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CatConv2d

Concat+Conv2d fused all-in-one CUDA kernel extension for Pytorch

• Optimized for Volta, 10% faster than torch concat+conv (batch size = 1)

    x = CatConv2d(x_list)
    
    # effectivly equal to
    x = torch.cat(x_list, 1)
    x = conv.forward(x)

Features

1. Conv1x1 (GEMM) CUDA kernel
2. Conv3x3 Winograd CUDA kernel, fused with input/weight transform
3. Take python list of Tensor as input

Limitations

1. Forward only
2. Maximum 16 Tensors for the input list
3. NCHW layout
4. Channel size >= 4 for each input Tensor in the list
5. Hardcoded zero padding (padding=1 for Conv3x3)
6. Optimized for small batch size
7. Optimized on Volta

TODO List

1. Backward path
2. SASS level optimization
3. TensorCore 16bit version

Requirement

• Cuda 10.2

Usage

python setup.py install

python test.py cuda

Result on Titan V

• Test target: HarDNet85 with 512x512 input size

 Torch Conv:  6.489803947205326 ms
    CatConv:  5.862883790766858 ms  lantency reduction: 9.660078509897597 %

• Applied on FCHarDNet gives a signifcant speed up (70 fps -> 99 fps)