# Copyright (c) 2020, 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 abc import ABC from typing import Any, Dict, Optional from nemo.core.classes import NeuralModule from nemo.core.neural_types import ChannelType, EncodedRepresentation, MaskType, NeuralType __all__ = ['DecoderModule'] class DecoderModule(NeuralModule, ABC): """ Base class for decoder neural module to be used in NLP models. """ @property def input_types(self) -> Optional[Dict[str, NeuralType]]: return { "input_ids": NeuralType(('B', 'T'), ChannelType()), "decoder_mask": NeuralType(('B', 'T'), MaskType(), optional=True), "encoder_embeddings": NeuralType(('B', 'T', 'D'), ChannelType(), optional=True), "encoder_mask": NeuralType(('B', 'T'), MaskType(), optional=True), "decoder_mems": NeuralType(('B', 'D', 'T', 'D'), EncodedRepresentation(), optional=True), } @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return {"last_hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())} @property def hidden_size(self) -> Optional[int]: raise NotImplementedError @property def vocab_size(self) -> Optional[int]: raise NotImplementedError @property def embedding(self) -> Optional[Any]: raise NotImplementedError @property def decoder(self) -> Optional[Any]: raise NotImplementedError @property def max_sequence_length(self) -> Optional[int]: raise NotImplementedError