07-enzyme.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40912857-c64b-439e-95e7-d1574e29cf31",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Minimisation of functions with Enzyme on the IPU\n",
    "\n",
    "Load the packages that we will use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a429c373-cf86-46fb-b75c-bc1ede5e8807",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: using IPUCompiler.Out in module Main conflicts with an existing identifier.\n",
      "WARNING: using IPUCompiler.In in module Main conflicts with an existing identifier.\n"
     ]
    }
   ],
   "source": [
    "using IPUToolkit.IPUCompiler, IPUToolkit.Poplar\n",
    "using Enzyme\n",
    "using Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3fccf6-c670-4aa1-a8ac-dca5e56d525c",
   "metadata": {},
   "source": [
    "Some settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28f82c57-36c7-46c4-84b7-6dbebda82243",
   "metadata": {},
   "outputs": [],
   "source": [
    "ENV[\"POPLAR_RUNTIME_OPTIONS\"] = \"\"\"{\"target.hostSyncTimeout\":\"60\"}\"\"\";\n",
    "IPUCompiler.KEEP_LLVM_FILES[] = true;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "960efbe7-92be-420e-89da-1ce8437c6bbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adam (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Define some functions we'll use in our program\n",
    "# Convenient method for computing the derivative of a 2-argument function `f` on points `x` and `y` with Enzyme\n",
    "∂(f, x, y) = first(autodiff_deferred(Reverse, f, Active(x), Active(y)));\n",
    "# The function we want to minimise\n",
    "rosenbrock(x, y) = (1 - x) ^ 2 + 100 * (y - x ^ 2) ^ 2\n",
    "# The derivative of the function we want to minimise,\n",
    "# computed with Enzyme.\n",
    "rosenbrock′(x, y) = ∂(rosenbrock, x, y)\n",
    "# Minimise a function with Adam method and return the number of iterations\n",
    "function adam(∂f, x₀::T, y₀::T) where {T}\n",
    "    x, y = x₀, y₀\n",
    "    # Some constants\n",
    "    α = T(0.001) # learning rate\n",
    "    β₁ = T(0.9)\n",
    "    β₂ = T(0.999)\n",
    "    ϵ = T(1e-8)\n",
    "    # Momenta\n",
    "    m = zero(T), zero(T)\n",
    "    v = zero(T), zero(T)\n",
    "    # Stopping criteria\n",
    "    Δ = 10 * eps(T)\n",
    "    δ_x, δ_y = one(T), one(T)\n",
    "    max_t = Int32(500_000)\n",
    "    t = one(max_t)\n",
    "    while abs(δ_x) > Δ < abs(δ_y) && t ≤ max_t\n",
    "        g = ∂f(x, y)\n",
    "        m = β₁ .* m .+ (1 - β₂) .* g\n",
    "        v = β₂ .* v .+ (1 - β₂) .* g .^ 2\n",
    "        m̂ = m ./ (1 - β₁ ^ t)\n",
    "        v̂ = v ./ (1 - β₂ ^ t)\n",
    "        δ_x, δ_y = α .* m̂ ./ (.√(v̂) .+ ϵ)\n",
    "        x -= δ_x\n",
    "        y -= δ_y\n",
    "        t += one(t)\n",
    "    end\n",
    "    # Subtract one because at the end of the last iteration we\n",
    "    # added 1 to the counter but didn't run the following iteration.\n",
    "    return t - one(t)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d27a044-39ec-40cd-9c0b-655cb7a78472",
   "metadata": {},
   "source": [
    "Run the program:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3baa195e-f21e-458c-91f1-7b438b6bac90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mTrying to attach to device 0...\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mSuccessfully attached to device 0\n",
      "\u001b[32m✓ Compiling codelet RosenAdam: \t Time: 0:00:20\u001b[39m\n",
      "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mLoading and running the program minimising the Rosenbrock function on 33856 points took 28.54 seconds\n"
     ]
    }
   ],
   "source": [
    "# Obtain a device and initialise the graph and the program\n",
    "device = Poplar.get_ipu_device()\n",
    "target = Poplar.DeviceGetTarget(device)\n",
    "graph = Poplar.Graph(target)\n",
    "prog = Poplar.ProgramSequence()\n",
    "\n",
    "# Number of total IPU tiles on the device\n",
    "num_tiles = Int(Poplar.TargetGetNumTiles(target))\n",
    "\n",
    "# Create grid of initial points\n",
    "make_grid(x::T, y::T) where {T<:AbstractVector} =\n",
    "    repeat(x; inner=length(y)), repeat(y; outer=length(x))\n",
    "\n",
    "# High resolution range\n",
    "x_range = y_range = collect(Float32.(range(-6; step=2^-4, length=23 * 8)))\n",
    "# # Low resolution range\n",
    "# x_range = collect(Float32.(range(-6; step=2^-2, length=23 * 2)))\n",
    "# y_range = collect(Float32.(range(-8; step=2^-2, length=64)))\n",
    "# Inputs to the codelet\n",
    "x₀, y₀ = make_grid(x_range, y_range)\n",
    "# Output of the codelet\n",
    "num_iterations = similar(x₀, Int32)\n",
    "\n",
    "# Compile the codelet in the graph\n",
    "@codelet graph function RosenAdam(x::VertexVector{Float32, IPUCompiler.In},\n",
    "                                  y::VertexVector{Float32, IPUCompiler.In},\n",
    "                                  num_iterations::VertexVector{Int32, IPUCompiler.Out})\n",
    "    for idx in eachindex(num_iterations)\n",
    "        num_iterations[idx] = adam(rosenbrock′, x[idx], y[idx])\n",
    "    end\n",
    "end\n",
    "\n",
    "# Create IPU arrays\n",
    "x₀_ipu = Poplar.GraphAddConstant(graph, x₀)\n",
    "y₀_ipu = Poplar.GraphAddConstant(graph, y₀)\n",
    "num_iterations_ipu = similar(graph, num_iterations, Int32)\n",
    "\n",
    "# Spread vertices across all tiles\n",
    "add_vertex(graph, prog, 0:(num_tiles - 1), RosenAdam, x₀_ipu, y₀_ipu, num_iterations_ipu)\n",
    "\n",
    "# Prepare for reading the output tensor\n",
    "Poplar.GraphCreateHostRead(graph, \"iterations-read\", num_iterations_ipu)\n",
    "\n",
    "# Run and time the program.  This will take about 30 seconds, be patient.\n",
    "engine = Poplar.Engine(graph, prog)\n",
    "elapsed = @elapsed Poplar.EngineLoadAndRun(engine, device)\n",
    "\n",
    "# Copy the output tensor to the CPU into a Julia vector\n",
    "Poplar.EngineReadTensor(engine, \"iterations-read\", num_iterations)\n",
    "\n",
    "# Release the device\n",
    "Poplar.detach_devices()\n",
    "\n",
    "# Reshape the vector into a matrix\n",
    "m = reshape(sqrt.(Int.(num_iterations)), length(y_range), length(x_range))\n",
    "\n",
    "# Print out how long it took to run the program\n",
    "@info \"Loading and running the program minimising the Rosenbrock function on $(length(x₀)) points took $(round(elapsed; sigdigits=4)) seconds\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d75189b-a0da-498b-9208-ada20b0cdef8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Always remember to release the device after use!\n",
    "Poplar.detach_devices()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b917ce28-a442-4a82-923a-07797972b4d6",
   "metadata": {},
   "source": [
    "Plot the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08098976-a520-4ff1-98c7-2efaafffc61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xticks = yticks = -20:1:20\n",
    "heatmap(x_range, y_range, m;\n",
    "        seriescolor = :linear_tritanopic_krjcw_5_95_c24_n256,\n",
    "        xlabel = \"x₀\",\n",
    "        ylabel = \"y₀\",\n",
    "        xticks,\n",
    "        yticks,\n",
    "        colorbar_title = \"sqrt(iterations)\",\n",
    "        title = \"Minimisation of Rosenbrock function\",\n",
    "    )"
   ]
  }
 ],
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