Make routing_key accept multiple values

- Implement `RoutableProvider` for all the inference adapters

llama_stack/distribution/datatypes.py

@@ -13,6 +13,10 @@ from llama_models.schema_utils import json_schema_type
 from pydantic import BaseModel, Field
 
 
+LLAMA_STACK_BUILD_CONFIG_VERSION = "v1"
+LLAMA_STACK_RUN_CONFIG_VERSION = "v1"
+
+
 @json_schema_type
 class Api(Enum):
     inference = "inference"
@@ -54,6 +58,12 @@ class RoutingTable(Protocol):
     def get_provider_impl(self, routing_key: str) -> Any: ...
 
 
+class RoutableProvider(Protocol):
+    async def register_routing_keys(self, keys: List[str]) -> None: ...
+
+    def get_routing_keys(self) -> List[str]: ...
+
+
 class GenericProviderConfig(BaseModel):
     provider_id: str
     config: Dict[str, Any]
@@ -65,8 +75,11 @@ class PlaceholderProviderConfig(BaseModel):
     providers: List[str]
 
 
+RoutingKey = Union[str, List[str]]
+
+
 class RoutableProviderConfig(GenericProviderConfig):
-    routing_key: str
+    routing_key: RoutingKey
 
 
 # Example: /inference, /safety
@@ -247,6 +260,7 @@ in the runtime configuration to help route to the correct provider.""",
 
 @json_schema_type
 class StackRunConfig(BaseModel):
+    version: str = LLAMA_STACK_RUN_CONFIG_VERSION
     built_at: datetime
 
     image_name: str = Field(
@@ -295,6 +309,7 @@ Provider configurations for each of the APIs provided by this package.
 
 @json_schema_type
 class BuildConfig(BaseModel):
+    version: str = LLAMA_STACK_BUILD_CONFIG_VERSION
     name: str
     distribution_spec: DistributionSpec = Field(
         description="The distribution spec to build including API providers. "

llama_stack/distribution/resolver.py

@@ -0,0 +1,129 @@
+# 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 Any, Dict, List, Set
+
+from llama_stack.distribution.datatypes import *  # noqa: F403
+from llama_stack.distribution.distribution import (
+    api_providers,
+    builtin_automatically_routed_apis,
+)
+from llama_stack.distribution.utils.dynamic import instantiate_provider
+
+
+async def resolve_impls_with_routing(run_config: StackRunConfig) -> Dict[Api, Any]:
+    """
+    Does two things:
+    - flatmaps, sorts and resolves the providers in dependency order
+    - for each API, produces either a (local, passthrough or router) implementation
+    """
+    all_providers = api_providers()
+    specs = {}
+    configs = {}
+
+    for api_str, config in run_config.api_providers.items():
+        api = Api(api_str)
+
+        # TODO: check that these APIs are not in the routing table part of the config
+        providers = all_providers[api]
+
+        # skip checks for API whose provider config is specified in routing_table
+        if isinstance(config, PlaceholderProviderConfig):
+            continue
+
+        if config.provider_id not in providers:
+            raise ValueError(
+                f"Unknown provider `{config.provider_id}` is not available for API `{api}`"
+            )
+        specs[api] = providers[config.provider_id]
+        configs[api] = config
+
+    apis_to_serve = run_config.apis_to_serve or set(
+        list(specs.keys()) + list(run_config.routing_table.keys())
+    )
+    for info in builtin_automatically_routed_apis():
+        source_api = info.routing_table_api
+
+        assert (
+            source_api not in specs
+        ), f"Routing table API {source_api} specified in wrong place?"
+        assert (
+            info.router_api not in specs
+        ), f"Auto-routed API {info.router_api} specified in wrong place?"
+
+        if info.router_api.value not in apis_to_serve:
+            continue
+
+        print("router_api", info.router_api)
+        if info.router_api.value not in run_config.routing_table:
+            raise ValueError(f"Routing table for `{source_api.value}` is not provided?")
+
+        routing_table = run_config.routing_table[info.router_api.value]
+
+        providers = all_providers[info.router_api]
+
+        inner_specs = []
+        inner_deps = []
+        for rt_entry in routing_table:
+            if rt_entry.provider_id not in providers:
+                raise ValueError(
+                    f"Unknown provider `{rt_entry.provider_id}` is not available for API `{api}`"
+                )
+            inner_specs.append(providers[rt_entry.provider_id])
+            inner_deps.extend(providers[rt_entry.provider_id].api_dependencies)
+
+        specs[source_api] = RoutingTableProviderSpec(
+            api=source_api,
+            module="llama_stack.distribution.routers",
+            api_dependencies=inner_deps,
+            inner_specs=inner_specs,
+        )
+        configs[source_api] = routing_table
+
+        specs[info.router_api] = AutoRoutedProviderSpec(
+            api=info.router_api,
+            module="llama_stack.distribution.routers",
+            routing_table_api=source_api,
+            api_dependencies=[source_api],
+        )
+        configs[info.router_api] = {}
+
+    sorted_specs = topological_sort(specs.values())
+    print(f"Resolved {len(sorted_specs)} providers in topological order")
+    for spec in sorted_specs:
+        print(f"  {spec.api}: {spec.provider_id}")
+    print("")
+    impls = {}
+    for spec in sorted_specs:
+        api = spec.api
+        deps = {api: impls[api] for api in spec.api_dependencies}
+        impl = await instantiate_provider(spec, deps, configs[api])
+
+        impls[api] = impl
+
+    return impls, specs
+
+
+def topological_sort(providers: List[ProviderSpec]) -> List[ProviderSpec]:
+    by_id = {x.api: x for x in providers}
+
+    def dfs(a: ProviderSpec, visited: Set[Api], stack: List[Api]):
+        visited.add(a.api)
+
+        for api in a.api_dependencies:
+            if api not in visited:
+                dfs(by_id[api], visited, stack)
+
+        stack.append(a.api)
+
+    visited = set()
+    stack = []
+
+    for a in providers:
+        if a.api not in visited:
+            dfs(a, visited, stack)
+
+    return [by_id[x] for x in stack]

llama_stack/distribution/routers/routing_tables.py

@@ -19,18 +19,31 @@ from llama_stack.distribution.datatypes import *  # noqa: F403
 class CommonRoutingTableImpl(RoutingTable):
     def __init__(
         self,
-        inner_impls: List[Tuple[str, Any]],
+        inner_impls: List[Tuple[RoutingKey, Any]],
         routing_table_config: Dict[str, List[RoutableProviderConfig]],
     ) -> None:
-        self.providers = {k: v for k, v in inner_impls}
-        self.routing_keys = list(self.providers.keys())
+        self.unique_providers = []
+        self.providers = {}
+        self.routing_keys = []
+
+        for key, impl in inner_impls:
+            keys = key if isinstance(key, list) else [key]
+            self.unique_providers.append((keys, impl))
+
+            for k in keys:
+                if k in self.providers:
+                    raise ValueError(f"Duplicate routing key {k}")
+                self.providers[k] = impl
+                self.routing_keys.append(k)
+
         self.routing_table_config = routing_table_config
 
     async def initialize(self) -> None:
-        pass
+        for keys, p in self.unique_providers:
+            await p.register_routing_keys(keys)
 
     async def shutdown(self) -> None:
-        for p in self.providers.values():
+        for _, p in self.unique_providers:
             await p.shutdown()
 
     def get_provider_impl(self, routing_key: str) -> Any:

llama_stack/distribution/server/server.py

@@ -17,16 +17,7 @@ from collections.abc import (
 from contextlib import asynccontextmanager
 from http import HTTPStatus
 from ssl import SSLError
-from typing import (
-    Any,
-    AsyncGenerator,
-    AsyncIterator,
-    Dict,
-    get_type_hints,
-    List,
-    Optional,
-    Set,
-)
+from typing import Any, AsyncGenerator, AsyncIterator, Dict, get_type_hints, Optional
 
 import fire
 import httpx
@@ -48,13 +39,9 @@ from llama_stack.providers.utils.telemetry.tracing import (
 )
 from llama_stack.distribution.datatypes import *  # noqa: F403
 
-from llama_stack.distribution.distribution import (
-    api_endpoints,
-    api_providers,
-    builtin_automatically_routed_apis,
-)
+from llama_stack.distribution.distribution import api_endpoints
 from llama_stack.distribution.request_headers import set_request_provider_data
-from llama_stack.distribution.utils.dynamic import instantiate_provider
+from llama_stack.distribution.resolver import resolve_impls_with_routing
 
 
 def is_async_iterator_type(typ):
@@ -289,125 +276,6 @@ def create_dynamic_typed_route(func: Any, method: str):
     return endpoint
 
 
-def topological_sort(providers: List[ProviderSpec]) -> List[ProviderSpec]:
-    by_id = {x.api: x for x in providers}
-
-    def dfs(a: ProviderSpec, visited: Set[Api], stack: List[Api]):
-        visited.add(a.api)
-
-        for api in a.api_dependencies:
-            if api not in visited:
-                dfs(by_id[api], visited, stack)
-
-        stack.append(a.api)
-
-    visited = set()
-    stack = []
-
-    for a in providers:
-        if a.api not in visited:
-            dfs(a, visited, stack)
-
-    return [by_id[x] for x in stack]
-
-
-def snake_to_camel(snake_str):
-    return "".join(word.capitalize() for word in snake_str.split("_"))
-
-
-async def resolve_impls_with_routing(run_config: StackRunConfig) -> Dict[Api, Any]:
-    """
-    Does two things:
-    - flatmaps, sorts and resolves the providers in dependency order
-    - for each API, produces either a (local, passthrough or router) implementation
-    """
-    all_providers = api_providers()
-    specs = {}
-    configs = {}
-
-    for api_str, config in run_config.api_providers.items():
-        api = Api(api_str)
-
-        # TODO: check that these APIs are not in the routing table part of the config
-        providers = all_providers[api]
-
-        # skip checks for API whose provider config is specified in routing_table
-        if isinstance(config, PlaceholderProviderConfig):
-            continue
-
-        if config.provider_id not in providers:
-            raise ValueError(
-                f"Unknown provider `{config.provider_id}` is not available for API `{api}`"
-            )
-        specs[api] = providers[config.provider_id]
-        configs[api] = config
-
-    apis_to_serve = run_config.apis_to_serve or set(
-        list(specs.keys()) + list(run_config.routing_table.keys())
-    )
-    for info in builtin_automatically_routed_apis():
-        source_api = info.routing_table_api
-
-        assert (
-            source_api not in specs
-        ), f"Routing table API {source_api} specified in wrong place?"
-        assert (
-            info.router_api not in specs
-        ), f"Auto-routed API {info.router_api} specified in wrong place?"
-
-        if info.router_api.value not in apis_to_serve:
-            continue
-
-        print("router_api", info.router_api)
-        if info.router_api.value not in run_config.routing_table:
-            raise ValueError(f"Routing table for `{source_api.value}` is not provided?")
-
-        routing_table = run_config.routing_table[info.router_api.value]
-
-        providers = all_providers[info.router_api]
-
-        inner_specs = []
-        inner_deps = []
-        for rt_entry in routing_table:
-            if rt_entry.provider_id not in providers:
-                raise ValueError(
-                    f"Unknown provider `{rt_entry.provider_id}` is not available for API `{api}`"
-                )
-            inner_specs.append(providers[rt_entry.provider_id])
-            inner_deps.extend(providers[rt_entry.provider_id].api_dependencies)
-
-        specs[source_api] = RoutingTableProviderSpec(
-            api=source_api,
-            module="llama_stack.distribution.routers",
-            api_dependencies=inner_deps,
-            inner_specs=inner_specs,
-        )
-        configs[source_api] = routing_table
-
-        specs[info.router_api] = AutoRoutedProviderSpec(
-            api=info.router_api,
-            module="llama_stack.distribution.routers",
-            routing_table_api=source_api,
-            api_dependencies=[source_api],
-        )
-        configs[info.router_api] = {}
-
-    sorted_specs = topological_sort(specs.values())
-    print(f"Resolved {len(sorted_specs)} providers in topological order")
-    for spec in sorted_specs:
-        print(f"  {spec.api}: {spec.provider_id}")
-    print("")
-    impls = {}
-    for spec in sorted_specs:
-        api = spec.api
-        deps = {api: impls[api] for api in spec.api_dependencies}
-        impl = await instantiate_provider(spec, deps, configs[api])
-
-        impls[api] = impl
-
-    return impls, specs
-
-
 def main(
     yaml_config: str = "llamastack-run.yaml",
     port: int = 5000,

llama_stack/providers/adapters/inference/bedrock/bedrock.py

@@ -12,7 +12,8 @@ from botocore.config import Config
 
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
+
+from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
 
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.adapters.inference.bedrock.config import BedrockConfig
@@ -25,7 +26,7 @@ BEDROCK_SUPPORTED_MODELS = {
 }
 
 
-class BedrockInferenceAdapter(Inference):
+class BedrockInferenceAdapter(Inference, RoutableProviderForModels):
 
     @staticmethod
     def _create_bedrock_client(config: BedrockConfig) -> BaseClient:
@@ -68,6 +69,9 @@ class BedrockInferenceAdapter(Inference):
         return boto3_session.client("bedrock-runtime", config=boto3_config)
 
     def __init__(self, config: BedrockConfig) -> None:
+        RoutableProviderForModels.__init__(
+            self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
+        )
         self._config = config
 
         self._client = BedrockInferenceAdapter._create_bedrock_client(config)
@@ -94,22 +98,6 @@ class BedrockInferenceAdapter(Inference):
     ) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
         raise NotImplementedError()
 
-    @staticmethod
-    def resolve_bedrock_model(model_name: str) -> str:
-        model = resolve_model(model_name)
-        assert (
-            model is not None
-            and model.descriptor(shorten_default_variant=True)
-            in BEDROCK_SUPPORTED_MODELS
-        ), (
-            f"Unsupported model: {model_name}, use one of the supported models: "
-            f"{','.join(BEDROCK_SUPPORTED_MODELS.keys())}"
-        )
-
-        return BEDROCK_SUPPORTED_MODELS.get(
-            model.descriptor(shorten_default_variant=True)
-        )
-
     @staticmethod
     def _bedrock_stop_reason_to_stop_reason(bedrock_stop_reason: str) -> StopReason:
         if bedrock_stop_reason == "max_tokens":
@@ -350,7 +338,7 @@ class BedrockInferenceAdapter(Inference):
     ) -> (
         AsyncGenerator
     ):  # Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]:
-        bedrock_model = BedrockInferenceAdapter.resolve_bedrock_model(model)
+        bedrock_model = self.map_to_provider_model(model)
         inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
             sampling_params
         )

llama_stack/providers/adapters/inference/fireworks/fireworks.py

@@ -12,7 +12,8 @@ from llama_models.llama3.api.chat_format import ChatFormat
 
 from llama_models.llama3.api.datatypes import Message, StopReason
 from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
+
+from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
 
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.utils.inference.augment_messages import (
@@ -28,8 +29,11 @@ FIREWORKS_SUPPORTED_MODELS = {
 }
 
 
-class FireworksInferenceAdapter(Inference):
+class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
     def __init__(self, config: FireworksImplConfig) -> None:
+        RoutableProviderForModels.__init__(
+            self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
+        )
         self.config = config
         tokenizer = Tokenizer.get_instance()
         self.formatter = ChatFormat(tokenizer)
@@ -65,18 +69,6 @@ class FireworksInferenceAdapter(Inference):
 
         return fireworks_messages
 
-    def resolve_fireworks_model(self, model_name: str) -> str:
-        model = resolve_model(model_name)
-        assert (
-            model is not None
-            and model.descriptor(shorten_default_variant=True)
-            in FIREWORKS_SUPPORTED_MODELS
-        ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(FIREWORKS_SUPPORTED_MODELS.keys())}"
-
-        return FIREWORKS_SUPPORTED_MODELS.get(
-            model.descriptor(shorten_default_variant=True)
-        )
-
     def get_fireworks_chat_options(self, request: ChatCompletionRequest) -> dict:
         options = {}
         if request.sampling_params is not None:
@@ -112,7 +104,7 @@ class FireworksInferenceAdapter(Inference):
 
         # accumulate sampling params and other options to pass to fireworks
         options = self.get_fireworks_chat_options(request)
-        fireworks_model = self.resolve_fireworks_model(request.model)
+        fireworks_model = self.map_to_provider_model(request.model)
 
         if not request.stream:
             r = await self.client.chat.completions.acreate(

llama_stack/providers/adapters/inference/ollama/ollama.py

@@ -11,7 +11,6 @@ import httpx
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.datatypes import Message, StopReason
 from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
 
 from ollama import AsyncClient
 
@@ -19,6 +18,7 @@ from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.utils.inference.augment_messages import (
     augment_messages_for_tools,
 )
+from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
 
 # TODO: Eventually this will move to the llama cli model list command
 # mapping of Model SKUs to ollama models
@@ -29,8 +29,11 @@ OLLAMA_SUPPORTED_SKUS = {
 }
 
 
-class OllamaInferenceAdapter(Inference):
+class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
     def __init__(self, url: str) -> None:
+        RoutableProviderForModels.__init__(
+            self, stack_to_provider_models_map=OLLAMA_SUPPORTED_SKUS
+        )
         self.url = url
         tokenizer = Tokenizer.get_instance()
         self.formatter = ChatFormat(tokenizer)
@@ -72,15 +75,6 @@ class OllamaInferenceAdapter(Inference):
 
         return ollama_messages
 
-    def resolve_ollama_model(self, model_name: str) -> str:
-        model = resolve_model(model_name)
-        assert (
-            model is not None
-            and model.descriptor(shorten_default_variant=True) in OLLAMA_SUPPORTED_SKUS
-        ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}"
-
-        return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True))
-
     def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict:
         options = {}
         if request.sampling_params is not None:
@@ -120,7 +114,7 @@ class OllamaInferenceAdapter(Inference):
         messages = augment_messages_for_tools(request)
         # accumulate sampling params and other options to pass to ollama
         options = self.get_ollama_chat_options(request)
-        ollama_model = self.resolve_ollama_model(request.model)
+        ollama_model = self.map_to_provider_model(request.model)
 
         res = await self.client.ps()
         need_model_pull = True

llama_stack/providers/adapters/inference/tgi/tgi.py

@@ -13,6 +13,8 @@ from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.datatypes import StopReason
 from llama_models.llama3.api.tokenizer import Tokenizer
 
+from llama_stack.distribution.datatypes import RoutableProvider
+
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.utils.inference.augment_messages import (
     augment_messages_for_tools,
@@ -23,7 +25,7 @@ from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImpl
 logger = logging.getLogger(__name__)
 
 
-class _HfAdapter(Inference):
+class _HfAdapter(Inference, RoutableProvider):
     client: AsyncInferenceClient
     max_tokens: int
     model_id: str
@@ -32,6 +34,14 @@ class _HfAdapter(Inference):
         self.tokenizer = Tokenizer.get_instance()
         self.formatter = ChatFormat(self.tokenizer)
 
+    async def register_routing_keys(self, routing_keys: list[str]) -> None:
+        # these are the model names the Llama Stack will use to route requests to this provider
+        # perform validation here if necessary
+        self.routing_keys = routing_keys
+
+    def get_routing_keys(self) -> list[str]:
+        return self.routing_keys
+
     async def shutdown(self) -> None:
         pass
 

llama_stack/providers/adapters/inference/together/together.py

@@ -10,7 +10,6 @@ from llama_models.llama3.api.chat_format import ChatFormat
 
 from llama_models.llama3.api.datatypes import Message, StopReason
 from llama_models.llama3.api.tokenizer import Tokenizer
-from llama_models.sku_list import resolve_model
 
 from together import Together
 
@@ -19,6 +18,7 @@ from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.utils.inference.augment_messages import (
     augment_messages_for_tools,
 )
+from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
 
 from .config import TogetherImplConfig
 
@@ -32,8 +32,13 @@ TOGETHER_SUPPORTED_MODELS = {
 }
 
 
-class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
+class TogetherInferenceAdapter(
+    Inference, NeedsRequestProviderData, RoutableProviderForModels
+):
     def __init__(self, config: TogetherImplConfig) -> None:
+        RoutableProviderForModels.__init__(
+            self, stack_to_provider_models_map=TOGETHER_SUPPORTED_MODELS
+        )
         self.config = config
         tokenizer = Tokenizer.get_instance()
         self.formatter = ChatFormat(tokenizer)
@@ -69,18 +74,6 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
 
         return together_messages
 
-    def resolve_together_model(self, model_name: str) -> str:
-        model = resolve_model(model_name)
-        assert (
-            model is not None
-            and model.descriptor(shorten_default_variant=True)
-            in TOGETHER_SUPPORTED_MODELS
-        ), f"Unsupported model: {model_name}, use one of the supported models: {','.join(TOGETHER_SUPPORTED_MODELS.keys())}"
-
-        return TOGETHER_SUPPORTED_MODELS.get(
-            model.descriptor(shorten_default_variant=True)
-        )
-
     def get_together_chat_options(self, request: ChatCompletionRequest) -> dict:
         options = {}
         if request.sampling_params is not None:
@@ -125,7 +118,7 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
 
         # accumulate sampling params and other options to pass to together
         options = self.get_together_chat_options(request)
-        together_model = self.resolve_together_model(request.model)
+        together_model = self.map_to_provider_model(request.model)
         messages = augment_messages_for_tools(request)
 
         if not request.stream:

llama_stack/providers/impls/meta_reference/inference/inference.py

@@ -6,21 +6,13 @@
 
 import asyncio
 
-from typing import AsyncIterator, Union
+from typing import AsyncIterator, List, Union
 
-from llama_models.llama3.api.datatypes import StopReason
 from llama_models.sku_list import resolve_model
 
-from llama_stack.apis.inference import (
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    ChatCompletionResponseEvent,
-    ChatCompletionResponseEventType,
-    ChatCompletionResponseStreamChunk,
-    Inference,
-    ToolCallDelta,
-    ToolCallParseStatus,
-)
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.distribution.datatypes import RoutableProvider
 from llama_stack.providers.utils.inference.augment_messages import (
     augment_messages_for_tools,
 )
@@ -28,15 +20,12 @@ from llama_stack.providers.utils.inference.augment_messages import (
 from .config import MetaReferenceImplConfig
 from .model_parallel import LlamaModelParallelGenerator
 
-from llama_models.llama3.api.datatypes import *  # noqa: F403
-from llama_stack.apis.inference import *  # noqa: F403
-
 # there's a single model parallel process running serving the model. for now,
 # we don't support multiple concurrent requests to this process.
 SEMAPHORE = asyncio.Semaphore(1)
 
 
-class MetaReferenceInferenceImpl(Inference):
+class MetaReferenceInferenceImpl(Inference, RoutableProvider):
     def __init__(self, config: MetaReferenceImplConfig) -> None:
         self.config = config
         model = resolve_model(config.model)
@@ -49,6 +38,15 @@ class MetaReferenceInferenceImpl(Inference):
         self.generator = LlamaModelParallelGenerator(self.config)
         self.generator.start()
 
+    async def register_routing_keys(self, routing_keys: List[str]) -> None:
+        assert (
+            len(routing_keys) == 1
+        ), f"Only one routing key is supported {routing_keys}"
+        assert routing_keys[0] == self.config.model
+
+    def get_routing_keys(self) -> List[str]:
+        return [self.config.model]
+
     async def shutdown(self) -> None:
         self.generator.stop()
 

llama_stack/providers/utils/inference/routable.py

@@ -0,0 +1,40 @@
+# 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 Dict
+
+from llama_models.sku_list import resolve_model
+
+from llama_stack.distribution.datatypes import RoutableProvider
+
+
+class RoutableProviderForModels(RoutableProvider):
+
+    def __init__(self, stack_to_provider_models_map: Dict[str, str]):
+        self.stack_to_provider_models_map = stack_to_provider_models_map
+
+    async def register_routing_keys(self, routing_keys: List[str]):
+        for routing_key in routing_keys:
+            if routing_key not in self.stack_to_provider_models_map:
+                raise ValueError(
+                    f"Routing key {routing_key} not found in map {self.stack_to_provider_models_map}"
+                )
+        self.routing_keys = routing_keys
+
+    def get_routing_keys(self) -> List[str]:
+        return self.routing_keys
+
+    def map_to_provider_model(self, routing_key: str) -> str:
+        model = resolve_model(routing_key)
+        if not model:
+            raise ValueError(f"Unknown model: `{routing_key}`")
+
+        if routing_key not in self.stack_to_provider_models_map:
+            raise ValueError(
+                f"Model {routing_key} not found in map {self.stack_to_provider_models_map}"
+            )
+
+        return self.stack_to_provider_models_map[routing_key]