neuralnetworks/1.3/vts/functional/ValidateModel.cpp

/*
 * 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.
 */

#define LOG_TAG "neuralnetworks_hidl_hal_test"

#include "1.0/Utils.h"
#include "1.3/Callbacks.h"
#include "1.3/Utils.h"
#include "GeneratedTestHarness.h"
#include "VtsHalNeuralnetworks.h"

namespace android::hardware::neuralnetworks::V1_3::vts::functional {

using implementation::PreparedModelCallback;
using V1_1::ExecutionPreference;
using V1_2::SymmPerChannelQuantParams;
using HidlToken =
        hidl_array<uint8_t, static_cast<uint32_t>(V1_2::Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;

///////////////////////// UTILITY FUNCTIONS /////////////////////////

static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
                                           const Model& model) {
    SCOPED_TRACE(message + " [getSupportedOperations_1_3]");

    Return<void> ret = device->getSupportedOperations_1_3(
            model, [&](ErrorStatus status, const hidl_vec<bool>&) {
                EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
            });
    EXPECT_TRUE(ret.isOk());
}

static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
                                 const Model& model, ExecutionPreference preference) {
    SCOPED_TRACE(message + " [prepareModel_1_3]");

    sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_3(
            model, preference, kDefaultPriority, {}, hidl_vec<hidl_handle>(),
            hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
    ASSERT_TRUE(prepareLaunchStatus.isOk());
    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));

    preparedModelCallback->wait();
    ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
    ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
    sp<IPreparedModel> preparedModel = getPreparedModel_1_3(preparedModelCallback);
    ASSERT_EQ(nullptr, preparedModel.get());
}

static bool validExecutionPreference(ExecutionPreference preference) {
    return preference == ExecutionPreference::LOW_POWER ||
           preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
           preference == ExecutionPreference::SUSTAINED_SPEED;
}

// Primary validation function. This function will take a valid model, apply a
// mutation to it to invalidate the model, then pass it to interface calls that
// use the model. Note that the model here is passed by value, and any mutation
// to the model does not leave this function.
static void validate(const sp<IDevice>& device, const std::string& message, Model model,
                     const std::function<void(Model*)>& mutation,
                     ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
    mutation(&model);
    if (validExecutionPreference(preference)) {
        validateGetSupportedOperations(device, message, model);
    }
    validatePrepareModel(device, message, model, preference);
}

static uint32_t addOperand(Model* model) {
    return hidl_vec_push_back(&model->main.operands,
                              {
                                      .type = OperandType::INT32,
                                      .dimensions = {},
                                      .numberOfConsumers = 0,
                                      .scale = 0.0f,
                                      .zeroPoint = 0,
                                      .lifetime = OperandLifeTime::SUBGRAPH_INPUT,
                                      .location = {.poolIndex = 0, .offset = 0, .length = 0},
                              });
}

static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
    uint32_t index = addOperand(model);
    model->main.operands[index].numberOfConsumers = 1;
    model->main.operands[index].lifetime = lifetime;
    return index;
}

///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////

static const uint32_t invalidOperandTypes[] = {
        static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MIN) - 1,
        static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MAX) + 1,
        static_cast<uint32_t>(OperandTypeRange::OEM_MIN) - 1,
        static_cast<uint32_t>(OperandTypeRange::OEM_MAX) + 1,
};

static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        for (uint32_t invalidOperandType : invalidOperandTypes) {
            const std::string message = "mutateOperandTypeTest: operand " +
                                        std::to_string(operand) + " set to value " +
                                        std::to_string(invalidOperandType);
            validate(device, message, model, [operand, invalidOperandType](Model* model) {
                model->main.operands[operand].type = static_cast<OperandType>(invalidOperandType);
            });
        }
    }
}

///////////////////////// VALIDATE OPERAND RANK /////////////////////////

static uint32_t getInvalidRank(OperandType type) {
    switch (type) {
        case OperandType::FLOAT16:
        case OperandType::FLOAT32:
        case OperandType::INT32:
        case OperandType::UINT32:
        case OperandType::BOOL:
            return 1;
        case OperandType::TENSOR_BOOL8:
        case OperandType::TENSOR_FLOAT16:
        case OperandType::TENSOR_FLOAT32:
        case OperandType::TENSOR_INT32:
        case OperandType::TENSOR_QUANT8_ASYMM:
        case OperandType::TENSOR_QUANT8_SYMM:
        case OperandType::TENSOR_QUANT16_ASYMM:
        case OperandType::TENSOR_QUANT16_SYMM:
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
            return 0;
        default:
            return 0;
    }
}

static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        const uint32_t invalidRank = getInvalidRank(model.main.operands[operand].type);
        if (invalidRank == 0) {
            continue;
        }
        const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
                                    " has rank of " + std::to_string(invalidRank);
        validate(device, message, model, [operand, invalidRank](Model* model) {
            model->main.operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
        });
    }
}

///////////////////////// VALIDATE OPERAND SCALE /////////////////////////

static float getInvalidScale(OperandType type) {
    switch (type) {
        case OperandType::FLOAT16:
        case OperandType::FLOAT32:
        case OperandType::INT32:
        case OperandType::UINT32:
        case OperandType::BOOL:
        case OperandType::TENSOR_BOOL8:
        case OperandType::TENSOR_FLOAT16:
        case OperandType::TENSOR_FLOAT32:
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
        case OperandType::SUBGRAPH:
            return 1.0f;
        case OperandType::TENSOR_INT32:
            return -1.0f;
        case OperandType::TENSOR_QUANT8_SYMM:
        case OperandType::TENSOR_QUANT8_ASYMM:
        case OperandType::TENSOR_QUANT16_ASYMM:
        case OperandType::TENSOR_QUANT16_SYMM:
            return 0.0f;
        default:
            return 0.0f;
    }
}

static void mutateOperandScaleTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        const float invalidScale = getInvalidScale(model.main.operands[operand].type);
        const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
                                    " has scale of " + std::to_string(invalidScale);
        validate(device, message, model, [operand, invalidScale](Model* model) {
            model->main.operands[operand].scale = invalidScale;
        });
    }
}

///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////

static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
    switch (type) {
        case OperandType::FLOAT16:
        case OperandType::FLOAT32:
        case OperandType::INT32:
        case OperandType::UINT32:
        case OperandType::BOOL:
        case OperandType::TENSOR_BOOL8:
        case OperandType::TENSOR_FLOAT16:
        case OperandType::TENSOR_FLOAT32:
        case OperandType::TENSOR_INT32:
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
        case OperandType::SUBGRAPH:
            return {1};
        case OperandType::TENSOR_QUANT8_ASYMM:
            return {-1, 256};
        case OperandType::TENSOR_QUANT8_SYMM:
            return {-129, -1, 1, 128};
        case OperandType::TENSOR_QUANT16_ASYMM:
            return {-1, 65536};
        case OperandType::TENSOR_QUANT16_SYMM:
            return {-32769, -1, 1, 32768};
        default:
            return {};
    }
}

static void mutateOperandZeroPointTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        const std::vector<int32_t> invalidZeroPoints =
                getInvalidZeroPoints(model.main.operands[operand].type);
        for (int32_t invalidZeroPoint : invalidZeroPoints) {
            const std::string message = "mutateOperandZeroPointTest: operand " +
                                        std::to_string(operand) + " has zero point of " +
                                        std::to_string(invalidZeroPoint);
            validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
                model->main.operands[operand].zeroPoint = invalidZeroPoint;
            });
        }
    }
}

///////////////////////// VALIDATE EXTRA ??? /////////////////////////

// TODO: Operand::lifetime
// TODO: Operand::location

///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////

static void mutateOperand(Operand* operand, OperandType type) {
    Operand newOperand = *operand;
    newOperand.type = type;
    switch (type) {
        case OperandType::FLOAT16:
        case OperandType::FLOAT32:
        case OperandType::INT32:
        case OperandType::UINT32:
        case OperandType::BOOL:
            newOperand.dimensions = hidl_vec<uint32_t>();
            newOperand.scale = 0.0f;
            newOperand.zeroPoint = 0;
            break;
        case OperandType::TENSOR_BOOL8:
        case OperandType::TENSOR_FLOAT16:
        case OperandType::TENSOR_FLOAT32:
            newOperand.dimensions =
                    operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
            newOperand.scale = 0.0f;
            newOperand.zeroPoint = 0;
            break;
        case OperandType::TENSOR_INT32:
            newOperand.dimensions =
                    operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
            newOperand.zeroPoint = 0;
            break;
        case OperandType::TENSOR_QUANT8_ASYMM:
        case OperandType::TENSOR_QUANT8_SYMM:
        case OperandType::TENSOR_QUANT16_ASYMM:
        case OperandType::TENSOR_QUANT16_SYMM:
            newOperand.dimensions =
                    operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
            newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
            break;
        case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
            newOperand.dimensions =
                    operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
            newOperand.scale = 0.0f;
            newOperand.zeroPoint = 0;

            SymmPerChannelQuantParams channelQuant;
            channelQuant.channelDim = 0;
            channelQuant.scales = hidl_vec<float>(
                    operand->dimensions.size() > 0 ? static_cast<size_t>(operand->dimensions[0])
                                                   : 0);
            for (size_t i = 0; i < channelQuant.scales.size(); ++i) {
                channelQuant.scales[i] = 1.0f;
            }
            newOperand.extraParams.channelQuant(std::move(channelQuant));
        } break;
        case OperandType::OEM:
        case OperandType::TENSOR_OEM_BYTE:
        default:
            break;
    }
    *operand = newOperand;
}

static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
    // Do not test OEM types
    if (type == model.main.operands[operand].type || type == OperandType::OEM ||
        type == OperandType::TENSOR_OEM_BYTE) {
        return true;
    }
    for (const Operation& operation : model.main.operations) {
        // Skip mutateOperationOperandTypeTest for the following operations.
        // - LSH_PROJECTION's second argument is allowed to have any type.
        // - ARGMIN and ARGMAX's first argument can be any of
        // TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
        // - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
        // - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
        // - DEQUANTIZE input can be any of
        // TENSOR_(QUANT8_ASYMM|QUANT8_ASYMM_SIGNED|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL),
        // output can be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
        // - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
        // - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
        // - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
        // - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
        // - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
        // - AXIS_ALIGNED_BBOX_TRANSFORM bounding boxes (arg 1) can be of
        //     TENSOR_QUANT8_ASYMM or TENSOR_QUANT8_ASYMM_SIGNED.
        // - RANK's input can have any TENSOR_* type.
        switch (operation.type) {
            case OperationType::LSH_PROJECTION: {
                if (operand == operation.inputs[1]) {
                    return true;
                }
            } break;
            case OperationType::CAST:
            case OperationType::ARGMAX:
            case OperationType::ARGMIN: {
                if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
                    type == OperandType::TENSOR_INT32 || type == OperandType::TENSOR_QUANT8_ASYMM ||
                    type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
                    return true;
                }
            } break;
            case OperationType::QUANTIZE: {
                if (operand == operation.inputs[0] &&
                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
                    return true;
                }
                if (operand == operation.outputs[0] &&
                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
                     type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
                    return true;
                }
            } break;
            case OperationType::RANDOM_MULTINOMIAL: {
                if (operand == operation.inputs[0] &&
                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
                    return true;
                }
            } break;
            case OperationType::DEQUANTIZE: {
                if (operand == operation.inputs[0] &&
                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
                     type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED ||
                     type == OperandType::TENSOR_QUANT8_SYMM ||
                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
                    return true;
                }
                if (operand == operation.outputs[0] &&
                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
                    return true;
                }
            } break;
            case OperationType::TRANSPOSE_CONV_2D:
            case OperationType::GROUPED_CONV_2D:
            case OperationType::DEPTHWISE_CONV_2D:
            case OperationType::CONV_2D: {
                if (operand == operation.inputs[1] &&
                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
                    return true;
                }
            } break;
            case OperationType::AXIS_ALIGNED_BBOX_TRANSFORM: {
                if (operand == operation.inputs[1] &&
                    (type == OperandType::TENSOR_QUANT8_ASYMM ||
                     type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
                    return true;
                }
            } break;
            case OperationType::RANK: {
                if (operand == operation.inputs[0] &&
                    (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
                     type == OperandType::TENSOR_INT32 ||
                     type == OperandType::TENSOR_QUANT8_ASYMM ||
                     type == OperandType::TENSOR_QUANT16_SYMM ||
                     type == OperandType::TENSOR_BOOL8 ||
                     type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL ||
                     type == OperandType::TENSOR_QUANT16_ASYMM ||
                     type == OperandType::TENSOR_QUANT8_SYMM ||
                     type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)) {
                    return true;
                }
            } break;
            default:
                break;
        }
    }
    return false;
}

static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
            if (mutateOperationOperandTypeSkip(operand, invalidOperandType, model)) {
                continue;
            }
            const std::string message = "mutateOperationOperandTypeTest: operand " +
                                        std::to_string(operand) + " set to type " +
                                        toString(invalidOperandType);
            validate(device, message, model, [operand, invalidOperandType](Model* model) {
                mutateOperand(&model->main.operands[operand], invalidOperandType);
            });
        }
    }
}

///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////

static const uint32_t invalidOperationTypes[] = {
        static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
        static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
        static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
};

static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        for (uint32_t invalidOperationType : invalidOperationTypes) {
            const std::string message = "mutateOperationTypeTest: operation " +
                                        std::to_string(operation) + " set to value " +
                                        std::to_string(invalidOperationType);
            validate(device, message, model, [operation, invalidOperationType](Model* model) {
                model->main.operations[operation].type =
                        static_cast<OperationType>(invalidOperationType);
            });
        }
    }
}

///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////

static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        const uint32_t invalidOperand = model.main.operands.size();
        for (size_t input = 0; input < model.main.operations[operation].inputs.size(); ++input) {
            const std::string message = "mutateOperationInputOperandIndexTest: operation " +
                                        std::to_string(operation) + " input " +
                                        std::to_string(input);
            validate(device, message, model, [operation, input, invalidOperand](Model* model) {
                model->main.operations[operation].inputs[input] = invalidOperand;
            });
        }
    }
}

///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////

static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        const uint32_t invalidOperand = model.main.operands.size();
        for (size_t output = 0; output < model.main.operations[operation].outputs.size();
             ++output) {
            const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
                                        std::to_string(operation) + " output " +
                                        std::to_string(output);
            validate(device, message, model, [operation, output, invalidOperand](Model* model) {
                model->main.operations[operation].outputs[output] = invalidOperand;
            });
        }
    }
}

///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////

static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
    if (vec) {
        // remove elements matching "value"
        auto last = std::remove(vec->begin(), vec->end(), value);
        vec->resize(std::distance(vec->begin(), last));

        // decrement elements exceeding "value"
        std::transform(vec->begin(), vec->end(), vec->begin(),
                       [value](uint32_t v) { return v > value ? v-- : v; });
    }
}

static void removeOperand(Model* model, uint32_t index) {
    hidl_vec_removeAt(&model->main.operands, index);
    for (Operation& operation : model->main.operations) {
        removeValueAndDecrementGreaterValues(&operation.inputs, index);
        removeValueAndDecrementGreaterValues(&operation.outputs, index);
    }
    removeValueAndDecrementGreaterValues(&model->main.inputIndexes, index);
    removeValueAndDecrementGreaterValues(&model->main.outputIndexes, index);
}

static bool removeOperandSkip(size_t operand, const Model& model) {
    for (const Operation& operation : model.main.operations) {
        // Skip removeOperandTest for the following operations.
        // - SPLIT's outputs are not checked during prepareModel.
        if (operation.type == OperationType::SPLIT) {
            for (const size_t outOprand : operation.outputs) {
                if (operand == outOprand) {
                    return true;
                }
            }
        }
        // BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have
        // either one, two, three or four outputs depending on their
        // mergeOutputs parameter and if state outputs are provided.
        // UNIDIRECTIONAL_SEQUENCE_LSTM and UNIDIRECTIONAL_SEQUENCE_RNN can have
        // either one or three outputs depending on whether state outputs are
        // provided.
        if (operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_LSTM ||
            operation.type == OperationType::UNIDIRECTIONAL_SEQUENCE_RNN ||
            operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
            operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
            for (const size_t outOprand : operation.outputs) {
                if (operand == outOprand) {
                    return true;
                }
            }
        }
    }
    return false;
}

static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operand = 0; operand < model.main.operands.size(); ++operand) {
        if (removeOperandSkip(operand, model)) {
            continue;
        }
        const std::string message = "removeOperandTest: operand " + std::to_string(operand);
        validate(device, message, model,
                 [operand](Model* model) { removeOperand(model, operand); });
    }
}

///////////////////////// REMOVE OPERATION /////////////////////////

static void removeOperation(Model* model, uint32_t index) {
    for (uint32_t operand : model->main.operations[index].inputs) {
        model->main.operands[operand].numberOfConsumers--;
    }
    hidl_vec_removeAt(&model->main.operations, index);
}

static void removeOperationTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        const std::string message = "removeOperationTest: operation " + std::to_string(operation);
        validate(device, message, model,
                 [operation](Model* model) { removeOperation(model, operation); });
    }
}

///////////////////////// REMOVE OPERATION INPUT /////////////////////////

static bool removeOperationInputSkip(const Operation& op, size_t input) {
    // Skip removeOperationInputTest for the following operations.
    // - CONCATENATION has at least 2 inputs, with the last element being INT32.
    // - CONV_2D, DEPTHWISE_CONV_2D, MAX_POOL_2D, AVERAGE_POOL_2D, L2_POOL_2D, RESIZE_BILINEAR,
    //   SPACE_TO_DEPTH, SPACE_TO_DEPTH, SPACE_TO_BATCH_ND, BATCH_TO_SPACE_ND can have an optional
    //   layout parameter.
    // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional axis
    //   parameter.
    switch (op.type) {
        case OperationType::CONCATENATION: {
            if (op.inputs.size() > 2 && input != op.inputs.size() - 1) {
                return true;
            }
        } break;
        case OperationType::DEPTHWISE_CONV_2D: {
            if ((op.inputs.size() == 12 && input == 11) || (op.inputs.size() == 9 && input == 8)) {
                return true;
            }
        } break;
        case OperationType::CONV_2D:
        case OperationType::AVERAGE_POOL_2D:
        case OperationType::MAX_POOL_2D:
        case OperationType::L2_POOL_2D: {
            if ((op.inputs.size() == 11 && input == 10) || (op.inputs.size() == 8 && input == 7)) {
                return true;
            }
        } break;
        case OperationType::RESIZE_BILINEAR: {
            if (op.inputs.size() == 4 && input == 3) {
                return true;
            }
        } break;
        case OperationType::SPACE_TO_DEPTH:
        case OperationType::DEPTH_TO_SPACE:
        case OperationType::BATCH_TO_SPACE_ND: {
            if (op.inputs.size() == 3 && input == 2) {
                return true;
            }
        } break;
        case OperationType::SPACE_TO_BATCH_ND: {
            if (op.inputs.size() == 4 && input == 3) {
                return true;
            }
        } break;
        case OperationType::L2_NORMALIZATION: {
            if (op.inputs.size() == 2 && input == 1) {
                return true;
            }
        } break;
        case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
            if (op.inputs.size() == 6 && input == 5) {
                return true;
            }
        } break;
        case OperationType::SOFTMAX: {
            if (op.inputs.size() == 3 && input == 2) {
                return true;
            }
        } break;
        default:
            break;
    }
    return false;
}

static void removeOperationInputTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        for (size_t input = 0; input < model.main.operations[operation].inputs.size(); ++input) {
            const Operation& op = model.main.operations[operation];
            if (removeOperationInputSkip(op, input)) {
                continue;
            }
            const std::string message = "removeOperationInputTest: operation " +
                                        std::to_string(operation) + ", input " +
                                        std::to_string(input);
            validate(device, message, model, [operation, input](Model* model) {
                uint32_t operand = model->main.operations[operation].inputs[input];
                model->main.operands[operand].numberOfConsumers--;
                hidl_vec_removeAt(&model->main.operations[operation].inputs, input);
            });
        }
    }
}

///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////

static void removeOperationOutputTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        for (size_t output = 0; output < model.main.operations[operation].outputs.size();
             ++output) {
            const std::string message = "removeOperationOutputTest: operation " +
                                        std::to_string(operation) + ", output " +
                                        std::to_string(output);
            validate(device, message, model, [operation, output](Model* model) {
                hidl_vec_removeAt(&model->main.operations[operation].outputs, output);
            });
        }
    }
}

///////////////////////// MODEL VALIDATION /////////////////////////

// TODO: remove model input
// TODO: remove model output
// TODO: add unused operation

///////////////////////// ADD OPERATION INPUT /////////////////////////

static bool addOperationInputSkip(const Operation& op) {
    // Skip addOperationInputTest for the following operations.
    // - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional INT32 axis
    //   parameter.
    if ((op.type == OperationType::L2_NORMALIZATION && op.inputs.size() == 1) ||
        (op.type == OperationType::LOCAL_RESPONSE_NORMALIZATION && op.inputs.size() == 5) ||
        (op.type == OperationType::SOFTMAX && op.inputs.size() == 2)) {
        return true;
    }
    return false;
}

static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        if (addOperationInputSkip(model.main.operations[operation])) {
            continue;
        }
        const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
        validate(device, message, model, [operation](Model* model) {
            uint32_t index = addOperand(model, OperandLifeTime::SUBGRAPH_INPUT);
            hidl_vec_push_back(&model->main.operations[operation].inputs, index);
            hidl_vec_push_back(&model->main.inputIndexes, index);
        });
    }
}

///////////////////////// ADD OPERATION OUTPUT /////////////////////////

static void addOperationOutputTest(const sp<IDevice>& device, const Model& model) {
    for (size_t operation = 0; operation < model.main.operations.size(); ++operation) {
        const std::string message =
                "addOperationOutputTest: operation " + std::to_string(operation);
        validate(device, message, model, [operation](Model* model) {
            uint32_t index = addOperand(model, OperandLifeTime::SUBGRAPH_OUTPUT);
            hidl_vec_push_back(&model->main.operations[operation].outputs, index);
            hidl_vec_push_back(&model->main.outputIndexes, index);
        });
    }
}

///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////

static const int32_t invalidExecutionPreferences[] = {
        static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1,        // lower bound
        static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1,  // upper bound
};

static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
    for (int32_t preference : invalidExecutionPreferences) {
        const std::string message =
                "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
        validate(
                device, message, model, [](Model*) {},
                static_cast<ExecutionPreference>(preference));
    }
}

////////////////////////// ENTRY POINT //////////////////////////////

void validateModel(const sp<IDevice>& device, const Model& model) {
    mutateOperandTypeTest(device, model);
    mutateOperandRankTest(device, model);
    mutateOperandScaleTest(device, model);
    mutateOperandZeroPointTest(device, model);
    mutateOperationOperandTypeTest(device, model);
    mutateOperationTypeTest(device, model);
    mutateOperationInputOperandIndexTest(device, model);
    mutateOperationOutputOperandIndexTest(device, model);
    removeOperandTest(device, model);
    removeOperationTest(device, model);
    removeOperationInputTest(device, model);
    removeOperationOutputTest(device, model);
    addOperationInputTest(device, model);
    addOperationOutputTest(device, model);
    mutateExecutionPreferenceTest(device, model);
}

}  // namespace android::hardware::neuralnetworks::V1_3::vts::functional