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.
CUDA kernel for Range Scale Gate activation with vectorized elementwise operations.

Optimizations:

Vectorized Memory Operations: Uses float4 loads/stores to process 4 elements per instruction.

Coalesced Memory Access: Threads access contiguous memory locations via vectorized operations.

Fast Math: Compiler flags enable fast approximate expf and sigmoid.

Elementwise operation:

Shifted sigmoid: gate_scaled = 2·sigmoid(x) - 1

Transforms range from (0,1) to (-1,1)

Gated multiplication: output = x * gate_scaled

Mathematically:
output = x·(2·sigmoid(x) - 1)

Characteristics:

Sigmoid creates symmetric gating (-1 to 1 multiplier).

Self-gating: input modulates its own magnitude and sign.

Output preserves input sign (if x > 0, gate ∈ (-1,1); if x < 0, gate ∈ (-1,1)).

Similar to tanh but with input-dependent scaling.

Use cases:

Attention mechanisms with signed gating.

Activation functions needing sign preservation.

Self-modulating neural units.

Specialized for scenarios where both magnitude scaling and potential sign flipping are desired based on input value.






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


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

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_scaled = 2.0 * torch.sigmoid(x) - 1.0
        return x * gate_scaled


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 []