Source code for nemo.collections.nlp.modules.common.bert_module

# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import os
import re
from typing import Dict, Optional

import torch

from nemo.core.classes import NeuralModule
from nemo.core.classes.exportable import Exportable
from nemo.core.neural_types import ChannelType, MaskType, NeuralType
from nemo.utils import logging

__all__ = ['BertModule']


[docs]class BertModule(NeuralModule, Exportable): @property def input_types(self) -> Optional[Dict[str, NeuralType]]: return { "input_ids": NeuralType(('B', 'T'), ChannelType()), "attention_mask": NeuralType(('B', 'T'), MaskType(), optional=True), "token_type_ids": NeuralType(('B', 'T'), ChannelType(), optional=True), } @property def output_types(self) -> Optional[Dict[str, NeuralType]]: return {"last_hidden_states": NeuralType(('B', 'T', 'D'), ChannelType())}
[docs] def restore_weights(self, restore_path: str): """Restores module/model's weights""" logging.info(f"Restoring weights from {restore_path}") if not os.path.exists(restore_path): logging.warning(f'Path {restore_path} not found') return pretrained_dict = torch.load(restore_path) # backward compatibility with NeMo0.11 if "state_dict" in pretrained_dict.keys(): pretrained_dict = pretrained_dict["state_dict"] # remove prefix from pretrained dict m = re.match("^bert.*?\.", list(pretrained_dict.keys())[0]) if m: prefix = m.group(0) pretrained_dict = {k[len(prefix) :]: v for k, v in pretrained_dict.items()} model_dict = self.state_dict() pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} # starting with transformers 3.1.0, embeddings.position_ids is added to the model's state dict and could be # missing in checkpoints trained with older transformers version if 'embeddings.position_ids' in model_dict and 'embeddings.position_ids' not in pretrained_dict: pretrained_dict['embeddings.position_ids'] = model_dict['embeddings.position_ids'] assert len(pretrained_dict) == len(model_dict) model_dict.update(pretrained_dict) self.load_state_dict(model_dict) logging.info(f"Weights for {type(self).__name__} restored from {restore_path}")
[docs] def input_example(self, max_batch=1, max_dim=256): """ Generates input examples for tracing etc. Returns: A tuple of input examples. """ sample = next(self.parameters()) sz = (max_batch, max_dim) input_ids = torch.randint(low=0, high=max_dim - 1, size=sz, device=sample.device) token_type_ids = torch.randint(low=0, high=1, size=sz, device=sample.device) attention_mask = torch.randint(low=0, high=1, size=sz, device=sample.device) input_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids, } return tuple([input_dict])