 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 via torch.utils.cpp_extension.load_inline.

Simple Element‑wise TD‑Error Kernel: Computes per‑element squared TD‑error:

td_target = rewards + gamma * next_values * (1 - dones)

td_error = values - td_target

loss = td_error^2

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

Built‑in Mean Reduction: Returns mean of squared TD‑errors directly in CUDA wrapper.

Lightweight Python Wrapper: Forward pass calls compiled CUDA function td_loss_cuda.

Hyperparameter Support: Constructor takes gamma and passes it to the kernel.

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

    def forward(self, values: torch.Tensor, rewards: torch.Tensor, next_values: torch.Tensor,
                dones: torch.Tensor) -> torch.Tensor:
        td_target = rewards + self.gamma * next_values * (1 - dones)
        td_error = values - td_target
        loss = (td_error ** 2).mean()
        return loss


batch_size = 32


def get_inputs():
    values = torch.randn(batch_size)
    rewards = torch.randn(batch_size)
    next_values = torch.randn(batch_size)
    dones = torch.randint(0, 2, (batch_size,)).float()
    return [values, rewards, next_values, dones]


def get_init_inputs():
    gamma = torch.tensor(0.99)
    return [gamma]