/*
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* Copyright (C) 2017 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "ValidateHal"
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#include "ValidateHal.h"
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#include "NeuralNetworks.h"
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#include "OperationsUtils.h"
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#include "Tracing.h"
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#include "Utils.h"
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#include <android-base/logging.h>
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namespace android {
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namespace nn {
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template <class T_Model>
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struct ModelToHalVersion;
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template <>
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struct ModelToHalVersion<V1_0::Model> {
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static constexpr HalVersion version = HalVersion::V1_0;
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};
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template <>
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struct ModelToHalVersion<V1_1::Model> {
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static constexpr HalVersion version = HalVersion::V1_1;
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};
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template <>
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struct ModelToHalVersion<V1_2::Model> {
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static constexpr HalVersion version = HalVersion::V1_2;
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};
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class MemoryAccessVerifier {
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public:
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MemoryAccessVerifier(const hidl_vec<hidl_memory>& pools)
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: mPoolCount(pools.size()), mPoolSizes(mPoolCount) {
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for (size_t i = 0; i < mPoolCount; i++) {
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mPoolSizes[i] = pools[i].size();
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}
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}
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bool validate(const DataLocation& location) {
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if (location.poolIndex >= mPoolCount) {
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LOG(ERROR) << "Invalid poolIndex " << location.poolIndex << "/" << mPoolCount;
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return false;
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}
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const size_t size = mPoolSizes[location.poolIndex];
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// Do the addition using size_t to avoid potential wrap-around problems.
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if (static_cast<size_t>(location.offset) + location.length > size) {
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LOG(ERROR) << "Reference to pool " << location.poolIndex << " with offset "
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<< location.offset << " and length " << location.length
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<< " exceeds pool size of " << size;
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return false;
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}
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return true;
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}
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private:
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size_t mPoolCount;
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std::vector<size_t> mPoolSizes;
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};
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static bool validateOperandExtraParams(const V1_2::Operand& operand, uint32_t index) {
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switch (operand.type) {
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case OperandType::FLOAT32:
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case OperandType::INT32:
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case OperandType::UINT32:
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case OperandType::BOOL:
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case OperandType::TENSOR_FLOAT32:
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case OperandType::TENSOR_FLOAT16:
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case OperandType::TENSOR_INT32:
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case OperandType::TENSOR_QUANT8_ASYMM:
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case OperandType::TENSOR_QUANT8_SYMM:
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case OperandType::TENSOR_QUANT16_ASYMM:
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case OperandType::TENSOR_QUANT16_SYMM:
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case OperandType::TENSOR_BOOL8: {
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NN_RET_CHECK(operand.extraParams.getDiscriminator() ==
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V1_2::Operand::ExtraParams::hidl_discriminator::none)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type)
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<< " has incorrect extraParams: " << toString(operand.extraParams);
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} break;
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case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
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NN_RET_CHECK(operand.extraParams.getDiscriminator() ==
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V1_2::Operand::ExtraParams::hidl_discriminator::channelQuant)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " without a Channel Quantization params";
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auto& channelQuant = operand.extraParams.channelQuant();
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size_t count = operand.dimensions.size();
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NN_RET_CHECK_LT(channelQuant.channelDim, count)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type)
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<< " with an invalid channelQuant.channelDim " << channelQuant.channelDim
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<< ", must be valid dimension index in range [0, " << count << ")";
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uint32_t expected = operand.dimensions[channelQuant.channelDim];
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NN_RET_CHECK_EQ(channelQuant.scales.size(), expected)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a wrong-sized scales, "
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<< "expected " << expected << " was " << channelQuant.scales.size();
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NN_RET_CHECK_NE(expected, 0)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " channel dimension "
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<< channelQuant.channelDim << " is underspecified (can't be 0)";
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for (uint32_t i = 0; i < expected; ++i) {
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NN_RET_CHECK_GT(channelQuant.scales[i], .0f)
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<< "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a negative value in scales["
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<< i << "]=" << channelQuant.scales[i];
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}
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} break;
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default: {
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if (isExtensionOperandType(operand.type)) {
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NN_RET_CHECK(operand.extraParams.getDiscriminator() ==
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V1_2::Operand::ExtraParams::hidl_discriminator::extension ||
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operand.extraParams.getDiscriminator() ==
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V1_2::Operand::ExtraParams::hidl_discriminator::none)
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<< "Operand " << index << ": Extension operand of type "
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<< getOperandTypeName(operand.type)
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<< " has incorrect extraParams: " << toString(operand.extraParams);
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}
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// No validation for OEM types.
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} break;
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}
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return true;
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}
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template <typename VersionedOperand>
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static bool validateOperands(const hidl_vec<VersionedOperand>& operands,
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const hidl_vec<uint8_t>& operandValues,
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const hidl_vec<hidl_memory>& pools, bool allowUnspecifiedRank) {
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uint32_t index = 0;
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MemoryAccessVerifier poolVerifier(pools);
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for (auto& versionedOperand : operands) {
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if (!validOperandType(versionedOperand.type)) {
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LOG(ERROR) << "Operand is not supported by this version: "
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<< toString(versionedOperand.type);
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return false;
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}
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// Once we are sure the operand is supported by its version, it is safe
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// to convert it to the latest version for the rest of the validations.
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V1_2::Operand operand = convertToV1_2(versionedOperand);
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// Validate type and dimensions.
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switch (operand.type) {
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case OperandType::FLOAT16:
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case OperandType::FLOAT32:
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case OperandType::INT32:
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case OperandType::UINT32:
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case OperandType::BOOL:
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case OperandType::OEM: {
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size_t count = operand.dimensions.size();
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if (count != 0) {
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LOG(ERROR) << "Operand " << index << ": Scalar data has dimensions of rank "
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<< count;
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return false;
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}
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break;
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}
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case OperandType::TENSOR_FLOAT16:
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case OperandType::TENSOR_FLOAT32:
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case OperandType::TENSOR_INT32:
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case OperandType::TENSOR_QUANT8_ASYMM:
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case OperandType::TENSOR_QUANT8_SYMM:
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case OperandType::TENSOR_QUANT16_ASYMM:
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case OperandType::TENSOR_QUANT16_SYMM:
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case OperandType::TENSOR_BOOL8:
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case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
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case OperandType::TENSOR_OEM_BYTE: {
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if ((!allowUnspecifiedRank || operand.lifetime == OperandLifeTime::CONSTANT_COPY ||
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operand.lifetime == OperandLifeTime::CONSTANT_REFERENCE) &&
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operand.dimensions.size() == 0) {
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LOG(ERROR) << "Operand " << index << ": Tensor has dimensions of rank 0";
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return false;
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}
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break;
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}
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default: {
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if (!isExtensionOperandType(operand.type)) {
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LOG(ERROR) << "Operand " << index << ": Invalid operand type "
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<< toString(operand.type);
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return false;
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}
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} break;
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}
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// TODO Validate the numberOfConsumers.
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// TODO Since we have to validate it, there was no point in including it. For the next
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// release, consider removing unless we have an additional process in system space
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// that creates this value. In that case, it would not have to be validated.
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// Validate the scale.
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switch (operand.type) {
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case OperandType::FLOAT16:
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case OperandType::FLOAT32:
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case OperandType::INT32:
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case OperandType::UINT32:
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case OperandType::BOOL:
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case OperandType::TENSOR_FLOAT16:
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case OperandType::TENSOR_FLOAT32:
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case OperandType::TENSOR_BOOL8:
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case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
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if (operand.scale != 0.f) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-zero scale ("
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<< operand.scale << ")";
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return false;
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}
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break;
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case OperandType::TENSOR_INT32:
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// TENSOR_INT32 may be used with or without scale, depending on the operation.
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if (operand.scale < 0.f) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a negative scale";
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return false;
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}
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break;
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case OperandType::TENSOR_QUANT8_ASYMM:
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case OperandType::TENSOR_QUANT8_SYMM:
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case OperandType::TENSOR_QUANT16_ASYMM:
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case OperandType::TENSOR_QUANT16_SYMM:
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if (operand.scale <= 0.f) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-positive scale";
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return false;
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}
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break;
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default:
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if (isExtensionOperandType(operand.type) && operand.scale != 0.f) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-zero scale ("
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<< operand.scale << ")";
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return false;
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}
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// No validation for OEM types.
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// TODO(b/119869082) We should have a separate type for TENSOR_INT32 with a scale.
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break;
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}
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// Validate the zeroPoint.
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switch (operand.type) {
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case OperandType::FLOAT16:
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case OperandType::FLOAT32:
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case OperandType::INT32:
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case OperandType::UINT32:
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case OperandType::BOOL:
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case OperandType::TENSOR_FLOAT16:
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case OperandType::TENSOR_FLOAT32:
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case OperandType::TENSOR_INT32:
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case OperandType::TENSOR_BOOL8:
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case OperandType::TENSOR_QUANT8_SYMM:
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case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
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if (operand.zeroPoint != 0) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-zero zeroPoint "
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<< operand.zeroPoint;
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return false;
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}
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break;
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case OperandType::TENSOR_QUANT8_ASYMM:
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if (operand.zeroPoint < 0 || operand.zeroPoint > 255) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with an invalid zeroPoint "
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<< operand.zeroPoint << ", must be in range [0, 255]";
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return false;
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}
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break;
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case OperandType::TENSOR_QUANT16_ASYMM:
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if (operand.zeroPoint < 0 || operand.zeroPoint > 65535) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with an invalid zeroPoint "
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<< operand.zeroPoint << ", must be in range [0, 65535]";
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return false;
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}
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break;
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case OperandType::TENSOR_QUANT16_SYMM:
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if (operand.zeroPoint != 0) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-zero zeroPoint "
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<< operand.zeroPoint;
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return false;
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}
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break;
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default:
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if (isExtensionOperandType(operand.type) && operand.zeroPoint != 0) {
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LOG(ERROR) << "Operand " << index << ": Operand of type "
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<< getOperandTypeName(operand.type) << " with a non-zero zeroPoint "
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<< operand.zeroPoint;
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return false;
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}
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// No validation for OEM types.
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break;
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}
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NN_RET_CHECK(validateOperandExtraParams(operand, index));
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// Validate the lifetime and the location.
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const DataLocation& location = operand.location;
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switch (operand.lifetime) {
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case OperandLifeTime::CONSTANT_COPY:
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if (location.poolIndex != 0) {
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LOG(ERROR) << "Operand " << index
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<< ": CONSTANT_COPY with a non-zero poolIndex "
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<< location.poolIndex;
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return false;
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}
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// Do the addition using size_t to avoid potential wrap-around problems.
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if (static_cast<size_t>(location.offset) + location.length > operandValues.size()) {
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LOG(ERROR) << "Operand " << index
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<< ": OperandValue location out of range. Starts at "
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<< location.offset << ", length " << location.length << ", max "
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<< operandValues.size();
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return false;
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}
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break;
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case OperandLifeTime::CONSTANT_REFERENCE:
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if (!poolVerifier.validate(location)) {
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return false;
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}
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break;
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case OperandLifeTime::TEMPORARY_VARIABLE:
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case OperandLifeTime::MODEL_INPUT:
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case OperandLifeTime::MODEL_OUTPUT:
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case OperandLifeTime::NO_VALUE:
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if (location.poolIndex != 0 || location.offset != 0 || location.length != 0) {
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LOG(ERROR) << "Operand " << index << ": Unexpected poolIndex "
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<< location.poolIndex << ", offset " << location.offset
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<< ", or length " << location.length << " for operand of lifetime "
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<< toString(operand.lifetime);
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return false;
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}
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break;
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default:
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LOG(ERROR) << "Operand " << index << ": Invalid lifetime "
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<< toString(operand.lifetime);
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return false;
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}
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// For constants, validate that the length is as expected. The other lifetimes
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// expect the length to be 0. Don't validate for OEM types.
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if (operand.lifetime == OperandLifeTime::CONSTANT_REFERENCE ||
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operand.lifetime == OperandLifeTime::CONSTANT_COPY) {
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if (!isExtensionOperandType(operand.type) && operand.type != OperandType::OEM &&
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operand.type != OperandType::TENSOR_OEM_BYTE) {
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uint32_t expectedLength = nonExtensionOperandSizeOfData(operand);
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if (location.length != expectedLength) {
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LOG(ERROR) << "Operand " << index << ": For operand " << toString(operand)
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<< " expected a size of " << expectedLength << " but got "
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<< location.length;
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return false;
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}
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}
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}
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index++;
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}
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return true;
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}
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static HalVersion getHalVersion(const V1_0::Operation&) {
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return HalVersion::V1_0;
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}
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static HalVersion getHalVersion(const V1_1::Operation&) {
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return HalVersion::V1_1;
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}
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static HalVersion getHalVersion(const V1_2::Operation&) {
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return HalVersion::V1_2;
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}
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template <typename VersionedOperation>
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static bool validateOperations(const hidl_vec<VersionedOperation>& operations,
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const hidl_vec<Operand>& operands) {
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const size_t operandCount = operands.size();
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// This vector keeps track of whether there's an operation that writes to
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// each operand. It is used to validate that temporary variables and
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// model outputs will be written to.
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std::vector<bool> writtenTo(operandCount, false);
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for (auto& op : operations) {
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// TODO Validate the shapes and any known values. This is currently
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// done in CpuExecutor but should be done here for all drivers.
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int error = validateOperation(
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static_cast<int32_t>(op.type), op.inputs.size(),
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op.inputs.size() > 0 ? op.inputs.data() : nullptr, op.outputs.size(),
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op.outputs.size() > 0 ? op.outputs.data() : nullptr, operands, getHalVersion(op));
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if (error != ANEURALNETWORKS_NO_ERROR) {
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LOG(ERROR) << "Invalid operation " << toString(op.type);
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return false;
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}
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for (uint32_t i : op.outputs) {
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const Operand& operand = operands[i];
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if (operand.lifetime != OperandLifeTime::TEMPORARY_VARIABLE &&
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operand.lifetime != OperandLifeTime::MODEL_OUTPUT) {
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LOG(ERROR) << "Writing to an operand with incompatible lifetime "
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<< toString(operand.lifetime);
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return false;
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}
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// Check that we only write once to an operand.
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if (writtenTo[i]) {
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LOG(ERROR) << "Operand " << i << " written a second time";
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return false;
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}
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writtenTo[i] = true;
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}
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}
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for (size_t i = 0; i < operandCount; i++) {
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if (!writtenTo[i]) {
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const Operand& operand = operands[i];
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if (operand.lifetime == OperandLifeTime::TEMPORARY_VARIABLE ||
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operand.lifetime == OperandLifeTime::MODEL_OUTPUT) {
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LOG(ERROR) << "Operand " << i << " with lifetime " << toString(operand.lifetime)
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<< " is not being written to.";
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return false;
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}
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}
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}
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// TODO More whole graph verifications are possible, for example that an
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// operand is not use as input & output for the same op, and more
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// generally that it is acyclic.
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return true;
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}
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bool validatePool(const hidl_memory& pool, HalVersion ver) {
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const auto& name = pool.name();
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if (name != "ashmem" && name != "mmap_fd" &&
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((ver < HalVersion::V1_2) ||
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(name != "hardware_buffer_blob" && name != "hardware_buffer"))) {
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LOG(ERROR) << "Unsupported memory type " << name;
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return false;
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}
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if (pool.handle() == nullptr) {
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LOG(ERROR) << "Memory of type " << name << " is null";
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return false;
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}
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return true;
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}
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static bool validatePools(const hidl_vec<hidl_memory>& pools, HalVersion ver) {
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return std::all_of(pools.begin(), pools.end(),
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[ver](const hidl_memory& pool) { return validatePool(pool, ver); });
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}
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static bool validateModelInputOutputs(const hidl_vec<uint32_t> indexes,
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const hidl_vec<Operand>& operands, OperandLifeTime lifetime) {
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const size_t operandCount = operands.size();
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for (uint32_t i : indexes) {
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if (i >= operandCount) {
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LOG(ERROR) << "Model input or output index out of range: " << i << "/" << operandCount;
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return false;
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}
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const Operand& operand = operands[i];
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if (operand.lifetime != lifetime) {
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LOG(ERROR) << "Model input or output has lifetime of " << toString(operand.lifetime)
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<< " instead of the expected " << toString(lifetime);
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return false;
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}
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}
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std::vector<uint32_t> sortedIndexes = indexes;
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std::sort(sortedIndexes.begin(), sortedIndexes.end());
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auto adjacentI = std::adjacent_find(sortedIndexes.begin(), sortedIndexes.end());
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if (adjacentI != sortedIndexes.end()) {
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LOG(ERROR) << "Model input or output occurs multiple times: " << *adjacentI;
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return false;
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}
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return true;
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}
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template <class T_Model>
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bool validateModel(const T_Model& model) {
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NNTRACE_FULL(NNTRACE_LAYER_UTILITY, NNTRACE_PHASE_UNSPECIFIED, "validateModel");
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HalVersion version = ModelToHalVersion<T_Model>::version;
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if (model.operations.size() == 0 || model.operands.size() == 0) {
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LOG(ERROR) << "Invalid empty model.";
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return false;
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}
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// We only need versioned operands for their validation. For all the other
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// validations we can use operands upcasted to the latest version.
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const hidl_vec<Operand> latestVersionOperands = convertToV1_2(model.operands);
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return (validateOperands(model.operands, model.operandValues, model.pools,
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/*allowUnspecifiedRank=*/version >= HalVersion::V1_2) &&
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validateOperations(model.operations, latestVersionOperands) &&
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validateModelInputOutputs(model.inputIndexes, latestVersionOperands,
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OperandLifeTime::MODEL_INPUT) &&
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validateModelInputOutputs(model.outputIndexes, latestVersionOperands,
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OperandLifeTime::MODEL_OUTPUT) &&
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validatePools(model.pools, version));
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}
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template bool validateModel<V1_0::Model>(const V1_0::Model& model);
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template bool validateModel<V1_1::Model>(const V1_1::Model& model);
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template bool validateModel<V1_2::Model>(const V1_2::Model& model);
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// Validates the arguments of a request. type is either "input" or "output" and is used
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// for printing error messages. The operandIndexes is the appropriate array of input
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// or output operand indexes that was passed to the ANeuralNetworksModel_identifyInputsAndOutputs.
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static bool validateRequestArguments(const hidl_vec<RequestArgument>& requestArguments,
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const hidl_vec<uint32_t>& operandIndexes,
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const hidl_vec<Operand>& operands,
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const hidl_vec<hidl_memory>& pools, bool allowUnspecified,
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const char* type) {
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MemoryAccessVerifier poolVerifier(pools);
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// The request should specify as many arguments as were described in the model.
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const size_t requestArgumentCount = requestArguments.size();
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if (requestArgumentCount != operandIndexes.size()) {
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LOG(ERROR) << "Request specifies " << requestArgumentCount << " " << type
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<< "s but the model has " << operandIndexes.size();
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return false;
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}
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for (size_t requestArgumentIndex = 0; requestArgumentIndex < requestArgumentCount;
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requestArgumentIndex++) {
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const RequestArgument& requestArgument = requestArguments[requestArgumentIndex];
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const DataLocation& location = requestArgument.location;
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// Get the operand index for this argument. We extract it from the list
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// that was provided in the call to ANeuralNetworksModel_identifyInputsAndOutputs.
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// We assume in this function that the model has been validated already.
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const uint32_t operandIndex = operandIndexes[requestArgumentIndex];
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const Operand& operand = operands[operandIndex];
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if (requestArgument.hasNoValue) {
|
if (location.poolIndex != 0 || location.offset != 0 || location.length != 0 ||
|
requestArgument.dimensions.size() != 0) {
|
LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
|
<< " has no value yet has details.";
|
return false;
|
}
|
} else {
|
// Validate the location.
|
if (!poolVerifier.validate(location)) {
|
return false;
|
}
|
// If the argument specified a dimension, validate it.
|
uint32_t rank = requestArgument.dimensions.size();
|
if (rank == 0) {
|
if (!allowUnspecified) {
|
// Validate that all the dimensions are specified in the model.
|
for (size_t i = 0; i < operand.dimensions.size(); i++) {
|
if (operand.dimensions[i] == 0) {
|
LOG(ERROR) << "Model has dimension " << i
|
<< " set to 0 but the request does specify the dimension.";
|
return false;
|
}
|
}
|
}
|
} else {
|
if (rank != operand.dimensions.size()) {
|
LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
|
<< " has number of dimensions (" << rank
|
<< ") different than the model's (" << operand.dimensions.size()
|
<< ")";
|
return false;
|
}
|
for (size_t i = 0; i < rank; i++) {
|
if (requestArgument.dimensions[i] != operand.dimensions[i] &&
|
operand.dimensions[i] != 0) {
|
LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
|
<< " has dimension " << i << " of "
|
<< requestArgument.dimensions[i]
|
<< " different than the model's " << operand.dimensions[i];
|
return false;
|
}
|
if (requestArgument.dimensions[i] == 0 && !allowUnspecified) {
|
LOG(ERROR) << "Request " << type << " " << requestArgumentIndex
|
<< " has dimension " << i << " of zero";
|
return false;
|
}
|
}
|
}
|
}
|
}
|
return true;
|
}
|
|
template <class T_Model>
|
bool validateRequest(const Request& request, const T_Model& model) {
|
HalVersion version = ModelToHalVersion<T_Model>::version;
|
return (validateRequestArguments(request.inputs, model.inputIndexes,
|
convertToV1_2(model.operands), request.pools,
|
/*allowUnspecified=*/false, "input") &&
|
validateRequestArguments(request.outputs, model.outputIndexes,
|
convertToV1_2(model.operands), request.pools,
|
/*allowUnspecified=*/version >= HalVersion::V1_2, "output") &&
|
validatePools(request.pools, version));
|
}
|
|
template bool validateRequest<V1_0::Model>(const Request& request, const V1_0::Model& model);
|
template bool validateRequest<V1_1::Model>(const Request& request, const V1_1::Model& model);
|
template bool validateRequest<V1_2::Model>(const Request& request, const V1_2::Model& model);
|
|
bool validateExecutionPreference(ExecutionPreference preference) {
|
return preference == ExecutionPreference::LOW_POWER ||
|
preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
|
preference == ExecutionPreference::SUSTAINED_SPEED;
|
}
|
|
bool validOperandType(V1_0::OperandType operandType) {
|
switch (operandType) {
|
case V1_0::OperandType::FLOAT32:
|
case V1_0::OperandType::INT32:
|
case V1_0::OperandType::UINT32:
|
case V1_0::OperandType::TENSOR_FLOAT32:
|
case V1_0::OperandType::TENSOR_INT32:
|
case V1_0::OperandType::TENSOR_QUANT8_ASYMM:
|
case V1_0::OperandType::OEM:
|
case V1_0::OperandType::TENSOR_OEM_BYTE:
|
return true;
|
default:
|
return false;
|
}
|
}
|
|
bool validOperandType(V1_2::OperandType operandType) {
|
switch (operandType) {
|
case V1_2::OperandType::FLOAT16:
|
case V1_2::OperandType::FLOAT32:
|
case V1_2::OperandType::INT32:
|
case V1_2::OperandType::UINT32:
|
case V1_2::OperandType::BOOL:
|
case V1_2::OperandType::TENSOR_FLOAT16:
|
case V1_2::OperandType::TENSOR_FLOAT32:
|
case V1_2::OperandType::TENSOR_INT32:
|
case V1_2::OperandType::TENSOR_QUANT8_ASYMM:
|
case V1_2::OperandType::TENSOR_QUANT8_SYMM:
|
case V1_2::OperandType::TENSOR_QUANT16_ASYMM:
|
case V1_2::OperandType::TENSOR_QUANT16_SYMM:
|
case V1_2::OperandType::TENSOR_BOOL8:
|
case V1_2::OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
|
case V1_2::OperandType::OEM:
|
case V1_2::OperandType::TENSOR_OEM_BYTE:
|
return true;
|
default:
|
return isExtensionOperandType(operandType);
|
}
|
}
|
|
} // namespace nn
|
} // namespace android
|
