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/stores

__ldg() for read-only caching through texture memory

Bit shifts for division (>> 2, << 2) for efficiency

Phish Activation Function

Computes Phish(x) = x * tanh(GELU(x))

Combination of GELU and tanh activations

Requires nested function evaluations

Optimized GELU Computation

Uses erff for error function approximation

Predefined constant M_SQRT1_2_F (1/√2)

Avoids repeated sqrtf calls

Memory Access

contiguous() tensors for coalescing

__restrict__ pointers

Grid-stride loop for arbitrary sizes

Performance Optimization

Compiler flags: -O3, --use_fast_math

Efficient kernel launch configuration

Block count limited to 65535

Uses tanhf for fast hyperbolic tangent

Mathematical Efficiency

Inline functions for GELU and Phish

Vectorized operations for 4 elements simultaneously

Single pass through data

Key Innovation: Vectorized Phish activation function combining GELU and tanh, optimized with mathematical constants and fast transcendental functions.


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


class Model(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Phish = x * tanh(GELU(x))
        return x * torch.tanh(F.gelu(x))


batch_size = 1024
feature_dim = 1024


def get_inputs():
    x = torch.randn(batch_size, feature_dim, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return []