(e) .env  In [1]   import torch

 (e) .env  In [2]   from torch.autograd import Function

 (e) .env  In [3]   from torch.cuda import comm

 (e) .env  In [4]   a = [torch.tensor([0], device=torch.device(i), requires_grad=True) for i in range(4)]

 (e) .env  In [5]   class Reduce(Function):
                         @staticmethod
                         def forward(ctx, inputs):
                             ctx.target_gpus = [inputs[i].get_device() for i in range(len(inputs))]
                             inputs = sorted(inputs, key=lambda i: i.get_device())
                             return comm.reduce_add(inputs)
                    
                         @staticmethod
                         def backward(ctx, gradOutput):
                             return Broadcast.apply(ctx.target_gpus, gradOutput)
                    

 (e) .env  In [6]   b = Reduce.apply(a)

 (e) .env  In [7]   b.requires_grad
            Out[7]   False

 (e) .env  In [8]   class Reduce(Function):
                         @staticmethod
                         def forward(ctx, *inputs):
                             ctx.target_gpus = [inputs[i].get_device() for i in range(len(inputs))]
                             inputs = sorted(inputs, key=lambda i: i.get_device())
                             return comm.reduce_add(inputs)
                    
                         @staticmethod
                         def backward(ctx, gradOutput):
                             return Broadcast.apply(ctx.target_gpus, gradOutput)
                    

 (e) .env  In [9]   c = Reduce.apply(*a)

 (e) .env  In [10]   c.requires_grad
            Out[10]   True

 (e) .env  In [11]  

Could anyone explains why are their behaviors different? Thank you.