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ov_tensor_test.cpp
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// Copyright (C) 2018-2025 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <gmock/gmock-spec-builders.h>
#include <gmock/gmock.h>
#include <gtest/gtest-param-test.h>
#include <gtest/gtest.h>
#include <cstdint>
#include <openvino/core/shape.hpp>
#include <openvino/core/strides.hpp>
#include <openvino/core/type/element_type.hpp>
#include "common_test_utils/test_assertions.hpp"
#include "openvino/core/except.hpp"
#include "openvino/core/partial_shape.hpp"
#include "openvino/core/type/element_type_traits.hpp"
#include "openvino/op/parameter.hpp"
#include "openvino/reference/utils/coordinate_transform.hpp"
#include "openvino/runtime/allocator.hpp"
#include "openvino/runtime/remote_tensor.hpp"
#include "openvino/runtime/tensor.hpp"
using OVTensorTest = ::testing::Test;
using testing::_;
const size_t string_size = ov::element::string.size();
inline ov::Strides byteStrides(const ov::Strides& strides, const ov::element::Type& type) {
ov::Strides byte_strides(strides.size());
for (size_t i = 0; i < strides.size(); ++i)
byte_strides[i] = strides[i] * type.size();
return byte_strides;
}
TEST_F(OVTensorTest, canCreateTensor) {
ov::Shape shape = {4, 3, 2};
ov::Tensor t{ov::element::f32, shape};
const std::size_t totalSize = ov::shape_size(shape);
ASSERT_EQ(totalSize, t.get_size());
ASSERT_NE(nullptr, t.data());
ASSERT_EQ(ov::element::f32, t.get_element_type());
ASSERT_EQ(shape, t.get_shape());
ASSERT_NE(shape, t.get_strides());
ASSERT_EQ(byteStrides(ov::Strides({6, 2, 1}), t.get_element_type()), t.get_strides());
ASSERT_EQ(ov::element::f32.size() * totalSize, t.get_byte_size());
ASSERT_THROW(t.data(ov::element::i64), ov::Exception);
ASSERT_THROW(t.data<std::int32_t>(), ov::Exception);
}
TEST_F(OVTensorTest, canCreateStringTensor) {
ov::Shape shape = {4, 3, 2};
ov::Tensor t{ov::element::string, shape};
const std::size_t totalSize = ov::shape_size(shape);
ASSERT_EQ(totalSize, t.get_size());
ASSERT_NE(nullptr, t.data());
ASSERT_EQ(ov::element::string, t.get_element_type());
ASSERT_EQ(shape, t.get_shape());
ASSERT_NE(shape, t.get_strides());
ASSERT_EQ(byteStrides(ov::Strides({6, 2, 1}), t.get_element_type()), t.get_strides());
ASSERT_EQ(string_size * totalSize, t.get_byte_size());
ASSERT_THROW(t.data(ov::element::i64), ov::Exception);
ASSERT_THROW(t.data<std::int32_t>(), ov::Exception);
}
TEST_F(OVTensorTest, createTensorFromPort) {
auto parameter1 = std::make_shared<ov::op::v0::Parameter>(ov::element::f64, ov::Shape{1, 3, 2, 2});
auto parameter2 = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::Shape{1, 3});
auto parameter3 = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape::dynamic());
float data[] = {5.f, 6.f, 7.f};
ov::Tensor t1{parameter1->output(0)};
ov::Tensor t2{parameter2->output(0), data};
ov::Tensor t3{parameter3->output(0)};
ov::Tensor t4{parameter3->output(0), data};
EXPECT_EQ(t1.get_shape(), parameter1->get_shape());
EXPECT_EQ(t1.get_element_type(), parameter1->get_element_type());
EXPECT_EQ(t2.get_shape(), parameter2->get_shape());
EXPECT_EQ(t2.get_element_type(), parameter2->get_element_type());
EXPECT_EQ(t3.get_shape(), ov::Shape{0});
EXPECT_EQ(t3.get_element_type(), parameter3->get_element_type());
EXPECT_EQ(t4.get_shape(), ov::Shape{0});
EXPECT_EQ(t4.get_element_type(), parameter3->get_element_type());
}
TEST_F(OVTensorTest, createStringTensorFromPort) {
auto parameter1 = std::make_shared<ov::op::v0::Parameter>(ov::element::string, ov::Shape{1, 3, 2, 2});
auto parameter2 = std::make_shared<ov::op::v0::Parameter>(ov::element::string, ov::Shape{1, 3});
auto parameter3 = std::make_shared<ov::op::v0::Parameter>(ov::element::string, ov::PartialShape::dynamic());
std::string data[] = {"one", "two sentence", "three 3 sentence"};
ov::Tensor t1{parameter1->output(0)};
ov::Tensor t2{parameter2->output(0), data};
ov::Tensor t3{parameter3->output(0)};
ov::Tensor t4{parameter3->output(0), data};
EXPECT_EQ(t1.get_shape(), parameter1->get_shape());
EXPECT_EQ(t1.get_element_type(), parameter1->get_element_type());
EXPECT_EQ(t2.get_shape(), parameter2->get_shape());
EXPECT_EQ(t2.get_element_type(), parameter2->get_element_type());
EXPECT_EQ(t3.get_shape(), ov::Shape{0});
EXPECT_EQ(t3.get_element_type(), parameter3->get_element_type());
EXPECT_EQ(t4.get_shape(), ov::Shape{0});
EXPECT_EQ(t4.get_element_type(), parameter3->get_element_type());
}
TEST_F(OVTensorTest, canAccessF16Tensor) {
ov::Shape shape = {4, 3, 2};
ov::Tensor t{ov::element::f16, shape};
EXPECT_NE(nullptr, t.data());
EXPECT_NO_THROW(t.data(ov::element::f16));
EXPECT_NO_THROW(t.data<ov::float16>());
EXPECT_NO_THROW(t.data<ov::bfloat16>());
EXPECT_THROW(t.data<std::uint16_t>(), ov::Exception);
EXPECT_THROW(t.data<std::int16_t>(), ov::Exception);
}
TEST_F(OVTensorTest, canAccessStringTensor) {
ov::Shape shape = {4, 3, 2};
ov::Tensor t{ov::element::string, shape};
EXPECT_NE(nullptr, t.data());
EXPECT_NO_THROW(t.data(ov::element::string));
EXPECT_NO_THROW(t.data<std::string>());
// check that all elements of string ov::Tensor are empty strings
auto string_elements = t.data<std::string>();
auto num_elements = t.get_size();
for (size_t ind = 0; ind < num_elements; ++ind) {
EXPECT_EQ(string_elements[ind], std::string());
}
EXPECT_THROW(t.data<std::uint16_t>(), ov::Exception);
EXPECT_THROW(t.data<std::int16_t>(), ov::Exception);
}
TEST_F(OVTensorTest, canAccessU8Tensor) {
ov::Shape shape = {4, 3, 2};
ov::Tensor t{ov::element::u8, shape};
EXPECT_NE(nullptr, t.data());
EXPECT_NO_THROW(t.data(ov::element::u8));
EXPECT_NO_THROW(t.data<char>());
EXPECT_NO_THROW(t.data<unsigned char>());
EXPECT_NO_THROW(t.data<bool>());
EXPECT_NO_THROW(t.data<uint8_t>());
EXPECT_NO_THROW(t.data<int8_t>());
EXPECT_THROW(t.data<float>(), ov::Exception);
EXPECT_THROW(t.data<double>(), ov::Exception);
EXPECT_THROW(t.data<uint32_t>(), ov::Exception);
}
TEST_F(OVTensorTest, emptySize) {
ov::Tensor t(ov::element::f32, {0});
ASSERT_NE(nullptr, t.data());
}
TEST_F(OVTensorTest, emptySizeStringTensor) {
ov::Tensor t(ov::element::string, {0});
ASSERT_NE(nullptr, t.data());
}
TEST_F(OVTensorTest, operators) {
ov::Tensor t;
ASSERT_FALSE(t);
ASSERT_TRUE(!t);
}
struct OVMockAllocator {
struct Impl {
MOCK_METHOD(void*, allocate, (size_t, size_t), ());
MOCK_METHOD(void, deallocate, (void*, size_t, size_t), ());
MOCK_METHOD(bool, is_equal, (const Impl&), (const, noexcept));
};
OVMockAllocator() : impl{std::make_shared<Impl>()} {}
void* allocate(size_t b, size_t a) {
return impl->allocate(b, a);
}
void deallocate(void* ptr, size_t b, size_t a) {
impl->deallocate(ptr, b, a);
}
bool is_equal(const OVMockAllocator& other) const {
return impl->is_equal(*other.impl);
}
std::shared_ptr<Impl> impl;
};
TEST_F(OVTensorTest, canCreateTensorUsingMockAllocator) {
constexpr size_t exp_size = 24;
ov::Shape shape = {1, 2, 3};
OVMockAllocator allocator;
EXPECT_CALL(*allocator.impl, allocate(exp_size, _)).WillRepeatedly(testing::Return(reinterpret_cast<void*>(1)));
EXPECT_CALL(*allocator.impl, deallocate(_, exp_size, _)).Times(1);
{ ov::Tensor t{ov::element::f32, shape, allocator}; }
}
TEST_F(OVTensorTest, canCreateTensorU2UsingMockAllocator) {
constexpr size_t exp_size = 2;
ov::Shape shape = {1, 2, 3};
OVMockAllocator allocator;
EXPECT_CALL(*allocator.impl, allocate(exp_size, _)).WillRepeatedly(testing::Return(reinterpret_cast<void*>(1)));
EXPECT_CALL(*allocator.impl, deallocate(_, exp_size, _)).Times(1);
{ ov::Tensor t{ov::element::u2, shape, allocator}; }
}
TEST_F(OVTensorTest, canCreateTensorU3UsingMockAllocator) {
constexpr size_t exp_size = 3;
ov::Shape shape = {1, 2, 3};
OVMockAllocator allocator;
EXPECT_CALL(*allocator.impl, allocate(exp_size, _)).WillRepeatedly(testing::Return(reinterpret_cast<void*>(1)));
EXPECT_CALL(*allocator.impl, deallocate(_, exp_size, _)).Times(1);
{ ov::Tensor t{ov::element::u3, shape, allocator}; }
}
TEST_F(OVTensorTest, canCreateTensorU6UsingMockAllocator) {
constexpr size_t exp_size = 6;
ov::Shape shape = {1, 2, 3};
OVMockAllocator allocator;
EXPECT_CALL(*allocator.impl, allocate(exp_size, _)).WillRepeatedly(testing::Return(reinterpret_cast<void*>(1)));
EXPECT_CALL(*allocator.impl, deallocate(_, exp_size, _)).Times(1);
{ ov::Tensor t{ov::element::u6, shape, allocator}; }
}
TEST_F(OVTensorTest, canAccessExternalData) {
ov::Shape shape = {1, 1, 3};
float data[] = {5.f, 6.f, 7.f};
ov::Tensor t{ov::element::f32, shape, data};
{
float* ptr = t.data<float>();
ASSERT_EQ(ptr[2], 7);
ASSERT_EQ(data, t.data(ov::element::f32));
ASSERT_EQ(data, ptr);
ASSERT_THROW(t.data<std::int16_t>(), ov::Exception);
ASSERT_EQ(byteStrides(ov::row_major_strides(shape), t.get_element_type()), t.get_strides());
ASSERT_EQ(ov::shape_size(shape), t.get_size());
ASSERT_EQ(ov::shape_size(shape) * ov::element::f32.size(), t.get_byte_size());
}
}
TEST_F(OVTensorTest, canAccessExternalDataStringTensor) {
ov::Shape shape = {1, 1, 3};
std::string data[] = {"one two three", "123", ""};
ov::Tensor t{ov::element::string, shape, data};
{
std::string* ptr = t.data<std::string>();
ASSERT_EQ(ptr[2], "");
ASSERT_EQ(data, t.data(ov::element::string));
ASSERT_EQ(data, ptr);
ASSERT_THROW(t.data<std::int16_t>(), ov::Exception);
ASSERT_EQ(byteStrides(ov::row_major_strides(shape), t.get_element_type()), t.get_strides());
ASSERT_EQ(ov::shape_size(shape), t.get_size());
ASSERT_EQ(ov::shape_size(shape) * string_size, t.get_byte_size());
}
}
TEST_F(OVTensorTest, canAccessExternalDataWithStrides) {
ov::Shape shape = {2, 3};
float data[] = {5.f, 6.f, 7.f, 0.f, 1.f, 42.f, 3.f, 0.f};
ov::Tensor t{ov::element::f32, shape, data, {16, 4}};
ASSERT_EQ(ov::Strides({16, 4}), t.get_strides());
{
ASSERT_EQ((ov::Shape{2, 3}), t.get_shape());
const float* ptr = t.data<const float>();
ASSERT_EQ(ptr[5], 42);
}
}
TEST_F(OVTensorTest, canAccessExternalDataWithStridesStringTensor) {
ov::Shape shape = {2, 3};
std::string data[] = {"abdcd efg hi", "01234", "xyz ", " ", "$%&%&& (*&&", "", "\n ", "\t "};
ov::Strides strides = {shape[1] * string_size + string_size, string_size};
ov::Tensor t{ov::element::string, shape, data, strides};
ASSERT_EQ(strides, t.get_strides());
{
ASSERT_EQ((ov::Shape{2, 3}), t.get_shape());
const std::string* ptr = t.data<const std::string>();
ASSERT_EQ(ptr[4], "$%&%&& (*&&");
}
}
TEST_F(OVTensorTest, cannotCreateTensorWithExternalNullptr) {
ov::Shape shape = {2, 3};
ASSERT_THROW(ov::Tensor(ov::element::f32, shape, nullptr), ov::Exception);
}
TEST_F(OVTensorTest, cannotCreateStringTensorWithExternalNullptr) {
ov::Shape shape = {2, 3};
ASSERT_THROW(ov::Tensor(ov::element::string, shape, nullptr), ov::Exception);
}
TEST_F(OVTensorTest, cannotCreateTensorWithWrongStrides) {
ov::Shape shape = {2, 3};
float data[] = {5.f, 6.f, 7.f, 0.f, 1.f, 42.f, 3.f, 0.f};
const auto el = ov::element::f32;
{
// strides.size() != shape.size()
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({6, 3, 1}, el)), ov::Exception);
}
{
// strides values are element-wise >= ov::row_major_strides(shape) values
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({2, 1}, el)), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({3, 0}, el)), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({3, 2}, el)), ov::Exception);
EXPECT_NO_THROW(ov::Tensor(el, shape, data, byteStrides({6, 2}, el)));
}
{
// strides are not divisible by elem_size
EXPECT_THROW(ov::Tensor(el, shape, data, {7, el.size()}), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, {3, 0}), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, {el.size(), 3}), ov::Exception);
}
}
TEST_F(OVTensorTest, cannotCreateStringTensorWithWrongStrides) {
ov::Shape shape = {2, 3};
std::string data[] = {"abdcd efg hi", "01234", "xyz ", " ", "$%&%&& (*&&", "", "\n ", "\t "};
const auto el = ov::element::string;
{
// strides.size() != shape.size()
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({6, 3, 1}, el)), ov::Exception);
}
{
// strides values are element-wise >= ov::row_major_strides(shape) values
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({2, 1}, el)), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({3, 0}, el)), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, byteStrides({3, 2}, el)), ov::Exception);
EXPECT_NO_THROW(ov::Tensor(el, shape, data, byteStrides({6, 2}, el)));
}
{
// strides are not divisible by elem_size
EXPECT_THROW(ov::Tensor(el, shape, data, {43, el.size()}), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, {3, 0}), ov::Exception);
EXPECT_THROW(ov::Tensor(el, shape, data, {el.size(), 61}), ov::Exception);
}
}
TEST_F(OVTensorTest, saveDimsAndSizeAfterMove) {
ov::Shape shape = {1, 2, 3};
ov::Tensor t{ov::element::f32, shape};
ov::Tensor new_tensor(std::move(t));
ASSERT_EQ(shape, new_tensor.get_shape());
ASSERT_EQ(ov::element::f32, new_tensor.get_element_type());
ASSERT_EQ(byteStrides(ov::row_major_strides(shape), new_tensor.get_element_type()), new_tensor.get_strides());
ASSERT_THROW(t.get_size(), ov::Exception);
ASSERT_THROW(t.get_element_type(), ov::Exception);
ASSERT_THROW(t.get_byte_size(), ov::Exception);
ASSERT_THROW(t.get_strides(), ov::Exception);
ASSERT_THROW(t.get_shape(), ov::Exception);
ASSERT_THROW(t.set_shape({}), ov::Exception);
ASSERT_THROW(t.data(), ov::Exception);
ASSERT_THROW(t.data<float>(), ov::Exception);
}
TEST_F(OVTensorTest, saveDimsAndSizeAfterMoveStringTensor) {
ov::Shape shape = {1, 2, 3};
ov::Tensor t{ov::element::string, shape};
ov::Tensor new_tensor(std::move(t));
ASSERT_EQ(shape, new_tensor.get_shape());
ASSERT_EQ(ov::element::string, new_tensor.get_element_type());
ASSERT_EQ(byteStrides(ov::row_major_strides(shape), new_tensor.get_element_type()), new_tensor.get_strides());
ASSERT_THROW(t.get_size(), ov::Exception);
ASSERT_THROW(t.get_element_type(), ov::Exception);
ASSERT_THROW(t.get_byte_size(), ov::Exception);
ASSERT_THROW(t.get_strides(), ov::Exception);
ASSERT_THROW(t.get_shape(), ov::Exception);
ASSERT_THROW(t.set_shape({}), ov::Exception);
ASSERT_THROW(t.data(), ov::Exception);
ASSERT_THROW(t.data<std::string>(), ov::Exception);
}
// set_shape
TEST_F(OVTensorTest, canSetShape) {
const ov::Shape origShape({1, 2, 3});
ov::Tensor t{ov::element::f32, origShape};
const ov::Shape newShape({4, 5, 6}), newShape2({4, 5, 6, 7});
const void* orig_data = t.data();
ASSERT_EQ(t.get_shape(), origShape);
OV_ASSERT_NO_THROW(t.set_shape(newShape));
ASSERT_EQ(newShape, t.get_shape());
ASSERT_EQ(byteStrides(ov::row_major_strides(newShape), t.get_element_type()), t.get_strides());
ASSERT_NE(orig_data, t.data());
// check that set_shape for copy changes original Tensor
{
ov::Tensor t2 = t;
OV_ASSERT_NO_THROW(t2.set_shape(newShape2));
ASSERT_EQ(newShape2, t.get_shape());
ASSERT_EQ(t2.get_shape(), t.get_shape());
ASSERT_EQ(t2.data(), t.data());
orig_data = t.data();
}
// set_shape for smaller memory - does not perform reallocation
{
t.set_shape(origShape);
ASSERT_EQ(origShape, t.get_shape());
ASSERT_EQ(orig_data, t.data());
}
}
TEST_F(OVTensorTest, canSetShapeStringTensor) {
const ov::Shape origShape({1, 2, 3});
ov::Tensor t{ov::element::string, {1, 2, 3}};
const ov::Shape newShape({4, 5, 6}), newShape2({4, 5, 6, 7});
const void* orig_data = t.data();
ASSERT_EQ(t.get_shape(), origShape);
OV_ASSERT_NO_THROW(t.set_shape(newShape));
ASSERT_EQ(newShape, t.get_shape());
ASSERT_EQ(byteStrides(ov::row_major_strides(newShape), t.get_element_type()), t.get_strides());
ASSERT_NE(orig_data, t.data());
// check that setShape for copy changes original Tensor
{
ov::Tensor t2 = t;
OV_ASSERT_NO_THROW(t2.set_shape(newShape2));
ASSERT_EQ(newShape2, t2.get_shape());
ASSERT_EQ(t2.get_shape(), t.get_shape());
ASSERT_EQ(t2.data(), t.data());
orig_data = t.data();
}
// set_shape for smaller memory - does not perform reallocation
{
OV_ASSERT_NO_THROW(t.set_shape(origShape));
ASSERT_EQ(origShape, t.get_shape());
ASSERT_EQ(orig_data, t.data());
}
}
TEST_F(OVTensorTest, cannotSetShapeOfBiggerSizeOnPreallocatedMemory) {
float data[4 * 5 * 6 * 2];
ov::Tensor t{ov::element::f32, {1, 2, 3}, data};
const ov::Shape newShape({4, 5, 6});
ASSERT_THROW(t.set_shape(newShape), ov::Exception);
}
TEST_F(OVTensorTest, cannotSetShapeOfBiggerSizeOnPreallocatedMemoryStringTensor) {
std::string data[4 * 5 * 6];
ov::Tensor t{ov::element::string, {1, 2, 3}, data};
const ov::Shape newShape({4, 5, 6});
ASSERT_THROW(t.set_shape(newShape), ov::Exception);
}
TEST_F(OVTensorTest, canSetShapeOfSmallerSizeOnPreallocatedMemory) {
float data[4 * 5 * 6 * 2];
ov::Tensor t{ov::element::f32, {4, 5, 6}, data};
const ov::Shape newShape({1, 2, 3});
OV_ASSERT_NO_THROW(t.set_shape(newShape));
}
TEST_F(OVTensorTest, canSetShapeOfSmallerSizeOnPreallocatedMemoryStringTensor) {
std::string data[4 * 5 * 6];
ov::Tensor t{ov::element::string, {4, 5, 6}, data};
const ov::Shape newShape({1, 2, 3});
OV_ASSERT_NO_THROW(t.set_shape(newShape));
}
TEST_F(OVTensorTest, canSetShapeOfSameSizeOnPreallocatedMemory) {
float data[4 * 5 * 6 * 2];
ov::Tensor t{ov::element::f32, {4, 5, 6}, data};
const ov::Shape newShape({4, 5, 6});
OV_ASSERT_NO_THROW(t.set_shape(newShape));
}
TEST_F(OVTensorTest, canSetShapeOfSameSizeOnPreallocatedMemoryStringTensor) {
std::string data[4 * 5 * 6];
ov::Tensor t{ov::element::string, {4, 5, 6}, data};
const ov::Shape newShape({4, 5, 6});
OV_ASSERT_NO_THROW(t.set_shape(newShape));
}
TEST_F(OVTensorTest, canSetShapeOfOriginalSizeAfterDecreasingOnPreallocatedMemory) {
float data[4 * 5 * 6 * 2];
ov::Tensor t{ov::element::f32, {4, 5, 6}, data};
const ov::Shape smallerShape({1, 2, 3});
const ov::Shape originalShape({4, 5, 6});
OV_ASSERT_NO_THROW(t.set_shape(smallerShape));
OV_ASSERT_NO_THROW(t.set_shape(originalShape));
}
TEST_F(OVTensorTest, canSetShapeOfOriginalSizeAfterDecreasingOnPreallocatedMemoryStringTensor) {
std::string data[4 * 5 * 6];
ov::Tensor t{ov::element::string, {4, 5, 6}, data};
const ov::Shape smallerShape({1, 2, 3});
const ov::Shape originalShape({4, 5, 6});
OV_ASSERT_NO_THROW(t.set_shape(smallerShape));
OV_ASSERT_NO_THROW(t.set_shape(originalShape));
}
TEST_F(OVTensorTest, canSetShapeOfOriginalSizeAfterDecreasing) {
const ov::Shape shape({4, 5, 6}), small_shape({1, 2, 3});
ov::Tensor t{ov::element::f32, shape};
void* data = t.data();
OV_ASSERT_NO_THROW(t.set_shape(small_shape));
EXPECT_EQ(data, t.data());
OV_ASSERT_NO_THROW(t.set_shape(shape));
EXPECT_EQ(data, t.data());
}
TEST_F(OVTensorTest, canSetShapeOfOriginalSizeAfterDecreasingStringTensor) {
const ov::Shape shape({4, 5, 6}), small_shape({1, 2, 3});
ov::Tensor t{ov::element::string, shape};
void* data = t.data();
OV_ASSERT_NO_THROW(t.set_shape(small_shape));
EXPECT_EQ(data, t.data());
OV_ASSERT_NO_THROW(t.set_shape(shape));
EXPECT_EQ(data, t.data());
}
TEST_F(OVTensorTest, canChangeShapeOnStridedTensor) {
float data[64 * 4];
ov::Tensor t{ov::element::f32, {4, 2, 2}, data, {64, 16, 4}};
const ov::Shape incorrect_shape({2, 4, 2});
const ov::Shape correct_shape({1, 1, 2});
ASSERT_THROW(t.set_shape(incorrect_shape), ov::Exception);
OV_ASSERT_NO_THROW(t.set_shape(correct_shape));
}
TEST_F(OVTensorTest, canChangeShapeOnStridedTensorStringTensor) {
std::string data[64 * 4];
ov::Tensor t{ov::element::string, {4, 2, 2}, data, {8 * string_size, 3 * string_size, string_size}};
const ov::Shape incorrect_shape({2, 2, 4});
const ov::Shape correct_shape({1, 1, 2});
ASSERT_THROW(t.set_shape(incorrect_shape), ov::Exception);
OV_ASSERT_NO_THROW(t.set_shape(correct_shape));
}
TEST_F(OVTensorTest, makeRangeRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}}; // RGBp picture of size (WxH) = 5x6
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 3, 5, 4}};
ov::Shape ref_shape = {1, 3, 4, 2};
ptrdiff_t ref_offset_elems = 7;
ptrdiff_t ref_offset_bytes = ref_offset_elems * ov::element::i32.size();
ov::Strides ref_strides = {90, 30, 5, 1};
ASSERT_EQ(roi_tensor.get_shape(), ref_shape);
ASSERT_EQ(roi_tensor.data<int32_t>() - t.data<int32_t>(), ref_offset_elems);
ASSERT_EQ(reinterpret_cast<uint8_t*>(roi_tensor.data()) - reinterpret_cast<uint8_t*>(t.data()), ref_offset_bytes);
ASSERT_EQ(roi_tensor.get_strides(), t.get_strides());
ASSERT_EQ(byteStrides(ref_strides, roi_tensor.get_element_type()), roi_tensor.get_strides());
ASSERT_EQ(roi_tensor.get_element_type(), t.get_element_type());
}
TEST_F(OVTensorTest, makeRangeRoiStringTensor) {
ov::Tensor t{ov::element::string, {1, 3, 6, 5}}; // RGBp picture of size (WxH) = 5x6
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 3, 5, 4}};
ov::Shape ref_shape = {1, 3, 4, 2};
ptrdiff_t ref_offset_elems = 7;
ptrdiff_t ref_offset_bytes = ref_offset_elems * string_size;
ov::Strides ref_strides = {90, 30, 5, 1};
ASSERT_EQ(roi_tensor.get_shape(), ref_shape);
ASSERT_EQ(roi_tensor.data<std::string>() - t.data<std::string>(), ref_offset_elems);
ASSERT_EQ(reinterpret_cast<uint8_t*>(roi_tensor.data()) - reinterpret_cast<uint8_t*>(t.data()), ref_offset_bytes);
ASSERT_EQ(roi_tensor.get_strides(), t.get_strides());
ASSERT_EQ(byteStrides(ref_strides, roi_tensor.get_element_type()), roi_tensor.get_strides());
ASSERT_EQ(roi_tensor.get_element_type(), t.get_element_type());
}
TEST_F(OVTensorTest, setSmallerShapeOnRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}};
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 2, 5, 4}};
const ov::Shape newShape({1, 1, 3, 2});
ASSERT_EQ(roi_tensor.get_shape(), ov::Shape({1, 2, 4, 2}));
roi_tensor.set_shape(newShape);
ASSERT_EQ(roi_tensor.get_shape(), newShape);
}
TEST_F(OVTensorTest, setMaxSizeShapeOnRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}};
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 2, 5, 5}};
const ov::Shape new_shape({1, 2, 1, 1});
const ov::Shape roi_capacity({1, 2, 4, 3});
ASSERT_EQ(roi_tensor.get_shape(), roi_capacity);
roi_tensor.set_shape(new_shape);
ASSERT_EQ(roi_tensor.get_shape(), new_shape);
roi_tensor.set_shape(roi_capacity);
ASSERT_EQ(roi_tensor.get_shape(), roi_capacity);
}
TEST_F(OVTensorTest, setShapeGtMaxOnRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}};
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 2, 5, 5}};
const ov::Shape newShape({0, 0, 0, 0});
roi_tensor.set_shape(newShape);
ASSERT_EQ(roi_tensor.get_shape(), newShape);
}
TEST_F(OVTensorTest, setMinShapeOnRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}};
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 2, 5, 5}};
const ov::Shape newShape({1, 3, 6, 3}); // ROI coordinate begin + newShape[2] is bigger than t.shape[2]
ASSERT_EQ(roi_tensor.get_shape(), ov::Shape({1, 2, 4, 3}));
ASSERT_THROW(roi_tensor.set_shape(newShape), ov::Exception);
}
TEST_F(OVTensorTest, cannotSetShapeOnRoiTensor) {
ov::Tensor t{ov::element::i32, {1, 3, 6, 5}}; // RGBp picture of size (WxH) = 5x6
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 3, 5, 4}};
const ov::Shape newShape({4, 5, 6});
ASSERT_THROW(roi_tensor.set_shape(newShape), ov::Exception);
}
TEST_F(OVTensorTest, cannotSetShapeOnRoiStringTensor) {
ov::Tensor t{ov::element::string, {1, 3, 6, 5}}; // RGBp picture of size (WxH) = 5x6
ov::Tensor roi_tensor{t, {0, 0, 1, 2}, {1, 3, 5, 4}};
const ov::Shape newShape({4, 5, 6});
ASSERT_THROW(roi_tensor.set_shape(newShape), ov::Exception);
}
TEST_F(OVTensorTest, tensorInt4DataAccess) {
ov::Tensor t{ov::element::i4, {1, 6, 5, 3}}; // RGB picture of size (WxH) = 5x6
ASSERT_THROW((ov::Tensor{t, {0, 1, 2, 0}, {1, 5, 4, 3}}), ov::Exception);
ASSERT_THROW(t.get_strides(), ov::Exception);
ASSERT_THROW(t.data<int8_t>(), ov::Exception);
OV_ASSERT_NO_THROW(t.data());
}
TEST_F(OVTensorTest, makeRangeRoiBlobWrongSize) {
ov::Tensor t{ov::element::f32, {1, 3, 4, 4}};
ASSERT_THROW((ov::Tensor{t, {0, 0, 1, 1}, {1, 3, 5, 5}}), ov::Exception);
ASSERT_THROW((ov::Tensor{t, {0, 0, 1, 1, 3}, {1, 3, 4, 4}}), ov::Exception);
}
TEST_F(OVTensorTest, readRangeRoiBlob) {
ov::Tensor t{ov::element::i32, {1, 3, 4, 8}};
{
const auto origPtr = t.data<int32_t>();
ASSERT_NE(nullptr, origPtr);
for (size_t i = 0; i < t.get_size(); ++i) {
origPtr[i] = static_cast<int32_t>(i);
}
}
ov::Tensor roi_tensor{t, {0, 0, 2, 4}, {1, 3, 4, 8}};
ASSERT_NE(false, static_cast<bool>(roi_tensor));
{
const std::uint8_t* roi = reinterpret_cast<const std::uint8_t*>(roi_tensor.data());
ASSERT_NE(nullptr, roi);
auto strides = roi_tensor.get_strides();
for (auto&& c : ov::CoordinateTransformBasic{roi_tensor.get_shape()}) {
auto actual_addr = roi + c[3] * strides[3] + c[2] * strides[2] + c[1] * strides[1] + c[0] * strides[0];
auto expected_addr = t.data<int32_t>() + ((c[3] + 4) * strides[3] + (c[2] + 2) * strides[2] +
(c[1] + 0) * strides[1] + (c[0] + 0) * strides[0]) /
t.get_element_type().size();
ASSERT_EQ(actual_addr, reinterpret_cast<const std::uint8_t*>(expected_addr));
}
}
}
TEST_F(OVTensorTest, readRangeRoiBlobStringTensor) {
ov::Tensor t{ov::element::string, {1, 3, 4, 8}};
{
const auto origPtr = t.data<std::string>();
ASSERT_NE(nullptr, origPtr);
for (size_t i = 0; i < t.get_size(); ++i) {
origPtr[i] = std::to_string(i);
}
}
ov::Tensor roi_tensor{t, {0, 0, 2, 4}, {1, 3, 4, 8}};
ASSERT_NE(false, static_cast<bool>(roi_tensor));
{
const std::uint8_t* roi = static_cast<const std::uint8_t*>(roi_tensor.data());
ASSERT_NE(nullptr, roi);
auto strides = roi_tensor.get_strides();
for (auto&& c : ov::CoordinateTransformBasic{roi_tensor.get_shape()}) {
auto actual_addr = roi + c[3] * strides[3] + c[2] * strides[2] + c[1] * strides[1] + c[0] * strides[0];
auto expected_addr = t.data<std::string>() + ((c[3] + 4) * strides[3] + (c[2] + 2) * strides[2] +
(c[1] + 0) * strides[1] + (c[0] + 0) * strides[0]) /
t.get_element_type().size();
ASSERT_EQ(actual_addr, static_cast<uint8_t*>(static_cast<void*>(expected_addr)));
}
}
}
TEST_F(OVTensorTest, checkIsContinuousTensorScalar) {
ov::Tensor tensor(ov::element::f32, ov::Shape{});
auto data = tensor.data();
auto strides = tensor.get_strides();
ov::Tensor view_tensor(ov::element::f32, ov::Shape{}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
}
TEST_F(OVTensorTest, checkIsContinuousTensor1Dimension) {
ov::Tensor tensor(ov::element::f32, ov::Shape{128});
auto data = tensor.data();
auto strides = tensor.get_strides();
ov::Tensor view_tensor;
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{16}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
}
TEST_F(OVTensorTest, checkIsContinuousTensor2Dimensions) {
ov::Tensor tensor(ov::element::f32, ov::Shape{32, 128});
auto data = tensor.data();
auto strides = tensor.get_strides();
ov::Tensor view_tensor;
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{16, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 16}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 16}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
}
TEST_F(OVTensorTest, checkIsContinuousTensor3Dimensions) {
ov::Tensor tensor(ov::element::f32, ov::Shape{5, 32, 128});
auto data = tensor.data();
auto strides = tensor.get_strides();
ov::Tensor view_tensor;
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 32, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 16, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 1, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 1, 64}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 16, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
}
TEST_F(OVTensorTest, checkIsContinuousTensor4Dimensions) {
ov::Tensor tensor(ov::element::f32, ov::Shape{3, 5, 32, 128});
auto data = tensor.data();
auto strides = tensor.get_strides();
ov::Tensor view_tensor;
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 2, 32, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 5, 32, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 2, 32, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 2, 5, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{3, 5, 32, 64}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 1, 16, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{2, 1, 16, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), false);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 1, 1, 128}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
view_tensor = ov::Tensor(ov::element::f32, ov::Shape{1, 1, 1, 32}, data, strides);
EXPECT_EQ(view_tensor.is_continuous(), true);
}
struct TestParams {
ov::Shape src_shape;
ov::Strides src_strides;
ov::Shape dst_shape;
ov::Strides dst_strides;
};
struct OVTensorTestCopy : ::testing::TestWithParam<std::tuple<ov::element::Type, TestParams>> {};
namespace {
template <class T>
std::vector<T> fill_data(const ov::Tensor& tensor) {
std::vector<T> actual;
const T* data = tensor.data<T>();
auto strides = tensor.get_strides();
for (auto&& c : ov::CoordinateTransformBasic{tensor.get_shape()}) {
size_t offset = 0;
for (size_t i = 0; i < strides.size(); i++)
offset += c[i] * strides[i];
actual.emplace_back(*(data + offset / tensor.get_element_type().size()));
}
return actual;
};
template <class T>
void compare_data(const ov::Tensor& src, const ov::Tensor& dst) {
auto source_vec = fill_data<T>(src);
auto dest_vec = fill_data<T>(dst);
ASSERT_EQ(source_vec.size(), dest_vec.size());
for (size_t i = 0; i < source_vec.size(); i++) {
EXPECT_EQ(source_vec[i], dest_vec[i]);
}
};
template <ov::element::Type_t ET,
typename T = typename ov::element_type_traits<ET>::value_type,
typename std::enable_if<ET != ov::element::Type_t::string, bool>::type = true>
void init_tensor(const ov::Tensor& tensor, bool input) {
const auto origPtr = tensor.data<T>();
ASSERT_NE(nullptr, origPtr);
for (size_t i = 0; i < tensor.get_size(); ++i) {
origPtr[i] = static_cast<T>(input ? i : -1);
}
}
template <ov::element::Type_t ET,
typename T = typename ov::element_type_traits<ET>::value_type,
typename std::enable_if<ET == ov::element::Type_t::string, bool>::type = true>
void init_tensor(const ov::Tensor& tensor, bool input) {
const auto origPtr = tensor.data<T>();
ASSERT_NE(nullptr, origPtr);
for (size_t i = 0; i < tensor.get_size(); ++i) {
origPtr[i] = std::to_string(i);
}
}
void init_tensor(const ov::Tensor& tensor, bool input) {
switch (tensor.get_element_type()) {
case ov::element::bf16:
init_tensor<ov::element::bf16>(tensor, input);
break;
case ov::element::f16:
init_tensor<ov::element::f16>(tensor, input);
break;
case ov::element::f32:
init_tensor<ov::element::f32>(tensor, input);
break;
case ov::element::f64:
init_tensor<ov::element::f64>(tensor, input);
break;
case ov::element::i8:
init_tensor<ov::element::i8>(tensor, input);
break;
case ov::element::i16:
init_tensor<ov::element::i16>(tensor, input);
break;
case ov::element::i32:
init_tensor<ov::element::i32>(tensor, input);
break;
case ov::element::i64:
init_tensor<ov::element::i64>(tensor, input);
break;
case ov::element::u8:
init_tensor<ov::element::u8>(tensor, input);
break;
case ov::element::u16:
init_tensor<ov::element::u16>(tensor, input);
break;
case ov::element::u32:
init_tensor<ov::element::u32>(tensor, input);
break;
case ov::element::u64:
init_tensor<ov::element::u64>(tensor, input);
break;
case ov::element::string:
init_tensor<ov::element::string>(tensor, input);
break;
default:
OPENVINO_THROW("Unsupported data type");
}
}
void compare_tensors(const ov::Tensor& src, const ov::Tensor& dst) {
ASSERT_EQ(src.get_byte_size(), dst.get_byte_size());
ASSERT_EQ(src.get_size(), dst.get_size());
ASSERT_EQ(src.get_element_type(), dst.get_element_type());
switch (src.get_element_type()) {
case ov::element::bf16:
compare_data<ov::element_type_traits<ov::element::bf16>::value_type>(src, dst);
break;
case ov::element::f16:
compare_data<ov::element_type_traits<ov::element::f16>::value_type>(src, dst);
break;
case ov::element::f32:
compare_data<ov::element_type_traits<ov::element::f32>::value_type>(src, dst);
break;
case ov::element::f64:
compare_data<ov::element_type_traits<ov::element::f64>::value_type>(src, dst);
break;
case ov::element::i8:
compare_data<ov::element_type_traits<ov::element::i8>::value_type>(src, dst);
break;
case ov::element::i16:
compare_data<ov::element_type_traits<ov::element::i16>::value_type>(src, dst);
break;
case ov::element::i32:
compare_data<ov::element_type_traits<ov::element::i32>::value_type>(src, dst);
break;
case ov::element::i64:
compare_data<ov::element_type_traits<ov::element::i64>::value_type>(src, dst);
break;
case ov::element::u8:
compare_data<ov::element_type_traits<ov::element::u8>::value_type>(src, dst);
break;
case ov::element::u16:
compare_data<ov::element_type_traits<ov::element::u16>::value_type>(src, dst);
break;
case ov::element::u32:
compare_data<ov::element_type_traits<ov::element::u32>::value_type>(src, dst);
break;
case ov::element::u64:
compare_data<ov::element_type_traits<ov::element::u64>::value_type>(src, dst);
break;
case ov::element::string:
compare_data<ov::element_type_traits<ov::element::string>::value_type>(src, dst);
break;
default:
OPENVINO_THROW("Unsupported data type");
}
}
} // namespace
TEST_P(OVTensorTestCopy, copy_to) {
ov::element::Type type;
TestParams p;
std::tie(type, p) = GetParam();
// Source tensors
ov::Tensor full_src_tensor;
ov::Tensor src_tensor;
if (!p.src_strides.empty()) {
full_src_tensor = ov::Tensor(type, ov::Shape{p.src_shape[0] * p.src_strides[0]});
src_tensor = ov::Tensor(type, p.src_shape, full_src_tensor.data(), p.src_strides);
} else {
src_tensor = full_src_tensor = ov::Tensor(type, p.src_shape);
}
init_tensor(full_src_tensor, true);
ov::Tensor full_dst_tensor;
ov::Tensor dst_tensor;
if (!p.dst_strides.empty()) {
full_dst_tensor = ov::Tensor(type, ov::Shape{p.dst_shape[0] * p.dst_strides[0]});
dst_tensor = ov::Tensor(type, p.dst_shape, full_dst_tensor.data(), p.dst_strides);
} else {
dst_tensor = full_dst_tensor = ov::Tensor(type, p.dst_shape);
}
init_tensor(full_src_tensor, false);
src_tensor.copy_to(dst_tensor);
compare_tensors(src_tensor, dst_tensor);
}
// clang-format off
INSTANTIATE_TEST_SUITE_P(copy_tests,
OVTensorTestCopy,
::testing::Combine(::testing::Values(
ov::element::bf16,
ov::element::f16,