1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
use super::{
    batcher::Batcher, BatchStrategy, DataLoader, DataLoaderIterator, MultiThreadDataLoader,
    Progress,
};
use burn_dataset::{
    transform::{PartialDataset, ShuffledDataset},
    Dataset,
};
use rand::{distributions::Standard, prelude::Distribution, rngs::StdRng, Rng, SeedableRng};
use std::sync::Arc;

/// A data loader that can be used to iterate over a dataset in batches.
pub struct BatchDataLoader<I, O> {
    strategy: Box<dyn BatchStrategy<I>>,
    dataset: Arc<dyn Dataset<I>>,
    batcher: Arc<dyn Batcher<I, O>>,
    rng: Option<spin::Mutex<rand::rngs::StdRng>>,
}

impl<I, O> BatchDataLoader<I, O> {
    /// Creates a new batch data loader.
    ///
    /// # Arguments
    ///
    /// * `strategy` - The batch strategy.
    /// * `dataset` - The dataset.
    /// * `batcher` - The batcher.
    /// * `rng`     - The rng determining if the dataset is shuffled each time a dataloader
    ///               iterator is created.
    ///
    /// # Returns
    ///
    /// The batch data loader.
    pub fn new(
        strategy: Box<dyn BatchStrategy<I>>,
        dataset: Arc<dyn Dataset<I>>,
        batcher: Arc<dyn Batcher<I, O>>,
        rng: Option<rand::rngs::StdRng>,
    ) -> Self {
        Self {
            strategy,
            dataset,
            batcher,
            rng: rng.map(spin::Mutex::new),
        }
    }
}

/// A data loader iterator that can be used to iterate over a data loader.
struct BatchDataloaderIterator<I, O> {
    current_index: usize,
    strategy: Box<dyn BatchStrategy<I>>,
    dataset: Arc<dyn Dataset<I>>,
    batcher: Arc<dyn Batcher<I, O>>,
}

impl<I, O> BatchDataLoader<I, O>
where
    I: Send + Sync + Clone + 'static,
    O: Send + Sync + Clone + 'static,
{
    /// Creates a new multi-threaded batch data loader.
    ///
    /// # Arguments
    ///
    /// * `strategy` - The batch strategy.
    /// * `dataset` - The dataset.
    /// * `batcher` - The batcher.
    /// * `num_threads` - The number of threads.
    ///
    /// # Returns
    ///
    /// The multi-threaded batch data loader.
    pub fn multi_thread(
        strategy: Box<dyn BatchStrategy<I>>,
        dataset: Arc<dyn Dataset<I>>,
        batcher: Arc<dyn Batcher<I, O>>,
        num_threads: usize,
        mut rng: Option<rand::rngs::StdRng>,
    ) -> MultiThreadDataLoader<O> {
        let datasets = PartialDataset::split(dataset, num_threads);

        let mut dataloaders: Vec<Arc<dyn DataLoader<_> + Send + Sync>> =
            Vec::with_capacity(num_threads);

        // Create more rngs from the first one, one for each new dataloader.
        let rngs = (0..num_threads).map(|_| {
            rng.as_mut()
                .map(|rng| StdRng::seed_from_u64(Distribution::sample(&Standard, rng)))
        });

        for (dataset, rng) in datasets.into_iter().zip(rngs) {
            let strategy = strategy.new_like();
            let dataloader =
                BatchDataLoader::new(strategy, Arc::new(dataset), batcher.clone(), rng);
            let dataloader = Arc::new(dataloader);
            dataloaders.push(dataloader);
        }
        MultiThreadDataLoader::new(dataloaders)
    }
}

impl<I: Send + Sync + Clone + 'static, O: Send + Sync> DataLoader<O> for BatchDataLoader<I, O> {
    fn iter<'a>(&'a self) -> Box<dyn DataLoaderIterator<O> + 'a> {
        // When starting a new iteration, we first check if the dataloader was created with an rng,
        // implying that we should shuffle the dataset beforehand, while advancing the current
        // rng to ensure that each new iteration shuffles the dataset differently.
        let dataset = match &self.rng {
            Some(rng) => {
                let mut rng = rng.lock();

                Arc::new(ShuffledDataset::with_seed(
                    self.dataset.clone(),
                    rng.sample(Standard),
                ))
            }
            None => self.dataset.clone(),
        };
        Box::new(BatchDataloaderIterator::new(
            self.strategy.new_like(),
            dataset,
            self.batcher.clone(),
        ))
    }

    fn num_items(&self) -> usize {
        self.dataset.len()
    }
}

impl<I, O> BatchDataloaderIterator<I, O> {
    /// Creates a new batch data loader iterator.
    ///
    /// # Arguments
    ///
    /// * `strategy` - The batch strategy.
    /// * `dataset` - The dataset.
    /// * `batcher` - The batcher.
    ///
    /// # Returns
    ///
    /// The batch data loader iterator.
    pub fn new(
        strategy: Box<dyn BatchStrategy<I>>,
        dataset: Arc<dyn Dataset<I>>,
        batcher: Arc<dyn Batcher<I, O>>,
    ) -> Self {
        BatchDataloaderIterator {
            current_index: 0,
            strategy,
            dataset,
            batcher,
        }
    }
}

impl<I, O> Iterator for BatchDataloaderIterator<I, O> {
    type Item = O;

    fn next(&mut self) -> Option<O> {
        while let Some(item) = self.dataset.get(self.current_index) {
            self.current_index += 1;
            self.strategy.add(item);

            if let Some(items) = self.strategy.batch(false) {
                return Some(self.batcher.batch(items));
            }
        }

        if let Some(items) = self.strategy.batch(true) {
            return Some(self.batcher.batch(items));
        }

        None
    }
}

impl<I, O> DataLoaderIterator<O> for BatchDataloaderIterator<I, O> {
    fn progress(&self) -> Progress {
        Progress::new(self.current_index, self.dataset.len())
    }
}

#[cfg(test)]
mod tests {
    use std::collections::HashSet;

    use super::*;
    use crate::data::dataloader::batcher::TestBatcher;
    use crate::data::dataloader::FixBatchStrategy;
    use crate::data::dataset::FakeDataset;

    #[test]
    fn test_batch_dataloader() {
        let batcher = Arc::new(TestBatcher::new());
        let dataset = Arc::new(FakeDataset::<String>::new(27));
        let dataloader = BatchDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset.clone(),
            batcher,
            None,
        );

        let mut items_dataset = HashSet::new();
        let mut items_dataloader = HashSet::new();

        for item in dataset.iter() {
            items_dataset.insert(item);
        }

        for items in dataloader.iter() {
            for item in items {
                items_dataloader.insert(item);
            }
        }

        assert_eq!(items_dataset, items_dataloader);
    }

    #[test]
    fn test_multi_thread_batch_dataloader() {
        let batcher = Arc::new(TestBatcher::new());
        let dataset = Arc::new(FakeDataset::<String>::new(27));
        let dataloader_single_thread = BatchDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset.clone(),
            batcher.clone(),
            None,
        );
        let dataloader_multi_thread = BatchDataLoader::multi_thread(
            Box::new(FixBatchStrategy::new(5)),
            dataset,
            batcher,
            4,
            None,
        );

        let mut items_single_thread = HashSet::new();
        let mut items_multi_thread = HashSet::new();

        for items in dataloader_single_thread.iter() {
            for item in items {
                items_single_thread.insert(item);
            }
        }

        for items in dataloader_multi_thread.iter() {
            for item in items {
                items_multi_thread.insert(item);
            }
        }

        assert_eq!(items_single_thread, items_multi_thread);
    }
}