mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 12:06:04 +00:00
added a minor fix
This commit is contained in:
Omar Abdelwahab
parent
b0a6adf3b2
commit
6adaca3d96
1 changed files with 109 additions and 103 deletions
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@ -15,17 +15,9 @@ from typing import Any
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from openai import AsyncStream
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from openai.types.chat import (
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ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage,
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)
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from openai.types.chat import (
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ChatCompletionChunk as OpenAIChatCompletionChunk,
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)
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from openai.types.chat import (
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ChatCompletionContentPartImageParam as OpenAIChatCompletionContentPartImageParam,
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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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from openai.types.chat import (
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ChatCompletionContentPartTextParam as OpenAIChatCompletionContentPartTextParam,
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)
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@ -37,56 +29,15 @@ 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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ChatCompletionMessageParam as OpenAIChatCompletionMessage,
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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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from openai.types.chat import (
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ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage,
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)
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from openai.types.chat import (
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ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage,
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)
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from openai.types.chat import (
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ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage,
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)
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from openai.types.chat.chat_completion import (
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Choice as OpenAIChoice,
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)
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from openai.types.chat.chat_completion import (
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ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs
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)
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from openai.types.chat.chat_completion_chunk import (
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Choice as OpenAIChatCompletionChunkChoice,
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)
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from openai.types.chat.chat_completion_chunk import (
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ChoiceDelta as OpenAIChoiceDelta,
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)
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from openai.types.chat.chat_completion_chunk import (
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ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall,
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)
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from openai.types.chat.chat_completion_chunk import (
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ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction,
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)
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from openai.types.chat.chat_completion_content_part_image_param import (
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ImageURL as OpenAIImageURL,
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)
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from openai.types.chat.chat_completion_message_tool_call import (
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Function as OpenAIFunction,
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)
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from pydantic import BaseModel
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from llama_stack.apis.common.content_types import (
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URL,
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_URLOrData,
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ImageContentItem,
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InterleavedContent,
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TextContentItem,
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TextDelta,
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ToolCallDelta,
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ToolCallParseStatus,
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_URLOrData,
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URL,
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)
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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@ -99,6 +50,7 @@ from llama_stack.apis.inference import (
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JsonSchemaResponseFormat,
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Message,
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OpenAIChatCompletion,
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OpenAIChoice as OpenAIChatCompletionChoice,
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OpenAIEmbeddingData,
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OpenAIMessageParam,
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OpenAIResponseFormatParam,
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@ -123,6 +75,30 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
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convert_image_content_to_url,
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decode_assistant_message,
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)
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from openai.types.chat import (
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ChatCompletionMessageParam as OpenAIChatCompletionMessage,
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ChatCompletionMessageToolCall,
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ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage,
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ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage,
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ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage,
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)
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from openai.types.chat.chat_completion import (
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Choice as OpenAIChoice,
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ChoiceLogprobs as OpenAIChoiceLogprobs, # same as chat_completion_chunk ChoiceLogprobs
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)
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from openai.types.chat.chat_completion_chunk import (
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Choice as OpenAIChatCompletionChunkChoice,
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ChoiceDelta as OpenAIChoiceDelta,
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ChoiceDeltaToolCall as OpenAIChoiceDeltaToolCall,
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ChoiceDeltaToolCallFunction as OpenAIChoiceDeltaToolCallFunction,
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)
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from openai.types.chat.chat_completion_content_part_image_param import (
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ImageURL as OpenAIImageURL,
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)
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from openai.types.chat.chat_completion_message_tool_call import (
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Function as OpenAIFunction,
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)
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from pydantic import BaseModel
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logger = get_logger(name=__name__, category="providers::utils")
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@ -221,12 +197,16 @@ def convert_openai_completion_logprobs(
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if logprobs.tokens and logprobs.token_logprobs:
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return [
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TokenLogProbs(logprobs_by_token={token: token_lp})
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for token, token_lp in zip(logprobs.tokens, logprobs.token_logprobs, strict=False)
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for token, token_lp in zip(
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logprobs.tokens, logprobs.token_logprobs, strict=False
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)
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]
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return None
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def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenAICompatLogprobs | None):
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def convert_openai_completion_logprobs_stream(
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text: str, logprobs: float | OpenAICompatLogprobs | None
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):
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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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@ -237,31 +217,6 @@ def convert_openai_completion_logprobs_stream(text: str, logprobs: float | OpenA
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return None
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# def process_completion_response(
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# response: OpenAICompatCompletionResponse,
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# ) -> CompletionResponse:
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# choice = response.choices[0]
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# # drop suffix <eot_id> if present and return stop reason as end of turn
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# if choice.text.endswith("<|eot_id|>"):
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# return CompletionResponse(
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# stop_reason=StopReason.end_of_turn,
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# content=choice.text[: -len("<|eot_id|>")],
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# logprobs=convert_openai_completion_logprobs(choice.logprobs),
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# )
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# # drop suffix <eom_id> if present and return stop reason as end of message
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# if choice.text.endswith("<|eom_id|>"):
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# return CompletionResponse(
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# stop_reason=StopReason.end_of_message,
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# content=choice.text[: -len("<|eom_id|>")],
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# logprobs=convert_openai_completion_logprobs(choice.logprobs),
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# )
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# return CompletionResponse(
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# stop_reason=get_stop_reason(choice.finish_reason),
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# content=choice.text,
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# logprobs=convert_openai_completion_logprobs(choice.logprobs),
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# )
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def process_chat_completion_response(
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response: OpenAICompatCompletionResponse,
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request: ChatCompletionRequest,
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@ -271,7 +226,9 @@ def process_chat_completion_response(
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if not choice.message or not choice.message.tool_calls:
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raise ValueError("Tool calls are not present in the response")
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tool_calls = [convert_tool_call(tool_call) for tool_call in choice.message.tool_calls]
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tool_calls = [
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convert_tool_call(tool_call) for tool_call in choice.message.tool_calls
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]
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if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
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# If we couldn't parse a tool call, jsonify the tool calls and return them
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return ChatCompletionResponse(
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@ -295,7 +252,9 @@ def process_chat_completion_response(
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# TODO: This does not work well with tool calls for vLLM remote provider
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# Ref: https://github.com/meta-llama/llama-stack/issues/1058
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raw_message = decode_assistant_message(text_from_choice(choice), get_stop_reason(choice.finish_reason))
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raw_message = decode_assistant_message(
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text_from_choice(choice), get_stop_reason(choice.finish_reason)
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)
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# NOTE: If we do not set tools in chat-completion request, we should not
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# expect the ToolCall in the response. Instead, we should return the raw
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@ -496,13 +455,17 @@ async def process_chat_completion_stream_response(
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)
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async def convert_message_to_openai_dict(message: Message, download: bool = False) -> dict:
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async def convert_message_to_openai_dict(
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message: Message, download: bool = False
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) -> dict:
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async def _convert_content(content) -> dict:
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if isinstance(content, ImageContentItem):
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return {
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"type": "image_url",
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"image_url": {
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"url": await convert_image_content_to_url(content, download=download),
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"url": await convert_image_content_to_url(
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content, download=download
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),
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},
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}
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else:
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@ -587,7 +550,11 @@ async def convert_message_to_openai_dict_new(
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) -> str | Iterable[OpenAIChatCompletionContentPartParam]:
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async def impl(
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content_: InterleavedContent,
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) -> str | OpenAIChatCompletionContentPartParam | list[OpenAIChatCompletionContentPartParam]:
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) -> (
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str
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| OpenAIChatCompletionContentPartParam
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| list[OpenAIChatCompletionContentPartParam]
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):
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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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@ -600,7 +567,9 @@ async def convert_message_to_openai_dict_new(
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return OpenAIChatCompletionContentPartImageParam(
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type="image_url",
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image_url=OpenAIImageURL(
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url=await convert_image_content_to_url(content_, download=download_images)
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url=await convert_image_content_to_url(
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content_, download=download_images
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)
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),
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)
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elif isinstance(content_, list):
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@ -627,7 +596,11 @@ async def convert_message_to_openai_dict_new(
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OpenAIChatCompletionMessageFunctionToolCall(
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id=tool.call_id,
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function=OpenAIFunction(
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name=(tool.tool_name if not isinstance(tool.tool_name, BuiltinTool) else tool.tool_name.value),
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name=(
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tool.tool_name
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if not isinstance(tool.tool_name, BuiltinTool)
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else tool.tool_name.value
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),
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arguments=tool.arguments, # Already a JSON string, don't double-encode
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),
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type="function",
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@ -807,7 +780,9 @@ 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: str | dict[str, Any] | None = None) -> ToolConfig:
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def _convert_openai_request_tool_config(
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tool_choice: str | dict[str, Any] | None = None
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) -> ToolConfig:
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tool_config = ToolConfig()
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if tool_choice:
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try:
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@ -818,7 +793,9 @@ def _convert_openai_request_tool_config(tool_choice: str | dict[str, Any] | None
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return tool_config
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def _convert_openai_request_tools(tools: list[dict[str, Any]] | None = None) -> list[ToolDefinition]:
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def _convert_openai_request_tools(
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tools: list[dict[str, Any]] | None = None
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) -> 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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@ -917,7 +894,11 @@ def _convert_openai_logprobs(
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return None
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return [
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TokenLogProbs(logprobs_by_token={logprobs.token: logprobs.logprob for logprobs in content.top_logprobs})
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TokenLogProbs(
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logprobs_by_token={
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logprobs.token: logprobs.logprob for logprobs in content.top_logprobs
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}
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)
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for content in logprobs.content
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]
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@ -956,9 +937,13 @@ def openai_messages_to_messages(
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converted_messages = []
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for message in messages:
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if message.role == "system":
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converted_message = SystemMessage(content=openai_content_to_content(message.content))
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converted_message = SystemMessage(
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content=openai_content_to_content(message.content)
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)
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elif message.role == "user":
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converted_message = UserMessage(content=openai_content_to_content(message.content))
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converted_message = UserMessage(
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content=openai_content_to_content(message.content)
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)
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elif message.role == "assistant":
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converted_message = CompletionMessage(
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content=openai_content_to_content(message.content),
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@ -990,7 +975,9 @@ def openai_content_to_content(
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if content.type == "text":
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return TextContentItem(type="text", text=content.text)
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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)))
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return ImageContentItem(
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type="image", image=_URLOrData(url=URL(uri=content.image_url.url))
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)
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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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@ -1030,14 +1017,17 @@ def convert_openai_chat_completion_choice(
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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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assert hasattr(choice, "message") and choice.message, "error in server response: message not found"
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assert hasattr(choice, "finish_reason") and choice.finish_reason, (
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"error in server response: finish_reason not found"
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)
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assert (
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hasattr(choice, "message") and choice.message
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), "error in server response: message not found"
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assert (
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hasattr(choice, "finish_reason") and choice.finish_reason
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), "error in server response: finish_reason not found"
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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content=choice.message.content or "", # CompletionMessage content is not optional
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content=choice.message.content
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or "", # CompletionMessage content is not optional
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stop_reason=_convert_openai_finish_reason(choice.finish_reason),
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tool_calls=_convert_openai_tool_calls(choice.message.tool_calls),
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),
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@ -1277,7 +1267,9 @@ class OpenAIChatCompletionToLlamaStackMixin:
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outstanding_responses.append(response)
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if stream:
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return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(self, model, outstanding_responses)
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return OpenAIChatCompletionToLlamaStackMixin._process_stream_response(
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self, model, outstanding_responses
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)
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return await OpenAIChatCompletionToLlamaStackMixin._process_non_stream_response(
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self, model, outstanding_responses
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@ -1286,21 +1278,29 @@ 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[
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Awaitable[AsyncIterator[ChatCompletionResponseStreamChunk]]
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],
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):
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id = f"chatcmpl-{uuid.uuid4()}"
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for i, outstanding_response in enumerate(outstanding_responses):
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response = await outstanding_response
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async for chunk in response:
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event = chunk.event
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finish_reason = _convert_stop_reason_to_openai_finish_reason(event.stop_reason)
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finish_reason = _convert_stop_reason_to_openai_finish_reason(
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event.stop_reason
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)
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if isinstance(event.delta, TextDelta):
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text_delta = event.delta.text
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delta = OpenAIChoiceDelta(content=text_delta)
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yield OpenAIChatCompletionChunk(
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id=id,
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choices=[OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)],
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choices=[
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OpenAIChatCompletionChunkChoice(
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index=i, finish_reason=finish_reason, delta=delta
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)
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],
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created=int(time.time()),
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model=model,
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object="chat.completion.chunk",
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@ -1322,7 +1322,9 @@ class OpenAIChatCompletionToLlamaStackMixin:
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yield OpenAIChatCompletionChunk(
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id=id,
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choices=[
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OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
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OpenAIChatCompletionChunkChoice(
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index=i, finish_reason=finish_reason, delta=delta
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)
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],
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created=int(time.time()),
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model=model,
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@ -1339,7 +1341,9 @@ class OpenAIChatCompletionToLlamaStackMixin:
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yield OpenAIChatCompletionChunk(
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id=id,
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choices=[
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OpenAIChatCompletionChunkChoice(index=i, finish_reason=finish_reason, delta=delta)
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OpenAIChatCompletionChunkChoice(
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index=i, finish_reason=finish_reason, delta=delta
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)
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],
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created=int(time.time()),
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model=model,
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@ -1354,7 +1358,9 @@ class OpenAIChatCompletionToLlamaStackMixin:
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response = await outstanding_response
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completion_message = response.completion_message
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message = await convert_message_to_openai_dict_new(completion_message)
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finish_reason = _convert_stop_reason_to_openai_finish_reason(completion_message.stop_reason)
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finish_reason = _convert_stop_reason_to_openai_finish_reason(
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completion_message.stop_reason
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)
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choice = OpenAIChatCompletionChoice(
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index=len(choices),
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|
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