You write custom CUDA kernels to replace the pytorch operators in the given GeGLU architecture to get speedups.

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.
Technologies Used in This Code
Core Libraries & Frameworks
PyTorch: Deep learning framework

CUDA: NVIDIA's parallel computing platform for GPU acceleration

C++: For high-performance kernel implementation

PyTorch Specific Components
torch.nn.Module: Base class for neural network modules

torch.utils.cpp_extension.load_inline: For inline compilation of CUDA/C++ extensions

PyTorch Tensors: Multi-dimensional arrays with automatic differentiation

CUDA/C++ Implementation Details
CUDA Kernels: Custom GPU kernel (bellman_loss_kernel)

CUDA Thread Management: Block/grid configuration for parallel execution

Memory Access Patterns: Using __restrict__ keyword for optimized memory access

Parallel Reduction: For finding maximum Q-value across actions

Reinforcement Learning Components
Bellman Equation: Q-learning update rule

Temporal Difference (TD) Error: Difference between current Q-value and target Q-value

Experience Components: Q-values, actions, rewards, next states, done flags

Discount Factor (gamma): Future reward discounting

Performance Optimizations
GPU Parallelization: Batch-level parallel processing

In-place Computation: Direct tensor operations without unnecessary copies

Fused Operations: Single kernel for complete loss computation




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=0.99):
        super(Model, self).__init__()
        self.gamma = gamma

    def forward(self, q_values, actions, rewards, next_q_values, dones):
        curr_q = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
        next_q_max = next_q_values.max(1)[0]
        target = rewards + self.gamma * next_q_max * (1.0 - dones)
        loss = (curr_q - target) ** 2
        return loss.mean()

batch_size = 1024
num_actions = 6

def get_inputs():
    q_values = torch.randn(batch_size, num_actions, requires_grad=True)
    actions = torch.randint(0, num_actions, (batch_size,))
    rewards = torch.randn(batch_size)
    next_q_values = torch.randn(batch_size, num_actions)
    dones = torch.zeros(batch_size)  # float 0.0 or 1.0
    return [q_values, actions, rewards, next_q_values, dones]

def get_init_inputs():
    return [0.99]