# Copyright (C) 2020 Unbabel
#
# 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.
r"""
Encoder Model base
====================
Module defining the common interface between all pretrained encoder models.
"""
import abc
from typing import Dict, List
import torch
import torch.nn as nn
[docs]class Encoder(nn.Module, metaclass=abc.ABCMeta):
"""Base class for an encoder model."""
@property
@abc.abstractmethod
def output_units(self):
"""Max number of tokens the encoder handles."""
pass
@property
@abc.abstractmethod
def max_positions(self):
"""Max number of tokens the encoder handles."""
pass
@property
@abc.abstractmethod
def num_layers(self):
"""Number of model layers available."""
pass
[docs] @classmethod
@abc.abstractmethod
def from_pretrained(cls, pretrained_model):
"""Function that loads a pretrained encoder and the respective tokenizer.
:return: Encoder model
"""
raise NotImplementedError
[docs] def prepare_sample(self, sample: List[str]) -> Dict[str, torch.Tensor]:
"""Receives a list of strings and applies tokenization and vectorization.
:param sample: List with text segments to be tokenized and padded.
:return: Dictionary with HF model inputs.
"""
tokenizer_output = self.tokenizer(
sample,
return_tensors="pt",
padding=True,
truncation=True,
max_length=self.max_positions - 2,
)
return tokenizer_output
[docs] def freeze(self) -> None:
"""Frezees the entire encoder."""
for param in self.parameters():
param.requires_grad = False
[docs] def unfreeze(self) -> None:
"""Unfrezees the entire encoder."""
for param in self.parameters():
param.requires_grad = True
[docs] @abc.abstractmethod
def freeze_embeddings(self) -> None:
"""Frezees the embedding layer."""
pass
[docs] @abc.abstractmethod
def layerwise_lr(self, lr: float, decay: float):
"""
:param lr: Learning rate for the highest encoder layer.
:param decay: decay percentage for the lower layers.
:return: List of model parameters with layer-wise decay learning rate
"""
pass
[docs] @abc.abstractmethod
def forward(
self, tokens: torch.Tensor, lengths: torch.Tensor
) -> Dict[str, torch.Tensor]:
pass