483 lines
16 KiB
Python
483 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2023-present, the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains utilities used by both the sync and async inference clients."""
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import base64
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import io
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import json
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import logging
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import time
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from contextlib import contextmanager
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from dataclasses import dataclass
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from pathlib import Path
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from typing import (
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TYPE_CHECKING,
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Any,
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AsyncIterable,
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BinaryIO,
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ContextManager,
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Dict,
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Generator,
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Iterable,
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List,
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Literal,
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NoReturn,
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Optional,
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Set,
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Union,
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overload,
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)
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from requests import HTTPError
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from huggingface_hub.errors import (
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GenerationError,
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IncompleteGenerationError,
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OverloadedError,
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TextGenerationError,
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UnknownError,
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ValidationError,
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)
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from ..constants import ENDPOINT
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from ..utils import (
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build_hf_headers,
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get_session,
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hf_raise_for_status,
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is_aiohttp_available,
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is_numpy_available,
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is_pillow_available,
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)
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from ._generated.types import (
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ChatCompletionStreamOutput,
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ChatCompletionStreamOutputChoice,
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ChatCompletionStreamOutputDelta,
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TextGenerationStreamOutput,
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)
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if TYPE_CHECKING:
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from aiohttp import ClientResponse, ClientSession
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from PIL import Image
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# TYPES
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UrlT = str
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PathT = Union[str, Path]
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BinaryT = Union[bytes, BinaryIO]
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ContentT = Union[BinaryT, PathT, UrlT]
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# Use to set a Accept: image/png header
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TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"}
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logger = logging.getLogger(__name__)
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# Add dataclass for ModelStatus. We use this dataclass in get_model_status function.
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@dataclass
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class ModelStatus:
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"""
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This Dataclass represents the the model status in the Hugging Face Inference API.
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Args:
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loaded (`bool`):
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If the model is currently loaded into Hugging Face's InferenceAPI. Models
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are loaded on-demand, leading to the user's first request taking longer.
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If a model is loaded, you can be assured that it is in a healthy state.
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state (`str`):
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The current state of the model. This can be 'Loaded', 'Loadable', 'TooBig'.
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If a model's state is 'Loadable', it's not too big and has a supported
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backend. Loadable models are automatically loaded when the user first
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requests inference on the endpoint. This means it is transparent for the
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user to load a model, except that the first call takes longer to complete.
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compute_type (`Dict`):
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Information about the compute resource the model is using or will use, such as 'gpu' type and number of
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replicas.
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framework (`str`):
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The name of the framework that the model was built with, such as 'transformers'
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or 'text-generation-inference'.
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"""
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loaded: bool
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state: str
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compute_type: Dict
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framework: str
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## IMPORT UTILS
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def _import_aiohttp():
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# Make sure `aiohttp` is installed on the machine.
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if not is_aiohttp_available():
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raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).")
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import aiohttp
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return aiohttp
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def _import_numpy():
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"""Make sure `numpy` is installed on the machine."""
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if not is_numpy_available():
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raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).")
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import numpy
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return numpy
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def _import_pil_image():
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"""Make sure `PIL` is installed on the machine."""
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if not is_pillow_available():
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raise ImportError(
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"Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be"
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" post-processed, use `client.post(...)` and get the raw response from the server."
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)
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from PIL import Image
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return Image
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## RECOMMENDED MODELS
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# Will be globally fetched only once (see '_fetch_recommended_models')
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_RECOMMENDED_MODELS: Optional[Dict[str, Optional[str]]] = None
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def _fetch_recommended_models() -> Dict[str, Optional[str]]:
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global _RECOMMENDED_MODELS
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if _RECOMMENDED_MODELS is None:
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response = get_session().get(f"{ENDPOINT}/api/tasks", headers=build_hf_headers())
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hf_raise_for_status(response)
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_RECOMMENDED_MODELS = {
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task: _first_or_none(details["widgetModels"]) for task, details in response.json().items()
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}
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return _RECOMMENDED_MODELS
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def _first_or_none(items: List[Any]) -> Optional[Any]:
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try:
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return items[0] or None
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except IndexError:
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return None
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## ENCODING / DECODING UTILS
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@overload
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def _open_as_binary(
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content: ContentT,
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) -> ContextManager[BinaryT]: ... # means "if input is not None, output is not None"
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@overload
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def _open_as_binary(
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content: Literal[None],
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) -> ContextManager[Literal[None]]: ... # means "if input is None, output is None"
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@contextmanager # type: ignore
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def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]:
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"""Open `content` as a binary file, either from a URL, a local path, or raw bytes.
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Do nothing if `content` is None,
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TODO: handle a PIL.Image as input
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TODO: handle base64 as input
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"""
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# If content is a string => must be either a URL or a path
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if isinstance(content, str):
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if content.startswith("https://") or content.startswith("http://"):
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logger.debug(f"Downloading content from {content}")
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yield get_session().get(content).content # TODO: retrieve as stream and pipe to post request ?
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return
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content = Path(content)
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if not content.exists():
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raise FileNotFoundError(
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f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local"
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" file. To pass raw content, please encode it as bytes first."
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)
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# If content is a Path => open it
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if isinstance(content, Path):
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logger.debug(f"Opening content from {content}")
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with content.open("rb") as f:
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yield f
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else:
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# Otherwise: already a file-like object or None
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yield content
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def _b64_encode(content: ContentT) -> str:
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"""Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL."""
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with _open_as_binary(content) as data:
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data_as_bytes = data if isinstance(data, bytes) else data.read()
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return base64.b64encode(data_as_bytes).decode()
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def _b64_to_image(encoded_image: str) -> "Image":
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"""Parse a base64-encoded string into a PIL Image."""
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Image = _import_pil_image()
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return Image.open(io.BytesIO(base64.b64decode(encoded_image)))
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def _bytes_to_list(content: bytes) -> List:
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"""Parse bytes from a Response object into a Python list.
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Expects the response body to be JSON-encoded data.
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NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a
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dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
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"""
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return json.loads(content.decode())
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def _bytes_to_dict(content: bytes) -> Dict:
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"""Parse bytes from a Response object into a Python dictionary.
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Expects the response body to be JSON-encoded data.
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NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a
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list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
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"""
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return json.loads(content.decode())
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def _bytes_to_image(content: bytes) -> "Image":
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"""Parse bytes from a Response object into a PIL Image.
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Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead.
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"""
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Image = _import_pil_image()
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return Image.open(io.BytesIO(content))
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## STREAMING UTILS
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def _stream_text_generation_response(
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bytes_output_as_lines: Iterable[bytes], details: bool
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) -> Union[Iterable[str], Iterable[TextGenerationStreamOutput]]:
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"""Used in `InferenceClient.text_generation`."""
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# Parse ServerSentEvents
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for byte_payload in bytes_output_as_lines:
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output = _format_text_generation_stream_output(byte_payload, details)
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if output is not None:
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yield output
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async def _async_stream_text_generation_response(
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bytes_output_as_lines: AsyncIterable[bytes], details: bool
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) -> Union[AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]:
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"""Used in `AsyncInferenceClient.text_generation`."""
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# Parse ServerSentEvents
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async for byte_payload in bytes_output_as_lines:
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output = _format_text_generation_stream_output(byte_payload, details)
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if output is not None:
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yield output
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def _format_text_generation_stream_output(
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byte_payload: bytes, details: bool
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) -> Optional[Union[str, TextGenerationStreamOutput]]:
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if not byte_payload.startswith(b"data:"):
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return None # empty line
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# Decode payload
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payload = byte_payload.decode("utf-8")
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json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
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# Either an error as being returned
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if json_payload.get("error") is not None:
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raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
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# Or parse token payload
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output = TextGenerationStreamOutput.parse_obj_as_instance(json_payload)
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return output.token.text if not details else output
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def _stream_chat_completion_response_from_text_generation(
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text_generation_output: Iterable[TextGenerationStreamOutput],
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) -> Iterable[ChatCompletionStreamOutput]:
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"""Used in `InferenceClient.chat_completion`."""
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created = int(time.time())
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for item in text_generation_output:
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yield _format_chat_completion_stream_output_from_text_generation(item, created)
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async def _async_stream_chat_completion_response_from_text_generation(
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text_generation_output: AsyncIterable[TextGenerationStreamOutput],
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) -> AsyncIterable[ChatCompletionStreamOutput]:
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"""Used in `AsyncInferenceClient.chat_completion`."""
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created = int(time.time())
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async for item in text_generation_output:
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yield _format_chat_completion_stream_output_from_text_generation(item, created)
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def _format_chat_completion_stream_output_from_text_generation(
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item: TextGenerationStreamOutput, created: int
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) -> ChatCompletionStreamOutput:
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if item.details is None:
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# new token generated => return delta
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return ChatCompletionStreamOutput(
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choices=[
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ChatCompletionStreamOutputChoice(
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delta=ChatCompletionStreamOutputDelta(
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role="assistant",
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content=item.token.text,
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),
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finish_reason=None,
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index=0,
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)
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],
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created=created,
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)
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else:
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# generation is completed => return finish reason
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return ChatCompletionStreamOutput(
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choices=[
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ChatCompletionStreamOutputChoice(
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delta=ChatCompletionStreamOutputDelta(),
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finish_reason=item.details.finish_reason,
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index=0,
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)
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],
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created=created,
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)
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def _stream_chat_completion_response_from_bytes(
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bytes_lines: Iterable[bytes],
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) -> Iterable[ChatCompletionStreamOutput]:
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"""Used in `InferenceClient.chat_completion` if model is served with TGI."""
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for item in bytes_lines:
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output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
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if output is not None:
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yield output
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async def _async_stream_chat_completion_response_from_bytes(
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bytes_lines: AsyncIterable[bytes],
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) -> AsyncIterable[ChatCompletionStreamOutput]:
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"""Used in `AsyncInferenceClient.chat_completion`."""
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async for item in bytes_lines:
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output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
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if output is not None:
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yield output
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def _format_chat_completion_stream_output_from_text_generation_from_bytes(
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byte_payload: bytes,
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) -> Optional[ChatCompletionStreamOutput]:
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if not byte_payload.startswith(b"data:"):
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return None # empty line
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# Decode payload
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payload = byte_payload.decode("utf-8")
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json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
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return ChatCompletionStreamOutput.parse_obj_as_instance(json_payload)
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async def _async_yield_from(client: "ClientSession", response: "ClientResponse") -> AsyncIterable[bytes]:
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async for byte_payload in response.content:
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yield byte_payload
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await client.close()
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# "TGI servers" are servers running with the `text-generation-inference` backend.
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# This backend is the go-to solution to run large language models at scale. However,
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# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference`
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# solution is still in use.
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#
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# Both approaches have very similar APIs, but not exactly the same. What we do first in
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# the `text_generation` method is to assume the model is served via TGI. If we realize
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# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fallback to the
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# default API with a warning message. We remember for each model if it's a TGI server
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# or not using `_NON_TGI_SERVERS` global variable.
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#
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# In addition, TGI servers have a built-in API route for chat-completion, which is not
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# available on the default API. We use this route to provide a more consistent behavior
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# when available.
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#
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# For more details, see https://github.com/huggingface/text-generation-inference and
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# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task.
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_NON_TGI_SERVERS: Set[Optional[str]] = set()
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def _set_as_non_tgi(model: Optional[str]) -> None:
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_NON_TGI_SERVERS.add(model)
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def _is_tgi_server(model: Optional[str]) -> bool:
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return model not in _NON_TGI_SERVERS
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_NON_CHAT_COMPLETION_SERVER: Set[str] = set()
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def _set_as_non_chat_completion_server(model: str) -> None:
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print("Set as non chat completion", model)
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_NON_CHAT_COMPLETION_SERVER.add(model)
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def _is_chat_completion_server(model: str) -> bool:
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return model not in _NON_CHAT_COMPLETION_SERVER
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# TEXT GENERATION ERRORS
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# ----------------------
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# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
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# inference project (https://github.com/huggingface/text-generation-inference).
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# ----------------------
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def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
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"""
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Try to parse text-generation-inference error message and raise HTTPError in any case.
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Args:
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error (`HTTPError`):
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The HTTPError that have been raised.
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"""
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# Try to parse a Text Generation Inference error
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try:
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# Hacky way to retrieve payload in case of aiohttp error
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payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
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error = payload.get("error")
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error_type = payload.get("error_type")
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except Exception: # no payload
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raise http_error
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# If error_type => more information than `hf_raise_for_status`
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if error_type is not None:
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exception = _parse_text_generation_error(error, error_type)
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raise exception from http_error
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# Otherwise, fallback to default error
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raise http_error
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def _parse_text_generation_error(error: Optional[str], error_type: Optional[str]) -> TextGenerationError:
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if error_type == "generation":
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return GenerationError(error) # type: ignore
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if error_type == "incomplete_generation":
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return IncompleteGenerationError(error) # type: ignore
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if error_type == "overloaded":
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return OverloadedError(error) # type: ignore
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if error_type == "validation":
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return ValidationError(error) # type: ignore
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return UnknownError(error) # type: ignore
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