mirror of
https://github.com/saymrwulf/onnxruntime.git
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* Remove allocator type from the key comparison in ExecutionProviders. Remove usage of DummyArena as it's no longer necessary. * Fix x86 tests where arena allocator is disabled. Make initialization of OrtMemoryInfo clearer by adding Invalid enum value. * Make OrtValueNameIdxMap::MaxIdx more intuitive.
199 lines
6.2 KiB
C++
199 lines
6.2 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "core/framework/tensor.h"
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#include "core/framework/allocatormgr.h"
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#include "test_utils.h"
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#include "gmock/gmock.h"
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#include "gtest/gtest.h"
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#include <sstream>
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namespace onnxruntime {
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namespace test {
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template <typename T>
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void CPUTensorTest(std::vector<int64_t> dims, const int offset = 0) {
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//not own the buffer
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TensorShape shape(dims);
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auto alloc = TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault);
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auto data = alloc->Alloc(sizeof(T) * (shape.Size() + offset));
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EXPECT_TRUE(data);
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Tensor t(DataTypeImpl::GetType<T>(), shape, data, alloc->Info(), offset);
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auto tensor_shape = t.Shape();
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//Use reinterpret_cast to bypass a gcc bug: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=51213
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EXPECT_EQ(*reinterpret_cast<const std::vector<int64_t>*>(&shape), *reinterpret_cast<const std::vector<int64_t>*>(&tensor_shape));
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EXPECT_EQ(t.DataType(), DataTypeImpl::GetType<T>());
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auto& location = t.Location();
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EXPECT_STREQ(location.name, CPU);
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EXPECT_EQ(location.id, 0);
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auto t_data = t.template MutableData<T>();
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EXPECT_TRUE(t_data);
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memset(t_data, 0, sizeof(T) * shape.Size());
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EXPECT_EQ(*(T*)((char*)data + offset), (T)0);
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alloc->Free(data);
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Tensor new_t(DataTypeImpl::GetType<T>(), shape, alloc, offset);
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tensor_shape = new_t.Shape();
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//Use reinterpret_cast to bypass a gcc bug: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=51213
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EXPECT_EQ(*reinterpret_cast<const std::vector<int64_t>*>(&shape), *reinterpret_cast<const std::vector<int64_t>*>(&tensor_shape));
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EXPECT_EQ(new_t.DataType(), DataTypeImpl::GetType<T>());
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auto& new_location = new_t.Location();
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ASSERT_STREQ(new_location.name, CPU);
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EXPECT_EQ(new_location.id, 0);
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auto new_data = new_t.template MutableData<T>();
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EXPECT_TRUE(new_data);
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memset(new_data, 0, sizeof(T) * shape.Size());
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EXPECT_EQ(*(T*)((char*)new_data + offset), (T)0);
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//no free op as the tensor own the buffer
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}
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TEST(TensorTest, CPUFloatTensorTest) {
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CPUTensorTest<float>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUInt32TensorTest) {
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CPUTensorTest<int32_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUUInt8TensorTest) {
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CPUTensorTest<uint8_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUUInt16TensorTest) {
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CPUTensorTest<uint16_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUInt16TensorTest) {
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CPUTensorTest<int16_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUInt64TensorTest) {
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CPUTensorTest<int64_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUDoubleTensorTest) {
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CPUTensorTest<double>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUUInt32TensorTest) {
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CPUTensorTest<uint32_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUUInt64TensorTest) {
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CPUTensorTest<uint64_t>(std::vector<int64_t>({3, 2, 4}));
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}
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TEST(TensorTest, CPUFloatTensorOffsetTest) {
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CPUTensorTest<float>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUInt32TensorOffsetTest) {
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CPUTensorTest<int32_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUUInt8TensorOffsetTest) {
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CPUTensorTest<uint8_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUUInt16TensorOffsetTest) {
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CPUTensorTest<uint16_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUInt16TensorOffsetTest) {
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CPUTensorTest<int16_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUInt64TensorOffsetTest) {
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CPUTensorTest<int64_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUDoubleTensorOffsetTest) {
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CPUTensorTest<double>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUUInt32TensorOffsetTest) {
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CPUTensorTest<uint32_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, CPUUInt64TensorOffsetTest) {
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CPUTensorTest<uint64_t>(std::vector<int64_t>({3, 2, 4}), 5);
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}
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TEST(TensorTest, EmptyTensorTest) {
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auto type = DataTypeImpl::GetType<float>();
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Tensor t(type, TensorShape({1, 0}), nullptr, TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault)->Info());
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auto& shape = t.Shape();
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EXPECT_EQ(shape.Size(), 0);
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EXPECT_EQ(t.DataType(), type);
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auto data = t.template MutableData<float>();
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EXPECT_TRUE(!data);
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auto& location = t.Location();
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ASSERT_STREQ(location.name, CPU);
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EXPECT_EQ(location.id, 0);
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// arena is disabled for CPUExecutionProvider on x86 and JEMalloc
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#if (defined(__amd64__) || defined(_M_AMD64)) && !defined(USE_JEMALLOC)
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EXPECT_EQ(location.alloc_type, OrtAllocatorType::OrtArenaAllocator);
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#else
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EXPECT_EQ(location.alloc_type, OrtAllocatorType::OrtDeviceAllocator);
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#endif
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}
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TEST(TensorTest, StringTensorTest) {
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//add scope to explicitly delete tensor
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#ifdef _MSC_VER
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std::string* string_ptr = nullptr;
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#else
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std::string* string_ptr __attribute__((unused)) = nullptr;
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#endif
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{
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TensorShape shape({2, 3});
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auto alloc = TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault);
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Tensor t(DataTypeImpl::GetType<std::string>(), shape, alloc);
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auto& tensor_shape = t.Shape();
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//Use reinterpret_cast to bypass a gcc bug: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=51213
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EXPECT_EQ(*reinterpret_cast<const std::vector<int64_t>*>(&shape), *reinterpret_cast<const std::vector<int64_t>*>(&tensor_shape));
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EXPECT_EQ(t.DataType(), DataTypeImpl::GetType<std::string>());
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auto& location = t.Location();
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ASSERT_STREQ(location.name, CPU);
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EXPECT_EQ(location.id, 0);
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std::string* new_data = t.template MutableData<std::string>();
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EXPECT_TRUE(new_data);
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new_data[0] = "a";
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new_data[1] = "b";
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auto tensor_data = t.template Data<std::string>();
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EXPECT_EQ(tensor_data[0], "a");
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EXPECT_EQ(tensor_data[1], "b");
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string_ptr = new_data;
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}
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}
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TEST(TensorTest, ConvertToString) {
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TensorShape shape({2, 3, 4});
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EXPECT_EQ(shape.ToString(), "{2,3,4}");
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std::ostringstream ss;
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ss << shape;
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EXPECT_EQ(ss.str(), "{2,3,4}");
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}
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TEST(TensorTest, Int64PtrConstructor) {
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int64_t dimensions[] = {2, 3, 4};
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TensorShape shape(dimensions, 2); // just use first 2
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EXPECT_EQ(shape.Size(), 6);
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EXPECT_EQ(shape.NumDimensions(), 2);
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EXPECT_THAT(shape.GetDims(), testing::ElementsAre(2, 3));
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}
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} // namespace test
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} // namespace onnxruntime
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