tutorials/2_Visualization.ipynb

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   "source": [
    "# Visualization in TorchOpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\">](https://colab.research.google.com/github/metaopt/torchopt/blob/main/tutorials/2_Visualization.ipynb)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In [PyTorch](https://pytorch.org), if the attribute `requires_grad` of a tensor is `True`, the computation graph will be created if we use the tensor to do any operations. The computation graph is implemented like link-list -- `Tensor`s are nodes and they are linked by their attribute `gran_fn`. [PyTorchViz](https://github.com/szagoruyko/pytorchviz) is a Python package that uses [Graphviz](https://graphviz.org) as a backend for plotting computation graphs. TorchOpt use PyTorchViz as the blueprint and provide more easy-to-use visualization functions on the premise of supporting all its functions."
   ]
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   "source": [
    "Let's start with a simple multiplication computation graph. We declared the variable `x` with flag `requires_grad=True` and compute `y = 2 * x`. Then we visualize the computation graph of `y`."
   ]
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      "<graphviz.graphs.Digraph object at 0x7fd0a30377f0>\n"
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   "source": [
    "from IPython.display import display\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torchopt\n",
    "\n",
    "\n",
    "x = torch.tensor(1.0, requires_grad=True)\n",
    "y = 2 * x\n",
    "display(torchopt.visual.make_dot(y, params={'x': x, 'y': y}))"
   ]
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    "The figure shows `y` is connected by the multiplication edge. The gradient of `y` will flow through the multiplication backward function then accumulated on `x`. Note that we pass a dictionary for adding node labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then let's plot a neural network. Note that we can pass the generator returned by method `named_parameters` for adding node labels."
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Linear(dim, 1, bias=True)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.fc(x)\n",
    "\n",
    "\n",
    "dim = 5\n",
    "batch_size = 2\n",
    "net = Net(dim)\n",
    "xs = torch.ones((batch_size, dim))\n",
    "ys = torch.ones((batch_size, 1))\n",
    "pred = net(xs)\n",
    "loss = F.mse_loss(pred, ys)\n",
    "\n",
    "display(torchopt.visual.make_dot(loss, params=(net.named_parameters(), {'loss': loss})))"
   ]
  },
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   "source": [
    "The computation graph of meta-learning algorithms will be much more complex. Our visualization tool allows users take as input the extracted network state for better visualization."
   ]
  },
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   "source": [
    "class MetaNet(nn.Module):\n",
    "    def __init__(self, dim):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Linear(dim, 1, bias=True)\n",
    "\n",
    "    def forward(self, x, meta_param):\n",
    "        return self.fc(x) + meta_param\n",
    "\n",
    "\n",
    "dim = 5\n",
    "batch_size = 2\n",
    "net = MetaNet(dim)\n",
    "\n",
    "xs = torch.ones((batch_size, dim))\n",
    "ys = torch.ones((batch_size, 1))\n",
    "\n",
    "optimizer = torchopt.MetaSGD(net, lr=1e-3)\n",
    "meta_param = torch.tensor(1.0, requires_grad=True)\n",
    "\n",
    "# Set enable_visual\n",
    "net_state_0 = torchopt.extract_state_dict(net, enable_visual=True, visual_prefix='step0.')\n",
    "\n",
    "pred = net(xs, meta_param)\n",
    "loss = F.mse_loss(pred, ys)\n",
    "optimizer.step(loss)\n",
    "\n",
    "# Set enable_visual\n",
    "net_state_1 = torchopt.extract_state_dict(net, enable_visual=True, visual_prefix='step1.')\n",
    "\n",
    "pred = net(xs, meta_param)\n",
    "loss = F.mse_loss(pred, torch.ones_like(pred))\n",
    "\n",
    "# Draw computation graph\n",
    "display(\n",
    "    torchopt.visual.make_dot(\n",
    "        loss, [net_state_0, net_state_1, {'meta_param': meta_param, 'loss': loss}]\n",
    "    )\n",
    ")"
   ]
  }
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