You write custom CUDA kernels to replace the pytorch operators in the given GeGLU 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 chunk+gelu+elementwise_mul), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.
Custom CUDA kernel extension via torch.utils.cpp_extension.load_inline

Polar coordinate transformation: (x,y) ↔ (r,θ) with learnable affine transforms

Element-wise parallelization using CUDA grid-stride loops over feature pairs

Per-feature-pair learnable parameters for radius and angle (weight_r, bias_r, weight_θ, bias_θ)

Contiguous tensor handling for all input tensors

Memory-efficient in-place-like computation with torch.empty_like

Mathematical operations: hypotf, atan2f, cosf, sinf

Fused multiply-add with __fmul_rn and __fadd_rn for precision control

Auto-tuning block/grid size based on number of feature pairs

Even-odd feature pairing for polar coordinate processing




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


class Model(nn.Module):
    def __init__(self, num_features=512):
        super().__init__()
        assert num_features % 2 == 0
        self.num_features = num_features
        self.weight_r = nn.Parameter(torch.ones(num_features // 2))
        self.bias_r = nn.Parameter(torch.zeros(num_features // 2))
        self.weight_theta = nn.Parameter(torch.ones(num_features // 2))
        self.bias_theta = nn.Parameter(torch.zeros(num_features // 2))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_even = x[:, 0::2]
        x_odd = x[:, 1::2]

        r = torch.hypot(x_even, x_odd)
        theta = torch.atan2(x_odd, x_even)

        r_new = r * self.weight_r + self.bias_r
        theta_new = theta * self.weight_theta + self.bias_theta

        x_even_new = r_new * torch.cos(theta_new)
        x_odd_new = r_new * torch.sin(theta_new)

        y = torch.empty_like(x)
        y[:, 0::2] = x_even_new
        y[:, 1::2] = x_odd_new
        return y


batch_size = 128
feature_dim = 512


def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []