forked from phoenix-oss/llama-stack-mirror
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>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
f294eac5f5
commit
9487ad8294
213 changed files with 1725 additions and 1204 deletions
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import multiprocessing
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import os
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import pickle
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import tempfile
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import time
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import uuid
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from typing import Callable, Generator
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import torch
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import zmq
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from fairscale.nn.model_parallel.initialize import (
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get_model_parallel_group,
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get_model_parallel_rank,
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get_model_parallel_src_rank,
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)
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from torch.distributed.launcher.api import elastic_launch, LaunchConfig
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_END_SENTINEL = "__end_sentinel__"
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_CANCEL_SENTINEL = "__cancel_sentinel__"
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def mp_rank_0() -> bool:
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return get_model_parallel_rank() == 0
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def retrieve_requests(reply_socket_url: str):
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if mp_rank_0():
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context = zmq.Context()
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reply_socket = context.socket(zmq.ROUTER)
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reply_socket.connect(reply_socket_url)
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while True:
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client_id, obj = maybe_get_work(reply_socket)
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if obj is None:
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time.sleep(0.01)
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continue
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reply_socket.send_multipart([client_id, pickle.dumps("YES READY")])
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break
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def send_obj(obj):
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reply_socket.send_multipart([client_id, pickle.dumps(obj)])
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while True:
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tasks = [None]
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if mp_rank_0():
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client_id, task = maybe_get_work(reply_socket)
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# there is still an unknown unclean GeneratorExit happening resulting in a
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# cancel sentinel getting queued _after_ we have finished sending everything :/
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# kind of a hack this is :/
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if task != _CANCEL_SENTINEL:
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tasks = [task]
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torch.distributed.broadcast_object_list(
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tasks,
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src=get_model_parallel_src_rank(),
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group=get_model_parallel_group(),
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)
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task = tasks[0]
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if task is None:
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time.sleep(0.1)
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else:
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try:
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out = yield task
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if out is None:
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break
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for obj in out:
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updates = [None]
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if mp_rank_0():
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_, update = maybe_get_work(reply_socket)
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if update == _CANCEL_SENTINEL:
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updates = [update]
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else:
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# only send the update if it's not cancelled otherwise the object sits in the socket
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# and gets pulled in the next request lol
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send_obj(obj)
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torch.distributed.broadcast_object_list(
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updates,
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src=get_model_parallel_src_rank(),
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group=get_model_parallel_group(),
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)
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if updates[0] == _CANCEL_SENTINEL:
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print("quitting generation loop because request was cancelled")
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break
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if mp_rank_0():
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send_obj(_END_SENTINEL)
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except Exception as e:
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print(f"[debug] got exception {e}")
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import traceback
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traceback.print_exc()
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if mp_rank_0():
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send_obj(e)
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if mp_rank_0():
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send_obj("DONE")
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def maybe_get_work(sock: zmq.Socket):
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message = None
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client_id = None
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try:
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client_id, obj = sock.recv_multipart(zmq.NOBLOCK)
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message = pickle.loads(obj)
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except zmq.ZMQError as e:
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if e.errno != zmq.EAGAIN:
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raise e
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return client_id, message
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def worker_process_entrypoint(
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reply_socket_url: str,
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init_model_cb: Callable,
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) -> None:
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model = init_model_cb()
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torch.distributed.barrier()
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time.sleep(1)
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# run the requests co-routine which retrieves requests from the socket
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# and sends responses (we provide) back to the caller
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req_gen = retrieve_requests(reply_socket_url)
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result = None
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while True:
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try:
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task = req_gen.send(result)
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if isinstance(task, str) and task == _END_SENTINEL:
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break
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result = model(task)
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except StopIteration:
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break
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print("[debug] worker process done")
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def launch_dist_group(
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reply_socket_url: str,
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model_parallel_size: int,
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init_model_cb: Callable,
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**kwargs,
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) -> None:
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id = uuid.uuid4().hex
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dist_url = f"file:///tmp/llama3_{id}_{time.time()}"
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with tempfile.TemporaryDirectory() as tmpdir:
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# TODO: track workers and if they terminate, tell parent process about it so cleanup can happen
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launch_config = LaunchConfig(
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max_nodes=1,
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min_nodes=1,
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nproc_per_node=model_parallel_size,
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start_method="fork",
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rdzv_backend="c10d",
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rdzv_endpoint=os.path.join(tmpdir, "rdzv"),
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rdzv_configs={"store_type": "file", "timeout": 90},
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max_restarts=0,
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monitor_interval=1,
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run_id=str(uuid.uuid4()),
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)
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elastic_launch(launch_config, entrypoint=worker_process_entrypoint)(
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reply_socket_url,
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init_model_cb,
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)
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def start_model_parallel_process(
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model_parallel_size: int,
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init_model_cb: Callable,
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**kwargs,
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):
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context = zmq.Context()
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request_socket = context.socket(zmq.DEALER)
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# Binding the request socket to a random port
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request_socket.bind("tcp://127.0.0.1:0")
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main_process_url = request_socket.getsockopt_string(zmq.LAST_ENDPOINT)
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ctx = multiprocessing.get_context("fork")
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process = ctx.Process(
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target=launch_dist_group,
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args=(
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main_process_url,
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model_parallel_size,
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init_model_cb,
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),
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kwargs=kwargs,
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)
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process.start()
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# wait until the model is loaded; rank 0 will send a message to indicate it's ready
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request_socket.send_pyobj("READY?")
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response = request_socket.recv_pyobj()
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print(f"Finished model load {response}")
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return request_socket, process
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class ModelParallelProcessGroup:
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def __init__(
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self,
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model_parallel_size: int,
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init_model_cb: Callable,
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**kwargs,
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):
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self.model_parallel_size = model_parallel_size
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self.init_model_cb = init_model_cb
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self.started = False
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self.running = False
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def start(self):
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assert not self.started, "process group already started"
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self.request_socket, self.process = start_model_parallel_process(
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self.model_parallel_size,
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self.init_model_cb,
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)
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self.started = True
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def stop(self):
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assert self.started, "process group not started"
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if self.process.is_alive():
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self.request_socket.send_pyobj(_END_SENTINEL, zmq.NOBLOCK)
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self.process.join()
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self.started = False
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def run_inference(self, request) -> Generator:
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assert not self.running, "inference already running"
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self.running = True
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self.request_socket.send_pyobj(request)
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try:
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while True:
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obj = self.request_socket.recv_pyobj()
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if obj == _END_SENTINEL:
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break
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if isinstance(obj, Exception):
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print(f"[debug] got exception {obj}")
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raise obj
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yield obj
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except GeneratorExit as e:
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self.request_socket.send_pyobj(_CANCEL_SENTINEL)
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while True:
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obj = self.request_socket.recv_pyobj()
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if obj == _END_SENTINEL:
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break
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finally:
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self.running = False
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