[GPU] Add unit tests for activation functions for blocked layouts (#11948)
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Oleksandr Kramskyi
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@@ -1499,14 +1499,12 @@ TEST(activation_f32_fw_gpu, b_fs_yx_fsv16_prelu) {
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}
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}
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struct activation_random_test_params {
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data_types input_type;
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format::type input_format;
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tensor input_size;
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activation_func func_type;
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activation_additional_params additional_params;
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padding padd;
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};
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using activation_random_test_params = std::tuple<data_types,
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format::type, // input_format
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tensor, // input_size
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activation_func, // func_type
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activation_additional_params, // additional_params
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padding>;
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struct activation_random_test : testing::TestWithParam<activation_random_test_params>
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{
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@@ -1598,15 +1596,23 @@ struct activation_random_test : testing::TestWithParam<activation_random_test_pa
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void execute_compare(const activation_random_test_params& params, bool check_result) {
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auto& engine = get_test_engine();
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auto in_layout = layout(params.input_type, format::bfyx, params.input_size);
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data_types input_type;
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format::type input_format;
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tensor input_size;
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activation_func func_type;
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activation_additional_params additional_params;
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padding padd;
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std::tie(input_type, input_format, input_size, func_type, additional_params, padd) = params;
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auto in_layout = layout(input_type, format::bfyx, input_size);
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auto in_mem = engine.allocate_memory(in_layout);
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fill_random(in_mem);
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/// bfyx
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cldnn::topology topo;
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topo.add(input_layout("in", in_layout));
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auto prim = activation("activation", "in", params.func_type);
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prim.additional_params = params.additional_params;
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auto prim = activation("activation", "in", func_type);
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prim.additional_params = additional_params;
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topo.add(prim);
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auto build_opts = build_options();
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@@ -1621,18 +1627,19 @@ struct activation_random_test : testing::TestWithParam<activation_random_test_pa
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cldnn::topology topo_opt;
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topo_opt.add(input_layout("in", in_layout));
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topo_opt.add(reorder("in_to_input_type", "in", params.input_format, params.input_type));
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auto prim_opt = activation("activation_blocked", "in_to_input_type", params.func_type);
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prim_opt.additional_params = params.additional_params;
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topo_opt.add(reorder("in_to_input_type", "in", input_format, input_type));
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auto prim_opt = activation("activation_blocked", "in_to_input_type", func_type);
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prim_opt.additional_params = additional_params;
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topo_opt.add(prim_opt);
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// force output format to input format.
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topo_opt.add(reorder("res_to_input_format", "activation_blocked", params.input_format, params.input_type));
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topo_opt.add(reorder("res_to_input_format", "activation_blocked", input_format, input_type));
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auto build_opts_opt = build_options();
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build_opts_opt.set_option(build_option::outputs({"activation_blocked", "res_to_input_format"}));
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auto activation_impl_desc = implementation_desc();
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activation_impl_desc.output_format = params.input_format;
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build_opts_opt.set_option(build_option::force_implementations({{"activation_blocked", {params.input_format, "activation_ref"} }}));
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activation_impl_desc.output_format = input_format;
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build_opts_opt.set_option(
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build_option::force_implementations({{"activation_blocked", {input_format, "activation_ref"}}}));
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network net_opt(engine, topo_opt, build_opts_opt);
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@@ -1645,16 +1652,16 @@ struct activation_random_test : testing::TestWithParam<activation_random_test_pa
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if (check_result == true) {
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// Check data_types
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if (params.input_type == data_types::f32) {
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if (input_type == data_types::f32) {
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compare_outputs<float>(output, output_opt);
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} else if (params.input_type == data_types::f16) {
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} else if (input_type == data_types::f16) {
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compare_outputs<FLOAT16>(output, output_opt);
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} else if (params.input_type == data_types::i8) {
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} else if (input_type == data_types::i8) {
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compare_outputs<int8_t>(output, output_opt);
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} else if (params.input_type == data_types::u8) {
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} else if (input_type == data_types::u8) {
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compare_outputs<uint8_t>(output, output_opt);
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} else {
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FAIL() << "Not supported data type: " << static_cast<size_t>(params.input_type);
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FAIL() << "Not supported data type: " << static_cast<size_t>(input_type);
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}
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}
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}
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@@ -1665,14 +1672,86 @@ TEST_P(activation_random_test, random) {
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execute_compare(param, true);
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}
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INSTANTIATE_TEST_SUITE_P(activation_blocked_tests,
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activation_random_test,
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testing::ValuesIn(
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std::vector<activation_random_test_params>{
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{ data_types::i8, format::b_fs_yx_fsv32, { 1, 32, 5, 5}, activation_func::relu, {}, {}},
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{ data_types::i8, format::bs_fs_yx_bsv32_fsv32, {32, 32, 5, 5}, activation_func::relu, {}, {}},
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{ data_types::f16, format::bs_fs_yx_bsv32_fsv16, {32, 32, 5, 5}, activation_func::relu, {}, {}},
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{ data_types::i8, format::bs_fs_yx_bsv32_fsv32, {16, 16, 5, 5}, activation_func::relu, {}, {}},
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{ data_types::f16, format::bs_fs_yx_bsv32_fsv16, {16, 16, 5, 5}, activation_func::relu, {}, {}},
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}
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));
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const auto reluParams = testing::ValuesIn(std::vector<activation_random_test_params>{
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{data_types::i8, format::b_fs_yx_fsv32, {1, 32, 5, 5}, activation_func::relu, {}, {}},
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{data_types::i8, format::bs_fs_yx_bsv32_fsv32, {32, 32, 5, 5}, activation_func::relu, {}, {}},
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{data_types::f16, format::bs_fs_yx_bsv32_fsv16, {32, 32, 5, 5}, activation_func::relu, {}, {}},
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{data_types::i8, format::bs_fs_yx_bsv32_fsv32, {16, 16, 5, 5}, activation_func::relu, {}, {}},
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{data_types::f16, format::bs_fs_yx_bsv32_fsv16, {16, 16, 5, 5}, activation_func::relu, {}, {}},
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});
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INSTANTIATE_TEST_SUITE_P(relu_activation_blocked_tests, activation_random_test, reluParams);
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const std::vector<data_types> dataTypes = {data_types::f16, data_types::f32};
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const std::vector<format::type> types = {format::bfyx,
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format::bfzyx,
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format::yxfb,
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format::byxf,
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format::fyxb,
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format::b_fs_yx_fsv2,
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format::b_fs_zyx_fsv2,
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format::bs_fs_yx_bsv32_fsv32,
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format::bs_fs_yx_bsv32_fsv16};
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// TODO: need to investigate input for commented activation functions
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const std::vector<activation_func> activationFunctions = {activation_func::none,
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activation_func::logistic,
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activation_func::gelu,
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activation_func::hyperbolic_tan,
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activation_func::relu,
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activation_func::relu_negative_slope,
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activation_func::clamp,
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activation_func::softrelu,
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activation_func::abs,
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activation_func::linear,
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activation_func::square,
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// activation_func::sqrt,
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activation_func::elu,
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activation_func::sin,
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// activation_func::asin,
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activation_func::sinh,
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// activation_func::asinh,
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activation_func::cos,
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// activation_func::acos,
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activation_func::cosh,
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// activation_func::acosh,
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// activation_func::log,
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// activation_func::log2,
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activation_func::exp,
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activation_func::tan,
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activation_func::atan,
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// activation_func::atanh,
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activation_func::floor,
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activation_func::ceil,
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activation_func::negative,
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activation_func::negation,
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activation_func::pow,
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activation_func::reciprocal,
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activation_func::erf,
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activation_func::hard_sigmoid,
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activation_func::hsigmoid,
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activation_func::selu,
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activation_func::sign,
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activation_func::softplus,
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activation_func::swish,
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activation_func::hswish,
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activation_func::mish,
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activation_func::round_half_to_even,
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activation_func::round_half_away_from_zero,
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activation_func::gelu_tanh,
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activation_func::softsign};
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const std::vector<tensor> inputShapes = {
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{1, 32, 5, 5},
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{32, 32, 5, 5},
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{16, 16, 5, 5},
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};
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const auto fpFunctionsParams = ::testing::Combine(::testing::ValuesIn(dataTypes),
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::testing::ValuesIn(types),
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::testing::ValuesIn(inputShapes),
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::testing::ValuesIn(activationFunctions),
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::testing::Values(activation_additional_params{}),
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::testing::Values(padding{}));
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INSTANTIATE_TEST_SUITE_P(fp_activation_blocked_tests, activation_random_test, fpFunctionsParams);
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