ML with Elixir

Getting comfortable with Nx

Mix.install([
  {:nx, "~> 0.5"},
  {:exla, "~> 0.5"},
  {:benchee, github: "bencheeorg/benchee", override: true}
])

Nx.tensor([1, 2, 3])

a = Nx.tensor([[1, 2, 3], [4, 5, 6]])
b = Nx.tensor(1.0)
c = Nx.tensor([[[[[[1.0, 2.0]]]]]])
dbg(a)
dbg(b)
dbg(c)

a = Nx.tensor([1, 2, 3])
b = Nx.tensor([1.0, 2.0, 3.0])
dbg(a)
dbg(b)

Nx.tensor(1.0e-45, type: {:f, 64})

Nx.tensor(128, type: {:s, 8})

Nx.tensor(300, type: {:s, 8})

Nx.tensor(10)

Nx.tensor([[1, 2, 3], [4, 5, 6]], names: [:x, :y])

a = Nx.tensor([[1, 2, 3], [4, 5, 6]])
Nx.to_binary(a)

<<1::64-signed-native, 2::64-signed-native, 3::64-signed-native>>
|> Nx.from_binary({:s, 64})
|> Nx.reshape({1, 3})

a = Nx.tensor([1, 2, 3])

a
|> Nx.as_type({:f, 32})
|> Nx.reshape({1, 3, 1})

Nx.bitcast(a, {:f, 64})

a = [-1, -2, -3, 0, 1, 2, 3]
Enum.map(a, &abs/1)

a = Nx.tensor([[[-1, -2, -3], [-4, -5, -6]], [[1, 2, 3], [4, 5, 6]]])

Nx.abs(a)

a = [1, 2, 3]
b = [4, 5, 6]
Enum.zip_with(a, b, fn x, y -> x + y end)

a = Nx.tensor([[1, 2, 3], [4, 5, 6]])
b = Nx.tensor([[6, 7, 8], [9, 10, 11]])
Nx.add(a, b)

Nx.multiply(a, b)

Nx.add(5, Nx.tensor([1, 2, 3]))

Nx.tensor([1, 2, 3])
IO.puts("#{inspect(Nx.shape(a))}")
b = Nx.tensor([[[4, 5, 6], [7, 8, 9]]])
IO.puts(inspect(Nx.shape(b)))
Nx.add(a, b)

revs = Nx.tensor([85, 76, 42, 34, 46, 23, 52, 99, 22, 32, 85, 51])

Nx.sum(revs)

revs =
  Nx.tensor(
    [
      [21, 64, 86, 26, 74, 81, 38, 79, 70, 48, 85, 33],
      [64, 82, 48, 39, 70, 71, 81, 53, 50, 67, 36, 50],
      [68, 74, 39, 78, 95, 62, 53, 21, 43, 59, 51, 88],
      [47, 74, 97, 51, 98, 47, 61, 36, 83, 55, 74, 43]
    ],
    names: [:year, :month]
  )

Nx.sum(revs, axes: [:year])

Nx.sum(revs, axes: [:month])

Nx expression can be thought of as a numerical assembly of your Nx function

defmodule MyModule do
  def adds_one(x) do
    Nx.add(x, 1)
  end
end

IO.puts(inspect(MyModule.adds_one(Nx.tensor([1, 2, 3]))))

defmodule MyModuleAccelerated do
  import Nx.Defn

  defn adds_one(x) do
    Nx.add(x, 1) |> print_expr()
  end
end

MyModuleAccelerated.adds_one(Nx.tensor([1, 2, 3]))

defmodule Softmax do
  import Nx.Defn

  defn(softmax(n), do: Nx.exp(n) / Nx.sum(Nx.exp(n)))
end

key = Nx.Random.key(42)
{tensor, _key} = Nx.Random.uniform(key, shape: {1_000_000})

Benchee.run(%{
  "JIT with EXLA" => fn ->
    apply(EXLA.jit(&Softmax.softmax/1), [tensor])
  end,
  "Regular Elixir" => fn ->
    Softmax.softmax(tensor)
  end
})