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Awesome Mojo🔥

Mojo

Mojo 🔥 — a new programming language for all developers, AI/ML scientists and software engineers.

A curated list of awesome Mojo🔥 code, problem-solving, solution, and in future libraries, frameworks, software and resources.

Let's accumulate here very new technology knowledge and best practices.

Mojo is a programming language that combines the user-friendliness of Python with the performance capabilities of C++ and Rust. Additionally, Mojo enables users to harness the vast ecosystem of Python libraries.

In a brief

  • Mojo allows you to leverage the entire Python ecosystem.
  • Mojo is designed to become a superset of Python.
  • Make Mojo compatible with existing Python programs.
  • Mojo as a member of the Python family.
  • Applied for AI systems and AI field.
  • Scalable programming for heterogeneous systems.
  • Building unify the world’s ML/AI infrastructure.
  • Innovations in compiler internals.
  • Support for current and emerging hardware accelerators.
  • Systems programming features.
  • Leverage the existing MLIR compiler ecosystem.

Hello Mojo🔥

Mojo is a new programming language that bridges the gap between research and production by combining the best of Python syntax with systems programming and metaprogramming.

hello.mojo or hello.🔥 the file extension can be an emoji!

You can read more about why Modular doing this Why Mojo🔥

What we wanted was an innovative and scalable programming model that could target accelerators and other heterogeneous systems that are pervasive in the AI field. ... Applied AI systems need to address all these issues, and we decided there was no reason it couldn’t be done with just one language. Thus, Mojo was born.

But Python has done its job very well =)

We didn’t see any need to innovate in language syntax or community. So we chose to embrace the Python ecosystem because it is so widely used, it is loved by the AI ecosystem, and because we believe it is a really nice language.

Why is it called Mojo?

Mojo🔥 means “a magical charm” or “magical powers.” We thought this was a fitting name for a language that brings magical powers to Python :python:, including unlocking an innovative programming model for accelerators and other heterogeneous systems pervasive in AI today.

Guido van Rossum benevolent dictator for life and Christopher Arthur Lattner distinguished inventor, creator and well-known leader about Mojo🔥pronunciation =)

According to the description

Background and influenced by

Who knows that these programming languages will be very happy, because Mojo benefits from tremendous lessons learned from other languages Rust, Swift, Julia, Zig, Nim, etc.

Mojo

News

[new]

[old]

Mojo🔥

Benchmarks

Tools

brew install hyperfine
  • macchina a system information frontend with an emphasis on performance.
brew install macchina
  • Python3 libs for plots
pip3 install numpy matplotlib scipy
  • Code to image PNG
brew install silicon

Benchmarking environment

Python / Mojo / Codon / Rust versions

> python3 --version
Python 3.11.6

> mojo --version
mojo 0.4.0 (9e33b013)

> codon --version
0.16.3

> rustc --version
rustc 1.65.0-nightly (9243168fa 2022-08-31)

Fibonacci Sequence

Lets find Fibonacci Sequence where

N = 100

Python Fibonacci Sequence Recursion

def fibonacci_recursion(n):
    return n if n < 2 else fibonacci_recursion(n - 1) + fibonacci_recursion(n - 2)
fibonacci_recursion(100)
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json python_recursion.json 'python3 benchmarks/fibonacci_sequence/python_recursion.py'

RESULT: TIMEOUT, I canceled computation after 1m

Python Fibonacci Sequence Iteration

def fibonacci_iteration(n):
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a+b
    return a
fibonacci_iteration(100)
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/python_iteration.json 'python3 benchmarks/fibonacci_sequence/python_iteration.py'

RESULT:
Benchmark 1: python3 benchmarks/fibonacci_sequence/python_iteration.py
Time (mean ± σ): 16374.7 µs ± 904.0 µs [User: 11483.5 µs, System: 3680.0 µs]
Range (min … max): 15361.0 µs … 22863.3 µs 100 runs

Compile Python byte code

python3 -m compileall benchmarks/fibonacci_sequence/python_recursion.py
python3 -m compileall benchmarks/fibonacci_sequence/python_iteration.py
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/python_recursion.cpython-311.json 'python3 benchmarks/fibonacci_sequence/__pycache__/python_recursion.cpython-311.pyc'
# TIMEOUT!

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/python_iteration.cpython-311.json 'python3 benchmarks/fibonacci_sequence/__pycache__/python_iteration.cpython-311.pyc'

RESULT:
Benchmark 1: python3 benchmarks/fibonacci_sequence/pycache/python_iteration.cpython-311.pyc
Time (mean ± σ): 16584.6 µs ± 761.5 µs [User: 11451.8 µs, System: 3813.3 µs]
Range (min … max): 15592.0 µs … 20953.2 µs 100 runs

Mojo Fibonacci Sequence Recursion

fn fibonacci_recursion(n: Int) -> Int:
    return n if n < 2 else fibonacci_recursion(n - 1) + fibonacci_recursion(n - 2)
fn main():
    _ = fibonacci_recursion(100)
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/mojo_recursion.json 'mojo run benchmarks/fibonacci_sequence/mojo_recursion.mojo'

RESULT: TIMEOUT, I canceled computation after 1m

Mojo Fibonacci Sequence Iteration

fn fibonacci_iteration(n: Int) -> Int:
    var a: Int = 0
    var b: Int = 1
    for _ in range(n):
        a = b
        b = a+b
    return a
fn main():
    _ = fibonacci_iteration(100)
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/mojo_iteration.json 'mojo run benchmarks/fibonacci_sequence/mojo_iteration.mojo'

RESULT:
Benchmark 1: mojo run benchmarks/fibonacci_sequence/mojo_iteration.mojo
Time (mean ± σ): 43852.7 µs ± 1353.5 µs [User: 38156.0 µs, System: 10407.3 µs]
Range (min … max): 42033.6 µs … 49357.3 µs 100 runs

Compile Mojo code

mojo build benchmarks/fibonacci_sequence/mojo_recursion.mojo
mojo build benchmarks/fibonacci_sequence/mojo_iteration.mojo
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/mojo_recursion.exe.json './benchmarks/fibonacci_sequence/mojo_recursion'
# TIMEOUT!

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/mojo_iteration.exe.json './benchmarks/fibonacci_sequence/mojo_iteration'

RESULT:
Benchmark 1: ./benchmarks/fibonacci_sequence/mojo_iteration
Time (mean ± σ): 934.6 µs ± 468.9 µs [User: 409.8 µs, System: 247.8 µs]
Range (min … max): 552.7 µs … 4522.9 µs 100 runs

Codon Fibonacci Sequence Recursion

def fibonacci_recursion(n):
    return n if n < 2 else fibonacci_recursion(n - 1) + fibonacci_recursion(n - 2)
fibonacci_recursion(100)

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/codon_recursion.json 'codon run --release benchmarks/fibonacci_sequence/codon_recursion.codon'

RESULT: TIMEOUT, I canceled computation after 1m

Codon Fibonacci Sequence Iteration

def fibonacci_iteration(n):
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a+b
    return a
fibonacci_iteration(100)

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/codon_iteration.json 'codon run --release benchmarks/fibonacci_sequence/codon_iteration.codon'

RESULT:
Benchmark 1: codon run --release benchmarks/fibonacci_sequence/codon_iteration.codon
Time (mean ± σ): 628060.1 µs ± 10430.5 µs [User: 584524.3 µs, System: 39358.5 µs]
Range (min … max): 612742.5 µs … 662716.9 µs 100 runs

Compile Codon code

codon build --release -exe benchmarks/fibonacci_sequence/codon_recursion.codon
codon build --release -exe benchmarks/fibonacci_sequence/codon_iteration.codon
hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json codon_recursion.exe.json './benchmarks/fibonacci_sequence/codon_recursion'
# TIMEOUT!

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/codon_iteration.exe.json './benchmarks/fibonacci_sequence/codon_iteration'

RESULT:
Benchmark 1: ./benchmarks/fibonacci_sequence/codon_iteration
Time (mean ± σ): 2732.7 µs ± 1145.5 µs [User: 1466.0 µs, System: 1061.5 µs]
Range (min … max): 2036.6 µs … 13236.3 µs 100 runs

Rust Fibonacci Sequence Recursion

fn fibonacci_recursive(n: i64) -> i64 {
    if n < 2 {
        return n;
    }
    return fibonacci_recursive(n - 1) + fibonacci_recursive( n - 2);
}
fn main() {
    let _ = fibonacci_recursive(100);
}
rustc -C opt-level=3 benchmarks/fibonacci_sequence/rust_recursion.rs -o benchmarks/fibonacci_sequence/rust_recursion

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/rust_recursion.json './benchmarks/fibonacci_sequence/rust_recursion'

RESULT: TIMEOUT, I canceled computation after 1m

Rust Fibonacci Sequence Iteration

fn fibonacci_iteration(n: usize) -> usize {
    let mut a = 1;
    let mut b = 1;
    for _ in 1..n {
        let old = a;
        a = b;
        b += old;
    }
    b
}
fn main() {
    let _ = fibonacci_iteration(100);
}
rustc -C opt-level=3 benchmarks/fibonacci_sequence/rust_iteration.rs -o benchmarks/fibonacci_sequence/rust_iteration

hyperfine --warmup 10 -r 100 --time-unit=microsecond --export-json benchmarks/fibonacci_sequence/rust_iteration.json './benchmarks/fibonacci_sequence/rust_iteration'

RESULT:
Benchmark 1: ./benchmarks/fibonacci_sequence/rust_iteration
Time (mean ± σ): 848.9 µs ± 283.2 µs [User: 371.8 µs, System: 261.4 µs]
Range (min … max): 525.9 µs … 2607.3 µs 100 runs

Summary Fibonacci Sequence

# Merge all JSON files into benchmarks.json
python3 benchmarks/hyperfine-scripts/merge_jsons.py benchmarks/fibonacci_sequence/ benchmarks/fibonacci_sequence/benchmarks.json

python3 benchmarks/hyperfine-scripts/plot2.py benchmarks/fibonacci_sequence/benchmarks.json

python3 benchmarks/hyperfine-scripts/plot3.py benchmarks/fibonacci_sequence/benchmarks.json

python3 benchmarks/hyperfine-scripts/advanced_statistics.py benchmarks/fibonacci_sequence/benchmarks.json > benchmarks/fibonacci_sequence/benchmarks.json.md

silicon benchmarks/fibonacci_sequence/benchmarks.json.md -l python -o benchmarks/fibonacci_sequence/benchmarks.json.md.png

Advanced statistics

All together

Zoomed

Detailed one by one

Places

  1. Rust
  2. Mojo
  3. Codon
  4. Python

But here a lot of questions:

  • How to optimize code/build/run?
  • Why mojo run so slow?
  • Why codon run --release so slow?
  • Why compiled Python byte code +/- equals Python interpreter?
  • Why Python interpreter faster than Mojo/Codon run?

So, we can say that Mojo🔥 is as fast as Rust on Mac!

Mandelbrot Set

Lets find Mandelbrot Set where

WIDTH = 960
HEIGHT = 960
MAX_ITERS = 200

MIN_X = -2.0
MAX_X = 0.6
MIN_Y = -1.5
MAX_Y = 1.5

Python Mandelbrot Set

def mandelbrot_kernel(c):
    z = c
    for i in range(MAX_ITERS):
        z = z * z + c  # Change this for different Multibrot sets (e.g., 2 for Mandelbrot)
        if z.real * z.real + z.imag * z.imag > 4:
            return i
    return MAX_ITERS


def compute_mandelbrot():
    t = [[0 for _ in range(WIDTH)] for _ in range(HEIGHT)] # Pixel matrix
    dx = (MAX_X - MIN_X) / WIDTH
    dy = (MAX_Y - MIN_Y) / HEIGHT
    for row in range(HEIGHT):
        for col in range(WIDTH):
            t[row][col] = mandelbrot_kernel(complex(MIN_X + col * dx, MIN_Y + row * dy))
    return t


compute_mandelbrot()
python3 -m compileall benchmarks/multibrot_set/multibrot.py

hyperfine --warmup 10 -r 10 --time-unit=microsecond --export-json benchmarks/multibrot_set/multibrot.cpython-311.json 'python3 benchmarks/multibrot_set/__pycache__/multibrot.cpython-311.pyc'

RESULT:
Benchmark 1: python3 benchmarks/multibrot_set/pycache/multibrot.cpython-311.pyc
Time (mean ± σ): 5444155.4 µs ± 23059.7 µs [User: 5419790.1 µs, System: 18131.3 µs]
Range (min … max): 5408155.3 µs … 5490548.4 µs 10 runs\

Mojo Mandelbrot Set

Mojo version with no optimization.

# Compute the number of steps to escape.
def multibrot_kernel(c: ComplexFloat64) -> Int:
    z = c
    for i in range(MAX_ITERS):
        z = z * z + c  # Change this for different Multibrot sets (e.g., 2 for Mandelbrot)
        if z.squared_norm() > 4:
            return i
    return MAX_ITERS


def compute_multibrot() -> Tensor[FloatType]:
    # create a matrix. Each element of the matrix corresponds to a pixel
    t = Tensor[FloatType](HEIGHT, WIDTH)

    dx = (MAX_X - MIN_X) / WIDTH
    dy = (MAX_Y - MIN_Y) / HEIGHT

    y = MIN_Y
    for row in range(HEIGHT):
        x = MIN_X
        for col in range(WIDTH):
            t[Index(row, col)] = multibrot_kernel(ComplexFloat64(x, y))
            x += dx
        y += dy
    return t


_ = compute_multibrot()
mojo build benchmarks/multibrot_set/multibrot.mojo

hyperfine --warmup 10 -r 10 --time-unit=microsecond --export-json benchmarks/multibrot_set/multibrot.exe.json './benchmarks/multibrot_set/multibrot'

RESULT:
Benchmark 1: ./benchmarks/multibrot_set/multibrot
Time (mean ± σ): 135880.5 µs ± 1175.4 µs [User: 133309.3 µs, System: 1700.1 µs]
Range (min … max): 134639.9 µs … 137621.4 µs 10 runs

Mojo Parallelized Mandelbrot Set

fn mandelbrot_kernel_SIMD[
    simd_width: Int
](c: ComplexSIMD[float_type, simd_width]) -> SIMD[float_type, simd_width]:
    """A vectorized implementation of the inner mandelbrot computation."""
    let cx = c.re
    let cy = c.im
    var x = SIMD[float_type, simd_width](0)
    var y = SIMD[float_type, simd_width](0)
    var y2 = SIMD[float_type, simd_width](0)
    var iters = SIMD[float_type, simd_width](0)

    var t: SIMD[DType.bool, simd_width] = True
    for i in range(MAX_ITERS):
        if not t.reduce_or():
            break
        y2 = y * y
        y = x.fma(y + y, cy)
        t = x.fma(x, y2) <= 4
        x = x.fma(x, cx - y2)
        iters = t.select(iters + 1, iters)
    return iters


fn compute_multibrot_parallelized() -> Tensor[float_type]:
    let t = Tensor[float_type](height, width)

    @parameter
    fn worker(row: Int):
        let scale_x = (max_x - min_x) / width
        let scale_y = (max_y - min_y) / height

        @parameter
        fn compute_vector[simd_width: Int](col: Int):
            """Each time we operate on a `simd_width` vector of pixels."""
            let cx = min_x + (col + iota[float_type, simd_width]()) * scale_x
            let cy = min_y + row * scale_y
            let c = ComplexSIMD[float_type, simd_width](cx, cy)
            t.data().simd_store[simd_width](
                row * width + col, mandelbrot_kernel_SIMD[simd_width](c)
            )

        # Vectorize the call to compute_vector where call gets a chunk of pixels.
        vectorize[simd_width, compute_vector](width)

    # Parallelized
    parallelize[worker](height, height)
    return t


def main():
    _ = compute_multibrot_parallelized()
mojo build benchmarks/multibrot_set/multibrot_mojo_parallelize.mojo

hyperfine --warmup 10 -r 10 --time-unit=microsecond --export-json benchmarks/multibrot_set/multibrot_mojo_parallelize.exe.json './benchmarks/multibrot_set/multibrot_mojo_parallelize'

RESULT:
Benchmark 1: ./benchmarks/multibrot_set/multibrot_mojo_parallelize
Time (mean ± σ): 7139.4 µs ± 596.4 µs [User: 36535.2 µs, System: 6670.1 µs]
Range (min … max): 6222.6 µs … 8269.7 µs 10 runs

Codon Mandelbrot Set

def mandelbrot_kernel(c):
    z = c
    for i in range(MAX_ITERS):
        z = z * z + c  # Change this for different Multibrot sets (e.g., 2 for Mandelbrot)
        if z.real * z.real + z.imag * z.imag > 4:
            return i
    return MAX_ITERS


def compute_mandelbrot():
    t = [[0 for _ in range(WIDTH)] for _ in range(HEIGHT)]  # Pixel matrix

    dx = (MAX_X - MIN_X) / WIDTH
    dy = (MAX_Y - MIN_Y) / HEIGHT

    @par(collapse=2)
    for row in range(HEIGHT):
        for col in range(WIDTH):
            t[row][col] = mandelbrot_kernel(complex(MIN_X + col * dx, MIN_Y + row * dy))
    return t


compute_mandelbrot()

For test run or plot (uncomment code in the file)

CODON_PYTHON=/opt/homebrew/opt/python@3.11/Frameworks/Python.framework/Versions/3.11/lib/libpython3.11.dylib codon run --release benchmarks/multibrot_set/multibrot.codon

Build and run

codon build --release -exe benchmarks/multibrot_set/multibrot.codon -o benchmarks/multibrot_set/multibrot_codon

hyperfine --warmup 10 -r 10 --time-unit=microsecond --export-json benchmarks/multibrot_set/multibrot_codon.json './benchmarks/multibrot_set/multibrot_codon'

RESULT:
Benchmark 1: ./benchmarks/multibrot_set/multibrot_codon
Time (mean ± σ): 44184.7 µs ± 1142.0 µs [User: 248773.9 µs, System: 72935.3 µs]
Range (min … max): 42963.8 µs … 46456.2 µs 10 runs

codon build --release -exe benchmarks/multibrot_set/multibrot_codon_par.codon -o benchmarks/multibrot_set/multibrot_codon_par

hyperfine --warmup 10 -r 10 --time-unit=microsecond --export-json benchmarks/multibrot_set/multibrot_codon_par.json './benchmarks/multibrot_set/multibrot_codon_par'

Summary Mandelbrot Set

# Merge all JSON files into benchmarks.json
python3 benchmarks/hyperfine-scripts/merge_jsons.py benchmarks/multibrot_set/ benchmarks/multibrot_set/benchmarks.json

python3 benchmarks/hyperfine-scripts/plot2.py benchmarks/multibrot_set/benchmarks.json

python3 benchmarks/hyperfine-scripts/plot3.py benchmarks/multibrot_set/benchmarks.json

python3 benchmarks/hyperfine-scripts/advanced_statistics.py benchmarks/multibrot_set/benchmarks.json > benchmarks/multibrot_set/benchmarks.json.md

silicon benchmarks/multibrot_set/benchmarks.json.md -l python -o benchmarks/multibrot_set/benchmarks.json.md.png

Advanced statistics

All together

Zoomed

Detailed one by one

Places

  1. Mojo (parallelize)
  2. Codon
  3. Mojo
  4. Python

Links:

Mandelbrot = Multibrot with power = 2

z = z**power + c  # You can change this for different set

Awesome Mojo🔥 code

Binary Search Algorithm

In computer science, binary search algorithm, also known as half-interval search, logarithmic search, or binary chop, is a search algorithm that finds the position of a target value within a sorted array.

Let's do some code with Python, Mojo🔥, Swift, V, Julia, Nim, Zig.

Note: For Python and Mojo versions, I leave some optimization and make the code similar for measurement and comparison.

Python Binary Search

from typing import List
import timeit

SIZE = 1000000
MAX_ITERS = 100
COLLECTION = tuple(i for i in range(SIZE))  # Make it aka at compile-time.


def python_binary_search(element: int, array: List[int]) -> int:
    start = 0
    stop = len(array) - 1
    while start <= stop:
        index = (start + stop) // 2
        pivot = array[index]
        if pivot == element:
            return index
        elif pivot > element:
            stop = index - 1
        elif pivot < element:
            start = index + 1
    return -1


def test_python_binary_search():
    _ = python_binary_search(SIZE - 1, COLLECTION)


print(
    "Average execution time of func in sec",
    timeit.timeit(lambda: test_python_binary_search(), number=MAX_ITERS),
)

Mojo🔥 Binary Search

"""Implements basic binary search."""

from Benchmark import Benchmark
from Vector import DynamicVector


alias SIZE = 1000000
alias NUM_WARMUP = 0
alias MAX_ITERS = 100


fn mojo_binary_search(element: Int, array: DynamicVector[Int]) -> Int:
    var start = 0
    var stop = len(array) - 1
    while start <= stop:
        let index = (start + stop) // 2
        let pivot = array[index]
        if pivot == element:
            return index
        elif pivot > element:
            stop = index - 1
        elif pivot < element:
            start = index + 1
    return -1


@parameter  # statement runs at compile-time.
fn get_collection() -> DynamicVector[Int]:
    var v = DynamicVector[Int](SIZE)
    for i in range(SIZE):
        v.push_back(i)
    return v


fn test_mojo_binary_search() -> F64:
    fn test_closure():
        _ = mojo_binary_search(SIZE - 1, get_collection())
    return F64(Benchmark(NUM_WARMUP, MAX_ITERS).run[test_closure]()) / 1e9


print(
    "Average execution time of func in sec ",
    test_mojo_binary_search(),
)

It is the first binary search written in Mojo🔥by community (@ego) and posted in mojo-chat.

Swift Binary Search

func binarySearch(items: [Int], elem: Int) -> Int {
    var low = 0
    var high = items.count - 1
    var mid = 0
    while low <= high {
        mid = Int((high + low) / 2)
        if items[mid] < elem {
            low = mid + 1
        } else if items[mid] > elem {
            high = mid - 1
        } else {
            return mid
        }
    }
    return -1
}

let items = [1, 2, 3, 4, 0].sorted()
let res = binarySearch(items: items, elem: 4)
print(res)

Julia Binary Search

function binarysearch(lst::Vector{T}, val::T) where T
    low = 1
    high = length(lst)
    while low ≤ high
        mid = (low + high) ÷ 2
        if lst[mid] > val
            high = mid - 1
        elseif lst[mid] < val
            low = mid + 1
        else
            return mid
        end
    end
    return 0
end

Nim Binary Search

proc binarySearch[T](a: openArray[T], key: T): int =
  var b = len(a)
  while result < b:
    var mid = (result + b) div 2
    if a[mid] < key: result = mid + 1
    else: b = mid
  if result >= len(a) or a[result] != key: result = -1


let res = @[2,3,4,5,6,7,8,9,10,12,14,16,18,20,22,25,27,30]
echo binarySearch(res, 10)

Zig Binary Search

const std = @import("std");

fn binarySearch(comptime T: type, arr: []const T, target: T) ?usize {
    var lo: usize = 0;
    var hi: usize = arr.len - 1;

    while (lo <= hi) {
        var mid: usize = (lo + hi) / 2;

        if (arr[mid] == target) {
            return mid;
        } else if (arr[mid] < target) {
            lo = mid + 1;
        } else {
            hi = mid - 1;
        }
    }

    return null;
}

V Binary Search

fn binary_search(a []int, value int) int {
    mut low := 0
    mut high := a.len - 1
    for low <= high {
        mid := (low + high) / 2
        if a[mid] > value {
            high = mid - 1
        } else if a[mid] < value {
            low = mid + 1
        } else {
            return mid
        }
    }
    return -1
}

fn main() {
    search_list := [1, 2, 3, 5, 6, 7, 8, 9, 10]
    println(binary_search(search_list, 9))
}

Bonus V Breadth First Search Path

fn breadth_first_search_path(graph map[string][]string, vertex string, target string) []string {
    mut path := []string{}
    mut visited := []string{init: vertex}
    mut queue := [][][]string{}
    queue << [[vertex], path]
    for queue.len > 0 {
        mut idx := queue.len - 1
        node := queue[idx][0][0]
        path = queue[idx][1]
        queue.delete(idx)
        if node == target {
            path << node
            return path
        }
        for child in graph[node] {
            mut tmp := path.clone()
            if child !in visited {
                visited << child
                tmp << node
                queue << [[child], tmp]
            }
        }
    }
    return path
}

fn main() {
    graph := map{
        'A': ['B', 'C']
        'B': ['A', 'D', 'E']
        'C': ['A', 'F']
        'D': ['B']
        'E': ['B', 'F']
        'F': ['C', 'E']
    }
    println('Graph: $graph')
    path := breadth_first_search_path(graph, 'A', 'F')
    println('The shortest path from node A to node F is: $path')
    assert path == ['A', 'C', 'F']
}

Fizz buzz

Python Fizz buzz

import timeit

SIZE = 100
MAX_ITERS = 100


def _fizz_buzz():  # Make it aka at compile-time.
  res = []
  for n in range(1, SIZE+1):
    if (n % 3 == 0) and (n % 5 == 0):
      s = "FizzBuzz"
    elif n % 3 == 0:
      s = "Fizz"
    elif n % 5 == 0:
      s = "Buzz"
    else:
      s = str(n)
    res.append(s)
  return res


DATA = _fizz_buzz()


def fizz_buzz():
  print("\n".join(DATA))


print(
  "Average execution time of Python func in sec",
  timeit.timeit(lambda: fizz_buzz(), number=MAX_ITERS),
)

# Average execution time of Python func in sec 0.005334990004485007

Clojure Fizz buzz

(import '[java.io OutputStream])
(require '[clojure.java.io :as io])

(def devnull (io/writer (OutputStream/nullOutputStream)))

(defmacro timeit [n expr]
  `(with-out-str (time
                   (dotimes [_# ~(Math/pow 1 n)]
                     (binding [*out* devnull]
                       ~expr)))))

(defmacro macro-fizz-buzz [n]
  `(fn []
    (print
      ~(apply str
        (for [i (range 1 (inc n))]
          (cond
            (zero? (mod i 15)) "FizzBuzz\n"
            (zero? (mod i 5))  "Buzz\n"
            (zero? (mod i 3))  "Fizz\n"
            :else              (str i "\n")))))))

(print (timeit 100 (macro-fizz-buzz 100)))

;; "Elapsed time: 0.175486 msecs"
;; Average execution time of Clojure func in sec 0.000175486 seconds

Mojo🔥Fizz buzz

from String import String
from Benchmark import Benchmark

alias SIZE = 100
alias NUM_WARMUP = 0
alias MAX_ITERS = 100


@parameter  # statement runs at compile-time.
fn _fizz_buzz() -> String:
    var res: String = ""
    for n in range(1, SIZE+1):
      if (n % 3 == 0) and (n % 5 == 0):
        res += "FizzBuzz"
      elif n % 3 == 0:
        res += "Fizz"
      elif n % 5 == 0:
        res += "Buzz"
      else:
        res += String(n)
      res += "\n"
    return res


fn fizz_buzz():
    print(_fizz_buzz())

fn run_benchmark() -> F64:
    fn _closure():
        _ = fizz_buzz()
    return F64(Benchmark(NUM_WARMUP, MAX_ITERS).run[_closure]()) / 1e9


print(
    "Average execution time of func in sec ",
    run_benchmark(),
)

# Average execution time of func in sec  0.000104 🔥

It is the first Fizz buzz written in Mojo🔥 ever by community (@Ego).

Merge sort

We will use algorithm from vell-known reference for algorithms book Introduction to Algorithms A3

Its fame has led to the common use of the abbreviation "CLRS" (Cormen, Leiserson, Rivest, Stein), or, in the first edition, "CLR" (Cormen, Leiserson, Rivest).

Chapter 2 "2.3.1 The divide-and-conquer approach".

Python Merge sort

%%python

import timeit

MAX_ITERS = 100


def merge(A, p, q, r):
    n1 = q - p + 1
    n2 = r - q
    L = [None] * n1
    R = [None] * n2
    for i in range(n1):
        L[i] = A[p + i]
    for j in range(n2):
        R[j] = A[q + 1 + j]
    i = 0
    j = 0
    k = p

    while i < n1 and j < n2:
        if L[i] <= R[j]:
            A[k] = L[i]
            i += 1
        else:
            A[k] = R[j]
            j += 1
        k += 1

    while i < n1:
        A[k] = L[i]
        i += 1
        k += 1

    while j < n2:
        A[k] = R[j]
        j += 1
        k += 1


def merge_sort(A, p, r):
    if p < r:
        q = (p + r) // 2
        merge_sort(A, p, q)
        merge_sort(A, q + 1, r)
        merge(A, p, q, r)


def run_benchmark_merge_sort():
    A = [14, 72, 50, 83, 18, 20, 13, 30, 17, 87, 94, 65, 24, 99, 70, 44, 5, 12, 74, 6, 32, 63, 91, 88, 43, 54, 27, 39, 64, 78, 29, 62, 58, 59, 61, 89, 2, 15, 41, 9, 93, 90, 23, 96, 73, 14, 8, 28, 11, 42, 77, 34, 52, 80, 57, 84, 21, 60, 66, 40, 7, 85, 47, 98, 97, 35, 82, 36, 49, 3, 68, 22, 67, 81, 56, 71, 4, 38, 69, 95, 16, 48, 1, 31, 75, 19, 10, 25, 79, 45, 76, 33, 53, 55, 46, 37, 26, 51, 92, 86]
    merge_sort(A, 0, len(A)-1)

print(
  "Average execution time of Python `merge_sort` in sec",
  timeit.timeit(lambda: run_benchmark_merge_sort(), number=MAX_ITERS),
)
# Average execution time of Python `merge_sort` in sec 0.019136679999064654


def run_benchmark_sort():
    A = [14, 72, 50, 83, 18, 20, 13, 30, 17, 87, 94, 65, 24, 99, 70, 44, 5, 12, 74, 6, 32, 63, 91, 88, 43, 54, 27, 39, 64, 78, 29, 62, 58, 59, 61, 89, 2, 15, 41, 9, 93, 90, 23, 96, 73, 14, 8, 28, 11, 42, 77, 34, 52, 80, 57, 84, 21, 60, 66, 40, 7, 85, 47, 98, 97, 35, 82, 36, 49, 3, 68, 22, 67, 81, 56, 71, 4, 38, 69, 95, 16, 48, 1, 31, 75, 19, 10, 25, 79, 45, 76, 33, 53, 55, 46, 37, 26, 51, 92, 86]
    A.sort()

print(
    "Average execution time of Python builtin `sort` in sec",
    timeit.timeit(lambda: run_benchmark_sort(), number=MAX_ITERS),
)
# Average execution time of Python builtin `sort` in sec 0.00019922800129279494

Mojo🔥 Merge sort

from Benchmark import Benchmark
from Vector import DynamicVector
from StaticTuple import StaticTuple
from Sort import sort

alias NUM_WARMUP = 0
alias MAX_ITERS = 100


fn merge(inout A: DynamicVector[Int], p: Int, q: Int, r: Int):
    let n1 = q - p + 1
    let n2 = r - q

    var L = DynamicVector[Int](n1)
    var R = DynamicVector[Int](n2)

    for i in range(n1):
        L[i] = A[p + i]
    for j in range(n2):
        R[j] = A[q + 1 + j]

    var i = 0
    var j = 0
    var k = p

    while i < n1 and j < n2:
        if L[i] <= R[j]:
            A[k] = L[i]
            i += 1
        else:
            A[k] = R[j]
            j += 1
        k += 1

    while i < n1:
        A[k] = L[i]
        i += 1
        k += 1

    while j < n2:
        A[k] = R[j]
        j += 1
        k += 1


fn merge_sort(inout A: DynamicVector[Int], p: Int, r: Int):
    if p < r:
        let q = (p + r) // 2
        merge_sort(A, p, q)
        merge_sort(A, q + 1, r)
        merge(A, p, q, r)


@parameter
fn create_vertor() -> DynamicVector[Int]:
    let st = StaticTuple[MAX_ITERS, Int](14, 72, 50, 83, 18, 20, 13, 30, 17, 87, 94, 65, 24, 99, 70, 44, 5, 12, 74, 6, 32, 63, 91, 88, 43, 54, 27, 39, 64, 78, 29, 62, 58, 59, 61, 89, 2, 15, 41, 9, 93, 90, 23, 96, 73, 14, 8, 28, 11, 42, 77, 34, 52, 80, 57, 84, 21, 60, 66, 40, 7, 85, 47, 98, 97, 35, 82, 36, 49, 3, 68, 22, 67, 81, 56, 71, 4, 38, 69, 95, 16, 48, 1, 31, 75, 19, 10, 25, 79, 45, 76, 33, 53, 55, 46, 37, 26, 51, 92, 86)
    var v = DynamicVector[Int](st.__len__())
    for i in range(st.__len__()):
        v.push_back(st[i])
    return v


fn run_benchmark_merge_sort() -> F64:
    fn _closure():
        var A = create_vertor()
        merge_sort(A, 0, len(A)-1)
    return F64(Benchmark(NUM_WARMUP, MAX_ITERS).run[_closure]()) / 1e9

print(
    "Average execution time of Mojo🔥 `merge_sort` in sec ",
    run_benchmark_merge_sort(),
)
# Average execution time of Mojo🔥 `merge_sort` in sec 1.1345999999999999e-05


fn run_benchmark_sort() -> F64:
    fn _closure():
        var A = create_vertor()
        sort(A)
    return F64(Benchmark(NUM_WARMUP, MAX_ITERS).run[_closure]()) / 1e9

print(
    "Average execution time of Mojo🔥 builtin `sort` in sec ",
    run_benchmark_sort(),
)
# Average execution time of Mojo🔥 builtin `sort` in sec 2.988e-06

You can use it like:

# Usage: merge_sort

var A = create_vertor()
merge_sort(A, 0, len(A)-1)

print(len(A))
print(A[0], A[99])

Builtin from Sort import sort quicksort a little bit faster than our implementation, but we can optimize it during deep in language and as usual with algorithms =) and programming paradigms.

  • Multithreaded Algorithms, Multithreaded merge sort at pages 797, 803 of the book CLRS above
  • Three Hungarians' Algorithm
  • Use insertion sort for small subarrays, hybrid merge sort
  • Merge in a different direction
  • Use an adaptive approach
  • Implement in-place merging
  • Optimize memory access
  • Mojo Stdlib Functional
  • Tiled merge sort like in Tiling Matmul
  • Parallel multiway merge sort

Summary for sorting algorithms merge sort and quicksort

Lang sec
Python merge_sort 0.019136679
Python builtin sort 0.000199228
Mojo merge_sort 0.000011346
Mojo builtin sort 0.000002988

Let's build a plot for this table.

#%%python

import matplotlib.pyplot as plt
import numpy as np

languages = ['Python merge_sort', 'Python builtin sort', 'Mojo merge_sort', 'Mojo builtin sort']
seconds = [0.019136679, 0.000199228, 0.000011346, 0.000002988]

# Apply a custom transformation to the values
transformed_seconds = [np.sqrt(1 / x) for x in seconds]

plt.barh(languages, transformed_seconds)
plt.xlabel('Custom Transformation')
plt.ylabel('Language and Sort Type')
plt.title('Comparison of Sorting Algorithms (Custom Transformation)')

plt.show()

Plot notes, more is better and faster.

Programming manual

Parameterization[]: compile time meta-programming

I strongly recommended start from here HelloMojo and understand [parameter] and [parameter expressions] parameterization here. Like in this example:

fn concat[len1: Int, len2: Int](lhs: MySIMD[len1], rhs: MySIMD[len2]) -> MySIMD[len1+len2]:
    let result = MySIMD[len1 + len2]()
    for i in range(len1):
        result[i] = lhs[i]
    for j in range(len2):
        result[len1 + j] = rhs[j]
    return result


let a = MySIMD[2](1, 2)
let x = concat[2,2](a, a)
x.dump()

Compile time [Parameters]: fn concat[len1: Int, len2: Int].

Run time (Args): fn concat(lhs: MySIMD, rhs: MySIMD).

Parameters PEP695 syntax in square [] brackets.

Now in Python:

def func(a: _T, b: _T) -> _T:
    ...

Now in Mojo🔥:

def func[T](a: T, b: T) -> T:
    ...

[Parameters] are named and have types like normal values in a Mojo program, but parameters[] are evaluated at compile time.

The runtime program may use the value of [parameters] - because the parameters are resolved at compile time before they are needed by the runtime program - but the compile time parameter expressions may not use runtime values.

Self type from PEP673

fn __sub__(self, rhs: Self) -> Self:
    let result = MySIMD[size]()
    for i in range(size):
        result[i] = self[i] - rhs[i]
    return result

In the docs you can find word Fields it is aka class Attributes in the Python.

So, you call them with dot.

from DType import DType
let bool_type = DType.bool

Data Type Model and alias

from DType import DType
DType.si8
from DType import DType
from SIMD import SIMD, SI8

alias MY_SIMD_DType_si8 = SIMD[DType.si8, 1]
alias MY_SI8 = SI8
print(MY_SIMD_DType_si8 == MY_SI8)
# true
  • Then a sequence of these types you can wrap with a container DynamicVector or similar.
from DType import DType
from SIMD import SIMD, SI8
from Vector import DynamicVector
from String import String

alias a = DynamicVector[SIMD[DType.si8, 1]]
alias b = DynamicVector[SI8]
print(a == b)
print(a == String)
print(b == String)
# all true

So the String is only alias for a something like DynamicVector[SIMD[DType.si8, 1]].

VariadicList for destructuring/unpacking/accessing arguments

from List import VariadicList

fn destructuring_arguments(*args: Int):
    let my_var_list = VariadicList(args)
    for i in range(len(my_var_list)):
        print("argument", i, ":", my_var_list[i])

destructuring_arguments(1, 2, 3, 4)

It is very useful for creating initial collections. We can write like this:

from Vector import DynamicVector
from StaticTuple import StaticTuple

fn create_vertor() -> DynamicVector[Int]:
    let st = StaticTuple[4, Int](1, 2, 3, 4)
    var v = DynamicVector[Int](st.__len__())
    for i in range(st.__len__()):
    v.push_back(st[i])
    return v

v = create_vertor()
print(v[0], v[3])

# or
from List import VariadicList

fn create_vertor() -> DynamicVector[Int]:
  let var_list = VariadicList(1, 2, 3, 4)
  var v = DynamicVector[Int](len(var_list))
  for i in range(len(var_list)):
    v.push_back(var_list[i])
  return v

v = create_vertor()
print(v[0], v[3])

Read more about function def and fn

String

from String import String
# String concatenation
print(String("'") + String(1) + "'\n")

# Python's join
print(String("|").join("a", "b", "c"))

# String format
from IO import _printf as print
let x: Int = 1
print("'%i'\n", x.value)

String and builtin slice

For a string you can use Builtin Slice with format string slice[start:end:step].

from String import String

let hello_mojo = String("Hello Mojo!")
print("Till the end:", hello_mojo[0::])
print("Before last 2 chars:", hello_mojo[0:-2])
print("From start to the end with step 2:", hello_mojo[0::2])
print("From start to the before last with step 3:", hello_mojo[0:-1:3])

There is some problem with unicode, when slicing 🔥:

let hello_mojo_unicode = String("Hello Mojo🔥!")
print("Unicode efore last 2 chars:", hello_mojo_unicode[0:-2])
# no result, silents

Here is an explanation and some discussion.

mbstowcs - convert a multibyte string to a wide-character string

Mojo🔥decorators

@value

struct decorator aka Python @dataclass. It will generate methods __init__, __copyinit__, __moveinit__ for you automatically.

@value
struct dataclass:
    var name: String
    var age: Int

Note that the @value decorator only works on types whose members are copyable and/or movable.

@value("trivial")

@register_passable("trivial")

Trivial types. This decorator tells Mojo that the type should be copyable __copyinit__ and movable __moveinit__. It also tells Mojo to prefer to pass the value in CPU registers. Allows structs to opt-in to being passed in a register instead of passing through memory.

@register_passable("trivial")
struct Int:
    var value: __mlir_type.`!pop.scalar<index>`

@always_inline

Decorators that provide full control over compiler optimizations. Instructs compiler to always inline this function when it’s called.

@always_inline
fn foo(x: Int, y: Int) -> Int:
    return x + y

fn bar(z: Int):
    let r = foo(z, z) # This call will be inlined

@parameter

It can be placed on nested functions that capture runtime values to create “parametric” capturing closures. It allows closures that capture runtime values to be passed as parameter values.

Decorators combination

@always_inline
@parameter
fn test(): return

Casting

Some casting examples

s: StringLiteral
let p = DTypePointer[DType.si8](s.data()).bitcast[DType.ui8]()
var result = 0
result += ((p.simd_load[64](offset) >> 6) != 0b10).cast[DType.ui8]().reduce_add().to_int()
let rest_p: DTypePointer[DType.ui8] = stack_allocation[simd_width, UI8, 1]()

from Bit import ctlz
s: String
i: Int
let code = s.buffer.data.load(i)
let byte_length_code = ctlz(~code).to_int()

Stack, Mem, Pointer, Allocation, Free

DTypePointer, Heap and Stack

DTypePointer - store an address with a given DType, allowing you to allocate, load and modify data with convenient access to SIMD operations.

from Pointer import DTypePointer
from DType import DType
from Random import rand
from Memory import memset_zero

# `heap`
var my_pointer_on_heap = DTypePointer[DType.ui8].alloc(8)
memset_zero(my_pointer_on_heap, 8)

# `stack or register`
var data = my_pointer_on_heap.simd_load[8](0)
print(data)

rand(my_pointer_on_heap, 4)

# `data` does not contain a reference to the `heap`, so load the data again
data = my_pointer_on_heap.simd_load[8](0)
print(data)

# simd_load and simd_store
var half = my_pointer_on_heap.simd_load[4](0)
half = half + 1
my_pointer_on_heap.simd_store[4](4, half)
print(my_pointer_on_heap.simd_load[8](0))

# Pointer move back
my_pointer_on_heap -= 1
print(my_pointer_on_heap.simd_load[8](0))

# Mast free memory
my_pointer_on_heap.free()

Struct can minimaze potential dangerous of pointers by limiting scoup.

Excellent article on Mojo Dojo blog about DTypePointer here

Plus his example Matrix Struct and DTypePointer

Pointer

Pointer store an address to any register_passable type, and allocate n amount of them to the heap.

from Pointer import Pointer
from Memory import memset_zero
from String import String

@register_passable  # for syntaxt like `let coord = p1[0]` and let it be passed through registers.
struct Coord:  # memory-only type
    var x: UI8
    var y: UI8

var p1 = Pointer[Coord].alloc(2)

memset_zero(p1, 2)
var coord = p1[0]  # is an identifier to memory on the stack or in a register
print(coord.x)

# Store the value

coord.x = 5
coord.y = 5
print(coord.x)

# We need to store the data.
p1.store(0, coord)
print(p1[0].x)

# Mast free memory
p1.free()

Full article about Pointer

Plus exemple Pointer and Struct

Advanced Mojo🔥features and Intrinsics module

Modular Intrinsics it is some kind of execution backends:

Mojo🔥-> MLIR Dialects -> execution backends with optimization code and architectures.

MLIR is a compiler infrastructure witch implementing various transformation and optimization passes for different programming languages and architectures.

Syscalls

MLIR itself does not directly provide functionality for interacting with operating system syscalls.

Which are low-level interfaces to operating system services, are typically handled at the level of the target programming language or the operating system itself. MLIR is designed to be language-and-target-agnostic, and its primary focus is on providing an intermediate representation for performing optimizations. To perform operating system syscalls in MLIR, we need to use a target-specific backend.

But with these execution backends, basically, we have access to OS syscalls. And we have the whole world of C/LLVM/Python stuff under the hood.

Lets have same quick look on it in practice:

from OS import getenv

print(getenv("PATH"))
print(getenv(StringRef("PATH")))

# or like this
from SIMD import SI8
from Intrinsics import external_call

var path1 = external_call["getenv", StringRef](StringRef("PATH"))
print(path1.data)

var path2 = external_call["getenv", StringRef]("PATH")
print(path2.data)

let abs_10 = external_call["abs", SI8, Int](-10)
print(abs_10)

In this simple example we used external_call to get OS environment variable with a casting type between Mojo and libc functions. Pretty cool, yeah!

I have a lot of ideas from this topic and I am eagerly awaiting the opportunity to implement them soon. Taking action can lead to amazing outcomes =)

MLIR libc gethostname

Let's do something interesting - call libc function gethostname.

Function has this interface int gethostname (char *name, size_t size).

For that we can use helper function external_call from Intrinsics module or write own MLIR.

Let's go code:

from Intrinsics import external_call
from SIMD import SIMD, SI8
from DType import DType
from Vector import DynamicVector
from DType import DType
from Pointer import DTypePointer, Pointer

# We can use `from String import String` but for clarification we will use a full form.
# DynamicVector[SIMD[DType.si8, 1]] == DynamicVector[SI8] == String

# Compile time stuff.
alias cArrayOfStrings = DynamicVector[SIMD[DType.si8, 1]]
alias capacity = 1024

var c_pointer_to_array_of_strings = DTypePointer[DType.si8](cArrayOfStrings(capacity).data)
var c_int_result = external_call["gethostname", Int, DTypePointer[DType.si8], Int](c_pointer_to_array_of_strings, capacity)
let mojo_string_result = String(c_pointer_to_array_of_strings.address)

print("C function gethostname result code:", c_int_result)
print("C function gethostname result value:", star_hostname(mojo_string_result))


@always_inline
fn star_hostname(hostname: String) -> String:
    # [Builtin Slice](https://docs.modular.com/mojo/MojoBuiltin/BuiltinSlice.html)
    # string slice[start:end:step]
    return hostname[0:-1:2]

Mojo🔥TCP Socket Server with PythonInterface

Let's do some things for a WEB with Mojo🔥. We do not have Internet access at playground.modular.com But we can steal do some interesting stuff like TCP on one machine.

Let's write the first TCP client-server code in Mojo🔥 with PythonInterface

You should create two separate notebooks, and run TCPSocketServer first then TCPSocketClient.

Python version of this code almost the same, except:

  • with syntax
  • let assigning
  • and destructuring like a, b = (1, 2)

Mojo🔥FastAPI with PythonInterface

After TCP Server in Mojo🔥 we are going forward =)

It's crazy, but let's try to run modern Python web server FastAPI with Mojo🔥!

Preparation

We need to upload FastAPI code to playground. So, on your local machine do

pip install --target=web fastapi uvicorn
tar -czPf web.tar.gz web

and upload web.tar.gz to playground via web interface.

Then we need to install it, just put into proper folder:

%%python
import os
import site

site_packages_path = site.getsitepackages()[0]
# install fastapi
os.system(f"tar xzf web.tar.gz -C {site_packages_path}")
os.system(f"cp -r {site_packages_path}/web/* {site_packages_path}/")
os.system(f"ls {site_packages_path} | grep fastapi")
# clean packages
os.system(f"rm -rf {site_packages_path}/web")
os.system(f"rm web.tar.gz")

Mojo🔥FastAPI Server

from PythonInterface import Python

# Python fastapi
let fastapi = Python.import_module("fastapi")
let uvicorn = Python.import_module("uvicorn")

var app = fastapi.FastAPI()
var router = fastapi.APIRouter()

# tricky part
let py = Python()
let py_code = """lambda: 'Hello Mojo🔥!'"""
let py_obj = py.evaluate(py_code)
print(py_obj)

router.add_api_route("/mojo", py_obj)
app.include_router(router)

print("Start FastAPI WEB Server")
uvicorn.run(app)
print("Done")

Mojo🔥FastAPI Client

from PythonInterface import Python
let http_client = Python.import_module("http.client")

let conn = http_client.HTTPConnection("localhost", 8000)
conn.request("GET", "/mojo")
let response = conn.getresponse()
print(response.status, response.reason, response.read())

As usual, you should create two separate notebooks, and run FastAPI first then FastAPIClient.

There are a lot of open questions, but basically we achieve the goal.

Mojo🔥 well done!

Some open questions:

  • Lack of Python syntax sugar
  • Lack of Mojo types implicitly converted into Python objects
  • How to pass Mojo function into Python space/function
from PythonInterface import Python

let pyfn = Python.evaluate("lambda x, y: x+y")
let functools = Python.import_module("functools")
print(functools.reduce(pyfn, [1, 2, 3, 4]))

# How to, without Mojo pyfn.so?
def pyfn(x, y):
    retyrn x+y

The future looks very optimistic!

Links:

Code implementation

Radiative transfer

Benchmark Mojo vs Numba by Nick Wogan

Instant and DateTimeLocal

Time utils by Samay Kapadia @Zalando

IDEA

Connecting to your mojo playground from VSCode or DataSpell

Python Interface and reading files

by Maxim Zaks

from String import String
from PythonInterface import Python

let pathlib = Python.import_module('pathlib')
let txt = pathlib.Path('nfl.csv').read_text()
let s: String = txt.to_string()

libc implementation

Pointer data

from DType import DType
from Buffer import Buffer
from Pointer import Pointer
from String import String, chr

let hello = "hello"
let pointer = Pointer(hello.data())

print("variant 1")
var result = String()
for i in range(len(hello)):
    result += chr(pointer.bitcast[Int8]().offset(i).load().to_int())
print(result)

print("variant 2")
print(StringRef(hello.data()))

print("variant 3")
print(StringRef(pointer.address))

print("variant 4")
let pm: Pointer[__mlir_type.`!pop.scalar<si8>`] = Pointer(hello.data())
print(StringRef(pm.address))

print("variant 5")
print(String(pointer.address))

print("variant 6")
let x = Buffer[8, DType.int8](pointer)
let array = x.simd_load[10](0)
var result = String()
for i in range(len(array)):
    result += chr(array[i].to_int())
print(result)

Code share from Mojo Playground

  1. From the Mojo Playground, right click the file in the explorer and press Open With > Editor
  2. Right click in the editor, select all and copy
  3. Create a new GitHub gist or put Jupyter notebook file into yor GitHub repository
  4. Paste in the contents and name the file with the Jupyter extension like test.ipynb
  5. Paste the link to the gist in the Discord chat

Github renders it properly, and then if someone wants to try out the code in their playground they can copy paste the raw code.

The Zen of Mojo🔥

Space for improvements

It is my personal view, so don't judge me too harshly.

I can't say that Mojo🔥 is an easy programming language for learning, like a Python as an example.

It requires a lot of understanding, patience and experience in any other programming languages.

If you want to build something not trivial, it will be hard but funny!

It has been 2 weeks since I embarked on this journey, and I am thrilled to share that I have now become well-acquainted with the Mojo🔥.

The intricacies of its structure and syntax have started to unravel before my eyes, and I am filled with a newfound understanding.

I am proud to say that I can now confidently craft code in this language, allowing me to bring to life a diverse range of ideas.

Major things to improve:

  1. Weak and unclear documentation.
  2. Lack of code examples. A good example is clojuredocs website.
  3. More explanation to the language paradigms
  4. Lack comparison to Python base things datatypes/functions/classes/expressions.
  5. Even though Mojo is superset of Python, the threshold of language entry is not so simple.
  6. A little bit raw, need some time for stabilizing language. Production release 1.0.
  7. Bugs, like in any one.
  8. Small standard library.
  9. Project Jupyter and notebooks environment. I understand that Jupyter plugin and custom kernel are a good solution, but it is makes development so slow.
  10. Not open-sourced yet. The community is waiting for open source compiler and REPL to start developing and producing libraries and applications. I think the community will easily rewrite the (bootstrapping) compiler from C++ to itself Mojo🔥.

Good and nice for win

  1. Modular team easily responds to requests/questions/letters/ideas
  2. Friendly and very smart community
  3. Problem-solving programming language with an idea to not create new syntax but deal with real word challenges.
  4. I don't know about you, but I was waiting and researching a long time for a Mojo🔥. I have been tried a hundred of programming languages. The World is ready for the revolution and the future of computation and AI. I'm really into Mojo🔥, it excites, fascinates me, and I hope it does the same for you.
  5. I believe that a well-known distinguished leader Ph.D. Computer Science Chris Lattner can build things, systems, teams and change the future.

Modular and Mojo🔥 history and etymology

Mojo🔥 is a Modular Inc programming language. Why Mojo we discussed here. About Company we know less, but it has a very cool name Modular, which can be referred to:

"In other words: Mojo isn’t magic, it’s modular."

All about computing, programming, AI/ML. A very good domain name that accurately describes the meaning of the Company.

There are some additional materials about Modular's Brand Story and Helping Modular Humanize AI Through Brand

Additional materials

MLIR and low-level implementation

Python / C++

AI

Python hints

Todat I would like to tell story about Python Enum problem. As a software engineers we often meet it in a WEB. Assume we have this DataBase Schema (PostgreSQL) with status enum:

CREATE TYPE public.status_type AS ENUM (
    'FIRST',
    'SECOND'
);

In a Python code we need names and values as strings (assume we use GraphQL with some ENUM type for our frontend side), and we need to maintain their order and have ability to compare these enums:

order2.status > order1.status > 'FIRST'

So it's a problem for most of common languages =) but we can use a little-known Python feature and override enum class method: __new__.

  • But Why?
  • Hm.. To assosiate each enum value with its INDEX! MALE -> 1, FEMALE -> 2, like PostgreSQL do.
  • and How?
  • Just COUNT its members with the len function!
import enum
from functools import total_ordering


@total_ordering
@enum.unique
class BaseUniqueSortedEnum(enum.Enum):
    """Base unique enum class with ordering."""

    def __new__(cls, *args, **kwargs):
        obj = object.__new__(cls)
        obj.index = len(cls.__members__) + 1  # This code line is a piece of advice, an insight and a tip!
        return obj

    # and then boring Python's magic methods as usual...

    def __hash__(self) -> int:
        return hash(
          f"{self.__module__}_{self.__class__.__name__}_{self.name}_{self.value}"
        )

    def __eq__(self, other) -> bool:
        self._check_type(other)
        return super().__eq__(other)

    def __lt__(self, other) -> bool:
        self._check_type(other)
        return self.index < other.index

    def _check_type(self, other) -> None:
        if type(self) != type(other):
            raise TypeError(f"Different types of Enum: {self} != {other}")


class Dog(BaseUniqueSortedEnum):
    # THIS ORDER MATTERS!
    BLOODHOUND = "BLOODHOUND"
    WEIMARANER = "WEIMARANER"
    SAME = "SAME"


class Cat(BaseUniqueSortedEnum)
    # THIS ORDER MATTERS!
    BRITISH = "BRITISH"
    SCOTTISH = "SCOTTISH"
    SAME = "SAME"


# and some tests
assert Dog.BLOODHOUND < Dog.WEIMARANER
assert Dog.BLOODHOUND <= Dog.WEIMARANER
assert Dog.BLOODHOUND != Dog.WEIMARANER
assert Dog.BLOODHOUND == Dog.BLOODHOUND
assert Dog.WEIMARANER == Dog.WEIMARANER
assert Dog.WEIMARANER > Dog.BLOODHOUND
assert Dog.WEIMARANER >= Dog.BLOODHOUND

assert Cat.BRITISH < Cat.SCOTTISH
assert Cat.BRITISH <= Cat.SCOTTISH
assert Cat.BRITISH != Cat.SCOTTISH
assert Cat.BRITISH == Cat.BRITISH
assert Cat.SCOTTISH == Cat.SCOTTISH
assert Cat.SCOTTISH > Cat.BRITISH
assert Cat.SCOTTISH >= Cat.BRITISH

assert hash(Dog.BLOODHOUND) == hash(Dog.BLOODHOUND)
assert hash(Dog.WEIMARANER) == hash(Dog.WEIMARANER)
assert hash(Dog.BLOODHOUND) != hash(Dog.WEIMARANER)
assert hash(Dog.SAME) != hash(Cat.SAME)

# raise TypeError
Dog.SAME <= Cat.SAME
Dog.SAME < Cat.SAME
Dog.SAME > Cat.SAME
Dog.SAME >= Cat.SAME
Dog.SAME != Cat.SAME

The end of the story. and use this Python ENUM insight for your well-codding!

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Awesome Mojo🔥

awesome.mojo-lang.dev

Topics

python programming-language benchmark machine-learning performance awesome mojo cpp llvm artificial-intelligence compilers hardware-acceleration hacktoberfest future-technologies mlir future-ai mojolang mojo-language awesome-mojo mojo-lang

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