
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 Clip 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:

Sigmoid gate: gate = sigmoid(x)

Gated multiplication: gated = x * gate

Range clipping: output = clamp(gated, clip_min, clip_max)

Mathematically:
output = clamp(x·sigmoid(x), min, max)

Characteristics:

Sigmoid creates soft gating (0-1 multiplier).

Self-gating: input gates itself.

Hard clipping to predefined range.

Similar to Swish/SiLU but with clipping.

Use cases:

Controlled activation magnitude.

Preventing activation explosion.

Self-attention gating mechanisms.

Default clipping: [0.0, 6.0] (similar to ReLU6 but with gating).

Specialized for stable activation with bounded output range and input-dependent gating.



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__()
        self.clip_min = 0.0
        self.clip_max = 6.0

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate = torch.sigmoid(x)
        gated_output = x * gate
        return torch.clamp(gated_output, min=self.clip_min, max=self.clip_max)


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 []