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:

Fused element-wise kernel: Single kernel computes entire Softsign operation

Grid-stride loops: Efficient memory access pattern for arbitrary input sizes

CUDA math intrinsics: fabsf for optimized absolute value calculation

Memory optimization: Contiguous tensor access with coalesced memory reads/writes

Fast math compilation: --use_fast_math flag for optimized floating-point operations

Inline CUDA extension: Runtime kernel compilation in PyTorch

Key optimization features:

No intermediate tensors: Eliminates torch.abs() temporary allocation

Single kernel launch: Replaces multiple PyTorch operator calls

Branch-free computation: Pure mathematical expression without conditionals

Optimal thread utilization: Automatic grid dimension calculation

Memory locality: Sequential access pattern across all elements


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


B, C = 128, 512


class Softsign(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        return input / (1.0 + torch.abs(input))


class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.op = Softsign()

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


def get_inputs():
    input = torch.randn(B, C, dtype=torch.float32)
    return [input]


def get_init_inputs():
    return []