Differences with torch.distributed.broadcast

torch.distributed.broadcast

torch.distributed.broadcast(
    tensor,
    src=0,
    group=None,
    async_op=False
)

For more information, see torch.distributed.broadcast。

mindspore.communication.comm_func.broadcast

mindspore.communication.comm_func.broadcast(tensor, src=0, group=GlobalComm.WORLD_COMM_GROUP)

For more information, see mindspore.communication.comm_func.broadcast。

Differences

API function of MindSpore is not consistent with that of PyTorch.

PyTorch：the inputs contains the tensor to be broadcast or received, the rank(global rank) src of the process that broadcast the tensor, the communication group to work on, and the async op flag async_op. The process will broadcast the tensor if it is the src process, otherwise it will receive the Tensor. The return is a async work handle if async_op=True, otherwise is None.

MindSpore: The inputs contains the tensor to be broadcast, the rank(global rank) src of the process that broadcast the tensor, the communication group to work on, and it will return the tensor with the same shape with the broadcasted tensor. The async op flag async_op and the working device lists device_ids are not supported.

Categories	Subcategories	PyTorch	MindSpore	Difference
Parameters	Parameters 1	tensor	tensor	PyTorch：tensor to be broadcast in src process，otherwise to be received. MindSpore：tensor to be broadcasted
	Parameters 2	src	src	No difference
	Parameters 3	group	group	No difference
	Parameters 4	async_op	-	PyTorch: the async op flag. MindSpore: does not have this parameter.