feat: add dynamic model registration support to TGI inference (#3417)

# What does this PR do?

adds dynamic model support to TGI

add new overwrite_completion_id feature to OpenAIMixin to deal with TGI
always returning id=""

## Test Plan

tgi: `docker run --gpus all --shm-size 1g -p 8080:80 -v /data:/data
ghcr.io/huggingface/text-generation-inference --model-id
Qwen/Qwen3-0.6B`

stack: `TGI_URL=http://localhost:8080 uv run llama stack build
--image-type venv --distro ci-tests --run`

test: `./scripts/integration-tests.sh --stack-config
http://localhost:8321 --setup tgi --subdirs inference --pattern openai`

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -8,6 +8,7 @@
 from collections.abc import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
+from pydantic import SecretStr
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -33,6 +34,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
+from llama_stack.apis.models.models import ModelType
 from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
@@ -41,16 +43,15 @@ from llama_stack.providers.utils.inference.model_registry import (
     build_hf_repo_model_entry,
 )
 from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
-    OpenAICompletionToLlamaStackMixin,
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
     process_completion_stream_response,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_model_input_info,
     completion_request_to_prompt_model_input_info,
@@ -73,26 +74,49 @@ def build_hf_repo_model_entries():
 
 
 class _HfAdapter(
+    OpenAIMixin,
     Inference,
-    OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
     ModelsProtocolPrivate,
 ):
-    client: AsyncInferenceClient
+    url: str
+    api_key: SecretStr
+
+    hf_client: AsyncInferenceClient
     max_tokens: int
     model_id: str
 
+    overwrite_completion_id = True  # TGI always returns id=""
+
     def __init__(self) -> None:
         self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
         self.huggingface_repo_to_llama_model_id = {
             model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo
         }
 
+    def get_api_key(self):
+        return self.api_key.get_secret_value()
+
+    def get_base_url(self):
+        return self.url
+
     async def shutdown(self) -> None:
         pass
 
+    async def list_models(self) -> list[Model] | None:
+        models = []
+        async for model in self.client.models.list():
+            models.append(
+                Model(
+                    identifier=model.id,
+                    provider_resource_id=model.id,
+                    provider_id=self.__provider_id__,
+                    metadata={},
+                    model_type=ModelType.llm,
+                )
+            )
+        return models
+
     async def register_model(self, model: Model) -> Model:
-        model = await self.register_helper.register_model(model)
         if model.provider_resource_id != self.model_id:
             raise ValueError(
                 f"Model {model.provider_resource_id} does not match the model {self.model_id} served by TGI."
@@ -176,7 +200,7 @@ class _HfAdapter(
         params = await self._get_params_for_completion(request)
 
         async def _generate_and_convert_to_openai_compat():
-            s = await self.client.text_generation(**params)
+            s = await self.hf_client.text_generation(**params)
             async for chunk in s:
                 token_result = chunk.token
                 finish_reason = None
@@ -194,7 +218,7 @@ class _HfAdapter(
 
     async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
         params = await self._get_params_for_completion(request)
-        r = await self.client.text_generation(**params)
+        r = await self.hf_client.text_generation(**params)
 
         choice = OpenAICompatCompletionChoice(
             finish_reason=r.details.finish_reason,
@@ -241,7 +265,7 @@ class _HfAdapter(
 
     async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
         params = await self._get_params(request)
-        r = await self.client.text_generation(**params)
+        r = await self.hf_client.text_generation(**params)
 
         choice = OpenAICompatCompletionChoice(
             finish_reason=r.details.finish_reason,
@@ -256,7 +280,7 @@ class _HfAdapter(
         params = await self._get_params(request)
 
         async def _generate_and_convert_to_openai_compat():
-            s = await self.client.text_generation(**params)
+            s = await self.hf_client.text_generation(**params)
             async for chunk in s:
                 token_result = chunk.token
 
@@ -308,18 +332,21 @@ class TGIAdapter(_HfAdapter):
         if not config.url:
             raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
         log.info(f"Initializing TGI client with url={config.url}")
-        self.client = AsyncInferenceClient(model=config.url, provider="hf-inference")
-        endpoint_info = await self.client.get_endpoint_info()
+        self.hf_client = AsyncInferenceClient(model=config.url, provider="hf-inference")
+        endpoint_info = await self.hf_client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
+        self.url = f"{config.url.rstrip('/')}/v1"
+        self.api_key = SecretStr("NO_KEY")
 
 
 class InferenceAPIAdapter(_HfAdapter):
     async def initialize(self, config: InferenceAPIImplConfig) -> None:
-        self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
-        endpoint_info = await self.client.get_endpoint_info()
+        self.hf_client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
+        endpoint_info = await self.hf_client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
+        # TODO: how do we set url for this?
 
 
 class InferenceEndpointAdapter(_HfAdapter):
@@ -331,6 +358,7 @@ class InferenceEndpointAdapter(_HfAdapter):
         endpoint.wait(timeout=60)
 
         # Initialize the adapter
-        self.client = endpoint.async_client
+        self.hf_client = endpoint.async_client
         self.model_id = endpoint.repository
         self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])
+        # TODO: how do we set url for this?