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

Proximal Policy Optimization (PPO) loss computation (clipped surrogate objective)

Probability ratio calculation: exp(new_log_prob - old_log_prob)

Clipping mechanism to bound ratio within [1-ε, 1+ε]

Advantage-weighted objective: min(ratio·A, clip(ratio)·A)

Element-wise parallelization across all timesteps/actions

Fixed block size (256 threads) with dynamic grid sizing

Contiguous tensor handling for memory coalescing

Mean reduction across all elements

Numerical stability via log-prob difference instead of division






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, clip_param=0.2):
        super(Model, self).__init__()
        self.clip_param = clip_param

    def forward(self, new_log_probs, old_log_probs, advantages):
        ratio = torch.exp(new_log_probs - old_log_probs)
        surr1 = ratio * advantages
        surr2 = torch.clamp(ratio, 1.0 - self.clip_param, 1.0 + self.clip_param) * advantages
        return -torch.min(surr1, surr2).mean()

batch_size = 1024

def get_inputs():
    new_log_probs = torch.randn(batch_size, requires_grad=True)
    old_log_probs = torch.randn(batch_size)
    advantages = torch.randn(batch_size)
    return [new_log_probs, old_log_probs, advantages]

def get_init_inputs():
    return [0.2]
