stochastic-computing.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stochastic Computing\n",
    "\n",
    "The goal of this part of the workshop is to explore a simple example \n",
    "of stochastic computing with spiking neurons.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Install Requirements\n",
    "\n",
    "First of all, we will need to install Norse. Please run the cell below. Read on while it's running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7ff732ab99f0>"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "!pip install norse --quiet\n",
    "    \n",
    "import torch\n",
    "import norse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Neuron Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "\n",
    "from norse.torch.module import LIFRefracRecurrentCell\n",
    "from norse.torch.functional.threshold import superspike_fn\n",
    "from norse.torch.functional.lif_refrac import lif_refrac_feed_forward_step, lif_refrac_step\n",
    "from norse.torch.functional.lif_refrac import LIFRefracFeedForwardState, LIFRefracState, LIFRefracParameters\n",
    "from norse.torch.functional.lif import LIFFeedForwardState, LIFState, LIFParameters\n",
    "from norse.torch.functional.encode import poisson_encode\n",
    "\n",
    "\n",
    "class LIFRefracNeurons(torch.nn.Module):\n",
    "  def __init__(self, K, N):\n",
    "    super(LIFRefracNeurons, self).__init__()\n",
    "\n",
    "    self.K = K\n",
    "    self.N = N\n",
    "\n",
    "    self.v_leak = torch.nn.Parameter(1.0*torch.ones(N, device=device), requires_grad=False)\n",
    "    self.rho = torch.nn.Parameter(10.0*torch.ones(N, device=device), requires_grad=False)\n",
    "    lif_parameter = LIFParameters(v_leak=self.v_leak, method=\"super\", alpha=100)\n",
    "    lif_refrac = LIFRefracParameters(lif_parameter, self.rho)\n",
    "    self.cell = LIFRefracRecurrentCell(K, N, p=lif_refrac)\n",
    "\n",
    "  def forward(self, z):\n",
    "    T, B, _ = z.shape\n",
    "    s0 = LIFRefracState(LIFState(torch.zeros(B,self.N, device=z.device), 0.9*torch.ones(B,self.N, device=z.device), torch.zeros(B,N, device=z.device)), torch.zeros(B, self.N, device=z.device))\n",
    "    refrac = []\n",
    "    voltages = []\n",
    "\n",
    "    for ts in range(T):\n",
    "      output, s0 = self.cell(z[ts,:], s0)\n",
    "      r = superspike_fn(s0.rho, torch.tensor(100.0, device=z.device))\n",
    "      refrac.append(r)\n",
    "      voltages.append(s0.lif.v)\n",
    "    return torch.stack(refrac), torch.stack(voltages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Task\n",
    "\n",
    "The goal will be to approximate a binary probability distribution on three binary variables\n",
    "$z_1, z_2, z_3$ represented by the refractory state $\\rho$ of three neurons. The correlation between the three refractory state variables can be computed in the following way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def three_point_correlations_func(rho):\n",
    "  T, B, _ = rho.shape\n",
    "\n",
    "  rb0 = torch.stack([1-rho[:,:,0], rho[:,:,0]])\n",
    "  rb1 = torch.stack([1-rho[:,:,1], rho[:,:,1]])\n",
    "  rb2 = torch.stack([1-rho[:,:,2], rho[:,:,2]])\n",
    "  return torch.einsum(\"atb,dtb,gtb -> badg\", rb0, rb1, rb2) / T"
   ]
  },
  {
   "source": [
    "Question (Optional): Can you think of a way how to implement this for an \n",
    "arbitrary number of neurons?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "We will generate the target distribution by sampling from a population of refractory\n",
    "neurons, that way we can be reasonably sure that the model can be trained quickly\n",
    "to approximate the target distribution.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_target_distribution(timesteps, batch_dimension, input_features):\n",
    "    T, B, H = timesteps, batch_dimension, input_features\n",
    "    z = poisson_encode(0.3 * torch.ones(B, H), T).detach().to(device)\n",
    "    n_target = LIFRefracNeurons(H,N).to(device)\n",
    "    refrac_t, voltages_t = n_target(z)\n",
    "    readout_t = refrac_t[:,:,:5]\n",
    "    p_target = three_point_correlations_func(readout_t).detach()\n",
    "    p_target = p_target.flatten(start_dim=1)[0].unsqueeze(0).repeat(B,1)\n",
    "    return p_target"
   ]
  },
  {
   "source": [
    "To plot the target we can use the following helper function"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def target_plot(ax, p_target, width=0.35):\n",
    "  ax.bar(np.arange(0,8), p_target[0], width, label='$p_{t}$')\n",
    "  ax.legend(loc='upper right')\n",
    "  ax.set_xticks(np.arange(0,8))\n",
    "  ax.set_xticklabels(['{0:03b}'.format(n) for n in np.arange(0,8)], rotation = 90)\n",
    "  ax.set_ylabel('$p(z)$')\n",
    "  ax.set_xlabel('$z$')"
   ]
  },
  {
   "source": [
    "Now we generate a target distribution.\n",
    "\n",
    "Once you've run the notebook to completion, you can\n",
    "consider doing one of the following Tasks: \n",
    "\n",
    "- Vary the number of timesteps, how to you expect fidelity of the approximation to behave?\n",
    "- Change the number of visible units (N) and number of poisson noise sources (H), what do you observe?"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 32 # number of visible units\n",
    "H = 256 # number of synapses connected to poisson noise sources\n",
    "B = 32 # batch dimension\n",
    "T = 1000 # number of timesteps to integrate (increase to get better sampling accuracy)\n",
    "rho = 20 # refractory time in timesteps\n",
    "device = \"cpu\"\n",
    "\n",
    "p_target = generate_target_distribution(\n",
    "    timesteps=T,\n",
    "    batch_dimension=B, \n",
    "    input_features=H\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot()\n",
    "target_plot(ax, p_target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Training\n",
    "\n",
    "The goal of the training procedure will be to minimize the [Kullback-Leibler divergence](https://en.wikipedia.org/wiki/Kullback–Leibler_divergence) between the target distribution $p_t$ and the batch-averaged sampled distribution $p_s$. In the machine-learning language the Kullback-Leibler divergence is one example of a loss-function and luckily PyTorch provides an [implementation](https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html). This is another good example where relying on a widely used library saves us a lot of implementation time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=40.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9c60ffa25591444db16dacb0c1041111"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import trange\n",
    "\n",
    "neurons = LIFRefracNeurons(H,N).to(device)\n",
    "optim = torch.optim.Adam(neurons.parameters())\n",
    "losses = []\n",
    "\n",
    "epochs = 40 # number of epochs to optimize\n",
    "pbar = trange(epochs)\n",
    "\n",
    "for i in pbar:\n",
    "  optim.zero_grad()\n",
    "\n",
    "  # sample new poisson noise for every iteration\n",
    "  z = poisson_encode(0.3 * torch.ones(B, H), T).detach().to(device)\n",
    "\n",
    "  # compute the refractory state and voltage traces\n",
    "  refrac, voltages = neurons(z)\n",
    "\n",
    "  # the first three neurons in each batch are our readout neurons\n",
    "  readout = refrac[:,:,:3]\n",
    "\n",
    "  # compute sampled probability distribution\n",
    "  p_f = three_point_correlations_func(refrac).flatten(start_dim=1)\n",
    "\n",
    "  # batch averaged Kullback-Leibler divergence \n",
    "  loss = torch.nn.functional.kl_div(torch.log(p_f.clamp(min=1e-7)), p_target, reduction='batchmean')\n",
    "\n",
    "  # propagate gradient to parameters through time and take optimisation step\n",
    "  loss.backward()\n",
    "  optim.step()\n",
    "\n",
    "  pbar.set_postfix({\"loss\": loss.detach().item()})\n",
    "  losses.append(loss.detach().item())\n",
    "\n",
    "\n",
    "# save all the training results\n",
    "basepath = \"results/stochastic\"\n",
    "os.makedirs(basepath, exist_ok=True)\n",
    "np.save(os.path.join(basepath, \"refrac.npy\"), refrac.detach().numpy())\n",
    "np.save(os.path.join(basepath, \"p_target.npy\"), p_target.detach().numpy())\n",
    "np.save(os.path.join(basepath, \"p_f.npy\"), p_f.detach().numpy())\n",
    "np.save(os.path.join(basepath, \"losses.npy\"), np.stack(losses))\n",
    "np.save(os.path.join(basepath, \"voltages.npy\"), voltages.detach().numpy())"
   ]
  },
  {
   "source": [
    "The model should converge to a loss of ~0.02-0.04.\n",
    "\n",
    "Tasks / Questions:\n",
    "- What could be done to improve upon this result?\n",
    "- Change the input poisson noise, what happens?\n",
    "- Find out about different [optimisers](https://pytorch.org/docs/stable/optim.html) and try another one."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Evaluating the Result\n",
    "\n",
    "Every scientist knows that a great plot is more than half of the reward.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "basepath = \"results/stochastic\"\n",
    "\n",
    "refrac = torch.tensor(np.load(os.path.join(basepath, \"refrac.npy\")))\n",
    "p_target = torch.tensor(np.load(os.path.join(basepath, \"p_target.npy\")))\n",
    "p_f = torch.tensor(np.load(os.path.join(basepath, \"p_f.npy\")))\n",
    "losses = torch.tensor(np.load(os.path.join(basepath, \"losses.npy\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code is not really part of the workshop, but is necessary to\n",
    "create a decent (enough) looking figure of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.collections import PatchCollection, LineCollection\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "def negedge(z, z_prev):\n",
    "  return (1-z) * z_prev\n",
    "\n",
    "def posedge(z, z_prev):\n",
    "  return z * (1 - z_prev)\n",
    "\n",
    "mpl.rcParams['ytick.left'] = True\n",
    "mpl.rcParams['ytick.labelleft'] = True\n",
    "mpl.rcParams['axes.spines.left'] = True\n",
    "mpl.rcParams['axes.spines.right'] = False\n",
    "mpl.rcParams['axes.spines.top'] = False\n",
    "mpl.rcParams['axes.spines.bottom'] = True\n",
    "mpl.rcParams['legend.frameon'] = False\n",
    "\n",
    "def sampling_figure(ax, refrac):\n",
    "  T, _, _ = refrac.shape\n",
    "  N = 3\n",
    "\n",
    "  posedges = []\n",
    "  negedges = []\n",
    "\n",
    "  z_prev = torch.zeros_like(refrac[0])\n",
    "  z = refrac[0,:]\n",
    "\n",
    "  ax.set_yticks(1.2 * np.arange(0,5) + 0.6)\n",
    "  ax.set_yticklabels(['$z_1$', '$z_2$', '$z_3$', '$z_4$', '$z_5$'])\n",
    "  ax.spines['left'].set_visible(False)\n",
    "\n",
    "  for ts in range(T):\n",
    "    z = refrac[ts]\n",
    "    posedges.append(posedge(z, z_prev))\n",
    "    negedges.append(negedge(z, z_prev))\n",
    "    z_prev = z\n",
    "\n",
    "  posedges = torch.stack(posedges)\n",
    "  negedges = torch.stack(negedges)\n",
    "\n",
    "  lines = []\n",
    "  rects = []\n",
    "\n",
    "  facecolor = \"black\"\n",
    "\n",
    "  for k in range(N):\n",
    "      t = posedges[200:400,0,k].to_sparse().coalesce().indices()[0]\n",
    "      t_r = negedges[200:400,0,k].to_sparse().coalesce().indices()[0]\n",
    "\n",
    "\n",
    "      for ts, tf in zip(t, t_r):\n",
    "        rect = Rectangle((ts, k * 1.2), tf - ts, 1.0)\n",
    "        line = [(ts, k*1.2), (ts, k*1.2 + 1.0)]\n",
    "        rects.append(rect)\n",
    "        lines.append(line)\n",
    "\n",
    "  pc = PatchCollection(rects, facecolor=facecolor, alpha=0.2, edgecolor=None)\n",
    "  lc = LineCollection(lines, color=\"black\")\n",
    "\n",
    "  for k in range(N):\n",
    "    ax.plot(voltages[200:400,0,k].detach() + k * 1.2, label=f\"$v_{k}$\")\n",
    "\n",
    "\n",
    "  ax.add_collection(pc)\n",
    "  ax.set_ylabel(\"Neuron\")\n",
    "  ax.set_xlabel(\"T [ms]\")\n",
    "  # ax.add_collection(lc)\n",
    "  ax.legend(loc=\"upper right\", bbox_to_anchor=(1.15, 1))\n",
    "\n",
    "\n",
    "def figure_stochastic_computing(p_f, p_target, losses, refrac):\n",
    "  width = 0.35\n",
    "\n",
    "  fig = plt.figure(constrained_layout=True, figsize=(7, 9))\n",
    "  gs = fig.add_gridspec(nrows=3, ncols=1)\n",
    "\n",
    "  ax_sampling = fig.add_subplot(gs[0,0])\n",
    "  ax_prob = fig.add_subplot(gs[1,0])\n",
    "  ax_loss = fig.add_subplot(gs[2,0])\n",
    "\n",
    "  p_f_mean = np.mean(p_f.detach().numpy(),axis=0)\n",
    "  p_f_min = np.min(p_f.detach().numpy(), axis=0)\n",
    "  p_f_max = np.max(p_f.detach().numpy(), axis=0) \n",
    "\n",
    "  ax_sampling.text(-0.1, 1.1, \"A\", transform=ax_sampling.transAxes, size=\"medium\", weight=\"normal\")\n",
    "  sampling_figure(ax_sampling, refrac)\n",
    "\n",
    "  # plot the probabilities\n",
    "\n",
    "  ax_prob.text(-0.1, 1.1, \"B\", transform=ax_prob.transAxes, size='medium', weight='normal')\n",
    "  ax_prob.bar(np.arange(0,8)-width/2, p_f[0].detach().numpy(), width, label='$p_s$')\n",
    "  ax_prob.bar(np.arange(0,8)+width/2, p_target[0], width, label='$p_{t}$')\n",
    "  ax_prob.legend(loc='upper right')\n",
    "  ax_prob.set_xticks(np.arange(0,8))\n",
    "  ax_prob.set_xticklabels(['{0:03b}'.format(n) for n in np.arange(0,8)], rotation = 90)\n",
    "  ax_prob.set_ylabel('$p(z)$')\n",
    "  ax_prob.set_xlabel('$z$')\n",
    "  ax_prob.errorbar(np.arange(0,8)-width/2, p_f_mean, yerr=[p_f_mean - p_f_min, p_f_max - p_f_mean], color='r', fmt='.k')\n",
    "\n",
    "  ax_loss.text(-0.1, 1.1, \"C\", transform=ax_loss.transAxes, size='medium', weight='normal')\n",
    "  ax_loss.semilogy(losses)\n",
    "  ax_loss.set_ylabel('$KL(p_s | p_t)$')\n",
    "  ax_loss.set_xlabel('Epoch')\n",
    "  return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting plot shows part of the neuron membrane traces of the three readout neurons, the value of the binary refractory state variables is indicated by gray shading (A). We also see how well the sampled distribution approximates the target distribution (B). Finally we plot the Kulback-Leibler divergence (C)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = figure_stochastic_computing(p_f, p_target, losses, refrac)\n",
    "fig.savefig('stochastic_computing.png', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "source": [
    "The same kind of experiment can be repeated for 5 variables. Below\n",
    "is an example of one such run.\n",
    "\n",
    "\n",
    "![](https://raw.githubusercontent.com/norse/norse-hbp-workshop/main/images/stochastic_computing.png)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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