Inference to use provider resource id to register and validate (#428)

This PR changes the way model id gets translated to the final model name
that gets passed through the provider.
Major changes include:
1) Providers are responsible for registering an object and as part of
the registration returning the object with the correct provider specific
name of the model provider_resource_id
2) To help with the common look ups different names a new ModelLookup
class is created.



Tested all inference providers including together, fireworks, vllm,
ollama, meta reference and bedrock

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -7,11 +7,15 @@
 from typing import *  # noqa: F403
 
 from botocore.client import BaseClient
+from llama_models.datatypes import CoreModelId
 
 from llama_models.llama3.api.chat_format import ChatFormat
 from llama_models.llama3.api.tokenizer import Tokenizer
 
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
 
 from llama_stack.apis.inference import *  # noqa: F403
 
@@ -19,19 +23,26 @@ from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
 
 
-BEDROCK_SUPPORTED_MODELS = {
-    "Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
-    "Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
-    "Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
-}
+model_aliases = [
+    build_model_alias(
+        "meta.llama3-1-8b-instruct-v1:0",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "meta.llama3-1-70b-instruct-v1:0",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+    build_model_alias(
+        "meta.llama3-1-405b-instruct-v1:0",
+        CoreModelId.llama3_1_405b_instruct.value,
+    ),
+]
 
 
 # NOTE: this is not quite tested after the recent refactors
 class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
     def __init__(self, config: BedrockConfig) -> None:
-        ModelRegistryHelper.__init__(
-            self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
-        )
+        ModelRegistryHelper.__init__(self, model_aliases)
         self._config = config
 
         self._client = create_bedrock_client(config)
@@ -49,7 +60,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
 
     async def completion(
         self,
-        model: str,
+        model_id: str,
         content: InterleavedTextMedia,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -286,7 +297,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
 
     async def chat_completion(
         self,
-        model: str,
+        model_id: str,
         messages: List[Message],
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
@@ -298,8 +309,9 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
     ) -> Union[
         ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
     ]:
+        model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
-            model=model,
+            model=model.provider_resource_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -404,7 +416,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
                 pass
 
     def _get_params_for_chat_completion(self, request: ChatCompletionRequest) -> Dict:
-        bedrock_model = self.map_to_provider_model(request.model)
+        bedrock_model = request.model
         inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
             request.sampling_params
         )
@@ -433,7 +445,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
 
     async def embeddings(
         self,
-        model: str,
+        model_id: str,
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()