In PyTorch,
`Tensor`

is the
primary object that we deal with (`Variable`

is just a thin wrapper class for
`Tensor`

). In this post, I will give a summary of pitfalls that we should avoid
when using Tensors. Since `FloatTensor`

and `LongTensor`

are the most popular
`Tensor`

types in PyTorch, I will focus on these two data types.

# Tensor operations#

## Tensor and Tensor operation#

For operations between Tensors, the rule is strict. Both Tensors of the operation must have the same data type, or you will see error messages like

```
TypeError: sub received an invalid combination of arguments - got (float), but expected one of:
* (int value)
didn't match because some of the arguments have invalid types: (!float!)
* (torch.LongTensor other)
didn't match because some of the arguments have invalid types: (!float!)
* (int value, torch.LongTensor other)
```

As another example, several loss functions like
`CrossEntropyLoss`

require that the target should be torch `LongTensor`

. So before doing
operations, make sure that your input `Tensor`

types match the function
definitions.

It is easy to convert the type of one `Tensor`

to another `Tensor`

. Suppose `x`

and `y`

are `Tensor`

of different types. You can use `x.type(y.type())`

or
`x.type_as(y)`

to convert `x`

to the type of `y`

.

## Tensor and scalar operation#

For `FloatTensor`

, you can do math operations (multiplication, addition,
division etc.) with a scalar of type `int`

or `float`

. But for `LongTensor`

,
you can only do math operation with `int`

scalar **but not float**.

# Why do some losses require target to be LongTensor?#

According to PyTorch
developers,
some use cases requires that the target be `LongTensor`

type and int just can
not hold the target value.

# FloatTensor or DoubleTensor#

For deep learning, precision is not a very important issue. Plus, GPU can not
process double precision very well. So `FloatTensor`

is enough, which is also
the default type for model parameters.

# NumPy array and torch Tensor#

## Shared memory or not?#

You can use `torch.from_numpy()`

method to convert a NumPy array to
corresponding torch `Tensor`

, which will share underlying memory with NumPy
array. To convert `Tensor`

`x`

to NumPy array, use `x.numpy()`

to convert it to
a NumPy array, which also shares the memory with original `Tensor`

.

Does torch `Tensor`

and Numpy array always share the underlying memory? The
short answer is no. If their underlying data type is not compatible, a copy of
original data will be made. For example, if you try to save torch `FloatTensor`

as numpy array of type `np.float64`

, it will trigger a deep copy.

## Correpsondece between NumPy and torch data type#

It should be noted that not all NumPy arrays can be converted to torch
`Tensor`

. Below is a table showing NumPy data types which is convertable to
torch `Tensor`

type.

NumPy data type | Tensor data type |
---|---|

`numpy.uint8` | `torch.ByteTensor` |

`numpy.int16` | `torch.ShortTensor` |

`numpy.int32` | `torch.IntTensor` |

`numpy.int` | `torch.LongTensor` |

`numpy.int64` | `torch.LongTensor` |

`numpy.float32` | `torch.FloatTensor` |

`numpy.float` | `torch.DoubleTensor` |

`numpy.float64` | `torch.DoubleTensor` |

## Speed comparison between NumPy and torch operations#

I am curious to know the speed difference between torch Tensor operation and
equivalent NumPy ndarray operations. I do it in Jupyter-console using the
built-in magic `%timeit`

.

```
import torch
import numpy as np
# torch Tensor on CPU
x = torch.rand(1, 64)
y = torch.rand(5000, 64)
%timeit z=(x*y).sum(dim=1)
# torch Tensor on GPU
x, y = x.cuda(), y.cuda()
%timeit z = (x*y).sum(dim=1)
# numpy ndarray on CPU
x = np.random.random((1, 64))
y = np.random.random((5000, 64))
%timeit z = (x*y).sum(axis=1)
```

The result is listed on the following table:

Data type and device | Average operation time |
---|---|

Tensor on CPU | 938 $\mu s$ |

Tensor on GPU | 38.9 $\mu s$ |

NumPy ndarray (on CPU) | 623 $\mu s$ |

It is pretty clear that Tensor operations on GPU runs orders of magnitute faster than operations on CPU. NumPy, due to its excellent implementation of its core in C, runs a little bit faster than Tensor on CPU.

# Convert scalar to torch Tensor#

You can convert a scalar to `Tensor`

by providing the scalr to the `Tensor`

constructor, which will not achieve what you want. For
example,`torch.Tensor(1)`

will not give you a `Tensor`

which contains float 1.
Instead, the produced `Tensor`

is something like

1.00000e-20 * 5.4514 [torch.FloatTensor of size 1]

To achieve what you want, you have to provide a list with single element 1 to
the `Tensor`

constructor.