mirror of https://github.com/hpcaitech/ColossalAI
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# coding=utf-8
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Blendable dataset."""
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import time
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import numpy as np
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import torch
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class BlendableDataset(torch.utils.data.Dataset):
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def __init__(self, datasets, weights):
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self.datasets = datasets
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num_datasets = len(datasets)
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assert num_datasets == len(weights)
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self.size = 0
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for dataset in self.datasets:
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self.size += len(dataset)
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# Normalize weights.
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weights = np.array(weights, dtype=np.float64)
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sum_weights = np.sum(weights)
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assert sum_weights > 0.0
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weights /= sum_weights
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# Build indices.
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start_time = time.time()
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assert num_datasets < 255
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self.dataset_index = np.zeros(self.size, dtype=np.uint8)
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self.dataset_sample_index = np.zeros(self.size, dtype=np.int64)
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from . import helpers
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helpers.build_blending_indices(self.dataset_index,
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self.dataset_sample_index,
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weights, num_datasets, self.size,
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torch.distributed.get_rank() == 0)
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print('> elapsed time for building blendable dataset indices: '
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'{:.2f} (sec)'.format(time.time() - start_time))
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def __len__(self):
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return self.size
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def __getitem__(self, idx):
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dataset_idx = self.dataset_index[idx]
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sample_idx = self.dataset_sample_index[idx]
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return self.datasets[dataset_idx][sample_idx]
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