
#include <onnx_test.hpp>

TEST_CASE(instance_norm_dyn_batch_test)
{
    // instancenorm with dynamic input in the 0'th (batch) dimension
    migraphx::shape s1{migraphx::shape::float_type, {{1, 2, {2}}, {2, 2}, {3, 3}, {3, 3}}};
    migraphx::shape s2{migraphx::shape::float_type, {2}};

    migraphx::program p;
    auto* mm   = p.get_main_module();
    auto x     = mm->add_parameter("0", s1);
    auto scale = mm->add_parameter("1", s2);
    auto bias  = mm->add_parameter("2", s2);

    auto mean     = mm->add_instruction(migraphx::make_op("reduce_mean", {{"axes", {2, 3}}}), x);
    auto l1       = add_common_op(*mm, migraphx::make_op("sub"), {x, mean});
    auto l0       = add_common_op(*mm, migraphx::make_op("sqdiff"), {x, mean});
    auto variance = mm->add_instruction(migraphx::make_op("reduce_mean", {{"axes", {2, 3}}}), l0);
    auto epsilon_literal =
        mm->add_literal(migraphx::literal{migraphx::shape{migraphx::shape::float_type}, {1e-5}});
    auto l2 = add_common_op(*mm, migraphx::make_op("add"), {variance, epsilon_literal});

    auto l3 = mm->add_instruction(migraphx::make_op("rsqrt"), l2);
    auto l4 = add_common_op(*mm, migraphx::make_op("mul"), {l1, l3});

    auto scale_bcast = mm->add_instruction(migraphx::make_op("broadcast", {{"axis", 1}}), scale, x);
    auto bias_bcast  = mm->add_instruction(migraphx::make_op("broadcast", {{"axis", 1}}), bias, x);
    auto l5          = mm->add_instruction(migraphx::make_op("mul"), l4, scale_bcast);
    auto ret         = mm->add_instruction(migraphx::make_op("add"), l5, bias_bcast);
    mm->add_return({ret});

    migraphx::onnx_options options;
    options.default_dyn_dim_value = {1, 2, {2}};
    auto prog = migraphx::parse_onnx("instance_norm_dyn_batch_test.onnx", options);

    EXPECT(p == prog);
}
