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| # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| # 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 torch | |
| from omegaconf import DictConfig, OmegaConf | |
| class CustomSimpleNamespace: | |
| """ | |
| A simple namespace class that supports both attribute-style and dictionary-style access. | |
| """ | |
| def __init__(self, d): | |
| self._d = d | |
| def __getattr__(self, attr): | |
| # Attribute-style access: config.key | |
| try: | |
| return self._d[attr] | |
| except KeyError: | |
| raise AttributeError(f"'CustomSimpleNamespace' object has no attribute '{attr}'") | |
| def __getitem__(self, key): | |
| # Dictionary-style access: config['key'] | |
| return self._d[key] | |
| def maybe_convert_to_namespace(config): | |
| """ | |
| This function cast a OmegaConf's DictConfig or a standard dict to CustomSimpleNamespace, which supports both | |
| attribute-style and dictionary-style access. | |
| Note: We need to convert OmegaConf's DictConfig since it is not compatible with torch.compile. | |
| """ | |
| # If input is OmegaConf's DictConfig, convert to a standard dict | |
| if isinstance(config, DictConfig): | |
| config = OmegaConf.to_container(config, resolve=True) | |
| if isinstance(config, dict): | |
| return CustomSimpleNamespace(config) | |
| else: | |
| return config | |
| def random_dropout(embeddings, drop_rate): | |
| r""" | |
| Function to perform random dropout for embeddings. | |
| When we drop embeddings, we zero them out. | |
| Args: | |
| embeddings (tensor): Input embeddings | |
| drop_rate (float): Rate of dropping the embedding. | |
| """ | |
| num_samples = embeddings.shape[0] | |
| # Create a shape (num_samples, 1, 1, 1, 1, ...) depending on embeddings dim. | |
| # This is done to ensure we can broadcast the zero_flag to the embeddings. | |
| # embeddings.ndim is 3 for images, and 4 for videos, and the corresponding | |
| # shapes are (num_samples, 1, 1) and (num_samples, 1, 1, 1) respectively. | |
| tensor_shape = (num_samples,) + tuple([1] * (embeddings.ndim - 1)) | |
| zero_flag = torch.ones(tensor_shape).to(embeddings.dtype) * (1 - drop_rate) | |
| zero_flag = torch.bernoulli(zero_flag).to(embeddings.device) | |
| embeddings = embeddings * zero_flag | |
| return embeddings | |