From 673abbe98a384c94b58aea10374fd0f09e3ef38a Mon Sep 17 00:00:00 2001 From: hli28146 Date: Wed, 10 Dec 2025 13:02:20 +0800 Subject: [PATCH] finish AHerfReLU #86 --- S1/hli28146_#86/AHerfReLU_cuda.py | 110 +++++++++++++++++++++++++++++ S1/hli28146_#86/AHerfReLU_torch.py | 41 +++++++++++ S1/hli28146_#86/prompt.txt | 67 ++++++++++++++++++ S1/hli28146_#86/run_code.py | 74 +++++++++++++++++++ 4 files changed, 292 insertions(+) create mode 100644 S1/hli28146_#86/AHerfReLU_cuda.py create mode 100644 S1/hli28146_#86/AHerfReLU_torch.py create mode 100644 S1/hli28146_#86/prompt.txt create mode 100644 S1/hli28146_#86/run_code.py diff --git a/S1/hli28146_#86/AHerfReLU_cuda.py b/S1/hli28146_#86/AHerfReLU_cuda.py new file mode 100644 index 00000000..6bc329e4 --- /dev/null +++ b/S1/hli28146_#86/AHerfReLU_cuda.py @@ -0,0 +1,110 @@ +import torch +import torch.nn as nn +from torch.utils.cpp_extension import load_inline + +# C++ 源代码 wrapper +cpp_source = """ +#include + +torch::Tensor aherf_relu_cuda_forward(const torch::Tensor& input, float alpha); +""" + +# CUDA 源代码 +cuda_source = """ +#include +#include +#include + +#define BLOCK_SIZE 256 + +struct __align__(16) Float4 { + float x, y, z, w; +}; + +// AHerfReLU Logic +__device__ __forceinline__ float compute_aherf_relu(float x, float alpha) { + if (x >= 0.0f) { + return x; + } else { + return alpha * x * erff(x) / (1.0f + x * x); + } +} + +__global__ void aherf_relu_kernel( + float* __restrict__ output, + const float* __restrict__ input, + const int n, + const float alpha) +{ + const int idx = blockIdx.x * blockDim.x + threadIdx.x; + const int vec_n = n / 4; + + int i = idx; + const int stride = blockDim.x * gridDim.x; + + // 1. Vectorized Loop + for (; i < vec_n; i += stride) { + Float4 in_vec = reinterpret_cast(input)[i]; + Float4 out_vec; + + out_vec.x = compute_aherf_relu(in_vec.x, alpha); + out_vec.y = compute_aherf_relu(in_vec.y, alpha); + out_vec.z = compute_aherf_relu(in_vec.z, alpha); + out_vec.w = compute_aherf_relu(in_vec.w, alpha); + + reinterpret_cast(output)[i] = out_vec; + } + + // 2. Scalar Tail + int start_scalar = vec_n * 4; + int global_tid = blockIdx.x * blockDim.x + threadIdx.x; + int total_threads = gridDim.x * gridDim.x; + + int current_idx = start_scalar + global_tid; + while (current_idx < n) { + output[current_idx] = compute_aherf_relu(input[current_idx], alpha); + current_idx += total_threads; + } +} + +torch::Tensor aherf_relu_cuda_forward(const torch::Tensor& input, float alpha) { + TORCH_CHECK(input.is_cuda(), "Input must be a CUDA tensor"); + TORCH_CHECK(input.is_contiguous(), "Input must be contiguous"); + + const int n = input.numel(); + auto output = torch::empty_like(input); + + const int vec_n = n / 4; + const int grid_size = (vec_n + BLOCK_SIZE - 1) / BLOCK_SIZE; + + int final_grid = (grid_size < 1) ? 1 : grid_size; + if (final_grid > 65535) final_grid = 65535; + + aherf_relu_kernel<<>>( + output.data_ptr(), + input.data_ptr(), + n, + alpha + ); + + return output; +} +""" + +aherf_relu_op_module = load_inline( + name='aherf_relu_op', + cpp_sources=cpp_source, + cuda_sources=cuda_source, + functions=['aherf_relu_cuda_forward'], + verbose=False, + extra_cuda_cflags=['-O3'] +) + +class ModelNew(nn.Module): + def __init__(self, alpha=0.87): + super(ModelNew, self).__init__() + self.alpha = alpha + self.op = aherf_relu_op_module + + def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: + return self.op.aherf_relu_cuda_forward(input_tensor.contiguous(), self.alpha) \ No newline at end of file diff --git a/S1/hli28146_#86/AHerfReLU_torch.py b/S1/hli28146_#86/AHerfReLU_torch.py new file mode 100644 index 00000000..2f50235f --- /dev/null +++ b/S1/hli28146_#86/AHerfReLU_torch.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn + +BATCH_SIZE = 4096 +HIDDEN_DIM = 4096 +SHAPE = (BATCH_SIZE, HIDDEN_DIM) + +# AHerfReLU 超参数 alpha ,论文中设为 0.87 +ALPHA_VALUE = 0.87 + +class AHerfReLU(nn.Module): + ''' + "AHerfReLU: A Novel Adaptive Activation Function Enhancing Deep Neural Network Performance" (Complexity, 2025) + https://onlinelibrary.wiley.com/doi/full/10.1155/cplx/8233876 + Formula: + f(x) = x if x >= 0 + f(x) = alpha * x * erf(x) / (1 + x^2) if x < 0 + ''' + def __init__(self, alpha=0.87): + super(AHerfReLU, self).__init__() + self.alpha = alpha + + def forward(self, x: torch.Tensor) -> torch.Tensor: + pos_part = x + neg_part = self.alpha * x * torch.erf(x) / (1 + x.pow(2)) + return torch.where(x >= 0, pos_part, neg_part) + +class Model(nn.Module): + def __init__(self, alpha=0.87): + super(Model, self).__init__() + self.act = AHerfReLU(alpha=alpha) + + def forward(self, x): + return self.act(x) + +def get_inputs(): + input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 2.0 + return [input_tensor.contiguous()] + +def get_init_inputs(): + return [ALPHA_VALUE] \ No newline at end of file diff --git a/S1/hli28146_#86/prompt.txt b/S1/hli28146_#86/prompt.txt new file mode 100644 index 00000000..fbceb2c3 --- /dev/null +++ b/S1/hli28146_#86/prompt.txt @@ -0,0 +1,67 @@ +Write a custom CUDA kernel to optimize `AHerfReLU`. + +Formula: + f(x) = x if x >= 0 + f(x) = alpha * x * erf(x) / (1 + x^2) if x < 0 + +Problem Analysis: +1. Computationally Intensive & Memory Bound: The operation is element-wise but involves the expensive `erf` function and several arithmetic operations for the negative part. +2. Operator Chaining: A PyTorch implementation using `torch.where` would create multiple intermediate tensors. + +Optimization Strategy: Fused Element-wise Kernel with Vectorization + +1. One-Thread-per-Element: Map each element to a CUDA thread. + +2. Vectorized Loads (float4): Use `float4` to process 128 bits per memory transaction. + +3. Fused Branching Logic: + - For each element `x`, check `if (x < 0)`. + - If true, compute `erf_val = erff(x)`, `denom = 1.0f + x*x`, `result = alpha * x * erf_val / denom`. + - If false, result is `x`. + +4. One-Pass: Fuse all steps into a single read-compute-write kernel. + +Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is: + +```python +import torch +import torch.nn as nn + +BATCH_SIZE = 4096 +HIDDEN_DIM = 4096 +SHAPE = (BATCH_SIZE, HIDDEN_DIM) + +# AHerfReLU 超参数 alpha ,论文中设为 0.87 +ALPHA_VALUE = 0.87 + +class AHerfReLU(nn.Module): + ''' + "AHerfReLU: A Novel Adaptive Activation Function Enhancing Deep Neural Network Performance" (Complexity, 2025) + https://onlinelibrary.wiley.com/doi/full/10.1155/cplx/8233876 + Formula: + f(x) = x if x >= 0 + f(x) = alpha * x * erf(x) / (1 + x^2) if x < 0 + ''' + def __init__(self, alpha=0.87): + super(AHerfReLU, self).__init__() + self.alpha = alpha + + def forward(self, x: torch.Tensor) -> torch.Tensor: + pos_part = x + neg_part = self.alpha * x * torch.erf(x) / (1 + x.pow(2)) + return torch.where(x >= 0, pos_part, neg_part) + +class Model(nn.Module): + def __init__(self, alpha=0.87): + super(Model, self).__init__() + self.act = AHerfReLU(alpha=alpha) + + def forward(self, x): + return self.act(x) + +def get_inputs(): + input_tensor = torch.randn(SHAPE, dtype=torch.float32) * 2.0 + return [input_tensor.contiguous()] + +def get_init_inputs(): + return [ALPHA_VALUE] \ No newline at end of file diff --git a/S1/hli28146_#86/run_code.py b/S1/hli28146_#86/run_code.py new file mode 100644 index 00000000..a225cf4d --- /dev/null +++ b/S1/hli28146_#86/run_code.py @@ -0,0 +1,74 @@ +########################################################### +# 性能和精度验证程序 +########################################################### +import torch +import torch.nn as nn +import time +from AHerfReLU_torch import Model,get_inputs,get_init_inputs +from AHerfReLU_cuda import ModelNew + +def run_benchmark(): + # 检查 CUDA 是否可用 + if not torch.cuda.is_available(): + print("CUDA 不可用，请确保您有可用的 NVIDIA GPU 并已正确安装 PyTorch CUDA 版本。") + return + else: + device = torch.device("cuda") + + # 初始化模型 + init_inputs = get_init_inputs() + init_inputs = [ + x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in init_inputs + ] + inputs = get_inputs() + inputs = [ + x.cuda(device=device) if isinstance(x, torch.Tensor) else x for x in inputs + ] + + torch_model = Model(*init_inputs).cuda() + cuda_model = ModelNew(*init_inputs).cuda() + + torch_model.eval() + cuda_model.eval() + + print("-------------------- 精度对齐验证 --------------------") + with torch.no_grad(): + output_torch = torch_model( *inputs) + output_cuda = cuda_model(*inputs) + + precision_flag = torch.allclose(output_torch, output_cuda,rtol=1e-03) + if precision_flag: + print("✅ 精度对齐：两个模型的输出结果非常接近。") + else: + print("❌ 精度不一致！") + + print("\n-------------------- 性能加速比测试 --------------------") + num_iterations = 100 + + # PyTorch 模型计时 + torch.cuda.synchronize() + start_time = time.time() + for _ in range(num_iterations): + _ = torch_model(*inputs) + torch.cuda.synchronize() + torch_time = (time.time() - start_time) / num_iterations + + # 自定义 CUDA 内核计时 + torch.cuda.synchronize() + start_time = time.time() + for _ in range(num_iterations): + _ = cuda_model(*inputs) + torch.cuda.synchronize() + cuda_time = (time.time() - start_time) / num_iterations + + print(f"PyTorch torch.relu 平均执行时间: {torch_time:.6f} 秒") + print(f"自定义 CUDA 内核 平均执行时间: {cuda_time:.6f} 秒") + speedup = 0 + if cuda_time > 0: + speedup = torch_time / cuda_time + print(f"加速比 (Speedup): {speedup:.2f}x") + else: + print("CUDA 内核执行时间为0，无法计算加速比。") + return precision_flag,speedup +if __name__ == "__main__": + precision_flag,speedup = run_benchmark() \ No newline at end of file