You write custom CUDA kernels to replace the PyTorch operators in the given EvoNorm architecture to get speedups.
You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining normalization+affine_transform+nonlinear_gating), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.


Techniques used:

CUDA inline extension in PyTorch

Float4 vectorization for memory coalescing

Element-wise kernel with grid-stride loops

CUDA math intrinsics: fabsf, copysignf, fmaxf

Branch-free implementation of softshrink formula

Mathematical optimization: copysignf for robust sign handling

Memory layout optimization: contiguous tensor access

Grid size tuning: automatic block calculation with 1024 limit

Parameter passing: lambda value as kernel argument



Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is:
import torch
import torch.nn as nn
import torch.nn.functional as F


N, C, H, W = 16, 16, 64, 64
LAMBDA = 0.5


class Softshrink(nn.Module):

    def __init__(self, lambd=LAMBDA):
        super().__init__()
        self.lambd = lambd

    def forward(self, input: torch.Tensor) -> torch.Tensor:



        abs_val_minus_lambda = torch.abs(input) - self.lambd


        thresholded_magnitude = torch.relu(abs_val_minus_lambda)


        return torch.sign(input) * thresholded_magnitude


class Model(nn.Module):
    def __init__(self, lambd=LAMBDA):
        super().__init__()
        self.op = Softshrink(lambd=lambd)

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return self.op(input)


# --- 辅助函数 ---

def get_inputs():

    torch.manual_seed(42)
    x = torch.randn(N, C, H, W, dtype=torch.float32) * 2.0
    return [x]


def get_init_inputs():
    return [LAMBDA]