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# gorgonia / tensor

package tensor provides efficient and generic n-dimensional arrays in Go that are useful for machine learning and deep learning purposes

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# Package `tensor`#

Package `tensor` is a package that provides efficient, generic (by some definitions of generic) n-dimensional arrays in Go. Also in this package are functions and methods that are used commonly in arithmetic, comparison and linear algebra operations.

The main purpose of this package is to support the operations required by Gorgonia.

## Introduction

In the data analysis world, Numpy and Matlab currently reign supreme. Both tools rely heavily on having performant n-dimensional arrays, or tensors. There is an obvious need for multidimensional arrays in Go.

While slices are cool, a large majority of scientific and numeric computing work relies heavily on matrices (two-dimensional arrays), three dimensional arrays and so on. In Go, the typical way of getting multidimensional arrays is to use something like `[][]T`. Applications that are more math heavy may opt to use the very excellent Gonum `matrix` package. What then if we want to go beyond having a `float64` matrix? What if we wanted a 3-dimensional `float32` array?

It comes to reason then there should be a data structure that handles these things. The `tensor` package fits in that niche.

### Basic Idea: Tensor

A tensor is a multidimensional array. It's like a slice, but works in multiple dimensions.

With slices, there are usage patterns that are repeated enough that warrant abstraction - `append`, `len`, `cap`, `range` are abstractions used to manipulate and query slices. Additionally slicing operations (`a[:1]` for example) are also abstractions provided by the language. Andrew Gerrand wrote a very good write up on Go's slice usage and internals.

Tensors come with their own set of usage patterns and abstractions. Most of these have analogues in slices, enumerated below (do note that certain slice operation will have more than one tensor analogue - this is due to the number of options available):

Slice Operation Tensor Operation
`len(a)` `T.Shape()`
`cap(a)` `T.DataSize()`
`a[:]` `T.Slice(...)`
`a[0]` `T.At(x,y)`
`append(a, ...)` `T.Stack(...)`, `T.Concat(...)`
`copy(dest, src)` `T.CopyTo(dest)`, `tensor.Copy(dest, src)`
`for _, v := range a` `for i, err := iterator.Next(); err == nil; i, err = iterator.Next()`

Some operations for a tensor does not have direct analogues to slice operations. However, they stem from the same idea, and can be considered a superset of all operations common to slices. They're enumerated below:

Tensor Operation Basic idea in slices
`T.Strides()` The stride of a slice will always be one element
`T.Dims()` The dimensions of a slice will always be one
`T.Size()` The size of a slice will always be its length
`T.Dtype()` The type of a slice is always known at compile time
`T.Reshape()` Given the shape of a slice is static, you can't really reshape a slice
`T.T(...)` / `T.Transpose()` / `T.UT()` No equivalent with slices

## The Types of Tensors

As of the current revision of this package, only dense tensors are supported. Support for sparse matrix (in form of a sparse column matrix and dictionary of keys matrix) will be coming shortly.

### Dense Tensors

The `*Dense` tensor is the primary tensor and is represented by a singular flat array, regardless of dimensions. See the Design of `*Dense` section for more information. It can hold any data type.

### Compressed Sparse Column Matrix

Documentation Coming soon

### Compressed Sparse Row Matrix

Documentation Coming soon

## Usage

To install: `go get -u "gorgonia.org/tensor"`

To create a matrix with package `tensor` is easy:

```// Creating a (2,2) matrix of int:
a := New(WithShape(2, 2), WithBacking([]int{1, 2, 3, 4}))
fmt.Printf("a:\n%v\n", a)

// Output:
// a:
// ⎡1  2⎤
// ⎣3  4⎦
//```

To create a 3-Tensor is just as easy - just put the correct shape and you're good to go:

```// Creating a (2,3,4) 3-Tensor of float32
b := New(WithBacking(Range(Float32, 0, 24)), WithShape(2, 3, 4))
fmt.Printf("b:\n%1.1f\n", b)

// Output:
// b:
// ⎡ 0.0   1.0   2.0   3.0⎤
// ⎢ 4.0   5.0   6.0   7.0⎥
// ⎣ 8.0   9.0  10.0  11.0⎦
//
// ⎡12.0  13.0  14.0  15.0⎤
// ⎢16.0  17.0  18.0  19.0⎥
// ⎣20.0  21.0  22.0  23.0⎦```

Accessing and Setting data is fairly easy. Dimensions are 0-indexed, so if you come from an R background, suck it up like I did. Be warned, this is the inefficient way if you want to do a batch access/setting:

```// Accessing data:
b := New(WithBacking(Range(Float32, 0, 24)), WithShape(2, 3, 4))
x, _ := b.At(0, 1, 2)
fmt.Printf("x: %v\n", x)

// Setting data
b.SetAt(float32(1000), 0, 1, 2)
fmt.Printf("b:\n%v", b)

// Output:
// x: 6
// b:
// ⎡   0     1     2     3⎤
// ⎢   4     5  1000     7⎥
// ⎣   8     9    10    11⎦

// ⎡  12    13    14    15⎤
// ⎢  16    17    18    19⎥
// ⎣  20    21    22    23⎦```

Bear in mind to pass in data of the correct type. This example will cause a panic:

```// Accessing data:
b := New(WithBacking(Range(Float32, 0, 24)), WithShape(2, 3, 4))
x, _ := b.At(0, 1, 2)
fmt.Printf("x: %v\n", x)

// Setting data
b.SetAt(1000, 0, 1, 2)
fmt.Printf("b:\n%v", b)```

There is a whole laundry list of methods and functions available at the godoc page

## Design of `*Dense`

The design of the `*Dense` tensor is quite simple in concept. However, let's start with something more familiar. This is a visual representation of a slice in Go (taken from rsc's excellent blog post on Go data structures):

The data structure for `*Dense` is similar, but a lot more complex. Much of the complexity comes from the need to do accounting work on the data structure as well as preserving references to memory locations. This is how the `*Dense` is defined:

```type Dense struct {
*AP
array
e Engine

// other fields elided for simplicity's sake
}```

And here's a visual representation of the `*Dense`.

`*Dense` draws its inspiration from Go's slice. Underlying it all is a flat array, and access to elements are controlled by `*AP`. Where a Go is able to store its metadata in a 3-word structure (obviating the need to allocate memory), a `*Dense` unfortunately needs to allocate some memory. The majority of the data is stored in the `*AP` structure, which contains metadata such as shape, stride, and methods for accessing the array.

`*Dense` embeds an `array` (not to be confused with Go's array), which is an abstracted data structure that looks like this:

``````type array struct {
t Dtype
v interface{}
}
``````

`*storage.Header` is the same structure as `reflect.SliceHeader`, except it stores a `unsafe.Pointer` instead of a `uintptr`. This is done so that eventually when more tests are done to determine how the garbage collector marks data, the `v` field may be removed.

The `storage.Header` field of the `array` (and hence `*Dense`) is there to provide a quick and easy way to translate back into a slice for operations that use familiar slice semantics, of which much of the operations are dependent upon.

By default, `*Dense` operations try to use the language builtin slice operations by casting the `*storage.Header` field into a slice. However, to accomodate a larger subset of types, the `*Dense` operations have a fallback to using pointer arithmetic to iterate through the slices for other types with non-primitive kinds (yes, you CAN do pointer arithmetic in Go. It's slow and unsafe). The result is slower operations for types with non-primitive kinds.

### Memory Allocation

`New()` functions as expected - it returns a pointer of `*Dense` to a array of zeroed memory. The underlying array is allocated, depending on what `ConsOpt` is passed in. With `New()`, `ConsOpt`s are used to determine the exact nature of the `*Dense`. It's a bit icky (I'd have preferred everything to have been known statically at compile time), but it works. Let's look at some examples:

```x := New(Of(Float64), WithShape(2,2)) // works
y := New(WithShape(2,2)) // panics
z := New(WithBacking([]int{1,2,3,4})) // works```

The following will happen:

• Line 1 works: This will allocate a `float64` array of size 4.
• Line 2 will cause a panic. This is because the function doesn't know what to allocate - it only knows to allocate an array of something for the size of 4.
• Line 3 will NOT fail, because the array has already been allocated (the `*Dense` reuses the same backing array as the slice passed in). Its shape will be set to `(4)`.

Alternatively you may also pass in an `Engine`. If that's the case then the allocation will use the `Alloc` method of the `Engine` instead:

`x := New(Of(Float64), WithEngine(myEngine), WithShape(2,2))`

The above call will use `myEngine` to allocate memory instead. This is useful in cases where you may want to manually manage your memory.

### Other failed designs

The alternative designs can be seen in the ALTERNATIVE DESIGNS document

## Generic Features

Example:

```x := New(WithBacking([]string{"hello", "world", "hello", "world"}), WithShape(2,2))
x = New(WithBacking([]int{1,2,3,4}), WithShape(2,2))```

The above code will not cause a compile error, because the structure holding the underlying array (of `string`s and then of `int`s) is a `*Dense`.

One could argue that this sidesteps the compiler's type checking system, deferring it to runtime (which a number of people consider dangerous). However, tools are being developed to type check these things, and until Go does support typechecked generics, unfortunately this will be the way it has to be.

Currently, the tensor package supports limited type of genericity - limited to a tensor of any primitive type.

# How This Package is Developed

Much of the code in this package is generated. The code to generate them is in the directory `genlib2`. `genlib2` requires `goimports` binary to be available in the \$PATH.

## Tests

Tests require python with numpy installed. You can select which python intepreter is being used by setting the environment variable `PYTHON_COMMAND` accordingly. The default value is `python`.

## Things Knowingly Untested For

• `complex64` and `complex128` are excluded from quick check generation process Issue #11

### TODO

• Identity optimizations for op
• Zero value optimizations
• fix Random() - super dodgy

# How To Get Support

The best way of support right now is to open a ticket on Github.

# Contributing

Obviously since you are most probably reading this on Github, Github will form the major part of the workflow for contributing to this package.

## Contributors and Significant Contributors

All contributions are welcome. However, there is a new class of contributor, called Significant Contributors.

A Significant Contributor is one who has shown deep understanding of how the library works and/or its environs. Here are examples of what constitutes a Significant Contribution:

• Wrote significant amounts of documentation pertaining to why/the mechanics of particular functions/methods and how the different parts affect one another
• Wrote code, and tests around the more intricately connected parts of Gorgonia
• Wrote code and tests, and have at least 5 pull requests accepted
• Provided expert analysis on parts of the package (for example, you may be a floating point operations expert who optimized one function)
• Answered at least 10 support questions.

Significant Contributors list will be updated once a month (if anyone even uses Gorgonia that is).

# Licence

Gorgonia and the `tensor` package are licenced under a variant of Apache 2.0. It's for all intents and purposes the same as the Apache 2.0 Licence, with the exception of not being able to commercially profit directly from the package unless you're a Significant Contributor (for example, providing commercial support for the package). It's perfectly fine to profit directly from a derivative of Gorgonia (for example, if you use Gorgonia as a library in your product)

Everyone is still allowed to use Gorgonia for commercial purposes (example: using it in a software for your business).

These are the packages and libraries which inspired and were adapted from in the process of writing Gorgonia (the Go packages that were used were already declared above):

Source How it's Used Licence
Numpy Inspired large portions. Directly adapted algorithms for a few methods (explicitly labelled in the docs) MIT/BSD-like. Numpy Licence

package tensor provides efficient and generic n-dimensional arrays in Go that are useful for machine learning and deep learning purposes

## Releases 23

Optimizations and Bugfixes Latest
Mar 29, 2021