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.

Here's a summary of the technologies used in the ReflectionPad2d CUDA implementation:

Key Technologies Used:

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

Fused GPU Kernel Design: Implements a single kernel that combines data loading and padding operations into one efficient pass, minimizing kernel launch overhead.

Shared Memory Optimization: Leverages CUDA shared memory (s_in[]) to cache the entire input feature map for each channel, enabling fast data access compared to global memory.

Two-Phase Execution Strategy:

Pass 1: Loads input data from global memory to shared memory using grid-strided loops

Pass 2: Performs reflection padding calculations reading from shared memory and writing to global output

2D Thread Blocking: Employs 2D thread blocks (BLOCK_DIM_X/Y = 16) for efficient parallelization across height and width dimensions.

Mathematical Reflection Indexing: Implements a device-side reflect_idx function that calculates reflection indices using arithmetic operations rather than conditional branching for better performance.

Grid-Strided Loops: Uses strided loops in both loading and computation phases to handle arbitrary tensor sizes while maintaining load balancing.

Batched Channel Processing: Processes multiple batches and channels concurrently through 2D grid dimensions (grid_dim(N, C)).

Memory Contiguity Enforcement: Explicitly ensures input tensor contiguity in Python (x.contiguous()) before passing to CUDA, with runtime validation.

Comprehensive Error Checking: Includes extensive bounds checking for padding values and tensor dimensions to ensure valid operations.

Performance Optimizations:

Shared memory caching of entire input feature maps

Coalesced memory access patterns

Single synchronization point between loading and computation phases

Compiler optimizations (-O3, --use_fast_math)

Grid-strided loops for efficient workload distribution

Restricted pointers for better compiler optimization

Architecture Features:

Separate C++ header and CUDA source code organization

Template-style parameter passing for padding values

Automatic output tensor allocation with correct dimensions

Efficient memory stride calculations for 4D tensor layout (N, C, H, W)




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

# -------------------------------------------------------------
# 常量定义
# -------------------------------------------------------------
BATCH_SIZE = 32
CHANNELS = 64
HEIGHT = 32  # H_in
WIDTH = 32  # W_in

# (pad_L, pad_R, pad_T, pad_B)
PADDING = (1, 1, 2, 0)


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

class Model(nn.Module):
    """
    nn.ReflectionPad2d 的纯 PyTorch 基准实现
    (使用 F.pad)
    """

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

        if isinstance(padding, int):
            # F.pad 需要 (left, right, top, bottom) 格式
            self.padding_tuple = (padding, padding, padding, padding)
        else:
            self.padding_tuple = padding

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # F.pad 的 padding 格式是 (pad_dim_0_left, pad_dim_0_right, ...)
        # 对应 (N, C, H, W)，我们需要 pad 最后两个维度
        # F.pad 接受的顺序是 (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom)
        return F.pad(x, self.padding_tuple, mode='reflect')


def get_inputs():
    """
    生成一个 (N, C, H, W) 形状的输入
    """
    x = torch.randn(BATCH_SIZE, CHANNELS, HEIGHT, WIDTH, dtype=torch.float32)
    return [x]


def get_init_inputs():
    return [PADDING]