Refactor NNAPI VTS to remove unreasonable dependence between versions

To make it easier to create the next version of NNAPI, this change
removes the following nonsensical dependence:
- NNAPI 1.0 VTS depends on NNAPI 1.1 and 1.2
- NNAPI 1.1 VTS depends on NNAPI 1.2

In particular, I made the following changes:
- split GeneratedTestHarness.cpp into three separate implementations,
- created a restricted version of Callbacks.h for 1.0 and 1.1,
- removed the dependency on frameworks/ml/nn/HalInterfaces.h,
- refactored Android.bp files for more autonomy between 1.0, 1.1, and 1.2,
- consolidated some common code into Utils.h,
- created structure for sharing code between VTS versions (VtsHalNeuralNetworksV1_0_utils).

Bug: 74827824
Bug: 124462414
Test: VtsHalNeuralnetworksV1_0TargetTest
Test: VtsHalNeuralnetworksV1_1TargetTest
Test: VtsHalNeuralnetworksV1_1CompatV1_0TargetTest
Test: VtsHalNeuralnetworksV1_2TargetTest
Test: VtsHalNeuralnetworksV1_2CompatV1_0TargetTest
Test: VtsHalNeuralnetworksV1_2CompatV1_1TargetTest
Change-Id: I4243d0b5e574255cef1070850f4d0a284f65f54e
diff --git a/neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..d9f64fd
--- /dev/null
+++ b/neuralnetworks/1.1/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,232 @@
+/*
+ * Copyright (C) 2017 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 "GeneratedTestHarness.h"
+
+#include <android-base/logging.h>
+#include <android/hardware/neuralnetworks/1.0/IPreparedModel.h>
+#include <android/hardware/neuralnetworks/1.0/types.h>
+#include <android/hardware/neuralnetworks/1.1/IDevice.h>
+#include <android/hidl/allocator/1.0/IAllocator.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+#include <iostream>
+
+#include "1.0/Callbacks.h"
+#include "1.0/Utils.h"
+#include "MemoryUtils.h"
+#include "TestHarness.h"
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace generated_tests {
+
+using ::android::hardware::neuralnetworks::V1_0::ErrorStatus;
+using ::android::hardware::neuralnetworks::V1_0::IPreparedModel;
+using ::android::hardware::neuralnetworks::V1_0::Request;
+using ::android::hardware::neuralnetworks::V1_0::RequestArgument;
+using ::android::hardware::neuralnetworks::V1_0::implementation::ExecutionCallback;
+using ::android::hardware::neuralnetworks::V1_0::implementation::PreparedModelCallback;
+using ::android::hardware::neuralnetworks::V1_1::ExecutionPreference;
+using ::android::hardware::neuralnetworks::V1_1::IDevice;
+using ::android::hardware::neuralnetworks::V1_1::Model;
+using ::android::hidl::memory::V1_0::IMemory;
+using ::test_helper::compare;
+using ::test_helper::filter;
+using ::test_helper::for_all;
+using ::test_helper::MixedTyped;
+using ::test_helper::MixedTypedExample;
+using ::test_helper::resize_accordingly;
+
+// Top level driver for models and examples generated by test_generator.py
+// Test driver for those generated from ml/nn/runtime/test/spec
+void EvaluatePreparedModel(sp<IPreparedModel>& preparedModel, std::function<bool(int)> is_ignored,
+                           const std::vector<MixedTypedExample>& examples,
+                           bool hasRelaxedFloat32Model, float fpAtol, float fpRtol) {
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    int example_no = 1;
+    for (auto& example : examples) {
+        SCOPED_TRACE(example_no++);
+        const MixedTyped& inputs = example.operands.first;
+        const MixedTyped& golden = example.operands.second;
+
+        const bool hasFloat16Inputs = !inputs.float16Operands.empty();
+        if (hasRelaxedFloat32Model || hasFloat16Inputs) {
+            // TODO: Adjust the error limit based on testing.
+            // If in relaxed mode, set the absolute tolerance to be 5ULP of FP16.
+            fpAtol = 5.0f * 0.0009765625f;
+            // Set the relative tolerance to be 5ULP of the corresponding FP precision.
+            fpRtol = 5.0f * 0.0009765625f;
+        }
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                    .location = {.poolIndex = INPUT,
+                                 .offset = 0,
+                                 .length = static_cast<uint32_t>(s)},
+                    .dimensions = {},
+            };
+            RequestArgument arg_empty = {
+                    .hasNoValue = true,
+            };
+            inputs_info[index] = s ? arg : arg_empty;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                if (!i.hasNoValue) i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        MixedTyped test;  // holding test results
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        resize_accordingly(golden, test);
+        for_all(golden, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                    .location = {.poolIndex = OUTPUT,
+                                 .offset = 0,
+                                 .length = static_cast<uint32_t>(s)},
+                    .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {nn::allocateSharedMemory(inputSize),
+                                          nn::allocateSharedMemory(outputSize)};
+        ASSERT_NE(0ull, pools[INPUT].size());
+        ASSERT_NE(0ull, pools[OUTPUT].size());
+
+        // load data
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
+        ASSERT_NE(nullptr, inputMemory.get());
+        ASSERT_NE(nullptr, outputMemory.get());
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        char* outputPtr = reinterpret_cast<char*>(static_cast<void*>(outputMemory->getPointer()));
+        ASSERT_NE(nullptr, inputPtr);
+        ASSERT_NE(nullptr, outputPtr);
+        inputMemory->update();
+        outputMemory->update();
+
+        // Go through all inputs, copy the values
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+
+        inputMemory->commit();
+        outputMemory->commit();
+
+        const Request request = {.inputs = inputs_info, .outputs = outputs_info, .pools = pools};
+
+        // launch execution
+        sp<ExecutionCallback> executionCallback = new ExecutionCallback();
+        ASSERT_NE(nullptr, executionCallback.get());
+        Return<ErrorStatus> executionLaunchStatus =
+                preparedModel->execute(request, executionCallback);
+        ASSERT_TRUE(executionLaunchStatus.isOk());
+        EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
+
+        // retrieve execution status
+        executionCallback->wait();
+        ASSERT_EQ(ErrorStatus::NONE, executionCallback->getStatus());
+
+        // validate results
+        outputMemory->read();
+        copy_back(&test, outputs_info, outputPtr);
+        outputMemory->commit();
+        // Filter out don't cares
+        MixedTyped filtered_golden = filter(golden, is_ignored);
+        MixedTyped filtered_test = filter(test, is_ignored);
+
+        // We want "close-enough" results for float
+        compare(filtered_golden, filtered_test, fpAtol, fpRtol);
+    }
+}
+
+void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,
+             std::function<bool(int)> is_ignored, const std::vector<MixedTypedExample>& examples) {
+    Model model = create_model();
+
+    // see if service can handle model
+    bool fullySupportsModel = false;
+    Return<void> supportedCall = device->getSupportedOperations_1_1(
+            model, [&fullySupportsModel](ErrorStatus status, const hidl_vec<bool>& supported) {
+                ASSERT_EQ(ErrorStatus::NONE, status);
+                ASSERT_NE(0ul, supported.size());
+                fullySupportsModel = std::all_of(supported.begin(), supported.end(),
+                                                 [](bool valid) { return valid; });
+            });
+    ASSERT_TRUE(supportedCall.isOk());
+
+    // launch prepare model
+    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
+    ASSERT_NE(nullptr, preparedModelCallback.get());
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+            model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
+    ASSERT_TRUE(prepareLaunchStatus.isOk());
+    ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
+
+    // retrieve prepared model
+    preparedModelCallback->wait();
+    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
+    sp<IPreparedModel> preparedModel = preparedModelCallback->getPreparedModel();
+
+    // early termination if vendor service cannot fully prepare model
+    if (!fullySupportsModel && prepareReturnStatus != ErrorStatus::NONE) {
+        ASSERT_EQ(nullptr, preparedModel.get());
+        LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
+                     "prepare model that it does not support.";
+        std::cout << "[          ]   Early termination of test because vendor service cannot "
+                     "prepare model that it does not support."
+                  << std::endl;
+        GTEST_SKIP();
+    }
+    EXPECT_EQ(ErrorStatus::NONE, prepareReturnStatus);
+    ASSERT_NE(nullptr, preparedModel.get());
+
+    EvaluatePreparedModel(preparedModel, is_ignored, examples,
+                          model.relaxComputationFloat32toFloat16, 1e-5f, 1e-5f);
+}
+
+}  // namespace generated_tests
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android