lowering.cpp

#include <migraphx/cpu/lowering.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/dfor.hpp>
#include <migraphx/operators.hpp>
#include <migraphx/shape_for_each.hpp>
#include <migraphx/iterator_for.hpp>
#include <migraphx/par_dfor.hpp>
#include <migraphx/cpu/gemm.hpp>
#include <unordered_map>
#include <utility>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace cpu {

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

template <class T>
typename std::conditional_t<std::is_integral<T>{}, std::make_signed<T>, std::enable_if<true, T>>::
    type
    make_signed(T x)
{
    return x;
}

//
// cpu implemenataion of batch norm for inference
//
// inputs are:
// args[0] -> input data buffer
// args[1] -> mini batch mean
// args[2] -> mini batch variance
// args[3] -> gamma
// args[4] -> bias
//
// The equation to compute batch norm for inference is:
//
// output[i] = bias + gamma * (input[i] + mean) / sqrt(variance + epsilon)
//
// the input data format should be nchw
//
struct cpu_batch_norm_inference
{
    op::batch_norm_inference op;

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

    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument output{output_shape};

        double epsilon           = op.epsilon;
        auto input               = args[0];
        auto arg_gamma           = args[1];
        auto arg_bias            = args[2];
        auto mini_batch_mean     = args[3];
        auto mini_batch_variance = args[4];

        auto num_batch    = output_shape.lens()[0];
        auto num_channels = output_shape.lens()[1];
        auto image_height = output_shape.lens()[2];
        auto image_width  = output_shape.lens()[3];

        if(op.bn_mode == op::batch_norm_inference::spatial)
        {
            visit_all(output, input, mini_batch_mean, mini_batch_variance, arg_gamma, arg_bias)(
                [&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {

                    par_dfor(num_batch, num_channels, image_height, image_width)(
                        [&](std::size_t n, std::size_t c, std::size_t h, std::size_t w) {
                            assert((variance[c] + epsilon) > 0);
                            result(n, c, h, w) = gamma[c] * (buffer(n, c, h, w) - mean[c]) /
                                                     std::sqrt(variance[c] + epsilon) +
                                                 bias[c];
                        });
                });
        }

        if(op.bn_mode == op::batch_norm_inference::per_activation)
        {
            visit_all(output, input, mini_batch_mean, mini_batch_mean, arg_gamma, arg_bias)(
                [&](auto result, auto buffer, auto mean, auto variance, auto gamma, auto bias) {

                    par_dfor(num_batch, num_channels, image_height, image_width)(
                        [&](std::size_t n, std::size_t c, std::size_t h, std::size_t w) {
                            assert((variance(c, h, w) + epsilon) > 0);
                            result(n, c, h, w) = gamma(c, h, w) *
                                                     (buffer(n, c, h, w) - mean(c, h, w)) /
                                                     std::sqrt(variance(c, h, w) + epsilon) +
                                                 bias(c, h, w);
                        });
                });
        }

        return output;
    }
};

struct cpu_lrn
{
    op::lrn op;

    std::string name() const { return "cpu::lrn"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, shape output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0])([&](auto output, auto input) {
            int n_batch         = output_shape.lens()[0];
            int channels        = output_shape.lens()[1];
            int height          = output_shape.lens()[2];
            int width           = output_shape.lens()[3];
            float alphaoverarea = op.alpha / op.size;
            int radius          = (op.size - 1) / 2;

            par_dfor(n_batch, height, width)([&](int b, int h, int w) {
                float scale = 0;
                dfor(channels)([&](int c) {
                    auto start = (c - radius) < 0 ? 0 : (c - radius);
                    auto end   = (c + radius) > channels ? channels : (c + radius);
                    for(auto k = start; k < end; ++k)
                    {
                        scale += std::pow(input(b, k, h, w), 2);
                    }
                    scale *= alphaoverarea;
                    scale += op.bias;
                    scale              = std::pow(scale, -op.beta);
                    output(b, c, h, w) = input(b, c, h, w) * scale;
                });
            });
        });
        return result;
    }
};

struct cpu_convolution
{
    op::convolution op;

    std::string name() const { return "cpu::convolution"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, 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   = input.get_shape().lens();
            auto in_h = in[2];
            auto in_w = in[3];

            auto wei   = weights.get_shape().lens();
            auto wei_n = wei[0];
            auto wei_c = wei[1];
            auto wei_h = wei[2];
            auto wei_w = wei[3];

            par_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];
                    const int group_id = w / (wei_n / op.group);

                    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;
                        const int in_ch = group_id * wei_c + k;
                        if(in_x >= 0 && in_x < in_h && in_y >= 0 && in_y < in_w)
                        {
                            acc += input(o, in_ch, in_x, in_y) * weights(w, k, x, y);
                        }
                    });
                    output(o, w, i, j) = acc;
                });
        });
        return result;
    }
};

struct cpu_im2col
{
    op::im2col op;

    static std::string name() { return "cpu::im2col"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        auto input_shape   = args[0].get_shape();
        auto weights_shape = args[1].get_shape();
        visit_all(result, args[0])([&](auto col, auto input) {
            const std::size_t& height   = input_shape.lens()[2];
            const std::size_t& width    = input_shape.lens()[3];
            const std::size_t& channels = weights_shape.lens()[1];
            const std::size_t& kernel_h = weights_shape.lens()[2];
            const std::size_t& kernel_w = weights_shape.lens()[3];
            const std::size_t& pad_h    = op.padding[0];
            const std::size_t& pad_w    = op.padding[1];
            const std::size_t& stride_h = op.stride[0];
            const std::size_t& stride_w = op.stride[1];

            int kdiv2_h;
            int kdiv2_w;
            kdiv2_h = kernel_h / 2;
            kdiv2_w = kernel_w / 2;
            // calculate output sizes
            const std::size_t col_height = (height - kernel_h + 2 * pad_h) / stride_h + 1;
            const std::size_t col_width  = (width - kernel_w + 2 * pad_w) / stride_w + 1;
            // account for padding for the starting position of the input pixels
            std::size_t iinput = kdiv2_h - pad_h;
            // loop over output pixels (ioutput, joutput)
            for(std::size_t ioutput = 0; ioutput < col_height; ioutput++, iinput += stride_h)
            {
                std::size_t jinput = kdiv2_w - pad_w;
                for(std::size_t joutput = 0; joutput < col_width; joutput++, jinput += stride_w)
                {
                    // compute linear index for output
                    std::size_t ldx = ioutput * col_width + joutput;
                    std::size_t p   = 0;
                    dfor(channels,
                         kernel_h,
                         kernel_w)([&](std::size_t c, std::size_t koffset, std::size_t loffset) {
                        int idx     = iinput + koffset - kdiv2_h;
                        int jdx     = jinput + loffset - kdiv2_w;
                        col(ldx, p) = ((idx >= 0) && (idx < height) && (jdx >= 0) && (jdx < width))
                                          ? input(0, c, idx, jdx)
                                          : 0;
                        p++;
                    });
                }
            }
        });
        return result;
    }
};

struct max_pool
{
    static std::string name() { return "max"; }
    static double start() { return std::numeric_limits<double>::lowest(); }

    static double apply(double x, double y)
    {
        double m = std::max(x, y);
        return (m);
    }

    static double final(double x, double) { return (x); }
};

struct avg_pool
{
    static std::string name() { return "average"; }
    static double start() { return 0.0; }

    static double apply(double x, double y) { return x + y; }

    static double final(double x, double y) { return x / y; }
};

template <class Op>
struct cpu_pooling
{
    op::pooling op;

    std::string name() const { return "cpu::pooling_" + Op::name(); }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        visit_all(result, args[0])([&](auto output, auto input) {
            using type = typename decltype(output)::value_type;
            auto in_h  = input.get_shape().lens()[2];
            auto in_w  = input.get_shape().lens()[3];

            par_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_x0 = i * op.stride[0] - op.padding[0];
                    const int start_y0 = j * op.stride[1] - op.padding[1];

                    const int hend = std::min(start_x0 + op.lengths[0], in_h);
                    const int wend = std::min(start_y0 + op.lengths[1], in_w);

                    const int start_x = std::max(start_x0, 0);
                    const int start_y = std::max(start_y0, 0);

                    const int w_h       = (hend - start_x);
                    const int w_w       = (wend - start_y);
                    const int pool_size = std::max(w_h * w_w, 1);

                    double acc = Op::start();
                    dfor(w_h, w_w)([&](int x, int 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 = Op::apply(acc, input(o, w, in_x, in_y));
                        }
                    });
                    output(o, w, i, j) = type(Op::final(acc, pool_size));
                });
        });
        return result;
    }
};

struct cpu_contiguous
{
    op::contiguous op;
    std::string name() const { return "cpu::contiguous"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        return op.compute(output_shape, std::move(args));
    }
};

struct cpu_pad
{
    op::pad op;
    std::string name() const { return "cpu::contiguous"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        assert(output_shape.standard());
        argument result{output_shape};
        result.visit([&](auto output) { std::fill(output.begin(), output.end(), op.value); });

        visit_all(result, args[0])([&](auto output, auto input) {
            shape_for_each(input.get_shape(), [&](const auto& idx) {
                std::vector<std::size_t> new_idx(idx.size());
                std::transform(
                    idx.begin(), idx.end(), op.pads.begin(), new_idx.begin(), [](auto i, auto j) {
                        return i + j;
                    });
                output(new_idx.begin(), new_idx.end()) = input(idx.begin(), idx.end());
            });
        });

        return result;
    }
};

struct cpu_concat
{
    op::concat op;
    std::string name() const { return "cpu::concat"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        return op.compute(output_shape, std::move(args));
    }
};

struct cpu_gemm
{
    op::dot op;
    std::string name() const { return "cpu::dot"; }
    shape compute_shape(const std::vector<shape>& inputs) const
    {
        if(inputs.size() == 3)
        {
            auto c_shape = inputs.at(2);
            check_shapes{{c_shape}}.not_broadcasted();
        }
        return op.compute_shape(inputs);
    }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        // 3 inputs, it is alpha * A * B + beta * C, then
        // A and B are matrics, and C is broadcastable to A * B
        if(args.size() == 3)
        {
            // no need to consider the value of args[2]
            if(op.beta == 0.0f)
            {
                result.visit([&](auto output) { std::fill(output.begin(), output.end(), 0); });
            }
            else
            {
                visit_all(result, args[2])([&](auto output, auto input) {
                    std::copy(input.begin(), input.end(), output.begin());
                });
            }

            migemm(result, args[0], args[1], op.alpha, op.beta);

            return result;
        }

        // 2 input arguments
        migemm(result, args[0], args[1], op.alpha, 0.0f);

        return result;
    }
};

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

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

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(make_signed(x)); };
    }
};

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

struct log_op
{
    std::string name() const { return "cpu::log"; }
    auto fcn() const
    {
        return [](auto x) { return std::log(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 sinh_op
{
    std::string name() const { return "cpu::sinh"; }
    auto fcn() const
    {
        return [](auto x) { return std::sinh(x); };
    }
};

struct cosh_op
{
    std::string name() const { return "cpu::cosh"; }
    auto fcn() const
    {
        return [](auto x) { return std::cosh(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 std::max(decltype(x){0}, x); };
    }
};

struct leaky_relu_op
{
    op::leaky_relu op;
    std::string name() const { return "cpu::leaky_relu"; }
    auto fcn() const
    {
        auto& a = op.alpha;
        return [a](auto x) { return x > 0 ? x : x * a; };
    }
};

struct elu_op
{
    op::elu op;
    std::string name() const { return "cpu::elu"; }
    auto fcn() const
    {
        auto& a = op.alpha;
        return [a](auto x) { return x > 0 ? x : a * std::expm1(x); };
    }
};

template <typename Op>
struct cpu_unary
{
    Op op;
    std::string name() const { return op.name(); }
    shape compute_shape(const std::vector<shape>& inputs) const
    {
        return {inputs.front().type(), inputs.front().lens()};
    }

    argument compute(context&, const 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(const std::vector<shape>& inputs) const { return inputs.front(); }
    argument compute(context&, const 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 cpu_logsoftmax
{
    op::logsoftmax op;
    std::string name() const { return "cpu::logsoftmax"; }
    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }

    template <typename T>
    std::size_t compute_batch_index(const T& idx, shape& batch_shape, int axis) const
    {
        if(axis == 0)
        {
            return 0;
        }
        else
        {
            std::vector<std::size_t> batch_idx(idx.begin(), idx.begin() + axis);
            return batch_shape.index(batch_idx.begin(), batch_idx.end());
        }
    }

    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
    {
        argument result{output_shape};
        auto lens = output_shape.lens();
        std::vector<std::size_t> batch_lens{};
        if(op.axis == 0)
        {
            batch_lens.push_back(1);
        }
        else
        {
            batch_lens.insert(batch_lens.begin(), lens.begin(), lens.begin() + op.axis);
        }
        shape batch_shape{migraphx::shape::uint32_type, batch_lens};
        visit_all(result, args[0])([&](auto output, auto input) {
            using value_type = typename decltype(input)::value_type;
            std::vector<value_type> batch_max(batch_shape.elements(),
                                              std::numeric_limits<value_type>::lowest());
            shape_for_each(output_shape, [&](auto idx) {
                auto index       = this->compute_batch_index(idx, batch_shape, op.axis);
                batch_max[index] = std::max(batch_max[index], input(idx.begin(), idx.end()));
            });

            shape_for_each(output_shape, [&](auto idx) {
                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
                output(idx.begin(), idx.end()) = input(idx.begin(), idx.end()) - batch_max[index];
            });

            std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
            shape_for_each(output_shape, [&](auto idx) {
                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
                batch_sum[index] += std::exp(output(idx.begin(), idx.end()));
            });

            for(std::size_t i = 0; i < batch_sum.size(); ++i)
            {
                batch_sum[i] = std::log(batch_sum[i]);
            }

            shape_for_each(output_shape, [&](auto idx) {
                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
                output(idx.begin(), idx.end()) -= batch_sum[index];
            });
        });

        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; };
    }
};

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

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

template <typename Op>
struct cpu_binary
{
    Op op;
    std::string name() const { return op.name(); }
    shape compute_shape(const std::vector<shape>& inputs) const
    {
        return {inputs.front().type(), inputs.front().lens()};
    }

    argument compute(context&, const 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) {
            if(input1.get_shape().packed() and input2.get_shape().packed())
            {
                std::transform(
                    input1.begin(), input1.end(), input2.begin(), output.begin(), op.fcn());
            }
            else
            {
                shape_for_each(output.get_shape(), [&](const auto& idx) {
                    output(idx.begin(), idx.end()) =
                        op.fcn()(input1(idx.begin(), idx.end()), input2(idx.begin(), idx.end()));
                });
            }
        });
        return result;
    }
};

struct cpu_apply
{
    program* prog;
    std::unordered_map<std::string, std::function<void(instruction_ref)>> apply_map{};

    template <class T>
    auto simple_op()
    {
        return [this](instruction_ref ins) { apply_simple_op<T>(ins); };
    }

    template <class T, class Op>
    auto extend_op()
    {
        return [this](instruction_ref ins) { apply_extend_op<T, Op>(ins); };
    }

    void init()
    {
        apply_map["im2col"]      = extend_op<cpu_im2col, op::im2col>();
        apply_map["convolution"] = extend_op<cpu_convolution, op::convolution>();
        apply_map["dot"]         = extend_op<cpu_gemm, op::dot>();
        apply_map["batch_norm_inference"] =
            extend_op<cpu_batch_norm_inference, op::batch_norm_inference>();
        apply_map["lrn"]        = extend_op<cpu_lrn, op::lrn>();
        apply_map["contiguous"] = extend_op<cpu_contiguous, op::contiguous>();
        apply_map["pad"]        = extend_op<cpu_pad, op::pad>();
        apply_map["concat"]     = extend_op<cpu_concat, op::concat>();
        apply_map["gather"]     = extend_op<cpu_gather, op::gather>();
        apply_map["logsoftmax"] = extend_op<cpu_logsoftmax, op::logsoftmax>();
        apply_map["leaky_relu"] = extend_op<cpu_unary<leaky_relu_op>, op::leaky_relu>();
        apply_map["elu"]        = extend_op<cpu_unary<elu_op>, op::elu>();
        apply_map["identity"]   = simple_op<cpu_unary<identity_op>>();
        apply_map["abs"]        = simple_op<cpu_unary<abs_op>>();
        apply_map["sinh"]       = simple_op<cpu_unary<sinh_op>>();
        apply_map["cosh"]       = simple_op<cpu_unary<cosh_op>>();
        apply_map["tanh"]       = simple_op<cpu_unary<tanh_op>>();
        apply_map["sigmoid"]    = simple_op<cpu_unary<sigmoid_op>>();
        apply_map["exp"]        = simple_op<cpu_unary<exp_op>>();
        apply_map["log"]        = simple_op<cpu_unary<log_op>>();
        apply_map["neg"]        = simple_op<cpu_unary<neg_op>>();
        apply_map["sin"]        = simple_op<cpu_unary<sin_op>>();
        apply_map["cos"]        = simple_op<cpu_unary<cos_op>>();
        apply_map["tan"]        = simple_op<cpu_unary<tan_op>>();
        apply_map["asin"]       = simple_op<cpu_unary<asin_op>>();
        apply_map["acos"]       = simple_op<cpu_unary<acos_op>>();
        apply_map["atan"]       = simple_op<cpu_unary<atan_op>>();
        apply_map["relu"]       = simple_op<cpu_unary<relu_op>>();
        apply_map["add"]        = simple_op<cpu_binary<add_op>>();
        apply_map["sub"]        = simple_op<cpu_binary<sub_op>>();
        apply_map["mul"]        = simple_op<cpu_binary<mul_op>>();
        apply_map["div"]        = simple_op<cpu_binary<div_op>>();
        apply_map["max"]        = simple_op<cpu_binary<max_op>>();
        apply_map["min"]        = simple_op<cpu_binary<min_op>>();

        apply_map["softmax"] = simple_op<softmax2d>();
    }

    void apply()
    {
        init();
        for(auto it : iterator_for(*prog))
        {
            if(it->name() == "pooling")
            {
                apply_pooling(it);
            }
            else if(apply_map.count(it->name()) > 0)
            {
                apply_map.at(it->name())(it);
            }
        }
    }

    template <class T>
    void apply_simple_op(instruction_ref ins)
    {
        prog->replace_instruction(ins, T{}, ins->inputs());
    }

    template <class T, class Op>
    void apply_extend_op(instruction_ref ins)
    {
        auto&& op = any_cast<Op>(ins->get_operator());
        prog->replace_instruction(ins, T{op}, ins->inputs());
    }

    void apply_pooling(instruction_ref ins)
    {
        auto&& op = any_cast<op::pooling>(ins->get_operator());
        if(op.mode == "max")
            prog->replace_instruction(ins, cpu_pooling<max_pool>{op}, ins->inputs());
        else if(op.mode == "average")
            prog->replace_instruction(ins, cpu_pooling<avg_pool>{op}, ins->inputs());
    }
};

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

} // namespace cpu
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx