composable_kernel/driver/conv.cu

#include <iostream>
#include <numeric>
#include <initializer_list>
#include "nvToolsExt.h"
#include "tensor.hpp"
#include "device_tensor.cuh"
#include "direct_convolution.cuh"

template <class T>
struct GeneratorConstant
{
    T value = 0;

    template <class... Is>
    T operator()(Is... is)
    {
        return value;
    }
};

template <class T>
struct GeneratorTensor
{
    template <class... Is>
    T operator()(Is... is)
    {
#if 0
        std::initializer_list<std::size_t> ls = {static_cast<std::size_t>(is)...};
        return std::accumulate(ls.begin(), ls.end(), std::size_t(0));
#else
        assert(sizeof...(Is) > 0);
        std::initializer_list<std::size_t> ids = {static_cast<std::size_t>(is)...};
        std::vector<std::size_t> lens(sizeof...(Is), 100);
        std::vector<std::size_t> strides(sizeof...(Is), 1);
        std::partial_sum(lens.rbegin(), lens.rbegin() + (sizeof...(Is) - 1), strides.rbegin() + 1);
        return std::inner_product(ids.begin(), ids.end(), strides.begin(), std::size_t(0)) + 1;
#endif
    }
};

template <typename T>
void host_convolution(const Tensor<T>& in,
                      const Tensor<T>& wei,
                      Tensor<T>& out,
                      std::size_t num_thread)
{
    auto f = [&](auto n, auto k, auto ho, auto wo) {
        double v = 0;
        for(int c = 0; c < wei.mDesc.GetLengths()[1]; ++c)
        {
            for(int y = 0; y < wei.mDesc.GetLengths()[2]; ++y)
            {
                int hi = ho + y;
                for(int x = 0; x < wei.mDesc.GetLengths()[3]; ++x)
                {
                    int wi = wo + x;
                    v += in(n, c, hi, wi) * wei(k, c, y, x);
                }
            }
        }
        out(n, k, ho, wo) = v;
    };

    auto f_par = make_ParallelTensorFunctor(f,
                                            out.mDesc.GetLengths()[0],
                                            out.mDesc.GetLengths()[1],
                                            out.mDesc.GetLengths()[2],
                                            out.mDesc.GetLengths()[3]);

    f_par(num_thread);
}

template <class T>
void device_convolution(const Tensor<T>& in, const Tensor<T>& wei, Tensor<T>& out)

{
    DeviceTensorDescriptor<4> in_desc_device(in.mDesc);
    DeviceTensorDescriptor<4> wei_desc_device(wei.mDesc);
    DeviceTensorDescriptor<4> out_desc_device(out.mDesc);

    printf("__func__: in_desc_device: {%u %u %u %u}, {%u %u %u %u}\n",
           in_desc_device.GetLength(0),
           in_desc_device.GetLength(1),
           in_desc_device.GetLength(2),
           in_desc_device.GetLength(3),
           in_desc_device.GetStride(0),
           in_desc_device.GetStride(1),
           in_desc_device.GetStride(2),
           in_desc_device.GetStride(3));

    std::size_t data_sz = sizeof(T);
    DeviceMem in_device_buf(data_sz * in.mDesc.GetElementSpace());
    DeviceMem wei_device_buf(data_sz * wei.mDesc.GetElementSpace());
    DeviceMem out_device_buf(data_sz * out.mDesc.GetElementSpace());

    int num_thread = std::thread::hardware_concurrency();

    out.GenerateTensorValue(GeneratorConstant<float>{0}, num_thread);

    in_device_buf.ToDevice(in.mData.data());
    wei_device_buf.ToDevice(wei.mData.data());
    out_device_buf.ToDevice(out.mData.data());

    dim3 block_dim(64, 1, 1);
    dim3 grid_dim(1, 1, 1);
    gridwise_convolution<T, 3, 3, 4, 4, 2, 2, 1, 1, 8, 8, 1>
        <<<grid_dim, block_dim>>>(in_desc_device,
                                  static_cast<T*>(in_device_buf.GetDeviceBuffer()),
                                  wei_desc_device,
                                  static_cast<T*>(wei_device_buf.GetDeviceBuffer()),
                                  out_desc_device,
                                  static_cast<T*>(out_device_buf.GetDeviceBuffer()));

    checkCudaErrors(cudaGetLastError());
    out_device_buf.FromDevice(out.mData.data());
}

int main()
{
#if 0
    Tensor<float> in({3, 16, 130, 130});
    Tensor<float> wei({4, 16, 3, 3});
    Tensor<float> out_host({3, 4, 128, 128});
#elif 0
    Tensor<float> in({1, 1, 130, 130});
    Tensor<float> wei({1, 1, 3, 3});
    Tensor<float> out_host({1, 1, 128, 128});
#elif 1
    Tensor<float> in({1, 1, 18, 18});
    Tensor<float> wei({1, 1, 3, 3});
    Tensor<float> out_host({1, 1, 16, 16});
#else
    Tensor<float> in({1, 1, 4, 4});
    Tensor<float> wei({1, 1, 3, 3});
    Tensor<float> out_host({1, 1, 2, 2});
#endif
    Tensor<float> out_device = out_host;

    int num_thread = std::thread::hardware_concurrency();

    std::cout << __func__ << ": num_thread " << num_thread << std::endl;

    in.GenerateTensorValue(GeneratorTensor<float>{}, num_thread);
    wei.GenerateTensorValue(GeneratorTensor<float>{}, num_thread);

    host_convolution(in, wei, out_host, num_thread);
    device_convolution(in, wei, out_device);

    std::cout << __func__ << ": done" << std::endl;

    LogRange(std::cout << __func__ << "in : ", in.mData, ",") << std::endl;
    LogRange(std::cout << __func__ << "wei: ", wei.mData, ",") << std::endl;
    LogRange(std::cout, out_host.mData, ",") << std::endl;
    LogRange(std::cout, out_device.mData, ",") << std::endl;

    float error = 0;
    for(int i = 0; i < out_host.mData.size(); ++i)
    {
        error += std::abs(out_host.mData[i] - out_device.mData[i]);
    }
    std::cout << "error: " << error << std::endl;
}