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/*
* Copyright (C) 2020 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 "GeneratedTestUtils.h"
#include <android-base/logging.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "TestHarness.h"
#include "TestNeuralNetworksWrapper.h"
namespace android::nn::generated_tests {
using namespace test_wrapper;
using namespace test_helper;
static OperandType getOperandType(const TestOperand& op, bool testDynamicOutputShape) {
auto dims = op.dimensions;
if (testDynamicOutputShape && op.lifetime == TestOperandLifeTime::SUBGRAPH_OUTPUT) {
dims.assign(dims.size(), 0);
}
if (op.type == TestOperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
return OperandType(
static_cast<Type>(op.type), dims,
SymmPerChannelQuantParams(op.channelQuant.scales, op.channelQuant.channelDim));
} else {
return OperandType(static_cast<Type>(op.type), dims, op.scale, op.zeroPoint);
}
}
// A Memory object that owns AHardwareBuffer
class MemoryAHWB : public Memory {
public:
static std::unique_ptr<MemoryAHWB> create(uint32_t size) {
const uint64_t usage =
AHARDWAREBUFFER_USAGE_CPU_READ_OFTEN | AHARDWAREBUFFER_USAGE_CPU_WRITE_OFTEN;
AHardwareBuffer_Desc desc = {
.width = size,
.height = 1,
.layers = 1,
.format = AHARDWAREBUFFER_FORMAT_BLOB,
.usage = usage,
};
AHardwareBuffer* ahwb = nullptr;
EXPECT_EQ(AHardwareBuffer_allocate(&desc, &ahwb), 0);
EXPECT_NE(ahwb, nullptr);
void* buffer = nullptr;
EXPECT_EQ(AHardwareBuffer_lock(ahwb, usage, -1, nullptr, &buffer), 0);
EXPECT_NE(buffer, nullptr);
return std::unique_ptr<MemoryAHWB>(new MemoryAHWB(ahwb, buffer));
}
~MemoryAHWB() override {
EXPECT_EQ(AHardwareBuffer_unlock(mAhwb, nullptr), 0);
AHardwareBuffer_release(mAhwb);
}
void* getPointer() const { return mBuffer; }
private:
MemoryAHWB(AHardwareBuffer* ahwb, void* buffer) : Memory(ahwb), mAhwb(ahwb), mBuffer(buffer) {}
AHardwareBuffer* mAhwb;
void* mBuffer;
};
static std::unique_ptr<MemoryAHWB> createConstantReferenceMemory(const TestModel& testModel) {
uint32_t size = 0;
auto processSubgraph = [&size](const TestSubgraph& subgraph) {
for (const TestOperand& operand : subgraph.operands) {
if (operand.lifetime == TestOperandLifeTime::CONSTANT_REFERENCE) {
size += operand.data.alignedSize();
}
}
};
processSubgraph(testModel.main);
for (const TestSubgraph& subgraph : testModel.referenced) {
processSubgraph(subgraph);
}
return size == 0 ? nullptr : MemoryAHWB::create(size);
}
static void createModelFromSubgraph(const TestSubgraph& subgraph, bool testDynamicOutputShape,
const std::vector<TestSubgraph>& refSubgraphs,
const std::unique_ptr<MemoryAHWB>& memory,
uint32_t* memoryOffset, Model* model, Model* refModels) {
// Operands.
for (const auto& operand : subgraph.operands) {
auto type = getOperandType(operand, testDynamicOutputShape);
auto index = model->addOperand(&type);
switch (operand.lifetime) {
case TestOperandLifeTime::CONSTANT_COPY: {
model->setOperandValue(index, operand.data.get<void>(), operand.data.size());
} break;
case TestOperandLifeTime::CONSTANT_REFERENCE: {
const uint32_t length = operand.data.size();
std::memcpy(static_cast<uint8_t*>(memory->getPointer()) + *memoryOffset,
operand.data.get<void>(), length);
model->setOperandValueFromMemory(index, memory.get(), *memoryOffset, length);
*memoryOffset += operand.data.alignedSize();
} break;
case TestOperandLifeTime::NO_VALUE: {
model->setOperandValue(index, nullptr, 0);
} break;
case TestOperandLifeTime::SUBGRAPH: {
uint32_t refIndex = *operand.data.get<uint32_t>();
CHECK_LT(refIndex, refSubgraphs.size());
const TestSubgraph& refSubgraph = refSubgraphs[refIndex];
Model* refModel = &refModels[refIndex];
if (!refModel->isFinished()) {
createModelFromSubgraph(refSubgraph, testDynamicOutputShape, refSubgraphs,
memory, memoryOffset, refModel, refModels);
ASSERT_EQ(refModel->finish(), Result::NO_ERROR);
ASSERT_TRUE(refModel->isValid());
}
model->setOperandValueFromModel(index, refModel);
} break;
case TestOperandLifeTime::SUBGRAPH_INPUT:
case TestOperandLifeTime::SUBGRAPH_OUTPUT:
case TestOperandLifeTime::TEMPORARY_VARIABLE: {
// Nothing to do here.
} break;
}
}
// Operations.
for (const auto& operation : subgraph.operations) {
model->addOperation(static_cast<int>(operation.type), operation.inputs, operation.outputs);
}
// Inputs and outputs.
model->identifyInputsAndOutputs(subgraph.inputIndexes, subgraph.outputIndexes);
}
void createModel(const TestModel& testModel, bool testDynamicOutputShape, GeneratedModel* model) {
ASSERT_NE(nullptr, model);
std::unique_ptr<MemoryAHWB> memory = createConstantReferenceMemory(testModel);
uint32_t memoryOffset = 0;
std::vector<Model> refModels(testModel.referenced.size());
createModelFromSubgraph(testModel.main, testDynamicOutputShape, testModel.referenced, memory,
&memoryOffset, model, refModels.data());
model->setRefModels(std::move(refModels));
model->setConstantReferenceMemory(std::move(memory));
// Relaxed computation.
model->relaxComputationFloat32toFloat16(testModel.isRelaxed);
if (!testModel.expectFailure) {
ASSERT_TRUE(model->isValid());
}
}
void createRequest(const TestModel& testModel, Execution* execution,
std::vector<TestBuffer>* outputs) {
ASSERT_NE(nullptr, execution);
ASSERT_NE(nullptr, outputs);
// Model inputs.
for (uint32_t i = 0; i < testModel.main.inputIndexes.size(); i++) {
const auto& operand = testModel.main.operands[testModel.main.inputIndexes[i]];
ASSERT_EQ(Result::NO_ERROR,
execution->setInput(i, operand.data.get<void>(), operand.data.size()));
}
// Model outputs.
for (uint32_t i = 0; i < testModel.main.outputIndexes.size(); i++) {
const auto& operand = testModel.main.operands[testModel.main.outputIndexes[i]];
// In the case of zero-sized output, we should at least provide a one-byte buffer.
// This is because zero-sized tensors are only supported internally to the runtime, or
// reported in output shapes. It is illegal for the client to pre-specify a zero-sized
// tensor as model output. Otherwise, we will have two semantic conflicts:
// - "Zero dimension" conflicts with "unspecified dimension".
// - "Omitted operand buffer" conflicts with "zero-sized operand buffer".
const size_t bufferSize = std::max<size_t>(operand.data.size(), 1);
outputs->emplace_back(bufferSize);
ASSERT_EQ(Result::NO_ERROR,
execution->setOutput(i, outputs->back().getMutable<void>(), bufferSize));
}
}
} // namespace android::nn::generated_tests
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