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: Uses extern __shared__ memory and tree‑based reduction to sum squared differences.

Strided Loop for Scalability: Each thread processes multiple elements with stride gridDim.x * blockDim.x.

Value‑Function MSE Loss: Computes squared error (values - returns)^2 per element.

Atomic Finalization: atomicAdd accumulates the block‑averaged loss into a single‑element output tensor.

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

Memory Contiguity: Ensures 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):
        super(Model, self).__init__()

    def forward(self, values: torch.Tensor, returns: torch.Tensor) -> torch.Tensor:
        loss = ((values - returns) ** 2).mean()
        return loss


batch_size = 32


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


def get_init_inputs():
    return []