DistributedDataParallel

[LanceGe]

Is there a way to make SupContrast work for DistributedDataParallel? By default each worker can only see its own sub-batch so the inter-sub-batch relationship of the samples will be utilized.

[HobbitLong]

You can use all_gather to gather features together. The caveat is that you need to manually propagate gradients through all_gather op, as it doesn't auto-bp.

[LanceGe]

You can use all_gather to gather features together. The caveat is that you need to manually propagate gradients through all_gather op, as it doesn't auto-bp.

I finally make it work with the help of diffdist, which provides a differentiable all_gather wrapper.

[ShijianXu]

Hi, can you share your code about how to implement this? I am not familiar with all_gather .etc operations. Thanks a lot.

[LanceGe]

Hi, can you share your code about how to implement this? I am not familiar with all_gather .etc operations. Thanks a lot.

First, install diffdist. Then put the following snippet before calling the criterion:

    import diffdist.functional as distops

    features = distops.all_gather(
        gather_list=[torch.zeros_like(features) for _ in range(torch.distributed.get_world_size())],
        tensor=features,
        next_backprop=None,
        inplace=True,
    )
    features = torch.cat(features)

    labels = distops.all_gather(
        gather_list=[torch.zeros_like(labels) for _ in range(torch.distributed.get_world_size())],
        tensor=labels,
        next_backprop=None,
        inplace=True,
    )
    labels = torch.cat(labels)

[ShijianXu]

Thank you for your quick reply.

So, then I can simply compute the loss as usual and then backward the gradient?

[LanceGe]

Yes, but I'm not sure this is bug-free.

On Tue, Jul 21, 2020, 5:27 PM XU Shijian [email protected] wrote:

Thank you for your quick reply.

So, then I can simply compute the loss as usual and then backward the gradient?

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[ShijianXu]

OK. Anyway, thanks a lot.

[ShijianXu]

Just for reference, this seems to be a reliable solution.