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.

Shared‑Memory Parallel Reduction: Kernel uses extern __shared__ memory and tree‑based reduction to compute the sum across threads.

Strided Loop for Large Tensors: Each thread processes multiple elements with stride gridDim.x * blockDim.x to handle arbitrary sizes.

Combined Actor‑Critic Loss:

Actor loss: -log_probs * advantages.

Critic loss: (values - returns)^2.

Weighted sum: actor_loss + value_coef * critic_loss.

Atomic Finalization: atomicAdd writes the block‑reduced average into a single‑element output tensor.

Block/Thread Configuration: 256 threads per block, up to 1024 blocks, with dynamic shared memory allocation.

Hyperparameter Support: Constructor accepts value_coef (converted from Tensor if needed) and passes it to the CUDA kernel.

Memory Contiguity: Ensures all input tensors are contiguous before kernel launch.



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

    def forward(self, log_probs: torch.Tensor, values: torch.Tensor, returns: torch.Tensor,
                advantages: torch.Tensor) -> torch.Tensor:
        actor_loss = -(log_probs * advantages).mean()
        critic_loss = ((values - returns) ** 2).mean()
        loss = actor_loss + self.value_coef * critic_loss
        return loss


batch_size = 256


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


def get_init_inputs():
    value_coef = torch.tensor(0.5)
    return [value_coef]