Canvas: End-to-End Kernel Architecture Search in Neural Networks

This is the development repository of Canvas, a library for sampling fine-grained PyTorch kernels (similar to NAS). Canvas samples from a rich set of fine-grained primitives to stochastically and iteratively construct new kernels and evaluate them according to user-specified constraints. Canvas supports freely adjustable tensor dimension sizes inside the kernel and uses two levels of solvers to satisfy structural legality and fully utilize model budgets.

Quick Start

Installation

python setup.py install

Interfaces

Canvas only has 3 interfaces:

• canvas.Placeholder(): A placeholder module that can be used to replace any module in a neural network, you can simply declare any placeholder module and use it as a normal module in your neural network. Note that the placeholder module will produce a same-shape output as the input. The shape of the input size should be [N, C*, H*, W*]. "*" means that the dimension can be non-existent.
• canvas.sample(): Sample an available kernel for a module from the search space. This function will find all placeholders in the module, and sample an available to substitute the originals.
• canvas.replace(): Replace all kernel placeholders of n with sample kernels in pack.

For more details, please refer to the doc-string of the Python interfaces.

Example

Below is a simple example of using Canvas to search for a kernel in a convolutional neural network.

import canvas
import torch
from torch import nn


class ExampleModel(nn.Module):
    def __init__(self):
        super(ExampleModel, self).__init__()
        self.proj = nn.Conv2d(3, 32, 1)
        # Initialize the module to be sampled with `canvas.Placeholder()`
        self.kernel_1 = canvas.Placeholder()
        self.bn = nn.BatchNorm2d(32)
        self.relu = nn.ReLU(inplace=True)
        # Initialize the module to be sampled with `canvas.Placeholder()`
        self.kernel_2 = canvas.Placeholder()

    def forward(self, x: torch.Tensor):
        x = self.proj(x)
        # All placeholders will produce a same-shape output as the input
        x = self.kernel_1(x)
        x = self.relu(self.bn(x))
        x = self.kernel_2(x)
        return x

if __name__ == "__main__":
    # Initialize the model
    model = ExampleModel()
    # Sample a kernel
    # You may also repeat the sampling process in a loop to find a better kernel
    kernel_pack = canvas.sample(model, example_input=torch.randn(1, 3, 224, 224))
    # Replace the original kernel with the sampled one
    canvas.replace(model, kernel_pack.module)
    # Print PyTorch implementation of the sampled kernel
    print(f"Sampled kernel code: {kernel_pack.torch_code}")

An example output of the PyTorch implementation of the sampled kernel is shown below:

class Kernel_4740052357514212317(nn.Module):
    def __init__(self, c: int, h: int, w: int):
        # Configurations
        super(Kernel_4740052357514212317, self).__init__()
        self.g = 4
        self.n, self.c, self.h, self.w = None, c, h, w
        
        # Kernels
        # Input: p_0
        pass
        # UnfoldW_K5_D2: p_1
        pass
        # BMM_0_1: p_2
        pass
        # ReLU: p_3
        pass
        # UnfoldH_K5_D1: p_4
        pass
        # GeLU: p_5
        pass
        # Scale_0/1/C_1/1/C_1/3/KW: p_6
        self.p_6_w = nn.Parameter(torch.ones((1, self.c, self.c, 5,)), requires_grad=True)
        nn.init.trunc_normal_(self.p_6_w, std=.02)
        # BMM_0_0: p_7
        pass
        # Output: p_8
        pass
    
    def forward(self, x: torch.Tensor):
        # Input: p_0
        t_0 = x
        self.n = t_0.size(0)
        assert (self.n, self.c, self.h, self.w) == tuple(t_0.size())
        # UnfoldW_K5_D2: p_1
        t_1 = F.unfold(t_0, (1, 5), dilation=(1, 2), padding=(0, 4))
        t_1 = t_1.view(self.n, self.c, 5, self.h, self.w)
        # BMM_0_1: p_2
        t_2_lhs = t_0.view(self.n, self.c, self.h * self.w)        
        t_2_rhs = t_1.view(self.n, self.c * 5, self.h * self.w).transpose(1, 2)        
        t_2 = torch.bmm(t_2_lhs, t_2_rhs) / math.sqrt(self.h * self.w)
        t_2 = t_2.view(self.n, self.c, self.c, 5)
        # ReLU: p_3
        t_3 = torch.relu(t_0)
        # UnfoldH_K5_D1: p_4
        t_4 = F.unfold(t_3, (5, 1), dilation=(1, 1), padding=(2, 0))
        t_4 = t_4.view(self.n, self.c, 5, self.h, self.w)
        # GeLU: p_5
        t_5 = F.gelu(t_4)
        # Scale_0/1/C_1/1/C_1/3/KW: p_6
        t_6 = self.p_6_w * t_2
        # BMM_0_0: p_7
        t_7_lhs = t_6.view(self.n, self.c, self.c * 5)        
        t_7_rhs = t_5.view(self.n, self.c * 5, self.h * self.w)        
        t_7 = torch.bmm(t_7_lhs, t_7_rhs) / math.sqrt(self.c * 5)
        t_7 = t_7.view(self.n, self.c, self.h, self.w)
        # Output: p_8
        return t_7.view(self.n, self.c, self.h, self.w)

Citation

@misc{zhao2023canvas,
      title={Canvas: End-to-End Kernel Architecture Search in Neural Networks}, 
      author={Chenggang Zhao and Genghan Zhang and Ao Shen and Mingyu Gao},
      year={2023},
      eprint={2304.07741},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}