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# 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