Make TGI adapter compatible with HF Inference API (#97)

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

@@ -5,54 +5,33 @@
 # the root directory of this source tree.
 
 
-from typing import Any, AsyncGenerator, Dict
+import logging
+from typing import AsyncGenerator
 
-import requests
-
-from huggingface_hub import HfApi, InferenceClient
+from huggingface_hub import AsyncInferenceClient, HfApi
 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.apis.inference import *  # noqa: F403
 from llama_stack.providers.utils.inference.augment_messages import (
     augment_messages_for_tools,
 )
 
-from .config import TGIImplConfig
+from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
+
+logger = logging.getLogger(__name__)
 
 
-class TGIAdapter(Inference):
-    def __init__(self, config: TGIImplConfig) -> None:
-        self.config = config
+class _HfAdapter(Inference):
+    client: AsyncInferenceClient
+    max_tokens: int
+    model_id: str
+
+    def __init__(self) -> None:
         self.tokenizer = Tokenizer.get_instance()
         self.formatter = ChatFormat(self.tokenizer)
 
-    @property
-    def client(self) -> InferenceClient:
-        return InferenceClient(model=self.config.url, token=self.config.api_token)
-
-    def _get_endpoint_info(self) -> Dict[str, Any]:
-        return {
-            **self.client.get_endpoint_info(),
-            "inference_url": self.config.url,
-        }
-
-    async def initialize(self) -> None:
-        try:
-            info = self._get_endpoint_info()
-            if "model_id" not in info:
-                raise RuntimeError("Missing model_id in model info")
-            if "max_total_tokens" not in info:
-                raise RuntimeError("Missing max_total_tokens in model info")
-            self.max_tokens = info["max_total_tokens"]
-
-            self.inference_url = info["inference_url"]
-        except Exception as e:
-            import traceback
-
-            traceback.print_exc()
-            raise RuntimeError(f"Error initializing TGIAdapter: {e}") from e
-
     async def shutdown(self) -> None:
         pass
 
@@ -111,7 +90,7 @@ class TGIAdapter(Inference):
 
         options = self.get_chat_options(request)
         if not request.stream:
-            response = self.client.text_generation(
+            response = await self.client.text_generation(
                 prompt=prompt,
                 stream=False,
                 details=True,
@@ -147,7 +126,7 @@ class TGIAdapter(Inference):
             stop_reason = None
             tokens = []
 
-            for response in self.client.text_generation(
+            async for response in await self.client.text_generation(
                 prompt=prompt,
                 stream=True,
                 details=True,
@@ -239,46 +218,36 @@ class TGIAdapter(Inference):
             )
 
 
-class InferenceEndpointAdapter(TGIAdapter):
-    def __init__(self, config: TGIImplConfig) -> None:
-        super().__init__(config)
-        self.config.url = self._construct_endpoint_url()
+class TGIAdapter(_HfAdapter):
+    async def initialize(self, config: TGIImplConfig) -> None:
+        self.client = AsyncInferenceClient(model=config.url, token=config.api_token)
+        endpoint_info = await self.client.get_endpoint_info()
+        self.max_tokens = endpoint_info["max_total_tokens"]
+        self.model_id = endpoint_info["model_id"]
 
-    def _construct_endpoint_url(self) -> str:
-        hf_endpoint_name = self.config.hf_endpoint_name
-        assert hf_endpoint_name.count("/") <= 1, (
-            "Endpoint name must be in the format of 'namespace/endpoint_name' "
-            "or 'endpoint_name'"
+
+class InferenceAPIAdapter(_HfAdapter):
+    async def initialize(self, config: InferenceAPIImplConfig) -> None:
+        self.client = AsyncInferenceClient(
+            model=config.model_id, token=config.api_token
         )
-        if "/" not in hf_endpoint_name:
-            hf_namespace: str = self.get_namespace()
-            endpoint_path = f"{hf_namespace}/{hf_endpoint_name}"
-        else:
-            endpoint_path = hf_endpoint_name
-        return f"https://api.endpoints.huggingface.cloud/v2/endpoint/{endpoint_path}"
+        endpoint_info = await self.client.get_endpoint_info()
+        self.max_tokens = endpoint_info["max_total_tokens"]
+        self.model_id = endpoint_info["model_id"]
 
-    def get_namespace(self) -> str:
-        return HfApi().whoami()["name"]
 
-    @property
-    def client(self) -> InferenceClient:
-        return InferenceClient(model=self.inference_url, token=self.config.api_token)
+class InferenceEndpointAdapter(_HfAdapter):
+    async def initialize(self, config: InferenceEndpointImplConfig) -> None:
+        # Get the inference endpoint details
+        api = HfApi(token=config.api_token)
+        endpoint = api.get_inference_endpoint(config.endpoint_name)
 
-    def _get_endpoint_info(self) -> Dict[str, Any]:
-        headers = {
-            "accept": "application/json",
-            "authorization": f"Bearer {self.config.api_token}",
-        }
-        response = requests.get(self.config.url, headers=headers)
-        response.raise_for_status()
-        endpoint_info = response.json()
-        return {
-            "inference_url": endpoint_info["status"]["url"],
-            "model_id": endpoint_info["model"]["repository"],
-            "max_total_tokens": int(
-                endpoint_info["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
-            ),
-        }
+        # Wait for the endpoint to be ready (if not already)
+        endpoint.wait(timeout=60)
 
-    async def initialize(self) -> None:
-        await super().initialize()
+        # Initialize the adapter
+        self.client = endpoint.async_client
+        self.model_id = endpoint.repository
+        self.max_tokens = int(
+            endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
+        )