6889773ea1f550042e37a219c63ee4f4200e983c,horovod/torch/functions.py,,broadcast_optimizer_state,#Any#Any#,61

Before Change

                occurrences[name] += 1
                key = "%s.%d" % (str(name), occurrences[name])

                if not torch.is_tensor(p):
                    // Wrap the scalar in a FloatTensor, and remember its type
                    // so we can cast it back after unwrapping
                    t = type(p)
                    p = torch.Tensor([p])
                    callbacks[key] = _create_callback(pid, name, t, p)

                params.append((key, p))

    // Synchronized broadcast of all parameters
    broadcast_parameters(params, root_rank)

After Change

        return

    params = []
    scalars = {}
    callbacks = {}
    occurrences = collections.defaultdict(int)

    // Returns the full type structure of the possibly nested objects for recursive casting back

Instances