chore(api): remove batch inference (#3261)

# What does this PR do?

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

llama_stack/apis/batch_inference/__init__.py

@@ -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 *

llama_stack/apis/batch_inference/batch_inference.py

@@ -1,79 +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.apis.version import LLAMA_STACK_API_V1
-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", level=LLAMA_STACK_API_V1)
-    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", level=LLAMA_STACK_API_V1)
-    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.
-        """
-        ...

llama_stack/apis/inference/inference.py

@@ -975,26 +975,6 @@ 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]
 
@@ -1051,27 +1031,6 @@ class InferenceProvider(Protocol):
         """
         ...
 
-    @webmethod(route="/inference/batch-completion", method="POST", experimental=True, level=LLAMA_STACK_API_V1)
-    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", level=LLAMA_STACK_API_V1)
     async def chat_completion(
         self,
@@ -1112,31 +1071,6 @@ class InferenceProvider(Protocol):
         """
         ...
 
-    @webmethod(route="/inference/batch-chat-completion", method="POST", experimental=True, level=LLAMA_STACK_API_V1)
-    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", level=LLAMA_STACK_API_V1)
     async def embeddings(
         self,