Implement VTS tests for NNAPI AIDL interface
The tests are copied from HIDL 1.0-3 VTS tests and updated to use AIDL.
Bug: 172922059
Test: VtsHalNeuralnetworksTargetTest
Change-Id: Ife08409e9b46420685a1ccb0b3256286c973dbf5
Merged-In: Ife08409e9b46420685a1ccb0b3256286c973dbf5
(cherry picked from commit b38bb4f12a1ceb33ebd0dd798650a74a8ef9d20e)
diff --git a/neuralnetworks/aidl/vts/functional/BasicTests.cpp b/neuralnetworks/aidl/vts/functional/BasicTests.cpp
new file mode 100644
index 0000000..b2f4507
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+++ b/neuralnetworks/aidl/vts/functional/BasicTests.cpp
@@ -0,0 +1,193 @@
+/*
+ * Copyright (C) 2021 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.
+ */
+
+#define LOG_TAG "neuralnetworks_aidl_hal_test"
+
+#include <aidl/android/hardware/neuralnetworks/Capabilities.h>
+#include <aidl/android/hardware/neuralnetworks/IDevice.h>
+#include <aidl/android/hardware/neuralnetworks/Operand.h>
+#include <aidl/android/hardware/neuralnetworks/OperandType.h>
+#include <aidl/android/hardware/neuralnetworks/Priority.h>
+#include <android/binder_interface_utils.h>
+
+#include "Utils.h"
+#include "VtsHalNeuralnetworks.h"
+
+namespace aidl::android::hardware::neuralnetworks::vts::functional {
+
+using implementation::PreparedModelCallback;
+
+// create device test
+TEST_P(NeuralNetworksAidlTest, CreateDevice) {}
+
+// initialization
+TEST_P(NeuralNetworksAidlTest, GetCapabilitiesTest) {
+ Capabilities capabilities;
+ const auto retStatus = kDevice->getCapabilities(&capabilities);
+ ASSERT_TRUE(retStatus.isOk());
+
+ auto isPositive = [](const PerformanceInfo& perf) {
+ return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
+ };
+
+ EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
+ EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
+ const auto& opPerf = capabilities.operandPerformance;
+ EXPECT_TRUE(
+ std::all_of(opPerf.begin(), opPerf.end(),
+ [isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
+ EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
+ [](const OperandPerformance& a, const OperandPerformance& b) {
+ return a.type < b.type;
+ }));
+ EXPECT_TRUE(std::all_of(opPerf.begin(), opPerf.end(), [](const OperandPerformance& a) {
+ return a.type != OperandType::SUBGRAPH;
+ }));
+ EXPECT_TRUE(isPositive(capabilities.ifPerformance));
+ EXPECT_TRUE(isPositive(capabilities.whilePerformance));
+}
+
+// detect cycle
+TEST_P(NeuralNetworksAidlTest, CycleTest) {
+ // opnd0 = TENSOR_FLOAT32 // model input
+ // opnd1 = TENSOR_FLOAT32 // model input
+ // opnd2 = INT32 // model input
+ // opnd3 = ADD(opnd0, opnd4, opnd2)
+ // opnd4 = ADD(opnd1, opnd3, opnd2)
+ // opnd5 = ADD(opnd4, opnd0, opnd2) // model output
+ //
+ // +-----+
+ // | |
+ // v |
+ // 3 = ADD(0, 4, 2) |
+ // | |
+ // +----------+ |
+ // | |
+ // v |
+ // 4 = ADD(1, 3, 2) |
+ // | |
+ // +----------------+
+ // |
+ // |
+ // +-------+
+ // |
+ // v
+ // 5 = ADD(4, 0, 2)
+
+ const std::vector<Operand> operands = {
+ {
+ // operands[0]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[1]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[2]
+ .type = OperandType::INT32,
+ .dimensions = {},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[3]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[4]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ {
+ // operands[5]
+ .type = OperandType::TENSOR_FLOAT32,
+ .dimensions = {1},
+ .scale = 0.0f,
+ .zeroPoint = 0,
+ .lifetime = OperandLifeTime::SUBGRAPH_OUTPUT,
+ .location = {.poolIndex = 0, .offset = 0, .length = 0},
+ },
+ };
+
+ const std::vector<Operation> operations = {
+ {.type = OperationType::ADD, .inputs = {0, 4, 2}, .outputs = {3}},
+ {.type = OperationType::ADD, .inputs = {1, 3, 2}, .outputs = {4}},
+ {.type = OperationType::ADD, .inputs = {4, 0, 2}, .outputs = {5}},
+ };
+
+ Subgraph subgraph = {
+ .operands = operands,
+ .operations = operations,
+ .inputIndexes = {0, 1, 2},
+ .outputIndexes = {5},
+ };
+ const Model model = {
+ .main = std::move(subgraph),
+ .referenced = {},
+ .operandValues = {},
+ .pools = {},
+ };
+
+ // ensure that getSupportedOperations() checks model validity
+ std::vector<bool> supportedOps;
+ const auto supportedOpsStatus = kDevice->getSupportedOperations(model, &supportedOps);
+ ASSERT_FALSE(supportedOpsStatus.isOk());
+ ASSERT_EQ(supportedOpsStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ ASSERT_EQ(static_cast<ErrorStatus>(supportedOpsStatus.getServiceSpecificError()),
+ ErrorStatus::INVALID_ARGUMENT);
+
+ // ensure that prepareModel() checks model validity
+ auto preparedModelCallback = ndk::SharedRefBase::make<PreparedModelCallback>();
+ auto prepareLaunchStatus =
+ kDevice->prepareModel(model, ExecutionPreference::FAST_SINGLE_ANSWER, kDefaultPriority,
+ kNoDeadline, {}, {}, kEmptyCacheToken, preparedModelCallback);
+ // Note that preparation can fail for reasons other than an
+ // invalid model (invalid model should result in
+ // INVALID_ARGUMENT) -- for example, perhaps not all
+ // operations are supported, or perhaps the device hit some
+ // kind of capacity limit.
+ ASSERT_FALSE(prepareLaunchStatus.isOk());
+ EXPECT_EQ(prepareLaunchStatus.getExceptionCode(), EX_SERVICE_SPECIFIC);
+ EXPECT_NE(static_cast<ErrorStatus>(prepareLaunchStatus.getServiceSpecificError()),
+ ErrorStatus::NONE);
+
+ EXPECT_NE(preparedModelCallback->getStatus(), ErrorStatus::NONE);
+ EXPECT_EQ(preparedModelCallback->getPreparedModel(), nullptr);
+}
+
+} // namespace aidl::android::hardware::neuralnetworks::vts::functional