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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 megatron import print_rank_0
- from megatron import mpu
- 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 megatron.data 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]
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