cpu_target.cpp

#include <rtg/cpu/cpu_target.hpp>
#include <rtg/instruction.hpp>
#include <rtg/dfor.hpp>
#include <rtg/operators.hpp>

namespace rtg {
namespace cpu {

template <typename T>
T zero(const T&)
{
    return T(0);
}

struct cpu_convolution
{
    convolution op;

    std::string name() const { return "cpu::convolution"; }
    shape compute_shape(std::vector<shape> inputs) const { return op.compute_shape(inputs); }
    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0], args[1])([&](auto output, auto input, auto weights) {
            auto in_h = input.get_shape().lens()[2];
            auto in_w = input.get_shape().lens()[3];

            auto wei_c = weights.get_shape().lens()[1];
            auto wei_h = weights.get_shape().lens()[2];
            auto wei_w = weights.get_shape().lens()[3];

            dfor(output_shape.lens()[0],
                 output_shape.lens()[1],
                 output_shape.lens()[2],
                 output_shape.lens()[3])(
                [&](std::size_t o, std::size_t w, std::size_t i, std::size_t j) {
                    const int start_x = i * op.stride[0] - op.padding[0];
                    const int start_y = j * op.stride[1] - op.padding[1];

                    double acc = 0;
                    dfor(wei_c, wei_h, wei_w)([&](std::size_t k, std::size_t x, std::size_t y) {
                        const int in_x = start_x + x;
                        const int in_y = start_y + y;
                        if(in_x >= 0 && in_x < in_h && in_y >= 0 && in_y < in_w)
                        {
                            acc += input(o, k, in_x, in_y) * weights(w, k, x, y);
                        }
                    });
                    output(o, w, i, j) = acc;
                });
        });
        return result;
    }
};

struct cpu_transpose
{
    transpose op;
   
    std::string name() const { return "cpu::transpose"; } 
    shape compute_shape(std::vector<shape> inputs) const { return op.compute_shape(inputs); }
    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0])([&](auto output, auto input) {
            using value_type = typename decltype(input)::value_type;
            value_type* ptr = static_cast<value_type*>(output.data());
            auto nb = output_shape.lens()[0];
            auto nc = output_shape.lens()[1]; 
            auto nh = output_shape.lens()[2]; 
            auto nw = output_shape.lens()[3];
            for (int kk = 0; kk < 4; kk++) {
                std::cout << "cpu_transpose: " << output_shape.lens()[kk] << "    " << output_shape.strides()[kk] << std::endl;
            }
            
            for (int b = 0; b < nb; b++) {
                for (int c = 0; c < nc; c++) {
                    for (int i = 0; i < nh; i++) {
                        for (int j = 0; j < nw; j++) {
                            *ptr++ = input(b,c,i,j);
                             std::cout << input(b,c,i,j) << "  ";
                        }
                    }
                }
            }
            std::cout << std::endl;
        });
        return result;
    }
};

struct cpu_reshape
{
    reshape op;
    std::string name() const { return "cpu::reshape"; }
    shape compute_shape(std::vector<shape> inputs) const { return op.compute_shape(inputs); }

    argument compute(shape output_shape, std::vector<argument> args) const
    {
        return {output_shape, std::move(args.front().data)};
    }
};

struct cpu_gemm
{
    gemm op;
    std::string name() const { return "cpu::gemm"; }
    shape compute_shape(std::vector<shape> inputs) const { return op.compute_shape(inputs); }

    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0], args[1])([&](auto cmat, auto amat, auto bmat) {
            auto m = amat.get_shape().lens()[0];
            auto n = bmat.get_shape().lens()[1];
            auto k = bmat.get_shape().lens()[0];

            auto a = amat.data();
            auto b = bmat.data();
            auto c = cmat.data();
            for(int ii = 0; ii < m; ii++)
            {
                for(int jj = 0; jj < n; jj++)
                {
                    c[ii * n + jj] = 0;
                }
            }
            for(int ii = 0; ii < m; ii++)
            {
                for(int kk = 0; kk < k; kk++)
                {
                    auto aik  = a[ii * k + kk];
                    auto* bkj = &b[kk * n];
                    auto* cij = &c[ii * n];
                    for(int jj = 0; jj < n; jj++, cij++, bkj++)
                    {
                        *cij += aik * (*bkj);
                    }
                }
            }
        });
        return result;
    }
};

struct identity_op
{
    std::string name() const { return "cpu::identity"; }
    auto fcn() const
    {
        return [](auto x) { return x; };
    }
};

struct abs_op
{
    std::string name() const { return "cpu::abs"; }
    auto fcn() const
    {
        return [](auto x) { return std::abs(x); };
    }
};

struct exp_op
{
    std::string name() const { return "cpu::exp"; }
    auto fcn() const
    {
        return [](auto x) { return std::exp(x); };
    }
};

struct sin_op
{
    std::string name() const { return "cpu::sin"; }
    auto fcn() const
    {
        return [](auto x) { return std::sin(x); };
    }
};

struct cos_op
{
    std::string name() const { return "cpu::cos"; }
    auto fcn() const
    {
        return [](auto x) { return std::cos(x); };
    }
};

struct tan_op
{
    std::string name() const { return "cpu::tan"; }
    auto fcn() const
    {
        return [](auto x) { return std::tan(x); };
    }
};

struct asin_op
{
    std::string name() const { return "cpu::asin"; }
    auto fcn() const
    {
        return [](auto x) { return std::asin(x); };
    }
};

struct acos_op
{
    std::string name() const { return "cpu::acos"; }
    auto fcn() const
    {
        return [](auto x) { return std::acos(x); };
    }
};

struct atan_op
{
    std::string name() const { return "cpu::atan"; }
    auto fcn() const
    {
        return [](auto x) { return std::atan(x); };
    }
};

struct tanh_op
{
    std::string name() const { return "cpu::tanh"; }
    auto fcn() const
    {
        return [](auto x) { return std::tanh(x); };
    }
};

struct sigmoid_op
{
    std::string name() const { return "cpu::sigmoid"; }
    auto fcn() const
    {
        return [](auto x) { return 1.f / (1.f + std::exp(-x)); };
    }
};

struct neg_op
{
    std::string name() const { return "cpu::neg"; }
    auto fcn() const
    {
        return [](auto x) { return -x; };
    }
};

struct relu_op
{
    std::string name() const { return "cpu::relu"; }
    auto fcn() const
    {
        return [](auto x) { return x > 0 ? x : 0; };
    }
};

template <typename Op>
struct cpu_unary
{
    Op op;
    std::string name() const { return op.name(); }
    shape compute_shape(std::vector<shape> inputs) const { return inputs.front(); }
    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        result.visit([&](auto output) {
            args[0].visit([&](auto input) {
                std::transform(input.begin(), input.end(), output.begin(), op.fcn());
            });
        });
        return result;
    }
};

struct softmax2d
{
    std::string name() const { return "cpu::softmax2d"; }
    shape compute_shape(std::vector<shape> inputs) const { return inputs.front(); }
    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0])([&](auto output, auto input) {
            using value_type = typename decltype(input)::value_type;
            auto nb          = input.get_shape().lens()[0];
            auto nc          = input.get_shape().lens()[1];
            auto nh          = input.get_shape().lens()[2];
            auto nw          = input.get_shape().lens()[3];
            dfor(nb, nh, nw)([&](std::size_t b, std::size_t i, std::size_t j) {
                value_type cmax = std::numeric_limits<value_type>::lowest();
                for(int c = 0; c < nc; c++)
                {
                    cmax = std::max(cmax, input(b, c, i, j));
                }
                for(int c = 0; c < nc; c++)
                {
                    output(b, c, i, j) = std::exp(input(b, c, i, j) - cmax);
                }
                value_type sum = value_type(0);
                for(int c = 0; c < nc; c++)
                {
                    sum += output(b, c, i, j);
                }
                for(int c = 0; c < nc; c++)
                {
                    output(b, c, i, j) = output(b, c, i, j) / sum;
                }
            });
        });
        return result;
    }
};

struct add_op
{
    std::string name() const { return "add"; }
    auto fcn() const
    {
        return [](auto x, auto y) { return x + y; };
    }
};

struct sub_op
{
    std::string name() const { return "sub"; }
    auto fcn() const
    {
        return [](auto x, auto y) { return x - y; };
    }
};

struct mul_op
{
    std::string name() const { return "mul"; }
    auto fcn() const
    {
        return [](auto x, auto y) { return x * y; };
    }
};

struct div_op
{
    std::string name() const { return "div"; }
    auto fcn() const
    {
        return [](auto x, auto y) { return x / y; };
    }
};

template <typename Op>
struct cpu_binary
{
    Op op;
    std::string name() const { return op.name(); }
    shape compute_shape(std::vector<shape> inputs) const { return inputs.front(); }
    argument compute(shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
            std::transform(input1.begin(), input1.end(), input2.begin(), output.begin(), op.fcn());
        });
        return result;
    }
};

struct cpu_apply
{
    program* prog;

    void apply()
    {
        for(auto it = prog->begin(); it != prog->end(); it++)
        {
            if(it->op.name() == "convolution")
            {
                apply_convolution(it);
            }
            else if(it->op.name() == "gemm")
            {
                apply_gemm(it);
            }
            else if(it->op.name() == "reshape")
            {
                apply_reshape(it);
            }
            else if(it->op.name() == "transpose")
            {
                apply_transpose(it);
            }
            else if(it->op.name() == "activation")
            {
                apply_activation(it);
            }
            else if(it->op.name() == "identity")
            {
                apply_identity(it);
            }
            else if(it->op.name() == "softmax")
            {
                apply_softmax(it);
            }
            else if(it->op.name() == "tanh")
            {
                apply_tanh(it);
            }
            else if(it->op.name() == "sigmoid")
            {
                apply_sigmoid(it);
            }
            else if(it->op.name() == "exp")
            {
                apply_exp(it);
            }
            else if(it->op.name() == "neg")
            {
                apply_neg(it);
            }
            else if(it->op.name() == "sin")
            {
                apply_sin(it);
            }
            else if(it->op.name() == "cos")
            {
                apply_cos(it);
            }
            else if(it->op.name() == "tan")
            {
                apply_tan(it);
            }
        }
    }

    void apply_convolution(instruction_ref ins)
    {
        auto&& op = any_cast<convolution>(ins->op);
        prog->replace_instruction(ins, cpu_convolution{op}, ins->arguments);
    }

    void apply_gemm(instruction_ref ins)
    {
        auto&& op = any_cast<gemm>(ins->op);
        prog->replace_instruction(ins, cpu_gemm{op}, ins->arguments);
    }

    void apply_reshape(instruction_ref ins)
    {
        auto&& op = any_cast<reshape>(ins->op);
        prog->replace_instruction(ins, cpu_reshape{op}, ins->arguments);
    }

    void apply_transpose(instruction_ref ins)
    {
        auto&& op = any_cast<transpose>(ins->op);
        prog->replace_instruction(ins, cpu_transpose{op}, ins->arguments);
    }

    void apply_activation(instruction_ref ins)
    {
        auto&& op = any_cast<activation>(ins->op);
        if(op.mode == "relu")
            prog->replace_instruction(ins, cpu_unary<relu_op>{}, ins->arguments);
    }

    void apply_identity(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<identity_op>{}, ins->arguments);
    }

    void apply_softmax(instruction_ref ins)
    {
        prog->replace_instruction(ins, softmax2d{}, ins->arguments);
    }

    void apply_tanh(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<tanh_op>{}, ins->arguments);
    }

    void apply_sigmoid(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<sigmoid_op>{}, ins->arguments);
    }

    void apply_exp(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<exp_op>{}, ins->arguments);
    }

    void apply_neg(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<neg_op>{}, ins->arguments);
    }

    void apply_sin(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<sin_op>{}, ins->arguments);
    }

    void apply_cos(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<cos_op>{}, ins->arguments);
    }

    void apply_tan(instruction_ref ins)
    {
        prog->replace_instruction(ins, cpu_unary<tan_op>{}, ins->arguments);
    }
};

std::string cpu_target::name() const { return "cpu"; }

void cpu_target::apply(program& p) const { cpu_apply{&p}.apply(); }

} // namespace cpu

} // namespace rtg