def update_with_local_losses()

in guided_diffusion/resample.py [0:0]

• 20 lines of code
• 9 McCabe index (conditional complexity)

    def update_with_local_losses(self, local_ts, local_losses):
        """
        Update the reweighting using losses from a model.

        Call this method from each rank with a batch of timesteps and the
        corresponding losses for each of those timesteps.
        This method will perform synchronization to make sure all of the ranks
        maintain the exact same reweighting.

        :param local_ts: an integer Tensor of timesteps.
        :param local_losses: a 1D Tensor of losses.
        """
        batch_sizes = [
            th.tensor([0], dtype=th.int32, device=local_ts.device)
            for _ in range(dist.get_world_size())
        ]
        dist.all_gather(
            batch_sizes,
            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
        )

        # Pad all_gather batches to be the maximum batch size.
        batch_sizes = [x.item() for x in batch_sizes]
        max_bs = max(batch_sizes)

        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
        dist.all_gather(timestep_batches, local_ts)
        dist.all_gather(loss_batches, local_losses)
        timesteps = [
            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
        ]
        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
        self.update_with_all_losses(timesteps, losses)