mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-27 18:50:41 +00:00
Make TGI adapter compatible with HF Inference API (#97)
This commit is contained in:
Lucain
GitHub
parent
851c30597a
commit
615ed4bfbc
7 changed files with 122 additions and 96 deletions
|
|
@ -0,0 +1,10 @@
|
|||
name: local-hf-endpoint
|
||||
distribution_spec:
|
||||
description: "Like local, but use Hugging Face Inference Endpoints for running LLM inference.\nSee https://hf.co/docs/api-endpoints."
|
||||
providers:
|
||||
inference: remote::hf::endpoint
|
||||
memory: meta-reference
|
||||
safety: meta-reference
|
||||
agents: meta-reference
|
||||
telemetry: meta-reference
|
||||
image_type: conda
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
name: local-hf-serverless
|
||||
distribution_spec:
|
||||
description: "Like local, but use Hugging Face Inference API (serverless) for running LLM inference.\nSee https://hf.co/docs/api-inference."
|
||||
providers:
|
||||
inference: remote::hf::serverless
|
||||
memory: meta-reference
|
||||
safety: meta-reference
|
||||
agents: meta-reference
|
||||
telemetry: meta-reference
|
||||
image_type: conda
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
name: local-tgi
|
||||
distribution_spec:
|
||||
description: Use TGI (local or with Hugging Face Inference Endpoints for running LLM inference. When using HF Inference Endpoints, you must provide the name of the endpoint).
|
||||
description: Like local, but use a TGI server for running LLM inference.
|
||||
providers:
|
||||
inference: remote::tgi
|
||||
memory: meta-reference
|
||||
|
|
|
|||
|
|
@ -4,21 +4,26 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
from .config import TGIImplConfig
|
||||
from .tgi import InferenceEndpointAdapter, TGIAdapter
|
||||
from typing import Union
|
||||
|
||||
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
|
||||
from .tgi import InferenceAPIAdapter, InferenceEndpointAdapter, TGIAdapter
|
||||
|
||||
|
||||
async def get_adapter_impl(config: TGIImplConfig, _deps):
|
||||
assert isinstance(config, TGIImplConfig), f"Unexpected config type: {type(config)}"
|
||||
|
||||
if config.url is not None:
|
||||
impl = TGIAdapter(config)
|
||||
elif config.is_inference_endpoint():
|
||||
impl = InferenceEndpointAdapter(config)
|
||||
async def get_adapter_impl(
|
||||
config: Union[InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig],
|
||||
_deps,
|
||||
):
|
||||
if isinstance(config, TGIImplConfig):
|
||||
impl = TGIAdapter()
|
||||
elif isinstance(config, InferenceAPIImplConfig):
|
||||
impl = InferenceAPIAdapter()
|
||||
elif isinstance(config, InferenceEndpointImplConfig):
|
||||
impl = InferenceEndpointAdapter()
|
||||
else:
|
||||
raise ValueError(
|
||||
"Invalid configuration. Specify either an URL or HF Inference Endpoint details (namespace and endpoint name)."
|
||||
f"Invalid configuration. Expected 'TGIAdapter', 'InferenceAPIImplConfig' or 'InferenceEndpointImplConfig'. Got {type(config)}."
|
||||
)
|
||||
|
||||
await impl.initialize()
|
||||
await impl.initialize(config)
|
||||
return impl
|
||||
|
|
|
|||
|
|
@ -12,18 +12,32 @@ from pydantic import BaseModel, Field
|
|||
|
||||
@json_schema_type
|
||||
class TGIImplConfig(BaseModel):
|
||||
url: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The URL for the local TGI endpoint (e.g., http://localhost:8080)",
|
||||
url: str = Field(
|
||||
description="The URL for the TGI endpoint (e.g. 'http://localhost:8080')",
|
||||
)
|
||||
api_token: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The HF token for Hugging Face Inference Endpoints (will default to locally saved token if not provided)",
|
||||
)
|
||||
hf_endpoint_name: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The name of the Hugging Face Inference Endpoint : can be either in the format of '{namespace}/{endpoint_name}' (namespace can be the username or organization name) or just '{endpoint_name}' if logged into the same account as the namespace",
|
||||
description="A bearer token if your TGI endpoint is protected.",
|
||||
)
|
||||
|
||||
def is_inference_endpoint(self) -> bool:
|
||||
return self.hf_endpoint_name is not None
|
||||
|
||||
@json_schema_type
|
||||
class InferenceEndpointImplConfig(BaseModel):
|
||||
endpoint_name: str = Field(
|
||||
description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
|
||||
)
|
||||
api_token: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Your Hugging Face user access token (will default to locally saved token if not provided)",
|
||||
)
|
||||
|
||||
|
||||
@json_schema_type
|
||||
class InferenceAPIImplConfig(BaseModel):
|
||||
model_id: str = Field(
|
||||
description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
|
||||
)
|
||||
api_token: Optional[str] = Field(
|
||||
default=None,
|
||||
description="Your Hugging Face user access token (will default to locally saved token if not provided)",
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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"]
|
||||
)
|
||||
|
|
|
|||
|
|
@ -48,11 +48,29 @@ def available_providers() -> List[ProviderSpec]:
|
|||
api=Api.inference,
|
||||
adapter=AdapterSpec(
|
||||
adapter_id="tgi",
|
||||
pip_packages=["huggingface_hub"],
|
||||
pip_packages=["huggingface_hub", "aiohttp"],
|
||||
module="llama_stack.providers.adapters.inference.tgi",
|
||||
config_class="llama_stack.providers.adapters.inference.tgi.TGIImplConfig",
|
||||
),
|
||||
),
|
||||
remote_provider_spec(
|
||||
api=Api.inference,
|
||||
adapter=AdapterSpec(
|
||||
adapter_id="hf::serverless",
|
||||
pip_packages=["huggingface_hub", "aiohttp"],
|
||||
module="llama_stack.providers.adapters.inference.tgi",
|
||||
config_class="llama_stack.providers.adapters.inference.tgi.InferenceAPIImplConfig",
|
||||
),
|
||||
),
|
||||
remote_provider_spec(
|
||||
api=Api.inference,
|
||||
adapter=AdapterSpec(
|
||||
adapter_id="hf::endpoint",
|
||||
pip_packages=["huggingface_hub", "aiohttp"],
|
||||
module="llama_stack.providers.adapters.inference.tgi",
|
||||
config_class="llama_stack.providers.adapters.inference.tgi.InferenceEndpointImplConfig",
|
||||
),
|
||||
),
|
||||
remote_provider_spec(
|
||||
api=Api.inference,
|
||||
adapter=AdapterSpec(
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue