transformers
copied
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Readme
Files and versions
376 lines
15 KiB
376 lines
15 KiB
# 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
|
|
import os
|
|
import torch
|
|
from pathlib import Path
|
|
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)
|
|
self.model.eval()
|
|
except Exception as e:
|
|
model_list = self.supported_model_names()
|
|
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 save_model(self, format: str = 'pytorch', path: str = 'default'):
|
|
if path == 'default':
|
|
path = str(Path(__file__).parent)
|
|
path = os.path.join(path, 'saved', format)
|
|
os.makedirs(path, exist_ok=True)
|
|
name = self.model_name.replace('/', '-')
|
|
path = os.path.join(path, name)
|
|
inputs = self.tokenizer('[CLS]', return_tensors='pt') # a dictionary
|
|
if format == 'pytorch':
|
|
path = path + '.pt'
|
|
torch.save(self.model, path)
|
|
elif format == 'torchscript':
|
|
path = path + '.pt'
|
|
inputs = list(inputs.values())
|
|
try:
|
|
try:
|
|
jit_model = torch.jit.script(self.model)
|
|
except Exception:
|
|
jit_model = torch.jit.trace(self.model, inputs, strict=False)
|
|
torch.jit.save(jit_model, path)
|
|
except Exception as e:
|
|
log.error(f'Fail to save as torchscript: {e}.')
|
|
raise RuntimeError(f'Fail to save as torchscript: {e}.')
|
|
elif format == 'onnx':
|
|
path = path + '.onnx'
|
|
try:
|
|
torch.onnx.export(self.model,
|
|
tuple(inputs.values()),
|
|
path,
|
|
input_names=["input_ids", "token_type_ids", "attention_mask"], # list(inputs.keys())
|
|
output_names=["last_hidden_state"],
|
|
opset_version=12,
|
|
dynamic_axes={
|
|
"input_ids": {0: "batch_size", 1: "input_length"},
|
|
"token_type_ids": {0: "batch_size", 1: "input_length"},
|
|
"attention_mask": {0: "batch_size", 1: "input_length"},
|
|
"last_hidden_state": {0: "batch_size"},
|
|
})
|
|
except Exception:
|
|
torch.onnx.export(self.model,
|
|
tuple(inputs.values()),
|
|
path,
|
|
input_names=["input_ids", "token_type_ids", "attention_mask"], # list(inputs.keys())
|
|
output_names=["last_hidden_state", "pooler_output"],
|
|
opset_version=12,
|
|
dynamic_axes={
|
|
"input_ids": {0: "batch_size", 1: "input_length"},
|
|
"token_type_ids": {0: "batch_size", 1: "input_length"},
|
|
"attention_mask": {0: "batch_size", 1: "input_length"},
|
|
"last_hidden_state": {0: "batch_size"},
|
|
"pooler_outputs": {0: "batch_size"}
|
|
})
|
|
elif format == 'tensorrt':
|
|
# os.system('pip install "git+https://github.com/grimoire/torch2trt_dynamic.git"')
|
|
pass
|
|
else:
|
|
log.error(f'Unsupported format "{format}".')
|
|
|
|
@staticmethod
|
|
def supported_model_names(format: str = None):
|
|
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",
|
|
"microsoft/deberta-v2-xlarge",
|
|
"microsoft/deberta-v2-xxlarge",
|
|
"microsoft/deberta-v2-xlarge-mnli",
|
|
"microsoft/deberta-v2-xxlarge-mnli",
|
|
"flaubert/flaubert_small_cased",
|
|
"flaubert/flaubert_base_uncased",
|
|
"flaubert/flaubert_base_cased",
|
|
"flaubert/flaubert_large_cased",
|
|
"camembert-base",
|
|
"Musixmatch/umberto-commoncrawl-cased-v1",
|
|
"Musixmatch/umberto-wikipedia-uncased-v1",
|
|
]
|
|
full_list.sort()
|
|
if format is None:
|
|
model_list = full_list
|
|
elif format == 'pytorch':
|
|
to_remove = []
|
|
assert set(to_remove).issubset(set(full_list))
|
|
model_list = list(set(full_list) - set(to_remove))
|
|
elif format == 'torchscript':
|
|
to_remove = [
|
|
'EleutherAI/gpt-j-6B',
|
|
'EleutherAI/gpt-neo-1.3B',
|
|
'allenai/led-base-16384',
|
|
'ctrl',
|
|
'distilgpt2',
|
|
'facebook/bart-large',
|
|
'google/bigbird-pegasus-large-arxiv',
|
|
'google/bigbird-pegasus-large-bigpatent',
|
|
'google/bigbird-pegasus-large-pubmed',
|
|
'google/canine-c',
|
|
'google/canine-s',
|
|
'google/reformer-crime-and-punishment',
|
|
'gpt2',
|
|
'gpt2-large',
|
|
'gpt2-medium',
|
|
'gpt2-xl',
|
|
'microsoft/deberta-base',
|
|
'microsoft/deberta-base-mnli',
|
|
'microsoft/deberta-large',
|
|
'microsoft/deberta-large-mnli',
|
|
'microsoft/deberta-xlarge',
|
|
'microsoft/deberta-xlarge-mnli',
|
|
'openai-gpt',
|
|
'transfo-xl-wt103',
|
|
'uw-madison/yoso-4096',
|
|
'xlm-clm-ende-1024',
|
|
'xlm-clm-enfr-1024',
|
|
'xlm-mlm-100-1280',
|
|
'xlm-mlm-17-1280',
|
|
'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',
|
|
'xlnet-base-cased',
|
|
'xlnet-large-cased',
|
|
'microsoft/deberta-v2-xlarge',
|
|
'microsoft/deberta-v2-xxlarge',
|
|
'microsoft/deberta-v2-xlarge-mnli',
|
|
'microsoft/deberta-v2-xxlarge-mnli',
|
|
'flaubert/flaubert_small_cased',
|
|
'flaubert/flaubert_base_uncased',
|
|
'flaubert/flaubert_base_cased',
|
|
'flaubert/flaubert_large_cased'
|
|
]
|
|
assert set(to_remove).issubset(set(full_list))
|
|
model_list = list(set(full_list) - set(to_remove))
|
|
elif format == 'onnx':
|
|
to_remove = [
|
|
'albert-xlarge-v1',
|
|
'albert-xlarge-v2',
|
|
'albert-xxlarge-v1',
|
|
'albert-xxlarge-v2',
|
|
'allenai/led-base-16384',
|
|
'ctrl',
|
|
'distilgpt2',
|
|
'EleutherAI/gpt-j-6B',
|
|
'EleutherAI/gpt-neo-1.3B',
|
|
'funnel-transformer/intermediate',
|
|
'funnel-transformer/large',
|
|
'funnel-transformer/medium',
|
|
'funnel-transformer/small',
|
|
'funnel-transformer/xlarge',
|
|
'google/bigbird-pegasus-large-arxiv',
|
|
'google/bigbird-pegasus-large-bigpatent',
|
|
'google/bigbird-pegasus-large-pubmed',
|
|
'google/canine-c',
|
|
'google/canine-s',
|
|
'google/fnet-base',
|
|
'google/fnet-large',
|
|
'google/reformer-crime-and-punishment',
|
|
'gpt2',
|
|
'gpt2-large',
|
|
'gpt2-medium',
|
|
'gpt2-xl',
|
|
'microsoft/deberta-v2-xlarge',
|
|
'microsoft/deberta-v2-xlarge-mnli',
|
|
'microsoft/deberta-v2-xxlarge',
|
|
'microsoft/deberta-v2-xxlarge-mnli',
|
|
'microsoft/deberta-xlarge',
|
|
'microsoft/deberta-xlarge-mnli',
|
|
'openai-gpt',
|
|
'transfo-xl-wt103',
|
|
'uw-madison/yoso-4096',
|
|
'xlm-mlm-100-1280',
|
|
'xlm-mlm-17-1280',
|
|
'xlm-mlm-en-2048',
|
|
'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'
|
|
]
|
|
assert set(to_remove).issubset(set(full_list))
|
|
model_list = list(set(full_list) - set(to_remove))
|
|
else: # todo: format in {'tensorrt'}
|
|
log.error(f'Invalid format "{format}". Currently supported formats: "pytorch", "torchscript".')
|
|
return model_list
|