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qnq's not quantization

Project description

QNQ -- QNQ's not quantization


The toolkit is for Techart algorithm team to quantize their custom neural network's pretrained model. The toolkit is beta now, you can contact me with email( for adding ops and fixing bugs.

How to install

pip install qnq

How to quantize

  1. Prepare your model.

    1. Check if your model contains non-class operator, like torch.matmul.
    2. If True, add from qnq.operators.torchfunc_ops import * to your code.
    3. Then use class replace non-class operator, you can refer fellow #! add by dongz
    class BasicBlock(nn.Module):
        expansion = 1
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(BasicBlock, self).__init__()
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn1 = nn.BatchNorm2d(planes)
            self.relu1 = nn.ReLU(inplace=True)
            self.relu2 = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = nn.BatchNorm2d(planes)
            self.downsample = downsample
            self.stride = stride
            #! add by dongz
            self.torch_add = TorchAdd()
        def forward(self, x):
            identity = x
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu1(out)
            out = self.conv2(out)
            out = self.bn2(out)
            if self.downsample is not None:
                identity = self.downsample(x)
            #! add by dongz
            out = self.torch_add(out, identity)
            # out += identity
            out = self.relu2(out)
            return out
  2. Prepare your loader.

    1. Your loader.__getitem__() should return a tuple like (data, label) or (data, index), qnq will use loader.__getitem__()[0] to forward your model.
  3. Prepare pretrained checkpoints.

    1. Train your model and use to save your checkpoints.
    2. Use checkpoints = torch.load(checkpoints_path) and model.load_state_dict(checkpoints) to load your checkpoints.
  4. Quantize

    1. Add from qnq import quantize
    2. Call quantize(model, bit_width, data_loader, path).

How to eval with quantization

  1. In the program
    1. quantize() will turn on 'eval mode' for model, that will automatically quantize activation, and weight already be fixed-point right now.
    2. Just call your origin version eval()
  2. Eval quantize.pth
    1. Coming soon!

How to debug

  1. Call quantize(model, bit_width, data_loader, path, is_debug=True).
  2. Debug mode will plot every layer's stats.

How QNQ work

Coming soon!

Operators supported

  • Convolution Layers
    • Conv
  • Pooling Layers
    • AveragePool
    • AdaptiveAvgPool
  • Activation
    • Relu
  • Normalization Layers
    • BatchNorm
  • Linear Layers
    • Linear
  • Torch Function
    • Add, Minus, DotMul, MatMul, Div
    • Sin, Cos
    • SoftMax

Project details

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