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.

Element‑wise Policy Gradient Loss Kernel: Computes -log_probs * advantages per element.

Fixed Block Configuration: 256 threads per block, grid size determined by element count.

Built‑in Mean Reduction: Returns the mean of the loss directly in the CUDA wrapper.

Minimal Python Interface: Forward pass calls the compiled CUDA function advantage_loss_cuda.

Verbose Compilation: Displays compilation details (verbose=True).




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):
        super(Model, self).__init__()

    def forward(self, log_probs: torch.Tensor, advantages: torch.Tensor) -> torch.Tensor:
        loss = -(log_probs * advantages).mean()
        return loss


batch_size = 32


def get_inputs():
    log_probs = torch.randn(batch_size)
    advantages = torch.randn(batch_size)
    return [log_probs, advantages]


def get_init_inputs():
    return []