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chore: remove batch-related APIs

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APIs removed:
 - POST /v1/batch-inference/completion
 - POST /v1/batch-inference/chat-completion
 - POST /v1/inference/batch-completion
 - POST /v1/inference/batch-chat-completion

note -
 - batch-completion & batch-chat-completion were only implemented for inference=inline::meta-reference
 - batch-inference were not implemented
This commit is contained in:
Matthew Farrellee 2025-08-26 15:22:50 -04:00
parent 963305c84d
commit f6d1867bf5
12 changed files with 190 additions and 972 deletions
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7
llama_stack/apis/batch_inference/__init__.py
View file

@ -1,7 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .batch_inference import *

78
llama_stack/apis/batch_inference/batch_inference.py
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@ -1,78 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Protocol, runtime_checkable
from llama_stack.apis.common.job_types import Job
from llama_stack.apis.inference import (
InterleavedContent,
LogProbConfig,
Message,
ResponseFormat,
SamplingParams,
ToolChoice,
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.schema_utils import webmethod
@runtime_checkable
class BatchInference(Protocol):
"""Batch inference API for generating completions and chat completions.
This is an asynchronous API. If the request is successful, the response will be a job which can be polled for completion.
NOTE: This API is not yet implemented and is subject to change in concert with other asynchronous APIs
including (post-training, evals, etc).
"""
@webmethod(route="/batch-inference/completion", method="POST")
async def completion(
self,
model: str,
content_batch: list[InterleavedContent],
sampling_params: SamplingParams | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> Job:
"""Generate completions for a batch of content.
:param model: The model to use for the completion.
:param content_batch: The content to complete.
:param sampling_params: The sampling parameters to use for the completion.
:param response_format: The response format to use for the completion.
:param logprobs: The logprobs to use for the completion.
:returns: A job for the completion.
"""
...
@webmethod(route="/batch-inference/chat-completion", method="POST")
async def chat_completion(
self,
model: str,
messages_batch: list[list[Message]],
sampling_params: SamplingParams | None = None,
# zero-shot tool definitions as input to the model
tools: list[ToolDefinition] | None = None,
tool_choice: ToolChoice | None = ToolChoice.auto,
tool_prompt_format: ToolPromptFormat | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> Job:
"""Generate chat completions for a batch of messages.
:param model: The model to use for the chat completion.
:param messages_batch: The messages to complete.
:param sampling_params: The sampling parameters to use for the completion.
:param tools: The tools to use for the chat completion.
:param tool_choice: The tool choice to use for the chat completion.
:param tool_prompt_format: The tool prompt format to use for the chat completion.
:param response_format: The response format to use for the chat completion.
:param logprobs: The logprobs to use for the chat completion.
:returns: A job for the chat completion.
"""
...

66
llama_stack/apis/inference/inference.py
View file

@ -973,26 +973,6 @@ class EmbeddingTaskType(Enum):
document = "document"
@json_schema_type
class BatchCompletionResponse(BaseModel):
"""Response from a batch completion request.
:param batch: List of completion responses, one for each input in the batch
"""
batch: list[CompletionResponse]
@json_schema_type
class BatchChatCompletionResponse(BaseModel):
"""Response from a batch chat completion request.
:param batch: List of chat completion responses, one for each conversation in the batch
"""
batch: list[ChatCompletionResponse]
class OpenAICompletionWithInputMessages(OpenAIChatCompletion):
input_messages: list[OpenAIMessageParam]
@ -1049,27 +1029,6 @@ class InferenceProvider(Protocol):
"""
...
@webmethod(route="/inference/batch-completion", method="POST", experimental=True)
async def batch_completion(
self,
model_id: str,
content_batch: list[InterleavedContent],
sampling_params: SamplingParams | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchCompletionResponse:
"""Generate completions for a batch of content using the specified model.
:param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
:param content_batch: The content to generate completions for.
:param sampling_params: (Optional) Parameters to control the sampling strategy.
:param response_format: (Optional) Grammar specification for guided (structured) decoding.
:param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
:returns: A BatchCompletionResponse with the full completions.
"""
raise NotImplementedError("Batch completion is not implemented")
return # this is so mypy's safe-super rule will consider the method concrete
@webmethod(route="/inference/chat-completion", method="POST")
async def chat_completion(
self,
@ -1110,31 +1069,6 @@ class InferenceProvider(Protocol):
"""
...
@webmethod(route="/inference/batch-chat-completion", method="POST", experimental=True)
async def batch_chat_completion(
self,
model_id: str,
messages_batch: list[list[Message]],
sampling_params: SamplingParams | None = None,
tools: list[ToolDefinition] | None = None,
tool_config: ToolConfig | None = None,
response_format: ResponseFormat | None = None,
logprobs: LogProbConfig | None = None,
) -> BatchChatCompletionResponse:
"""Generate chat completions for a batch of messages using the specified model.
:param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
:param messages_batch: The messages to generate completions for.
:param sampling_params: (Optional) Parameters to control the sampling strategy.
:param tools: (Optional) List of tool definitions available to the model.
:param tool_config: (Optional) Configuration for tool use.
:param response_format: (Optional) Grammar specification for guided (structured) decoding.
:param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
:returns: A BatchChatCompletionResponse with the full completions.
"""
raise NotImplementedError("Batch chat completion is not implemented")
return # this is so mypy's safe-super rule will consider the method concrete
@webmethod(route="/inference/embeddings", method="POST")
async def embeddings(
self,