File size: 3,387 Bytes
7934b29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Copyright (c) 2021, NVIDIA CORPORATION. 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.
from typing import Optional
from hydra.utils import instantiate
from transformers import AutoConfig, AutoModel
from nemo.collections.nlp.modules.common.decoder_module import DecoderModule
from nemo.collections.nlp.modules.common.huggingface.huggingface_utils import get_huggingface_pretrained_lm_models_list
from nemo.utils import logging
class HuggingFaceDecoderModule(DecoderModule):
"""Gets HuggingFace based model to be used as an Decoder in NeMo NLP.
Use the model_name arg to get a named model architecture.
Available model names can be found with get_huggingface_pretrained_lm_models_list() or
by going to https://huggingface.co/models.
Use the pretrained arg to get the named model architecture with or without pretrained weights.
If model_name is None, then we can pass in a custom configuration via the config_dict.
For example, to instantiate a HuggingFace BERT model with custom configuration we would do:
config_dict={
'_target_': 'transformers.BertConfig',
'hidden_size': 1536
}
Args:
model_name (Optional[str]): Named model architecture from HuggingFace. Defaults to None.
pretrained (bool): Use True to get pretrained weights.
False will use the same architecture but with randomly initialized weights.
Defaults to False.
config_dict (Optional[dict], optional): Use for custom configuration of the HuggingFace model. Defaults to None.
checkpoint_file (Optional[str], optional): Provide weights for the transformer from a local checkpoint. Defaults to None.
"""
def __init__(
self,
model_name: Optional[str] = None,
pretrained: bool = False,
config_dict: Optional[dict] = None,
checkpoint_file: Optional[str] = None,
):
super().__init__()
model = None
if model_name is not None:
if model_name in get_huggingface_pretrained_lm_models_list():
if pretrained:
model = AutoModel.from_pretrained(model_name)
else:
cfg = AutoConfig.from_pretrained(model_name)
model = AutoModel.from_config(cfg)
else:
logging.error(f'{model_name} not found in list of HuggingFace pretrained models')
else:
cfg = instantiate(config_dict)
model = AutoModel.from_config(cfg)
self._hidden_size = model.config.hidden_size
self._vocab_size = model.config.vocab_size
@property
def hidden_size(self) -> Optional[int]:
return self._hidden_size
@property
def vocab_size(self) -> Optional[int]:
return self._vocab_size
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