eliminate_pad_test.cpp

#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_pad.hpp>
#include <migraphx/instruction.hpp>
#include <basic_ops.hpp>
#include <migraphx/operators.hpp>
#include <test.hpp>

struct eliminate_pad_target
{
    std::string name() const { return "eliminate_pad"; }
    std::vector<migraphx::pass> get_passes(migraphx::context&) const
    {
        return {migraphx::eliminate_pad{}, migraphx::dead_code_elimination{}};
    }
    migraphx::context get_context() const { return {}; }
};

migraphx::instruction_ref
create_im2col(migraphx::instruction_ref& l_img, size_t channels, migraphx::program& p)
{
    size_t f[2] = {1, 1};
    std::vector<int32_t> weights(channels * f[0] * f[1]);

    migraphx::shape s_weights{migraphx::shape::int32_type, {1, channels, f[0], f[1]}};
    auto l_weights = p.add_literal(migraphx::literal{s_weights, weights});
    return p.add_instruction(migraphx::op::im2col{}, l_img, l_weights);
}

migraphx::instruction_ref
create_conv(migraphx::instruction_ref& l_img, size_t channels, migraphx::program& p, migraphx::op::padding_mode_t padding_mode = migraphx::op::padding_mode_t::default_)
{
    migraphx::shape s_weights{migraphx::shape::int32_type, {4, channels, 3, 3}};
    std::vector<int32_t> weights(4 * channels * 3 * 3);

    auto l_weights = p.add_literal(migraphx::literal{s_weights, weights});
    migraphx::op::convolution op;
    op.padding_mode = padding_mode;
    return p.add_instruction(op, l_img, l_weights);
}

TEST_CASE(rewrite_test)
{
    migraphx::program p;

    size_t img_dim[2] = {2, 2};
    size_t channels   = 1;
    std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
    std::iota(input.begin(), input.end(), 0);

    migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
    auto l_img      = p.add_literal(migraphx::literal{s_img, input});
    auto padded_img = p.add_instruction(migraphx::op::pad{{0, 0, 1, 1, 0, 0, 1, 1}}, l_img);

    auto l0 = create_im2col(padded_img, channels, p);
    auto l1 = create_conv(padded_img, channels, p);
    auto l2 = p.add_instruction(migraphx::op::pooling{}, padded_img);
    p.add_instruction(migraphx::op::identity{}, l0, l1, l2);

    p.compile(eliminate_pad_target{});
    EXPECT(std::none_of(
        p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}

TEST_CASE(rewrite_test_asymmetric)
{
    migraphx::program p;
    size_t img_dim[2] = {2, 2};
    size_t channels   = 1;
    std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
    std::iota(input.begin(), input.end(), 0);

    migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
    auto l_img      = p.add_literal(migraphx::literal{s_img, input});
    auto padded_img = p.add_instruction(migraphx::op::pad{{0, 0, 0, 0, 0, 0, 2, 2}}, l_img);

    create_im2col(padded_img, channels, p);

    p.compile(eliminate_pad_target{});
    EXPECT(std::any_of(
        p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}

TEST_CASE(rewrite_test_same_padding)
{
    migraphx::program p;
    size_t img_dim[2] = {2, 2};
    size_t channels   = 1;
    std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
    std::iota(input.begin(), input.end(), 0);

    migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
    auto l_img      = p.add_literal(migraphx::literal{s_img, input});
    auto padded_img = p.add_instruction(migraphx::op::pad{{0, 0, 1, 1, 0, 0, 1, 1}}, l_img);
    
    create_conv(padded_img, channels, p, migraphx::op::padding_mode_t::same);

    p.compile(eliminate_pad_target{});
    EXPECT(std::any_of(
        p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
}

int main(int argc, const char* argv[]) { test::run(argc, argv); }