#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ["SentenceTransformerEmbeddings", "InferenceAPIEmbeddings"]
import logging
import os
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Mapping
from typing import Optional
from typing import Sequence
import requests
import apache_beam as beam
from apache_beam.ml.inference.base import ModelHandler
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.transforms.base import EmbeddingsManager
from apache_beam.ml.transforms.base import _ImageEmbeddingHandler
from apache_beam.ml.transforms.base import _TextEmbeddingHandler
try:
from sentence_transformers import SentenceTransformer
except ImportError:
SentenceTransformer = None
_LOGGER = logging.getLogger(__name__)
# TODO: https://github.com/apache/beam/issues/29621
# Use HuggingFaceModelHandlerTensor once the import issue is fixed.
# Right now, the hugging face model handler import torch and tensorflow
# at the same time, which adds too much weigth to the container unnecessarily.
class _SentenceTransformerModelHandler(ModelHandler):
"""
Note: Intended for internal use and guarantees no backwards compatibility.
"""
def __init__(
self,
model_name: str,
model_class: Callable,
load_model_args: Optional[dict] = None,
min_batch_size: Optional[int] = None,
max_batch_size: Optional[int] = None,
max_seq_length: Optional[int] = None,
large_model: bool = False,
**kwargs):
self._max_seq_length = max_seq_length
self.model_name = model_name
self._model_class = model_class
self._load_model_args = load_model_args
self._min_batch_size = min_batch_size
self._max_batch_size = max_batch_size
self._large_model = large_model
self._kwargs = kwargs
if not SentenceTransformer:
raise ImportError(
"sentence-transformers is required to use "
"SentenceTransformerEmbeddings."
"Please install it with using `pip install sentence-transformers`.")
def run_inference(
self,
batch: Sequence[str],
model: SentenceTransformer,
inference_args: Optional[Dict[str, Any]] = None,
):
inference_args = inference_args or {}
return model.encode(batch, **inference_args)
def load_model(self):
model = self._model_class(self.model_name, **self._load_model_args)
if self._max_seq_length:
model.max_seq_length = self._max_seq_length
return model
def share_model_across_processes(self) -> bool:
return self._large_model
def batch_elements_kwargs(self) -> Mapping[str, Any]:
batch_sizes = {}
if self._min_batch_size:
batch_sizes["min_batch_size"] = self._min_batch_size
if self._max_batch_size:
batch_sizes["max_batch_size"] = self._max_batch_size
return batch_sizes
def __repr__(self) -> str:
# ModelHandler is internal to the user and is not exposed.
# Hence we need to override the __repr__ method to expose
# the name of the class.
return 'SentenceTransformerEmbeddings'
class _InferenceAPIHandler(ModelHandler):
def __init__(self, config: 'InferenceAPIEmbeddings'):
super().__init__()
self._config = config
def load_model(self):
session = requests.Session()
# if the token is not provided during construction time, it might have
# been provided with custom container, which we can get it during runtume.
if not self._config.hf_token:
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
raise ValueError(
'HF_TOKEN environment variable not set. '
'Please set the environment variable or pass the token as an '
'argument.')
session.headers.update({"Authorization": f"Bearer {hf_token}"})
return session
session.headers.update(self._config.authorization_token)
return session
def run_inference(
self, batch, session: requests.Session, inference_args=None):
response = session.post(
self._config.api_url,
headers=self._config.authorization_token,
json={
"inputs": batch, "options": inference_args
})
return response.json()
[docs]
class InferenceAPIEmbeddings(EmbeddingsManager):
"""
Feature extraction using HuggingFace's Inference API.
Intended to be used for feature-extraction. For other tasks, please
refer to https://huggingface.co/inference-api.
Args:
hf_token: HuggingFace token.
columns: List of columns to be embedded.
model_name: Model name used for feature extraction.
api_url: API url for feature extraction. If specified, model_name will be
ignored. If none, the default url for feature extraction
will be used.
"""
def __init__(
self,
hf_token: Optional[str],
columns: List[str],
model_name: Optional[str] = None, # example: "sentence-transformers/all-MiniLM-l6-v2" # pylint: disable=line-too-long
api_url: Optional[str] = None,
**kwargs,
):
super().__init__(columns, **kwargs)
self._authorization_token = {"Authorization": f"Bearer {hf_token}"}
self._model_name = model_name
self.hf_token = hf_token
if not api_url:
if not self._model_name:
raise ValueError("Either api_url or model_name must be provided.")
self._api_url = (
f"https://api-inference.huggingface.co/pipeline/feature-extraction/{self._model_name}" # pylint: disable=line-too-long
)
else:
self._api_url = api_url
_LOGGER.info("HuggingFace API URL: %s")
[docs]
def get_token(self):
return os.environ.get('HF_TOKEN')
@property
def api_url(self):
return self._api_url
@property
def authorization_token(self):
return self._authorization_token
[docs]
def get_model_handler(self):
return _InferenceAPIHandler(self)