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328 lines
14 KiB
328 lines
14 KiB
4 months ago
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/*
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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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// This file contains pre-canonical-types utility code and does not includes HAL
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// utilities. LegacyHalUtils.h is a superset of these utilities that includes
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// HAL utilities.
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#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H
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#define ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H
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#include <android-base/logging.h>
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#include <nnapi/TypeUtils.h>
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#include <nnapi/Types.h>
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#include <string>
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#include <tuple>
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#include <utility>
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#include <vector>
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#include "NeuralNetworks.h"
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#include "OperationResolver.h"
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#include "nnapi/TypeUtils.h"
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#include "nnapi/Types.h"
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namespace android {
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namespace nn {
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// The number of data types (OperandCode) defined in NeuralNetworks.h.
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const int kNumberOfDataTypes = 16;
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// The number of operation types (OperationCode) defined in NeuralNetworks.h.
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const int kNumberOfOperationTypes = 102;
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static_assert(kNumberOfOperationTypes == BuiltinOperationResolver::kNumberOfOperationTypes);
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// The number of execution preferences defined in NeuralNetworks.h.
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const int kNumberOfPreferences = 3;
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// The number of data types (OperandCode) defined in NeuralNetworksOEM.h.
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const int kNumberOfDataTypesOEM = 2;
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// The number of operation types (OperationCode) defined in NeuralNetworksOEM.h.
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const int kNumberOfOperationTypesOEM = 1;
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// The lowest number assigned to any OEM Code in NeuralNetworksOEM.h.
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const int kOEMCodeBase = 10000;
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/* IMPORTANT: if you change the following list, don't
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* forget to update the corresponding 'tags' table in
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* the initVlogMask() function implemented in Utils.cpp.
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*/
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enum VLogFlags { MODEL = 0, COMPILATION, EXECUTION, CPUEXE, MANAGER, DRIVER, MEMORY };
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#define VLOG_IS_ON(TAG) ((vLogMask & (1 << (TAG))) != 0)
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#define VLOG(TAG) \
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if (LIKELY(!VLOG_IS_ON(TAG))) \
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; \
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else \
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LOG(INFO)
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extern int vLogMask;
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void initVLogMask();
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#ifdef NN_DEBUGGABLE
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#define SHOW_IF_DEBUG(msg) msg
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#else
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#define SHOW_IF_DEBUG(msg) ""
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#endif
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// DEPRECATED(b/118737105). Use CHECK.
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#define nnAssert(v) CHECK(v)
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#define NN_RETURN_IF_ERROR(expr) \
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do { \
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int _errorCode = (expr); \
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if (_errorCode != ANEURALNETWORKS_NO_ERROR) { \
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return _errorCode; \
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} \
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} while (0)
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// Make a Duration from a duration in nanoseconds. If the value exceeds the max duration, return the
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// maximum expressible duration.
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Duration makeTimeoutDuration(uint64_t nanoseconds);
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// Make a Duration from a duration in nanoseconds. If the value exceeds the max duration, return the
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// maximum expressible duration. If nanoseconds == -1, the duration is omitted. Precondition:
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// nanoseconds >= -1
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OptionalDuration makeTimeoutDuration(int64_t nanoseconds);
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// Make a deadline from a duration. If the sum of the current time and the
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// duration exceeds the max time, return a time point holding the maximum
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// expressible time.
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TimePoint makeDeadline(Duration duration);
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inline TimePoint makeDeadline(uint64_t duration) {
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return makeDeadline(makeTimeoutDuration(duration));
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}
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// Convenience function. If the duration is provided, this function creates a
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// deadline using makeDeadline. If the duration is not provided, this function
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// returns std::nullopt.
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inline OptionalTimePoint makeDeadline(OptionalDuration duration) {
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return duration.has_value() ? std::make_optional(makeDeadline(*duration)) : OptionalTimePoint{};
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}
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inline OptionalTimePoint makeDeadline(std::optional<uint64_t> duration) {
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return duration.has_value() ? std::make_optional(makeDeadline(*duration)) : OptionalTimePoint{};
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}
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inline OptionalTimePoint makeDeadline(int64_t duration) {
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return makeDeadline(makeTimeoutDuration(duration));
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}
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// Returns true if the deadline has passed. Returns false if either the deadline
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// has not been exceeded or if the deadline is not present.
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bool hasDeadlinePassed(const OptionalTimePoint& deadline);
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// Returns true if an operand type is an extension type.
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bool isExtensionOperandType(OperandType type);
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// Returns true if an operation type is an extension type.
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bool isExtensionOperationType(OperationType type);
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// Returns the amount of space needed to store a value of the specified
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// dimensions and type. For a tensor with unspecified rank or at least one
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// unspecified dimension, returns zero.
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//
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// Aborts if the specified type is an extension type.
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// Aborts if the size would overflow the return type.
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//
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// See also TypeManager::getSizeOfData(OperandType, const std::vector<uint32_t>&).
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uint32_t nonExtensionOperandSizeOfData(OperandType type, const std::vector<uint32_t>& dimensions);
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// Returns the amount of space needed to store a value of the dimensions and
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// type of this operand. For a tensor with unspecified rank or at least one
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// unspecified dimension, returns zero.
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//
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// Aborts if the specified type is an extension type.
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// Aborts if the size would overflow the return type.
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//
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// See also TypeManager::getSizeOfData(const Operand&).
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inline uint32_t nonExtensionOperandSizeOfData(const Operand& operand) {
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return nonExtensionOperandSizeOfData(operand.type, operand.dimensions);
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}
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// Returns the amount of space needed to store a value of the specified
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// dimensions and element size. For a tensor with unspecified rank or at least
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// one unspecified dimension, returns zero.
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//
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// Aborts if the size would overflow the return type.
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//
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// See also TypeManager::getSizeOfData(const Operand&).
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uint32_t sizeOfTensorData(uint32_t sizeOfElement, const std::vector<uint32_t>& dimensions);
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// Returns true if the amount of space needed to store a value of the specified
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// dimensions and element size overflows the uint32_t type.
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//
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// Aborts if the specified type is an extension type.
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//
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// See also TypeManager::sizeOfDataOverflowsUInt32(OperandType, const std::vector<uint32_t>&).
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bool nonExtensionOperandSizeOfDataOverflowsUInt32(OperandType type,
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const std::vector<uint32_t>& dimensions);
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// Returns true if the amount of space needed to store a value of the specified
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// dimensions and element size overflows the uint32_t type.
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//
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// See also TypeManager::sizeOfDataOverflowsUInt32(OperandType, const std::vector<uint32_t>&).
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bool sizeOfTensorDataOverflowsUInt32(uint32_t elementSize, const std::vector<uint32_t>& dimensions);
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// Returns true if a non-extension operand type is a scalar type.
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//
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// Aborts if the specified type is an extension type.
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//
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// See also TypeManager::isTensorType(OperandType).
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bool nonExtensionOperandTypeIsScalar(int type);
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// Whether an operand of tensor type has unspecified dimensions.
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//
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// Undefined behavior if the operand type is a scalar type.
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bool tensorHasUnspecifiedDimensions(int type, const uint32_t* dim, uint32_t dimCount);
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bool tensorHasUnspecifiedDimensions(OperandType type, const std::vector<uint32_t>& dimensions);
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bool tensorHasUnspecifiedDimensions(OperandType type, const Dimensions& dimensions);
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bool tensorHasUnspecifiedDimensions(const Operand& operand);
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bool tensorHasUnspecifiedDimensions(const ANeuralNetworksOperandType* type);
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// Returns the number of padding bytes needed to align data starting at `index` with `length` number
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// of bytes such that `index` + returned number of padding bytes is aligned. Refer to
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// `getAlignmentForLength` for more information on alignment (such as what the current alignments
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// are for different data lengths).
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uint32_t alignBytesNeeded(uint32_t index, size_t length);
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// Does a detailed LOG(INFO) of the model
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void logModelToInfo(const Model& model);
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inline std::string toString(uint32_t obj) {
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return std::to_string(obj);
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}
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template <typename Type>
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std::string toString(const std::vector<Type>& range) {
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std::string os = "[";
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for (size_t i = 0; i < range.size(); ++i) {
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os += (i == 0 ? "" : ", ") + toString(range[i]);
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}
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return os += "]";
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}
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template <typename A, typename B>
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std::string toString(const std::pair<A, B>& pair) {
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std::ostringstream oss;
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oss << "(" << pair.first << ", " << pair.second << ")";
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return oss.str();
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}
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inline bool validCode(uint32_t codeCount, uint32_t codeCountOEM, uint32_t code) {
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return (code < codeCount) || (code >= kOEMCodeBase && (code - kOEMCodeBase) < codeCountOEM);
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}
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// Validates an operand type.
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//
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// extensionOperandTypeInfo must be nullptr iff the type is not an extension type.
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//
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// If allowPartial is true, the dimensions may be underspecified.
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int validateOperandType(const ANeuralNetworksOperandType& type,
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const Extension::OperandTypeInformation* const extensionOperandTypeInfo,
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const char* tag, bool allowPartial);
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int validateOperandList(uint32_t count, const uint32_t* list, uint32_t operandCount,
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const char* tag);
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// A set of functions to help validate models containing IF or WHILE operations.
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struct SubgraphValidationHelper {
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// Checks if a given operand is a SUBGRAPH operand with a valid offset.
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std::function<bool(const Operand&)> isValidSubgraphReference;
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// Gets the input count of a subgraph referenced by a given operand.
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std::function<uint32_t(const Operand&)> getSubgraphInputCount;
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// Gets the output count of a subgraph referenced by a given operand.
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std::function<uint32_t(const Operand&)> getSubgraphOutputCount;
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// Gets the specified input operand of a subgraph referenced by a given operand.
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std::function<const Operand*(const Operand&, uint32_t)> getSubgraphInputOperand;
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// Gets the specified output operand of a subgraph referenced by a given operand.
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std::function<const Operand*(const Operand&, uint32_t)> getSubgraphOutputOperand;
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// Whether control flow operations with inner or outer input or output
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// operands of unknown size are allowed.
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bool allowControlFlowOperationWithOperandOfUnknownSize;
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};
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// Returns ANEURALNETWORKS_NO_ERROR if the corresponding operation is defined and can handle the
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// provided operand types in the given HAL version, otherwise returns ANEURALNETWORKS_BAD_DATA.
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// The last argument is only used for validating IF and WHILE operations.
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int validateOperation(ANeuralNetworksOperationType opType, uint32_t inputCount,
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const uint32_t* inputIndexes, uint32_t outputCount,
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const uint32_t* outputIndexes, const std::vector<Operand>& operands,
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HalVersion halVersion, const SubgraphValidationHelper& helper);
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inline size_t getSizeFromInts(int lower, int higher) {
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return (uint32_t)(lower) + ((uint64_t)(uint32_t)(higher) << 32);
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}
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// Convert ANEURALNETWORKS_* result code to ErrorStatus.
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// Not guaranteed to be a 1-to-1 mapping.
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ErrorStatus convertResultCodeToErrorStatus(int resultCode);
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// Convert ErrorStatus to ANEURALNETWORKS_* result code.
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// Not guaranteed to be a 1-to-1 mapping.
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int convertErrorStatusToResultCode(ErrorStatus status);
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// Convert execution results to runtime format. Additionally checks that the
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// returned results abide by the HAL specification, and logs an error if the
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// result violates the specification.
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std::tuple<int, std::vector<OutputShape>, Timing> getExecutionResult(
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ErrorStatus status, std::vector<OutputShape> outputShapes, Timing timing);
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constexpr Priority convertToCanonicalPriority(int32_t priority) {
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switch (priority) {
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case ANEURALNETWORKS_PRIORITY_LOW:
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return Priority::LOW;
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case ANEURALNETWORKS_PRIORITY_MEDIUM:
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return Priority::MEDIUM;
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case ANEURALNETWORKS_PRIORITY_HIGH:
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return Priority::HIGH;
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}
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LOG(FATAL) << "unrecognized priority: " << priority;
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return {};
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}
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// The function syncWait() has the same semantics as the system function
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// ::sync_wait(), except that the syncWait() return value is semantically
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// richer. The timeout parameter is in msecs.
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enum class FenceState {
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ACTIVE, // fence has not been signaled
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SIGNALED, // fence has been signaled
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ERROR, // fence has been placed in the error state
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UNKNOWN, // either bad argument passed to syncWait(), or internal error
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};
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FenceState syncWait(int fd, int timeout);
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#ifdef NN_DEBUGGABLE
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uint32_t getProp(const char* str, uint32_t defaultValue = 0);
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#endif // NN_DEBUGGABLE
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struct ApiVersion {
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Version canonical;
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int64_t featureLevel;
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};
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constexpr auto kHalVersionV1_0ToApi = ApiVersion{.canonical = Version::ANDROID_OC_MR1,
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.featureLevel = ANEURALNETWORKS_FEATURE_LEVEL_1};
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constexpr auto kHalVersionV1_1ToApi = ApiVersion{.canonical = Version::ANDROID_P,
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.featureLevel = ANEURALNETWORKS_FEATURE_LEVEL_2};
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constexpr auto kHalVersionV1_2ToApi = ApiVersion{.canonical = Version::ANDROID_Q,
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.featureLevel = ANEURALNETWORKS_FEATURE_LEVEL_3};
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constexpr auto kHalVersionV1_3ToApi = ApiVersion{.canonical = Version::ANDROID_R,
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.featureLevel = ANEURALNETWORKS_FEATURE_LEVEL_4};
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} // namespace nn
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} // namespace android
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#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H
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