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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Pruning Quickstart\n\nModel pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.\nIt usually has following paths:\n\n#. Pre-training a model -> Pruning the model -> Fine-tuning the model\n#. Pruning the model aware training -> Fine-tuning the model\n#. Pruning the model -> Pre-training the compact model\n\nNNI supports the above three modes and mainly focuses on the pruning stage.\nFollow this tutorial for a quick look at how to use NNI to prune a model in a common practice.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preparation\n\nIn this tutorial, we use a simple model and pre-train on MNIST dataset.\nIf you are familiar with defining a model and training in pytorch, you can skip directly to `Pruning Model`_.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\n\nfrom scripts.compression_mnist_model import TorchModel, trainer, evaluator, device\n\n# define the model\nmodel = TorchModel().to(device)\n\n# show the model structure, note that pruner will wrap the model layer.\nprint(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# define the optimizer and criterion for pre-training\n\noptimizer = SGD(model.parameters(), 1e-2)\ncriterion = F.nll_loss\n\n# pre-train and evaluate the model on MNIST dataset\nfor epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Pruning Model\n\nUsing L1NormPruner pruning the model and generating the masks.\nUsually, pruners require original model and ``config_list`` as parameters.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\nThis `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,\nexcept the layer named `fc3`, because `fc3` is `exclude`.\nThe final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "config_list = [{\n    'sparsity_per_layer': 0.5,\n    'op_types': ['Linear', 'Conv2d']\n}, {\n    'exclude': True,\n    'op_names': ['fc3']\n}]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Pruners usually require `model` and `config_list` as input arguments.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner\npruner = L1NormPruner(model, config_list)\n\n# show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.\nprint(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# compress the model and generate the masks\n_, masks = pruner.compress()\n# show the masks sparsity\nfor name, mask in masks.items():\n    print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Speed up the original model with masks, note that `ModelSpeedup` requires an unwrapped model.\nThe model becomes smaller after speed-up,\nand reaches a higher sparsity ratio because `ModelSpeedup` will propagate the masks across layers.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# need to unwrap the model, if the model is wrapped before speed up\npruner._unwrap_model()\n\n# speed up the model\nfrom nni.compression.pytorch.speedup import ModelSpeedup\n\nModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "the model will become real smaller after speed up\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fine-tuning Compacted Model\nNote that if the model has been sped up, you need to re-initialize a new optimizer for fine-tuning.\nBecause speed up will replace the masked big layers with dense small ones.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "optimizer = SGD(model.parameters(), 1e-2)\nfor epoch in range(3):\n    trainer(model, optimizer, criterion)"
      ]
    }
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