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https://github.com/meta-llama/llama-stack.git
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refactor: remove dead code from openai_compat utility
Removed 9 unused functions and their associated imports (~210 lines): - `openai_content_to_content()` and content type converters - Sampling parameter converters (`get_sampling_options`, `_convert_openai_sampling_params`, etc.) - OpenAI request/response format converters - Stop reason and finish reason converters - Tool and tool call converters These functions had no callers in the codebase and were accumulating technical debt. The removal also eliminates a potential future issue where `openai_content_to_content()` would raise `ValueError` on the newly introduced `OpenAIFile` content type. Test Plan: - All 120 provider utils unit tests pass - Ruff linting passes
This commit is contained in:
Ashwin Bharambe
parent
72ea95e2e0
commit
34af80675e
1 changed files with 0 additions and 209 deletions
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@ -3,23 +3,10 @@
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from collections.abc import Iterable
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from typing import (
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Any,
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)
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from openai.types.chat import (
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ChatCompletionContentPartParam as OpenAIChatCompletionContentPartParam,
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)
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try:
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from openai.types.chat import (
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ChatCompletionMessageFunctionToolCall as OpenAIChatCompletionMessageFunctionToolCall,
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)
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except ImportError:
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from openai.types.chat.chat_completion_message_tool_call import (
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ChatCompletionMessageToolCall as OpenAIChatCompletionMessageFunctionToolCall,
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)
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from openai.types.chat import (
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ChatCompletionMessageToolCall,
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)
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@ -32,18 +19,6 @@ from llama_stack.models.llama.datatypes import (
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ToolCall,
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ToolDefinition,
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)
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from llama_stack_api import (
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URL,
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GreedySamplingStrategy,
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ImageContentItem,
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JsonSchemaResponseFormat,
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OpenAIResponseFormatParam,
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SamplingParams,
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TextContentItem,
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TopKSamplingStrategy,
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TopPSamplingStrategy,
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_URLOrData,
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)
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logger = get_logger(name=__name__, category="providers::utils")
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@ -73,42 +48,6 @@ class OpenAICompatCompletionResponse(BaseModel):
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choices: list[OpenAICompatCompletionChoice]
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def get_sampling_strategy_options(params: SamplingParams) -> dict:
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options = {}
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if isinstance(params.strategy, GreedySamplingStrategy):
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options["temperature"] = 0.0
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elif isinstance(params.strategy, TopPSamplingStrategy):
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if params.strategy.temperature is not None:
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options["temperature"] = params.strategy.temperature
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if params.strategy.top_p is not None:
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options["top_p"] = params.strategy.top_p
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elif isinstance(params.strategy, TopKSamplingStrategy):
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options["top_k"] = params.strategy.top_k
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else:
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raise ValueError(f"Unsupported sampling strategy: {params.strategy}")
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return options
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def get_sampling_options(params: SamplingParams | None) -> dict:
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if not params:
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return {}
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options = {}
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if params:
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options.update(get_sampling_strategy_options(params))
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if params.max_tokens:
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options["max_tokens"] = params.max_tokens
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if params.repetition_penalty is not None and params.repetition_penalty != 1.0:
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options["repeat_penalty"] = params.repetition_penalty
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if params.stop is not None:
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options["stop"] = params.stop
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return options
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def text_from_choice(choice) -> str:
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if hasattr(choice, "delta") and choice.delta:
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return choice.delta.content # type: ignore[no-any-return] # external OpenAI types lack precise annotations
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@ -253,154 +192,6 @@ def convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
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return out
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def _convert_stop_reason_to_openai_finish_reason(stop_reason: StopReason) -> str:
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"""
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Convert a StopReason to an OpenAI chat completion finish_reason.
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"""
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return {
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StopReason.end_of_turn: "stop",
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StopReason.end_of_message: "tool_calls",
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StopReason.out_of_tokens: "length",
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}.get(stop_reason, "stop")
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def _convert_openai_finish_reason(finish_reason: str) -> StopReason:
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"""
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Convert an OpenAI chat completion finish_reason to a StopReason.
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finish_reason: Literal["stop", "length", "tool_calls", ...]
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- stop: model hit a natural stop point or a provided stop sequence
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- length: maximum number of tokens specified in the request was reached
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- tool_calls: model called a tool
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->
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class StopReason(Enum):
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end_of_turn = "end_of_turn"
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end_of_message = "end_of_message"
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out_of_tokens = "out_of_tokens"
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"""
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# TODO(mf): are end_of_turn and end_of_message semantics correct?
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return {
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"stop": StopReason.end_of_turn,
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"length": StopReason.out_of_tokens,
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"tool_calls": StopReason.end_of_message,
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}.get(finish_reason, StopReason.end_of_turn)
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def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]:
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lls_tools: list[ToolDefinition] = []
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if not tools:
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return lls_tools
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for tool in tools:
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tool_fn = tool.get("function", {})
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tool_name = tool_fn.get("name", None)
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tool_desc = tool_fn.get("description", None)
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tool_params = tool_fn.get("parameters", None)
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lls_tool = ToolDefinition(
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tool_name=tool_name,
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description=tool_desc,
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input_schema=tool_params, # Pass through entire JSON Schema
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)
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lls_tools.append(lls_tool)
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return lls_tools
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def _convert_openai_request_response_format(
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response_format: OpenAIResponseFormatParam | None = None,
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):
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if not response_format:
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return None
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# response_format can be a dict or a pydantic model
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response_format_dict = dict(response_format) # type: ignore[arg-type] # OpenAIResponseFormatParam union needs dict conversion
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if response_format_dict.get("type", "") == "json_schema":
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return JsonSchemaResponseFormat(
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type="json_schema", # type: ignore[arg-type] # Literal["json_schema"] incompatible with expected type
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json_schema=response_format_dict.get("json_schema", {}).get("schema", ""),
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)
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return None
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def _convert_openai_tool_calls(
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tool_calls: list[OpenAIChatCompletionMessageFunctionToolCall],
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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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OpenAI ChatCompletionMessageToolCall:
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id: str
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function: Function
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type: Literal["function"]
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OpenAI Function:
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arguments: str
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name: str
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->
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ToolCall:
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call_id: str
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tool_name: str
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arguments: Dict[str, ...]
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"""
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if not tool_calls:
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return [] # CompletionMessage tool_calls is not optional
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return [
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ToolCall(
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call_id=call.id,
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tool_name=call.function.name,
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arguments=call.function.arguments,
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)
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for call in tool_calls
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]
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def _convert_openai_sampling_params(
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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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if max_tokens:
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sampling_params.max_tokens = max_tokens
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# Map an explicit temperature of 0 to greedy sampling
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if temperature == 0:
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sampling_params.strategy = GreedySamplingStrategy()
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else:
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# OpenAI defaults to 1.0 for temperature and top_p if unset
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if temperature is None:
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temperature = 1.0
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if top_p is None:
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top_p = 1.0
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sampling_params.strategy = TopPSamplingStrategy(temperature=temperature, top_p=top_p) # type: ignore[assignment] # SamplingParams.strategy union accepts this type
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return sampling_params
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def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionContentPartParam] | None):
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if content is None:
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return ""
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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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return [openai_content_to_content(c) for c in content]
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elif hasattr(content, "type"):
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if content.type == "text":
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return TextContentItem(type="text", text=content.text) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track
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elif content.type == "image_url":
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return ImageContentItem(type="image", image=_URLOrData(url=URL(uri=content.image_url.url))) # type: ignore[attr-defined] # Iterable narrowed by hasattr check but mypy doesn't track
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else:
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raise ValueError(f"Unknown content type: {content.type}")
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else:
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raise ValueError(f"Unknown content type: {content}")
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async def prepare_openai_completion_params(**params):
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async def _prepare_value(value: Any) -> Any:
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new_value = value
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