Differences with torch.distributed.all_gather

torch.distributed.all_gather

torch.distributed.all_gather(
    tensor_list,
    tensor,
    group=None,
    async_op=False
)

For more information, see torch.distributed.all_gather.

mindspore.ops.AllGather

class mindspore.ops.AllGather(group=GlobalComm.WORLD_COMM_GROUP)(input_x)

For more information, see mindspore.ops.AllGather.

Differences

PyTorch: The inputs are the tensor broadcasted by the current process tensor, the communication group group and the async op flag async_op. The output is tensor_list after AllGather op, whose type is list[Tensor] and the length is the number of devices in the communication group. The return is a async work handle if async_op=True, otherwise is None.

MindSpore: The input of this interface is input_x that is a tensor. The output is a tensor, whose first dimension equals the number of devices in the communication group. This interface currently does not support the configuration of async_op.

Class	Sub-class	PyTorch	MindSpore	Difference
Param	Param 1	tensor_list	-	PyTorch: the output after AllGather. MindSpore does not have this parameter
	Param 2	tensor	-	PyTorch: the tensor broadcasted by the current process. MindSpore does not have this parameter
	Param 3	group	group	No dfference
	Param 4	async_op	-	PyTorch: the async op flag. MindSpore does not have this parameter
Input	Single input	-	input_x	PyTorch: not applied. MindSpore: the input tensor of AllGather.