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.

This CUDA kernel implements a Mish-B activation with the same core optimizations as previous kernels, plus a notable numerical stability enhancement:

Vectorized Memory Operations: Uses float4 loads/stores to process 4 elements per instruction, improving memory bandwidth utilization.

Coalesced Memory Access: Threads access contiguous memory locations via vectorized operations, enabling efficient memory coalescing.

Fast Math & Loop Unrolling: Compiler flags enable fast approximate math (tanhf, expf, log1pf) and implicit loop unrolling improves instruction-level parallelism.

Key Numerical Optimization: The softplus_stable_op function uses a numerically stable softplus implementation:

fmaxf(0.0f, y) + log1pf(expf(-fabsf(y)))

Avoids overflow for large positive y (via log1pf)

Avoids underflow for large negative y (via expf(-fabsf(y)))

More robust than naive log(1 + exp(y))

The kernel precomputes y = beta * x to avoid redundant multiplication in the elementwise function.



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, beta=1.0):
        super().__init__()
        self.beta = beta

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x * torch.tanh(F.softplus(self.beta * x))


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 [1.0]