index 000000000000..9eed17922976 --- /dev/null +++ b/python/samples/concepts/local_models/onnx_chat_completion.py @@ -0,0 +1,75 @@ +# Copyright (c) Microsoft. All rights reserved. + + +import asyncio + +from semantic_kernel.connectors.ai.onnx import OnnxGenAIChatCompletion, OnnxGenAIPromptExecutionSettings +from semantic_kernel.contents.chat_history import ChatHistory +from semantic_kernel.kernel import Kernel + +# This concept sample shows how to use the Onnx connector with +# a local model running in Onnx + +kernel = Kernel() + +service_id = "phi3" +############################################# +# Make sure to download an ONNX model +# (https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) +# If onnxruntime-genai is used: +# use the model stored in /cpu folder +# If onnxruntime-genai-cuda is installed for gpu use: +# use the model stored in /cuda folder +# Then set ONNX_GEN_AI_CHAT_MODEL_FOLDER environment variable to the path to the model folder +############################################# +streaming = True + +chat_completion = OnnxGenAIChatCompletion(ai_model_id=service_id, template="phi3") +settings = OnnxGenAIPromptExecutionSettings() + +system_message = """You are a helpful assistant.""" +chat_history = ChatHistory(system_message=system_message) + + +async def chat() -> bool: + try: + user_input = input("User:> ") + except KeyboardInterrupt: + print("\n\nExiting chat...") + return False + except EOFError: + print("\n\nExiting chat...") + return False + + if user_input == "exit": + print("\n\nExiting chat...") + return False + chat_history.add_user_message(user_input) + if streaming: + print("Mosscap:> ", end="") + message = "" + async for chunk in chat_completion.get_streaming_chat_message_content( + chat_history=chat_history, settings=settings, kernel=kernel + ): + if chunk: + print(str(chunk), end="") + message += str(chunk) + chat_history.add_assistant_message(message) + print("") + else: + answer = await chat_completion.get_chat_message_content( + chat_history=chat_history, settings=settings, kernel=kernel + ) + print(f"Mosscap:> {answer}") + chat_history.add_message(answer) + return True + + +async def main() -> None: + chatting = True + while chatting: + chatting = await chat() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/concepts/local_models/onnx_phi3_vision_completion.py b/python/samples/concepts/local_models/onnx_phi3_vision_completion.py new file mode 100644 index 000000000000..b983a3dba208 --- /dev/null +++ b/python/samples/concepts/local_models/onnx_phi3_vision_completion.py @@ -0,0 +1,91 @@ +# Copyright (c) Microsoft. All rights reserved. + + +import asyncio + +from semantic_kernel.connectors.ai.onnx import OnnxGenAIChatCompletion, OnnxGenAIPromptExecutionSettings +from semantic_kernel.contents import AuthorRole, ChatHistory, ChatMessageContent, ImageContent +from semantic_kernel.kernel import Kernel + +# This concept sample shows how to use the Onnx connector with +# a local model running in Onnx + +kernel = Kernel() + +service_id = "phi3" +############################################# +# Make sure to download an ONNX model +# If onnxruntime-genai is used: +# (https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cpu) +# If onnxruntime-genai-cuda is installed for gpu use: +# (https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-gpu) +# Then set ONNX_GEN_AI_CHAT_MODEL_FOLDER environment variable to the path to the model folder +############################################# +streaming = True + +chat_completion = OnnxGenAIChatCompletion(ai_model_id=service_id, template="phi3v") + +# Max length property is important to allocate RAM +# If the value is too big, you ran out of memory +# If the value is too small, your input is limited +settings = OnnxGenAIPromptExecutionSettings(max_length=4096) + +system_message = """ +You are a helpful assistant. +You know about provided images and the history of the conversation. +""" +chat_history = ChatHistory(system_message=system_message) + + +async def chat() -> bool: + try: + user_input = input("User:> ") + except KeyboardInterrupt: + print("\n\nExiting chat...") + return False + except EOFError: + print("\n\nExiting chat...") + return False + + if user_input == "exit": + print("\n\nExiting chat...") + return False + chat_history.add_user_message(user_input) + if streaming: + print("Mosscap:> ", end="") + message = "" + async for chunk in chat_completion.get_streaming_chat_message_content( + chat_history=chat_history, settings=settings, kernel=kernel + ): + print(chunk.content, end="") + if chunk.content: + message += chunk.content + chat_history.add_assistant_message(message) + print("") + else: + answer = await chat_completion.get_chat_message_content( + chat_history=chat_history, settings=settings, kernel=kernel + ) + print(f"Mosscap:> {answer}") + chat_history.add_message(message) + return True + + +async def main() -> None: + chatting = True + image_path = input("Image Path (leave empty if no image): ") + if image_path: + chat_history.add_message( + ChatMessageContent( + role=AuthorRole.USER, + items=[ + ImageContent.from_image_path(image_path=image_path), + ], + ), + ) + while chatting: + chatting = await chat() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/samples/concepts/local_models/onnx_text_completion.py b/python/samples/concepts/local_models/onnx_text_completion.py new file mode 100644 index 000000000000..f5acc598cd39 --- /dev/null +++ b/python/samples/concepts/local_models/onnx_text_completion.py @@ -0,0 +1,76 @@ +# Copyright (c) Microsoft. All rights reserved. + + +import asyncio + +from semantic_kernel.connectors.ai.onnx import OnnxGenAITextCompletion +from semantic_kernel.functions.kernel_arguments import KernelArguments +from semantic_kernel.kernel import Kernel + +# This concept sample shows how to use the Onnx connector with +# a local model running in Onnx + +kernel = Kernel() + +service_id = "phi3" +############################################# +# Make sure to download an ONNX model +# (https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) +# If onnxruntime-genai is used: +# use the model stored in /cpu folder +# If onnxruntime-genai-cuda is installed for gpu use: +# use the model stored in /cuda folder +# Then set ONNX_GEN_AI_TEXT_MODEL_FOLDER environment variable to the path to the model folder +############################################# +streaming = True + +kernel.add_service(OnnxGenAITextCompletion(ai_model_id=service_id)) + +settings = kernel.get_prompt_execution_settings_from_service_id(service_id) + +# Phi3 Model is using chat templates to generate responses +# With the Chat Template the model understands +# the context and roles of the conversation better +# https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format +chat_function = kernel.add_function( + plugin_name="ChatBot", + function_name="Chat", + prompt="<|user|>{{$user_input}}<|end|><|assistant|>", + template_format="semantic-kernel", + prompt_execution_settings=settings, +) + + +async def chat() -> bool: + try: + user_input = input("User:> ") + except KeyboardInterrupt: + print("\n\nExiting chat...") + return False + except EOFError: + print("\n\nExiting chat...") + return False + + if user_input == "exit": + print("\n\nExiting chat...") + return False + + if streaming: + print("Mosscap:> ", end="") + async for chunk in kernel.invoke_stream(chat_function, KernelArguments(user_input=user_input)): + print(chunk[0].text, end="") + print("\n") + else: + answer = await kernel.invoke(chat_function, KernelArguments(user_input=user_input)) + print(f"Mosscap:> {answer}") + return True + + +async def main() -> None: + chatting = True + while chatting: + chatting = await chat() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/python/semantic_kernel/connectors/ai/ollama/services/ollama_text_completion.py b/python/semantic_kernel/connectors/ai/ollama/services/ollama_text_completion.py index c492f6e08a78..2ee657069598 100644 --- a/python/semantic_kernel/connectors/ai/ollama/services/ollama_text_completion.py +++ b/python/semantic_kernel/connectors/ai/ollama/services/ollama_text_completion.py @@ -68,6 +68,9 @@ def __init__( except ValidationError as ex: raise ServiceInitializationError("Failed to create Ollama settings.", ex) from ex + if not ollama_settings.model: + raise ServiceInitializationError("Please provide ai_model_id or OLLAMA_MODEL env variable is required") + super().__init__( service_id=service_id or ollama_settings.model, ai_model_id=ollama_settings.model, diff --git a/python/semantic_kernel/connectors/ai/onnx/__init__.py b/python/semantic_kernel/connectors/ai/onnx/__init__.py new file mode 100644 index 000000000000..974187cb000c --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Microsoft. All rights reserved. + +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_prompt_execution_settings import ( + OnnxGenAIPromptExecutionSettings, +) +from semantic_kernel.connectors.ai.onnx.services.onnx_gen_ai_chat_completion import OnnxGenAIChatCompletion +from semantic_kernel.connectors.ai.onnx.services.onnx_gen_ai_text_completion import OnnxGenAITextCompletion + +__all__ = ['OnnxGenAIChatCompletion', 'OnnxGenAIPromptExecutionSettings', 'OnnxGenAITextCompletion'] diff --git a/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_prompt_execution_settings.py b/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_prompt_execution_settings.py new file mode 100644 index 000000000000..aebc2d231cb2 --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_prompt_execution_settings.py @@ -0,0 +1,25 @@ +# Copyright (c) Microsoft. All rights reserved. + + +from pydantic import Field + +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings + + +class OnnxGenAIPromptExecutionSettings(PromptExecutionSettings): + """OnnxGenAI prompt execution settings.""" + + diversity_penalty: float | None = Field(None, ge=0.0, le=1.0) + do_sample: bool = False + early_stopping: bool = True + length_penalty: float | None = Field(None, ge=0.0, le=1.0) + max_length: int = Field(3072, gt=0) + min_length: int | None = Field(None, gt=0) + no_repeat_ngram_size: int = 0 + num_beams: int | None = Field(None, gt=0) + num_return_sequences: int | None = Field(None, gt=0) + past_present_share_buffer: int = True + repetition_penalty: float | None = Field(None, ge=0.0, le=1.0) + temperature: float | None = Field(None, ge=0.0, le=2.0) + top_k: int | None = Field(None, gt=0) + top_p: float | None = Field(None, ge=0.0, le=1.0) diff --git a/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_settings.py b/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_settings.py new file mode 100644 index 000000000000..61e5e2391796 --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/onnx_gen_ai_settings.py @@ -0,0 +1,23 @@ +# Copyright (c) Microsoft. All rights reserved. + +from typing import ClassVar + +from semantic_kernel.kernel_pydantic import KernelBaseSettings + + +class OnnxGenAISettings(KernelBaseSettings): + """Onnx Gen AI model settings. + + The settings are first loaded from environment variables with the prefix 'ONNX_GEN_AI_'. If the + environment variables are not found, the settings can be loaded from a .env file with the + encoding 'utf-8'. If the settings are not found in the .env file, the settings are ignored; + however, validation will fail alerting that the settings are missing. + + Optional settings for prefix 'ONNX_GEN_AI_' are: + - chat_model_folder: Path to the Onnx chat model folder (ENV: ONNX_GEN_AI_CHAT_MODEL_FOLDER). + - text_model_folder: Path to the Onnx text model folder (ENV: ONNX_GEN_AI_TEXT_MODEL_FOLDER). + """ + + env_prefix: ClassVar[str] = "ONNX_GEN_AI_" + chat_model_folder: str | None = None + text_model_folder: str | None = None diff --git a/python/semantic_kernel/connectors/ai/onnx/services/__init__.py b/python/semantic_kernel/connectors/ai/onnx/services/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py new file mode 100644 index 000000000000..3f627e12c665 --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_chat_completion.py @@ -0,0 +1,179 @@ +# Copyright (c) Microsoft. All rights reserved. + +import logging +import sys +from collections.abc import AsyncGenerator +from typing import Any, ClassVar + +if sys.version_info >= (3, 12): + from typing import override # pragma: no cover +else: + from typing_extensions import override # pragma: no cover + + +from pydantic import ValidationError + +from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_prompt_execution_settings import OnnxGenAIPromptExecutionSettings +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_settings import OnnxGenAISettings +from semantic_kernel.connectors.ai.onnx.services.onnx_gen_ai_completion_base import OnnxGenAICompletionBase +from semantic_kernel.connectors.ai.onnx.utils import ONNXTemplate, apply_template +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings +from semantic_kernel.contents import ( + ChatHistory, + ChatMessageContent, + ImageContent, + StreamingChatMessageContent, + TextContent, +) +from semantic_kernel.contents.utils.author_role import AuthorRole +from semantic_kernel.exceptions import ServiceInitializationError, ServiceInvalidExecutionSettingsError +from semantic_kernel.utils.experimental_decorator import experimental_class + +logger: logging.Logger = logging.getLogger(__name__) + + +@experimental_class +class OnnxGenAIChatCompletion(ChatCompletionClientBase, OnnxGenAICompletionBase): + """OnnxGenAI text completion service.""" + + template: ONNXTemplate + SUPPORTS_FUNCTION_CALLING: ClassVar[bool] = False + + def __init__( + self, + template: ONNXTemplate, + ai_model_path: str | None = None, + ai_model_id: str | None = None, + env_file_path: str | None = None, + env_file_encoding: str | None = None, + ) -> None: + """Initializes a new instance of the OnnxGenAITextCompletion class. + + Args: + template : The chat template configuration. + ai_model_path : Local path to the ONNX model Folder. + ai_model_id : The ID of the AI model. Defaults to None. + env_file_path : Use the environment settings file as a fallback + to environment variables. + env_file_encoding : The encoding of the environment settings file. + """ + try: + settings = OnnxGenAISettings.create( + chat_model_folder=ai_model_path, + env_file_path=env_file_path, + env_file_encoding=env_file_encoding, + ) + except ValidationError as e: + raise ServiceInitializationError(f"Error creating OnnxGenAISettings: {e}") from e + + if settings.chat_model_folder is None: + raise ServiceInitializationError( + "AI model path is not provided. Please provide the 'ai_model_path' parameter in the constructor. " + "OR set the 'ONNX_GEN_AI_CHAT_MODEL_FOLDER' environment variable." + ) + + if ai_model_id is None: + ai_model_id = settings.chat_model_folder + + super().__init__(ai_model_id=ai_model_id, ai_model_path=settings.chat_model_folder, template=template) + + @override + async def _inner_get_chat_message_contents( + self, + chat_history: "ChatHistory", + settings: "PromptExecutionSettings", + ) -> list["ChatMessageContent"]: + """Create chat message contents, in the number specified by the settings. + + Args: + chat_history : A list of chats in a chat_history object, that can be + rendered into messages from system, user, assistant and tools. + settings : Settings for the request. + + Returns: + A list of chat message contents representing the response(s) from the LLM. + """ + if not isinstance(settings, OnnxGenAIPromptExecutionSettings): + settings = self.get_prompt_execution_settings_from_settings(settings) + assert isinstance(settings, OnnxGenAIPromptExecutionSettings) # nosec + prompt = self._prepare_chat_history_for_request(chat_history) + images = self._get_images_from_history(chat_history) + choices = await self._generate_next_token(prompt, settings, images) + return [self._create_chat_message_content(choice) for choice in choices] + + @override + async def _inner_get_streaming_chat_message_contents( + self, + chat_history: "ChatHistory", + settings: "PromptExecutionSettings", + ) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]: + """Create streaming chat message contents, in the number specified by the settings. + + Args: + chat_history : A list of chat chat_history, that can be rendered into a + set of chat_history, from system, user, assistant and function. + settings : Settings for the request. + + Yields: + A stream representing the response(s) from the LLM. + """ + if not isinstance(settings, OnnxGenAIPromptExecutionSettings): + settings = self.get_prompt_execution_settings_from_settings(settings) + assert isinstance(settings, OnnxGenAIPromptExecutionSettings) # nosec + prompt = self._prepare_chat_history_for_request(chat_history) + images = self._get_images_from_history(chat_history) + async for chunk in self._generate_next_token_async(prompt, settings, images): + yield [ + self._create_streaming_chat_message_content(choice_index, new_token) + for choice_index, new_token in enumerate(chunk) + ] + + def _create_chat_message_content(self, choice: str) -> ChatMessageContent: + return ChatMessageContent( + role=AuthorRole.ASSISTANT, + items=[ + TextContent( + text=choice, + ai_model_id=self.ai_model_id, + ) + ], + ) + + def _create_streaming_chat_message_content(self, choice_index: int, choice: str) -> StreamingChatMessageContent: + return StreamingChatMessageContent( + role=AuthorRole.ASSISTANT, + choice_index=choice_index, + content=choice, + ai_model_id=self.ai_model_id, + ) + + @override + def _prepare_chat_history_for_request( + self, chat_history: ChatHistory, role_key: str = "role", content_key: str = "content" + ) -> Any: + return apply_template(chat_history, self.template) + + def _get_images_from_history(self, chat_history: "ChatHistory") -> ImageContent | None: + images = [] + for message in chat_history.messages: + for image in message.items: + if isinstance(image, ImageContent): + if not self.enable_multi_modality: + raise ServiceInvalidExecutionSettingsError("The model does not support multi-modality") + if image.uri: + images.append(image) + else: + raise ServiceInvalidExecutionSettingsError( + "Image Content URI needs to be set, because onnx can only work with file paths" + ) + # Currently Onnx Runtime only supports one image + # Later we will add support for multiple images + if len(images) > 1: + raise ServiceInvalidExecutionSettingsError("The model does not support more than one image") + return images[-1] if images else None + + @override + def get_prompt_execution_settings_class(self) -> type["PromptExecutionSettings"]: + """Create a request settings object.""" + return OnnxGenAIPromptExecutionSettings diff --git a/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_completion_base.py b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_completion_base.py new file mode 100644 index 000000000000..c7e2c47d12d4 --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_completion_base.py @@ -0,0 +1,105 @@ +# Copyright (c) Microsoft. All rights reserved. + +import json +import os +from collections.abc import AsyncGenerator +from typing import Any + +import onnxruntime_genai as OnnxRuntimeGenAi + +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_prompt_execution_settings import OnnxGenAIPromptExecutionSettings +from semantic_kernel.contents import ImageContent +from semantic_kernel.exceptions import ServiceInitializationError, ServiceInvalidResponseError +from semantic_kernel.kernel_pydantic import KernelBaseModel + + +class OnnxGenAICompletionBase(KernelBaseModel): + """Base class for OnnxGenAI Completion services.""" + + model: Any + tokenizer: Any + tokenizer_stream: Any + enable_multi_modality: bool = False + + def __init__(self, ai_model_path: str, **kwargs) -> None: + """Creates a new instance of the OnnxGenAICompletionBase class, loads model & tokenizer. + + Args: + ai_model_path : Path to Onnx Model. + **kwargs: Additional keyword arguments. + + Raises: + ServiceInitializationError: When model cannot be loaded + """ + try: + json_gen_ai_config = os.path.join(ai_model_path + "/genai_config.json") + with open(json_gen_ai_config) as file: + config: dict = json.load(file) + enable_multi_modality = "vision" in config.get("model", {}) + model = OnnxRuntimeGenAi.Model(ai_model_path) + if enable_multi_modality: + tokenizer = model.create_multimodal_processor() + else: + tokenizer = OnnxRuntimeGenAi.Tokenizer(model) + tokenizer_stream = tokenizer.create_stream() + except Exception as ex: + raise ServiceInitializationError("Failed to initialize OnnxTextCompletion service", ex) from ex + + super().__init__( + model=model, + tokenizer=tokenizer, + tokenizer_stream=tokenizer_stream, + enable_multi_modality=enable_multi_modality, + **kwargs, + ) + + def _prepare_input_params( + self, prompt: str, settings: OnnxGenAIPromptExecutionSettings, image: ImageContent | None = None + ) -> Any: + params = OnnxRuntimeGenAi.GeneratorParams(self.model) + params.set_search_options(**settings.prepare_settings_dict()) + if not self.enable_multi_modality: + input_tokens = self.tokenizer.encode(prompt) + params.input_ids = input_tokens + else: + if image is not None: + # With the use of Pybind there is currently no way to load images from bytes + # We can only open images from a file path currently + image = OnnxRuntimeGenAi.Images.open(str(image.uri)) + input_tokens = self.tokenizer(prompt, images=image) + params.set_inputs(input_tokens) + return params + + async def _generate_next_token_async( + self, + prompt: str, + settings: OnnxGenAIPromptExecutionSettings, + image: ImageContent | None = None, + ) -> AsyncGenerator[list[str], Any]: + try: + params = self._prepare_input_params(prompt, settings, image) + generator = OnnxRuntimeGenAi.Generator(self.model, params) + + while not generator.is_done(): + generator.compute_logits() + generator.generate_next_token() + new_token_choices = [self.tokenizer_stream.decode(token) for token in generator.get_next_tokens()] + yield new_token_choices + del generator + except Exception as ex: + raise ServiceInvalidResponseError("Failed Inference with ONNX", ex) from ex + + async def _generate_next_token( + self, + prompt: str, + settings: OnnxGenAIPromptExecutionSettings, + image: ImageContent | None = None, + ): + token_choices: list[str] = [] + async for new_token_choice in self._generate_next_token_async(prompt, settings, image): + # zip only works if the lists are the same length + if len(token_choices) == 0: + token_choices = new_token_choice + else: + token_choices = [old_token + new_token for old_token, new_token in zip(token_choices, new_token_choice)] + return token_choices diff --git a/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_text_completion.py b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_text_completion.py new file mode 100644 index 000000000000..11aa88165be4 --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/services/onnx_gen_ai_text_completion.py @@ -0,0 +1,133 @@ +# Copyright (c) Microsoft. All rights reserved. + +import logging +import sys +from collections.abc import AsyncGenerator +from typing import Any + +if sys.version_info >= (3, 12): + from typing import override # pragma: no cover +else: + from typing_extensions import override # pragma: no cover + +from pydantic import ValidationError + +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_prompt_execution_settings import OnnxGenAIPromptExecutionSettings +from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_settings import OnnxGenAISettings +from semantic_kernel.connectors.ai.onnx.services.onnx_gen_ai_completion_base import OnnxGenAICompletionBase +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings +from semantic_kernel.connectors.ai.text_completion_client_base import TextCompletionClientBase +from semantic_kernel.contents.streaming_text_content import StreamingTextContent +from semantic_kernel.contents.text_content import TextContent +from semantic_kernel.exceptions import ServiceInitializationError +from semantic_kernel.utils.experimental_decorator import experimental_class + +logger: logging.Logger = logging.getLogger(__name__) + + +@experimental_class +class OnnxGenAITextCompletion(TextCompletionClientBase, OnnxGenAICompletionBase): + """OnnxGenAI text completion service.""" + + def __init__( + self, + ai_model_path: str | None = None, + ai_model_id: str | None = None, + env_file_path: str | None = None, + env_file_encoding: str | None = None, + ) -> None: + """Initializes a new instance of the OnnxGenAITextCompletion class. + + Args: + ai_model_path : Local path to the ONNX model Folder. + ai_model_id : The ID of the AI model. Defaults to None. + env_file_path : Use the environment settings file as a fallback + to environment variables. + env_file_encoding : The encoding of the environment settings file. + """ + try: + settings = OnnxGenAISettings.create( + text_model_folder=ai_model_path, + env_file_path=env_file_path, + env_file_encoding=env_file_encoding, + ) + except ValidationError as e: + raise ServiceInitializationError(f"Invalid settings for OnnxGenAITextCompletion: {e}") + + if settings.text_model_folder is None: + raise ServiceInitializationError( + "AI model path is not provided. Please provide the 'ai_model_path' parameter in the constructor. " + "OR set the 'ONNX_GEN_AI_TEXT_MODEL_FOLDER' environment variable." + ) + + if ai_model_id is None: + ai_model_id = settings.text_model_folder + + super().__init__( + ai_model_id=ai_model_id, + ai_model_path=settings.text_model_folder, + ) + + @override + async def _inner_get_text_contents( + self, + prompt: str, + settings: PromptExecutionSettings, + ) -> list[TextContent]: + """This is the method that is called from the kernel to get a response from a text-optimized LLM. + + Args: + prompt : The prompt to send to the LLM. + settings : Settings for the request. + + Returns: + List[TextContent]: A list of TextContent objects representing the response(s) from the LLM. + """ + if not isinstance(settings, OnnxGenAIPromptExecutionSettings): + settings = self.get_prompt_execution_settings_from_settings(settings) + assert isinstance(settings, OnnxGenAIPromptExecutionSettings) # nosec + + choices = await self._generate_next_token(prompt, settings) + return [ + TextContent( + text=choice, + ai_model_id=self.ai_model_id, + ) + for choice in choices + ] + + @override + async def _inner_get_streaming_text_contents( + self, + prompt: str, + settings: PromptExecutionSettings, + ) -> AsyncGenerator[list[StreamingTextContent], Any]: + """Streams a text completion using a Onnx model. + + Note that this method does not support multiple responses. + + Args: + prompt : Prompt to complete. + settings : Request settings. + + Yields: + List[StreamingTextContent]: List of StreamingTextContent objects. + """ + if not isinstance(settings, OnnxGenAIPromptExecutionSettings): + settings = self.get_prompt_execution_settings_from_settings(settings) + assert isinstance(settings, OnnxGenAIPromptExecutionSettings) # nosec + + async for token_choice in self._generate_next_token_async(prompt, settings): + yield [ + StreamingTextContent( + choice_index=index, inner_content=new_token, text=new_token, ai_model_id=self.ai_model_id + ) + for index, new_token in enumerate(token_choice) + ] + + return + + @override + def get_prompt_execution_settings_class(self) -> type["PromptExecutionSettings"]: + """Create a request settings object.""" + return OnnxGenAIPromptExecutionSettings diff --git a/python/semantic_kernel/connectors/ai/onnx/utils.py b/python/semantic_kernel/connectors/ai/onnx/utils.py new file mode 100644 index 000000000000..80c93e0ebceb --- /dev/null +++ b/python/semantic_kernel/connectors/ai/onnx/utils.py @@ -0,0 +1,135 @@ +# Copyright (c) Microsoft. All rights reserved. +from enum import Enum + +from semantic_kernel.contents import AuthorRole, ChatHistory, ImageContent, TextContent +from semantic_kernel.exceptions import ServiceException, ServiceInvalidRequestError + + +class ONNXTemplate(str, Enum): + """ONNXTemplate is an enumeration that represents different ONNX model templates. + + Attributes: + PHI3 (str): Represents the "phi3" ONNX model template. + PHI3V (str): Represents the "phi3v" ONNX model template. + GEMMA (str): Represents the "gemma" ONNX model template. + LLAMA (str): Represents the "llama" ONNX model template. + NONE (str): Can be chosen if no Template should be used. + + """ + + PHI3 = "phi3" + PHI3V = "phi3v" + GEMMA = "gemma" + LLAMA = "llama" + NONE = "none" + + +def apply_template(history: ChatHistory, template: ONNXTemplate) -> str: + """Apply the specified ONNX template to the given chat history. + + Args: + history (ChatHistory): The chat history to which the template will be applied. + template (ONNXTemplate): The ONNX template to apply. + + Returns: + str: The result of applying the template to the chat history. + + Raises: + ServiceException: If an error occurs while applying the template. + """ + template_functions = { + ONNXTemplate.PHI3: phi3_template, + ONNXTemplate.GEMMA: gemma_template, + ONNXTemplate.LLAMA: llama_template, + ONNXTemplate.PHI3V: phi3v_template, + ONNXTemplate.NONE: lambda text: text, + } + + try: + return template_functions[template](history) + except Exception as e: + raise ServiceException(f"An error occurred while applying the template: {template.value}") from e + + +def phi3_template(history: ChatHistory) -> str: + """Generates a formatted string from the chat history for use with the phi3 model. + + Args: + history (ChatHistory): An object containing the chat history with a list of messages. + + Returns: + str: A formatted string where each message is prefixed with the role and suffixed with an end marker. + """ + phi3_input = "" + for message in history.messages: + phi3_input += f"<|{message.role.value}|>\n{message.content}<|end|>\n" + phi3_input += "<|assistant|>\n" + return phi3_input + + +def phi3v_template(history: ChatHistory) -> str: + """Generates a formatted string from a given chat history for use with the phi3v model. + + Args: + history (ChatHistory): An object containing the chat history with messages. + + Returns: + str: A formatted string representing the chat history, with special tokens indicating + the role of each message (system, user, assistant) and the type of content (text, image). + """ + phi3v_input = "" + for message in history.messages: + if message.role == AuthorRole.SYSTEM: + phi3v_input += f"<|system|>\n{message.content}<|end|>\n" + if message.role == AuthorRole.USER: + for item in message.items: + if isinstance(item, TextContent): + phi3v_input += f"<|user|>\n{item.text}<|end|>\n" + # Currently only one image is supported in Onnx + if isinstance(item, ImageContent): + phi3v_input += "<|image_1|>\n" + if message.role == AuthorRole.ASSISTANT: + phi3v_input += f"<|assistant|>\n{message.content}<|end|>\n" + phi3v_input += "<|assistant|>\n" + return phi3v_input + + +def gemma_template(history: ChatHistory) -> str: + """Generates a formatted string for the Gemma model based on the provided chat history. + + Args: + history (ChatHistory): An object containing the chat history with messages. + + Returns: + str: A formatted string representing the chat history for the Gemma model. + + Raises: + ServiceInvalidRequestError: If a system message is encountered in the chat history. + """ + gemma_input = "" + for message in history.messages: + if message.role == AuthorRole.SYSTEM: + raise ServiceInvalidRequestError("System messages are not supported in Gemma") + if message.role == AuthorRole.USER: + gemma_input += f"user\n{message.content}\n" + if message.role == AuthorRole.ASSISTANT: + gemma_input += f"model\n{message.content}\n" + gemma_input += "model\n" + return gemma_input + + +def llama_template(history: ChatHistory) -> str: + """Generates a formatted string from a given chat history for use with the LLaMA model. + + Args: + history (ChatHistory): An object containing the chat history, which includes a list of messages. + + Returns: + str: A formatted string where each message is wrapped with specific header and end tags, + and the final string ends with an assistant header tag. + """ + llama_input = "" + for message in history.messages: + llama_input += f"<|start_header_id|>{message.role.value}<|end_header_id|>\n\n{message.content}<|eot_id|>" + llama_input += "<|start_header_id|>assistant<|end_header_id|>" + return llama_input diff --git a/python/semantic_kernel/contents/binary_content.py b/python/semantic_kernel/contents/binary_content.py index d6ee57546cb2..cafba6ade0bc 100644 --- a/python/semantic_kernel/contents/binary_content.py +++ b/python/semantic_kernel/contents/binary_content.py @@ -1,10 +1,11 @@ # Copyright (c) Microsoft. All rights reserved. import logging +import os from typing import Annotated, Any, ClassVar, Literal, TypeVar from xml.etree.ElementTree import Element # nosec -from pydantic import Field, UrlConstraints, computed_field +from pydantic import Field, FilePath, UrlConstraints, computed_field from pydantic_core import Url from semantic_kernel.contents.const import BINARY_CONTENT_TAG, ContentTypes @@ -38,7 +39,7 @@ class BinaryContent(KernelContent): """ content_type: Literal[ContentTypes.BINARY_CONTENT] = Field(BINARY_CONTENT_TAG, init=False) # type: ignore - uri: Url | None = None + uri: Url | FilePath | None = None default_mime_type: ClassVar[str] = "text/plain" tag: ClassVar[str] = BINARY_CONTENT_TAG _data_uri: DataUri | None = None @@ -83,6 +84,9 @@ def __init__( _data_uri = DataUri( data_bytes=data, data_format=data_format, mime_type=mime_type or self.default_mime_type ) + if uri is not None: + uri = FilePath(uri) if os.path.exists(uri) else Url(uri) + super().__init__(uri=uri, **kwargs) self._data_uri = _data_uri diff --git a/python/semantic_kernel/contents/image_content.py b/python/semantic_kernel/contents/image_content.py index ccb472bbf60c..9125a9f0a58f 100644 --- a/python/semantic_kernel/contents/image_content.py +++ b/python/semantic_kernel/contents/image_content.py @@ -56,7 +56,7 @@ def from_image_path(cls: type[_T], image_path: str) -> _T: """Create an instance from an image file.""" mime_type = mimetypes.guess_type(image_path)[0] with open(image_path, "rb") as image_file: - return cls(data=image_file.read(), data_format="base64", mime_type=mime_type) + return cls(data=image_file.read(), data_format="base64", mime_type=mime_type, uri=image_path) def to_dict(self) -> dict[str, Any]: """Convert the instance to a dictionary.""" diff --git a/python/tests/integration/completions/chat_completion_test_base.py b/python/tests/integration/completions/chat_completion_test_base.py index d222cee41ca8..2a5a7dc53032 100644 --- a/python/tests/integration/completions/chat_completion_test_base.py +++ b/python/tests/integration/completions/chat_completion_test_base.py @@ -1,6 +1,7 @@ # Copyright (c) Microsoft. All rights reserved. +import platform import sys from functools import reduce from typing import Annotated, Any @@ -10,7 +11,6 @@ from azure.identity import DefaultAzureCredential from openai import AsyncAzureOpenAI -from semantic_kernel.connectors.ai.anthropic import AnthropicChatCompletion from semantic_kernel.connectors.ai.azure_ai_inference.azure_ai_inference_prompt_execution_settings import ( AzureAIInferenceChatPromptExecutionSettings, ) @@ -45,51 +45,29 @@ from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings from semantic_kernel.contents.chat_history import ChatHistory from semantic_kernel.core_plugins.math_plugin import MathPlugin -from semantic_kernel.exceptions import ServiceInitializationError from semantic_kernel.functions.kernel_function_decorator import kernel_function from semantic_kernel.kernel import Kernel from semantic_kernel.kernel_pydantic import KernelBaseModel from tests.integration.completions.completion_test_base import CompletionTestBase, ServiceType +from tests.integration.completions.test_utils import is_service_setup_for_testing if sys.version_info >= (3, 12): from typing import override # pragma: no cover else: from typing_extensions import override # pragma: no cover -mistral_ai_setup: bool = False -try: - MistralAIChatCompletion() - mistral_ai_setup = True -except ServiceInitializationError: - mistral_ai_setup = False - -ollama_setup: bool = False -try: - OllamaChatCompletion() - ollama_setup = True -except ServiceInitializationError: - ollama_setup = False - -google_ai_setup: bool = False -try: - GoogleAIChatCompletion() - google_ai_setup = True -except ServiceInitializationError: - google_ai_setup = False - -vertex_ai_setup: bool = False -try: - VertexAIChatCompletion() - vertex_ai_setup = True -except ServiceInitializationError: - vertex_ai_setup = False - -anthropic_setup: bool = False -try: - AnthropicChatCompletion() - anthropic_setup = True -except ServiceInitializationError: - anthropic_setup = False +# This can later also be simplified as map probably +mistral_ai_setup: bool = is_service_setup_for_testing("MISTRALAI_API_KEY") +ollama_setup: bool = is_service_setup_for_testing("OLLAMA_MODEL") +google_ai_setup: bool = is_service_setup_for_testing("GOOGLE_AI_API_KEY") +vertex_ai_setup: bool = is_service_setup_for_testing("VERTEX_AI_PROJECT_ID") +anthropic_setup: bool = is_service_setup_for_testing("ANTHROPIC_API_KEY") +onnx_setup: bool = is_service_setup_for_testing("ONNX_GEN_AI_CHAT_MODEL_FOLDER") + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx import OnnxGenAIChatCompletion, OnnxGenAIPromptExecutionSettings + from semantic_kernel.connectors.ai.onnx.utils import ONNXTemplate # A mock plugin that contains a function that returns a complex object. @@ -149,6 +127,10 @@ def services(self) -> dict[str, tuple[ServiceType, type[PromptExecutionSettings] "ollama": (OllamaChatCompletion() if ollama_setup else None, OllamaChatPromptExecutionSettings), "google_ai": (GoogleAIChatCompletion() if google_ai_setup else None, GoogleAIChatPromptExecutionSettings), "vertex_ai": (VertexAIChatCompletion() if vertex_ai_setup else None, VertexAIChatPromptExecutionSettings), + "onnx_gen_ai": ( + OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3V) if onnx_setup else None, + OnnxGenAIPromptExecutionSettings if not skip_on_mac_available else None, + ), } def setup(self, kernel: Kernel): diff --git a/python/tests/integration/completions/test_chat_completion_with_image_input_text_output.py b/python/tests/integration/completions/test_chat_completion_with_image_input_text_output.py index b70b7585c81d..3e7cbfcdbfc3 100644 --- a/python/tests/integration/completions/test_chat_completion_with_image_input_text_output.py +++ b/python/tests/integration/completions/test_chat_completion_with_image_input_text_output.py @@ -15,6 +15,7 @@ from tests.integration.completions.chat_completion_test_base import ( ChatCompletionTestBase, google_ai_setup, + onnx_setup, vertex_ai_setup, ) from tests.integration.completions.completion_test_base import ServiceType @@ -101,6 +102,25 @@ {}, id="azure_image_input_file", ), + pytest.param( + "onnx_gen_ai", + {}, + [ + ChatMessageContent( + role=AuthorRole.USER, + items=[ + TextContent(text="What is in this image?"), + ImageContent.from_image_path( + image_path=os.path.join(os.path.dirname(__file__), "../../", "assets/sample_image.jpg") + ), + ], + ), + ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="Where was it made?")]), + ], + {}, + marks=pytest.mark.skipif(not onnx_setup, reason="Need a Onnx Model setup"), + id="onnx_gen_ai_image_input_file", + ), pytest.param( "azure_ai_inference", { diff --git a/python/tests/integration/completions/test_chat_completions.py b/python/tests/integration/completions/test_chat_completions.py index 505f7100a2a9..6de673f3a50c 100644 --- a/python/tests/integration/completions/test_chat_completions.py +++ b/python/tests/integration/completions/test_chat_completions.py @@ -17,6 +17,7 @@ anthropic_setup, mistral_ai_setup, ollama_setup, + onnx_setup, vertex_ai_setup, ) from tests.integration.completions.completion_test_base import ServiceType @@ -113,6 +114,17 @@ class Reasoning(KernelBaseModel): marks=pytest.mark.skipif(not ollama_setup, reason="Need local Ollama setup"), id="ollama_text_input", ), + pytest.param( + "onnx_gen_ai", + {}, + [ + ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="Hello")]), + ChatMessageContent(role=AuthorRole.USER, items=[TextContent(text="How are you today?")]), + ], + ["Hello", "well"], + marks=pytest.mark.skipif(not onnx_setup, reason="Need a Onnx Model setup"), + id="onnx_gen_ai", + ), pytest.param( "anthropic", {}, diff --git a/python/tests/integration/completions/test_text_completion.py b/python/tests/integration/completions/test_text_completion.py index ae835fd6ac16..f7c0903935f1 100644 --- a/python/tests/integration/completions/test_text_completion.py +++ b/python/tests/integration/completions/test_text_completion.py @@ -1,6 +1,6 @@ # Copyright (c) Microsoft. All rights reserved. -import os +import platform import sys from functools import partial, reduce from typing import Any @@ -38,14 +38,16 @@ from semantic_kernel import Kernel from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings from tests.integration.completions.completion_test_base import CompletionTestBase, ServiceType -from tests.integration.completions.test_utils import retry +from tests.integration.completions.test_utils import is_service_setup_for_testing, retry -ollama_setup: bool = False -try: - if os.environ["OLLAMA_MODEL"]: - ollama_setup = True -except KeyError: - ollama_setup = False +ollama_setup: bool = is_service_setup_for_testing("OLLAMA_MODEL") +google_ai_setup: bool = is_service_setup_for_testing("GOOGLE_AI_API_KEY") +vertex_ai_setup: bool = is_service_setup_for_testing("VERTEX_AI_PROJECT_ID") +onnx_setup: bool = is_service_setup_for_testing("ONNX_GEN_AI_TEXT_MODEL_FOLDER") + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx import OnnxGenAIPromptExecutionSettings, OnnxGenAITextCompletion pytestmark = pytest.mark.parametrize( @@ -115,8 +117,17 @@ {}, ["Repeat the word Hello"], {}, + marks=pytest.mark.skipif(not vertex_ai_setup, reason="Need VertexAI setup"), id="vertex_ai_text_completion", ), + pytest.param( + "onnx_gen_ai", + {}, + ["<|user|>Repeat the word Hello<|end|><|assistant|>"], + {}, + marks=pytest.mark.skipif(not onnx_setup, reason="Need local Onnx setup"), + id="onnx_gen_ai_text_completion", + ), ], ) @@ -148,8 +159,8 @@ def services(self) -> dict[str, tuple[ServiceType, type[PromptExecutionSettings] "azure": (AzureTextCompletion(), OpenAITextPromptExecutionSettings), "azure_custom_client": (azure_custom_client, OpenAITextPromptExecutionSettings), "ollama": (OllamaTextCompletion() if ollama_setup else None, OllamaTextPromptExecutionSettings), - "google_ai": (GoogleAITextCompletion(), GoogleAITextPromptExecutionSettings), - "vertex_ai": (VertexAITextCompletion(), VertexAITextPromptExecutionSettings), + "google_ai": (GoogleAITextCompletion() if google_ai_setup else None, GoogleAITextPromptExecutionSettings), + "vertex_ai": (VertexAITextCompletion() if vertex_ai_setup else None, VertexAITextPromptExecutionSettings), "hf_t2t": ( HuggingFaceTextCompletion( service_id="patrickvonplaten/t5-tiny-random", @@ -174,6 +185,10 @@ def services(self) -> dict[str, tuple[ServiceType, type[PromptExecutionSettings] ), HuggingFacePromptExecutionSettings, ), + "onnx_gen_ai": ( + OnnxGenAITextCompletion() if onnx_setup else None, + OnnxGenAIPromptExecutionSettings if not skip_on_mac_available else None, + ), } async def get_text_completion_response( diff --git a/python/tests/integration/completions/test_utils.py b/python/tests/integration/completions/test_utils.py index cb49a892e83d..9631bdea46ab 100644 --- a/python/tests/integration/completions/test_utils.py +++ b/python/tests/integration/completions/test_utils.py @@ -2,6 +2,7 @@ import asyncio import logging +import os logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger() @@ -19,3 +20,7 @@ async def retry(func, retries=20): raise await asyncio.sleep(max(min(i, max_delay), min_delay)) return None + + +def is_service_setup_for_testing(env_var_name: str) -> bool: + return env_var_name in os.environ and os.environ[env_var_name] diff --git a/python/tests/unit/connectors/onnx/conftest.py b/python/tests/unit/connectors/onnx/conftest.py new file mode 100644 index 000000000000..89cb12931de5 --- /dev/null +++ b/python/tests/unit/connectors/onnx/conftest.py @@ -0,0 +1,32 @@ +# Copyright (c) Microsoft. All rights reserved. +from pytest import fixture + + +@fixture() +def onnx_unit_test_env(monkeypatch, exclude_list, override_env_param_dict): + """Fixture to set environment variables for OnnxGenAISettings.""" + if exclude_list is None: + exclude_list = [] + + if override_env_param_dict is None: + override_env_param_dict = {} + + env_vars = { + "ONNX_GEN_AI_CHAT_MODEL_FOLDER": "test", + "ONNX_GEN_AI_TEXT_MODEL_FOLDER": "test", + } + + env_vars.update(override_env_param_dict) + + for key, value in env_vars.items(): + if key not in exclude_list: + monkeypatch.setenv(key, value) + else: + monkeypatch.delenv(key, raising=False) + + return env_vars + + +gen_ai_config = {"model": {"test": "test"}} + +gen_ai_config_vision = {"model": {"vision": "test"}} diff --git a/python/tests/unit/connectors/onnx/services/test_onnx_chat_completion.py b/python/tests/unit/connectors/onnx/services/test_onnx_chat_completion.py new file mode 100644 index 000000000000..b529907e465e --- /dev/null +++ b/python/tests/unit/connectors/onnx/services/test_onnx_chat_completion.py @@ -0,0 +1,191 @@ +# Copyright (c) Microsoft. All rights reserved. +import json +import os +import platform +from unittest.mock import MagicMock, mock_open, patch + +import pytest + +from semantic_kernel.contents import AuthorRole, ChatHistory, ChatMessageContent, ImageContent +from semantic_kernel.exceptions import ServiceInitializationError, ServiceInvalidExecutionSettingsError +from semantic_kernel.kernel import Kernel +from tests.unit.connectors.onnx.conftest import ( + gen_ai_config, + gen_ai_config_vision, +) + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx import ( # noqa: E402 + OnnxGenAIChatCompletion, + OnnxGenAIPromptExecutionSettings, + ) + from semantic_kernel.connectors.ai.onnx.utils import ONNXTemplate # noqa: E402 + + +@pytest.mark.skipif(skip_on_mac_available, reason="OnnxRuntime is not available on macOS") +class TestOnnxChatCompletion: + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_valid_env_variable(self, gen_ai_config, model, tokenizer, onnx_unit_test_env): + service = OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3, env_file_path="test.env") + assert not service.enable_multi_modality + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config_vision)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_vision_valid_env_variable( + self, gen_ai_vision_config, model, tokenizer, onnx_unit_test_env + ): + service = OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3, env_file_path="test.env") + assert service.enable_multi_modality + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_valid_parameter(self, gen_ai_config, model, tokenizer): + assert OnnxGenAIChatCompletion(ai_model_path="/valid_path", template=ONNXTemplate.PHI3) + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_str_template(self, gen_ai_config, model, tokenizer): + assert OnnxGenAIChatCompletion(ai_model_path="/valid_path", template="phi3") + + def test_onnx_chat_completion_with_invalid_model(self): + with pytest.raises(ServiceInitializationError): + OnnxGenAIChatCompletion( + ai_model_path="/invalid_path", + template=ONNXTemplate.PHI3, + ) + + def test_onnx_chat_completion_without_prompt_template(self): + with pytest.raises(TypeError): + OnnxGenAIChatCompletion() + + def test_onnx_chat_completion_with_invalid_env_variable(self, onnx_unit_test_env): + with pytest.raises(ServiceInitializationError): + OnnxGenAIChatCompletion( + template=ONNXTemplate.PHI3, + ) + + @pytest.mark.parametrize("exclude_list", [["ONNX_GEN_AI_CHAT_MODEL_FOLDER"]], indirect=True) + def test_onnx_chat_completion_with_missing_ai_path(self, onnx_unit_test_env): + with pytest.raises(ServiceInitializationError): + OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3, env_file_path="test.env") + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + @pytest.mark.asyncio + async def test_onnx_chat_completion(self, gen_ai_config, model, tokenizer): + generator_mock = MagicMock() + generator_mock.__aiter__.return_value = [["H"], ["e"], ["l"], ["l"], ["o"]] + + chat_completion = OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3, ai_model_path="test") + + history = ChatHistory() + history.add_system_message("test") + history.add_user_message("test") + + with patch.object(chat_completion, "_generate_next_token_async", return_value=generator_mock): + completed_text: ChatMessageContent = await chat_completion.get_chat_message_content( + prompt="test", chat_history=history, settings=OnnxGenAIPromptExecutionSettings(), kernel=Kernel() + ) + + assert str(completed_text) == "Hello" + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + @pytest.mark.asyncio + async def test_onnx_chat_completion_streaming(self, gen_ai_config, model, tokenizer): + generator_mock = MagicMock() + generator_mock.__aiter__.return_value = [["H"], ["e"], ["l"], ["l"], ["o"]] + + chat_completion = OnnxGenAIChatCompletion(template=ONNXTemplate.PHI3, ai_model_path="test") + + history = ChatHistory() + history.add_system_message("test") + history.add_user_message("test") + + completed_text: str = "" + + with patch.object(chat_completion, "_generate_next_token_async", return_value=generator_mock): + async for chunk in chat_completion.get_streaming_chat_message_content( + prompt="test", chat_history=history, settings=OnnxGenAIPromptExecutionSettings(), kernel=Kernel() + ): + completed_text += str(chunk) + + assert completed_text == "Hello" + + @patch("onnxruntime_genai.Model") + def test_onnx_chat_get_image_history(self, model): + builtin_open = open # save the unpatched version + + def patch_open(*args, **kwargs): + if "genai_config.json" in str(args[0]): + # mocked open for path "genai_config.json" + return mock_open(read_data=json.dumps(gen_ai_config_vision))(*args, **kwargs) + # unpatched version for every other path + return builtin_open(*args, **kwargs) + + with patch("builtins.open", patch_open): + chat_completion = OnnxGenAIChatCompletion( + template=ONNXTemplate.PHI3, + ai_model_path="test", + ) + + image_content = ImageContent.from_image_path( + image_path=os.path.join(os.path.dirname(__file__), "../../../../", "assets/sample_image.jpg") + ) + + history = ChatHistory() + history.add_system_message("test") + history.add_user_message("test") + history.add_message( + ChatMessageContent( + role=AuthorRole.USER, + items=[image_content], + ), + ) + + last_image = chat_completion._get_images_from_history(history) + assert last_image == image_content + + @pytest.mark.asyncio + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + async def test_onnx_chat_get_image_history_with_not_multimodal(self, model, tokenizer): + builtin_open = open # save the unpatched version + + def patch_open(*args, **kwargs): + if "genai_config.json" in str(args[0]): + # mocked open for path "genai_config.json" + return mock_open(read_data=json.dumps(gen_ai_config))(*args, **kwargs) + # unpatched version for every other path + return builtin_open(*args, **kwargs) + + with patch("builtins.open", patch_open): + chat_completion = OnnxGenAIChatCompletion( + template=ONNXTemplate.PHI3, + ai_model_path="test", + ) + + image_content = ImageContent.from_image_path( + image_path=os.path.join(os.path.dirname(__file__), "../../../../", "assets/sample_image.jpg") + ) + + history = ChatHistory() + history.add_system_message("test") + history.add_user_message("test") + history.add_message( + ChatMessageContent( + role=AuthorRole.USER, + items=[image_content], + ), + ) + + with pytest.raises(ServiceInvalidExecutionSettingsError): + _ = await chat_completion._get_images_from_history(history) diff --git a/python/tests/unit/connectors/onnx/services/test_onnx_text_completion.py b/python/tests/unit/connectors/onnx/services/test_onnx_text_completion.py new file mode 100644 index 000000000000..0d9252e976f7 --- /dev/null +++ b/python/tests/unit/connectors/onnx/services/test_onnx_text_completion.py @@ -0,0 +1,79 @@ +# Copyright (c) Microsoft. All rights reserved. +import json +import platform +from unittest.mock import MagicMock, mock_open, patch + +import pytest + +from semantic_kernel.contents import TextContent +from semantic_kernel.exceptions import ServiceInitializationError +from tests.unit.connectors.onnx.conftest import gen_ai_config + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx import ( # noqa: E402 + OnnxGenAIPromptExecutionSettings, + OnnxGenAITextCompletion, + ) + + +@pytest.mark.skipif(skip_on_mac_available, reason="OnnxRuntime is not available on macOS") +class TestOnnxTextCompletion: + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_valid_env_variable(self, gen_ai_config, model, tokenizer, onnx_unit_test_env): + assert OnnxGenAITextCompletion(env_file_path="test.env") + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + def test_onnx_chat_completion_with_valid_parameter(self, gen_ai_config, model, tokenizer): + assert OnnxGenAITextCompletion(ai_model_path="/valid_path") + + def test_onnx_chat_completion_with_invalid_model(self): + with pytest.raises(ServiceInitializationError): + OnnxGenAITextCompletion(ai_model_path="/invalid_path") + + def test_onnx_chat_completion_with_invalid_env_variable(self, onnx_unit_test_env): + with pytest.raises(ServiceInitializationError): + OnnxGenAITextCompletion() + + @pytest.mark.parametrize("exclude_list", [["ONNX_GEN_AI_TEXT_MODEL_FOLDER"]], indirect=True) + def test_onnx_chat_completion_with_missing_ai_path(self, onnx_unit_test_env): + with pytest.raises(ServiceInitializationError): + OnnxGenAITextCompletion(env_file_path="test.env") + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + @pytest.mark.asyncio + async def test_onnx_text_completion(self, gen_ai_config, model, tokenizer): + generator_mock = MagicMock() + generator_mock.__aiter__.return_value = [["H"], ["e"], ["l"], ["l"], ["o"]] + + text_completion = OnnxGenAITextCompletion(ai_model_path="test") + with patch.object(text_completion, "_generate_next_token_async", return_value=generator_mock): + completed_text: TextContent = await text_completion.get_text_content( + prompt="test", settings=OnnxGenAIPromptExecutionSettings() + ) + + assert completed_text.text == "Hello" + + @patch("builtins.open", new_callable=mock_open, read_data=json.dumps(gen_ai_config)) + @patch("onnxruntime_genai.Model") + @patch("onnxruntime_genai.Tokenizer") + @pytest.mark.asyncio + async def test_onnx_text_completion_streaming(self, gen_ai_config, model, tokenizer): + generator_mock = MagicMock() + generator_mock.__aiter__.return_value = [["H"], ["e"], ["l"], ["l"], ["o"]] + + text_completion = OnnxGenAITextCompletion(ai_model_path="test") + completed_text: str = "" + with patch.object(text_completion, "_generate_next_token_async", return_value=generator_mock): + async for chunk in text_completion.get_streaming_text_content( + prompt="test", settings=OnnxGenAIPromptExecutionSettings() + ): + completed_text += chunk.text + + assert completed_text == "Hello" diff --git a/python/tests/unit/connectors/onnx/services/test_onnx_utils.py b/python/tests/unit/connectors/onnx/services/test_onnx_utils.py new file mode 100644 index 000000000000..e746a1624a01 --- /dev/null +++ b/python/tests/unit/connectors/onnx/services/test_onnx_utils.py @@ -0,0 +1,97 @@ +# Copyright (c) Microsoft. All rights reserved. +import platform + +import pytest + +from semantic_kernel.contents import AuthorRole, ChatHistory, ImageContent, TextContent + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx.utils import ( # noqa: E402 + gemma_template, + llama_template, + phi3_template, + phi3v_template, + ) + + +@pytest.mark.skipif(skip_on_mac_available, reason="OnnxRuntime is not available on macOS") +class TestOnnxUtils: + def test_phi3v_template_with_text_and_image(self): + history = ChatHistory( + messages=[ + {"role": AuthorRole.SYSTEM, "content": "System message"}, + { + "role": AuthorRole.USER, + "items": [TextContent(text="User text message"), ImageContent(url="http://example.com/image.png")], + }, + {"role": AuthorRole.ASSISTANT, "content": "Assistant message"}, + ] + ) + + expected_output = ( + "<|system|>\nSystem message<|end|>\n" + "<|user|>\nUser text message<|end|>\n" + "<|image_1|>\n" + "<|assistant|>\nAssistant message<|end|>\n" + "<|assistant|>\n" + ) + + assert phi3v_template(history) == expected_output + + def test_phi3_template_with_only_text(self): + history = ChatHistory(messages=[{"role": AuthorRole.USER, "items": [TextContent(text="User text message")]}]) + + expected_output = "<|user|>\nUser text message<|end|>\n<|assistant|>\n" + + assert phi3_template(history) == expected_output + + def test_gemma_template_with_user_and_assistant_messages(self): + history = ChatHistory( + messages=[ + {"role": AuthorRole.USER, "content": "User text message"}, + {"role": AuthorRole.ASSISTANT, "content": "Assistant message"}, + ] + ) + + expected_output = ( + "" + "user\nUser text message\n" + "model\nAssistant message\n" + "model\n" + ) + + assert gemma_template(history) == expected_output + + def test_gemma_template_with_only_user_message(self): + history = ChatHistory(messages=[{"role": AuthorRole.USER, "content": "User text message"}]) + + expected_output = "user\nUser text message\nmodel\n" + + assert gemma_template(history) == expected_output + + def test_llama_template_with_user_and_assistant_messages(self): + history = ChatHistory( + messages=[ + {"role": AuthorRole.USER, "content": "User text message"}, + {"role": AuthorRole.ASSISTANT, "content": "Assistant message"}, + ] + ) + + expected_output = ( + "<|start_header_id|>user<|end_header_id|>\n\nUser text message<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\nAssistant message<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>" + ) + + assert llama_template(history) == expected_output + + def test_llama_template_with_only_user_message(self): + history = ChatHistory(messages=[{"role": AuthorRole.USER, "content": "User text message"}]) + + expected_output = ( + "<|start_header_id|>user<|end_header_id|>\n\nUser text message<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>" + ) + + assert llama_template(history) == expected_output diff --git a/python/tests/unit/connectors/onnx/test_onnx_prompt_execution_settings.py b/python/tests/unit/connectors/onnx/test_onnx_prompt_execution_settings.py new file mode 100644 index 000000000000..80c84619d0a2 --- /dev/null +++ b/python/tests/unit/connectors/onnx/test_onnx_prompt_execution_settings.py @@ -0,0 +1,85 @@ +# Copyright (c) Microsoft. All rights reserved. +import platform + +import pytest +from pydantic import ValidationError + +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings + +skip_on_mac_available = platform.system() == "Darwin" +if not skip_on_mac_available: + from semantic_kernel.connectors.ai.onnx.onnx_gen_ai_prompt_execution_settings import ( + OnnxGenAIPromptExecutionSettings, + ) + + +@pytest.mark.skipif(skip_on_mac_available, reason="OnnxRuntime is not available on macOS") +class TestOnnxPromptExecutionSettings: + def test_default_onnx_chat_prompt_execution_settings(self): + settings = OnnxGenAIPromptExecutionSettings() + assert settings.temperature is None + assert settings.top_p is None + + def test_custom_onnx_chat_prompt_execution_settings(self): + settings = OnnxGenAIPromptExecutionSettings( + temperature=0.5, + top_p=0.5, + max_length=128, + ) + assert settings.temperature == 0.5 + assert settings.top_p == 0.5 + assert settings.max_length == 128 + + def test_onnx_chat_prompt_execution_settings_from_default_completion_config(self): + settings = PromptExecutionSettings(service_id="test_service") + chat_settings = OnnxGenAIPromptExecutionSettings.from_prompt_execution_settings(settings) + assert chat_settings.service_id == "test_service" + assert chat_settings.temperature is None + assert chat_settings.top_p is None + + def test_onnx_chat_prompt_execution_settings_from_onnx_prompt_execution_settings(self): + chat_settings = OnnxGenAIPromptExecutionSettings(service_id="test_service", temperature=1.0) + new_settings = OnnxGenAIPromptExecutionSettings(service_id="test_2", temperature=0.0) + chat_settings.update_from_prompt_execution_settings(new_settings) + assert chat_settings.service_id == "test_2" + assert chat_settings.temperature == 0.0 + + def test_onnx_chat_prompt_execution_settings_from_custom_completion_config(self): + settings = PromptExecutionSettings( + service_id="test_service", + extension_data={ + "temperature": 0.5, + "top_p": 0.5, + "max_length": 128, + }, + ) + chat_settings = OnnxGenAIPromptExecutionSettings.from_prompt_execution_settings(settings) + assert chat_settings.temperature == 0.5 + assert chat_settings.top_p == 0.5 + assert chat_settings.max_length == 128 + + def test_create_options(self): + settings = OnnxGenAIPromptExecutionSettings( + service_id="test_service", + extension_data={ + "temperature": 0.5, + "top_p": 0.5, + "max_length": 128, + }, + ) + options = settings.prepare_settings_dict() + assert options["temperature"] == 0.5 + assert options["top_p"] == 0.5 + assert options["max_length"] == 128 + + def test_create_options_with_wrong_parameter(self): + with pytest.raises(ValidationError): + OnnxGenAIPromptExecutionSettings( + service_id="test_service", + function_choice_behavior="auto", + extension_data={ + "temperature": 10.0, + "top_p": 0.5, + "max_length": 128, + }, + ) diff --git a/python/uv.lock b/python/uv.lock index b6168f0fbb68..cfd79d7b4840 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -1,21 +1,87 @@ version = 1 requires-python = ">=3.10, <3.13" resolution-markers = [ + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", "python_full_version < '3.11' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and sys_platform == 'darwin'", "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version >= '3.11' and sys_platform == 'darwin'", "python_full_version >= '3.12' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version < '3.11' and sys_platform == 'linux'", + "python_full_version == '3.11.*' and sys_platform 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