Converting script
This commit is contained in:
parent
4f3a54bfc8
commit
c987545592
|
@ -107,7 +107,7 @@ if is_torch_available():
|
|||
CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
|
||||
|
||||
from .modeling_albert import (AlbertModel, AlbertForMaskedLM, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
from .modeling_albert import (AlbertModel, AlbertForMaskedLM, load_tf_weights_in_albert, ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
|
||||
|
||||
# Optimization
|
||||
from .optimization import (AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup,
|
||||
|
|
|
@ -1,18 +1,39 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2018 The HuggingFace Inc. team.
|
||||
#
|
||||
# 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.
|
||||
"""Convert ALBERT checkpoint."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import argparse
|
||||
import torch
|
||||
|
||||
from transformers import AlbertConfig, BertForPreTraining, load_tf_weights_in_bert
|
||||
|
||||
from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert
|
||||
|
||||
import logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
|
||||
# Initialise PyTorch model
|
||||
config = BertConfig.from_json_file(bert_config_file)
|
||||
config = AlbertConfig.from_json_file(bert_config_file)
|
||||
print("Building PyTorch model from configuration: {}".format(str(config)))
|
||||
model = BertForPreTraining(config)
|
||||
model = AlbertForMaskedLM(config)
|
||||
|
||||
# Load weights from tf checkpoint
|
||||
load_tf_weights_in_bert(model, config, tf_checkpoint_path)
|
||||
load_tf_weights_in_albert(model, config, tf_checkpoint_path)
|
||||
|
||||
# Save pytorch-model
|
||||
print("Save PyTorch model to {}".format(pytorch_dump_path))
|
||||
|
@ -31,7 +52,7 @@ if __name__ == "__main__":
|
|||
default = None,
|
||||
type = str,
|
||||
required = True,
|
||||
help = "The config json file corresponding to the pre-trained BERT model. \n"
|
||||
help = "The config json file corresponding to the pre-trained ALBERT model. \n"
|
||||
"This specifies the model architecture.")
|
||||
parser.add_argument("--pytorch_dump_path",
|
||||
default = None,
|
||||
|
@ -40,5 +61,6 @@ if __name__ == "__main__":
|
|||
help = "Path to the output PyTorch model.")
|
||||
args = parser.parse_args()
|
||||
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path,
|
||||
args.bert_config_file,
|
||||
args.albert_config_file,
|
||||
args.pytorch_dump_path)
|
||||
|
Loading…
Reference in New Issue