| /* |
| * Copyright (C) 2018 The Android Open Source Project |
| * |
| * 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 <gmock/gmock.h> |
| #include <gtest/gtest.h> |
| |
| #include <limits> |
| #include <utility> |
| #include <vector> |
| |
| #include "HalInterfaces.h" |
| #include "MemoryUtils.h" |
| #include "OperationsUtils.cpp" |
| #include "QuantUtils.h" |
| #include "Utils.h" |
| #include "ValidateHal.h" |
| #include "nnapi/TypeUtils.h" |
| #include "nnapi/Types.h" |
| |
| namespace android { |
| namespace nn { |
| namespace wrapper { |
| |
| namespace { |
| using ::testing::ElementsAreArray; |
| } // namespace |
| |
| TEST(CalculateBroadcastedShapeTest, Basic) { |
| Shape shape1; |
| Shape shape2; |
| shape1.dimensions = {4, 3, 2, 1}; |
| shape2.dimensions = {3, 1, 5}; |
| |
| Shape expectedOutputShape; |
| expectedOutputShape.dimensions = {4, 3, 2, 5}; |
| |
| Shape actualOutputShape; |
| EXPECT_TRUE(calculateBroadcastedShape(shape1, shape2, &actualOutputShape)); |
| EXPECT_THAT(actualOutputShape.dimensions, ElementsAreArray(expectedOutputShape.dimensions)); |
| |
| EXPECT_TRUE(calculateBroadcastedShape(shape2, shape1, &actualOutputShape)); |
| EXPECT_THAT(actualOutputShape.dimensions, ElementsAreArray(expectedOutputShape.dimensions)); |
| } |
| |
| TEST(CalculateBroadcastedShapeTest, FailsOnIncompatible) { |
| Shape shape1; |
| Shape shape2; |
| shape1.dimensions = {5}; |
| shape2.dimensions = {3}; |
| |
| Shape actualOutputShape; |
| EXPECT_FALSE(calculateBroadcastedShape(shape1, shape2, &actualOutputShape)); |
| EXPECT_FALSE(calculateBroadcastedShape(shape2, shape1, &actualOutputShape)); |
| } |
| |
| static int32_t getExtensionType(uint16_t extensionPrefix, uint16_t typeWithinExtension) { |
| int32_t type = (extensionPrefix << kExtensionTypeBits) | typeWithinExtension; |
| EXPECT_TRUE(isExtensionOperandType(static_cast<V1_3::OperandType>(type))); |
| return type; |
| } |
| |
| TEST(TensorHasUnspecifiedDimensionsTest, ExtensionTensorWithUnspecifiedRank) { |
| // Regression test for b/124285861. |
| EXPECT_TRUE(tensorHasUnspecifiedDimensions(getExtensionType(1, 0), /*dim=*/nullptr, |
| /*dimCount=*/0)); |
| } |
| |
| TEST(ValidateOperandTypeTest, ExtensionTensorWithUnspecifiedRank) { |
| // Regression test for b/124104123. |
| constexpr uint16_t kExtensionPrefix = 1; |
| constexpr uint16_t kTypeWithinExtension = 0; |
| int32_t extensionType = getExtensionType(kExtensionPrefix, kTypeWithinExtension); |
| ANeuralNetworksOperandType type = { |
| .type = extensionType, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| }; |
| Extension::OperandTypeInformation info = { |
| .type = kTypeWithinExtension, |
| .isTensor = true, |
| .byteSize = 4, |
| }; |
| EXPECT_EQ(validateOperandType(type, &info, /*tag=*/"test", /*allowPartial=*/true), |
| ANEURALNETWORKS_NO_ERROR); |
| EXPECT_EQ(validateOperandType(type, &info, /*tag=*/"test", /*allowPartial=*/false), |
| ANEURALNETWORKS_BAD_DATA); |
| } |
| |
| TEST(ValidateOperandTypeTest, ExtensionTypeDimensionProductOverflow) { |
| // Regression test for b/146044137. |
| constexpr uint16_t kExtensionPrefix = 1; |
| constexpr uint16_t kTypeWithinExtension = 0; |
| int32_t extensionType = getExtensionType(kExtensionPrefix, kTypeWithinExtension); |
| uint32_t dimensions[] = {5, 4, 4, 786433, 5, 3, 16777216, 4, 5}; |
| ANeuralNetworksOperandType type = { |
| .type = extensionType, |
| .dimensionCount = std::size(dimensions), |
| .dimensions = dimensions, |
| }; |
| Extension::OperandTypeInformation info = { |
| .type = kTypeWithinExtension, |
| .isTensor = true, |
| .byteSize = 1, |
| }; |
| EXPECT_EQ(validateOperandType(type, &info, /*tag=*/"test", /*allowPartial=*/true), |
| ANEURALNETWORKS_BAD_DATA); |
| } |
| |
| TEST(ValidateOperandTypeTest, TensorSizeDimensionProductOverflow) { |
| // Regression test for b/146044137. |
| uint32_t dimensions[] = {256, 256, 256, 256}; |
| ANeuralNetworksOperandType type = { |
| .type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = std::size(dimensions), |
| .dimensions = dimensions, |
| }; |
| EXPECT_EQ(validateOperandType(type, nullptr, /*tag=*/"test", /*allowPartial=*/true), |
| ANEURALNETWORKS_BAD_DATA); |
| } |
| |
| TEST(ValidateRequestTest, UnknownOutputRank) { |
| V1_3::Request::MemoryPool pool; |
| pool.hidlMemory(allocateSharedMemory(2 * sizeof(float))); |
| ASSERT_TRUE(pool.hidlMemory().valid()); |
| const V1_3::Model model = { |
| .main = |
| { |
| .operands = {{ |
| .type = V1_3::OperandType::TENSOR_FLOAT32, |
| .dimensions = {1}, |
| .numberOfConsumers = 1, |
| .lifetime = V1_3::OperandLifeTime::SUBGRAPH_INPUT, |
| }, |
| { |
| .type = V1_3::OperandType::TENSOR_FLOAT32, |
| .dimensions = {}, // unknown output rank |
| .numberOfConsumers = 0, |
| .lifetime = V1_3::OperandLifeTime::SUBGRAPH_OUTPUT, |
| }}, |
| .operations = {{ |
| .type = V1_3::OperationType::ABS, |
| .inputs = {0}, |
| .outputs = {1}, |
| }}, |
| .inputIndexes = {0}, |
| .outputIndexes = {1}, |
| }, |
| }; |
| const V1_3::Request request = { |
| .inputs = {{ |
| .location = {.poolIndex = 0, .offset = 0, .length = sizeof(float)}, |
| .dimensions = {}, |
| }}, |
| .outputs = {{ |
| .location = {.poolIndex = 0, .offset = sizeof(float), .length = sizeof(float)}, |
| .dimensions = {}, |
| }}, |
| .pools = {std::move(pool)}, |
| }; |
| EXPECT_FALSE(validateRequest(request, model, /*allowUnspecifiedOutput=*/false)); |
| } |
| |
| TEST(ValidateRequestTest, ScalarOutput) { |
| V1_3::Request::MemoryPool pool; |
| pool.hidlMemory(allocateSharedMemory(sizeof(float) + sizeof(int32_t))); |
| ASSERT_TRUE(pool.hidlMemory().valid()); |
| const V1_3::Model model = { |
| .main = |
| { |
| .operands = {{ |
| .type = V1_3::OperandType::TENSOR_FLOAT32, |
| .dimensions = {1}, |
| .numberOfConsumers = 1, |
| .lifetime = V1_3::OperandLifeTime::SUBGRAPH_INPUT, |
| }, |
| { |
| .type = V1_3::OperandType::INT32, |
| .dimensions = {}, |
| .numberOfConsumers = 0, |
| .lifetime = V1_3::OperandLifeTime::SUBGRAPH_OUTPUT, |
| }}, |
| .operations = {{ |
| .type = V1_3::OperationType::RANK, |
| .inputs = {0}, |
| .outputs = {1}, |
| }}, |
| .inputIndexes = {0}, |
| .outputIndexes = {1}, |
| }, |
| }; |
| const V1_3::Request request = { |
| .inputs = {{ |
| .location = {.poolIndex = 0, .offset = 0, .length = sizeof(float)}, |
| .dimensions = {}, |
| }}, |
| .outputs = {{ |
| .location = {.poolIndex = 0, |
| .offset = sizeof(float), |
| .length = sizeof(int32_t)}, |
| .dimensions = {}, |
| }}, |
| .pools = {std::move(pool)}, |
| }; |
| EXPECT_TRUE(validateRequest(request, model, /*allowUnspecifiedOutput=*/false)); |
| } |
| |
| class CombineDimensionsTest : public ::testing::Test { |
| protected: |
| void testCompatible(const std::vector<uint32_t>& lhs, const std::vector<uint32_t>& rhs, |
| const std::vector<uint32_t>& expected) { |
| SCOPED_TRACE("lhs = " + toString(lhs) + ", rhs = " + toString(rhs)); |
| const auto res = combineDimensions(lhs, rhs); |
| ASSERT_TRUE(res.has_value()); |
| EXPECT_EQ(res.value(), expected); |
| } |
| |
| void testIncompatible(const std::vector<uint32_t>& lhs, const std::vector<uint32_t>& rhs) { |
| SCOPED_TRACE("lhs = " + toString(lhs) + ", rhs = " + toString(rhs)); |
| const auto res = combineDimensions(lhs, rhs); |
| EXPECT_FALSE(res.has_value()); |
| } |
| }; |
| |
| TEST_F(CombineDimensionsTest, Rank) { |
| testCompatible({}, {1, 2, 3, 4}, {1, 2, 3, 4}); |
| testCompatible({1, 2, 3, 4}, {}, {1, 2, 3, 4}); |
| testCompatible({}, {}, {}); |
| testIncompatible({1, 2, 3}, {1, 2, 3, 4}); |
| testIncompatible({1, 2, 3, 4}, {1, 2, 3}); |
| } |
| |
| TEST_F(CombineDimensionsTest, Dimensions) { |
| testCompatible({0, 0, 0, 0}, {1, 2, 3, 4}, {1, 2, 3, 4}); |
| testCompatible({1, 2, 3, 4}, {0, 0, 0, 0}, {1, 2, 3, 4}); |
| testCompatible({0, 0, 0, 0}, {0, 0, 0, 0}, {0, 0, 0, 0}); |
| testIncompatible({1, 2, 3, 4}, {2, 2, 3, 4}); |
| testIncompatible({1, 2, 3, 4}, {1, 2, 3, 3}); |
| } |
| |
| TEST(QuantizationUtilsTest, QuantizeMultiplierSmallerThanOneExp) { |
| auto checkInvalidQuantization = [](double value) { |
| int32_t q; |
| int s; |
| EXPECT_FALSE(QuantizeMultiplierSmallerThanOneExp(value, &q, &s)); |
| }; |
| |
| checkInvalidQuantization(-0.1); |
| checkInvalidQuantization(0.0); |
| // If we get close enough to 1.0 it crashes and dies in one of two ways: |
| // Either the shift becomes negative or we trigger the 'less-than-one' CHECK. |
| checkInvalidQuantization(1 - 1e-15); |
| checkInvalidQuantization(1 - 1e-17); |
| checkInvalidQuantization(1.0); |
| |
| auto checkQuantization = [](double value, int32_t goldenQuantized, int goldenShift) { |
| int32_t q; |
| int s; |
| EXPECT_TRUE(QuantizeMultiplierSmallerThanOneExp(value, &q, &s)); |
| EXPECT_EQ(q, goldenQuantized); |
| EXPECT_EQ(s, goldenShift); |
| }; |
| |
| checkQuantization(0.25, 1073741824, -1); |
| checkQuantization(0.50 - 5e-9, 2147483627, -1); |
| checkQuantization(0.50 - 1e-10, 1073741824, 0); |
| checkQuantization(0.50, 1073741824, 0); |
| checkQuantization(0.75, 1610612736, 0); |
| checkQuantization(1 - 1e-9, 2147483646, 0); |
| } |
| |
| TEST(QuantizationUtilsTest, QuantizeMultiplierGreaterThanOne) { |
| auto checkInvalidQuantization = [](double value) { |
| int32_t q; |
| int s; |
| EXPECT_FALSE(QuantizeMultiplierGreaterThanOne(value, &q, &s)); |
| }; |
| |
| checkInvalidQuantization(1 + 1e-16); |
| |
| auto checkQuantization = [](double value, int32_t goldenQuantized, int goldenShift) { |
| int32_t q; |
| int s; |
| EXPECT_TRUE(QuantizeMultiplierGreaterThanOne(value, &q, &s)); |
| EXPECT_EQ(q, goldenQuantized); |
| EXPECT_EQ(s, goldenShift); |
| }; |
| |
| checkQuantization(1 + 1e-11, 1073741824, 1); |
| checkQuantization(1.25, 1342177280, 1); |
| checkQuantization(1.50, 1610612736, 1); |
| checkQuantization(1.50, 1610612736, 1); |
| checkQuantization(1.75, 1879048192, 1); |
| checkQuantization(2 - 1e-9, 2147483647, 1); |
| checkQuantization(2 - 1e-11, 1073741824, 2); |
| checkQuantization(2, 1073741824, 2); |
| } |
| |
| TEST(QuantizationUtilTest, QuantizeMultiplier) { |
| auto checkQuantization = [](double value, int32_t goldenQuantized, int goldenShift) { |
| int32_t q; |
| int s; |
| EXPECT_TRUE(QuantizeMultiplier(value, &q, &s)); |
| EXPECT_EQ(q, goldenQuantized); |
| EXPECT_EQ(s, goldenShift); |
| }; |
| |
| checkQuantization(-4, -1073741824, 3); |
| checkQuantization(-2, -1073741824, 2); |
| checkQuantization(-1, -1073741824, 1); |
| checkQuantization(-0.5, -1073741824, 0); |
| checkQuantization(-0.25, -1073741824, -1); |
| checkQuantization(-0.125, -1073741824, -2); |
| checkQuantization(0, 0, 0); |
| checkQuantization(0.125, 1073741824, -2); |
| checkQuantization(0.25, 1073741824, -1); |
| checkQuantization(0.5, 1073741824, 0); |
| checkQuantization(1, 1073741824, 1); |
| checkQuantization(2, 1073741824, 2); |
| checkQuantization(4, 1073741824, 3); |
| } |
| |
| TEST(QuantizationUtilTest, QuantizeMultiplierUnderflow) { |
| auto checkQuantization = [](double value, int32_t goldenQuantized, int goldenShift) { |
| int32_t q; |
| int s; |
| EXPECT_TRUE(QuantizeMultiplier(value, &q, &s)); |
| EXPECT_EQ(q, goldenQuantized); |
| EXPECT_EQ(s, goldenShift); |
| }; |
| |
| checkQuantization(std::ldexp(1.0f, -31), 1073741824, -30); |
| checkQuantization(std::ldexp(1.0f, -32), 1073741824, -31); |
| checkQuantization(std::ldexp(0.99f, -32), 0, 0); |
| checkQuantization(std::ldexp(1.0f, -33), 0, 0); |
| } |
| |
| TEST(QuantizationUtilTest, GetInvSqrtQuantizedMultiplierExp) { |
| auto checkInvSqrtQuantization = [](int32_t input, int32_t goldenInvSqrt, int goldenShift) { |
| int32_t q; |
| int s; |
| EXPECT_TRUE(GetInvSqrtQuantizedMultiplierExp(input, 1, &q, &s)); |
| EXPECT_EQ(q, goldenInvSqrt); |
| EXPECT_EQ(s, goldenShift); |
| }; |
| |
| const auto kInt32Max = std::numeric_limits<std::int32_t>::max(); |
| checkInvSqrtQuantization(0, kInt32Max, 0); |
| checkInvSqrtQuantization(1, kInt32Max, 0); |
| checkInvSqrtQuantization(2, 1518498372, 0); |
| checkInvSqrtQuantization(3, 1239850284, 0); |
| checkInvSqrtQuantization(4, 1073741828, 0); |
| checkInvSqrtQuantization(100, 214748363, 0); |
| checkInvSqrtQuantization(10000, 343597361, 4); |
| checkInvSqrtQuantization(1000000, 274877901, 7); |
| checkInvSqrtQuantization(100000000, 219902323, 10); |
| checkInvSqrtQuantization((1 << 30), 268435457, 12); |
| checkInvSqrtQuantization(kInt32Max, 189812531, 12); |
| } |
| |
| } // namespace wrapper |
| } // namespace nn |
| } // namespace android |
