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.
python setup.py installCanvas 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.
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)@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}
}