rmsnorm_kernel/rmsnorm.cu

#include <torch/all.h>
#include <thrust/execution_policy.h>
#include <thrust/for_each.h>
#include <thrust/iterator/counting_iterator.h>
#include <cmath>
#include <thrust/device_vector.h>
#include <thrust/copy.h>
#include <iostream>
#include <iomanip> // For formatting output

const float EPS = 1e-5f;

// CPU implementation of RMSNorm
torch::Tensor rmsnorm_forward_cpu(torch::Tensor x, torch::Tensor gamma) {
    int B = x.size(0), S = x.size(1), H = x.size(2);
    auto out = torch::empty_like(x);
    
    auto x_accessor = x.accessor<float, 3>();
    auto gamma_accessor = gamma.accessor<float, 1>();
    auto out_accessor = out.accessor<float, 3>();
    
    // Process each row
    for (int b = 0; b < B; ++b) {
        for (int s = 0; s < S; ++s) {
            // Calculate root mean square
            float sum_sq = 0.0f;
            for (int h = 0; h < H; ++h) {
                float val = x_accessor[b][s][h];
                sum_sq += val * val;
            }
            float rms = std::sqrt(sum_sq / H + EPS);
            
            // Normalize and scale
            for (int h = 0; h < H; ++h) {
                out_accessor[b][s][h] = (x_accessor[b][s][h] / rms) * gamma_accessor[h];
            }
        }
    }
    
    return out;
}

struct RmsnormFunctor {
    const float* x;
    const float* gamma;
    float* out;
    int hidden_dim;

    RmsnormFunctor(const float* x_, const float* gamma_, float* out_, int h_)
        : x(x_), gamma(gamma_), out(out_), hidden_dim(h_) {}

    __device__
    void operator()(int row_idx) {
        const float* row_x = x + row_idx * hidden_dim;
        float* row_out = out + row_idx * hidden_dim;

        float sum_sq = 0.0f;
        for (int i = 0; i < hidden_dim; ++i)
            sum_sq += row_x[i] * row_x[i];

        float rms = sqrtf(sum_sq / hidden_dim + EPS);

        for (int i = 0; i < hidden_dim; ++i)
            row_out[i] = (row_x[i] / rms) * gamma[i];
    }
};

torch::Tensor rmsnorm_forward(torch::Tensor x, torch::Tensor gamma) {
    int B = x.size(0), S = x.size(1), H = x.size(2);
    int rows = B * S;

    // Create output tensor with same shape as input
    auto out = torch::empty_like(x);

    const float* x_ptr = x.data_ptr<float>();
    const float* gamma_ptr = gamma.data_ptr<float>();
    float* out_ptr = out.data_ptr<float>();

    thrust::counting_iterator<int> iter(0);
    thrust::for_each(
        thrust::device,
        iter, iter + rows,
        RmsnormFunctor(x_ptr, gamma_ptr, out_ptr, H)
    );
    
    return out;
}

// int main() {
//     int B = 2, S = 2, H = 4;

//     // Create tensors directly on CPU first
//     auto options_cpu = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCPU);
    
//     // Initialize with CPU data
//     torch::Tensor x_cpu = torch::tensor({
//         {
//             {1.0f, 2.0f, 3.0f, 4.0f},
//             {5.0f, 6.0f, 7.0f, 8.0f}
//         },
//         {
//             {2.0f, 2.0f, 2.0f, 2.0f},
//             {9.0f, 10.0f, 11.0f, 12.0f}
//         }
//     }, options_cpu);
    
//     torch::Tensor gamma_cpu = torch::tensor({1.0f, 1.0f, 1.0f, 1.0f}, options_cpu);

//     // Run CPU version
//     std::cout << "===== CPU IMPLEMENTATION RESULTS =====" << std::endl;
//     torch::Tensor out_cpu = rmsnorm_forward_cpu(x_cpu, gamma_cpu);
//     auto cpu_accessor = out_cpu.accessor<float, 3>();

//     for (int b = 0; b < B; ++b) {
//         for (int s = 0; s < S; ++s) {
//             std::cout << "Row " << (b * S + s) << ": ";
//             for (int h = 0; h < H; ++h) {
//                 std::cout << std::fixed << std::setprecision(6) << cpu_accessor[b][s][h] << " ";
//             }
//             std::cout << "\n";
//         }
//     }

//     // Move tensors to CUDA for GPU version
//     auto cuda_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
//     torch::Tensor x_cuda = x_cpu.to(torch::kCUDA);
//     torch::Tensor gamma_cuda = gamma_cpu.to(torch::kCUDA);

//     // Call the CUDA kernel wrapper
//     std::cout << "\n===== GPU IMPLEMENTATION RESULTS =====" << std::endl;
//     torch::Tensor out_cuda = rmsnorm_forward(x_cuda, gamma_cuda);

//     // Copy result back to CPU and print
//     auto gpu_result_on_cpu = out_cuda.cpu();
//     auto gpu_accessor = gpu_result_on_cpu.accessor<float, 3>();

//     for (int b = 0; b < B; ++b) {
//         for (int s = 0; s < S; ++s) {
//             std::cout << "Row " << (b * S + s) << ": ";
//             for (int h = 0; h < H; ++h) {
//                 std::cout << std::fixed << std::setprecision(6) << gpu_accessor[b][s][h] << " ";
//             }
//             std::cout << "\n";
//         }
//     }

//     // Check if results match
//     std::cout << "\n===== COMPARISON =====" << std::endl;
//     float max_diff = 0.0f;
//     for (int b = 0; b < B; ++b) {
//         for (int s = 0; s < S; ++s) {
//             for (int h = 0; h < H; ++h) {
//                 float diff = std::abs(cpu_accessor[b][s][h] - gpu_accessor[b][s][h]);
//                 max_diff = std::max(max_diff, diff);
//             }
//         }
//     }
//     std::cout << "Maximum difference between CPU and GPU results: " 
//               << std::scientific << max_diff << std::endl;
//     std::cout << (max_diff < 1e-5 ? "PASSED: Results match!" : "FAILED: Results don't match!") << std::endl;

//     return 0;
// }