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[Inference] Use huggingface_hub inference client for TGI adapter (#53)

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* Use huggingface_hub inference client for TGI inference

* Update the default value for TGI URL

* Use InferenceClient.text_generation for TGI inference

* Fixes post-review and split TGI adapter into local and Inference Endpoints ones

* Update CLI reference and add typing

* Rename TGI Adapter class

* Use HfApi to get the namespace when not provide in the hf endpoint name

* Remove unecessary method argument

* Improve TGI adapter initialization condition

* Move helper into impl file + fix merging conflicts
This commit is contained in:
Celina Hanouti 2024-09-12 18:11:35 +02:00 • committed by GitHub
parent 191cd28831
commit 736092f6bc
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6 changed files with 171 additions and 72 deletions
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7
docs/cli_reference.md
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@ -286,6 +286,13 @@ i+-------------------------------+---------------------------------------+------
| | "memory": "meta-reference-faiss" | |
| | } | |
+--------------------------------+---------------------------------------+----------------------------------------------------------------------+
| local-plus-tgi-inference | { | Use TGI (local or with [Hugging Face Inference Endpoints](https:// |
| | "inference": "remote::tgi", | huggingface.co/inference-endpoints/dedicated)) for running LLM |
| | "safety": "meta-reference", | inference. When using HF Inference Endpoints, you must provide the |
| | "agentic_system": "meta-reference", | name of the endpoint. |
| | "memory": "meta-reference-faiss" | |
| | } | |
+--------------------------------+---------------------------------------+----------------------------------------------------------------------+
</pre>
As you can see above, each “distribution” details the “providers” it is composed of. For example, `local` uses the “meta-reference” provider for inference while local-ollama relies on a different provider (Ollama) for inference. Similarly, you can use Fireworks or Together.AI for running inference as well.

14
llama_toolchain/core/distribution_registry.py
View file

@ -65,11 +65,23 @@ def available_distribution_specs() -> List[DistributionSpec]:
Api.telemetry: "console",
},
),
DistributionSpec(
distribution_type="local-plus-tgi-inference",
description="Use TGI for running LLM inference",
providers={
Api.inference: remote_provider_type("tgi"),
Api.safety: "meta-reference",
Api.agentic_system: "meta-reference",
Api.memory: "meta-reference-faiss",
},
),
]
@lru_cache()
def resolve_distribution_spec(distribution_type: str) -> Optional[DistributionSpec]:
def resolve_distribution_spec(
distribution_type: str,
) -> Optional[DistributionSpec]:
for spec in available_distribution_specs():
if spec.distribution_type == distribution_type:
return spec

17
llama_toolchain/inference/adapters/tgi/__init__.py
View file

@ -4,12 +4,21 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_toolchain.core.datatypes import RemoteProviderConfig
from .config import TGIImplConfig
from .tgi import InferenceEndpointAdapter, TGIAdapter
async def get_adapter_impl(config: RemoteProviderConfig, _deps):
from .tgi import TGIInferenceAdapter
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)
else:
raise ValueError(
"Invalid configuration. Specify either an URL or HF Inference Endpoint details (namespace and endpoint name)."
)
impl = TGIInferenceAdapter(config.url)
await impl.initialize()
return impl

29
llama_toolchain/inference/adapters/tgi/config.py Normal file
View file

@ -0,0 +1,29 @@
# 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 Optional
from llama_models.schema_utils import json_schema_type
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)",
)
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",
)
def is_inference_endpoint(self) -> bool:
return self.hf_endpoint_name is not None

173
llama_toolchain/inference/adapters/tgi/tgi.py
View file

@ -4,63 +4,68 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import AsyncGenerator, List
import httpx
from typing import Any, AsyncGenerator, Dict
import requests
from huggingface_hub import HfApi, InferenceClient
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.datatypes import StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from text_generation import Client
from llama_toolchain.inference.api import * # noqa: F403
from llama_toolchain.inference.prepare_messages import prepare_messages
from .config import TGIImplConfig
SUPPORTED_MODELS = {
HF_SUPPORTED_MODELS = {
"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
}
class TGIInferenceAdapter(Inference):
def __init__(self, url: str) -> None:
self.url = url.rstrip("/")
class TGIAdapter(Inference):
def __init__(self, config: TGIImplConfig) -> None:
self.config = config
self.tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(self.tokenizer)
self.model = None
self.max_tokens = None
@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:
hf_models = {v: k for k, v in SUPPORTED_MODELS.items()}
try:
print(f"Connecting to TGI server at: {self.url}")
async with httpx.AsyncClient() as client:
response = await client.get(f"{self.url}/info")
response.raise_for_status()
info = response.json()
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"]
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"]
model_id = info["model_id"]
if model_id not in hf_models:
raise RuntimeError(
f"TGI is serving model: {model_id}, use one of the supported models: {','.join(hf_models.keys())}"
)
self.model = hf_models[model_id]
model_id = info["model_id"]
model_name = next(
(name for name, id in HF_SUPPORTED_MODELS.items() if id == model_id),
None,
)
if model_name is None:
raise RuntimeError(
f"TGI is serving model: {model_id}, use one of the supported models: {', '.join(HF_SUPPORTED_MODELS.values())}"
)
self.model_name = model_name
self.inference_url = info["inference_url"]
except Exception as e:
import traceback
traceback.print_exc()
raise RuntimeError("Could not connect to TGI server") from e
raise RuntimeError(f"Error initializing TGIAdapter: {e}") from e
async def shutdown(self) -> None:
pass
@ -68,16 +73,6 @@ class TGIInferenceAdapter(Inference):
async def completion(self, request: CompletionRequest) -> AsyncGenerator:
raise NotImplementedError()
def _convert_messages(self, messages: List[Message]) -> List[Message]:
ret = []
for message in messages:
if message.role == "ipython":
role = "tool"
else:
role = message.role
ret.append({"role": role, "content": message.content})
return ret
def get_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
@ -89,47 +84,47 @@ class TGIInferenceAdapter(Inference):
async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
messages = prepare_messages(request)
model_input = self.formatter.encode_dialog_prompt(messages)
prompt = self.tokenizer.decode(model_input.tokens)
input_tokens = len(model_input.tokens)
max_new_tokens = min(
request.sampling_params.max_tokens or self.max_tokens,
self.max_tokens - len(model_input.tokens) - 1,
request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
self.max_tokens - input_tokens - 1,
)
if request.model != self.model:
raise ValueError(
f"Model mismatch, expected: {self.model}, got: {request.model}"
)
print(f"Calculated max_new_tokens: {max_new_tokens}")
assert (
request.model == self.model_name
), f"Model mismatch, expected {self.model_name}, got {request.model}"
options = self.get_chat_options(request)
client = Client(base_url=self.url)
if not request.stream:
r = client.generate(
prompt,
response = self.client.text_generation(
prompt=prompt,
stream=False,
details=True,
max_new_tokens=max_new_tokens,
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
**options,
)
if r.details.finish_reason:
if r.details.finish_reason == "stop":
stop_reason = None
if response.details.finish_reason:
if response.details.finish_reason == "stop":
stop_reason = StopReason.end_of_turn
elif r.details.finish_reason == "length":
elif response.details.finish_reason == "length":
stop_reason = StopReason.out_of_tokens
else:
stop_reason = StopReason.end_of_message
else:
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
r.generated_text, stop_reason
response.generated_text,
stop_reason,
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
@ -137,14 +132,15 @@ class TGIInferenceAdapter(Inference):
delta="",
)
)
buffer = ""
ipython = False
stop_reason = None
tokens = []
for response in client.generate_stream(
prompt,
for response in self.client.text_generation(
prompt=prompt,
stream=True,
details=True,
max_new_tokens=max_new_tokens,
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
**options,
@ -231,3 +227,48 @@ class TGIInferenceAdapter(Inference):
stop_reason=stop_reason,
)
)
class InferenceEndpointAdapter(TGIAdapter):
def __init__(self, config: TGIImplConfig) -> None:
super().__init__(config)
self.config.url = self._construct_endpoint_url()
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'"
)
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}"
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)
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"]
),
}
async def initialize(self) -> None:
await super().initialize()

3
llama_toolchain/inference/providers.py
View file

@ -39,8 +39,9 @@ def available_providers() -> List[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_id="tgi",
pip_packages=["text-generation"],
pip_packages=["huggingface_hub"],
module="llama_toolchain.inference.adapters.tgi",
config_class="llama_toolchain.inference.adapters.tgi.TGIImplConfig",
),
),
remote_provider_spec(