def _add_allreduce_graph(model):
    """Construct the graph that performs Allreduce on the gradients."""
    # Need to all-reduce the per-GPU gradients if training with more than 1 GPU
    all_params = model.TrainableParams()
    assert len(all_params) % cfg.NUM_GPUS == 0
    # The model parameters are replicated on each GPU, get the number
    # distinct parameter blobs (i.e., the number of parameter blobs on
    # each GPU)
    params_per_gpu = int(len(all_params) / cfg.NUM_GPUS)
    with c2_utils.CudaScope(0):
        # Iterate over distinct parameter blobs
        for i in range(params_per_gpu):
            # Gradients from all GPUs for this parameter blob
            gradients = [
                model.param_to_grad[p] for p in all_params[i::params_per_gpu]
            ]
            if len(gradients) > 0:
                if cfg.USE_NCCL:
                    model.net.NCCLAllreduce(gradients, gradients)
                else:
                    muji.Allreduce(model.net, gradients, reduced_affix='')
Construct the graph that performs Allreduce on the gradients.
detectron/modeling/optimizer.py