1027 lines
43 KiB
C++
1027 lines
43 KiB
C++
/*
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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 "ModelBuilder"
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#include "ModelBuilder.h"
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#include <GraphDump.h>
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#include <LegacyUtils.h>
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#include <ModelUtils.h>
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#include <android-base/logging.h>
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#include <nnapi/Validation.h>
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#include <algorithm>
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#include <map>
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#include <memory>
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#include <set>
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#include <utility>
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#include <vector>
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#include "CompilationBuilder.h"
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#include "Manager.h"
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#include "ModelArchHasher.h"
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#include "TypeManager.h"
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namespace android {
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namespace nn {
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// The maximum number of operands and operations that a model may have.
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const uint32_t MAX_NUMBER_OF_OPERANDS = 0xFFFFFFFE;
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const uint32_t MAX_NUMBER_OF_OPERATIONS = 0xFFFFFFFE;
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#define NN_VALIDATE_NULL_OR_SIZED(tag, data, length) \
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if ((data == nullptr) != (length == 0)) { \
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LOG(ERROR) << "ANeuralNetworksModel_" << tag << " " << #data << " is " \
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<< (data == nullptr ? "null" : "not null") << " but " << #length << " is " \
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<< length; \
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return ANEURALNETWORKS_BAD_DATA; \
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}
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template <typename Type>
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static std::vector<Type> makeVector(const Type* data, uint32_t length) {
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return length > 0 ? std::vector<Type>(data, data + length) : std::vector<Type>();
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}
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bool ModelBuilder::badState(const char* name) {
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if (mCompletedModel) {
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LOG(ERROR) << "ANeuralNetworksModel_" << name << " can't modify after model finished";
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return true;
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}
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if (mInvalidModel) {
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LOG(ERROR) << "ANeuralNetworksModel_" << name << " can't modify an invalid model";
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return true;
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}
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return false;
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}
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int ModelBuilder::getExtensionType(const char* extensionName, uint16_t typeWithinExtension,
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int32_t* type) {
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return TypeManager::get()->getExtensionType(extensionName, typeWithinExtension, type)
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? ANEURALNETWORKS_NO_ERROR
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: ANEURALNETWORKS_BAD_DATA;
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}
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int ModelBuilder::addOperand(const ANeuralNetworksOperandType& type) {
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if (badState("addOperand")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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OperandType operandType = static_cast<OperandType>(type.type);
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if (isExtension(operandType) && !TypeManager::get()->areExtensionsAllowed()) {
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LOG(ERROR) << "Extensions are not supported for this process.";
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return ANEURALNETWORKS_BAD_DATA;
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}
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bool isOemOperand =
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operandType == OperandType::OEM || operandType == OperandType::TENSOR_OEM_BYTE;
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if (isOemOperand && !mHasOEMOperand) {
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LOG(WARNING) << "OEM data type is deprecated. Use Extensions instead.";
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}
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const Extension::OperandTypeInformation* info = nullptr;
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if (isExtension(operandType) &&
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!TypeManager::get()->getExtensionOperandTypeInfo(operandType, &info)) {
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LOG(ERROR) << "Extension operand type " << operandType << " is not registered";
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return ANEURALNETWORKS_BAD_DATA;
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}
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NN_VALIDATE_NULL_OR_SIZED("addOperand", type.dimensions, type.dimensionCount);
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Operand operand = {
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.type = operandType,
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.dimensions = makeVector(type.dimensions, type.dimensionCount),
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.scale = type.scale,
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.zeroPoint = type.zeroPoint,
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.lifetime = Operand::LifeTime::TEMPORARY_VARIABLE,
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.location = {.poolIndex = 0, .offset = 0, .length = 0},
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.extraParams = {},
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};
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if (auto result = validateOperandType(operand, info, "ANeuralNetworksModel_addOperand", true);
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!result.ok()) {
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LOG(ERROR) << result.error();
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return ANEURALNETWORKS_BAD_DATA;
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}
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size_t idx = mOperands.size();
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if (idx >= MAX_NUMBER_OF_OPERANDS) {
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LOG(ERROR) << "ANeuralNetworksModel_addOperand exceed max operands";
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return ANEURALNETWORKS_BAD_DATA;
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}
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mOperands.push_back(std::move(operand));
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mHasOEMOperand |= isOemOperand;
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mHasControlFlow |= (operandType == OperandType::SUBGRAPH);
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::setOperandValue(uint32_t index, const void* buffer, size_t length) {
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VLOG(MODEL) << __func__ << " for operand " << index << " size " << length;
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if (badState("setOperandValue")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= operandCount()) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValue setting operand " << index << " of "
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<< operandCount();
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return ANEURALNETWORKS_BAD_DATA;
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}
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Operand& operand = mOperands[index];
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NN_VALIDATE_NULL_OR_SIZED("setOperandValue", buffer, length);
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if (buffer == nullptr) {
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operand.lifetime = Operand::LifeTime::NO_VALUE;
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// The location is unused and is set to zeros.
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operand.location = {.poolIndex = 0, .offset = 0, .length = 0};
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} else {
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if (TypeManager::get()->isTensorType(operand.type) &&
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tensorHasUnspecifiedDimensions(operand)) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValue setting operand " << index
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<< " which has operand type that is not fully specified";
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return ANEURALNETWORKS_BAD_DATA;
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}
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if (length > 0xFFFFFFFF) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValue value length of " << length
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<< " exceeds max size";
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return ANEURALNETWORKS_BAD_DATA;
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}
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uint32_t valueLength = static_cast<uint32_t>(length);
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if (operand.type != OperandType::OEM) {
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uint32_t neededLength = TypeManager::get()->getSizeOfData(operand);
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if (neededLength != valueLength) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValue setting " << valueLength
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<< " bytes when needing " << neededLength;
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return ANEURALNETWORKS_BAD_DATA;
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}
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}
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if (valueLength <= ANEURALNETWORKS_MAX_SIZE_OF_IMMEDIATELY_COPIED_VALUES) {
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uint32_t existingSize = static_cast<uint32_t>(mSmallOperandValues.size());
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uint32_t extraBytes = alignBytesNeeded(existingSize, valueLength);
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mSmallOperandValues.resize(existingSize + extraBytes + valueLength);
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operand.lifetime = Operand::LifeTime::CONSTANT_COPY;
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operand.location = {
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.poolIndex = 0, .offset = existingSize + extraBytes, .length = valueLength};
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memcpy(&mSmallOperandValues[operand.location.offset], buffer, valueLength);
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VLOG(MODEL) << "Copied small value to offset " << operand.location.offset;
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} else {
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VLOG(MODEL) << "Saving large value";
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operand.lifetime = Operand::LifeTime::CONSTANT_REFERENCE;
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// The values for poolIndex and offset will be set when the model is finished.
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typedef decltype(operand.location.poolIndex) PoolIndexType;
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typedef decltype(operand.location.offset) OffsetType;
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operand.location = {.poolIndex = ~PoolIndexType(0),
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.offset = ~OffsetType(0),
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.length = valueLength};
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// We keep track of the buffers. We'll allocate the shared memory only
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// once we know the total size, to avoid needless copies.
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mLargeOperandValues.push_back(LargeValue{.operandIndex = index, .buffer = buffer});
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}
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}
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::setOperandValueFromModel(uint32_t index, const ModelBuilder* value) {
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VLOG(MODEL) << __func__ << " for operand " << index << " model " << value;
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if (badState("setOperandValueFromModel")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (!value->mCompletedModel) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromModel value model must be finished";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (value->mInvalidModel) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromModel value model is invalid";
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= operandCount()) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromModel setting operand " << index
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<< " of " << operandCount();
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return ANEURALNETWORKS_BAD_DATA;
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}
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Operand& operand = mOperands[index];
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operand.lifetime = Operand::LifeTime::SUBGRAPH;
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operand.location = {
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.poolIndex = 0,
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.offset = static_cast<uint32_t>(mReferencedModels.size()),
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.length = 0,
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};
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mReferencedModels.push_back(value);
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mReferencedSubgraphsForValidation.push_back(value->makeModel().main);
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::setOperandSymmPerChannelQuantParams(
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uint32_t index, const ANeuralNetworksSymmPerChannelQuantParams& channelQuant) {
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if (badState("setOperandSymmPerChannelQuantParams")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= operandCount()) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandSymmPerChannelQuantParams "
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<< "setting per-channel quantization parameters for operand " << index << " of "
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<< operandCount();
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return ANEURALNETWORKS_BAD_DATA;
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}
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Operand& operand = mOperands[index];
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NN_VALIDATE_NULL_OR_SIZED("setOperandSymmPerChannelQuantParams", channelQuant.scales,
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channelQuant.scaleCount);
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Operand::SymmPerChannelQuantParams extraParams = {
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.scales = makeVector(channelQuant.scales, channelQuant.scaleCount),
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.channelDim = channelQuant.channelDim,
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};
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if (auto result = validateOperandSymmPerChannelQuantParams(
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operand, extraParams, "ANeuralNetworksModel_setOperandSymmPerChannelQuantParams");
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!result.ok()) {
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LOG(ERROR) << result.error();
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return ANEURALNETWORKS_BAD_DATA;
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}
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switch (operand.type) {
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case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
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operand.extraParams = std::move(extraParams);
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break;
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default:
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LOG(ERROR) << "ANeuralNetworksModel_setOperandSymmPerChannelQuantParams "
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<< "invalid operand type " << static_cast<int32_t>(operand.type);
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return ANEURALNETWORKS_BAD_DATA;
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}
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::setOperandExtensionData(uint32_t index, const void* data, size_t length) {
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if (badState("setOperandExtensionData")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= operandCount()) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandExtensionData "
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<< "setting extension data for operand " << index << " of " << operandCount();
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return ANEURALNETWORKS_BAD_DATA;
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}
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Operand& operand = mOperands[index];
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if (!isExtension(operand.type)) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandExtensionData "
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<< "setting extension data for a base operand type "
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<< static_cast<int32_t>(operand.type);
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return ANEURALNETWORKS_BAD_DATA;
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}
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NN_VALIDATE_NULL_OR_SIZED("setOperandExtensionData", data, length);
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if (data == nullptr) {
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operand.extraParams = {};
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} else {
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operand.extraParams = Operand::ExtensionParams(
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std::vector<uint8_t>(reinterpret_cast<const uint8_t*>(data),
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reinterpret_cast<const uint8_t*>(data) + length));
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}
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::copyLargeValuesToSharedMemory() {
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VLOG(MODEL) << __func__ << " has " << mLargeOperandValues.size() << " values.";
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if (!mLargeOperandValues.empty()) {
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// Calculate the size of the shared memory needed for all the large values.
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// Also sets the offset for each value within the memory.
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size_t poolSize = 0;
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for (LargeValue& l : mLargeOperandValues) {
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Operand& operand = mOperands[l.operandIndex];
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CHECK_EQ(operand.lifetime, Operand::LifeTime::CONSTANT_REFERENCE);
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poolSize += alignBytesNeeded(poolSize, operand.location.length);
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operand.location.offset = poolSize;
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poolSize += operand.location.length;
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}
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// Allocate the shared memory.
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int n;
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std::tie(n, mLargeValueMemory) = MemoryAshmem::create(poolSize);
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NN_RETURN_IF_ERROR(n);
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uint8_t* memoryPointer = mLargeValueMemory->getPointer();
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uint32_t poolIndex = mMemories.add(mLargeValueMemory.get());
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VLOG(MODEL) << "Allocated large value pool of size " << poolSize << " at index "
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<< poolIndex;
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// Copy the values to this memory.
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for (LargeValue& l : mLargeOperandValues) {
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Operand& operand = mOperands[l.operandIndex];
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operand.location.poolIndex = poolIndex;
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memcpy(memoryPointer + operand.location.offset, l.buffer, operand.location.length);
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}
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}
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::setOperandValueFromMemory(uint32_t index, const RuntimeMemory* memory,
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uint32_t offset, size_t length) {
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VLOG(MODEL) << __func__ << " for operand " << index << " offset " << offset << " size "
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<< length;
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if (badState("setOperandValueFromMemory")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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if (index >= operandCount()) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromMemory setting operand " << index
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<< " of " << operandCount();
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return ANEURALNETWORKS_BAD_DATA;
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}
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Operand& operand = mOperands[index];
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if (TypeManager::get()->isTensorType(operand.type) && tensorHasUnspecifiedDimensions(operand)) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromMemory setting operand " << index
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<< " which has operand type that is not fully specified";
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return ANEURALNETWORKS_BAD_DATA;
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}
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uint32_t neededLength = TypeManager::get()->getSizeOfData(operand);
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if (neededLength != length) {
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LOG(ERROR) << "ANeuralNetworksModel_setOperandValueFromMemory setting " << length
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<< " bytes when needing " << neededLength;
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return ANEURALNETWORKS_BAD_DATA;
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}
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// Set compilation = nullptr to indicate that the memory is used for a model constant.
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// In this case, IOType::INPUT is a placeholder value that is ignored by the validator.
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if (!memory->getValidator().validate(/*compilation=*/nullptr, /*placeholder*/ IOType::INPUT,
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index, nullptr, offset, length)) {
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return ANEURALNETWORKS_BAD_DATA;
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}
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operand.lifetime = Operand::LifeTime::CONSTANT_REFERENCE;
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operand.location = {.poolIndex = mMemories.add(memory),
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.offset = offset,
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.length = static_cast<uint32_t>(length)};
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::addOperation(ANeuralNetworksOperationType type, uint32_t inputCount,
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const uint32_t* inputs, uint32_t outputCount,
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const uint32_t* outputs) {
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if (badState("addOperation")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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OperationType operationType = static_cast<OperationType>(type);
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if (isExtension(operationType) && !TypeManager::get()->areExtensionsAllowed()) {
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LOG(ERROR) << "Extensions are not supported for this process.";
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return ANEURALNETWORKS_BAD_DATA;
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}
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if (operationType == OperationType::OEM_OPERATION && !mHasOEMOperation) {
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LOG(WARNING) << "OEM_OPERATION is deprecated. Use Extensions instead.";
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}
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if (!isExtension(operationType)) {
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bool allowExperimental = false;
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#ifdef NN_EXPERIMENTAL_FEATURE
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if (type >= BuiltinOperationResolver::kStartOfExperimentalOperations &&
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type < BuiltinOperationResolver::kStartOfExperimentalOperations +
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BuiltinOperationResolver::kNumberOfExperimentalOperationTypes) {
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allowExperimental = true;
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}
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#endif // NN_EXPERIMENTAL_FEATURE
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if (!validCode(kNumberOfOperationTypes, kNumberOfOperationTypesOEM, type) &&
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!allowExperimental) {
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LOG(ERROR) << "ANeuralNetworksModel_addOperation invalid operation type " << type;
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return ANEURALNETWORKS_BAD_DATA;
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}
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} else {
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const Extension* extension;
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uint16_t extensionPrefix = getExtensionPrefix(static_cast<uint32_t>(operationType));
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if (!TypeManager::get()->getExtensionInfo(extensionPrefix, &extension)) {
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LOG(ERROR) << "Extension operation type " << operationType << " is not recognized";
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return ANEURALNETWORKS_BAD_DATA;
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}
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}
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NN_VALIDATE_NULL_OR_SIZED("addOperation", inputs, inputCount);
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NN_VALIDATE_NULL_OR_SIZED("addOperation", outputs, outputCount);
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Operation operation = {
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.type = operationType,
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.inputs = makeVector(inputs, inputCount),
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.outputs = makeVector(outputs, outputCount),
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};
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if (auto result = validateOperationButNotOperands(operation, mOperands,
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mReferencedSubgraphsForValidation);
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!result.ok()) {
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LOG(ERROR) << "Invalid Operation: " << result.error();
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return ANEURALNETWORKS_BAD_DATA;
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}
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uint32_t operationIndex = operationCount();
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if (operationIndex >= MAX_NUMBER_OF_OPERATIONS) {
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LOG(ERROR) << "ANeuralNetworksModel_addOperation exceed max operations";
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return ANEURALNETWORKS_BAD_DATA;
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}
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mOperations.push_back(std::move(operation));
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mHasOEMOperation |= (operationType == OperationType::OEM_OPERATION);
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mHasExtensionOperation |= isExtension(operationType);
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mHasControlFlow |=
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(operationType == OperationType::IF || operationType == OperationType::WHILE);
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return ANEURALNETWORKS_NO_ERROR;
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}
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int ModelBuilder::identifyInputsAndOutputs(uint32_t inputCount, const uint32_t* inputs,
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uint32_t outputCount, const uint32_t* outputs) {
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if (badState("identifyInputsAndOutputs")) {
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return ANEURALNETWORKS_BAD_STATE;
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}
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NN_VALIDATE_NULL_OR_SIZED("identifyInputsAndOutputs", inputs, inputCount);
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if (auto result = validateOperandList(makeVector(inputs, inputCount), operandCount(),
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"ANeuralNetworksModel_identifyInputsAndOutputs inputs");
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!result.ok()) {
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LOG(ERROR) << result.error();
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
NN_VALIDATE_NULL_OR_SIZED("identifyInputsAndOutputs", outputs, outputCount);
|
|
if (auto result = validateOperandList(makeVector(outputs, outputCount), operandCount(),
|
|
"ANeuralNetworksModel_identifyInputsAndOutputs outputs");
|
|
!result.ok()) {
|
|
LOG(ERROR) << result.error();
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
|
|
// Makes a copy of the index list, validates the arguments, and changes
|
|
// the lifetime info of the corresponding operand.
|
|
auto setArguments = [&](std::vector<uint32_t>* indexVector, uint32_t indexCount,
|
|
const uint32_t* indexList, Operand::LifeTime lifetime) -> bool {
|
|
indexVector->resize(indexCount);
|
|
for (uint32_t i = 0; i < indexCount; i++) {
|
|
const uint32_t operandIndex = indexList[i];
|
|
if (operandIndex >= mOperands.size()) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_identifyInputsAndOutputs Can't set input or "
|
|
"output "
|
|
"to be "
|
|
<< operandIndex << " as this exceeds the numbe of operands "
|
|
<< mOperands.size();
|
|
return false;
|
|
}
|
|
(*indexVector)[i] = operandIndex;
|
|
Operand& operand = mOperands[operandIndex];
|
|
if (operand.lifetime != Operand::LifeTime::TEMPORARY_VARIABLE) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_identifyInputsAndOutputs Can't set operand "
|
|
<< operandIndex
|
|
<< " to be an input or output. Check that it's not a constant or "
|
|
"already an input or output";
|
|
return false;
|
|
}
|
|
operand.lifetime = lifetime;
|
|
}
|
|
return true;
|
|
};
|
|
|
|
if (!setArguments(&mInputIndexes, inputCount, inputs, Operand::LifeTime::SUBGRAPH_INPUT) ||
|
|
!setArguments(&mOutputIndexes, outputCount, outputs, Operand::LifeTime::SUBGRAPH_OUTPUT)) {
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
|
|
return ANEURALNETWORKS_NO_ERROR;
|
|
}
|
|
|
|
int ModelBuilder::relaxComputationFloat32toFloat16(bool allow) {
|
|
if (badState("relaxComputationFloat32toFloat16")) {
|
|
return ANEURALNETWORKS_BAD_STATE;
|
|
}
|
|
|
|
mRelaxComputationFloat32toFloat16 = allow;
|
|
|
|
return ANEURALNETWORKS_NO_ERROR;
|
|
}
|
|
|
|
int ModelBuilder::createCompilation(CompilationBuilder** compilation,
|
|
const std::vector<std::shared_ptr<Device>>& devices,
|
|
bool explicitDeviceList) {
|
|
if (!mCompletedModel || mInvalidModel) {
|
|
LOG(ERROR) << "ANeuralNetworksCompilation_create passed an unfinished or invalid model";
|
|
*compilation = nullptr;
|
|
return ANEURALNETWORKS_BAD_STATE;
|
|
}
|
|
*compilation = new (std::nothrow) CompilationBuilder(this, devices, explicitDeviceList);
|
|
return (*compilation ? ANEURALNETWORKS_NO_ERROR : ANEURALNETWORKS_OUT_OF_MEMORY);
|
|
}
|
|
|
|
int ModelBuilder::finish() {
|
|
if (mCompletedModel) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_finish called more than once";
|
|
return ANEURALNETWORKS_BAD_STATE;
|
|
}
|
|
if (mInvalidModel) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_finish called on an invalid model";
|
|
return ANEURALNETWORKS_BAD_STATE;
|
|
}
|
|
|
|
int n = copyLargeValuesToSharedMemory();
|
|
if (n != ANEURALNETWORKS_NO_ERROR) {
|
|
return n;
|
|
}
|
|
|
|
// We sort the operations so that they will be in the appropriate
|
|
// order for a single-threaded, op at a time execution.
|
|
// TODO: we don't need this if we always run the partitioner.
|
|
if (!sortIntoRunOrder()) {
|
|
// We expect sortIntoRunOrder() to have logged an appropriate error message.
|
|
mInvalidModel = true;
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
|
|
// TODO: Modify validation so that it can be called without creating a Model.
|
|
// NOTE: Must sortIntoRunOrder() before validation; validator expects operations
|
|
// to have been sorted.
|
|
// NOTE: Must copyLargeValuesToSharedMemory() before validation; otherwise,
|
|
// a CONSTANT_REFERENCE operand will not have correct .poolIndex, and
|
|
// validation will not work properly.
|
|
const Model modelForValidation = makeModel();
|
|
const auto maybeVersion = validate(modelForValidation);
|
|
if (!maybeVersion.ok()) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_finish called on invalid model: "
|
|
<< maybeVersion.error();
|
|
mInvalidModel = true;
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
if (!isCompliantVersion(maybeVersion.value(), DeviceManager::get()->getRuntimeVersion())) {
|
|
LOG(ERROR) << "ANeuralNetworksModel_finish called on a model that is newer what is "
|
|
"allowed. Model version needed: "
|
|
<< maybeVersion.value() << ", current runtime version supported: "
|
|
<< DeviceManager::get()->getRuntimeVersion();
|
|
mInvalidModel = true;
|
|
return ANEURALNETWORKS_BAD_DATA;
|
|
}
|
|
if (VLOG_IS_ON(MODEL)) {
|
|
graphDump("ModelBuilder::finish", modelForValidation, nullptr);
|
|
}
|
|
|
|
removeTrailingArgumentsWithDefaultValues();
|
|
simplifyModel();
|
|
|
|
mCompletedModel = true;
|
|
CHECK(calcModelArchHash(modelForValidation, mModelArchHash))
|
|
<< "Failed to calculate model arch hash";
|
|
return ANEURALNETWORKS_NO_ERROR;
|
|
}
|
|
|
|
static void logRemoval(const Operation& operation, uint32_t count,
|
|
const std::vector<Operand>& operands) {
|
|
std::ostringstream message;
|
|
message << "Operation " << operation.type << " with inputs {";
|
|
for (uint32_t i = 0; i < operation.inputs.size(); ++i) {
|
|
if (i != 0) {
|
|
message << ", ";
|
|
}
|
|
message << operands[operation.inputs[i]].type;
|
|
}
|
|
message << "} has trailing optional inputs set to default values. Removing " << count
|
|
<< " trailing inputs.";
|
|
VLOG(MODEL) << message.str();
|
|
}
|
|
|
|
void ModelBuilder::removeTrailingArgumentsWithDefaultValues() {
|
|
for (Operation& operation : mOperations) {
|
|
const uint32_t count = getNumTrailingArgumentsToRemove(operation);
|
|
if (count == 0) {
|
|
continue;
|
|
}
|
|
if (VLOG_IS_ON(MODEL)) {
|
|
logRemoval(operation, count, mOperands);
|
|
}
|
|
const uint32_t inputCount = operation.inputs.size();
|
|
CHECK_LT(count, inputCount);
|
|
const uint32_t newInputCount = inputCount - count;
|
|
operation.inputs.resize(newInputCount);
|
|
}
|
|
}
|
|
|
|
// See countMatchingTrailingArguments().
|
|
enum class TailSpec {
|
|
BOOL_FALSE,
|
|
INT32_ONE,
|
|
INT32_NEGATIVE_ONE,
|
|
};
|
|
|
|
// See countMatchingTrailingArguments().
|
|
static bool matchesSpec(TailSpec spec, const Operand& operand,
|
|
const std::vector<uint8_t>& mSmallOperandValues) {
|
|
const void* valuePtr = nullptr;
|
|
if (operand.lifetime == Operand::LifeTime::CONSTANT_COPY) {
|
|
valuePtr = static_cast<const void*>(&mSmallOperandValues[operand.location.offset]);
|
|
} else if (operand.lifetime == Operand::LifeTime::POINTER) {
|
|
valuePtr = std::get<const void*>(operand.location.pointer);
|
|
} else {
|
|
// CONSTANT_REFERENCE operands are not supported to avoid mapping memory
|
|
// during compilation.
|
|
return false;
|
|
}
|
|
switch (spec) {
|
|
case TailSpec::BOOL_FALSE:
|
|
return operand.type == OperandType::BOOL &&
|
|
*static_cast<const bool8*>(valuePtr) == false;
|
|
case TailSpec::INT32_ONE:
|
|
return operand.type == OperandType::INT32 &&
|
|
*static_cast<const int32_t*>(valuePtr) == 1;
|
|
case TailSpec::INT32_NEGATIVE_ONE:
|
|
return operand.type == OperandType::INT32 &&
|
|
*static_cast<const int32_t*>(valuePtr) == -1;
|
|
default:
|
|
CHECK(false) << "Unhandled TailSpec: " << static_cast<int>(spec);
|
|
}
|
|
}
|
|
|
|
// Returns the number of trailing operation inputs that match the specification.
|
|
//
|
|
// Example:
|
|
// opeation.inputs = {BOOL_TRUE, BOOL_TRUE, INT32_ONE, INT32_NEGATIVE_ONE}
|
|
// tail = {BOOL_FALSE, INT32_ONE, INT32_NEGATIVE_ONE}
|
|
// tailStartIndex = 1 matching elements: ^^^^^^^^^ ^^^^^^^^^^^^^^^^^^
|
|
static uint32_t countMatchingTrailingArguments(uint32_t tailStartIndex,
|
|
const std::vector<TailSpec>& tail,
|
|
const Operation& operation,
|
|
const std::vector<Operand>& operands,
|
|
const std::vector<uint8_t>& smallOperandValues) {
|
|
const uint32_t inputCount = operation.inputs.size();
|
|
uint32_t count = 0;
|
|
for (uint32_t i = inputCount - 1; i >= tailStartIndex; --i) {
|
|
const Operand& operand = operands[operation.inputs[i]];
|
|
if (!matchesSpec(tail[i - tailStartIndex], operand, smallOperandValues)) {
|
|
break;
|
|
}
|
|
++count;
|
|
}
|
|
return count;
|
|
}
|
|
|
|
uint32_t ModelBuilder::getNumTrailingArgumentsToRemove(const Operation& operation) const {
|
|
const uint32_t inputCount = operation.inputs.size();
|
|
auto getCount = [this, &operation](uint32_t tailStartIndex, const std::vector<TailSpec>& tail) {
|
|
return countMatchingTrailingArguments(tailStartIndex, tail, operation, mOperands,
|
|
mSmallOperandValues);
|
|
};
|
|
using TS = TailSpec;
|
|
// Check if the operation has optional arguments that might be set to default
|
|
// values. Skip the counting if no optional arguments are present.
|
|
switch (operation.type) {
|
|
case OperationType::AVERAGE_POOL_2D: {
|
|
if (inputCount == 11 && mOperands[operation.inputs[7]].type == OperandType::INT32) {
|
|
// Explicit padding
|
|
// API level 29: 10 to 11 inputs
|
|
// API level 27: 10 inputs
|
|
return getCount(10, {TS::BOOL_FALSE});
|
|
} else if (inputCount == 8 &&
|
|
mOperands[operation.inputs[7]].type == OperandType::BOOL) {
|
|
// Implicit padding
|
|
// API level 29: 7 to 8 inputs
|
|
// API level 27: 7 inputs
|
|
return getCount(7, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::CONV_2D: {
|
|
if (10 < inputCount && inputCount <= 13 &&
|
|
mOperands[operation.inputs[7]].type == OperandType::INT32) {
|
|
// Explicit padding
|
|
// API level 29: 10 to 13 inputs
|
|
// API level 27: 10 inputs
|
|
uint32_t count = getCount(10, {TS::BOOL_FALSE, TS::INT32_ONE, TS::INT32_ONE});
|
|
// Inputs 11 and 12 must come together.
|
|
return inputCount - count == 12 ? 0 : count;
|
|
} else if (7 < inputCount && inputCount <= 10 &&
|
|
mOperands[operation.inputs[7]].type == OperandType::BOOL) {
|
|
// Implicit padding
|
|
// API level 29: 7 to 10 inputs
|
|
// API level 27: 7 inputs
|
|
uint32_t count = getCount(7, {TS::BOOL_FALSE, TS::INT32_ONE, TS::INT32_ONE});
|
|
// Inputs 8 and 9 must come together.
|
|
return inputCount - count == 9 ? 0 : count;
|
|
}
|
|
} break;
|
|
case OperationType::DEPTHWISE_CONV_2D: {
|
|
if (11 < inputCount && inputCount <= 14 &&
|
|
mOperands[operation.inputs[8]].type == OperandType::INT32) {
|
|
// Explicit padding
|
|
// API level 29: 11 to 14 inputs
|
|
// API level 27: 11 inputs
|
|
uint32_t count = getCount(11, {TS::BOOL_FALSE, TS::INT32_ONE, TS::INT32_ONE});
|
|
// Inputs 12 and 13 must come together.
|
|
return inputCount - count == 13 ? 0 : count;
|
|
} else if (8 < inputCount && inputCount <= 11 &&
|
|
mOperands[operation.inputs[8]].type == OperandType::BOOL) {
|
|
// Implicit padding
|
|
// API level 29: 8 to 11 inputs
|
|
// API level 27: 8 inputs
|
|
uint32_t count = getCount(8, {TS::BOOL_FALSE, TS::INT32_ONE, TS::INT32_ONE});
|
|
// Inputs 9 and 10 must come together.
|
|
return inputCount - count == 10 ? 0 : count;
|
|
}
|
|
} break;
|
|
case OperationType::DEPTH_TO_SPACE: {
|
|
if (inputCount == 3) {
|
|
// API level 29: 2 to 3 inputs
|
|
// API level 27: 2 inputs
|
|
return getCount(2, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::L2_NORMALIZATION: {
|
|
if (inputCount == 2) {
|
|
// API level 29: 1 to 2 inputs
|
|
// API level 27: 1 inputs
|
|
return getCount(1, {TS::INT32_NEGATIVE_ONE});
|
|
}
|
|
} break;
|
|
case OperationType::L2_POOL_2D: {
|
|
if (inputCount == 11 && mOperands[operation.inputs[7]].type == OperandType::INT32) {
|
|
// Explicit padding
|
|
// API level 29: 10 to 11 inputs
|
|
// API level 27: 10 inputs
|
|
return getCount(10, {TS::BOOL_FALSE});
|
|
} else if (inputCount == 8 &&
|
|
mOperands[operation.inputs[7]].type == OperandType::BOOL) {
|
|
// Implicit padding
|
|
// API level 29: 7 to 8 inputs
|
|
// API level 27: 7 inputs
|
|
return getCount(7, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
|
|
if (inputCount == 6) {
|
|
// API level 29: 5 to 6 inputs
|
|
// API level 27: 5 inputs
|
|
return getCount(5, {TS::INT32_NEGATIVE_ONE});
|
|
}
|
|
} break;
|
|
case OperationType::MAX_POOL_2D: {
|
|
if (inputCount == 11 && mOperands[operation.inputs[7]].type == OperandType::INT32) {
|
|
// Explicit padding
|
|
// API level 29: 10 to 11 inputs
|
|
// API level 27: 10 inputs
|
|
return getCount(10, {TS::BOOL_FALSE});
|
|
} else if (inputCount == 8 &&
|
|
mOperands[operation.inputs[7]].type == OperandType::BOOL) {
|
|
// Implicit padding
|
|
// API level 29: 7 to 8 inputs
|
|
// API level 27: 7 inputs
|
|
return getCount(7, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::RESIZE_BILINEAR: {
|
|
if (3 < inputCount && inputCount <= 6) {
|
|
// By shape:
|
|
// API level 30: 3 to 6 inputs
|
|
// API level 29: 3 to 4 inputs
|
|
// API level 27: 3 inputs
|
|
// By scale:
|
|
// API level 30: 3 to 6 inputs
|
|
// API level 29: 3 to 4 inputs
|
|
return getCount(3, {TS::BOOL_FALSE, TS::BOOL_FALSE, TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::SOFTMAX: {
|
|
if (inputCount == 3) {
|
|
// API level 29: 2 to 3 inputs
|
|
// API level 27: 2 inputs
|
|
return getCount(2, {TS::INT32_NEGATIVE_ONE});
|
|
}
|
|
} break;
|
|
case OperationType::SPACE_TO_DEPTH: {
|
|
if (inputCount == 3) {
|
|
// API level 29: 2 to 3 inputs
|
|
// API level 27: 2 inputs
|
|
return getCount(2, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::BATCH_TO_SPACE_ND: {
|
|
if (inputCount == 3) {
|
|
// API level 29: 2 to 3 inputs
|
|
// API level 28: 2 inputs
|
|
return getCount(2, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::SPACE_TO_BATCH_ND: {
|
|
if (inputCount == 4) {
|
|
// API level 29: 3 to 4 inputs
|
|
// API level 28: 3 inputs
|
|
return getCount(3, {TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
case OperationType::RESIZE_NEAREST_NEIGHBOR: {
|
|
if (4 < inputCount && inputCount <= 6) {
|
|
// By shape or scale
|
|
// API level 30: 4 to 6 inputs
|
|
// API level 29: 4 inputs
|
|
return getCount(4, {TS::BOOL_FALSE, TS::BOOL_FALSE});
|
|
}
|
|
} break;
|
|
default: {
|
|
// Do nothing.
|
|
} break;
|
|
}
|
|
// No trailing optional arguments to check.
|
|
return 0;
|
|
}
|
|
|
|
bool ModelBuilder::sortIntoRunOrder() {
|
|
// Note that this may be called before the model has been
|
|
// validated, so we must code defensively. However, we can assume
|
|
// an Operation's inputs and outputs have legal indices -- this
|
|
// should have been checked in addOperation().
|
|
|
|
if (!mSortedOperationIndexMap.empty()) {
|
|
LOG(ERROR) << "Operations were already sorted into run order.";
|
|
return true;
|
|
}
|
|
|
|
// Tracks the operations that can be executed.
|
|
std::vector<uint32_t> sortedOperationIndexMap;
|
|
std::vector<uint32_t> opsReadyToRun;
|
|
std::vector<Operation> runOrder;
|
|
|
|
// Tracks how many inputs are needed for each operation to be ready to run.
|
|
std::multimap<uint32_t, uint32_t> operandToOperations;
|
|
std::vector<uint32_t> unknownInputCount(operationCount());
|
|
for (uint32_t operationIndex = 0; operationIndex < operationCount(); operationIndex++) {
|
|
uint32_t& count = unknownInputCount[operationIndex];
|
|
count = 0;
|
|
for (uint32_t operandIndex : mOperations[operationIndex].inputs) {
|
|
auto lifetime = mOperands[operandIndex].lifetime;
|
|
if (lifetime == Operand::LifeTime::TEMPORARY_VARIABLE ||
|
|
lifetime == Operand::LifeTime::SUBGRAPH_OUTPUT) {
|
|
count++;
|
|
operandToOperations.insert(
|
|
std::pair<uint32_t, uint32_t>(operandIndex, operationIndex));
|
|
}
|
|
}
|
|
if (count == 0) {
|
|
opsReadyToRun.push_back(operationIndex);
|
|
}
|
|
}
|
|
|
|
while (opsReadyToRun.size() > 0) {
|
|
// Execute the next op
|
|
int opIndex = opsReadyToRun.back();
|
|
opsReadyToRun.pop_back();
|
|
const Operation& operation = mOperations[opIndex];
|
|
|
|
runOrder.push_back(mOperations[opIndex]);
|
|
sortedOperationIndexMap.push_back(opIndex);
|
|
|
|
// Mark all its outputs as known.
|
|
for (uint32_t operandIndex : operation.outputs) {
|
|
auto range = operandToOperations.equal_range(operandIndex);
|
|
for (auto i = range.first; i != range.second; i++) {
|
|
uint32_t& count = unknownInputCount[i->second];
|
|
if (--count == 0) {
|
|
opsReadyToRun.push_back(i->second);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (runOrder.size() != mOperations.size()) {
|
|
CHECK_LT(runOrder.size(), mOperations.size());
|
|
// Graph must contain at least one cycle or one never-written
|
|
// operand, because there is at least one Operation that never
|
|
// became ready.
|
|
LOG(ERROR) << "Graph contains at least one cycle or one never-written operand";
|
|
return false;
|
|
}
|
|
|
|
mSortedOperationIndexMap = std::move(sortedOperationIndexMap);
|
|
mOperations = std::move(runOrder);
|
|
return true;
|
|
}
|
|
|
|
// A helper class to simplify state management when creating a Model.
|
|
class ModelBuilder::ModelMaker {
|
|
public:
|
|
static Model run(const ModelBuilder* model, bool simplifyModel);
|
|
|
|
private:
|
|
static Model::Subgraph makeSubgraph(const ModelBuilder* model);
|
|
explicit ModelMaker(bool simplifyModel) : mSimplifyModel(simplifyModel) {}
|
|
Model makeModel(const ModelBuilder* mainModel);
|
|
uint32_t addSubgraph(const ModelBuilder* refModel);
|
|
void updateOperandLocations(const ModelBuilder* refModel, Model::Subgraph* subgraph);
|
|
void addExtensions(const ModelBuilder* model);
|
|
void addExtensionWithPrefix(uint16_t prefix);
|
|
|
|
bool mSimplifyModel;
|
|
std::vector<Model::Subgraph> mRefSubgraphs;
|
|
Model::OperandValues mOperandValues;
|
|
MemoryTracker mMemories;
|
|
std::vector<ExtensionNameAndPrefix> mExtensionNameToPrefix;
|
|
std::set<uint16_t> mPrefixSet;
|
|
};
|
|
|
|
void ModelBuilder::simplifyModel() {
|
|
mSimplifyModel = true;
|
|
}
|
|
|
|
Model ModelBuilder::makeModel() const {
|
|
// TODO: Cache the Model to speed up subsequent calls.
|
|
return ModelMaker::run(this, mSimplifyModel);
|
|
}
|
|
|
|
Model ModelBuilder::ModelMaker::run(const ModelBuilder* model, bool simplifyModel) {
|
|
// run() ensures the state of ModelMaker is destroyed after the call.
|
|
return ModelMaker(simplifyModel).makeModel(model);
|
|
}
|
|
|
|
Model ModelBuilder::ModelMaker::makeModel(const ModelBuilder* mainModel) {
|
|
addExtensions(mainModel);
|
|
Model model;
|
|
model.main = makeSubgraph(mainModel);
|
|
updateOperandLocations(mainModel, &model.main);
|
|
model.referenced = std::move(mRefSubgraphs);
|
|
model.operandValues = std::move(mOperandValues);
|
|
model.pools.reserve(mMemories.size());
|
|
std::transform(mMemories.begin(), mMemories.end(), std::back_inserter(model.pools),
|
|
[](const RuntimeMemory* m) { return m->getMemory(); });
|
|
model.relaxComputationFloat32toFloat16 = mainModel->mRelaxComputationFloat32toFloat16;
|
|
model.extensionNameToPrefix = std::move(mExtensionNameToPrefix);
|
|
if (mSimplifyModel) {
|
|
removeDeadOperands(&model);
|
|
}
|
|
return model;
|
|
}
|
|
|
|
Model::Subgraph ModelBuilder::ModelMaker::makeSubgraph(const ModelBuilder* model) {
|
|
Model::Subgraph subgraph;
|
|
subgraph.operands = model->mOperands;
|
|
subgraph.operations = model->mOperations;
|
|
subgraph.inputIndexes = model->mInputIndexes;
|
|
subgraph.outputIndexes = model->mOutputIndexes;
|
|
return subgraph;
|
|
}
|
|
|
|
void ModelBuilder::ModelMaker::updateOperandLocations(const ModelBuilder* refModel,
|
|
Model::Subgraph* subgraph) {
|
|
for (Operand& operand : subgraph->operands) {
|
|
if (operand.lifetime == Operand::LifeTime::CONSTANT_COPY) {
|
|
uint32_t valueLength = operand.location.length;
|
|
uint32_t originalOffset = operand.location.offset;
|
|
operand.location = mOperandValues.append(&refModel->mSmallOperandValues[originalOffset],
|
|
valueLength);
|
|
} else if (operand.lifetime == Operand::LifeTime::CONSTANT_REFERENCE) {
|
|
uint32_t originalPoolIndex = operand.location.poolIndex;
|
|
operand.location.poolIndex = mMemories.add(refModel->mMemories[originalPoolIndex]);
|
|
}
|
|
}
|
|
// Do recursive calls at the end to improve locality of mOperandValues.
|
|
for (Operand& operand : subgraph->operands) {
|
|
if (operand.lifetime == Operand::LifeTime::SUBGRAPH) {
|
|
uint32_t refModelIndex = operand.location.offset;
|
|
// TODO(b/147875885): Avoid creating duplicate refSubgraphs when
|
|
// a single refModel is referenced multiple times.
|
|
operand.location.offset = addSubgraph(refModel->mReferencedModels[refModelIndex]);
|
|
}
|
|
}
|
|
}
|
|
|
|
uint32_t ModelBuilder::ModelMaker::addSubgraph(const ModelBuilder* refModel) {
|
|
uint32_t index = mRefSubgraphs.size();
|
|
mRefSubgraphs.push_back(makeSubgraph(refModel));
|
|
updateOperandLocations(refModel, &mRefSubgraphs.back());
|
|
return index;
|
|
}
|
|
|
|
void ModelBuilder::ModelMaker::addExtensions(const ModelBuilder* model) {
|
|
for (const auto& operand : model->mOperands) {
|
|
if (isExtension(operand.type)) {
|
|
addExtensionWithPrefix(static_cast<uint32_t>(operand.type) >> kExtensionTypeBits);
|
|
}
|
|
}
|
|
for (const auto& operation : model->mOperations) {
|
|
if (isExtension(operation.type)) {
|
|
addExtensionWithPrefix(static_cast<uint32_t>(operation.type) >> kExtensionTypeBits);
|
|
}
|
|
}
|
|
for (const auto& refModel : model->mReferencedModels) {
|
|
addExtensions(refModel);
|
|
}
|
|
}
|
|
|
|
void ModelBuilder::ModelMaker::addExtensionWithPrefix(uint16_t prefix) {
|
|
if (!mPrefixSet.insert(prefix).second) {
|
|
return;
|
|
}
|
|
const Extension* extension;
|
|
CHECK(TypeManager::get()->getExtensionInfo(prefix, &extension));
|
|
mExtensionNameToPrefix.push_back({
|
|
.name = extension->name,
|
|
.prefix = prefix,
|
|
});
|
|
}
|
|
|
|
const uint8_t* ModelBuilder::getModelArchHash() const {
|
|
CHECK(mCompletedModel) << "Calling getModelArchHash on non completed model";
|
|
return mModelArchHash;
|
|
}
|
|
|
|
#undef NN_VALIDATE_NULL_OR_SIZED
|
|
|
|
} // namespace nn
|
|
} // namespace android
|