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Diffstat (limited to 'nn/common/include/LegacyUtils.h')
-rw-r--r-- | nn/common/include/LegacyUtils.h | 313 |
1 files changed, 313 insertions, 0 deletions
diff --git a/nn/common/include/LegacyUtils.h b/nn/common/include/LegacyUtils.h new file mode 100644 index 000000000..64ee835f8 --- /dev/null +++ b/nn/common/include/LegacyUtils.h @@ -0,0 +1,313 @@ +/* + * Copyright (C) 2017 The Android Open Source Project + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +// This file contains pre-canonical-types utility code and does not includes HAL +// utilities. LegacyHalUtils.h is a superset of these utilities that includes +// HAL utilities. + +#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H +#define ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H + +#include <android-base/logging.h> + +#include <string> +#include <tuple> +#include <utility> +#include <vector> + +#include <nnapi/TypeUtils.h> +#include <nnapi/Types.h> +#include "NeuralNetworks.h" +#include "OperationResolver.h" + +namespace android { +namespace nn { + +// The number of data types (OperandCode) defined in NeuralNetworks.h. +const int kNumberOfDataTypes = 16; + +// The number of operation types (OperationCode) defined in NeuralNetworks.h. +const int kNumberOfOperationTypes = 102; +static_assert(kNumberOfOperationTypes == BuiltinOperationResolver::kNumberOfOperationTypes); + +// The number of execution preferences defined in NeuralNetworks.h. +const int kNumberOfPreferences = 3; + +// The number of data types (OperandCode) defined in NeuralNetworksOEM.h. +const int kNumberOfDataTypesOEM = 2; + +// The number of operation types (OperationCode) defined in NeuralNetworksOEM.h. +const int kNumberOfOperationTypesOEM = 1; + +// The lowest number assigned to any OEM Code in NeuralNetworksOEM.h. +const int kOEMCodeBase = 10000; + +/* IMPORTANT: if you change the following list, don't + * forget to update the corresponding 'tags' table in + * the initVlogMask() function implemented in Utils.cpp. + */ +enum VLogFlags { MODEL = 0, COMPILATION, EXECUTION, CPUEXE, MANAGER, DRIVER, MEMORY }; + +#define VLOG_IS_ON(TAG) ((vLogMask & (1 << (TAG))) != 0) + +#define VLOG(TAG) \ + if (LIKELY(!VLOG_IS_ON(TAG))) \ + ; \ + else \ + LOG(INFO) + +extern int vLogMask; +void initVLogMask(); + +#ifdef NN_DEBUGGABLE +#define SHOW_IF_DEBUG(msg) msg +#else +#define SHOW_IF_DEBUG(msg) "" +#endif + +// DEPRECATED(b/118737105). Use CHECK. +#define nnAssert(v) CHECK(v) + +#define NN_RETURN_IF_ERROR(expr) \ + do { \ + int _errorCode = (expr); \ + if (_errorCode != ANEURALNETWORKS_NO_ERROR) { \ + return _errorCode; \ + } \ + } while (0) + +// Make an TimeoutDuration from a duration in nanoseconds. If the value exceeds +// the max duration, return the maximum expressible duration. +TimeoutDuration makeTimeoutDuration(uint64_t nanoseconds); + +// Type to represent a deadline time point across processes. +using Deadline = std::chrono::steady_clock::time_point; + +// Make an Deadline from a duration. If the sum of the current time and the +// duration exceeds the max time, return a time point holding the maximum +// expressible time. +Deadline makeDeadline(TimeoutDuration duration); +inline Deadline makeDeadline(uint64_t duration) { + return makeDeadline(makeTimeoutDuration(duration)); +} + +// Convenience function. If the duration is provided, this function creates a +// Deadline using makeDeadline. If the duration is not provided, this function +// returns std::nullopt. +inline std::optional<Deadline> makeDeadline(OptionalTimeoutDuration duration) { + return duration.has_value() ? makeDeadline(*duration) : std::optional<Deadline>{}; +} +inline std::optional<Deadline> makeDeadline(std::optional<uint64_t> duration) { + return duration.has_value() ? makeDeadline(*duration) : std::optional<Deadline>{}; +} + +// Returns true if the deadline has passed. Returns false if either the deadline +// has not been exceeded or if the deadline is not present. +bool hasDeadlinePassed(const std::optional<Deadline>& deadline); + +// Make an OptionalTimePoint from an optional Deadline. If the Deadline is not +// provided, this function returns none for OptionalTimePoint. +OptionalTimePoint makeTimePoint(const std::optional<Deadline>& deadline); + +// Returns true if an operand type is an extension type. +bool isExtensionOperandType(OperandType type); + +// Returns true if an operation type is an extension type. +bool isExtensionOperationType(OperationType type); + +// Returns the amount of space needed to store a value of the specified +// dimensions and type. For a tensor with unspecified rank or at least one +// unspecified dimension, returns zero. +// +// Aborts if the specified type is an extension type. +// Aborts if the size would overflow the return type. +// +// See also TypeManager::getSizeOfData(OperandType, const std::vector<uint32_t>&). +uint32_t nonExtensionOperandSizeOfData(OperandType type, const std::vector<uint32_t>& dimensions); + +// Returns the amount of space needed to store a value of the dimensions and +// type of this operand. For a tensor with unspecified rank or at least one +// unspecified dimension, returns zero. +// +// Aborts if the specified type is an extension type. +// Aborts if the size would overflow the return type. +// +// See also TypeManager::getSizeOfData(const Operand&). +inline uint32_t nonExtensionOperandSizeOfData(const Operand& operand) { + return nonExtensionOperandSizeOfData(operand.type, operand.dimensions); +} + +// Returns the amount of space needed to store a value of the specified +// dimensions and element size. For a tensor with unspecified rank or at least +// one unspecified dimension, returns zero. +// +// Aborts if the size would overflow the return type. +// +// See also TypeManager::getSizeOfData(const Operand&). +uint32_t sizeOfTensorData(uint32_t sizeOfElement, const std::vector<uint32_t>& dimensions); + +// Returns true if the amount of space needed to store a value of the specified +// dimensions and element size overflows the uint32_t type. +// +// Aborts if the specified type is an extension type. +// +// See also TypeManager::sizeOfDataOverflowsUInt32(OperandType, const std::vector<uint32_t>&). +bool nonExtensionOperandSizeOfDataOverflowsUInt32(OperandType type, + const std::vector<uint32_t>& dimensions); + +// Returns true if the amount of space needed to store a value of the specified +// dimensions and element size overflows the uint32_t type. +// +// See also TypeManager::sizeOfDataOverflowsUInt32(OperandType, const std::vector<uint32_t>&). +bool sizeOfTensorDataOverflowsUInt32(uint32_t elementSize, const std::vector<uint32_t>& dimensions); + +// Returns true if a non-extension operand type is a scalar type. +// +// Aborts if the specified type is an extension type. +// +// See also TypeManager::isTensorType(OperandType). +bool nonExtensionOperandTypeIsScalar(int type); + +// Whether an operand of tensor type has unspecified dimensions. +// +// Undefined behavior if the operand type is a scalar type. +bool tensorHasUnspecifiedDimensions(int type, const uint32_t* dim, uint32_t dimCount); +bool tensorHasUnspecifiedDimensions(OperandType type, const std::vector<uint32_t>& dimensions); +bool tensorHasUnspecifiedDimensions(OperandType type, const Dimensions& dimensions); +bool tensorHasUnspecifiedDimensions(const Operand& operand); +bool tensorHasUnspecifiedDimensions(const ANeuralNetworksOperandType* type); + +// Returns the number of padding bytes needed to align data of the +// specified length. It aligns object of length: +// 2, 3 on a 2 byte boundary, +// 4+ on a 4 byte boundary. +// We may want to have different alignments for tensors. +// TODO: This is arbitrary, more a proof of concept. We need +// to determine what this should be. +uint32_t alignBytesNeeded(uint32_t index, size_t length); + +// Does a detailed LOG(INFO) of the model +void logModelToInfo(const Model& model); + +inline std::string toString(uint32_t obj) { + return std::to_string(obj); +} + +template <typename Type> +std::string toString(const std::vector<Type>& range) { + std::string os = "["; + for (size_t i = 0; i < range.size(); ++i) { + os += (i == 0 ? "" : ", ") + toString(range[i]); + } + return os += "]"; +} + +template <typename A, typename B> +std::string toString(const std::pair<A, B>& pair) { + std::ostringstream oss; + oss << "(" << pair.first << ", " << pair.second << ")"; + return oss.str(); +} + +inline bool validCode(uint32_t codeCount, uint32_t codeCountOEM, uint32_t code) { + return (code < codeCount) || (code >= kOEMCodeBase && (code - kOEMCodeBase) < codeCountOEM); +} + +// Validates an operand type. +// +// extensionOperandTypeInfo must be nullptr iff the type is not an extension type. +// +// If allowPartial is true, the dimensions may be underspecified. +int validateOperandType(const ANeuralNetworksOperandType& type, + const Extension::OperandTypeInformation* const extensionOperandTypeInfo, + const char* tag, bool allowPartial); +int validateOperandList(uint32_t count, const uint32_t* list, uint32_t operandCount, + const char* tag); + +// A set of functions to help validate models containing IF or WHILE operations. +struct SubgraphValidationHelper { + // Checks if a given operand is a SUBGRAPH operand with a valid offset. + std::function<bool(const Operand&)> isValidSubgraphReference; + // Gets the input count of a subgraph referenced by a given operand. + std::function<uint32_t(const Operand&)> getSubgraphInputCount; + // Gets the output count of a subgraph referenced by a given operand. + std::function<uint32_t(const Operand&)> getSubgraphOutputCount; + // Gets the specified input operand of a subgraph referenced by a given operand. + std::function<const Operand*(const Operand&, uint32_t)> getSubgraphInputOperand; + // Gets the specified output operand of a subgraph referenced by a given operand. + std::function<const Operand*(const Operand&, uint32_t)> getSubgraphOutputOperand; + // Whether control flow operations with inner or outer input or output + // operands of unknown size are allowed. + bool allowControlFlowOperationWithOperandOfUnknownSize; +}; + +// Returns ANEURALNETWORKS_NO_ERROR if the corresponding operation is defined and can handle the +// provided operand types in the given HAL version, otherwise returns ANEURALNETWORKS_BAD_DATA. +// The last argument is only used for validating IF and WHILE operations. +int validateOperation(ANeuralNetworksOperationType opType, uint32_t inputCount, + const uint32_t* inputIndexes, uint32_t outputCount, + const uint32_t* outputIndexes, const std::vector<Operand>& operands, + HalVersion halVersion, const SubgraphValidationHelper& helper); + +inline size_t getSizeFromInts(int lower, int higher) { + return (uint32_t)(lower) + ((uint64_t)(uint32_t)(higher) << 32); +} + +// Convert ANEURALNETWORKS_* result code to ErrorStatus. +// Not guaranteed to be a 1-to-1 mapping. +ErrorStatus convertResultCodeToErrorStatus(int resultCode); + +// Convert ErrorStatus to ANEURALNETWORKS_* result code. +// Not guaranteed to be a 1-to-1 mapping. +int convertErrorStatusToResultCode(ErrorStatus status); + +// Convert execution results to runtime format. Additionally checks that the +// returned results abide by the HAL specification, and logs an error if the +// result violates the specification. +std::tuple<int, std::vector<OutputShape>, Timing> getExecutionResult( + ErrorStatus status, std::vector<OutputShape> outputShapes, Timing timing); + +constexpr Priority convertToCanonicalPriority(int32_t priority) { + switch (priority) { + case ANEURALNETWORKS_PRIORITY_LOW: + return Priority::LOW; + case ANEURALNETWORKS_PRIORITY_MEDIUM: + return Priority::MEDIUM; + case ANEURALNETWORKS_PRIORITY_HIGH: + return Priority::HIGH; + } + LOG(FATAL) << "unrecognized priority: " << priority; + return {}; +} + +// The function syncWait() has the same semantics as the system function +// ::sync_wait(), except that the syncWait() return value is semantically +// richer. The timeout parameter is in msecs. +enum class FenceState { + ACTIVE, // fence has not been signaled + SIGNALED, // fence has been signaled + ERROR, // fence has been placed in the error state + UNKNOWN, // either bad argument passed to syncWait(), or internal error +}; +FenceState syncWait(int fd, int timeout); + +#ifdef NN_DEBUGGABLE +uint32_t getProp(const char* str, uint32_t defaultValue = 0); +#endif // NN_DEBUGGABLE + +} // namespace nn +} // namespace android + +#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_LEGACY_UTILS_H |