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chore: enable pyupgrade fixes (#1806)

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# 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 2025-05-01 17:23:50 -04:00 • committed by GitHub
parent ffe3d0b2cd
commit 9e6561a1ec
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GPG key ID: B5690EEEBB952194
319 changed files with 2843 additions and 3033 deletions
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125
llama_stack/providers/remote/inference/passthrough/passthrough.py
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@ -4,7 +4,8 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
from collections.abc import AsyncGenerator, AsyncIterator
from typing import Any
from llama_stack_client import AsyncLlamaStackClient
@ -93,10 +94,10 @@ class PassthroughInferenceAdapter(Inference):
self,
model_id: str,
content: InterleavedContent,
sampling_params: Optional[SamplingParams] = None,
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
sampling_params: SamplingParams | None = None,
response_format: ResponseFormat | None = None,
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
) -> AsyncGenerator:
if sampling_params is None:
sampling_params = SamplingParams()
@ -123,15 +124,15 @@ class PassthroughInferenceAdapter(Inference):
async def chat_completion(
self,
model_id: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = None,
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = None,
response_format: Optional[ResponseFormat] = None,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
tool_config: Optional[ToolConfig] = None,
messages: list[Message],
sampling_params: SamplingParams | None = None,
tools: list[ToolDefinition] | None = None,
tool_choice: ToolChoice | None = ToolChoice.auto,
tool_prompt_format: ToolPromptFormat | None = None,
response_format: ResponseFormat | None = None,
stream: bool | None = False,
logprobs: LogProbConfig | None = None,
tool_config: ToolConfig | None = None,
) -> AsyncGenerator:
if sampling_params is None:
sampling_params = SamplingParams()
@ -165,7 +166,7 @@ class PassthroughInferenceAdapter(Inference):
else:
return await self._nonstream_chat_completion(json_params)
async def _nonstream_chat_completion(self, json_params: Dict[str, Any]) -> ChatCompletionResponse:
async def _nonstream_chat_completion(self, json_params: dict[str, Any]) -> ChatCompletionResponse:
client = self._get_client()
response = await client.inference.chat_completion(**json_params)
@ -178,7 +179,7 @@ class PassthroughInferenceAdapter(Inference):
logprobs=response.logprobs,
)
async def _stream_chat_completion(self, json_params: Dict[str, Any]) -> AsyncGenerator:
async def _stream_chat_completion(self, json_params: dict[str, Any]) -> AsyncGenerator:
client = self._get_client()
stream_response = await client.inference.chat_completion(**json_params)
@ -193,10 +194,10 @@ class PassthroughInferenceAdapter(Inference):
async def embeddings(
self,
model_id: str,
contents: List[InterleavedContent],
text_truncation: Optional[TextTruncation] = TextTruncation.none,
output_dimension: Optional[int] = None,
task_type: Optional[EmbeddingTaskType] = None,
contents: list[InterleavedContent],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
client = self._get_client()
model = await self.model_store.get_model(model_id)
@ -212,24 +213,24 @@ class PassthroughInferenceAdapter(Inference):
async def openai_completion(
self,
model: str,
prompt: Union[str, List[str], List[int], List[List[int]]],
best_of: Optional[int] = None,
echo: Optional[bool] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[Dict[str, float]] = None,
logprobs: Optional[bool] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[float] = None,
seed: Optional[int] = None,
stop: Optional[Union[str, List[str]]] = None,
stream: Optional[bool] = None,
stream_options: Optional[Dict[str, Any]] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
user: Optional[str] = None,
guided_choice: Optional[List[str]] = None,
prompt_logprobs: Optional[int] = None,
prompt: str | list[str] | list[int] | list[list[int]],
best_of: int | None = None,
echo: bool | None = None,
frequency_penalty: float | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_tokens: int | None = None,
n: int | None = None,
presence_penalty: float | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
top_p: float | None = None,
user: str | None = None,
guided_choice: list[str] | None = None,
prompt_logprobs: int | None = None,
) -> OpenAICompletion:
client = self._get_client()
model_obj = await self.model_store.get_model(model)
@ -261,29 +262,29 @@ class PassthroughInferenceAdapter(Inference):
async def openai_chat_completion(
self,
model: str,
messages: List[OpenAIMessageParam],
frequency_penalty: Optional[float] = None,
function_call: Optional[Union[str, Dict[str, Any]]] = None,
functions: Optional[List[Dict[str, Any]]] = None,
logit_bias: Optional[Dict[str, float]] = None,
logprobs: Optional[bool] = None,
max_completion_tokens: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
parallel_tool_calls: Optional[bool] = None,
presence_penalty: Optional[float] = None,
response_format: Optional[OpenAIResponseFormatParam] = None,
seed: Optional[int] = None,
stop: Optional[Union[str, List[str]]] = None,
stream: Optional[bool] = None,
stream_options: Optional[Dict[str, Any]] = None,
temperature: Optional[float] = None,
tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
top_logprobs: Optional[int] = None,
top_p: Optional[float] = None,
user: Optional[str] = None,
) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
messages: list[OpenAIMessageParam],
frequency_penalty: float | None = None,
function_call: str | dict[str, Any] | None = None,
functions: list[dict[str, Any]] | None = None,
logit_bias: dict[str, float] | None = None,
logprobs: bool | None = None,
max_completion_tokens: int | None = None,
max_tokens: int | None = None,
n: int | None = None,
parallel_tool_calls: bool | None = None,
presence_penalty: float | None = None,
response_format: OpenAIResponseFormatParam | None = None,
seed: int | None = None,
stop: str | list[str] | None = None,
stream: bool | None = None,
stream_options: dict[str, Any] | None = None,
temperature: float | None = None,
tool_choice: str | dict[str, Any] | None = None,
tools: list[dict[str, Any]] | None = None,
top_logprobs: int | None = None,
top_p: float | None = None,
user: str | None = None,
) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
client = self._get_client()
model_obj = await self.model_store.get_model(model)
@ -315,7 +316,7 @@ class PassthroughInferenceAdapter(Inference):
return await client.inference.openai_chat_completion(**params)
def cast_value_to_json_dict(self, request_params: Dict[str, Any]) -> Dict[str, Any]:
def cast_value_to_json_dict(self, request_params: dict[str, Any]) -> dict[str, Any]:
json_params = {}
for key, value in request_params.items():
json_input = convert_pydantic_to_json_value(value)