model_config_handling.py

# 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

    @classmethod
    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