API Updates (#73)

* API Keys passed from Client instead of distro configuration

* delete distribution registry

* Rename the "package" word away

* Introduce a "Router" layer for providers

Some providers need to be factorized and considered as thin routing
layers on top of other providers. Consider two examples:

- The inference API should be a routing layer over inference providers,
  routed using the "model" key
- The memory banks API is another instance where various memory bank
  types will be provided by independent providers (e.g., a vector store
  is served by Chroma while a keyvalue memory can be served by Redis or
  PGVector)

This commit introduces a generalized routing layer for this purpose.

* update `apis_to_serve`

* llama_toolchain -> llama_stack

* Codemod from llama_toolchain -> llama_stack

- added providers/registry
- cleaned up api/ subdirectories and moved impls away
- restructured api/api.py
- from llama_stack.apis.<api> import foo should work now
- update imports to do llama_stack.apis.<api>
- update many other imports
- added __init__, fixed some registry imports
- updated registry imports
- create_agentic_system -> create_agent
- AgenticSystem -> Agent

* Moved some stuff out of common/; re-generated OpenAPI spec

* llama-toolchain -> llama-stack (hyphens)

* add control plane API

* add redis adapter + sqlite provider

* move core -> distribution

* Some more toolchain -> stack changes

* small naming shenanigans

* Removing custom tool and agent utilities and moving them client side

* Move control plane to distribution server for now

* Remove control plane from API list

* no codeshield dependency randomly plzzzzz

* Add "fire" as a dependency

* add back event loggers

* stack configure fixes

* use brave instead of bing in the example client

* add init file so it gets packaged

* add init files so it gets packaged

* Update MANIFEST

* bug fix

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Xi Yan <xiyan@meta.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>

llama_toolchain/inference/meta_reference/model_parallel.py

@@ -1,110 +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.
-
-import os
-from copy import deepcopy
-from dataclasses import dataclass
-from functools import partial
-from typing import Generator, List, Optional
-
-from llama_models.llama3.api.chat_format import ChatFormat
-from llama_models.llama3.api.datatypes import Message, ToolPromptFormat
-from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
-
-from .config import MetaReferenceImplConfig
-from .generation import Llama, model_checkpoint_dir
-from .parallel_utils import ModelParallelProcessGroup
-
-
-@dataclass
-class InferenceArgs:
-    messages: List[Message]
-    temperature: float
-    top_p: float
-    max_gen_len: int
-    logprobs: bool
-    tool_prompt_format: ToolPromptFormat
-
-
-class ModelRunner:
-    def __init__(self, llama):
-        self.llama = llama
-
-    # the `task` object is the same that is sent to `ModelParallelProcessGroup.run_inference()`
-    def __call__(self, task: InferenceArgs):
-        return self.llama.chat_completion(
-            task.messages,
-            task.temperature,
-            task.top_p,
-            task.max_gen_len,
-            task.logprobs,
-            task.tool_prompt_format,
-        )
-
-
-def init_model_cb(config: MetaReferenceImplConfig):
-    llama = Llama.build(config)
-    return ModelRunner(llama)
-
-
-class LlamaModelParallelGenerator:
-    """
-    This abstraction exists so
-     - we can run model parallel code without needing to run the CLIs via torchrun
-     - this also enables use model parallel code within a notebook context.
-
-    A Context Manager is used to ensure that the model parallel process is started and stopped
-    correctly. This does make the ergonomics a little awkward, because it isn't immediately
-    clear at the callsite why we need to use a context manager.
-    """
-
-    def __init__(self, config: MetaReferenceImplConfig):
-        self.config = config
-        self.model = resolve_model(self.config.model)
-        # this is a hack because Agent's loop uses this to tokenize and check if input is too long
-        # while the tool-use loop is going
-        checkpoint_dir = model_checkpoint_dir(self.model)
-        tokenizer_path = os.path.join(checkpoint_dir, "tokenizer.model")
-        self.formatter = ChatFormat(Tokenizer(tokenizer_path))
-
-    def start(self):
-        self.__enter__()
-
-    def stop(self):
-        self.__exit__(None, None, None)
-
-    def __enter__(self):
-        self.group = ModelParallelProcessGroup(
-            self.config.model_parallel_size,
-            init_model_cb=partial(init_model_cb, self.config),
-        )
-        self.group.start()
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.group.stop()
-
-    def chat_completion(
-        self,
-        messages: List[Message],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-        tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
-    ) -> Generator:
-        req_obj = InferenceArgs(
-            messages=deepcopy(messages),
-            temperature=temperature,
-            top_p=top_p,
-            max_gen_len=max_gen_len,
-            logprobs=logprobs,
-            tool_prompt_format=tool_prompt_format,
-        )
-
-        gen = self.group.run_inference(req_obj)
-        yield from gen