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| # coding=utf-8 | |
| # 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. | |
| """Blendable dataset.""" | |
| import time | |
| import numpy as np | |
| import torch | |
| from fengshen.data.megatron_dataloader.utils import print_rank_0 | |
| class BlendableDataset(torch.utils.data.Dataset): | |
| def __init__(self, datasets, weights): | |
| self.datasets = datasets | |
| num_datasets = len(datasets) | |
| assert num_datasets == len(weights) | |
| self.size = 0 | |
| for dataset in self.datasets: | |
| self.size += len(dataset) | |
| # Normalize weights. | |
| weights = np.array(weights, dtype=np.float64) | |
| sum_weights = np.sum(weights) | |
| assert sum_weights > 0.0 | |
| weights /= sum_weights | |
| # Build indecies. | |
| start_time = time.time() | |
| assert num_datasets < 255 | |
| self.dataset_index = np.zeros(self.size, dtype=np.uint8) | |
| self.dataset_sample_index = np.zeros(self.size, dtype=np.int64) | |
| from fengshen.data.megatron_dataloader import helpers | |
| helpers.build_blending_indices(self.dataset_index, | |
| self.dataset_sample_index, | |
| weights, num_datasets, self.size, | |
| torch.distributed.get_rank() == 0) | |
| print_rank_0('> elapsed time for building blendable dataset indices: ' | |
| '{:.2f} (sec)'.format(time.time() - start_time)) | |
| def __len__(self): | |
| return self.size | |
| def __getitem__(self, idx): | |
| dataset_idx = self.dataset_index[idx] | |
| sample_idx = self.dataset_sample_index[idx] | |
| return self.datasets[dataset_idx][sample_idx] | |