broadcast_object_list与broadcast类似,将特定rank中的列表数据广播到其余rank中,不同rank中的列表长度需要一致。 #将src_rank的列表数据广播到其余rank中 src_rank= 1 if dist.get_rank() == src_rank: objs = ["hello", "torch", 1, { "map" : 3 }] else : objs = [None, None, None, None]...
pytorch/torch/distributed/distributed_c10d.py Line 2712 in71795f1 group_src=_canonicalize_group_rank(group,src,group_src,return_global=False) so it is safer and better to usegroup_srcas well inbroadcast_object_listinstead ofglobal_src. I'm willing to submit a pr if this is confirmed. Vers...
ranks """ src = 0 if rank == src: data = [torch.arange(10, dtype=torch.float32) + 2 * rank] else: data = [torch.zeros(1, dtype=torch.float32)] print(f"Rank {rank} has data: {data}") dist.broadcast_object_list(data, src=src) print(f"Rank {rank} received data: {data}...
p_list_names = broadcast_object(p_list_names, root_rank=0) p_list =sorted(p_list, key=lambdap : p_list_names.index(self._parameter_names.get(p)))# Form groupsifisinstance(self._groups,list): p_groups = [] grouped_id =set() p_list_ids = [id(p)forpinp_list]forgroupinself._...
# To ensure parameter order and group formation is consistent, broadcast p_list order # from rank 0 and use for every worker p_list_names = [self._parameter_names.get(p) for p in p_list] p_list_names = broadcast_object(p_list_names, root_rank=0) ...
🐛 Describe the bug After the torch.distributed.recv_object_list(obj, dst) method returns, the obj resides on the sender GPU's memory, not on the receiver GPU's memory. I would expect obj to be residing on the receiving GPU. import torch ...
output = tree_unflatten(passthrough_tensor_list, treespec) return output 其中,使用 _sync_params 来同步模型参数,具体是使用 _distributed_broadcast_coalesced 进行完成。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 def _sync_params(self): with torch.no_grad(): # module buffer sync if self....
(xmin, ymin, xmax, ymax) set_2: a tensor of dimensions (n2, 4), anchor表示成(xmin, ymin, xmax, ymax) Returns: intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2) """ # PyTorch auto-broadcasts singleton dimensions lower_...
(xmin, ymin, xmax, ymax)Returns:intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, shape: (n1, n2)"""# PyTorch auto-broadcasts singleton dimensionslower_bounds=torch.max(set_1[:,:2].unsqueeze(1),set_2[:,:2].unsqueeze(0))# (n1, n2, 2...
4. PyTorch Tensor 与 Python List 和 Aumpy Array 的转换 5. Tensor 综合练习 PyTorch 基础 0. 概述 在开始深度学习项目之前,选择一个合适的框架是非常重要的。常见的深度学习框架包括 TensorFlow, Keras, PyTorch, Caffe, Theano, CNTK, PaddlePaddle 等。本次实验我们将选用 PyTorch,并学习 PyTorch 的背景知识...