Remove "routing_table" and "routing_key" concepts for the user (#201)

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.
This commit is contained in:
Ashwin Bharambe 2024-10-10 10:24:13 -07:00 committed by GitHub
parent 8c3010553f
commit 6bb57e72a7
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GPG key ID: B5690EEEBB952194
93 changed files with 4697 additions and 4457 deletions

View file

@ -22,7 +22,7 @@ def available_templates_specs() -> List[BuildConfig]:
import yaml
template_specs = []
for p in TEMPLATES_PATH.rglob("*.yaml"):
for p in TEMPLATES_PATH.rglob("*build.yaml"):
with open(p, "r") as f:
build_config = BuildConfig(**yaml.safe_load(f))
template_specs.append(build_config)
@ -105,8 +105,7 @@ class StackBuild(Subcommand):
import yaml
from llama_stack.distribution.build import ApiInput, build_image, ImageType
from llama_stack.distribution.build import build_image, ImageType
from llama_stack.distribution.utils.config_dirs import DISTRIBS_BASE_DIR
from llama_stack.distribution.utils.serialize import EnumEncoder
from termcolor import cprint
@ -150,9 +149,6 @@ class StackBuild(Subcommand):
def _run_template_list_cmd(self, args: argparse.Namespace) -> None:
import json
import yaml
from llama_stack.cli.table import print_table
# eventually, this should query a registry at llama.meta.com/llamastack/distributions
@ -178,9 +174,11 @@ class StackBuild(Subcommand):
)
def _run_stack_build_command(self, args: argparse.Namespace) -> None:
import textwrap
import yaml
from llama_stack.distribution.distribution import get_provider_registry
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
from prompt_toolkit.validation import Validator
from termcolor import cprint
@ -244,26 +242,29 @@ class StackBuild(Subcommand):
)
cprint(
"\n Llama Stack is composed of several APIs working together. Let's configure the providers (implementations) you want to use for these APIs.",
textwrap.dedent(
"""
Llama Stack is composed of several APIs working together. Let's select
the provider types (implementations) you want to use for these APIs.
""",
),
color="green",
)
print("Tip: use <TAB> to see options for the providers.\n")
providers = dict()
for api, providers_for_api in get_provider_registry().items():
available_providers = [
x for x in providers_for_api.keys() if x != "remote"
]
api_provider = prompt(
"> Enter provider for the {} API: (default=meta-reference): ".format(
api.value
),
"> Enter provider for API {}: ".format(api.value),
completer=WordCompleter(available_providers),
complete_while_typing=True,
validator=Validator.from_callable(
lambda x: x in providers_for_api,
error_message="Invalid provider, please enter one of the following: {}".format(
list(providers_for_api.keys())
),
),
default=(
"meta-reference"
if "meta-reference" in providers_for_api
else list(providers_for_api.keys())[0]
lambda x: x in available_providers,
error_message="Invalid provider, use <TAB> to see options",
),
)