Differences with torch.ger

The following mapping relationships can be found in this file.

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

torch.ger

torch.ger(input, vec2, *, out=None)

For more information, see torch.ger.

mindspore.ops.ger

mindspore.ops.ger(input, other)

For more information, see mindspore.ops.ger.

Differences

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

PyTorch: The parameters input and vec2 support all data types of uint, int and float, and can be different data types. The data type of the return value selects a larger range of data types in the input parameter.

MindSpore: The data types of parameters input and other support float16/32/64, and must be the same data type. The data type of the return value is the same as the input.

There is no difference in function.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameters	Parameter 1	input	input	PyTorch supports all data types of uint, int and float, and MindSpore only supports float16/32/64
	Parameter 2	vec2	other	PyTorch supports all data types of uint, int and float, and MindSpore only supports float16/32/64
	Parameter 3	out	-	For details, see General Difference Parameter Table

Code Example 1

The data type of the input is int, and the data type of the return value is also int.

# PyTorch
import torch
import numpy as np

x1 = np.arange(3)
x2 = np.arange(6)

input = torch.tensor(x1, dtype=torch.int32)
other = torch.tensor(x2, dtype=torch.int32)
output = torch.ger(input, other)
print(output)
# tensor([[ 0,  0,  0,  0,  0,  0],
#         [ 0,  1,  2,  3,  4,  5],
#         [ 0,  2,  4,  6,  8, 10]], dtype=torch.int32)
print(output.dtype)
# torch.int32

# MindSpore doesn't support this feature currently.

Code Example 2

The data type of the input is float, and the data type of the return value is also float.

# PyTorch
import torch
import numpy as np
x1 = np.arange(3)
x2 = np.arange(6)
input = torch.tensor(x1, dtype=torch.float32)
other = torch.tensor(x2, dtype=torch.float32)
output = torch.ger(input, other)
print(output)
# tensor([[ 0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  1.,  2.,  3.,  4.,  5.],
#         [ 0.,  2.,  4.,  6.,  8., 10.]])
print(output.dtype)
# torch.float32

# MindSpore
import mindspore as ms
import numpy as np
x1 = np.arange(3)
x2 = np.arange(6)
input = ms.Tensor(x1, ms.float32)
other = ms.Tensor(x2, ms.float32)
output = ms.ops.ger(input, other)
print(output)
# [[ 0.  0.  0.  0.  0.  0.]
#  [ 0.  1.  2.  3.  4.  5.]
#  [ 0.  2.  4.  6.  8. 10.]]
print(output.dtype)
# Float32