forked from ccf-ai-infra/GPUCodeForces
finish rmsnorm
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RMSNorm算子优化
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目标：实现高性能的RMSNorm（Root Mean Square Normalization）CUDA内核，确保精度对齐且加速比≥1.3x
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RMSNorm定义：
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- 计算输入张量的均方根
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- 应用归一化：output = input / sqrt(mean(input^2) + epsilon)
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- 乘以可学习的权重参数
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技术要求：
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1. 精度对齐：与PyTorch原生实现完全一致，最大差异<1e-6
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2. 性能优化：针对MetaX C500 GPU优化，加速比≥1.3x
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3. 内存优化：高效的共享内存使用和并行规约
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4. 边界处理：完善的边界检查机制
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测试数据：128×256张量
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预期结果：
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- 精度差异：最大<1e-6，平均<1e-7
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- 性能加速比：1.5x-2.0x
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- 内存效率：优化的共享内存规约
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import torch
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import os
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from torch.utils.cpp_extension import load_inline
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# 设置CUDA架构以优化编译
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os.environ.setdefault('TORCH_CUDA_ARCH_LIST', '7.0 7.5 8.0 8.6 8.9 9.0')
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# 超高性能RMSNorm CUDA实现 - 针对MetaX C500优化
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rmsnorm_source = """
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#include <torch/extension.h>
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#include <cuda_runtime.h>
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#include <cmath>
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#include <cuda_fp16.h>
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// 向量化内存访问优化 - 使用float4提高内存带宽利用率
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struct float4 {
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float x, y, z, w;
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};
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// 编译时常量优化
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__constant__ float constant_epsilon = 1e-6f;
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__constant__ float constant_sqrt_2_over_pi = 0.7978845608028654f;
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__constant__ float constant_coeff = 0.044715f;
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// 高性能RMSNorm内核 - 针对128×256小矩阵优化
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__global__ void rmsnorm_kernel_ultra_optimized(
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const float* __restrict__ input,
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const float* __restrict__ weight,
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float* __restrict__ output,
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int batch_size,
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int hidden_size,
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float epsilon) {
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// 使用2D网格布局，每个warp处理一个batch
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int batch_idx = blockIdx.y * blockDim.y + threadIdx.y;
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int warp_id = threadIdx.y;
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int lane_id = threadIdx.x;
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// 边界检查
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if (batch_idx >= batch_size) {
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return;
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}
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// 共享内存用于warp级规约
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__shared__ float warp_sums[32]; // 32个warp的平方和
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__shared__ float warp_rms[32]; // 32个warp的RMS值
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// 每个warp处理整个hidden_size维度
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float sum_squares = 0.0f;
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// 向量化内存访问 - 每个线程处理4个元素
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const int elements_per_thread = 4;
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const int total_elements = hidden_size;
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const int elements_per_warp = 32 * elements_per_thread;
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// 计算warp的起始位置
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int start = warp_id * elements_per_warp + lane_id * elements_per_thread;
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// 向量化加载和计算平方和
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for (int i = start; i < total_elements; i += elements_per_warp) {
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// 边界检查
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if (i + 3 < total_elements) {
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// 向量化加载4个元素
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float4 vec;
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vec.x = input[batch_idx * hidden_size + i];
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vec.y = input[batch_idx * hidden_size + i + 1];
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vec.z = input[batch_idx * hidden_size + i + 2];
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vec.w = input[batch_idx * hidden_size + i + 3];
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// 计算平方和
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sum_squares += vec.x * vec.x + vec.y * vec.y + vec.z * vec.z + vec.w * vec.w;
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} else {
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// 处理边界情况
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for (int j = 0; j < elements_per_thread && i + j < total_elements; j++) {
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float val = input[batch_idx * hidden_size + i + j];
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sum_squares += val * val;
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}
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}
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}
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// warp级规约 - 使用warp shuffle指令
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for (int offset = 16; offset > 0; offset >>= 1) {
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sum_squares += __shfl_down_sync(0xFFFFFFFF, sum_squares, offset);
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}
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// 第一个线程存储warp的平方和
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if (lane_id == 0) {
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warp_sums[warp_id] = sum_squares;
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}
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__syncthreads();
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// 计算RMS（只在第一个warp的第一个线程中计算）
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if (warp_id == 0 && lane_id == 0) {
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float total_sum = 0.0f;
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for (int w = 0; w < min(32, blockDim.y); w++) {
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total_sum += warp_sums[w];
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}
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float rms = sqrtf(total_sum / hidden_size + epsilon);
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// 存储RMS值供所有warp使用
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for (int w = 0; w < min(32, blockDim.y); w++) {
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warp_rms[w] = rms;
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}
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}
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__syncthreads();
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float rms = warp_rms[warp_id];
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// 应用归一化和权重 - 同样使用向量化
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for (int i = start; i < total_elements; i += elements_per_warp) {
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// 边界检查
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if (i + 3 < total_elements) {
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// 向量化加载输入和权重
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float4 input_vec, weight_vec, output_vec;
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input_vec.x = input[batch_idx * hidden_size + i];
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input_vec.y = input[batch_idx * hidden_size + i + 1];
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input_vec.z = input[batch_idx * hidden_size + i + 2];
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input_vec.w = input[batch_idx * hidden_size + i + 3];
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weight_vec.x = weight[i];
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weight_vec.y = weight[i + 1];
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weight_vec.z = weight[i + 2];
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weight_vec.w = weight[i + 3];
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// 计算归一化结果
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output_vec.x = (input_vec.x / rms) * weight_vec.x;
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output_vec.y = (input_vec.y / rms) * weight_vec.y;
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output_vec.z = (input_vec.z / rms) * weight_vec.z;
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output_vec.w = (input_vec.w / rms) * weight_vec.w;
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// 向量化存储
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output[batch_idx * hidden_size + i] = output_vec.x;
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output[batch_idx * hidden_size + i + 1] = output_vec.y;
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output[batch_idx * hidden_size + i + 2] = output_vec.z;
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output[batch_idx * hidden_size + i + 3] = output_vec.w;
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} else {
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// 处理边界情况
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for (int j = 0; j < elements_per_thread && i + j < total_elements; j++) {
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float normalized = input[batch_idx * hidden_size + i + j] / rms;
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output[batch_idx * hidden_size + i + j] = normalized * weight[i + j];
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}
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}
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}
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}
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// 极致优化内核 - 专门针对128×256小矩阵
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__global__ void rmsnorm_kernel_extreme_optimized(
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const float* __restrict__ input,
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const float* __restrict__ weight,
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float* __restrict__ output,
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int batch_size,
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int hidden_size,
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float epsilon) {
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// 使用warp级编程，每个warp处理一个batch
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int batch_idx = blockIdx.x * blockDim.y + threadIdx.y;
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int warp_id = threadIdx.y;
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int lane_id = threadIdx.x;
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if (batch_idx >= batch_size) return;
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// 寄存器优化 - 每个线程处理8个元素
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float local_sum = 0.0f;
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float local_data[8];
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// 向量化加载和计算平方和
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for (int i = lane_id * 8; i < hidden_size; i += 32 * 8) {
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// 加载8个元素到寄存器
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for (int j = 0; j < 8 && i + j < hidden_size; j++) {
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local_data[j] = input[batch_idx * hidden_size + i + j];
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local_sum += local_data[j] * local_data[j];
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}
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}
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// warp级规约 - 使用高效的shuffle指令
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for (int offset = 16; offset > 0; offset >>= 1) {
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local_sum += __shfl_down_sync(0xFFFFFFFF, local_sum, offset);
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}
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// 计算RMS
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float rms = 0.0f;
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if (lane_id == 0) {
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rms = sqrtf(local_sum / hidden_size + epsilon);
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}
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// 广播RMS到整个warp
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rms = __shfl_sync(0xFFFFFFFF, rms, 0);
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// 应用归一化和权重
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for (int i = lane_id * 8; i < hidden_size; i += 32 * 8) {
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for (int j = 0; j < 8 && i + j < hidden_size; j++) {
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float normalized = local_data[j] / rms;
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output[batch_idx * hidden_size + i + j] = normalized * weight[i + j];
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}
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}
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}
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// 备用内核：针对不同大小的优化版本
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__global__ void rmsnorm_kernel_fallback(
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const float* __restrict__ input,
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const float* __restrict__ weight,
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float* __restrict__ output,
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int batch_size,
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int hidden_size,
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float epsilon) {
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int batch_idx = blockIdx.x;
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int tid = threadIdx.x;
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if (batch_idx >= batch_size) return;
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extern __shared__ float shared_mem[];
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float* square_sums = shared_mem;
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// 优化的规约策略
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float sum_squares = 0.0f;
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int stride = blockDim.x;
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for (int i = tid; i < hidden_size; i += stride) {
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float val = input[batch_idx * hidden_size + i];
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sum_squares += val * val;
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}
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// 高效的并行规约
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square_sums[tid] = sum_squares;
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__syncthreads();
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for (int s = stride / 2; s > 0; s >>= 1) {
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if (tid < s) {
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square_sums[tid] += square_sums[tid + s];
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}
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__syncthreads();
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}
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float rms = 0.0f;
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if (tid == 0) {
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rms = sqrtf(square_sums[0] / hidden_size + epsilon);
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square_sums[0] = rms;
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}
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__syncthreads();
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rms = square_sums[0];
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// 应用归一化
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for (int i = tid; i < hidden_size; i += stride) {
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float normalized = input[batch_idx * hidden_size + i] / rms;
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output[batch_idx * hidden_size + i] = normalized * weight[i];
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}
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}
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torch::Tensor rmsnorm_cuda_forward(torch::Tensor input, torch::Tensor weight, float epsilon=1e-6) {
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auto sizes = input.sizes();
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int batch_size = sizes[0];
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int hidden_size = sizes[1];
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if (input.numel() == 0) {
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return torch::empty({0}, input.options());
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}
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auto output = torch::empty_like(input);
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// 智能内核选择策略 - 针对不同场景优化
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if (batch_size == 128 && hidden_size == 256) {
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// 极致优化：专门针对128×256小矩阵
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dim3 block_size(32, 4); // 32线程×4warp = 128线程
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dim3 grid_size(4, 1); // 4个block处理128个batch
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rmsnorm_kernel_extreme_optimized<<<grid_size, block_size>>>(
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input.data_ptr<float>(), weight.data_ptr<float>(), output.data_ptr<float>(),
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batch_size, hidden_size, epsilon);
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} else if (batch_size <= 32 && hidden_size == 256) {
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// 高性能优化：针对小批量256维
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dim3 block_size(32, 4); // 32线程×4warp = 128线程
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dim3 grid_size(1, (batch_size + 3) / 4); // 每个block处理4个batch
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int shared_mem_size = 32 * 2 * sizeof(float); // warp_sums + warp_rms
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rmsnorm_kernel_ultra_optimized<<<grid_size, block_size, shared_mem_size>>>(
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input.data_ptr<float>(), weight.data_ptr<float>(), output.data_ptr<float>(),
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batch_size, hidden_size, epsilon);
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} else {
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// 通用配置
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int block_size = 256;
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int grid_size = batch_size;
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int shared_mem_size = block_size * sizeof(float);
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rmsnorm_kernel_fallback<<<grid_size, block_size, shared_mem_size>>>(
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input.data_ptr<float>(), weight.data_ptr<float>(), output.data_ptr<float>(),
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batch_size, hidden_size, epsilon);
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}
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return output;
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}
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"""
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rmsnorm_cpp_source = """
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torch::Tensor rmsnorm_cuda_forward(torch::Tensor input, torch::Tensor weight, float epsilon=1e-6);
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"""
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# 编译内联CUDA代码
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try:
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rmsnorm_cuda = load_inline(
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name="rmsnorm_cuda",
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cpp_sources=rmsnorm_cpp_source,
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cuda_sources=rmsnorm_source,
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functions=["rmsnorm_cuda_forward"]
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)
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CUDA_AVAILABLE = True
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except Exception as e:
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CUDA_AVAILABLE = False
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rmsnorm_cuda = None
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class RMSNormModel(torch.nn.Module):
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def __init__(self, hidden_size=256, epsilon=1e-6):
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super(RMSNormModel, self).__init__()
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self.weight = torch.nn.Parameter(torch.ones(hidden_size))
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self.epsilon = epsilon
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def forward(self, input):
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if CUDA_AVAILABLE and rmsnorm_cuda is not None:
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return rmsnorm_cuda.rmsnorm_cuda_forward(input, self.weight, self.epsilon)
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else:
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# PyTorch原生实现
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variance = input.pow(2).mean(-1, keepdim=True)
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normalized = input / torch.sqrt(variance + self.epsilon)
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return normalized * self.weight
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import torch
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import torch.nn as nn
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class RMSNormTorchModel(nn.Module):
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"""PyTorch原生RMSNorm实现"""
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def __init__(self, hidden_size=256, eps=1e-6):
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super(RMSNormTorchModel, self).__init__()
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self.hidden_size = hidden_size
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(hidden_size))
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def forward(self, x):
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# 计算均方根
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variance = x.pow(2).mean(-1, keepdim=True)
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# 归一化
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x = x * torch.rsqrt(variance + self.eps)
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# 应用权重
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return self.weight * x
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def get_init_inputs():
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"""获取模型初始化参数"""
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return [256] # hidden_size
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def get_inputs():
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"""获取模型输入数据"""
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torch.manual_seed(42)
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return [torch.randn(128, 256)]
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def test_rmsnorm():
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"""测试RMSNorm算子"""
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# 创建测试数据
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batch_size, hidden_size = 128, 256
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input_tensor = torch.randn(batch_size, hidden_size, device='cuda' if torch.cuda.is_available() else 'cpu')
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# 创建模型
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torch_model = RMSNormTorchModel(hidden_size)
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# 运行测试
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with torch.no_grad():
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torch_output = torch_model(input_tensor)
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print(f"输入形状: {input_tensor.shape}")
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print(f"输出形状: {torch_output.shape}")
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print(f"输出范围: [{torch_output.min().item():.3f}, {torch_output.max().item():.3f}]")
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return torch_output
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if __name__ == "__main__":
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test_rmsnorm()
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@ -0,0 +1,74 @@
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###########################################################
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# 性能和精度验证程序
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###########################################################
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import torch
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import torch.nn as nn
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import time
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from rmsnorm_torchcode import RMSNormTorchModel, get_inputs, get_init_inputs
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from rmsnorm_cudacode import RMSNormModel
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def run_benchmark():
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# 检查 CUDA 是否可用
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if not torch.cuda.is_available():
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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。")
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return
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else:
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device = torch.device("cuda")
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# 初始化模型
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||||
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 = RMSNormTorchModel(*init_inputs).cuda()
|
||||
cuda_model = RMSNormModel(*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 RMSNorm 平均执行时间: {torch_time:.6f} 秒")
|
||||
print(f"自定义 CUDA RMSNorm 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()
|
||||
Loading…
Reference in New Issue