You write custom CUDA kernels to replace the PyTorch operators in the given EvoNorm 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 normalization+affine_transform+nonlinear_gating), or algorithmic changes (such as optimized memory access patterns). You are only limited by your imagination.


The provided code implements a custom CUDA kernel for 3D reflection padding in PyTorch using several advanced techniques:

Key Technologies Used:

Inline CUDA Extension in PyTorch: Uses torch.utils.cpp_extension.load_inline to compile and load CUDA code directly within Python, avoiding separate compilation steps.

Fused GPU Kernel Design: Implements a single kernel that handles both data loading and padding operations, reducing kernel launch overhead.

Shared Memory Optimization: Leverages CUDA shared memory (s_in[]) to cache input data, enabling faster data access patterns compared to global memory.

Multi-dimensional Thread Blocking: Employs 3D thread blocks (BLOCK_DIM_X/Y/Z) for efficient parallelization across depth, height, and width dimensions.

Strided Memory Access Patterns: Calculates explicit strides for both input and output tensors to optimize memory access.

Reflective Index Calculation: Implements a device-side reflect_idx function that handles boundary reflection using mathematical calculations rather than conditional branching.

Grid-Strided Loops: Uses grid-strided loops in the kernel to handle arbitrary output sizes while maintaining coalesced memory access.

Batched Channel Processing: Processes multiple batches and channels concurrently through grid dimensions (grid_dim(N, C)).

Runtime Bounds Checking: Includes comprehensive error checking for tensor dimensions and padding values.

Memory Contiguity Enforcement: Ensures input tensor is contiguous for optimal memory access patterns.

Performance Optimizations:

Shared memory caching of input data

Coalesced global memory accesses

Minimal synchronization points (single __syncthreads())

Compiler optimizations (-O3, --use_fast_math)

Grid-strided loops for load balancing


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

BATCH_SIZE = 8
CHANNELS = 16
DEPTH = 16  # D_in
HEIGHT = 16  # H_in
WIDTH = 16  # W_in

PADDING = (1, 1, 2, 2, 1, 0)


# -------------------------------------------------------------

class Model(nn.Module):

    def __init__(self, padding):
        super().__init__()

        if isinstance(padding, int):
            # F.pad 需要 6-tuple
            self.padding_tuple = (padding,) * 6
        else:
            self.padding_tuple = padding

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # F.pad 5D 张量 (N, C, D, H, W)
        # 填充顺序: (pad_W_L, pad_W_R, pad_H_T, pad_H_B, pad_D_F, pad_D_K)
        # 这与 nn.ReflectionPad3d 的构造函数顺序一致
        return F.pad(x, self.padding_tuple, mode='reflect')


def get_inputs():
    x = torch.randn(BATCH_SIZE, CHANNELS, DEPTH, HEIGHT, WIDTH, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return [PADDING]