mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-12 20:12:33 +00:00
Removed additional dead code
This commit is contained in:
Omar Abdelwahab
parent
f6080040da
commit
8809a34b23
2 changed files with 166 additions and 169 deletions
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@ -351,34 +351,6 @@ class CompletionRequest(BaseModel):
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logprobs: LogProbConfig | None = None
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# @json_schema_type
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# class CompletionResponse(MetricResponseMixin):
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# """Response from a completion request.
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# :param content: The generated completion text
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# :param stop_reason: Reason why generation stopped
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# :param logprobs: Optional log probabilities for generated tokens
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# """
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# content: str
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# stop_reason: StopReason
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# logprobs: list[TokenLogProbs] | None = None
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# @json_schema_type
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# class CompletionResponseStreamChunk(MetricResponseMixin):
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# """A chunk of a streamed completion response.
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# :param delta: New content generated since last chunk. This can be one or more tokens.
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# :param stop_reason: Optional reason why generation stopped, if complete
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# :param logprobs: Optional log probabilities for generated tokens
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# """
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# delta: str
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# stop_reason: StopReason | None = None
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# logprobs: list[TokenLogProbs] | None = None
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class SystemMessageBehavior(Enum):
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"""Config for how to override the default system prompt.
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@ -10,24 +10,14 @@ 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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)
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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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@ -39,56 +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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@ -97,8 +46,6 @@ from llama_stack.apis.inference import (
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ChatCompletionResponseEventType,
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ChatCompletionResponseStreamChunk,
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CompletionMessage,
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CompletionResponse,
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CompletionResponseStreamChunk,
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GreedySamplingStrategy,
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JsonSchemaResponseFormat,
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Message,
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@ -116,9 +63,6 @@ from llama_stack.apis.inference import (
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TopPSamplingStrategy,
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UserMessage,
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)
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from llama_stack.apis.inference import (
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OpenAIChoice as OpenAIChatCompletionChoice,
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)
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from llama_stack.log import get_logger
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from llama_stack.models.llama.datatypes import (
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BuiltinTool,
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@ -130,6 +74,30 @@ 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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@ -228,12 +196,16 @@ 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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@ -244,29 +216,29 @@ 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_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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@ -278,7 +250,9 @@ 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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@ -302,7 +276,9 @@ 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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@ -335,40 +311,40 @@ def process_chat_completion_response(
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)
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async def process_completion_stream_response(
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stream: AsyncGenerator[OpenAICompatCompletionResponse, None],
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) -> AsyncGenerator[CompletionResponseStreamChunk, None]:
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stop_reason = None
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# async def process_completion_stream_response(
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# stream: AsyncGenerator[OpenAICompatCompletionResponse, None],
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# ) -> AsyncGenerator[CompletionResponseStreamChunk, None]:
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# stop_reason = None
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async for chunk in stream:
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choice = chunk.choices[0]
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finish_reason = choice.finish_reason
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# async for chunk in stream:
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# choice = chunk.choices[0]
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# finish_reason = choice.finish_reason
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text = text_from_choice(choice)
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if text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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continue
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elif text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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continue
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yield CompletionResponseStreamChunk(
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delta=text,
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stop_reason=stop_reason,
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logprobs=convert_openai_completion_logprobs_stream(text, choice.logprobs),
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)
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if finish_reason:
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if finish_reason in ["stop", "eos", "eos_token"]:
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stop_reason = StopReason.end_of_turn
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elif finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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break
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# text = text_from_choice(choice)
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# if text == "<|eot_id|>":
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# stop_reason = StopReason.end_of_turn
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# text = ""
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# continue
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# elif text == "<|eom_id|>":
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# stop_reason = StopReason.end_of_message
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# text = ""
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# continue
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# yield CompletionResponseStreamChunk(
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# delta=text,
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# stop_reason=stop_reason,
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# logprobs=convert_openai_completion_logprobs_stream(text, choice.logprobs),
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# )
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# if finish_reason:
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# if finish_reason in ["stop", "eos", "eos_token"]:
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# stop_reason = StopReason.end_of_turn
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# elif finish_reason == "length":
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# stop_reason = StopReason.out_of_tokens
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# break
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yield CompletionResponseStreamChunk(
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delta="",
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stop_reason=stop_reason,
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)
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# yield CompletionResponseStreamChunk(
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# delta="",
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# stop_reason=stop_reason,
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# )
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async def process_chat_completion_stream_response(
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@ -503,13 +479,17 @@ 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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@ -594,7 +574,11 @@ 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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@ -607,7 +591,9 @@ 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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|
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@ -634,7 +620,11 @@ 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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@ -814,7 +804,9 @@ 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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@ -825,7 +817,9 @@ 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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|
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@ -924,7 +918,11 @@ 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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@ -963,9 +961,13 @@ 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":
|
||||
converted_message = CompletionMessage(
|
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content=openai_content_to_content(message.content),
|
||||
|
|
@ -984,7 +986,9 @@ def openai_messages_to_messages(
|
|||
return converted_messages
|
||||
|
||||
|
||||
def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionContentPartParam] | None):
|
||||
def openai_content_to_content(
|
||||
content: str | Iterable[OpenAIChatCompletionContentPartParam] | None,
|
||||
):
|
||||
if content is None:
|
||||
return ""
|
||||
if isinstance(content, str):
|
||||
|
|
@ -995,7 +999,9 @@ def openai_content_to_content(content: str | Iterable[OpenAIChatCompletionConten
|
|||
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:
|
||||
|
|
@ -1035,14 +1041,17 @@ 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),
|
||||
),
|
||||
|
|
@ -1282,7 +1291,9 @@ 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
|
||||
|
|
@ -1291,21 +1302,29 @@ 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",
|
||||
|
|
@ -1327,7 +1346,9 @@ 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,
|
||||
|
|
@ -1344,7 +1365,9 @@ 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,
|
||||
|
|
@ -1359,7 +1382,9 @@ 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),
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue