feat(nn): Add ReLU² (Squared ReLU) activation function

[m96-chan]

Summary

Implement ReLU² (Squared ReLU) activation function for MLP layers.

Formula

ReLU²(x) = (max(0, x))² = ReLU(x)²

Background

ReLU² was introduced in the Primer paper and has shown benefits:

• Sparsity: Stronger sparsity than standard ReLU
• Smoothness: Continuous first derivative (unlike ReLU)
• Performance: Improved training dynamics in some architectures

Used in:

• Primer (Google, 2021)
• Some MoE implementations
• Sparse attention variants

Comparison

Activation	Formula	Derivative
ReLU	max(0, x)	1 if x > 0 else 0
ReLU²	max(0, x)²	2x if x > 0 else 0
GELU	x * Φ(x)	complex
SiLU	x * σ(x)	σ(x) + xσ(x)(1-σ(x))

Proposed Implementation

Native Kernels

native/ops/nn/activation/
├── gelu.inl      # Existing
├── silu.inl      # Existing
├── relu2.inl     # NEW

Add to activation_kernels.cuh:

// ReLU² kernel
__global__ void relu2_f32_kernel(
    const float* __restrict__ x,
    float* __restrict__ y,
    size_t n
) {
    size_t idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        float val = x[idx];
        float relu_val = fmaxf(0.0f, val);
        y[idx] = relu_val * relu_val;
    }
}

Python API

from pygpukit.ops.nn import relu2

# Basic usage
y = relu2(x)

# With pre-allocated output (for CUDA Graph)
relu2(x, out=y)

Tasks

• Implement relu2 kernels (f32, f16, bf16)
• Add in-place variant relu2_
• Add Python bindings
• Add Python API in ops/nn.py
• Add tests
• Add to benchmark suite

References

• Primer paper: https://arxiv.org/abs/2109.08668
• "So You Want to Train a Transformer": discusses activation choices

[m96-chan]

Implemented in PR #174. Added ReLU squared (relu2) activation function with GPU kernel and CPU fallback.