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# 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.
from typing import Any, List, Optional, Tuple
import numpy as np
import torch.utils.data as pt_data
from torch.utils.data import Dataset, IterableDataset
__all__ = ['ConcatDataset', 'ConcatMapDataset']
class ConcatDataset(IterableDataset):
"""
A dataset that accepts as argument multiple datasets and then samples from them based on the specified
sampling technique.
Args:
datasets (list): A list of datasets to sample from.
shuffle (bool): Whether to shuffle individual datasets. Only works with non-iterable datasets.
Defaults to True.
sampling_technique (str): Sampling technique to choose which dataset to draw a sample from.
Defaults to 'temperature'. Currently supports 'temperature', 'random' and 'round-robin'.
sampling_temperature (int): Temperature value for sampling. Only used when sampling_technique = 'temperature'.
Defaults to 5.
sampling_probabilities (list): Probability values for sampling. Only used when sampling_technique = 'random'.
global_rank (int): Worker rank, used for partitioning map style datasets. Defaults to 0.
world_size (int): Total number of processes, used for partitioning map style datasets. Defaults to 1.
"""
def __init__(
self,
datasets: List[Any],
shuffle: bool = True,
sampling_technique: str = 'temperature',
sampling_temperature: int = 5,
sampling_probabilities: List[float] = None,
global_rank: int = 0,
world_size: int = 1,
):
super().__init__()
supported_sampling_techniques = ['temperature', 'random', 'round-robin']
self.datasets = datasets
self.iterables = [None] * len(datasets)
self.shuffle = shuffle
self.global_rank = global_rank
self.world_size = world_size
self.sampling_kwargs = {}
if sampling_technique == 'temperature':
self.index_generator = ConcatDataset.temperature_generator
self.sampling_kwargs['temperature'] = sampling_temperature
elif sampling_technique == 'random':
self.index_generator = ConcatDataset.random_generator
self.sampling_kwargs['p'] = sampling_probabilities
elif sampling_technique == 'round-robin':
self.index_generator = ConcatDataset.round_robin_generator
else:
raise ValueError(f"Currently we only support sampling techniques in {supported_sampling_techniques}.")
self.length = 0
if isinstance(datasets[0], IterableDataset):
self.kind = 'iterable'
else:
self.kind = 'map'
for idx, dataset in enumerate(datasets):
isiterable = isinstance(dataset, IterableDataset)
if (isiterable and not self.kind == 'iterable') or (not isiterable and self.kind == 'iterable'):
raise ValueError("All datasets in ConcatDataset must be of the same kind (Iterable or Map).")
if self.kind == 'map':
self.length += len(dataset) // world_size
else:
self.length += len(dataset)
def get_iterable(self, dataset):
if isinstance(dataset, IterableDataset):
return dataset.__iter__()
else:
indices = np.arange(len(dataset))
if self.shuffle:
np.random.shuffle(indices)
return iter(indices)
def __iter__(self):
worker_info = pt_data.get_worker_info()
if worker_info is None:
max_elements = self.length
wid = 0
wnum = 1
else:
wid = worker_info.id
wnum = worker_info.num_workers
max_elements = len(range(wid, self.length, wnum))
if self.kind == 'map':
for idx in range(len(self.datasets)):
start_idx = (len(self.datasets[idx]) // self.world_size) * self.global_rank
end_idx = start_idx + (len(self.datasets[idx]) // self.world_size)
if self.global_rank == self.world_size - 1:
end_idx = len(self.datasets[idx])
indices = range(start_idx + wid, end_idx, wnum)
self.datasets[idx] = pt_data.Subset(self.datasets[idx], indices)
for idx, dataset in enumerate(self.datasets):
iterable = self.get_iterable(dataset)
self.iterables[idx] = iterable
n = 0
ind_gen = self.index_generator(self.datasets, **self.sampling_kwargs)
while n < max_elements:
n += 1
try:
ind = next(ind_gen)
except StopIteration:
return
try:
val = next(self.iterables[ind])
if self.kind == 'map':
val = self.datasets[ind][val]
yield val
except StopIteration:
self.iterables[ind] = self.get_iterable(self.datasets[ind])
n -= 1
def __len__(self):
return self.length
@staticmethod
def temperature_generator(datasets, **kwargs):
temp = kwargs.get('temperature')
if not temp:
raise ValueError("Temperature generator expects a 'temperature' keyword argument.")
lengths = []
num = len(datasets)
for dataset in datasets:
lengths.append(len(dataset))
p = np.array(lengths) / np.sum(lengths)
p = np.power(p, 1 / temp)
p = p / np.sum(p)
while True:
ind = np.random.choice(np.arange(num), p=p)
yield ind
@staticmethod
def round_robin_generator(datasets, **kwargs):
num = len(datasets)
while True:
for i in range(num):
yield i
@staticmethod
def random_generator(datasets, **kwargs):
p = kwargs.get('p')
if not p:
raise ValueError("Random generator expects a 'p' keyowrd argument for sampling probabilities.")
num = len(datasets)
if len(p) != num:
raise ValueError("Length of probabilities list must be equal to the number of datasets.")
while True:
ind = np.random.choice(np.arange(num), p=p)
yield ind
class ConcatMapDataset(Dataset):
"""
A dataset that accepts as argument multiple datasets and then samples from them based on the specified
sampling technique.
Args:
datasets (list): A list of datasets to sample from.
sampling_technique (str): Sampling technique to choose which dataset to draw a sample from.
Defaults to 'temperature'. Currently supports 'temperature', 'random' and 'round-robin'.
sampling_temperature (int): Temperature value for sampling. Only used when sampling_technique = 'temperature'.
Defaults to 5.
sampling_probabilities (list): Probability values for sampling. Only used when sampling_technique = 'random'.
seed: Optional value to seed the numpy RNG.
"""
def __init__(
self,
datasets: List[Any],
sampling_technique: str = 'temperature',
sampling_temperature: int = 5,
sampling_probabilities: Optional[List[float]] = None,
seed: Optional[int] = None,
):
super().__init__()
self.datasets = datasets
self.lengths = [len(x) for x in self.datasets]
self.sampling_technique = sampling_technique
self.sampling_temperature = sampling_temperature
self.sampling_probabilities = sampling_probabilities
self.np_rng = np.random.RandomState(seed)
# Build a list of size `len(self)`. Each tuple contains (dataset_id, dataset_index)
self.indices: List[Tuple[int, int]] = []
# Current position as we consume indices from each data set
dataset_positions = [0] * len(self.datasets)
# Random permutation of each dataset. Will be regenerated when exhausted.
shuffled_indices = [self.np_rng.permutation(len(x)) for x in self.datasets]
# Build the list of randomly-chosen datasets spanning the entire length, adhering to sampling technique
if self.sampling_technique == "round-robin":
# To exhaust longest dataset, need to draw `num_datasets * max_dataset_len` samples
total_length = max(self.lengths) * len(self.lengths)
# For round robin, iterate through each dataset
dataset_ids = np.arange(total_length) % len(self.datasets)
for dataset_id in dataset_ids:
position = dataset_positions[dataset_id]
index = shuffled_indices[dataset_id][position]
self.indices.append((dataset_id, index))
dataset_positions[dataset_id] += 1
if dataset_positions[dataset_id] == len(shuffled_indices[dataset_id]):
dataset_positions[dataset_id] = 0
shuffled_indices[dataset_id] = self.np_rng.permutation(len(self.datasets[dataset_id]))
else:
# Resolve probabilities of drawing from each data set
if self.sampling_technique == "random":
if sampling_probabilities is None or len(sampling_probabilities) != len(self.datasets):
raise ValueError(
f"Need {len(self.datasets)} probabilities; got "
f"{len(sampling_probabilities) if sampling_probabilities is not None else 'None'}"
)
p = np.array(self.sampling_probabilities)
elif self.sampling_technique == "temperature":
p = np.array([len(x) for x in self.datasets])
p = np.power(p, 1 / self.sampling_temperature)
else:
raise ValueError(f"Couldn't interpret sampling technique: {sampling_technique}")
# Normalize probabilities
p = p / np.sum(p)
# Will randomly choose from datasets
choices = np.arange(len(self.datasets))
# Keep going until largest dataset is exhausted.
exhausted_datasets = set()
while len(exhausted_datasets) < len(self.datasets):
# Randomly choose a dataset for each position in accordance with p
dataset_id = self.np_rng.choice(a=choices, p=p)
dataset = self.datasets[dataset_id]
# Pick next index from dataset
position = dataset_positions[dataset_id]
index = shuffled_indices[dataset_id][position]
self.indices.append((dataset_id, index))
# Maybe reset this dataset's permutation
dataset_positions[dataset_id] += 1
if dataset_positions[dataset_id] >= len(dataset):
shuffled_indices[dataset_id] = self.np_rng.permutation(len(dataset))
dataset_positions[dataset_id] = 0
exhausted_datasets.add(dataset_id)
def __len__(self):
return len(self.indices)
def __getitem__(self, idx):
dataset_id, dataset_index = self.indices[idx]
return self.datasets[dataset_id][dataset_index]
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