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import torch |
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import math |
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import numpy as np |
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class SequentialDistributedSampler(torch.utils.data.sampler.Sampler): |
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""" |
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Distributed Sampler that subsamples indicies sequentially, |
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making it easier to collate all results at the end. |
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Even though we only use this sampler for eval and predict (no training), |
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which means that the model params won't have to be synced (i.e. will not hang |
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for synchronization even if varied number of forward passes), we still add extra |
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samples to the sampler to make it evenly divisible (like in `DistributedSampler`) |
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to make it easy to `gather` or `reduce` resulting tensors at the end of the loop. |
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""" |
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def __init__(self, dataset, batch_size, rank=None, num_replicas=None): |
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if num_replicas is None: |
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if not torch.distributed.is_available(): |
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raise RuntimeError("Requires distributed package to be available") |
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num_replicas = torch.distributed.get_world_size() |
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if rank is None: |
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if not torch.distributed.is_available(): |
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raise RuntimeError("Requires distributed package to be available") |
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rank = torch.distributed.get_rank() |
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self.dataset = dataset |
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self.num_replicas = num_replicas |
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self.rank = rank |
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self.batch_size = batch_size |
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self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.batch_size / self.num_replicas)) * self.batch_size |
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self.total_size = self.num_samples * self.num_replicas |
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def __iter__(self): |
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indices = list(range(len(self.dataset))) |
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indices += [indices[-1]] * (self.total_size - len(indices)) |
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indices = indices[self.rank * self.num_samples : (self.rank + 1) * self.num_samples] |
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return iter(indices) |
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def __len__(self): |
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return self.num_samples |
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def distributed_concat(tensor, num_total_examples): |
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output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())] |
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torch.distributed.all_gather(output_tensors, tensor) |
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concat = torch.cat(output_tensors, dim=0) |
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return concat[:num_total_examples] |
