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chore: enable pyupgrade fixes (#1806)
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
This commit is contained in:
Ihar Hrachyshka
GitHub
parent
ffe3d0b2cd
commit
9e6561a1ec
319 changed files with 2843 additions and 3033 deletions
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@ -8,16 +8,9 @@ import logging
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import time
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import uuid
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import warnings
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from collections.abc import AsyncGenerator, AsyncIterator, Awaitable, Iterable
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from typing import (
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Any,
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AsyncGenerator,
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AsyncIterator,
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Awaitable,
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Dict,
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Iterable,
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List,
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Optional,
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Union,
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)
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from openai import AsyncStream
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@ -141,24 +134,24 @@ class OpenAICompatCompletionChoiceDelta(BaseModel):
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class OpenAICompatLogprobs(BaseModel):
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text_offset: Optional[List[int]] = None
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text_offset: list[int] | None = None
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token_logprobs: Optional[List[float]] = None
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token_logprobs: list[float] | None = None
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tokens: Optional[List[str]] = None
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tokens: list[str] | None = None
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top_logprobs: Optional[List[Dict[str, float]]] = None
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top_logprobs: list[dict[str, float]] | None = None
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class OpenAICompatCompletionChoice(BaseModel):
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finish_reason: Optional[str] = None
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text: Optional[str] = None
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delta: Optional[OpenAICompatCompletionChoiceDelta] = None
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logprobs: Optional[OpenAICompatLogprobs] = None
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finish_reason: str | None = None
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text: str | None = None
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delta: OpenAICompatCompletionChoiceDelta | None = None
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logprobs: OpenAICompatLogprobs | None = None
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class OpenAICompatCompletionResponse(BaseModel):
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choices: List[OpenAICompatCompletionChoice]
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choices: list[OpenAICompatCompletionChoice]
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def get_sampling_strategy_options(params: SamplingParams) -> dict:
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@ -217,8 +210,8 @@ def get_stop_reason(finish_reason: str) -> StopReason:
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def convert_openai_completion_logprobs(
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logprobs: Optional[OpenAICompatLogprobs],
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) -> Optional[List[TokenLogProbs]]:
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logprobs: OpenAICompatLogprobs | None,
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) -> list[TokenLogProbs] | None:
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if not logprobs:
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return None
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if hasattr(logprobs, "top_logprobs"):
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@ -235,7 +228,7 @@ def convert_openai_completion_logprobs(
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return None
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def convert_openai_completion_logprobs_stream(text: str, logprobs: Optional[Union[float, OpenAICompatLogprobs]]):
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def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenAICompatLogprobs | None):
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if logprobs is None:
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return None
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if isinstance(logprobs, float):
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@ -562,7 +555,7 @@ class UnparseableToolCall(BaseModel):
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async def convert_message_to_openai_dict_new(
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message: Message | Dict,
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message: Message | dict,
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) -> OpenAIChatCompletionMessage:
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"""
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Convert a Message to an OpenAI API-compatible dictionary.
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@ -591,14 +584,10 @@ async def convert_message_to_openai_dict_new(
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# List[...] -> List[...]
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async def _convert_message_content(
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content: InterleavedContent,
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) -> Union[str, Iterable[OpenAIChatCompletionContentPartParam]]:
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) -> str | Iterable[OpenAIChatCompletionContentPartParam]:
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async def impl(
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content_: InterleavedContent,
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) -> Union[
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str,
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OpenAIChatCompletionContentPartParam,
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List[OpenAIChatCompletionContentPartParam],
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]:
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) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]:
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# Llama Stack and OpenAI spec match for str and text input
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if isinstance(content_, str):
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return content_
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@ -670,7 +659,7 @@ async def convert_message_to_openai_dict_new(
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def convert_tool_call(
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tool_call: ChatCompletionMessageToolCall,
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) -> Union[ToolCall, UnparseableToolCall]:
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) -> ToolCall | UnparseableToolCall:
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"""
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Convert a ChatCompletionMessageToolCall tool call to either a
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ToolCall or UnparseableToolCall. Returns an UnparseableToolCall
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@ -846,7 +835,7 @@ def _convert_openai_finish_reason(finish_reason: str) -> StopReason:
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}.get(finish_reason, StopReason.end_of_turn)
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def _convert_openai_request_tool_config(tool_choice: Optional[Union[str, Dict[str, Any]]] = None) -> ToolConfig:
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def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None = None) -> ToolConfig:
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tool_config = ToolConfig()
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if tool_choice:
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try:
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@ -857,7 +846,7 @@ def _convert_openai_request_tool_config(tool_choice: Optional[Union[str, Dict[st
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return tool_config
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def _convert_openai_request_tools(tools: Optional[List[Dict[str, Any]]] = None) -> List[ToolDefinition]:
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def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]:
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lls_tools = []
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if not tools:
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return lls_tools
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@ -903,8 +892,8 @@ def _convert_openai_request_response_format(
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def _convert_openai_tool_calls(
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tool_calls: List[OpenAIChatCompletionMessageToolCall],
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) -> List[ToolCall]:
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tool_calls: list[OpenAIChatCompletionMessageToolCall],
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) -> list[ToolCall]:
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"""
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Convert an OpenAI ChatCompletionMessageToolCall list into a list of ToolCall.
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@ -940,7 +929,7 @@ def _convert_openai_tool_calls(
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def _convert_openai_logprobs(
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logprobs: OpenAIChoiceLogprobs,
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) -> Optional[List[TokenLogProbs]]:
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) -> list[TokenLogProbs] | None:
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"""
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Convert an OpenAI ChoiceLogprobs into a list of TokenLogProbs.
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@ -973,9 +962,9 @@ def _convert_openai_logprobs(
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def _convert_openai_sampling_params(
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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max_tokens: int | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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) -> SamplingParams:
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sampling_params = SamplingParams()
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@ -998,8 +987,8 @@ def _convert_openai_sampling_params(
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def openai_messages_to_messages(
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messages: List[OpenAIChatCompletionMessage],
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) -> List[Message]:
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messages: list[OpenAIChatCompletionMessage],
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) -> list[Message]:
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"""
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Convert a list of OpenAIChatCompletionMessage into a list of Message.
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"""
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@ -1027,7 +1016,7 @@ def openai_messages_to_messages(
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return converted_messages
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def openai_content_to_content(content: Union[str, Iterable[OpenAIChatCompletionContentPartParam]]):
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def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionContentPartParam]):
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if isinstance(content, str):
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return content
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elif isinstance(content, list):
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@ -1273,24 +1262,24 @@ class OpenAICompletionToLlamaStackMixin:
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async def openai_completion(
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self,
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model: str,
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prompt: Union[str, List[str], List[int], List[List[int]]],
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best_of: Optional[int] = None,
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echo: Optional[bool] = None,
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frequency_penalty: Optional[float] = None,
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logit_bias: Optional[Dict[str, float]] = None,
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logprobs: Optional[bool] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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presence_penalty: Optional[float] = None,
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seed: Optional[int] = None,
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stop: Optional[Union[str, List[str]]] = None,
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stream: Optional[bool] = None,
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stream_options: Optional[Dict[str, Any]] = None,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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user: Optional[str] = None,
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guided_choice: Optional[List[str]] = None,
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prompt_logprobs: Optional[int] = None,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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) -> OpenAICompletion:
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if stream:
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raise ValueError(f"{self.__class__.__name__} doesn't support streaming openai completions")
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@ -1342,29 +1331,29 @@ class OpenAIChatCompletionToLlamaStackMixin:
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async def openai_chat_completion(
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self,
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model: str,
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messages: List[OpenAIChatCompletionMessage],
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frequency_penalty: Optional[float] = None,
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function_call: Optional[Union[str, Dict[str, Any]]] = None,
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functions: Optional[List[Dict[str, Any]]] = None,
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logit_bias: Optional[Dict[str, float]] = None,
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logprobs: Optional[bool] = None,
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max_completion_tokens: Optional[int] = None,
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max_tokens: Optional[int] = None,
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n: Optional[int] = None,
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parallel_tool_calls: Optional[bool] = None,
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presence_penalty: Optional[float] = None,
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response_format: Optional[OpenAIResponseFormatParam] = None,
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seed: Optional[int] = None,
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stop: Optional[Union[str, List[str]]] = None,
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stream: Optional[bool] = None,
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stream_options: Optional[Dict[str, Any]] = None,
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temperature: Optional[float] = None,
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tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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top_logprobs: Optional[int] = None,
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top_p: Optional[float] = None,
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user: Optional[str] = None,
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) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
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messages: list[OpenAIChatCompletionMessage],
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frequency_penalty: float | None = None,
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function_call: str | dict[str, Any] | None = None,
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functions: list[dict[str, Any]] | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_completion_tokens: int | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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parallel_tool_calls: bool | None = None,
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presence_penalty: float | None = None,
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response_format: OpenAIResponseFormatParam | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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tool_choice: str | dict[str, Any] | None = None,
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tools: list[dict[str, Any]] | None = None,
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top_logprobs: int | None = None,
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top_p: float | None = None,
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user: str | None = None,
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) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
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messages = openai_messages_to_messages(messages)
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response_format = _convert_openai_request_response_format(response_format)
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sampling_params = _convert_openai_sampling_params(
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@ -1403,7 +1392,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
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async def _process_stream_response(
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self,
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model: str,
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outstanding_responses: List[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]],
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outstanding_responses: list[Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]],
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):
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id = f"chatcmpl-{uuid.uuid4()}"
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for outstanding_response in outstanding_responses:
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@ -1466,7 +1455,7 @@ class OpenAIChatCompletionToLlamaStackMixin:
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i = i + 1
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async def _process_non_stream_response(
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self, model: str, outstanding_responses: List[Awaitable[ChatCompletionResponse]]
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self, model: str, outstanding_responses: list[Awaitable[ChatCompletionResponse]]
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) -> OpenAIChatCompletion:
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choices = []
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for outstanding_response in outstanding_responses:
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