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.
PyTorch C++/CUDA Extension: Inline compilation using torch.utils.cpp_extension.load_inline.

Custom TRPO Loss Kernel: Computes two components per batch item:

Surrogate objective: -ratio * advantages, where ratio = exp(log_probs - old_log_probs).

KL divergence: Σ old_p * (log(old_p) - log(new_p)) across action dimensions.

Serial KL Summation: Each thread loops over all action dimensions to compute the KL term.

Fixed Block Configuration: 256 threads per block, grid size based on batch size.

Regularized Loss Combination: Returns surrogate_loss + max_kl * kl_loss after taking means.

Numerical Safety: Checks probability values > 1e-8 before log operations.

Python Wrapper with Hyperparameter: Accepts max_kl as a constructor argument, passed to the CUDA 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


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

    def forward(self, log_probs: torch.Tensor, old_log_probs: torch.Tensor, advantages: torch.Tensor,
                old_probs: torch.Tensor, new_probs: torch.Tensor) -> torch.Tensor:
        ratio = torch.exp(log_probs - old_log_probs)
        surrogate_loss = -(ratio * advantages).mean()
        kl_div = (old_probs * (torch.log(old_probs) - torch.log(new_probs))).sum(dim=-1).mean()
        loss = surrogate_loss + self.max_kl * kl_div
        return loss


batch_size = 32
action_dim = 4


def get_inputs():
    log_probs = torch.randn(batch_size)
    old_log_probs = torch.randn(batch_size)
    advantages = torch.randn(batch_size)
    old_probs = torch.softmax(torch.randn(batch_size, action_dim), dim=-1)
    new_probs = torch.softmax(torch.randn(batch_size, action_dim), dim=-1)
    return [log_probs, old_log_probs, advantages, old_probs, new_probs]


def get_init_inputs():
    max_kl = torch.tensor(0.01)
    return [max_kl]