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Kill model archive maps (#4636)

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* Kill model archive maps

* Fixup

* Also kill model_archive_map for MaskedBertPreTrainedModel

* Unhook config_archive_map

* Tokenizers: align with model id changes

* make style && make quality

* Fix CI
This commit is contained in:
Julien Chaumond
2020-06-02 09:39:33 -04:00
committed by GitHub
parent 47a551d17b
commit d4c2cb402d
115 changed files with 792 additions and 1323 deletions
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9
examples/adversarial/test_hans.py
View File

@@ -65,13 +65,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
(
tuple(conf.pretrained_config_archive_map.keys())
for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
),
(),
)
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
@@ -389,7 +382,7 @@ def main():
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--task_name",

66
examples/contrib/mm-imdb/run_mmimdb.py
View File

@@ -34,26 +34,11 @@ from tqdm import tqdm, trange
from transformers import (
WEIGHTS_NAME,
AdamW,
AlbertConfig,
AlbertModel,
AlbertTokenizer,
BertConfig,
BertModel,
BertTokenizer,
DistilBertConfig,
DistilBertModel,
DistilBertTokenizer,
AutoConfig,
AutoModel,
AutoTokenizer,
MMBTConfig,
MMBTForClassification,
RobertaConfig,
RobertaModel,
RobertaTokenizer,
XLMConfig,
XLMModel,
XLMTokenizer,
XLNetConfig,
XLNetModel,
XLNetTokenizer,
get_linear_schedule_with_warmup,
)
from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
@@ -67,23 +52,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
(
tuple(conf.pretrained_config_archive_map.keys())
for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
),
(),
)
MODEL_CLASSES = {
"bert": (BertConfig, BertModel, BertTokenizer),
"xlnet": (XLNetConfig, XLNetModel, XLNetTokenizer),
"xlm": (XLMConfig, XLMModel, XLMTokenizer),
"roberta": (RobertaConfig, RobertaModel, RobertaTokenizer),
"distilbert": (DistilBertConfig, DistilBertModel, DistilBertTokenizer),
"albert": (AlbertConfig, AlbertModel, AlbertTokenizer),
}
def set_seed(args):
random.seed(args.seed)
@@ -351,19 +319,12 @@ def main():
required=True,
help="The input data dir. Should contain the .jsonl files for MMIMDB.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",
@@ -385,7 +346,7 @@ def main():
)
parser.add_argument(
"--cache_dir",
default="",
default=None,
type=str,
help="Where do you want to store the pre-trained models downloaded from s3",
)
@@ -526,18 +487,14 @@ def main():
# Setup model
labels = get_mmimdb_labels()
num_labels = len(labels)
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
transformer_config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path
)
tokenizer = tokenizer_class.from_pretrained(
transformer_config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
cache_dir=args.cache_dir,
)
transformer = model_class.from_pretrained(
args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir if args.cache_dir else None
transformer = AutoModel.from_pretrained(
args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir
)
img_encoder = ImageEncoder(args)
config = MMBTConfig(transformer_config, num_labels=num_labels)
@@ -583,13 +540,12 @@ def main():
# Load a trained model and vocabulary that you have fine-tuned
model = MMBTForClassification(config, transformer, img_encoder)
model.load_state_dict(torch.load(os.path.join(args.output_dir, WEIGHTS_NAME)))
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(

46
examples/contrib/run_swag.py
View File

@@ -31,14 +31,8 @@ from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Tenso
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from transformers import (
WEIGHTS_NAME,
AdamW,
BertConfig,
BertForMultipleChoice,
BertTokenizer,
get_linear_schedule_with_warmup,
)
from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoTokenizer, get_linear_schedule_with_warmup
from transformers.modeling_auto import AutoModelForMultipleChoice
try:
@@ -49,12 +43,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in [BertConfig]), ())
MODEL_CLASSES = {
"bert": (BertConfig, BertForMultipleChoice, BertTokenizer),
}
class SwagExample(object):
"""A single training/test example for the SWAG dataset."""
@@ -492,19 +480,12 @@ def main():
required=True,
help="SWAG csv for predictions. E.g., val.csv or test.csv",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",
@@ -536,9 +517,6 @@ def main():
parser.add_argument(
"--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
)
parser.add_argument(
"--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
)
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument(
@@ -652,13 +630,9 @@ def main():
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case
)
model = model_class.from_pretrained(
config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,)
model = AutoModelForMultipleChoice.from_pretrained(
args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
)
@@ -694,8 +668,8 @@ def main():
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
@@ -718,8 +692,8 @@ def main():
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
tokenizer = tokenizer_class.from_pretrained(checkpoint)
model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model.to(args.device)
# Evaluate

5
examples/distillation/run_squad_w_distillation.py
View File

@@ -67,9 +67,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig)), ()
)
MODEL_CLASSES = {
"bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
@@ -505,7 +502,7 @@ def main():
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",

2
examples/movement-pruning/emmental/configuration_bert_masked.py
View File

@@ -19,7 +19,6 @@ and adapts it to the specificities of MaskedBert (`pruning_method`, `mask_init`
import logging
from transformers.configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from transformers.configuration_utils import PretrainedConfig
@@ -31,7 +30,6 @@ class MaskedBertConfig(PretrainedConfig):
A class replicating the `~transformers.BertConfig` with additional parameters for pruning/masking configuration.
"""
pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
model_type = "masked_bert"
def __init__(

8
examples/movement-pruning/emmental/modeling_bert_masked.py
View File

@@ -29,12 +29,7 @@ from torch.nn import CrossEntropyLoss, MSELoss
from emmental import MaskedBertConfig
from emmental.modules import MaskedLinear
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
from transformers.modeling_bert import (
ACT2FN,
BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
BertLayerNorm,
load_tf_weights_in_bert,
)
from transformers.modeling_bert import ACT2FN, BertLayerNorm, load_tf_weights_in_bert
from transformers.modeling_utils import PreTrainedModel, prune_linear_layer
@@ -395,7 +390,6 @@ class MaskedBertPreTrainedModel(PreTrainedModel):
"""
config_class = MaskedBertConfig
pretrained_model_archive_map = BERT_PRETRAINED_MODEL_ARCHIVE_MAP
load_tf_weights = load_tf_weights_in_bert
base_model_prefix = "bert"

4
examples/movement-pruning/masked_run_glue.py
View File

@@ -53,8 +53,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig,)), (),)
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
"masked_bert": (MaskedBertConfig, MaskedBertForSequenceClassification, BertTokenizer),
@@ -576,7 +574,7 @@ def main():
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--task_name",

4
examples/movement-pruning/masked_run_squad.py
View File

@@ -57,8 +57,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig,)), (),)
MODEL_CLASSES = {
"bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
"masked_bert": (MaskedBertConfig, MaskedBertForQuestionAnswering, BertTokenizer),
@@ -673,7 +671,7 @@ def main():
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",

4
examples/question-answering/run_squad.py
View File

@@ -58,8 +58,6 @@ logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)
def set_seed(args):
random.seed(args.seed)
@@ -491,7 +489,7 @@ def main():
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",

1
examples/summarization/bertabs/configuration_bertabs.py
View File

@@ -61,7 +61,6 @@ class BertAbsConfig(PretrainedConfig):
the decoder.
"""
pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
model_type = "bertabs"
def __init__(

7
examples/summarization/bertabs/modeling_bertabs.py
View File

@@ -33,14 +33,13 @@ from transformers import BertConfig, BertModel, PreTrainedModel
MAX_SIZE = 5000
BERTABS_FINETUNED_MODEL_MAP = {
"bertabs-finetuned-cnndm": "https://cdn.huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin",
}
BERTABS_FINETUNED_MODEL_ARCHIVE_LIST = [
"remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization",
]
class BertAbsPreTrainedModel(PreTrainedModel):
config_class = BertAbsConfig
pretrained_model_archive_map = BERTABS_FINETUNED_MODEL_MAP
load_tf_weights = False
base_model_prefix = "bert"

3
examples/text-classification/README.md
View File

@@ -258,7 +258,7 @@ TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recal
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is a crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
#### Fine-tuning on XNLI
@@ -273,7 +273,6 @@ on a single tesla V100 16GB. The data for XNLI can be downloaded with the follow
export XNLI_DIR=/path/to/XNLI
python run_xnli.py \
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--language de \
--train_language en \

57
examples/text-classification/run_xnli.py
View File

@@ -13,7 +13,7 @@
# 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.
""" Finetuning multi-lingual models on XNLI (Bert, DistilBERT, XLM).
""" Finetuning multi-lingual models on XNLI (e.g. Bert, DistilBERT, XLM).
Adapted from `examples/text-classification/run_glue.py`"""
@@ -32,15 +32,9 @@ from tqdm import tqdm, trange
from transformers import (
WEIGHTS_NAME,
AdamW,
BertConfig,
BertForSequenceClassification,
BertTokenizer,
DistilBertConfig,
DistilBertForSequenceClassification,
DistilBertTokenizer,
XLMConfig,
XLMForSequenceClassification,
XLMTokenizer,
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
get_linear_schedule_with_warmup,
)
from transformers import glue_convert_examples_to_features as convert_examples_to_features
@@ -57,16 +51,6 @@ except ImportError:
logger = logging.getLogger(__name__)
ALL_MODELS = sum(
(tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, DistilBertConfig, XLMConfig)), ()
)
MODEL_CLASSES = {
"bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
"xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
"distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
}
def set_seed(args):
random.seed(args.seed)
@@ -377,19 +361,12 @@ def main():
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--language",
@@ -421,7 +398,7 @@ def main():
)
parser.add_argument(
"--cache_dir",
default="",
default=None,
type=str,
help="Where do you want to store the pre-trained models downloaded from s3",
)
@@ -562,24 +539,23 @@ def main():
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
config = AutoConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name,
cache_dir=args.cache_dir if args.cache_dir else None,
cache_dir=args.cache_dir,
)
tokenizer = tokenizer_class.from_pretrained(
args.model_type = config.model_type
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
cache_dir=args.cache_dir,
)
model = model_class.from_pretrained(
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
cache_dir=args.cache_dir,
)
if args.local_rank == 0:
@@ -614,14 +590,13 @@ def main():
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model = AutoModelForSequenceClassification.from_pretrained(args.output_dir)
tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
@@ -633,7 +608,7 @@ def main():
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
model = model_class.from_pretrained(checkpoint)
model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())

2
examples/token-classification/run_ner.py
View File

@@ -13,7 +13,7 @@
# 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.
""" Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """
""" Fine-tuning the library models for named entity recognition on CoNLL-2003. """
import logging