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__ and tree‑based reduction to sum loss across threads.

Strided Loop for Large Batches: Each thread processes multiple batch items with stride gridDim.x * blockDim.x.

DQN TD‑Error Calculation:

Extracts Q‑value for taken action via indexing.

Computes max(next_q_values) across action dimension (serial loop).

Target: reward + gamma * max_next_q * (1 - done).

Loss: squared difference (q_pred - q_target)^2.

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

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

Hyperparameter Handling: Constructor takes gamma (converted from Tensor if needed) and passes it to the kernel.

Mixed Datatypes: Uses long for action indices and float for Q‑values, rewards, and dones.

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

    def forward(self, q_values: torch.Tensor, actions: torch.Tensor, rewards: torch.Tensor, next_q_values: torch.Tensor,
                dones: torch.Tensor) -> torch.Tensor:
        batch_size = q_values.shape[0]
        q_pred = q_values[torch.arange(batch_size), actions.long()]
        q_target = rewards + self.gamma * next_q_values.max(dim=1)[0] * (1 - dones)
        loss = ((q_pred - q_target) ** 2).mean()
        return loss


batch_size = 32
action_dim = 4


def get_inputs():
    q_values = torch.randn(batch_size, action_dim)
    actions = torch.randint(0, action_dim, (batch_size,))
    rewards = torch.randn(batch_size)
    next_q_values = torch.randn(batch_size, action_dim)
    dones = torch.randint(0, 2, (batch_size,)).float()
    return [q_values, actions, rewards, next_q_values, dones]


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