transformers/auto_transformers.py

# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed 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.

import numpy
from transformers import AutoTokenizer, AutoModel

from towhee.operator import NNOperator
from towhee import register

import warnings
import logging

warnings.filterwarnings('ignore')
logging.getLogger("transformers").setLevel(logging.ERROR)
log = logging.getLogger()


@register(output_schema=['vec'])
class AutoTransformers(NNOperator):
    """
    NLP embedding operator that uses the pretrained transformers model gathered by huggingface.
    Args:
        model_name (`str`):
            Which model to use for the embeddings.
    """

    def __init__(self, model_name: str = "bert-base-uncased") -> None:
        super().__init__()
        self.model_name = model_name
        try:
            self.model = AutoModel.from_pretrained(model_name)
        except Exception as e:
            model_list = get_model_list()
            if model_name not in model_list:
                log.error(f"Invalid model name: {model_name}. Supported model names: {model_list}")
            else:
                log.error(f"Fail to load model by name: {self.model_name}")
            raise e
        try:
            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        except Exception as e:
            log.error(f'Fail to load tokenizer by name: {self.model_name}')
            raise e

    def __call__(self, txt: str) -> numpy.ndarray:
        try:
            inputs = self.tokenizer(txt, return_tensors="pt")
        except Exception as e:
            log.error(f'Invalid input for the tokenizer: {self.model_name}')
            raise e
        try:
            outs = self.model(**inputs)
        except Exception as e:
            log.error(f'Invalid input for the model: {self.model_name}')
            raise e
        try:
            features = outs.last_hidden_state.squeeze(0)
        except Exception as e:
            log.error(f'Fail to extract features by model: {self.model_name}')
            raise e
        vec = features.detach().numpy()
        return vec


def get_model_list():
    full_list = [
        "bert-large-uncased",
        "bert-base-cased",
        "bert-large-cased",
        "bert-base-multilingual-uncased",
        "bert-base-multilingual-cased",
        "bert-base-chinese",
        "bert-base-german-cased",
        "bert-large-uncased-whole-word-masking",
        "bert-large-cased-whole-word-masking",
        "bert-large-uncased-whole-word-masking-finetuned-squad",
        "bert-large-cased-whole-word-masking-finetuned-squad",
        "bert-base-cased-finetuned-mrpc",
        "bert-base-german-dbmdz-cased",
        "bert-base-german-dbmdz-uncased",
        "cl-tohoku/bert-base-japanese-whole-word-masking",
        "cl-tohoku/bert-base-japanese-char",
        "cl-tohoku/bert-base-japanese-char-whole-word-masking",
        "TurkuNLP/bert-base-finnish-cased-v1",
        "TurkuNLP/bert-base-finnish-uncased-v1",
        "wietsedv/bert-base-dutch-cased",
        "google/bigbird-roberta-base",
        "google/bigbird-roberta-large",
        "google/bigbird-base-trivia-itc",
        "albert-base-v1",
        "albert-large-v1",
        "albert-xlarge-v1",
        "albert-xxlarge-v1",
        "albert-base-v2",
        "albert-large-v2",
        "albert-xlarge-v2",
        "albert-xxlarge-v2",
        "facebook/bart-large",
        "google/bert_for_seq_generation_L-24_bbc_encoder",
        "google/bigbird-pegasus-large-arxiv",
        "google/bigbird-pegasus-large-pubmed",
        "google/bigbird-pegasus-large-bigpatent",
        "google/canine-s",
        "google/canine-c",
        "YituTech/conv-bert-base",
        "YituTech/conv-bert-medium-small",
        "YituTech/conv-bert-small",
        "ctrl",
        "microsoft/deberta-base",
        "microsoft/deberta-large",
        "microsoft/deberta-xlarge",
        "microsoft/deberta-base-mnli",
        "microsoft/deberta-large-mnli",
        "microsoft/deberta-xlarge-mnli",
        "distilbert-base-uncased",
        "distilbert-base-uncased-distilled-squad",
        "distilbert-base-cased",
        "distilbert-base-cased-distilled-squad",
        "distilbert-base-german-cased",
        "distilbert-base-multilingual-cased",
        "distilbert-base-uncased-finetuned-sst-2-english",
        "google/electra-small-generator",
        "google/electra-base-generator",
        "google/electra-large-generator",
        "google/electra-small-discriminator",
        "google/electra-base-discriminator",
        "google/electra-large-discriminator",
        "google/fnet-base",
        "google/fnet-large",
        "facebook/wmt19-ru-en",
        "funnel-transformer/small",
        "funnel-transformer/small-base",
        "funnel-transformer/medium",
        "funnel-transformer/medium-base",
        "funnel-transformer/intermediate",
        "funnel-transformer/intermediate-base",
        "funnel-transformer/large",
        "funnel-transformer/large-base",
        "funnel-transformer/xlarge-base",
        "funnel-transformer/xlarge",
        "gpt2",
        "gpt2-medium",
        "gpt2-large",
        "gpt2-xl",
        "distilgpt2",
        "EleutherAI/gpt-neo-1.3B",
        "EleutherAI/gpt-j-6B",
        "kssteven/ibert-roberta-base",
        "allenai/led-base-16384",
        "google/mobilebert-uncased",
        "microsoft/mpnet-base",
        "uw-madison/nystromformer-512",
        "openai-gpt",
        "google/reformer-crime-and-punishment",
        "tau/splinter-base",
        "tau/splinter-base-qass",
        "tau/splinter-large",
        "tau/splinter-large-qass",
        "squeezebert/squeezebert-uncased",
        "squeezebert/squeezebert-mnli",
        "squeezebert/squeezebert-mnli-headless",
        "transfo-xl-wt103",
        "xlm-mlm-en-2048",
        "xlm-mlm-ende-1024",
        "xlm-mlm-enfr-1024",
        "xlm-mlm-enro-1024",
        "xlm-mlm-tlm-xnli15-1024",
        "xlm-mlm-xnli15-1024",
        "xlm-clm-enfr-1024",
        "xlm-clm-ende-1024",
        "xlm-mlm-17-1280",
        "xlm-mlm-100-1280",
        "xlm-roberta-base",
        "xlm-roberta-large",
        "xlm-roberta-large-finetuned-conll02-dutch",
        "xlm-roberta-large-finetuned-conll02-spanish",
        "xlm-roberta-large-finetuned-conll03-english",
        "xlm-roberta-large-finetuned-conll03-german",
        "xlnet-base-cased",
        "xlnet-large-cased",
        "uw-madison/yoso-4096",
    ]
    full_list.sort()
    return full_list