Differences with torch.all

The following mapping relationships can be found in this file.

PyTorch APIs	MindSpore APIs
torch.all	mindspore.ops.all
torch.Tensor.all	mindspore.Tensor.all

torch.all

torch.all(input, dim, keepdim=False, *, out=None) -> Tensor

For more information, see torch.all.

mindspore.ops.all

mindspore.ops.all(x, axis=(), keep_dims=False) -> Tensor

For more information, see mindspore.ops.all.

Differences

PyTorch: Perform logic AND on the elements of input according to the specified dim. keepdim controls whether the output and input have the same dimension. out can fetch the output.

MindSpore: Perform logic AND on the elements of x according to the specified axis. The keep_dims has the same function as PyTorch, and MindSpore does not have the out parameter. MindSpore has a default value for axis, and performs the logical AND on all elements of x if axis is the default.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	x	Same function, different parameter names
	Parameter 2	dim	axis	PyTorch must pass dim and only one integer. MindSpore axis can be passed as an integer, a tuples of integers or a list of integers
	Parameter 3	keepdim	keep_dims	Same function, different parameter names
	Parameter 4	out	-	PyTorch out can get the output. MindSpore does not have this parameter

Code Example

# PyTorch
import torch

input = torch.tensor([[False, True, False, True], [False, True, False, False]])
print(torch.all(input, dim=0, keepdim=True))
# tensor([[False,  True, False, False]])

# MindSpore
import mindspore

x = mindspore.Tensor([[False, True, False, True], [False, True, False, False]])
print(mindspore.ops.all(x, axis=0, keep_dims=True))
# [[False  True False False]]