//***************************************************************************** // Copyright 2017-2019 Intel Corporation // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. //***************************************************************************** #include <algorithm> #include <cinttypes> #include <cmath> #include <cstdlib> #include <random> #include <string> #include "gtest/gtest.h" #include "ngraph/ngraph.hpp" #include "util/all_close.hpp" #include "util/all_close_f.hpp" #include "util/ndarray.hpp" #include "util/random.hpp" #include "util/test_control.hpp" #include "util/test_tools.hpp" static std::mt19937_64 random_generator; using namespace std; using namespace ngraph; static string s_manifest = "${MANIFEST}"; // Trivial case with no summed axes. NGRAPH_TEST(${BACKEND_NAME}, sum_trivial) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape); copy_data(a, vector<float>{1, 2, 3, 4}); auto result = backend->create_tensor(element::f32, shape); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{1, 2, 3, 4}), read_vector<float>(result))); } // Failure has been reported at 5D for some reason NGRAPH_TEST(${BACKEND_NAME}, sum_trivial_5d) { Shape shape{2, 2, 2, 2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape); copy_data(a, vector<float>{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); auto result = backend->create_tensor(element::f32, shape); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_to_scalar) { Shape shape{2, 2}; auto A = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape); copy_data(a, vector<float>{1, 2, 3, 4}); auto result = backend->create_tensor(element::f32, Shape{}); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{10}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{1, 2, 3, 4}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_large_1d_to_scalar) { Shape shape{1000000}; auto A = make_shared<op::Parameter>(element::f32, shape); auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output random_generator.seed(2); vector<float> v_a(1000000, 0); double r = 0; for (int i = 0; i < 1000000; i++) { v_a[i] = static_cast<float>(random_generator() % 255); r += static_cast<double>(v_a[i]); } auto a = backend->create_tensor(element::f32, shape); copy_data(a, v_a); auto result = backend->create_tensor(element::f32, Shape{}); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE( test::all_close_f(vector<float>{static_cast<float>(r)}, read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_columns) { Shape shape_a{3, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{2}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{9, 12}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{1, 2, 3, 4, 5, 6}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_6d) { Shape shape_a{2, 6, 4, 5, 7, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{2, 4, 5, 3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1, 4}), ParameterVector{A}); auto backend_wrk = runtime::Backend::create("${BACKEND_NAME}"); auto backend_ref = runtime::Backend::create("INTERPRETER"); // Create some tensors for input/output auto a_wrk = backend_wrk->create_tensor(element::f32, shape_a); auto a_ref = backend_ref->create_tensor(element::f32, shape_a); auto result_wrk = backend_wrk->create_tensor(element::f32, shape_rt); auto result_ref = backend_ref->create_tensor(element::f32, shape_rt); vector<float> inp_data(shape_size<const Shape>(shape_a)); iota(inp_data.begin(), inp_data.end(), 1.f); copy_data(a_wrk, inp_data); copy_data(a_ref, inp_data); auto handle_wrk = backend_wrk->compile(f); auto handle_ref = backend_ref->compile(f); handle_wrk->call_with_validate({result_wrk}, {a_wrk}); handle_ref->call_with_validate({result_ref}, {a_ref}); EXPECT_TRUE(test::all_close_f(read_vector<float>(result_ref), read_vector<float>(result_wrk))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_rows) { Shape shape_a{3, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{3, 7, 11}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{1, 2, 3, 4, 5, 6}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_rows_zero) { Shape shape_a{3, 0}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{}); auto result = backend->create_tensor(element::f32, shape_rt); copy_data(result, vector<float>({3, 3, 3})); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{0, 0, 0}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_cols_zero) { // Now the reduction (g(x:float32[2,2],y:float32[]) = reduce(x,y,f,axes={})). Shape shape_a{0, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{2}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{}); auto result = backend->create_tensor(element::f32, shape_rt); copy_data(result, vector<float>({3, 3})); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{0, 0}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_vector_zero) { Shape shape_a{0}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{}); auto result = backend->create_tensor(element::f32, shape_rt); copy_data(result, vector<float>({3})); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{0}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_matrix_to_scalar_zero_by_zero) { Shape shape_a{0, 0}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{}); auto result = backend->create_tensor(element::f32, shape_rt); copy_data(result, vector<float>({3})); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{0}), read_vector<float>(result))); // For some reason I'm feeling extra paranoid about making sure reduction doesn't clobber the // input tensors, so let's do this too. EXPECT_TRUE(test::all_close_f((vector<float>{}), read_vector<float>(a))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_to_matrix_most_sig) { Shape shape_a{3, 3, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3, 3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{1 + 10 + 19, 2 + 11 + 20, 3 + 12 + 21, 4 + 13 + 22, 5 + 14 + 23, 6 + 15 + 24, 7 + 16 + 25, 8 + 17 + 26, 9 + 18 + 27}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_to_matrix_least_sig) { Shape shape_a{3, 3, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3, 3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{2}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{1 + 2 + 3, 4 + 5 + 6, 7 + 8 + 9, 10 + 11 + 12, 13 + 14 + 15, 16 + 17 + 18, 19 + 20 + 21, 22 + 23 + 24, 25 + 26 + 27}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_to_vector) { Shape shape_a{3, 3, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{1 + 10 + 19 + 4 + 13 + 22 + 7 + 16 + 25, 2 + 11 + 20 + 5 + 14 + 23 + 8 + 17 + 26, 3 + 12 + 21 + 6 + 15 + 24 + 9 + 18 + 27}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_to_scalar) { Shape shape_a{3, 3, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1, 2}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27}); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f( (vector<float>{1 + 10 + 19 + 4 + 13 + 22 + 7 + 16 + 25 + 2 + 11 + 20 + 5 + 14 + 23 + 8 + 17 + 26 + 3 + 12 + 21 + 6 + 15 + 24 + 9 + 18 + 27}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_to_scalar_int32) { Shape shape_a{3, 3, 3}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1, 2}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{0x40000001, 10, 19, 4, 13, 22, 7, 16, 25, 2, 11, 20, 5, 14, 23, 8, 17, 26, 3, 12, 21, 6, 15, 24, 9, 18, 27}); auto result = backend->create_tensor(element::i32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{0x40000001 + 10 + 19 + 4 + 13 + 22 + 7 + 16 + 25 + 2 + 11 + 20 + 5 + 14 + 23 + 8 + 17 + 26 + 3 + 12 + 21 + 6 + 15 + 24 + 9 + 18 + 27}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_eliminate_zero_dim) { Shape shape_a{3, 0, 2}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{3, 2}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, vector<float>{}); auto result = backend->create_tensor(element::f32, shape_rt); // Overwrite the initial result vector to make sure we're not just coincidentally getting the right value. copy_data(result, vector<float>{2112, 2112, 2112, 2112, 2112, 2112}); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<float>{0, 0, 0, 0, 0, 0}), read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_3d_eliminate_zero_dim_int32) { Shape shape_a{3, 0, 2}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_rt{3, 2}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, vector<int32_t>{}); auto result = backend->create_tensor(element::i32, shape_rt); // Overwrite the initial result vector to make sure we're not just coincidentally getting the right value. copy_data(result, vector<int32_t>{2112, 2112, 2112, 2112, 2112, 2112}); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ((vector<int32_t>{0, 0, 0, 0, 0, 0}), read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, sum_5d_to_scalar) { Shape shape_a{3, 3, 3, 3, 3}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1, 2, 3, 4}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f32, shape_a); copy_data(a, std::vector<float>(std::pow(3, 5), 1)); auto result = backend->create_tensor(element::f32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f(std::vector<float>{243.}, read_vector<float>(result))); } NGRAPH_TEST(${BACKEND_NAME}, sum_5d_to_scalar_int32) { Shape shape_a{3, 3, 3, 3, 3}; auto A = make_shared<op::Parameter>(element::i32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1, 2, 3, 4}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i32, shape_a); copy_data(a, std::vector<int32_t>(std::pow(3, 5), 1)); auto result = backend->create_tensor(element::i32, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ(std::vector<int32_t>{243}, read_vector<int32_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, sum_2d_to_scalar_int8) { Shape shape_a{3, 3}; auto A = make_shared<op::Parameter>(element::i8, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0, 1}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::i8, shape_a); copy_data(a, std::vector<int8_t>{1, 2, 3, 4, 5, 6, 7, 8, 9}); auto result = backend->create_tensor(element::i8, shape_rt); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_EQ(std::vector<int8_t>{45}, read_vector<int8_t>(result)); } NGRAPH_TEST(${BACKEND_NAME}, sum_trivial_in_double) { Shape shape{4, 3}; Shape rshape{3}; auto A = make_shared<op::Parameter>(element::f64, shape); auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); auto backend = runtime::Backend::create("${BACKEND_NAME}"); // Create some tensors for input/output auto a = backend->create_tensor(element::f64, shape); copy_data(a, vector<double>{12, 2, 10, 9, 8, 4, 6, 1, 5, 3, 11, 7}); auto result = backend->create_tensor(element::f64, rshape); auto handle = backend->compile(f); handle->call_with_validate({result}, {a}); EXPECT_TRUE(test::all_close_f((vector<double>{30, 22, 26}), read_vector<double>(result))); } #if NGRAPH_INTERPRETER_ENABLE #ifndef _WIN32 NGRAPH_TEST(${BACKEND_NAME}, sum_stable_acc) { std::string backend_name = "${BACKEND_NAME}"; if (backend_name == "INTERPRETER") { return; } Shape shape_a{10, 10, 10, 30}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{10}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1, 2, 3}), ParameterVector{A}); test::Uniform<float> rng(1000.0f, 1000.1f, 2112); vector<vector<float>> args; for (shared_ptr<op::Parameter> param : f->get_parameters()) { vector<float> tensor_val(shape_size(param->get_shape())); rng.initialize(tensor_val); args.push_back(tensor_val); } auto ref_func = clone_function(*f); auto bk_func = clone_function(*f); auto ref_results = execute(ref_func, args, "INTERPRETER"); auto bk_results = execute(bk_func, args, "${BACKEND_NAME}"); EXPECT_TRUE( test::all_close_f(ref_results.at(0), bk_results.at(0), DEFAULT_FLOAT_TOLERANCE_BITS + 1)); } #endif NGRAPH_TEST(${BACKEND_NAME}, sum_stable_acc_double) { std::string backend_name = "${BACKEND_NAME}"; if (backend_name == "INTERPRETER") { return; } Shape shape_a{10, 10, 20, 300}; auto A = make_shared<op::Parameter>(element::f64, shape_a); Shape shape_rt{10}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{1, 2, 3}), ParameterVector{A}); test::Uniform<double> rng(1000000000.0L, 1000000000.001L, 2112); vector<vector<double>> args; for (shared_ptr<op::Parameter> param : f->get_parameters()) { vector<double> tensor_val(shape_size(param->get_shape())); rng.initialize(tensor_val); args.push_back(tensor_val); } auto ref_func = clone_function(*f); auto bk_func = clone_function(*f); auto ref_results = execute(ref_func, args, "INTERPRETER"); auto bk_results = execute(bk_func, args, "${BACKEND_NAME}"); EXPECT_TRUE(test::all_close(ref_results.at(0), bk_results.at(0), 0.0, 1e-5)); } NGRAPH_TEST(${BACKEND_NAME}, sum_stable_simple_float) { std::string backend_name = "${BACKEND_NAME}"; if (backend_name == "INTERPRETER") { return; } Shape shape_a{20}; auto A = make_shared<op::Parameter>(element::f32, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); vector<vector<float>> args; args.push_back(vector<float>{10000000.0f, 0.9f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.1f, 0.9f, 0.5f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 0.1f}); auto ref_func = clone_function(*f); auto bk_func = clone_function(*f); auto ref_results = execute(ref_func, args, "INTERPRETER"); auto bk_results = execute(bk_func, args, "${BACKEND_NAME}"); EXPECT_TRUE( test::all_close_f(ref_results.at(0), bk_results.at(0), DEFAULT_FLOAT_TOLERANCE_BITS - 1)); } #ifndef _WIN32 NGRAPH_TEST(${BACKEND_NAME}, sum_stable_simple_double) { std::string backend_name = "${BACKEND_NAME}"; if (backend_name == "INTERPRETER") { return; } Shape shape_a{20}; auto A = make_shared<op::Parameter>(element::f64, shape_a); Shape shape_rt{}; auto f = make_shared<Function>(make_shared<op::Sum>(A, AxisSet{0}), ParameterVector{A}); vector<vector<double>> args; args.push_back(vector<double>{10000000000000000.0L, 0.2L, 0.3L, 0.4L, 0.5L, 0.6L, 0.7L, 0.8L, 0.9L, 0.7L, 0.9L, 0.7L, 0.3L, 0.6L, 0.8L, 0.4L, 0.6L, 0.5L, 0.8L, 0.7L}); auto ref_func = clone_function(*f); auto bk_func = clone_function(*f); auto ref_results = execute(ref_func, args, "INTERPRETER"); auto bk_results = execute(bk_func, args, "${BACKEND_NAME}"); EXPECT_TRUE(test::all_close(ref_results.at(0), bk_results.at(0), 0.0, 2.0)); } #endif #endif