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.

CUDA Optimization Strategies:

Vectorized Memory Access

Uses float4 for 4-element vector loads

Reduces memory instructions by 4x

Better memory bandwidth utilization

Fast Math Operations

Compiler flag: --use_fast_math

Optimized logf computations

-O3 for maximum optimization

Parallel Reduction

Warp shuffle with #pragma unroll

Shared memory for block-level reduction

Double precision accumulation

Grid-Stride Loop

Processes vector elements with stride

Efficient GPU utilization

Handles arbitrary tensor sizes

Conditional Optimization

Branch for y > 0 condition

Zero assignment for invalid cases

Minimal branching overhead

Tail Handling

Single thread processes remainder elements

Maintains correctness for all sizes

Minimal performance impact

Performance Tuning

Fixed 256 threads per block

Block count capped at 1024

Ensures at least 1 block



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().__init__()
        self.loss = nn.KLDivLoss(reduction='mean', log_target=False)

    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        return self.loss(x, y)

batch_size = 1024
feature_dim = 512

def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    x = torch.nn.functional.log_softmax(x, dim=1) # KLDiv expects log-probs
    y = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    y = torch.nn.functional.softmax(y, dim=1)     # KLDiv expects probs
    return [x, y]

def get_init_inputs():
    return []]