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# Copyright (c) 2024, EleutherAI
# This file is based on code by the authors denoted below and has been modified from its original version.
#
# Copyright (c) 2024, 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 indices.
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 {} elapsed time for building blendable dataset indices: "
"{:.2f} (sec)".format(
torch.distributed.get_rank(), time.time() - start_time
)
)
def __len__(self):
return self.size
def __getitem__(self, idx):
try:
dataset_idx = self.dataset_index[idx]
sample_idx = self.dataset_sample_index[idx]
return self.datasets[dataset_idx][sample_idx]
except IndexError:
new_idx = idx % len(self)
print(
f"WARNING: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})"
)
return self[new_idx]
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