# Tensors

## What is a tensor?

* Similar to `numpy`'s n-d arrays
* Can be used on GPU

## Tensor Attributes

Each `torch.Tensor` has these:

1. [`torch.dtype`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.dtype)
2. [`torch.device`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device)
3. [`torch.layout`](https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.layout)

### `torch.dtype`

* Object
* Represents data type of the tensor
* [`is_floating_point`](https://pytorch.org/docs/stable/torch.html#torch.is_floating_point) returns boolean of whether a tensor is a floating point data type

#### Operations with Tensors

When the dtypes of inputs to operation (add,sub,div,mul) **differ**, find the **minimum dtype** that satisfies the following rules:

**Tensor Operands**: floating > integral > boolean

1. If type of a scalar operand > tensor operands → Promote to type with sufficient size to hold all scalar operands of that category.
2. If a 0-dimension tensor operand > dimensioned operands → Promote to type with sufficient size and category to hold all 0-dim tensor operands of that category.
3. If there are 0 higher-category zero-dim operands → Promote to type with sufficient size and category to hold all dimensioned operands

### `torch.device`

* Object
* Represents device that the tensor is allocated
* Cuda
  * Keeps track of selected GPU

```python
>>> torch.device('cuda:0')
device(type='cuda', index=0)

>>> torch.device('cpu')
device(type='cpu')

>>> torch.device('cuda')  # current cuda device
device(type='cuda')
```

### `torch.layout`

* Object
* Represents memory layout of tensor
* Perform tensor operations efficiently


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