burn-core 0.22.0-pre.1

use burn_dataset::Dataset;
use burn_dataset::transform::PartialDataset;
use burn_tensor::Device;
use rand::distr::{Distribution, StandardUniform};
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};

use super::batcher::Batcher;
use super::{BatchDataLoader, BatchStrategy, DataLoader, DataLoaderIterator, Progress};
use std::sync::{Arc, OnceLock, mpsc, mpsc::SyncSender};
use std::thread;

const MAX_QUEUED_ITEMS: usize = 100;

type RngSeed = <StdRng as SeedableRng>::Seed;

/// A multi-threaded data loader that can be used to iterate over a dataset.
pub struct MultiThreadDataLoader<I, O> {
    // Configuration parameters needed for initialization
    strategy: Box<dyn BatchStrategy<I>>,
    dataset: Arc<dyn Dataset<I>>,
    batcher: Arc<dyn Batcher<I, O>>,
    device: Device,
    seed: Option<RngSeed>,
    num_threads: usize,

    // The lazily initialized data loaders
    dataloaders: OnceLock<Vec<BatchDataLoader<I, O>>>,

    // Spawned once and reused across every `iter()` call so each worker keeps a
    // stable CubeCL stream (and its memory pool) instead of leaking one per epoch (#4792).
    workers: OnceLock<WorkerPool<O>>,
}

/// A message that can be sent between threads.
#[derive(Debug)]
pub enum Message<O> {
    /// A batch of items.
    Batch(usize, O, Progress),

    /// The thread is done.
    Done,

    /// The worker hit an unrecoverable error (e.g. `Dataset::get` failed) and stopped early.
    Error(usize, String),
}

struct MultiThreadsDataloaderIterator<O> {
    num_done: usize,
    num_workers: usize,
    receiver: mpsc::Receiver<Message<O>>,
    progresses: Vec<Progress>,
}

/// Per-epoch channel a worker streams its batches into; handed to the worker to start a pass.
type WorkerCommand<O> = SyncSender<Message<O>>;

struct WorkerPool<O> {
    /// One command channel per worker; sending a per-epoch sender starts a pass.
    senders: Vec<mpsc::Sender<WorkerCommand<O>>>,
    handles: Vec<thread::JoinHandle<()>>,
    item_counts: Vec<usize>,
}

impl<O> Drop for WorkerPool<O> {
    fn drop(&mut self) {
        // Dropping the senders makes each worker's `recv()` return Err, ending its loop.
        self.senders.clear();
        for handle in self.handles.drain(..) {
            let _ = handle.join();
        }
    }
}

impl<I, O> MultiThreadDataLoader<I, O>
where
    I: Send + Sync + Clone + 'static,
    O: Send + '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.
    /// * `device`  - The device to use when loading a batch.
    /// * `rng`     - The rng determining if the dataset is shuffled each time a dataloader
    ///   iterator is created.
    ///
    /// # Returns
    ///
    /// The multi-threaded batch data loader.
    pub fn new(
        strategy: Box<dyn BatchStrategy<I>>,
        dataset: Arc<dyn Dataset<I>>,
        batcher: Arc<dyn Batcher<I, O>>,
        num_threads: usize,
        device: Device,
        rng: Option<rand::rngs::StdRng>,
    ) -> Self {
        let mut seed = None;
        if let Some(mut rng) = rng {
            // RNG stream splitting (not state cloning): derive a new seed from the RNG's output.
            // This is exactly what `rng.fork()` does.
            let mut s = RngSeed::default();
            rng.fill_bytes(&mut s);

            seed = Some(s);
        }
        Self::from_seed(strategy, dataset, batcher, num_threads, device, seed)
    }

    fn from_seed(
        strategy: Box<dyn BatchStrategy<I>>,
        dataset: Arc<dyn Dataset<I>>,
        batcher: Arc<dyn Batcher<I, O>>,
        num_threads: usize,
        device: Device,
        seed: Option<RngSeed>,
    ) -> Self {
        Self {
            strategy,
            dataset,
            batcher,
            num_threads,
            device,
            seed,
            dataloaders: OnceLock::new(),
            workers: OnceLock::new(),
        }
    }

    /// Force initialization if needed.
    fn initialize(&self) -> &[BatchDataLoader<I, O>] {
        self.dataloaders
            .get_or_init(|| {
                let mut dataset = self.dataset.clone();
                if let Some(seed) = self.seed.as_ref() {
                    // Pre-shuffle the dataset before split if shuffle is enabled.
                    // This ensures that each thread gets a uniform random sample of the dataset.
                    let mut rng = StdRng::from_seed(*seed);
                    dataset = Arc::new(burn_dataset::transform::ShuffledDataset::new(
                        dataset, &mut rng,
                    ));
                }

                let datasets = match self.strategy.batch_size() {
                    Some(batch_size) => {
                        PartialDataset::split_chunks(dataset, self.num_threads, batch_size)
                    }
                    None => PartialDataset::split(dataset, self.num_threads),
                };

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

                datasets
                    .into_iter()
                    .zip(rngs)
                    .map(|(dataset, rng)| {
                        let strategy = self.strategy.clone_dyn();
                        BatchDataLoader::new(
                            strategy,
                            Arc::new(dataset),
                            self.batcher.clone(),
                            self.device.clone(),
                            rng,
                        )
                    })
                    .collect()
            })
            .as_ref()
    }

    /// Lazily spawns the persistent worker pool (once) and returns it.
    fn workers(&self) -> &WorkerPool<O> {
        self.workers.get_or_init(|| {
            let dataloaders = self.initialize();
            let item_counts: Vec<usize> = dataloaders.iter().map(|d| d.num_items()).collect();

            let mut senders = Vec::with_capacity(dataloaders.len());
            let mut handles = Vec::with_capacity(dataloaders.len());

            for (index, dataloader) in dataloaders.iter().enumerate() {
                let dataloader = dataloader.clone();
                let (command_sender, command_receiver) = mpsc::channel::<WorkerCommand<O>>();

                let handle = thread::Builder::new()
                    .name(std::format!("dataloader-{index}"))
                    .spawn(move || {
                        while let Ok(sender) = command_receiver.recv() {
                            let mut iterator = dataloader.iter();
                            loop {
                                match iterator.next() {
                                    Some(Ok(item)) => {
                                        let progress = iterator.progress();

                                        if sender
                                            .send(Message::Batch(index, item, progress))
                                            .is_err()
                                        {
                                            break;
                                        }
                                    }
                                    None => break,
                                    Some(Err(dataset_err)) => {
                                        sender
                                            .send(Message::Error(index, dataset_err.to_string()))
                                            .ok();
                                        break;
                                    }
                                }
                            }
                            sender.send(Message::Done).ok();
                        }
                    })
                    .unwrap();

                senders.push(command_sender);
                handles.push(handle);
            }

            WorkerPool {
                senders,
                handles,
                item_counts,
            }
        })
    }
}

impl<I, O> DataLoader<O> for MultiThreadDataLoader<I, O>
where
    I: Send + Sync + Clone + 'static,
    O: Send + 'static + std::fmt::Debug,
{
    fn iter<'a>(&'a self) -> Box<dyn DataLoaderIterator<O> + 'a> {
        let workers = self.workers();

        let (sender, receiver) = mpsc::sync_channel::<Message<O>>(MAX_QUEUED_ITEMS);
        let unit: Option<String> = Some("items".to_string());

        let mut progresses = Vec::with_capacity(workers.senders.len());
        for (command_sender, &num_items) in workers.senders.iter().zip(workers.item_counts.iter()) {
            progresses.push(Progress::new(0, num_items, unit.clone()));
            command_sender
                .send(sender.clone())
                .expect("Dataloader worker thread should be alive");
        }
        let num_workers = workers.senders.len();

        // Drop our sender so the channel disconnects once every worker is done.
        drop(sender);

        Box::new(MultiThreadsDataloaderIterator::new(
            receiver,
            num_workers,
            progresses,
        ))
    }

    fn num_items(&self) -> usize {
        // For num_items, we can directly use the dataset size without
        // necessarily initializing the full loader
        self.dataset.len()
    }

    fn to_device(&self, device: &Device) -> Arc<dyn DataLoader<O>> {
        Arc::new(Self::from_seed(
            self.strategy.clone_dyn(),
            self.dataset.clone(),
            self.batcher.clone(),
            self.num_threads,
            device.clone(),
            self.seed,
        ))
    }

    fn slice(&self, start: usize, end: usize) -> Arc<dyn DataLoader<O>> {
        let dataloader = Self::from_seed(
            self.strategy.clone_dyn(),
            Arc::new(PartialDataset::new(self.dataset.clone(), start, end)),
            self.batcher.clone(),
            self.num_threads,
            self.device.clone(),
            self.seed,
        );
        Arc::new(dataloader)
    }
}

impl<O> MultiThreadsDataloaderIterator<O> {
    pub fn new(
        receiver: mpsc::Receiver<Message<O>>,
        num_workers: usize,
        progresses: Vec<Progress>,
    ) -> Self {
        MultiThreadsDataloaderIterator {
            num_done: 0,
            num_workers,
            receiver,
            progresses,
        }
    }
}
impl<O: std::fmt::Debug> DataLoaderIterator<O> for MultiThreadsDataloaderIterator<O> {
    fn progress(&self) -> Progress {
        let mut items_total = 0;
        let mut items_processed = 0;
        let unit: Option<String> = Some("items".to_string());

        for progress in self.progresses.iter() {
            items_total += progress.items_total;
            items_processed += progress.items_processed;
        }

        Progress::new(items_processed, items_total, unit)
    }
}

impl<O: std::fmt::Debug> Iterator for MultiThreadsDataloaderIterator<O> {
    type Item = Result<O, burn_dataset::DatasetError>;

    fn next(&mut self) -> Option<Self::Item> {
        if self.num_workers == 0 {
            return None;
        }

        loop {
            match self.receiver.recv() {
                Ok(Message::Batch(index, item, progress)) => {
                    if let Some(current) = self.progresses.get_mut(index) {
                        *current = progress;
                    }
                    return Some(Ok(item));
                }
                Ok(Message::Done) => {
                    self.num_done += 1;
                    if self.num_done == self.num_workers {
                        // Workers stay alive for the next epoch; nothing to join.
                        return None;
                    }
                }
                Ok(Message::Error(index, msg)) => {
                    return Some(Err(burn_dataset::DatasetError::new(std::io::Error::other(
                        format!("dataloader worker {index} failed: {msg}"),
                    ))));
                }
                Err(_) => return None,
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::data::dataloader::FixBatchStrategy;
    use crate::data::dataloader::batcher::TestBatcher;
    use crate::data::dataset::FakeDataset;
    use burn_dataset::DatasetError;
    use burn_dataset::InMemDataset;
    use std::collections::HashSet;
    use std::sync::mpsc;
    use std::time::Duration;

    /// A dataset that returns a real error (not an out-of-bounds panic) at one index,
    /// to exercise the worker-error-propagation path below.
    struct FlakyDataset {
        len: usize,
        fail_at: usize,
    }

    impl Dataset<usize> for FlakyDataset {
        fn get(&self, index: usize) -> Result<usize, DatasetError> {
            assert!(index < self.len, "index out of bounds");
            if index == self.fail_at {
                return Err(DatasetError::new(std::io::Error::other(
                    "simulated dataset failure",
                )));
            }
            Ok(index)
        }

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

    #[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(),
            Default::default(),
            None,
        );
        let dataloader_multi_thread = MultiThreadDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset,
            batcher,
            4,
            Default::default(),
            None,
        );

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

        for items in dataloader_single_thread.iter().map(Result::unwrap) {
            for item in items {
                items_single_thread.insert(item);
            }
        }

        for items in dataloader_multi_thread.iter().map(Result::unwrap) {
            for item in items {
                items_multi_thread.insert(item);
            }
        }

        assert_eq!(items_single_thread, items_multi_thread);
    }

    #[test]
    fn test_multi_thread_batch_dataloader_shuffle() {
        let num_classes = 2;
        let class_size = 100;
        let batch_size = 10;

        // Items is a deliberately ordered dataset.
        let mut items = Vec::new();
        for class in 0..num_classes {
            items.extend(vec![class; class_size]);
        }

        {
            // Unshuffled multithreaded loader
            let dataset = Arc::new(InMemDataset::new(items.clone()));
            let batcher = Arc::new(TestBatcher::new());

            let loader = MultiThreadDataLoader::new(
                Box::new(FixBatchStrategy::new(batch_size)),
                dataset,
                batcher,
                num_classes,
                Default::default(),
                // No rng means no shuffling.
                None,
            );

            for batch in loader.iter().map(Result::unwrap) {
                let mut batch_items = HashSet::new();
                for item in batch {
                    batch_items.insert(item);
                }

                // Since the dataset is not shuffled, we expect each batch to contain the same item.
                assert_eq!(batch_items.len(), 1);
            }
        }

        {
            // Shuffled multithreaded loader
            let dataset = Arc::new(InMemDataset::new(items.clone()));
            let batcher = Arc::new(TestBatcher::new());

            let loader = MultiThreadDataLoader::new(
                Box::new(FixBatchStrategy::new(batch_size)),
                dataset.clone(),
                batcher.clone(),
                num_classes,
                Default::default(),
                // The rng enables shuffling.
                Some(StdRng::seed_from_u64(42)),
            );

            for batch in loader.iter().map(Result::unwrap) {
                let mut batch_items = HashSet::new();
                for item in batch {
                    batch_items.insert(item);
                }

                // Since the dataset is shuffled, we expect to see all items.
                assert_eq!(batch_items.len(), num_classes);
            }
        }
    }

    #[test]
    fn test_multi_thread_batch_dataloader_incomplete_batches() {
        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(),
            Default::default(),
            None,
        );
        let dataloader_multi_thread = MultiThreadDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset,
            batcher,
            4,
            Default::default(),
            None,
        );

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

        let mut single_thread_cnt = 0;
        let mut multi_thread_cnt = 0;
        for items in dataloader_single_thread.iter().map(Result::unwrap) {
            items_single_thread.insert(items);
            single_thread_cnt += 1;
        }

        for items in dataloader_multi_thread.iter().map(Result::unwrap) {
            items_multi_thread.insert(items);
            multi_thread_cnt += 1;
        }

        assert_eq!(single_thread_cnt, multi_thread_cnt);
        assert_eq!(items_single_thread, items_multi_thread);
    }

    // Iterating the same loader over several epochs must keep yielding the full dataset (#4792).
    #[test]
    fn test_multi_thread_batch_dataloader_multiple_epochs() {
        let batcher = Arc::new(TestBatcher::new());
        let dataset = Arc::new(FakeDataset::<String>::new(27));

        let expected: HashSet<_> = dataset.iter().map(Result::unwrap).collect();

        let dataloader = MultiThreadDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset,
            batcher,
            4,
            Default::default(),
            None,
        );

        for _epoch in 0..3 {
            let mut items = HashSet::new();
            for batch in dataloader.iter().map(Result::unwrap) {
                for item in batch {
                    items.insert(item);
                }
            }
            assert_eq!(items, expected);
        }
    }

    // Dropping an iterator early must not kill the workers; the next pass still yields everything.
    #[test]
    fn test_multi_thread_batch_dataloader_resumes_after_early_drop() {
        let batcher = Arc::new(TestBatcher::new());
        let dataset = Arc::new(FakeDataset::<String>::new(27));

        let expected: HashSet<_> = dataset.iter().map(Result::unwrap).collect();

        let dataloader = MultiThreadDataLoader::new(
            Box::new(FixBatchStrategy::new(5)),
            dataset,
            batcher,
            4,
            Default::default(),
            None,
        );

        // Consume a single batch then drop the iterator early.
        {
            let mut iterator = dataloader.iter();
            let _ = iterator.next();
        }

        let mut items = HashSet::new();
        for batch in dataloader.iter().map(Result::unwrap) {
            for item in batch {
                items.insert(item);
            }
        }
        assert_eq!(items, expected);
    }

    #[test]
    fn test_multi_thread_batch_dataloader_propagates_worker_error_instead_of_hanging() {
        let batcher = Arc::new(TestBatcher::new());
        let dataset = Arc::new(FlakyDataset {
            len: 40,
            fail_at: 20,
        });
        let dataloader = MultiThreadDataLoader::new(
            Box::new(FixBatchStrategy::new(1)),
            dataset,
            batcher,
            4,
            Default::default(),
            None,
        );

        let (done_tx, done_rx) = mpsc::channel();
        let handle = thread::spawn(move || {
            let saw_error = dataloader.iter().any(|batch| batch.is_err());
            done_tx.send(saw_error).ok();
        });

        let saw_error = done_rx
            .recv_timeout(Duration::from_secs(10))
            .expect("dataloader hung instead of reporting the worker error");
        assert!(
            saw_error,
            "expected the dataloader to yield an Err on a worker error"
        );
        let _ = handle.join();
    }
}