quantization_speedup.ipynb

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        "\n# SpeedUp Model with Calibration Config\n\n\n## Introduction\n\nDeep learning network has been computational intensive and memory intensive \nwhich increases the difficulty of deploying deep neural network model. Quantization is a \nfundamental technology which is widely used to reduce memory footprint and speedup inference \nprocess. Many frameworks begin to support quantization, but few of them support mixed precision \nquantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\\, only support simulated mixed precision quantization which will \nnot speedup the inference process. To get real speedup of mixed precision quantization and \nhelp people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different \nDL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model \nwith quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects \nTensorRT at this stage, and will support more backends in the future.\n\n\n## Design and Implementation\n\nTo support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  \nFrontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference \nframework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and \nTensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph \nrepresentation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx \nmodel to generate inference engine. \n\n\nQuantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.\nUsers should set config to train quantized model using QAT algorithm(please refer to `NNI Quantization Algorithms <https://nni.readthedocs.io/en/stable/Compression/Quantizer.html>`__\\  ).\nAfter quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.\n\n\nAfter getting mixed precision engine, users can do inference with input data.\n\n\nNote\n\n\n* Recommend using \"cpu\"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not \"cpu\"(host), this tool will transpose it to \"cpu\" which may increases unnecessary overhead.\n* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).\n* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.\n\n\n## Prerequisite\nCUDA version >= 11.0\n\nTensorRT version >= 7.2\n\nNote\n\n* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\\  \n\n## Usage\n"
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        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\nfrom scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt\n\nconfig_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}]\n\nmodel = TorchModel().to(device)\noptimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)\ncriterion = F.nll_loss\ndummy_input = torch.rand(32, 1, 28,28).to(device)\n\nfrom nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\nquantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)\nquantizer.compress()"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "finetuning the model by using QAT\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "for epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "export model and get calibration_config\n\n"
      ]
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        "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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "build tensorRT engine to make a real speedup\n\n"
      ]
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      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT\n# input_shape = (32, 1, 28, 28)\n# engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)\n# engine.compress()\n# test_trt(engine)"
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        "Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.\n\nFor complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.\n\nFor more parameters about the class 'TensorRTModelSpeedUp', you can refer to `Model Compression API Reference <https://nni.readthedocs.io/en/stable/Compression/CompressionReference.html#quantization-speedup>`__\\.\n\n### Mnist test\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 1, 28, 28)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.001199961\n     - 96%\n   * - mixed precision(average bit 20.4)\n     - 0.000753688\n     - 96%\n   * - all in 8bit\n     - 0.000229869\n     - 93.7%\n\n### Cifar10 resnet18 test (train one epoch)\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.003286268\n     - 54.21%\n   * - mixed precision(average bit 11.55)\n     - 0.001358022\n     - 54.78%\n   * - all in 8bit\n     - 0.000859139\n     - 52.81%\n\n"
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