quantization_quick_start_mnist.ipynb

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        "%matplotlib inline"
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        "\n# Quantization Quickstart\n\nQuantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.\n\nIn NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.\nHere we use `QAT_Quantizer` as an example to show the usage of quantization in NNI.\n"
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      "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 `Quantizing Model`_.\n\n"
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      "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# 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)"
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      "source": [
        "## Quantizing Model\n\nInitialize a `config_list`.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\n"
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        "config_list = [{\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_names': ['conv1']\n}, {\n    'quant_types': ['output'],\n    'quant_bits': {'output': 8},\n    'op_names': ['relu1']\n}, {\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_names': ['conv2']\n}, {\n    'quant_types': ['output'],\n    'quant_bits': {'output': 8},\n    'op_names': ['relu2']\n}]"
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        "finetuning the model by using QAT\n\n"
      ]
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      "source": [
        "from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\ndummy_input = torch.rand(32, 1, 28, 28).to(device)\nquantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)\nquantizer.compress()"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "The model has now been wrapped, and quantization targets ('quant_types' setting in `config_list`)\nwill be quantized & dequantized for simulated quantization in the wrapped layers.\nQAT is a training-aware quantizer, it will update scale and zero point during training.\n\n"
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      "execution_count": null,
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      "source": [
        "for epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
      ]
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      "source": [
        "export model and get calibration_config\n\n"
      ]
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      "execution_count": null,
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      "source": [
        "model_path = \"./log/mnist_model.pth\"\ncalibration_path = \"./log/mnist_calibration.pth\"\ncalibration_config = quantizer.export_model(model_path, calibration_path)\n\nprint(\"calibration_config: \", calibration_config)"
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