diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index 897b5d6e8..91c448bc6 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -8,6 +8,9 @@ from collections.abc import AsyncIterator from enum import Enum from typing import Annotated, Any, Literal, Protocol, runtime_checkable +from pydantic import BaseModel, Field, field_validator +from typing_extensions import TypedDict + from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent from llama_stack.apis.common.responses import Order from llama_stack.apis.models import Model @@ -23,9 +26,6 @@ from llama_stack.models.llama.datatypes import ( from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_stack.schema_utils import json_schema_type, register_schema, webmethod -from pydantic import BaseModel, Field, field_validator -from typing_extensions import TypedDict - register_schema(ToolCall) register_schema(ToolDefinition) @@ -381,9 +381,7 @@ class ToolConfig(BaseModel): tool_choice: ToolChoice | str | None = Field(default=ToolChoice.auto) tool_prompt_format: ToolPromptFormat | None = Field(default=None) - system_message_behavior: SystemMessageBehavior | None = Field( - default=SystemMessageBehavior.append - ) + system_message_behavior: SystemMessageBehavior | None = Field(default=SystemMessageBehavior.append) def model_post_init(self, __context: Any) -> None: if isinstance(self.tool_choice, str): @@ -512,21 +510,15 @@ class OpenAIFile(BaseModel): OpenAIChatCompletionContentPartParam = Annotated[ - OpenAIChatCompletionContentPartTextParam - | OpenAIChatCompletionContentPartImageParam - | OpenAIFile, + OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam | OpenAIFile, Field(discriminator="type"), ] -register_schema( - OpenAIChatCompletionContentPartParam, name="OpenAIChatCompletionContentPartParam" -) +register_schema(OpenAIChatCompletionContentPartParam, name="OpenAIChatCompletionContentPartParam") OpenAIChatCompletionMessageContent = str | list[OpenAIChatCompletionContentPartParam] -OpenAIChatCompletionTextOnlyMessageContent = ( - str | list[OpenAIChatCompletionContentPartTextParam] -) +OpenAIChatCompletionTextOnlyMessageContent = str | list[OpenAIChatCompletionContentPartTextParam] @json_schema_type @@ -694,9 +686,7 @@ class OpenAIResponseFormatJSONObject(BaseModel): OpenAIResponseFormatParam = Annotated[ - OpenAIResponseFormatText - | OpenAIResponseFormatJSONSchema - | OpenAIResponseFormatJSONObject, + OpenAIResponseFormatText | OpenAIResponseFormatJSONSchema | OpenAIResponseFormatJSONObject, Field(discriminator="type"), ] register_schema(OpenAIResponseFormatParam, name="OpenAIResponseFormatParam") @@ -986,16 +976,8 @@ class InferenceProvider(Protocol): async def rerank( self, model: str, - query: ( - str - | OpenAIChatCompletionContentPartTextParam - | OpenAIChatCompletionContentPartImageParam - ), - items: list[ - str - | OpenAIChatCompletionContentPartTextParam - | OpenAIChatCompletionContentPartImageParam - ], + query: (str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam), + items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam], max_num_results: int | None = None, ) -> RerankResponse: """Rerank a list of documents based on their relevance to a query. diff --git a/llama_stack/core/routers/inference.py b/llama_stack/core/routers/inference.py index d2944ec81..ff86a89ed 100644 --- a/llama_stack/core/routers/inference.py +++ b/llama_stack/core/routers/inference.py @@ -7,9 +7,17 @@ import asyncio import time from collections.abc import AsyncGenerator, AsyncIterator -from datetime import datetime, UTC +from datetime import UTC, datetime from typing import Annotated, Any +from openai.types.chat import ( + ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam, +) +from openai.types.chat import ( + ChatCompletionToolParam as OpenAIChatCompletionToolParam, +) +from pydantic import Field, TypeAdapter + from llama_stack.apis.common.content_types import InterleavedContent from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError from llama_stack.apis.inference import ( @@ -48,12 +56,6 @@ from llama_stack.providers.utils.telemetry.tracing import ( get_current_span, ) -from openai.types.chat import ( - ChatCompletionToolChoiceOptionParam as OpenAIChatCompletionToolChoiceOptionParam, - ChatCompletionToolParam as OpenAIChatCompletionToolParam, -) -from pydantic import Field, TypeAdapter - logger = get_logger(name=__name__, category="core::routers") @@ -96,9 +98,7 @@ class InferenceRouter(Inference): logger.debug( f"InferenceRouter.register_model: {model_id=} {provider_model_id=} {provider_id=} {metadata=} {model_type=}", ) - await self.routing_table.register_model( - model_id, provider_model_id, provider_id, metadata, model_type - ) + await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type) def _construct_metrics( self, @@ -153,16 +153,11 @@ class InferenceRouter(Inference): total_tokens: int, model: Model, ) -> list[MetricInResponse]: - metrics = self._construct_metrics( - prompt_tokens, completion_tokens, total_tokens, model - ) + metrics = self._construct_metrics(prompt_tokens, completion_tokens, total_tokens, model) if self.telemetry: for metric in metrics: enqueue_event(metric) - return [ - MetricInResponse(metric=metric.metric, value=metric.value) - for metric in metrics - ] + return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] async def _count_tokens( self, @@ -256,9 +251,7 @@ class InferenceRouter(Inference): # these metrics will show up in the client response. response.metrics = ( - metrics - if not hasattr(response, "metrics") or response.metrics is None - else response.metrics + metrics + metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics ) return response @@ -296,13 +289,9 @@ class InferenceRouter(Inference): # Use the OpenAI client for a bit of extra input validation without # exposing the OpenAI client itself as part of our API surface if tool_choice: - TypeAdapter(OpenAIChatCompletionToolChoiceOptionParam).validate_python( - tool_choice - ) + TypeAdapter(OpenAIChatCompletionToolChoiceOptionParam).validate_python(tool_choice) if tools is None: - raise ValueError( - "'tool_choice' is only allowed when 'tools' is also provided" - ) + raise ValueError("'tool_choice' is only allowed when 'tools' is also provided") if tools: for tool in tools: TypeAdapter(OpenAIChatCompletionToolParam).validate_python(tool) @@ -367,9 +356,7 @@ class InferenceRouter(Inference): enqueue_event(metric) # these metrics will show up in the client response. response.metrics = ( - metrics - if not hasattr(response, "metrics") or response.metrics is None - else response.metrics + metrics + metrics if not hasattr(response, "metrics") or response.metrics is None else response.metrics + metrics ) return response @@ -405,31 +392,19 @@ class InferenceRouter(Inference): ) -> ListOpenAIChatCompletionResponse: if self.store: return await self.store.list_chat_completions(after, limit, model, order) - raise NotImplementedError( - "List chat completions is not supported: inference store is not configured." - ) + raise NotImplementedError("List chat completions is not supported: inference store is not configured.") - async def get_chat_completion( - self, completion_id: str - ) -> OpenAICompletionWithInputMessages: + async def get_chat_completion(self, completion_id: str) -> OpenAICompletionWithInputMessages: if self.store: return await self.store.get_chat_completion(completion_id) - raise NotImplementedError( - "Get chat completion is not supported: inference store is not configured." - ) + raise NotImplementedError("Get chat completion is not supported: inference store is not configured.") - async def _nonstream_openai_chat_completion( - self, provider: Inference, params: dict - ) -> OpenAIChatCompletion: + async def _nonstream_openai_chat_completion(self, provider: Inference, params: dict) -> OpenAIChatCompletion: response = await provider.openai_chat_completion(**params) for choice in response.choices: # some providers return an empty list for no tool calls in non-streaming responses # but the OpenAI API returns None. So, set tool_calls to None if it's empty - if ( - choice.message - and choice.message.tool_calls is not None - and len(choice.message.tool_calls) == 0 - ): + if choice.message and choice.message.tool_calls is not None and len(choice.message.tool_calls) == 0: choice.message.tool_calls = None return response @@ -449,9 +424,7 @@ class InferenceRouter(Inference): message=f"Health check timed out after {timeout} seconds", ) except NotImplementedError: - health_statuses[provider_id] = HealthResponse( - status=HealthStatus.NOT_IMPLEMENTED - ) + health_statuses[provider_id] = HealthResponse(status=HealthStatus.NOT_IMPLEMENTED) except Exception as e: health_statuses[provider_id] = HealthResponse( status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}" @@ -486,11 +459,7 @@ class InferenceRouter(Inference): else: if hasattr(chunk, "delta"): completion_text += chunk.delta - if ( - hasattr(chunk, "stop_reason") - and chunk.stop_reason - and self.telemetry - ): + if hasattr(chunk, "stop_reason") and chunk.stop_reason and self.telemetry: complete = True completion_tokens = await self._count_tokens(completion_text) # if we are done receiving tokens @@ -515,14 +484,9 @@ class InferenceRouter(Inference): # Return metrics in response async_metrics = [ - MetricInResponse(metric=metric.metric, value=metric.value) - for metric in completion_metrics + MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics ] - chunk.metrics = ( - async_metrics - if chunk.metrics is None - else chunk.metrics + async_metrics - ) + chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics else: # Fallback if no telemetry completion_metrics = self._construct_metrics( @@ -532,14 +496,9 @@ class InferenceRouter(Inference): model, ) async_metrics = [ - MetricInResponse(metric=metric.metric, value=metric.value) - for metric in completion_metrics + MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics ] - chunk.metrics = ( - async_metrics - if chunk.metrics is None - else chunk.metrics + async_metrics - ) + chunk.metrics = async_metrics if chunk.metrics is None else chunk.metrics + async_metrics yield chunk async def count_tokens_and_compute_metrics( @@ -553,9 +512,7 @@ class InferenceRouter(Inference): content = [response.completion_message] else: content = response.content - completion_tokens = await self._count_tokens( - messages=content, tool_prompt_format=tool_prompt_format - ) + completion_tokens = await self._count_tokens(messages=content, tool_prompt_format=tool_prompt_format) total_tokens = (prompt_tokens or 0) + (completion_tokens or 0) # Create a separate span for completion metrics @@ -575,10 +532,7 @@ class InferenceRouter(Inference): enqueue_event(metric) # Return metrics in response - return [ - MetricInResponse(metric=metric.metric, value=metric.value) - for metric in completion_metrics - ] + return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in completion_metrics] # Fallback if no telemetry metrics = self._construct_metrics( @@ -587,10 +541,7 @@ class InferenceRouter(Inference): total_tokens, model, ) - return [ - MetricInResponse(metric=metric.metric, value=metric.value) - for metric in metrics - ] + return [MetricInResponse(metric=metric.metric, value=metric.value) for metric in metrics] async def stream_tokens_and_compute_metrics_openai_chat( self, @@ -631,48 +582,33 @@ class InferenceRouter(Inference): if choice_delta.delta: delta = choice_delta.delta if delta.content: - current_choice_data["content_parts"].append( - delta.content - ) + current_choice_data["content_parts"].append(delta.content) if delta.tool_calls: for tool_call_delta in delta.tool_calls: tc_idx = tool_call_delta.index - if ( - tc_idx - not in current_choice_data["tool_calls_builder"] - ): - current_choice_data["tool_calls_builder"][ - tc_idx - ] = { + if tc_idx not in current_choice_data["tool_calls_builder"]: + current_choice_data["tool_calls_builder"][tc_idx] = { "id": None, "type": "function", "function_name_parts": [], "function_arguments_parts": [], } - builder = current_choice_data["tool_calls_builder"][ - tc_idx - ] + builder = current_choice_data["tool_calls_builder"][tc_idx] if tool_call_delta.id: builder["id"] = tool_call_delta.id if tool_call_delta.type: builder["type"] = tool_call_delta.type if tool_call_delta.function: if tool_call_delta.function.name: - builder["function_name_parts"].append( - tool_call_delta.function.name - ) + builder["function_name_parts"].append(tool_call_delta.function.name) if tool_call_delta.function.arguments: builder["function_arguments_parts"].append( tool_call_delta.function.arguments ) if choice_delta.finish_reason: - current_choice_data["finish_reason"] = ( - choice_delta.finish_reason - ) + current_choice_data["finish_reason"] = choice_delta.finish_reason if choice_delta.logprobs and choice_delta.logprobs.content: - current_choice_data["logprobs_content_parts"].extend( - choice_delta.logprobs.content - ) + current_choice_data["logprobs_content_parts"].extend(choice_delta.logprobs.content) # Compute metrics on final chunk if chunk.choices and chunk.choices[0].finish_reason: @@ -702,12 +638,8 @@ class InferenceRouter(Inference): if choice_data["tool_calls_builder"]: for tc_build_data in choice_data["tool_calls_builder"].values(): if tc_build_data["id"]: - func_name = "".join( - tc_build_data["function_name_parts"] - ) - func_args = "".join( - tc_build_data["function_arguments_parts"] - ) + func_name = "".join(tc_build_data["function_name_parts"]) + func_args = "".join(tc_build_data["function_arguments_parts"]) assembled_tool_calls.append( OpenAIChatCompletionToolCall( id=tc_build_data["id"], @@ -720,16 +652,10 @@ class InferenceRouter(Inference): message = OpenAIAssistantMessageParam( role="assistant", content=content_str if content_str else None, - tool_calls=( - assembled_tool_calls if assembled_tool_calls else None - ), + tool_calls=(assembled_tool_calls if assembled_tool_calls else None), ) logprobs_content = choice_data["logprobs_content_parts"] - final_logprobs = ( - OpenAIChoiceLogprobs(content=logprobs_content) - if logprobs_content - else None - ) + final_logprobs = OpenAIChoiceLogprobs(content=logprobs_content) if logprobs_content else None assembled_choices.append( OpenAIChoice( @@ -748,6 +674,4 @@ class InferenceRouter(Inference): object="chat.completion", ) logger.debug(f"InferenceRouter.completion_response: {final_response}") - asyncio.create_task( - self.store.store_chat_completion(final_response, messages) - ) + asyncio.create_task(self.store.store_chat_completion(final_response, messages)) diff --git a/llama_stack/providers/remote/inference/runpod/runpod.py b/llama_stack/providers/remote/inference/runpod/runpod.py index 28c65cda5..a7802b282 100644 --- a/llama_stack/providers/remote/inference/runpod/runpod.py +++ b/llama_stack/providers/remote/inference/runpod/runpod.py @@ -7,10 +7,9 @@ from llama_stack.apis.inference import * # noqa: F403 from llama_stack.apis.inference import OpenAIEmbeddingsResponse - from llama_stack.providers.utils.inference.model_registry import ( - build_hf_repo_model_entry, ModelRegistryHelper, + build_hf_repo_model_entry, ) from llama_stack.providers.utils.inference.openai_compat import ( get_sampling_options, @@ -51,9 +50,7 @@ class RunpodInferenceAdapter( Inference, ): def __init__(self, config: RunpodImplConfig) -> None: - ModelRegistryHelper.__init__( - self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS - ) + ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS) self.config = config def _get_params(self, request: ChatCompletionRequest) -> dict: diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py index 0c69318b9..63b877b5e 100644 --- a/llama_stack/providers/remote/inference/watsonx/watsonx.py +++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py @@ -9,6 +9,7 @@ from typing import Any from ibm_watsonx_ai.foundation_models import Model from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams +from openai import AsyncOpenAI from llama_stack.apis.inference import ( ChatCompletionRequest, @@ -33,7 +34,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( completion_request_to_prompt, request_has_media, ) -from openai import AsyncOpenAI from . import WatsonXConfig from .models import MODEL_ENTRIES @@ -65,9 +65,7 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): self._project_id = self._config.project_id def _get_client(self, model_id) -> Model: - config_api_key = ( - self._config.api_key.get_secret_value() if self._config.api_key else None - ) + config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None config_url = self._config.url project_id = self._config.project_id credentials = {"url": config_url, "apikey": config_api_key} @@ -82,46 +80,28 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper): ) return self._openai_client - async def _get_params(self, request: ChatCompletionRequest) -> dict: - input_dict = {"params": {}} media_present = request_has_media(request) llama_model = self.get_llama_model(request.model) if isinstance(request, ChatCompletionRequest): - input_dict["prompt"] = await chat_completion_request_to_prompt( - request, llama_model - ) + input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model) else: - assert ( - not media_present - ), "Together does not support media for Completion requests" + assert not media_present, "Together does not support media for Completion requests" input_dict["prompt"] = await completion_request_to_prompt(request) if request.sampling_params: if request.sampling_params.strategy: - input_dict["params"][ - GenParams.DECODING_METHOD - ] = request.sampling_params.strategy.type + input_dict["params"][GenParams.DECODING_METHOD] = request.sampling_params.strategy.type if request.sampling_params.max_tokens: - input_dict["params"][ - GenParams.MAX_NEW_TOKENS - ] = request.sampling_params.max_tokens + input_dict["params"][GenParams.MAX_NEW_TOKENS] = request.sampling_params.max_tokens if request.sampling_params.repetition_penalty: - input_dict["params"][ - GenParams.REPETITION_PENALTY - ] = request.sampling_params.repetition_penalty + input_dict["params"][GenParams.REPETITION_PENALTY] = request.sampling_params.repetition_penalty if isinstance(request.sampling_params.strategy, TopPSamplingStrategy): - input_dict["params"][ - GenParams.TOP_P - ] = request.sampling_params.strategy.top_p - input_dict["params"][ - GenParams.TEMPERATURE - ] = request.sampling_params.strategy.temperature + input_dict["params"][GenParams.TOP_P] = request.sampling_params.strategy.top_p + input_dict["params"][GenParams.TEMPERATURE] = request.sampling_params.strategy.temperature if isinstance(request.sampling_params.strategy, TopKSamplingStrategy): - input_dict["params"][ - GenParams.TOP_K - ] = request.sampling_params.strategy.top_k + input_dict["params"][GenParams.TOP_K] = request.sampling_params.strategy.top_k if isinstance(request.sampling_params.strategy, GreedySamplingStrategy): input_dict["params"][GenParams.TEMPERATURE] = 0.0 diff --git a/llama_stack/providers/utils/inference/openai_compat.py b/llama_stack/providers/utils/inference/openai_compat.py index 6a0fe9417..a3e272d20 100644 --- a/llama_stack/providers/utils/inference/openai_compat.py +++ b/llama_stack/providers/utils/inference/openai_compat.py @@ -15,9 +15,17 @@ from typing import Any from openai import AsyncStream from openai.types.chat import ( ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage, +) +from openai.types.chat import ( ChatCompletionChunk as OpenAIChatCompletionChunk, +) +from openai.types.chat import ( ChatCompletionContentPartImageParam as OpenAIChatCompletionContentPartImageParam, +) +from openai.types.chat import ( ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam, +) +from openai.types.chat import ( ChatCompletionContentPartTextParam as OpenAIChatCompletionContentPartTextParam, ) @@ -29,15 +37,56 @@ except ImportError: from openai.types.chat.chat_completion_message_tool_call import ( ChatCompletionMessageToolCall as OpenAIChatCompletionMessageFunctionToolCall, ) +from openai.types.chat import ( + ChatCompletionMessageParam as OpenAIChatCompletionMessage, +) +from openai.types.chat import ( + ChatCompletionMessageToolCall, +) +from openai.types.chat import ( + ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage, +) +from openai.types.chat import ( + ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage, +) +from openai.types.chat import ( + ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage, +) +from openai.types.chat.chat_completion import ( + Choice as OpenAIChoice, +) +from openai.types.chat.chat_completion import ( + ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs +) +from openai.types.chat.chat_completion_chunk import ( + Choice as OpenAIChatCompletionChunkChoice, +) +from openai.types.chat.chat_completion_chunk import ( + ChoiceDelta as OpenAIChoiceDelta, +) +from openai.types.chat.chat_completion_chunk import ( + ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall, +) +from openai.types.chat.chat_completion_chunk import ( + ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction, +) +from openai.types.chat.chat_completion_content_part_image_param import ( + ImageURL as OpenAIImageURL, +) +from openai.types.chat.chat_completion_message_tool_call import ( + Function as OpenAIFunction, +) +from pydantic import BaseModel + from llama_stack.apis.common.content_types import ( - _URLOrData, + URL, ImageContentItem, InterleavedContent, TextContentItem, TextDelta, ToolCallDelta, ToolCallParseStatus, - URL, + _URLOrData, ) from llama_stack.apis.inference import ( ChatCompletionRequest, @@ -74,30 +123,6 @@ from llama_stack.providers.utils.inference.prompt_adapter import ( convert_image_content_to_url, decode_assistant_message, ) -from openai.types.chat import ( - ChatCompletionMessageParam as OpenAIChatCompletionMessage, - ChatCompletionMessageToolCall, - ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage, - ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage, - ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage, -) -from openai.types.chat.chat_completion import ( - Choice as OpenAIChoice, - ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs -) -from openai.types.chat.chat_completion_chunk import ( - Choice as OpenAIChatCompletionChunkChoice, - ChoiceDelta as OpenAIChoiceDelta, - ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall, - ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction, -) -from openai.types.chat.chat_completion_content_part_image_param import ( - ImageURL as OpenAIImageURL, -) -from openai.types.chat.chat_completion_message_tool_call import ( - Function as OpenAIFunction, -) -from pydantic import BaseModel logger = get_logger(name=__name__, category="providers::utils") @@ -196,16 +221,12 @@ def convert_openai_completion_logprobs( if logprobs.tokens and logprobs.token_logprobs: return [ TokenLogProbs(logprobs_by_token={token: token_lp}) - for token, token_lp in zip( - logprobs.tokens, logprobs.token_logprobs, strict=False - ) + for token, token_lp in zip(logprobs.tokens, logprobs.token_logprobs, strict=False) ] return None -def convert_openai_completion_logprobs_stream( - text: str, logprobs: float | OpenAICompatLogprobs | None -): +def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenAICompatLogprobs | None): if logprobs is None: return None if isinstance(logprobs, float): @@ -250,9 +271,7 @@ def process_chat_completion_response( if not choice.message or not choice.message.tool_calls: raise ValueError("Tool calls are not present in the response") - tool_calls = [ - convert_tool_call(tool_call) for tool_call in choice.message.tool_calls - ] + tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls] if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls): # If we couldn't parse a tool call, jsonify the tool calls and return them return ChatCompletionResponse( @@ -276,9 +295,7 @@ def process_chat_completion_response( # TODO: This does not work well with tool calls for vLLM remote provider # Ref: https://github.com/meta-llama/llama-stack/issues/1058 - raw_message = decode_assistant_message( - text_from_choice(choice), get_stop_reason(choice.finish_reason) - ) + raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason)) # NOTE: If we do not set tools in chat-completion request, we should not # expect the ToolCall in the response. Instead, we should return the raw @@ -479,17 +496,13 @@ async def process_chat_completion_stream_response( ) -async def convert_message_to_openai_dict( - message: Message, download: bool = False -) -> dict: +async def convert_message_to_openai_dict(message: Message, download: bool = False) -> dict: async def _convert_content(content) -> dict: if isinstance(content, ImageContentItem): return { "type": "image_url", "image_url": { - "url": await convert_image_content_to_url( - content, download=download - ), + "url": await convert_image_content_to_url(content, download=download), }, } else: @@ -574,11 +587,7 @@ async def convert_message_to_openai_dict_new( ) -> str | Iterable[OpenAIChatCompletionContentPartParam]: async def impl( content_: InterleavedContent, - ) -> ( - str - | OpenAIChatCompletionContentPartParam - | list[OpenAIChatCompletionContentPartParam] - ): + ) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]: # Llama Stack and OpenAI spec match for str and text input if isinstance(content_, str): return content_ @@ -591,9 +600,7 @@ async def convert_message_to_openai_dict_new( return OpenAIChatCompletionContentPartImageParam( type="image_url", image_url=OpenAIImageURL( - url=await convert_image_content_to_url( - content_, download=download_images - ) + url=await convert_image_content_to_url(content_, download=download_images) ), ) elif isinstance(content_, list): @@ -620,11 +627,7 @@ async def convert_message_to_openai_dict_new( OpenAIChatCompletionMessageFunctionToolCall( id=tool.call_id, function=OpenAIFunction( - name=( - tool.tool_name - if not isinstance(tool.tool_name, BuiltinTool) - else tool.tool_name.value - ), + name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value), arguments=tool.arguments, # Already a JSON string, don't double-encode ), type="function", @@ -804,9 +807,7 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason: }.get(finish_reason, StopReason.end_of_turn) -def _convert_openai_request_tool_config( - tool_choice: str | dict[str, Any] | None = None -) -> ToolConfig: +def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None = None) -> ToolConfig: tool_config = ToolConfig() if tool_choice: try: @@ -817,9 +818,7 @@ def _convert_openai_request_tool_config( return tool_config -def _convert_openai_request_tools( - tools: list[dict[str, Any]] | None = None -) -> list[ToolDefinition]: +def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]: lls_tools = [] if not tools: return lls_tools @@ -918,11 +917,7 @@ def _convert_openai_logprobs( return None return [ - TokenLogProbs( - logprobs_by_token={ - logprobs.token: logprobs.logprob for logprobs in content.top_logprobs - } - ) + TokenLogProbs(logprobs_by_token={logprobs.token: logprobs.logprob for logprobs in content.top_logprobs}) for content in logprobs.content ] @@ -961,13 +956,9 @@ def openai_messages_to_messages( converted_messages = [] for message in messages: if message.role == "system": - converted_message = SystemMessage( - content=openai_content_to_content(message.content) - ) + converted_message = SystemMessage(content=openai_content_to_content(message.content)) elif message.role == "user": - converted_message = UserMessage( - content=openai_content_to_content(message.content) - ) + converted_message = UserMessage(content=openai_content_to_content(message.content)) elif message.role == "assistant": converted_message = CompletionMessage( content=openai_content_to_content(message.content), @@ -999,9 +990,7 @@ def openai_content_to_content( if content.type == "text": return TextContentItem(type="text", text=content.text) elif content.type == "image_url": - return ImageContentItem( - type="image", image=_URLOrData(url=URL(uri=content.image_url.url)) - ) + return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) else: raise ValueError(f"Unknown content type: {content.type}") else: @@ -1041,17 +1030,14 @@ def convert_openai_chat_completion_choice( end_of_message = "end_of_message" out_of_tokens = "out_of_tokens" """ - assert ( - hasattr(choice, "message") and choice.message - ), "error in server response: message not found" - assert ( - hasattr(choice, "finish_reason") and choice.finish_reason - ), "error in server response: finish_reason not found" + assert hasattr(choice, "message") and choice.message, "error in server response: message not found" + assert hasattr(choice, "finish_reason") and choice.finish_reason, ( + "error in server response: finish_reason not found" + ) return ChatCompletionResponse( completion_message=CompletionMessage( - content=choice.message.content - or "", # CompletionMessage content is not optional + content=choice.message.content or "", # CompletionMessage content is not optional stop_reason=_convert_openai_finish_reason(choice.finish_reason), tool_calls=_convert_openai_tool_calls(choice.message.tool_calls), ), @@ -1291,9 +1277,7 @@ class OpenAIChatCompletionToLlamaStackMixin: outstanding_responses.append(response) if stream: - return OpenAIChatCompletionToLlamaStackMixin._process_stream_response( - self, model, outstanding_responses - ) + return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses) return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response( self, model, outstanding_responses @@ -1302,29 +1286,21 @@ class OpenAIChatCompletionToLlamaStackMixin: async def _process_stream_response( self, model: str, - outstanding_responses: list[ - Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]] - ], + outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]], ): id = f"chatcmpl-{uuid.uuid4()}" for i, outstanding_response in enumerate(outstanding_responses): response = await outstanding_response async for chunk in response: event = chunk.event - finish_reason = _convert_stop_reason_to_openai_finish_reason( - event.stop_reason - ) + finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason) if isinstance(event.delta, TextDelta): text_delta = event.delta.text delta = OpenAIChoiceDelta(content=text_delta) yield OpenAIChatCompletionChunk( id=id, - choices=[ - OpenAIChatCompletionChunkChoice( - index=i, finish_reason=finish_reason, delta=delta - ) - ], + choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)], created=int(time.time()), model=model, object="chat.completion.chunk", @@ -1346,9 +1322,7 @@ class OpenAIChatCompletionToLlamaStackMixin: yield OpenAIChatCompletionChunk( id=id, choices=[ - OpenAIChatCompletionChunkChoice( - index=i, finish_reason=finish_reason, delta=delta - ) + OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) ], created=int(time.time()), model=model, @@ -1365,9 +1339,7 @@ class OpenAIChatCompletionToLlamaStackMixin: yield OpenAIChatCompletionChunk( id=id, choices=[ - OpenAIChatCompletionChunkChoice( - index=i, finish_reason=finish_reason, delta=delta - ) + OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta) ], created=int(time.time()), model=model, @@ -1382,9 +1354,7 @@ class OpenAIChatCompletionToLlamaStackMixin: response = await outstanding_response completion_message = response.completion_message message = await convert_message_to_openai_dict_new(completion_message) - finish_reason = _convert_stop_reason_to_openai_finish_reason( - completion_message.stop_reason - ) + finish_reason = _convert_stop_reason_to_openai_finish_reason(completion_message.stop_reason) choice = OpenAIChatCompletionChoice( index=len(choices), diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 13c055b66..90838f273 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -87,9 +87,7 @@ def pytest_configure(config): suite = config.getoption("--suite") if suite: if suite not in SUITE_DEFINITIONS: - raise pytest.UsageError( - f"Unknown suite: {suite}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}" - ) + raise pytest.UsageError(f"Unknown suite: {suite}. Available: {', '.join(sorted(SUITE_DEFINITIONS.keys()))}") # Apply setups (global parameterizations): env + defaults setup = config.getoption("--setup") @@ -127,9 +125,7 @@ def pytest_addoption(parser): """ ), ) - parser.addoption( - "--env", action="append", help="Set environment variables, e.g. --env KEY=value" - ) + parser.addoption("--env", action="append", help="Set environment variables, e.g. --env KEY=value") parser.addoption( "--text-model", help="comma-separated list of text models. Fixture name: text_model_id", @@ -169,7 +165,9 @@ def pytest_addoption(parser): ) available_suites = ", ".join(sorted(SUITE_DEFINITIONS.keys())) - suite_help = f"Single test suite to run (narrows collection). Available: {available_suites}. Example: --suite=responses" + suite_help = ( + f"Single test suite to run (narrows collection). Available: {available_suites}. Example: --suite=responses" + ) parser.addoption("--suite", help=suite_help) # Global setups for any suite @@ -241,11 +239,7 @@ def pytest_generate_tests(metafunc): # Generate test IDs test_ids = [] - non_empty_params = [ - (i, values) - for i, values in enumerate(param_values.values()) - if values[0] is not None - ] + non_empty_params = [(i, values) for i, values in enumerate(param_values.values()) if values[0] is not None] # Get actual function parameters using inspect test_func_params = set(inspect.signature(metafunc.function).parameters.keys()) @@ -262,9 +256,7 @@ def pytest_generate_tests(metafunc): if parts: test_ids.append(":".join(parts)) - metafunc.parametrize( - params, value_combinations, scope="session", ids=test_ids if test_ids else None - ) + metafunc.parametrize(params, value_combinations, scope="session", ids=test_ids if test_ids else None) def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: @@ -274,9 +266,7 @@ def pytest_ignore_collect(path: str, config: pytest.Config) -> bool: return False sobj = SUITE_DEFINITIONS.get(suite) - roots: list[str] = ( - sobj.get("roots", []) if isinstance(sobj, dict) else getattr(sobj, "roots", []) - ) + roots: list[str] = sobj.get("roots", []) if isinstance(sobj, dict) else getattr(sobj, "roots", []) if not roots: return False diff --git a/tests/unit/server/test_resolver.py b/tests/unit/server/test_resolver.py index 484b12623..df2274754 100644 --- a/tests/unit/server/test_resolver.py +++ b/tests/unit/server/test_resolver.py @@ -9,15 +9,15 @@ import sys from typing import Any, Protocol from unittest.mock import AsyncMock, MagicMock -from llama_stack.apis.inference import Inference, SamplingParams +from pydantic import BaseModel, Field + +from llama_stack.apis.inference import Inference from llama_stack.core.datatypes import Api, Provider, StackRunConfig from llama_stack.core.resolver import resolve_impls from llama_stack.core.routers.inference import InferenceRouter from llama_stack.core.routing_tables.models import ModelsRoutingTable from llama_stack.providers.datatypes import InlineProviderSpec, ProviderSpec -from pydantic import BaseModel, Field - def add_protocol_methods(cls: type, protocol: type[Protocol]) -> None: """Dynamically add protocol methods to a class by inspecting the protocol."""