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6bb57e72a7
This PR makes several core changes to the developer experience surrounding Llama Stack. Background: PR #92 introduced the notion of "routing" to the Llama Stack. It introduces three object types: (1) models, (2) shields and (3) memory banks. Each of these objects can be associated with a distinct provider. So you can get model A to be inferenced locally while model B, C can be inference remotely (e.g.) However, this had a few drawbacks: you could not address the provider instances -- i.e., if you configured "meta-reference" with a given model, you could not assign an identifier to this instance which you could re-use later. the above meant that you could not register a "routing_key" (e.g. model) dynamically and say "please use this existing provider I have already configured" for a new model. the terms "routing_table" and "routing_key" were exposed directly to the user. in my view, this is way too much overhead for a new user (which almost everyone is.) people come to the stack wanting to do ML and encounter a completely unexpected term. What this PR does: This PR structures the run config with only a single prominent key: - providers Providers are instances of configured provider types. Here's an example which shows two instances of the remote::tgi provider which are serving two different models. providers: inference: - provider_id: foo provider_type: remote::tgi config: { ... } - provider_id: bar provider_type: remote::tgi config: { ... } Secondly, the PR adds dynamic registration of { models | shields | memory_banks } to the API surface. The distribution still acts like a "routing table" (as previously) except that it asks the backing providers for a listing of these objects. For example it asks a TGI or Ollama inference adapter what models it is serving. Only the models that are being actually served can be requested by the user for inference. Otherwise, the Stack server will throw an error. When dynamically registering these objects, you can use the provider IDs shown above. Info about providers can be obtained using the Api.inspect set of endpoints (/providers, /routes, etc.) The above examples shows the correspondence between inference providers and models registry items. Things work similarly for the safety <=> shields and memory <=> memory_banks pairs. Registry: This PR also makes it so that Providers need to implement additional methods for registering and listing objects. For example, each Inference provider is now expected to implement the ModelsProtocolPrivate protocol (naming is not great!) which consists of two methods register_model list_models The goal is to inform the provider that a certain model needs to be supported so the provider can make any relevant backend changes if needed (or throw an error if the model cannot be supported.) There are many other cleanups included some of which are detailed in a follow-up comment.
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# 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 argparse
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from llama_stack.cli.subcommand import Subcommand
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class StackRun(Subcommand):
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def __init__(self, subparsers: argparse._SubParsersAction):
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super().__init__()
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self.parser = subparsers.add_parser(
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"run",
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prog="llama stack run",
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description="""start the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.""",
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formatter_class=argparse.RawTextHelpFormatter,
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)
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self._add_arguments()
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self.parser.set_defaults(func=self._run_stack_run_cmd)
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def _add_arguments(self):
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self.parser.add_argument(
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"config",
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type=str,
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help="Path to config file to use for the run",
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)
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self.parser.add_argument(
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"--port",
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type=int,
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help="Port to run the server on. Defaults to 5000",
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default=5000,
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)
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self.parser.add_argument(
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"--disable-ipv6",
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action="store_true",
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help="Disable IPv6 support",
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default=False,
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)
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def _run_stack_run_cmd(self, args: argparse.Namespace) -> None:
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from pathlib import Path
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import pkg_resources
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import yaml
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from termcolor import cprint
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from llama_stack.distribution.build import ImageType
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from llama_stack.distribution.configure import parse_and_maybe_upgrade_config
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from llama_stack.distribution.utils.config_dirs import BUILDS_BASE_DIR
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from llama_stack.distribution.utils.exec import run_with_pty
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if not args.config:
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self.parser.error("Must specify a config file to run")
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return
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config_file = Path(args.config)
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if not config_file.exists() and not args.config.endswith(".yaml"):
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# check if it's a build config saved to conda dir
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config_file = Path(
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BUILDS_BASE_DIR / ImageType.conda.value / f"{args.config}-run.yaml"
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)
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if not config_file.exists() and not args.config.endswith(".yaml"):
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# check if it's a build config saved to docker dir
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config_file = Path(
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BUILDS_BASE_DIR / ImageType.docker.value / f"{args.config}-run.yaml"
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)
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if not config_file.exists():
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self.parser.error(
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f"File {str(config_file)} does not exist. Please run `llama stack build` and `llama stack configure <name>` to generate a run.yaml file"
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)
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return
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cprint(f"Using config `{config_file}`", "green")
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with open(config_file, "r") as f:
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config_dict = yaml.safe_load(config_file.read_text())
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config = parse_and_maybe_upgrade_config(config_dict)
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if config.docker_image:
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script = pkg_resources.resource_filename(
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"llama_stack",
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"distribution/start_container.sh",
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)
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run_args = [script, config.docker_image]
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else:
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script = pkg_resources.resource_filename(
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"llama_stack",
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"distribution/start_conda_env.sh",
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)
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run_args = [
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script,
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config.conda_env,
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]
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run_args.extend([str(config_file), str(args.port)])
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if args.disable_ipv6:
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run_args.append("--disable-ipv6")
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run_with_pty(run_args)
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