Source code for dgl.distributed.dist_dataloader

# pylint: disable=global-variable-undefined, invalid-name
"""Multiprocess dataloader for distributed training"""
from .. import backend as F
from .dist_context import get_sampler_pool

__all__ = ["DistDataLoader"]


[docs]class DistDataLoader: """DGL customized multiprocessing dataloader. DistDataLoader provides a similar interface to Pytorch's DataLoader to generate mini-batches with multiprocessing. It utilizes the worker processes created by :func:`dgl.distributed.initialize` to parallelize sampling. Parameters ---------- dataset: a tensor Tensors of node IDs or edge IDs. batch_size: int The number of samples per batch to load. shuffle: bool, optional Set to ``True`` to have the data reshuffled at every epoch (default: ``False``). collate_fn: callable, optional The function is typically used to sample neighbors of the nodes in a batch or the endpoint nodes of the edges in a batch. drop_last: bool, optional Set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. (default: ``False``) queue_size: int, optional Size of multiprocessing queue Examples -------- >>> g = dgl.distributed.DistGraph('graph-name') >>> def sample(seeds): ... seeds = th.LongTensor(np.asarray(seeds)) ... frontier = dgl.distributed.sample_neighbors(g, seeds, 10) ... return dgl.to_block(frontier, seeds) >>> dataloader = dgl.distributed.DistDataLoader(dataset=nodes, batch_size=1000, collate_fn=sample, shuffle=True) >>> for block in dataloader: ... feat = g.ndata['features'][block.srcdata[dgl.NID]] ... labels = g.ndata['labels'][block.dstdata[dgl.NID]] ... pred = model(block, feat) Note ---- When performing DGL's distributed sampling with multiprocessing, users have to use this class instead of Pytorch's DataLoader because DGL's RPC requires that all processes establish connections with servers before invoking any DGL's distributed API. Therefore, this dataloader uses the worker processes created in :func:`dgl.distributed.initialize`. Note ---- This dataloader does not guarantee the iteration order. For example, if dataset = [1, 2, 3, 4], batch_size = 2 and shuffle = False, the order of [1, 2] and [3, 4] is not guaranteed. """ def __init__( self, dataset, batch_size, shuffle=False, collate_fn=None, drop_last=False, queue_size=None, ): self.pool, self.num_workers = get_sampler_pool() if queue_size is None: queue_size = self.num_workers * 4 if self.num_workers > 0 else 4 self.queue_size = queue_size # prefetch size self.batch_size = batch_size self.num_pending = 0 self.collate_fn = collate_fn self.current_pos = 0 self.queue = [] # Only used when pool is None self.drop_last = drop_last self.recv_idxs = 0 self.shuffle = shuffle self.is_closed = False self.dataset = dataset self.data_idx = F.arange(0, len(dataset)) self.expected_idxs = len(dataset) // self.batch_size if not self.drop_last and len(dataset) % self.batch_size != 0: self.expected_idxs += 1 # We need to have a unique ID for each data loader to identify itself # in the sampler processes. global DATALOADER_ID = "dataloader-" + str(DATALOADER_ID) DATALOADER_ID += 1 if self.pool is not None: self.pool.set_collate_fn(self.collate_fn, def __del__(self): # When the process exits, the process pool may have been closed. We should try # and get the process pool again and see if we need to clean up the process pool. self.pool, self.num_workers = get_sampler_pool() if self.pool is not None: self.pool.delete_collate_fn( def __next__(self): if self.pool is None: num_reqs = 1 else: num_reqs = self.queue_size - self.num_pending for _ in range(num_reqs): self._request_next_batch() if self.recv_idxs < self.expected_idxs: result = self._get_data_from_result_queue() self.recv_idxs += 1 self.num_pending -= 1 return result else: assert self.num_pending == 0 raise StopIteration def _get_data_from_result_queue(self, timeout=1800): if self.pool is None: ret = self.queue.pop(0) else: ret = self.pool.get_result(, timeout=timeout) return ret def __iter__(self): if self.shuffle: self.data_idx = F.rand_shuffle(self.data_idx) self.recv_idxs = 0 self.current_pos = 0 self.num_pending = 0 return self def _request_next_batch(self): next_data = self._next_data() if next_data is None: return elif self.pool is not None: self.pool.submit_task(, next_data) else: result = self.collate_fn(next_data) self.queue.append(result) self.num_pending += 1 def _next_data(self): if self.current_pos == len(self.dataset): return None end_pos = 0 if self.current_pos + self.batch_size > len(self.dataset): if self.drop_last: return None else: end_pos = len(self.dataset) else: end_pos = self.current_pos + self.batch_size idx = self.data_idx[self.current_pos : end_pos].tolist() ret = [self.dataset[i] for i in idx] # Sharing large number of tensors between processes will consume too many # file descriptors, so let's convert each tensor to scalar value beforehand. if isinstance(ret[0], tuple): ret = [(type, F.as_scalar(id)) for (type, id) in ret] else: ret = [F.as_scalar(id) for id in ret] self.current_pos = end_pos return ret