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245 lines
11 KiB
245 lines
11 KiB
/*
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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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// Class used to build a model through a succession of successive calls
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// to the NN API.
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#ifndef ANDROID_FRAMEWORKS_ML_NN_RUNTIME_MODEL_BUILDER_H
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#define ANDROID_FRAMEWORKS_ML_NN_RUNTIME_MODEL_BUILDER_H
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#include <LegacyUtils.h>
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#include <memory>
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#include <vector>
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#include "Memory.h"
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#include "NeuralNetworks.h"
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namespace android {
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namespace nn {
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class CompilationBuilder;
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class Device;
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class ExecutionPlan;
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class RuntimeMemory;
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class ModelBuilder {
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public:
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ModelBuilder() {}
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// Returns an operand/operation type corresponding to a given extension operand/operation type.
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int getExtensionType(const char* extensionName, uint16_t typeWithinExtension, int32_t* type);
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// Adds an operand to the model.
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int addOperand(const ANeuralNetworksOperandType& type);
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int setOperandValue(uint32_t index, const void* buffer, size_t length);
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int setOperandValueFromMemory(uint32_t index, const RuntimeMemory* memory, uint32_t offset,
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size_t length);
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int setOperandValueFromModel(uint32_t index, const ModelBuilder* value);
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int setOperandSymmPerChannelQuantParams(
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uint32_t index, const ANeuralNetworksSymmPerChannelQuantParams& extraParams);
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int setOperandExtensionData(uint32_t index, const void* data, size_t length);
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int addOperation(ANeuralNetworksOperationType type, uint32_t inputCount, const uint32_t* inputs,
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uint32_t outputCount, const uint32_t* outputs);
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int identifyInputsAndOutputs(uint32_t inputCount, const uint32_t* inputs, uint32_t outputCount,
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const uint32_t* outputs);
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int relaxComputationFloat32toFloat16(bool allow);
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bool isComputationFloat32RelaxedToFloat16() const { return mRelaxComputationFloat32toFloat16; }
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int finish();
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bool isFinished() const { return mCompletedModel; }
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bool isValid() const { return !mInvalidModel; }
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bool hasOEMOperation() const { return mHasOEMOperation; }
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bool hasExtensionOperation() const { return mHasExtensionOperation; }
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// explicitDeviceList is true if the list of devices was provided explicitly
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// via the ANeuralNetworksModel_createForDevices API (which has certain
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// special semantics) and false otherwise.
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int createCompilation(CompilationBuilder** compilation,
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const std::vector<std::shared_ptr<Device>>& devices,
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bool explicitDeviceList = false);
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Model makeModel() const;
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uint32_t operandCount() const {
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// We don't allow more than uint32_t worth of operands
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return static_cast<uint32_t>(mOperands.size());
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}
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uint32_t operationCount() const {
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// We don't allow more than uint32_t worth of operations
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return static_cast<uint32_t>(mOperations.size());
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}
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uint32_t inputCount() const { return static_cast<uint32_t>(mInputIndexes.size()); }
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uint32_t outputCount() const { return static_cast<uint32_t>(mOutputIndexes.size()); }
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uint32_t getInputOperandIndex(uint32_t i) const {
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CHECK_LT(i, mInputIndexes.size());
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return mInputIndexes[i];
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}
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const std::vector<uint32_t>& getInputOperandIndexes() const { return mInputIndexes; }
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const Operand& getInputOperand(uint32_t i) const {
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uint32_t index = getInputOperandIndex(i);
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CHECK_LT(index, mOperands.size());
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return mOperands[index];
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}
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uint32_t getOutputOperandIndex(uint32_t i) const {
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CHECK_LT(i, mOutputIndexes.size());
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return mOutputIndexes[i];
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}
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const std::vector<uint32_t>& getOutputOperandIndexes() const { return mOutputIndexes; }
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const Operand& getOutputOperand(uint32_t i) const {
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uint32_t index = getOutputOperandIndex(i);
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CHECK_LT(index, mOperands.size());
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return mOperands[index];
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}
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const Operand& getOperand(uint32_t index) const { return mOperands[index]; }
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const Operation& getOperation(uint32_t index) const { return mOperations[index]; }
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const MemoryTracker& getMemories() const { return mMemories; }
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const std::vector<Operation>& getOperations() const { return mOperations; }
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const std::vector<uint32_t>& getSortedOperationMapping() const {
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return mSortedOperationIndexMap;
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}
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const uint8_t* getPointerToOperandValue(uint32_t offset) const {
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return mSmallOperandValues.data() + offset;
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}
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uint32_t referencedModelCount() const {
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return static_cast<uint32_t>(mReferencedModels.size());
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}
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const ModelBuilder* getReferencedModel(uint32_t i) const {
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CHECK_LT(i, mReferencedModels.size());
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return mReferencedModels[i];
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}
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const ModelBuilder* getReferencedModel(const Operand& operand) const {
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CHECK(operand.lifetime == Operand::LifeTime::SUBGRAPH);
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return getReferencedModel(operand.location.offset);
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}
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// simulateFailureResultCode == ANEURALNETWORKS_NO_ERROR means behave normally.
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int partitionTheWork(const std::vector<std::shared_ptr<Device>>& devices, uint32_t preference,
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uint32_t priority, const OptionalTimePoint& deadline, ExecutionPlan* plan,
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int simulateFailureResultCode = ANEURALNETWORKS_NO_ERROR) const;
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private:
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// TODO(b/132322449): move partitionTheWork, findBestDeviceForEachOperation,
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// getPerformance, supportedByControlFlowInterpreter,
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// isControlFlowOperationWithOperandOfUnknownSize, partitionTheWorkInternal,
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// sortIntoRunOrder to CompilationBuilder?
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// Populates bestDeviceForOperation
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//
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// For 0 <= i < operationCount(), produces
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//
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// 0 <= (*bestDeviceForOperation)[i] <= devices.size()
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//
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// (*bestDeviceForOperation)[i] == devices.size() is a special value meaning
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// that this is a control flow operation scheduled for interpreted execution
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// (see LogicalStep).
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int findBestDeviceForEachOperation(uint32_t preference,
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const std::vector<std::shared_ptr<Device>>& devices,
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std::vector<int>* bestDeviceForOperation) const;
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float getPerformance(uint32_t preference, const std::shared_ptr<Device> device) const;
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float getPerformance(uint32_t preference, const std::shared_ptr<Device> device,
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uint32_t operationIndex) const;
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bool supportedByControlFlowInterpreter(uint32_t operationIndex) const;
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// Returns true if the operation is IF or WHILE and has an inner or outer
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// input or output of unknown size.
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bool isControlFlowOperationWithOperandOfUnknownSize(uint32_t operationIndex) const;
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int partitionTheWorkInternal(uint32_t sourceModelIndex,
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const std::vector<std::shared_ptr<Device>>& devices,
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uint32_t preference, uint32_t priority,
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const OptionalTimePoint& deadline, ExecutionPlan* plan) const;
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// Return true if either mCompleteModel or mInvalidModel is true.
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bool badState(const char* name);
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// Removes some trailing operation inputs that are set to default values.
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//
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// Some drivers reject operations based on the argument count even when the
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// optional arguments are set to default values. This transformation enables
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// more drivers to execute the model. See http://b/147105700.
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void removeTrailingArgumentsWithDefaultValues();
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uint32_t getNumTrailingArgumentsToRemove(const Operation& operation) const;
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// Sorts the operations to be in the correct order for single threaded
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// node-at-a-time execution.
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bool sortIntoRunOrder();
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// Copies the large values to a shared memory, if we have any.
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int copyLargeValuesToSharedMemory();
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// The operations of the graph.
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std::vector<Operation> mOperations;
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// The mapping from sorted index to the original index of operations in mOperations.
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// mSortedOperationIndexMap is empty before sortIntoRunOrder() is called.
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std::vector<uint32_t> mSortedOperationIndexMap;
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// Is at least one of those operations an OEM_OPERATION?
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bool mHasOEMOperation = false;
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// Is at least one of those operations an extension operation?
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bool mHasExtensionOperation = false;
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// The description of the operands of the graph.
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std::vector<Operand> mOperands;
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// Is at least one of those operands an OEM operand?
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bool mHasOEMOperand = false;
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// The indexes of input operands of the model.
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std::vector<uint32_t> mInputIndexes;
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// The indexes of output operands of the model.
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std::vector<uint32_t> mOutputIndexes;
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MemoryTracker mMemories;
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// The value of the small operands that are defined at model
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// creation time.
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std::vector<uint8_t> mSmallOperandValues;
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struct LargeValue {
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uint32_t operandIndex;
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const void* buffer;
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};
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// Operand index and buffer pointer for all the large operand values of this model.
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std::vector<LargeValue> mLargeOperandValues;
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// The shared memory region that will contain the large values.
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std::unique_ptr<MemoryAshmem> mLargeValueMemory;
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// Once the model has been finished, we should not allow further
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// modifications to the model.
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bool mCompletedModel = false;
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// Any invalid manipulation of the model will mark the model invalid.
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// No further modifications are allowed to the model.
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bool mInvalidModel = false;
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// 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
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// precision as low as that of the IEEE 754 16-bit floating-point format.
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// 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
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// range and precision of the IEEE 754 32-bit floating-point format.
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bool mRelaxComputationFloat32toFloat16 = false;
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// Models referenced by operands in this model.
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std::vector<const ModelBuilder*> mReferencedModels;
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// Main subgraphs of models referenced by operands in this model. Required
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// for validateOperation().
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std::vector<Model::Subgraph> mReferencedSubgraphsForValidation;
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class ModelMaker;
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};
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} // namespace nn
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} // namespace android
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#endif // ANDROID_FRAMEWORKS_ML_NN_RUNTIME_MODEL_BUILDER_H
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