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.


Core Optimization Techniques:

Performance Optimizations

Fast Math Compilation - Uses --use_fast_math flag for accelerated math operations

Two-Stage Reduction - Block-level reduction followed by final reduction kernel

Grid Size Limiting - Caps grid size at MAX_GRID_SIZE (4096) for optimal resource usage

Compiler Optimizations - -O3 flag for aggressive performance tuning

Memory Optimizations

Memory Coalescing - Ensures contiguous memory access patterns

Efficient Reduction - Partial sums stored in shared memory for block-level reduction

Strided Access - Processes elements with grid-stride loops for load balancing

Numerical Stability

Stable BCE Loss - Implements numerically stable binary cross-entropy:

float maxi = (x > 0.0f) ? x : 0.0f;
float loss = maxi - x * t + __logf(1.0f + __expf(-fabsf(x)));
Fast Math Functions - Uses CUDA intrinsic functions (__logf, __expf, fabsf)

Kernel Design

Separate Reduction Paths - Distinct handling for reduction vs non-reduction cases

Dual Gradient Computation - Computes gradients for both input and target tensors

Efficient Sigmoid - Uses 1.0f / (1.0f + __expf(-x)) for sigmoid calculation

API & Usability

Multiple Reduction Support - Full support for 'none', 'mean', and 'sum' reductions

Proper Gradient Scaling - Correctly scales gradients based on reduction type

Automatic Device Handling - Moves tensors to GPU if not already there

Key Features

Generator-Specific Loss - Optimized for generative model training

Fast Backward Pass - Efficient gradient computation using sigmoid derivatives

Memory Efficient - Minimal intermediate storage requirements

This implementation provides high-performance generator loss computation with numerical stability, specifically optimized for training generative adversarial networks.


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
import torch.nn.functional as F

N, C, H, W = 32, 1, 64, 64


class GeneratorLoss(nn.Module):
    def __init__(self, reduction='mean', beta=1.0):
        super().__init__()
        self.reduction = reduction
        if reduction not in ['none', 'mean', 'sum']:
            raise ValueError("Invalid reduction mode")

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:

        loss = F.relu(input) - input * target + torch.log1p(torch.exp(-torch.abs(input)))

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:
            return loss


class Model(nn.Module):
    def __init__(self, reduction='mean', beta=1.0):
        super().__init__()
        self.op = GeneratorLoss(reduction, beta)

    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
        if isinstance(input, (list, tuple)) and len(input) > 0:
            input = input[0]
            target = target[0] if len(target) > 0 else target

        return self.op(input, target)


def get_inputs():
    input = torch.randn(N, C, H, W, dtype=torch.float32)
    target = torch.ones(N, C, H, W, dtype=torch.float32)
    return [input, target]


def get_init_inputs():
    return ['mean', 1.0]