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add tokenizer support for hugging face models in the macro #13

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66 changes: 66 additions & 0 deletions python-lib/macro/model_configurations.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,15 @@
from transformers import (tokenization_bert,
tokenization_gpt2,
tokenization_transfo_xl,
tokenization_xlnet,
tokenization_roberta,
tokenization_distilbert,
tokenization_ctrl,
tokenization_camembert,
tokenization_albert,
tokenization_t5,
tokenization_bart)

NON_TRANSFORMER_MODELS = ["word2vec","fasttext","glove","elmo","use"]
TRANSFORMERS_MODELS = ['bert-base-uncased', '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', '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', 'facebook/bart-large', 'facebook/bart-large-mnli', 'facebook/bart-large-cnn', 'facebook/mbart-large-en-ro', 'openai-gpt', 'transfo-xl-wt103', 'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl', 'distilgpt2', 'ctrl', 'xlnet-base-cased', 'xlnet-large-cased', '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', 'roberta-base', 'roberta-large', 'roberta-large-mnli', 'distilroberta-base', 'roberta-base-openai-detector', 'roberta-large-openai-detector', 'distilbert-base-uncased', 'distilbert-base-uncased-distilled-squad', 'distilbert-base-cased', 'distilbert-base-cased-distilled-squad', 'distilbert-base-german-cased', 'distilbert-base-multilingual-cased', 'albert-base-v1', 'albert-large-v1', 'albert-xlarge-v1', 'albert-xxlarge-v1', 'albert-base-v2', 'albert-large-v2', 'albert-xlarge-v2', 'albert-xxlarge-v2', 'camembert-base', 't5-small', 't5-base', 't5-large', 'xlm-roberta-base', 'xlm-roberta-large', 'flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased', 'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased', 'allenai/longformer-base-4096', 'allenai/longformer-large-4096']
MODEL_CONFIFURATIONS = {
Expand Down Expand Up @@ -1077,4 +1089,58 @@

}

BART_URL_MAP = {
'vocab_file': {model: tokenization_bart.vocab_url for model in tokenization_bart._all_bart_models},
'merges_file': {model: tokenization_bart.merges_url for model in tokenization_bart._all_bart_models}
}
BART_FILE_NAMES = tokenization_roberta.VOCAB_FILES_NAMES


TOKENIZER_CONFIGURATIONS = {
'BERT': {
'url_map': tokenization_bert.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_bert.VOCAB_FILES_NAMES
},
'GPT-2': {
'url_map': tokenization_gpt2.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_gpt2.VOCAB_FILES_NAMES
},
'CamemBERT': {
'url_map': tokenization_camembert.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_camembert.VOCAB_FILES_NAMES
},
'ALBERT': {
'url_map': tokenization_albert.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_albert.VOCAB_FILES_NAMES
},
'CTRL': {
'url_map': tokenization_ctrl.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_ctrl.VOCAB_FILES_NAMES
},
'DistilBERT': {
'url_map': tokenization_distilbert.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_distilbert.VOCAB_FILES_NAMES
},
'RoBERTa': {
'url_map': tokenization_roberta.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_roberta.VOCAB_FILES_NAMES
},
'Bart': {
'url_map': BART_URL_MAP,
'file_names': BART_FILE_NAMES
},
'T5': {
'url_map': tokenization_t5.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_t5.VOCAB_FILES_NAMES
},
'Transformer-XL': {
'url_map': tokenization_transfo_xl.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_transfo_xl.VOCAB_FILES_NAMES
},
'XLNet': {
'url_map': tokenization_xlnet.PRETRAINED_VOCAB_FILES_MAP,
'file_names': tokenization_xlnet.VOCAB_FILES_NAMES
},
}


22 changes: 20 additions & 2 deletions python-lib/macro/model_downloaders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
import io
import zipfile
import logging
from macro.model_configurations import MODEL_CONFIFURATIONS
from macro.model_configurations import MODEL_CONFIFURATIONS, TOKENIZER_CONFIGURATIONS
import time
from transformers.file_utils import (S3_BUCKET_PREFIX,
CLOUDFRONT_DISTRIB_PREFIX,
Expand All @@ -21,7 +21,7 @@

WORD2VEC_BASE_URL = "http://vectors.nlpl.eu/repository/20/{}.zip"
FASTTEXT_BASE_URL = "https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.{}.300.vec.gz"
HG_FILENAMES = ["pytorch_model.bin","config.json","vocab.txt"]
HG_FILENAMES = ["pytorch_model.bin", "config.json"]

class BaseDownloader(object):
def __init__(self,folder,macro_inputs,proxy,progress_callback):
Expand Down Expand Up @@ -293,6 +293,16 @@ def run(self):
bytes_so_far = self.download_plain(response, bytes_so_far)
elif response.status_code == 404:
pass
tokenizer_dict = TOKENIZER_CONFIGURATIONS[self.embedding_family]
for file_type in tokenizer_dict['url_map'].keys():
self.archive_name = self.language + '/' + self.embedding_family + '/' + self.model_shortcut_name.replace("/","_") + '/' + tokenizer_dict['file_names'][file_type]
download_link = tokenizer_dict['url_map'][file_type][self.model_shortcut_name]
response = self.get_stream(download_link)
if response.status_code == 200:
bytes_so_far = self.download_plain(response, bytes_so_far)
elif response.status_code == 404:
pass


def get_file_size(self, response=None):
total_size = 0
Expand All @@ -303,6 +313,14 @@ def get_file_size(self, response=None):
total_size += int(response.headers.get('content-length'))
elif response.status_code == 404:
total_size += 0
tokenizer_dict = TOKENIZER_CONFIGURATIONS[self.embedding_family]
for file_type in tokenizer_dict['url_map'].keys():
download_link = tokenizer_dict['url_map'][file_type][self.model_shortcut_name]
response = self.get_stream(download_link)
if response.status_code == 200:
total_size += int(response.headers.get('content-length'))
elif response.status_code == 404:
total_size += 0
return total_size


Expand Down