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| # coding=utf-8 | |
| # Copyright 2020 The HuggingFace Inc. team. | |
| # Copyright (c) 2018, 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. | |
| import copy | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers import BertConfig | |
| from transformers.utils import logging | |
| # from model_handling import DecoderSpokenNorm | |
| logger = logging.get_logger(__name__) | |
| class DecoderSpokenNormConfig(BertConfig): | |
| # model_type = "decoder-spoken-norm" | |
| def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs): | |
| """Constructs RobertaConfig.""" | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
| self.num_hidden_layers=2 | |
| # self.hidden_layers_from_pretrained = list(range(self.num_hidden_layers)) | |
| # self.hidden_layers_from_pretrained = [0, 3] | |
| # if len(self.hidden_layers_from_pretrained) < self.num_hidden_layers: | |
| # self.num_hidden_layers = len(self.hidden_layers_from_pretrained) | |
| class EncoderDecoderSpokenNormConfig(PretrainedConfig): | |
| # model_type = "encoder-decoder-spoken-norm" | |
| is_composition = True | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| assert ( | |
| "encoder" in kwargs and "decoder" in kwargs | |
| ), "Config has to be initialized with encoder and decoder config" | |
| encoder_config = kwargs.pop("encoder") | |
| encoder_model_type = encoder_config.pop("model_type") | |
| decoder_config = kwargs.pop("decoder") | |
| decoder_model_type = decoder_config.pop("model_type") | |
| from transformers.models.auto.configuration_auto import AutoConfig | |
| self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config) | |
| self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config) | |
| self.is_encoder_decoder = True | |
| def from_encoder_decoder_configs( | |
| cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs | |
| ) -> PretrainedConfig: | |
| r""" | |
| Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model | |
| configuration and decoder model configuration. | |
| Returns: | |
| :class:`EncoderDecoderConfig`: An instance of a configuration object | |
| """ | |
| logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") | |
| decoder_config.is_decoder = True | |
| decoder_config.add_cross_attention = True | |
| return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) | |
| def to_dict(self): | |
| """ | |
| Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig`. | |
| Returns: | |
| :obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, | |
| """ | |
| output = copy.deepcopy(self.__dict__) | |
| output["encoder"] = self.encoder.to_dict() | |
| output["decoder"] = self.decoder.to_dict() | |
| output["model_type"] = self.__class__.model_type | |
| return output | |