Sensitivity analysis of network of Epileptors

benchmark-autodiff.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77d934af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import os\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0852638",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arm64\r\n"
     ]
    }
   ],
   "source": [
    "!arch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "513b4e48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[91mWarning at epinet.fut:105:13-13:\u001b[0m\r\n",
      "  Unused variable \"i\" .\r\n"
     ]
    }
   ],
   "source": [
    "!futhark c --library epinet.fut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c2c82c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected platform: darwin\r\n",
      "Detected backend:  c\r\n"
     ]
    }
   ],
   "source": [
    "!build_futhark_ffi epinet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f975cdcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from futhark_ffi import Futhark\n",
    "import _epinet\n",
    "epinet = Futhark(_epinet)\n",
    "w = np.loadtxt('weights.txt').astype('f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99b1d8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tvb.simulator.models import Epileptor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdb9b674",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = (np.random.randn(76)/10 + 3.0).astype('f')\n",
    "dt = 0.1\n",
    "nt = 50000\n",
    "zt = epinet.from_futhark(epinet.run_z_array(nt, dt, z, w))\n",
    "lfp = zt[:,:,3] - zt[:,:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "637a6a58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86.5 ms ± 1.61 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "epinet.from_futhark(epinet.run_z_array(nt, 0.1, z, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5420042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x124ad5fa0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(121); imshow(lfp,aspect='auto',vmin=-5,vmax=5)\n",
    "subplot(122);\n",
    "plot(z, epinet.from_futhark(epinet.forward_array(zt)), 'bo')\n",
    "plot(z, lfp.std(axis=0) + 0.1, 'ko', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "881cd4c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf = 100\n",
    "dt = 1e-2\n",
    "zt = epinet.from_futhark(epinet.run_z_array(int(tf/dt), dt, z, w))\n",
    "lfp = zt[:,:,3] - zt[:,:,0]\n",
    "plot(lfp);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3893a451",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 76/76 [00:21<00:00,  3.57it/s]\n"
     ]
    }
   ],
   "source": [
    "model = Epileptor(\n",
    "    a = np.r_[1.0],\n",
    "    b = np.r_[3.0],\n",
    "    c = np.r_[1.0],\n",
    "    d = np.r_[5.0],\n",
    "    r = np.r_[0.00015],\n",
    "    s = np.r_[4.0],\n",
    "    Iext = np.r_[3.1],\n",
    "    slope = np.r_[0.0],\n",
    "    Iext2 = np.r_[0.45],\n",
    "    tau = np.r_[10.0],\n",
    "    aa = np.r_[6.0],\n",
    "    bb = np.r_[2.0],\n",
    "    Kvf = np.r_[0.0],\n",
    "    Kf = np.r_[0.00],\n",
    "    Ks = np.r_[0.00],\n",
    "    tt = np.r_[1.0],\n",
    "    modification = np.r_[False],\n",
    "    x0 = np.r_[-2.0],\n",
    ")\n",
    "\n",
    "def compare_dx(x):\n",
    "    c = 0.01\n",
    "    dx1 = model.dfun(x.reshape(6,1,1), np.r_[c,c].reshape(2,1)).reshape(6)\n",
    "    dx2 = epinet.from_futhark(epinet.node_eval(x.astype('f'), c,c))\n",
    "    return dx1-dx2\n",
    "\n",
    "for i in tqdm.trange(76):\n",
    "    acc = []\n",
    "    for x in zt[:,i,:]:\n",
    "        acc.append(np.abs(compare_dx(x)))\n",
    "    acc = np.array(acc)\n",
    "    if np.any(acc>0.01):\n",
    "        print(i, acc.sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4f16347f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING  File 'hemispheres' not found in ZIP.\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "WARNING  random_state supplied for non-stochastic integration\n",
      "986 ms ± 13 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "17 ms ± 138 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(10000.0, 'steps')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tvb.simulator.lab import *\n",
    "initial = np.r_[-1.5, -11.3, 3.0, -0.82, 0.04, -0.15]\n",
    "initial = np.zeros((100, 1, 76, 1)) + initial[:, None, None]\n",
    "initial[:,2] = z[:,None]\n",
    "sim = simulator.Simulator(\n",
    "    connectivity=connectivity.Connectivity.from_file(),\n",
    "    coupling=coupling.Difference(a=np.r_[0.0]),\n",
    "    model=model,\n",
    "    integrator=integrators.HeunDeterministic(dt=dt),\n",
    "    initial_conditions=initial,\n",
    "    monitors=[monitors.TemporalAverage(period=0.1)]\n",
    ")\n",
    "sim.configure()\n",
    "(t,y),*_ = sim.run(simulation_length=100)\n",
    "%timeit sim.run(simulation_length=100)\n",
    "%timeit epinet.from_futhark(epinet.run_z_array(int(tf/dt), dt, z, w))\n",
    "subplot(211); plot(lfp)\n",
    "subplot(212); plot(y[:,0,:,0]);\n",
    "100/dt, 'steps'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1c6a59",
   "metadata": {},
   "source": [
    "Let's check the online and regular loss functions,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea6f0a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(609.18115234375, 609.1732788085938)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epinet.loss(nt,dt,w,z,z+1e-2), epinet.loss_online(nt,dt,w,z,z+1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faea919c",
   "metadata": {},
   "source": [
    "Close enough\n",
    "\n",
    "We can check the forward and backward derivatives against each other,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bfe2258a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nt = 100\n",
    "losses = [\n",
    "    epinet.from_futhark(epinet.vjp_loss(nt,dt,w,z,z+1e-2)),\n",
    "    epinet.from_futhark(epinet.jvp_loss(nt,dt,w,z,z+1e-2)),\n",
    "    epinet.from_futhark(epinet.vjp_loss_online(nt,dt,w,z,z+1e-2)),\n",
    "    epinet.from_futhark(epinet.jvp_loss_online(nt,dt,w,z,z+1e-2))  ,\n",
    "]\n",
    "for l in losses[1:]:\n",
    "    np.testing.assert_allclose(losses[0], l, 1e-6, 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8967f5f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18 ms ± 15.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "1.99 ms ± 15.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "nt = 1000\n",
    "%timeit l2 = epinet.loss(nt,dt,w,z,z+1e-2)\n",
    "%timeit l2 = epinet.loss_online(nt,dt,w,z,z+1e-2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698d1edd",
   "metadata": {},
   "source": [
    "As a baseline, we can check against numerical difference,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "667c74e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "nt = 100\n",
    "zh = z + 1e-2\n",
    "l2_nd = []\n",
    "l2 = epinet.loss(nt,dt,w,z,zh)\n",
    "h = 1e-1\n",
    "for i in range(76):\n",
    "    zh_ = zh.copy()\n",
    "    zh_[i] += h\n",
    "    l2_nd.append((epinet.loss(nt,dt,w,z,zh_) - l2)/h)\n",
    "l2_nd = np.array(l2_nd)\n",
    "l2 = epinet.from_futhark(epinet.jvp_loss(nt,dt,w,z,zh))\n",
    "np.testing.assert_allclose(l2_nd, l2, 1e-3, 1e-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61fad7e",
   "metadata": {},
   "source": [
    "Forward mode is not as efficient as we'd like because it takes more time than equivalent numerical differences,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3a188885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.2 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "36.1 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "\n",
      "1.28 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1000 loops each)\n",
      "2.95 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "\n",
      "14.2 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "31.9 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n",
      "\n",
      "154 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n",
      "316 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for nt in [1, 10, 100, 1000]:\n",
    "    %timeit -r 1 l2 = epinet.from_futhark(epinet.jvp_loss(nt,dt,w,z,z+1e-2))\n",
    "    %timeit -r 1 l2 = epinet.from_futhark(epinet.jvp_loss_online(nt,dt,w,z,z+1e-2))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40114dbc",
   "metadata": {},
   "source": [
    "Backwards mode is fast for a short number of time steps, but memory is an issue for longer sims."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4692300b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "130 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n",
      "166 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "160 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n",
      "422 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "235 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n",
      "995 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "337 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n",
      "1.62 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "534 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n",
      "2.8 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "666 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 50 loops each)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for nt in [8,16,32,64,100,128]:\n",
    "    %timeit -r 1 -n 50 l2 = epinet.from_futhark(epinet.vjp_loss(nt,dt,w,z,z+1e-2))\n",
    "    %timeit -r 1 -n 50 l2 = epinet.from_futhark(epinet.vjp_loss_online(nt,dt,w,z,z+1e-2))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c574851",
   "metadata": {},
   "source": [
    "this isn't linear because of memory usage, so there's a cross over point around nt=32, for the batch version, while the online form is better for longer runs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c31d1a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.22 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 100000 loops each)\n",
      "7.49 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 100000 loops each)\n",
      "12.3 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 100000 loops each)\n",
      "33.6 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "\n",
      "16.8 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 100000 loops each)\n",
      "25.4 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "60.3 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "1.26 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1000 loops each)\n",
      "\n",
      "116 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 10000 loops each)\n",
      "206 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1000 loops each)\n",
      "531 µs ± 0 ns per loop (mean ± std. dev. of 1 run, 1000 loops each)\n",
      "14.4 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "\n",
      "1.15 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1000 loops each)\n",
      "1.96 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "5.31 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "151 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n",
      "\n",
      "12.8 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "19.7 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 100 loops each)\n",
      "53.9 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 10 loops each)\n",
      "1.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for nt in [1, 10, 100, 1000, 10000]:\n",
    "    %timeit -r 1 l2 = epinet.loss(nt,dt,w,z,z+1e-2)\n",
    "    %timeit -r 1 l2 = epinet.loss_online(nt,dt,w,z,z+1e-2)\n",
    "    %timeit -r 1 l2 = epinet.from_futhark(epinet.vjp_loss_online(nt,dt,w,z,z+1e-2))\n",
    "    %timeit -r 1 l2 = epinet.from_futhark(epinet.jvp_loss(nt,dt,w,z,z+1e-2))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289e1650",
   "metadata": {},
   "source": [
    "Reverse AD with online loss is a good approach, as it is just over 2x cost of the loss itself. This might be something to pursue else where, i.e. when comparing time series with sum squared error, do it in online fashion."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "275e0195",
   "metadata": {},
   "source": [
    "We can look at work precision. Well, at least, convergence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d732e36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = 100\n",
    "dt = 0.001 # ms\n",
    "l2 = epinet.from_futhark(epinet.jvp_loss(int(tf/dt),dt,w,z,z+1e-2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d823afca",
   "metadata": {},
   "outputs": [],
   "source": [
    "dts = dt * 2**np.r_[:8:0.1]\n",
    "l2s = [epinet.from_futhark(epinet.jvp_loss(int(tf/dt_),dt_,w,z,z+1e-2))\n",
    "       for dt_ in dts]\n",
    "l2s = np.array(l2s)\n",
    "sse = np.sum((l2s - l2)**2,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3fd08356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1106ea310>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loglog(dts, sse, 'x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "eabdfec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subplot(121); imshow(l2s.T)\n",
    "subplot(122); plot(l2); plot(l2s[40]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed83ef5",
   "metadata": {},
   "source": [
    "Interesting that the \"true\" solution is different from most, until the simulation diverges at around dt=0.128."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "arm64",
   "language": "python",
   "name": "arm64"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

epinet.fut

import "util"

let a = 1.0f32
let b = 3.0f32
let c = 1.0f32
let d = 5.0f32
let r = 0.00015f32
let s = 4.0f32
let Iext = 3.1f32
let slope = 0.0f32
let Iext2 = 0.45f32
let tau = 10.0f32
let aa = 6.0f32
let bb = 2.0f32
let Kvf = 0.0f32
let Kf = 0.00f32
let Ks = 0.00f32
let tt = 1.0f32
let modification = 0.0f32
let x0 = -2f32

type node = (f32,f32,f32, f32,f32,f32)

let coupling (w:[][]f32) (x:[]f32): []f32 =
    let per_node wi xi = map2 (\wij xj -> wij * (xj - xi)) wi x |> reduce (+) 0f32
    in map2 per_node w x

let node (y:node) (c_pop1:f32) (c_pop2:f32): node =
    let (x1,y1,z,x2,y2,g) = y
    let dx1' = if x1 < 0
        then ((-a) * x1*x1) + b * x1
        else (slope - x2) + 0.6f32 * (z - 4f32)*(z - 4f32)
    let dx1 = tt * (y1 - z + Iext + Kvf * c_pop1 + dx1' * x1)
    let dy1 = tt * (c - d * x1*x1 - y1)
    let dz = if z < 0
        then -0.1f32 * z*z*z*z*z*z*z
        else 0f32
    let h = if modification>0
        then x0 + 3f32/(1f32 + f32.exp(-(x1 + 0.5f32)/0.1f32))
        else 4f32 * (x1 - x0) + dz
    let dz = tt * (r * (h - z + Ks * c_pop1))
    let dx2 = tt * (-y2 + x2 - x2*x2*x2 + Iext2 + bb * g - 0.3f32 * (z - 3.5) + Kf * c_pop2)
    let dy2 = if x2 < -0.25f32 then 0f32 else aa * (x2 + 0.25f32)
    let dy2 = tt * ((-y2 + dy2) / tau)
    let dg = tt * (-0.01f32 * (g - 0.1 * x1))
    in (dx1, dy1, dz, dx2, dy2, dg)

entry node_eval (y:[6]f32) (c_pop1:f32) (c_pop2:f32): [6]f32 =
    let y' = (y[0],y[1],y[2],y[3],y[4],y[5])
    let (d1,d2,d3,d4,d5,d6) = node y' c_pop1 c_pop2
    in [d1,d2,d3,d4,d5,d6]

let network (y:[]node) (w:[][]f32): []node =
    let (x1, y1, z, x2, y2, g) = unzip6 y
    let c_pop1 = coupling w x1
    let c_pop2 = coupling w x2
    in map3 node y c_pop1 c_pop2

let heun (dt:f32) (y:[]node) (w:[][]f32): []node =
    let half_dt:f32 = dt / 2
    let dy1 = network y w
    let yi = map2 (\(x1, y1, z, x2, y2, g) (dx1, dy1, dz, dx2, dy2, dg) -> 
        (x1 + dt * dx1, y1 + dt * dy1, z + dt * dz, x2 + dt * dx2, y2 + dt * dy2, g + dt * dg))
        y dy1
    let dy2 = network yi w
    in map3 (\(x1, y1, z, x2, y2, g) (dx1, dy1, dz, dx2, dy2, dg) (ex1, ey1, ez, ex2, ey2, eg) -> 
        (x1 + half_dt * (dx1 + ex1),
        y1 + half_dt * (dy1 + ey1),
        z + half_dt * (dz + ez),
        x2 + half_dt * (dx2 + ex2),
        y2 + half_dt * (dy2 + ey2),
        g + half_dt * (dg + eg)))
        y dy1 dy2

let init = (-1.5f32, -11.3f32, 3.0f32, -0.82f32, 0.04f32, -0.15f32)

let run [n] (nt:i64) (dt:f32) (init:[n]node) (w:[n][n]f32): [][n]node = 
    let y = tabulate nt (\_ -> init)
    in loop (y) for i < (nt - 1) do
        let y[i+1] = heun dt (copy y[i]) w
        in y

let lfp ((x1,_,_,x2,_,_):node): f32 = x2 - x1

-- welford online variance algorithm
type welf = {count: i64, mean: f32, m2: f32}

let welford_init (y:node): welf = {count=1, mean=lfp y, m2=0f32}

let welford_update ({count,mean,m2}:welf) (y:node): welf =
    let lfp' = lfp y
    let count = count + 1
    let delta = lfp' - mean
    let mean = mean + delta / (f32.i64 count)
    let delta2 = lfp' - mean
    let m2 = m2 + delta * delta2
    in {count, mean, m2}

let welford_finalize ({count,mean,m2}:welf): (f32,f32) =
    (mean, m2 / (f32.i64 count))

let run_online_std [n] (nt:i64) (dt:f32) (init:[n]node) (w:[n][n]f32): [n]f32 =
    let v = map welford_init init
    let (_, v) = loop (y, v) = (init, v)
        for i < (nt - 1) do
            let y = heun dt y w
            let v = map2 welford_update v y
            in (y, v)
    in map (\v -> f32.sqrt (welford_finalize v).1) v

let run_z [n] (nt:i64) (dt:f32) (z:[n]f32) (w:[n][n]f32): [][n]node = 
    let (x1,y1,_,x2,y2,g) = init
    let y0 = map (\z -> (x1,y1,z,x2,y2,g)) z
    in run nt dt y0 w

let run_z_online [n] (nt:i64) (dt:f32) (z:[n]f32) (w:[n][n]f32): []f32 = 
    let (x1,y1,_,x2,y2,g) = init
    let y0 = map (\z -> (x1,y1,z,x2,y2,g)) z
    in run_online_std nt dt y0 w

entry run_z_array [n] (nt:i64) (dt:f32) (z:[n]f32) (w:[n][n]f32): [nt][n][6]f32 =
  let y = run_z nt dt z w
  let to_array (yi:[n]node): [n][6]f32 = map (\(a,b,c,d,e,f) -> [a,b,c,d,e,f]) yi
  in map to_array y

let std [n] (xs:[n]f32): f32 =
    let mu = (reduce (+) 0f32 xs) / (f32.i64 n)
    let n_var = map (\x -> (x - mu)*(x - mu)) xs |> reduce (+) 0f32
    in f32.sqrt (n_var / (f32.i64 n))

let forward [nt] [n] (y:[nt][n]node) = 
    let lfp: [n][nt]f32 = map (map (\(x1,_,_,x2,_,_) -> x2 - x1)) y |> transpose
    in map std lfp

entry forward_array [nt] [n] (y:[nt][n][6]f32): [n]f32 =
    let lfp: [n][nt]f32 = map (map (\x -> x[3] - x[0])) y |> transpose
    in map std lfp

let sse [n] (x:[n]f32) (y:[n]f32): f32 =
    map2 (\x y -> (x - y)*(x - y)) x y |> reduce (+) 0f32

entry loss [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): f32 =
    run_z nt dt zh w |> forward |> sse z

entry loss_online [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): f32 =
    run_z_online nt dt zh w |> sse z

entry vjp_loss_online [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): [n]f32 =
  vjp (loss_online nt dt w z) zh 1f32

entry jvp_loss_online [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): [n]f32 =
  let one i = jvp (loss_online nt dt w z) zh (tabulate n (\j -> if i==j then 1f32 else 0f32))
  in map one (iota n)

entry vjp_loss [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): [n]f32 =
  vjp (loss nt dt w z) zh 1f32

entry jvp_loss [n] (nt:i64) (dt:f32) (w:[n][n]f32) (z:[n]f32) (zh:[n]f32): [n]f32 =
  let one i = jvp (loss nt dt w z) zh (tabulate n (\j -> if i==j then 1f32 else 0f32))
  in map one (iota n)
-- because of the type of jvp we need to write the overall loss differently to
-- push jvp further down.


-- ==
-- random input { 75i64 [76]f32 [76]f32 [76][76]f32 0i64 }
-- random input { 75i64 [76]f32 [76]f32 [76][76]f32 1i64 }
-- random input { 75i64 [76]f32 [76]f32 [76][76]f32 2i64 }
-- random input { 750i64 [76]f32 [76]f32 [76][76]f32 0i64 }
-- random input { 750i64 [76]f32 [76]f32 [76][76]f32 1i64 }
-- random input { 750i64 [76]f32 [76]f32 [76][76]f32 2i64 }
let main [n] (nt:i64) (z:[n]f32) (zh:[n]f32) (w:[n][n]f32) (mode:i64): [n]f32 =
  let dt: f32 = 0.01 in
  match mode
  case 0i64 -> loss nt dt w z zh |> replicate n
  case 1i64 -> vjp_loss nt dt w z zh
  case 2i64 -> jvp_loss nt dt w z zh
  case _ -> replicate n 0f32

epinet.jl

# using OrdinaryDiffEq
using DelimitedFiles
# using Plots
# using Statistics
# using DiffEqSensitivity
# using ForwardDiff
# using Zygote
# using SliceMap
# using BenchmarkTools

# TVB conn_76
weights = Float32.(readdlm("weights.txt"));
# tract_lengths = Float32.(readdlm("mpr-append/tract_lengths.txt"));

function make_epileptor(weights)
    a = 1.0f0
    b = 3.0f0
    c = 1.0f0
    d = 5.0f0
    r = 0.00015f0
    s = 4.0f0
    Iext = 3.1f0
    slope = 0.0f0
    Iext2 = 0.45f0
    tau = 10.0f0
    aa = 6.0f0
    bb = 2.0f0
    Kvf = 0.0f0
    Kf = 0.01f0
    Ks = 0.01f0
    tt = 1.0f0
    modification = 0.0f0
    x0 = -2f0
    weights = Float32.(weights)

    @inline function node(i,y,p,t,c_pop1,c_pop2)
        if y[1] < 0.0
            ydot1 = - a * y[1] ^ 2 + b * y[1]
        else
            ydot1 = slope - y[3] + 0.6 * (y[3] - 4.0) ^ 2
        end
        ydot1 = tt * (y[2] - y[3] + Iext + Kvf * c_pop1 + ydot1 * y[1])
        ydot2 = tt * (c - d * y[1] ^ 2 - y[2])

        # energy
        if y[3] < 0.0
            ydot3 = - 0.1 * y[3] ^ 7
        else
            ydot3 = 0.0
        end
        if modification>0
            h =  x0 + 3/(1 + exp(-(y[1]+0.5)/0.1))
        else
            h = 4 * (y[1] - x0) + ydot3
        end
        ydot3 = tt * (r * (h - y[3]  + Ks * c_pop1))

        # population 2
        ydot4 = tt * (-y[5] + y[4] - y[4] ^ 3 + Iext2 + bb * y[6] - 0.3 * (y[3] - 3.5) + Kf * c_pop2)
        if y[4] < -0.25
            ydot5 = 0.0
        else
            ydot5 = aa * (y[4] + 0.25)
        end
        ydot5 = tt * ((-y[5] + ydot5) / tau)

        # filter
        ydot6 = tt * (-0.01 * (y[6] - 0.1 * y[1]))
        return [ydot1, ydot2, ydot3, ydot4, ydot5, ydot6]
    end

    function network(ydot,y,p,t)
        c_pop1 = dropdims(sum(weights .* (y[:,1] .- y[:,1]'); dims=2); dims=2)
        c_pop2 = dropdims(sum(weights .* (y[:,4] .- y[:,4]'); dims=2); dims=2)
        for i=1:size(weights,1)
            node(i,ydot,(y[:,i]),p,t,c_pop1[i],c_pop2[i])
        end
    end

    return network
end

network = make_epileptor(weights)

function make_predict(nwin=33,sensealg=ForwardSensitivity())
    function predict(z)
        u0 = [[-1.5, -11.3, 3.0, -0.82, 0.04, -0.15] for i=1:size(weights,1)]
        u0 = Float32.(reduce(hcat, u0)')
        u0 = reduce(hcat, [u0[:,1], u0[:,2], z, u0[:,4], u0[:,5], u0[:,6]])
        win = 15f0
        prob = ODEProblem(network,u0,(0f0,nwin * win))
        @show nwin*win/0.1f0
        sol = solve(prob, Heun(), dt=0.1f0, saveat=win,sensealg=sensealg)
        zt = reduce(hcat,[u[:,4]-u[:,1] for u in sol.u])
        zs = [std(zt[i,:]) for i=1:76]
        zs, zt
    end
    predict
end

function make_loss(sz, nwin=33, sensealg=ForwardSensitivity())
    predict = make_predict(nwin, sensealg)
    function loss(zh)
        szh, _ = predict(zh)
        sum((szh .- sz).^2)
    end
    loss
end

function runstuff()
    z = Float32.(randn(76)) .+ 3f0;
    sz, zt = make_predict(5)(z);
    # p1=heatmap(zt)
    # p2=plot(z, sz, seriestype = :scatter)
    # plot(p1,p2)

    loss = make_loss(sz, 5, ForwardSensitivity())
    @time loss(z)
    @time loss(z)
    @time loss(z)
    ForwardDiff.gradient(loss, z) 
    @time ForwardDiff.gradient(loss, z)  # ~200s?
    @time ForwardDiff.gradient(loss, z)

    # for sa in [ForwardSensitivity(), ForwardDiffSensitivity(), BacksolveAdjoint(), InterpolatingAdjoint(), QuadratureAdjoint(), ReverseDiffAdjoint(), ReverseDiffVJP(true), SensitivityADPassThrough()]
    #     @show sa
    #     loss = make_loss(sz, 5, ForwardSensitivity())
    #     loss(z)
    #     @time loss(z)
    #     ForwardDiff.gradient(loss, z) 
    #     @time ForwardDiff.gradient(loss, z)
    # end

    # using ReverseDiff

    # loss = make_loss(sz, 5, ReverseDiffVJP(false))
    # @time ReverseDiff.gradient(loss, z);
    # @time ReverseDiff.gradient(loss, z);
    # @time ReverseDiff.gradient(loss, z);
end

epinettest.py

aimport os
import numpy as np
import pylab as pl
if 1:
    #os.system('futhark c --library epinet.fut')
    os.system('build_futhark_ffi epinet')
from futhark_ffi import Futhark
import _epinet
epinet = Futhark(_epinet)
w = np.loadtxt('weights.txt').astype('f')
z = (np.random.randn(76)+ 3.0).astype('f')
nt = 750
zt = epinet.from_futhark(epinet.run_z_array(nt, z, w))
lfp = zt[:,:,3] - zt[:,:,0]
pl.subplot(121); pl.imshow(lfp,aspect='auto',vmin=-5,vmax=5)
pl.subplot(122);
pl.plot(z, epinet.from_futhark(epinet.forward_array(zt)), 'bo')
pl.plot(z, lfp.std(axis=0) + 0.1, 'ko', alpha=0.3)
pl.show()

l1 = epinet.from_futhark(epinet.vjp_loss(nt,w,z,z+1e-2))
l2 = epinet.from_futhark(epinet.jvp_loss(nt,w,z,z+1e-2))
print(np.c_[l1, l2])
# they're the same!
#
import tqdm
for i in tqdm.trange(10):
    l2 = epinet.jvp_loss(nt,w,z,z+1e-2)
for i in tqdm.trange(10):
    l2 = epinet.from_futhark(epinet.jvp_loss(nt,w,z,z+1e-2))
for i in tqdm.trange(10):
    l2 = epinet.from_futhark(epinet.vjp_loss(nt,w,z,z+1e-2))

spatepi.fut

type node = (f32,f32,f32, f32,f32,f32)
type lc = (f32,f32,f32)

let spatepi_dfun
            (gamma11:f32) (gamma22:f32)
            (gamma12:f32) (gamma_glob:f32)
            (Iext:f32) (Iext2:f32) (tt:f32)
            (py0:f32) (tau0:f32) (tau2:f32)
            (y:node) (c:f32) (lc:lc) (x0:f32)
        : node =

    let loc11 = gamma11 * lc.0
    let loc22 = gamma22 * lc.1
    let loc12 = gamma12 * lc.2
    let c_pop = gamma_glob * c
    let (y0,y1,y2,y3,y4,y5) = y

    -- -- pop 1
    let y02 = y0 * y0
    let dy0 = if y0 < 0 
        then (y0 * y02 - 3.0f32 * y02)
        else (y3 - 0.6f32 * (y2 - 4.0f32) * (y2 - 4.0f32)) * y0
    
    let dy0 = tt * (y1 - dy0 - y2 + Iext + loc11 + c_pop)
    let dy1 = tt * (py0 - 5 * y02 - y1)

    -- -- energy
    let y22 = y2 * y2
    let y23 = y22 * y2
    let dy2 = if y2 < 0 then (-0.1f32 * y22 * y22 * y23) else 0f32
    let dy2 = tt * (1.0f32 / tau0 * (4.0f32 * (y0 - x0) - y2 + dy2))

    -- pop 2
    let y33 = y3 * y3 * y3
    let dy3 = tt * (-y4 + y3 - y33 + Iext2 + 2.0f32 * y5 - 0.3f32 * (y2 - 3.5f32) + loc22)
    let dy4 = if (y3 < (-0.25f32)) then 0f32 else 6.0f32 * (y3 + 0.25f32)
    let dy4 = tt * ((-y4 + dy4) / tau2)

    -- filter
    let dy5 = tt * (-0.01f32 * y5 + 0.003f32 * y0 + 0.01f32 * loc12)

    in (dy0, dy1, dy2, dy3, dy4, dy5)

let npsign (x:f32): f32 =
    if x < 0f32 then -1f32 else if x > 0f32 then 1f32 else 0f32

let spatepi_lcfun (theta11:f32) (theta22:f32) (theta12:f32)
                  (y:node): lc =
    let (y0,y1,y2,y3,y4,y5) = y
    in (
        0.5f32 * ((npsign (y0 - theta11)) + 1f32),
        0.5f32 * ((npsign (y3 - theta11)) + 1f32),
        0.5f32 * ((npsign (y0 - theta12)) + 1f32)
    )

-- let full_dfun nodes = 
--     let lcs = map2 (spatepi_lcfun 0f32 0f32 0f32) node |> sht3x2
--     in map4 dfun nodes cs lcs x0s
let unzip6 [n] 'a 'b 'c 'd 'e 'f (xs: [n](a,b,c,d,e,f)): ([n]a, [n]b, [n]c, [n]d, [n]e, [n]f) =
  let (as, bs, cs, ds, efs) = unzip5 (map (\(a,b,c,d,e,f) -> (a,b,c,d,(e,f))) xs)
  let (es, fs) = unzip efs
  in (as, bs, cs, ds, es, fs)

-- ==
-- input { 1024i64 0 }
-- input { 8192i64 0 }
-- input { 16384i64 0 }
-- input { 32768i64 0 }
-- input { 66000i64 0 }
-- input { 400000i64 0 }
entry main (n:i64) (grad:i32): [n]f32 = 
    let t = 1i64
    let nodes = tabulate n (\i -> (-1.4624261f32+((f32.i64 i)*1e-3), -9.6934490f32+((f32.i64 i)*1e-3),  2.9502959f32+((f32.i64 i)*1e-3), -1.1118182f32+((f32.i64 i)*1e-3),
 -9.5610595e-20+((f32.i64 i)*1e-3), -4.3872780e-01+((f32.i64 i)*1e-3)))
    let lcs = tabulate n (\i -> (0.1f32+((f32.i64 i)*1e-3), 0.1f32+((f32.i64 i)*1e-3), 0.1f32+((f32.i64 i)*1e-3)))
    let cs = tabulate n (\i -> 0.1f32+((f32.i64 i) * 1e-3))
    let x0s = tabulate n (\i -> -1.8f32+((f32.i64 i) * 1e-3))
    --     (gamma11:f32) (gamma22:f32)
    -- (gamma12:f32) (gamma_glob:f32)
    -- (Iext:f32) (Iext2:f32) (tt:f32)
    -- (py0:f32) (tau0:f32) (tau2:f32)
    let dfun = spatepi_dfun 0.169 0.318 0.0318 1.0 3.1 0.45 0.17 1.0 2857.0 1.0
    let h = 0.1f32
    let eu (a,b,c,d,e,f) (da,db,dc,dd,de,df) = (a+h*da,b+h*db,c+h*dc,d+h*dd,e+h*de,f+h*df)
    let f (n, c, l, x) = loop (n) for i < t do (eu n (dfun n c l x))
    let g_vjp = f (nodes[0],cs[0],lcs[0],x0s[0])
    let g_jvp =   (nodes[0],cs[0],lcs[0],x0s[0])
    in
    -- if grad==0
    --     then 
        map4 (\n c l x ->     f (n,c,l,x)  ) nodes cs lcs x0s |> unzip6 |> (.0)
    -- else if grad==1
    --     then map4 (\n c l x -> vjp f (n,c,l,x) g_vjp) nodes cs lcs x0s |> unzip4 |> (.0) |> unzip6 |> (.0)
    --     else map4 (\n c l x -> jvp f (n,c,l,x) g_jvp) nodes cs lcs x0s |> unzip6 |> (.0)

util.fut

let unzip6 [n] 'a 'b 'c 'd 'e 'f (xs: [n](a,b,c,d,e,f)): ([n]a, [n]b, [n]c, [n]d, [n]e, [n]f) =
  let (as, bs, cs, ds, efs) = unzip5 (map (\(a,b,c,d,e,f) -> (a,b,c,d,(e,f))) xs)
  let (es, fs) = unzip efs
  in (as, bs, cs, ds, es, fs)

weights.txt

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