empty_strided in PyTorch

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*My post explains empty() and empty_like().

empty_strided() can create the 0D or more D strided tensor with the zero or more floating-point numbers(Default), integers, complex numbers or boolean values from uninitialized memory which are called uninitialized data as shown below:

*Memos:

• empty_strided() can be used with torch but not with a tensor.
• The 1st argument with torch is size(Required-Type:tuple of int, list of int, or size()).
• The 2nd argument with torch is stride(Required-Type:tuple of int or list of int).
• There is dtype argument with torch(Optional-Default:None-Type:dtype): *Memos:
  • If it's None, get_default_dtype() is used. *My post explains get_default_dtype() and set_default_dtype().
  • dtype= must be used.
  • My post explains dtype argument.
• There is device argument with torch(Optional-Defalut:None-Type:str, int or device()): *Memos:
  • If it's None, get_default_device() is used. *My post explains get_default_device() and set_default_device().
  • device= must be used.
  • My post explains device argument.
• There is requires_grad argument with torch(Optional-Default:False-Type:bool): *Memos:
  • requires_grad= must be used.
  • My post explains requires_grad argument.
• The number of size and stride must be the same.
• Uninitialized memory has data but the data is unknown.

import torch

torch.empty_strided(size=(), stride=())
torch.empty_strided(size=torch.tensor(8).size(), stride=())
# tensor(1.2770e+19)

torch.empty_strided(size=(0,), stride=(0,))
torch.empty_strided(size=(0,), stride=(1,))
torch.empty_strided(size=(0,), stride=(2,))
torch.empty_strided(size=torch.tensor([]).size(), stride=(0,))
# tensor([])

torch.empty_strided(size=(3,), stride=(0,))
torch.empty_strided(size=torch.tensor([8, 3, 6]).size(), stride=(0,))
# tensor([7.4511e-33, 7.4511e-33, 7.4511e-33])

torch.empty_strided(size=(3,), stride=(1,))
torch.empty_strided(size=torch.tensor([8, 3, 6]).size(), stride=(1,))
# tensor([9.7245e-33, 3.1678e-41, 9.6997e-33])

torch.empty_strided(size=(3,), stride=(2,))
torch.empty_strided(size=torch.tensor([8, 3, 6]).size(), stride=(2,))
# tensor([-5.0667e-38,  4.4842e-44,  8.9683e-44])

torch.empty_strided((3, 2), stride=(0, 0))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(0, 0))
# tensor([[-8.4397e-35, -8.4397e-35],
#         [-8.4397e-35, -8.4397e-35],
#         [-8.4397e-35, -8.4397e-35]])

torch.empty_strided((3, 2), stride=(0, 1))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(0, 1))
# tensor([[9.7001e-33, 3.1678e-41],
#         [9.7001e-33, 3.1678e-41],
#         [9.7001e-33, 3.1678e-41]])

torch.empty_strided((3, 2), stride=(0, 2))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(0, 2))
# tensor([[1.4013e-45, -1.7014e+38],
#         [1.4013e-45, -1.7014e+38],
#         [1.4013e-45, -1.7014e+38]])

torch.empty_strided((3, 2), stride=(1, 0))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(1, 0))
# tensor([[-8.4397e-35, -8.4397e-35],
#         [4.5188e-41, 4.5188e-41],
#         [9.7611e-33, 9.7611e-33]])

torch.empty_strided((3, 2), stride=(1, 1))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(1, 1))
# tensor([[-8.4397e-35, 4.5188e-41],
#         [4.5188e-41, 9.7396e-33],
#         [9.7396e-33, 3.1678e-41]])

torch.empty_strided((3, 2), stride=(1, 2))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(1, 2))
# tensor([[1.7340e-07, 6.8988e-07],
#         [1.6599e-07, 1.2539e+16],
#         [6.8988e-07, 2.1707e-18]])

torch.empty_strided((3, 2), stride=(2, 0))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(2, 0))
# tensor([[-8.4397e-35, -8.4397e-35],
#         [9.7265e-33, 9.7265e-33],
#         [6.6757e-07, 6.6757e-07]])

torch.empty_strided((3, 2), stride=(2, 1))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(2, 1))
# tensor([[-8.4397e-35, 4.5188e-41],
#         [9.6884e-33, 3.1678e-41],
#         [4.4842e-44, 0.0000e+00]])

torch.empty_strided((3, 2), stride=(2, 2))
torch.empty_strided(size=torch.tensor([[8, 3], [6, 0], [2, 9]]).size(),
                    stride=(2, 2))
# tensor([[6.7121e-07, 1.3085e-11],
#         [1.3085e-11, 1.6690e+22],
#         [1.6690e+22, 2.1707e-18]])
etc.

torch.empty_strided(size=(3, 2, 4), stride=(0, 1, 2))
# tensor([[[-8.4397e-35, -8.4397e-35, 4.4842e-44, 1.1210e-43],
#          [4.5188e-41, 4.5188e-41, 0.0000e+00, 0.0000e+00]],
#         [[-8.4397e-35, -8.4397e-35, 4.4842e-44, 1.1210e-43],
#          [4.5188e-41, 4.5188e-41, 0.0000e+00, 0.0000e+00]],
#         [[-8.4397e-35, -8.4397e-35, 4.4842e-44, 1.1210e-43],
#          [4.5188e-41, 4.5188e-41, 0.0000e+00, 0.0000e+00]]])

torch.empty_strided(size=(3, 2, 4), stride=(0, 1, 2), dtype=torch.int64)
# tensor([[[0, 97092179969056, 0, 0],
#          [97092200351808, 138498049810784, 0, 0]],
#         [[0, 97092179969056, 0, 0],
#          [97092200351808, 138498049810784, 0, 0]],
#         [[0, 97092179969056, 0, 0],
#          [97092200351808, 138498049810784, 0, 0]]])

torch.empty_strided(size=(3, 2, 4), stride=(0, 1, 2), dtype=torch.complex64)
# tensor([[[-8.4397e-35+4.5188e-41j, 9.6886e-33+3.1678e-41j,
#           0.0000e+00+0.0000e+00j, 1.3829e-33+3.1678e-41j],
#          [9.6840e-33+3.1678e-41j, 0.0000e+00+0.0000e+00j,
#           9.8336e-33+3.1678e-41j, -3.8910e-25+4.5186e-41j]],
#         [[-8.4397e-35+4.5188e-41j, 9.6886e-33+3.1678e-41j,
#           0.0000e+00+0.0000e+00j, 1.3829e-33+3.1678e-41j],
#          [9.6840e-33+3.1678e-41j, 0.0000e+00+0.0000e+00j,
#           9.8336e-33+3.1678e-41j, -3.8910e-25+4.5186e-41j]],
#         [[-8.4397e-35+4.5188e-41j, 9.6886e-33+3.1678e-41j,
#           0.0000e+00+0.0000e+00j, 1.3829e-33+3.1678e-41j],
#          [9.6840e-33+3.1678e-41j, 0.0000e+00+0.0000e+00j,
#           9.8336e-33+3.1678e-41j, -3.8910e-25+4.5186e-41j]]])

torch.empty_strided(size=(3, 2, 4), stride=(0, 1, 2), dtype=torch.bool)
# tensor([[[True, True, True, False],
#          [True, True, True, False]],
#         [[True, True, True, False],
#          [True, True, True, False]],
#         [[True, True, True, False],
#          [True, True, True, False]]])