#include <migraphx/config.hpp>
#include <migraphx/cpu/dnnl.hpp>
#include <migraphx/op/deconvolution.hpp>

namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace cpu {

struct dnnl_deconvolution
    : dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::deconvolution>
{
    std::vector<int> arg_map(int) const { return {DNNL_ARG_SRC, DNNL_ARG_WEIGHTS}; }

    shape adjust_shape(const shape& x, int i, const shape& output) const
    {
        auto s = base_adjust_shape(x, output);
        if(i == 1)
        {
            // The input and output channels are flipped for dnnl
            auto lens = s.lens();
            std::swap(lens[0], lens[1]);
            auto strides = s.strides();
            std::swap(strides[0], strides[1]);
            return {s.type(), lens, strides};
        }
        return s;
    }

    dnnl::deconvolution_forward::desc
    get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
    {
        // In DNNL dilation is zero-based
        auto dilation = op.dilation;
        std::transform(
            dilation.begin(), dilation.end(), dilation.begin(), [](auto x) { return x - 1; });
        return {dnnl::prop_kind::forward_inference,
                dnnl::algorithm::deconvolution_direct,
                m.at(DNNL_ARG_SRC),
                m.at(DNNL_ARG_WEIGHTS),
                m.at(DNNL_ARG_DST),
                to_dnnl_dims(op.stride),
                to_dnnl_dims(dilation),
                to_dnnl_dims(op.padding),
                to_dnnl_dims(op.padding)};
    }
};

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