/*
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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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#ifndef ANDROID_ML_NN_COMMON_CPU_EXECUTOR_H
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#define ANDROID_ML_NN_COMMON_CPU_EXECUTOR_H
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#include "HalInterfaces.h"
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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#include "Utils.h"
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#include <android-base/macros.h>
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#include <ui/GraphicBuffer.h>
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#include <algorithm>
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#include <optional>
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#include <vector>
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namespace android {
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namespace nn {
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// Information we maintain about each operand during execution that
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// may change during execution.
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struct RunTimeOperandInfo {
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// TODO Storing the type here is redundant, as it won't change during execution.
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OperandType type;
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// The type and dimensions of the operand. The dimensions can
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// change at runtime. We include the type because it's useful
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// to pass together with the dimension to the functions implementing
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// the operators.
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//
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// A dimension being zero has different meanings for different operands at different stages:
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// - Model inputs:
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// * Specified in model: implies "dynamic", and must be fully-specified in request.
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// * Specified in request: illegal.
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// - Constant operands: illegal.
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// - Model outputs and internal operands:
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// * Before evaluation: implies unknown and to be deduced from execution.
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// * After evaluation:
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// - If isSufficient reports true: the tensor is zero-sized.
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// - Otherwise: implies unknown.
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std::vector<uint32_t> dimensions;
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float scale;
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int32_t zeroPoint;
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// Where the operand's data is stored. Check the corresponding
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// location information in the model to figure out if this points
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// to memory we have allocated for an temporary operand.
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uint8_t* buffer;
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// The length of the buffer.
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uint32_t length;
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// Whether this is a temporary variable, a model input, a constant, etc.
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OperandLifeTime lifetime;
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// Keeps track of how many operations have yet to make use
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// of this temporary variable. When the count is decremented to 0,
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// we free the buffer. For non-temporary variables, this count is
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// always 0.
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uint32_t numberOfUsesLeft;
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Operand::ExtraParams extraParams;
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Shape shape() const {
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return {
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.type = type,
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.dimensions = dimensions,
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.scale = scale,
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.offset = zeroPoint,
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.extraParams = extraParams,
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};
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}
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bool isSufficient() const {
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if (isExtensionOperandType(type)) {
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// We don't know sizes of extension types.
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return true;
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}
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return length >= nonExtensionOperandSizeOfData(type, dimensions);
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}
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};
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// Used to keep a pointer to each of the memory pools.
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//
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// RunTimePoolInfo references a region of memory. Other RunTimePoolInfo objects
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// may reference the same region of memory by either:
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// (1) copying an existing RunTimePoolInfo object, or
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// (2) creating multiple RunTimePoolInfo objects from the same memory resource
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// (e.g., "createFromHidlMemory" or "createFromExistingBuffer")
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//
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// If the underlying region of memory is mapped by "createFromHidlMemory", the
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// mapping will be sustained until it is no longer referenced by any
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// RunTimePoolInfo objects.
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class RunTimePoolInfo {
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public:
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static std::optional<RunTimePoolInfo> createFromHidlMemory(const hidl_memory& hidlMemory);
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static RunTimePoolInfo createFromExistingBuffer(uint8_t* buffer);
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uint8_t* getBuffer() const;
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bool update() const;
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hidl_memory getHidlMemory() const;
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private:
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class RunTimePoolInfoImpl;
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RunTimePoolInfo(const std::shared_ptr<const RunTimePoolInfoImpl>& impl);
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std::shared_ptr<const RunTimePoolInfoImpl> mImpl;
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};
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bool setRunTimePoolInfosFromHidlMemories(std::vector<RunTimePoolInfo>* poolInfos,
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const hidl_vec<hidl_memory>& pools);
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// This class is used to execute a model on the CPU.
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class CpuExecutor {
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public:
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// This constructor allows clients of CpuExecutor to provide custom CPU
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// operation implementations. It is used by a sample driver to test
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// extension support.
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//
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// Note that it is not possible to provide custom CPU implementations for
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// non-OperationResolver operations (b/124041202).
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//
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// The operation resolver must outlive the executor.
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explicit CpuExecutor(const IOperationResolver* operationResolver)
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: mOperationResolver(operationResolver) {}
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CpuExecutor() : CpuExecutor(BuiltinOperationResolver::get()) {}
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// Executes the model. The results will be stored at the locations
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// specified in the constructor.
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// The model must outlive the executor. We prevent it from being modified
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// while this is executing.
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int run(const Model& model, const Request& request,
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const std::vector<RunTimePoolInfo>& modelPoolInfos,
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const std::vector<RunTimePoolInfo>& requestPoolInfos);
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const std::vector<OutputShape>& getOutputShapes() const {
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CHECK(mFinished) << "getOutputShapes() called by an unfinished CpuExecutor.";
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return mOutputShapes;
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}
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private:
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bool initializeRunTimeInfo(const std::vector<RunTimePoolInfo>& modelPoolInfos,
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const std::vector<RunTimePoolInfo>& requestPoolInfos);
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// Runs one operation of the graph.
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int executeOperation(const Operation& entry);
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// Decrement the usage count for the operands listed. Frees the memory
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// allocated for any temporary variable with a count of zero.
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void freeNoLongerUsedOperands(const std::vector<uint32_t>& inputs);
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// Frees the memory allocated for any temporary variable, and sets the
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// output operand shapes returning to the runtime.
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void finish(int result);
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// The model and the request that we'll execute. Only valid while run()
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// is being executed.
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const Model* mModel = nullptr;
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const Request* mRequest = nullptr;
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// We're copying the list of all the dimensions from the model, as
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// these may be modified when we run the operations. Since we're
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// making a full copy, the indexes used in the operand description
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// stay valid.
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// std::vector<uint32_t> mDimensions;
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// Runtime information about all the operands.
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std::vector<RunTimeOperandInfo> mOperands;
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// The output operand shapes returning to the runtime.
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std::vector<OutputShape> mOutputShapes;
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// Whether execution is finished and mOutputShapes is ready
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bool mFinished = false;
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const IOperationResolver* mOperationResolver;
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};
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// Class for setting reasonable OpenMP threading settings. (OpenMP is used by
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// the Eigen matrix library.)
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//
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// Currently sets a low blocktime: the time OpenMP threads busy-wait for more
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// work before going to sleep. See b/79159165, https://reviews.llvm.org/D18577.
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// The default is 200ms, we set to 20ms here, see b/109645291. This keeps the
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// cores enabled throughout inference computation without too much extra power
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// consumption afterwards.
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//
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// The OpenMP settings are thread-local (applying only to worker threads formed
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// from that thread), see https://software.intel.com/en-us/node/522688 and
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// http://lists.llvm.org/pipermail/openmp-dev/2016-July/001432.html. This class
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// ensures that within the scope in which an object is instantiated we use the
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// right settings (scopes may be nested), as long as no other library changes
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// them. (Note that in current NNAPI usage only one instance is used in the
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// CpuExecutor thread).
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//
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// TODO(mikie): consider also setting the number of threads used. Using as many
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// threads as there are cores results in more variable performance: if we don't
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// get all cores for our threads, the latency is doubled as we wait for one core
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// to do twice the amount of work. Reality is complicated though as not all
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// cores are the same. Decision to be based on benchmarking against a
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// representative set of workloads and devices. I'm keeping the code here for
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// reference.
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// b/109953668, disable OpenMP
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#ifdef NNAPI_OPENMP
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class ScopedOpenmpSettings {
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public:
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ScopedOpenmpSettings();
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~ScopedOpenmpSettings();
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DISALLOW_COPY_AND_ASSIGN(ScopedOpenmpSettings);
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private:
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int mBlocktimeInitial;
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#if NNAPI_LIMIT_CPU_THREADS
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int mMaxThreadsInitial;
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#endif
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};
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#endif // NNAPI_OPENMP
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namespace {
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template <typename T>
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T getScalarData(const RunTimeOperandInfo& info) {
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// TODO: Check buffer is at least as long as size of data.
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T* data = reinterpret_cast<T*>(info.buffer);
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return data[0];
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}
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inline bool IsNullInput(const RunTimeOperandInfo *input) {
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return input->lifetime == OperandLifeTime::NO_VALUE;
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}
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inline int NumInputsWithValues(const Operation &operation,
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std::vector<RunTimeOperandInfo> &operands) {
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const std::vector<uint32_t> &inputs = operation.inputs;
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return std::count_if(inputs.begin(), inputs.end(),
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[&operands](uint32_t i) {
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return !IsNullInput(&operands[i]);
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});
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}
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inline int NumOutputs(const Operation &operation) {
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return operation.outputs.size();
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}
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inline size_t NumDimensions(const RunTimeOperandInfo *operand) {
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return operand->shape().dimensions.size();
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}
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inline uint32_t SizeOfDimension(const RunTimeOperandInfo *operand, int i) {
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return operand->shape().dimensions[i];
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}
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inline RunTimeOperandInfo *GetInput(const Operation &operation,
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std::vector<RunTimeOperandInfo> &operands,
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int index) {
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return &operands[operation.inputs[index]];
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}
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inline RunTimeOperandInfo *GetOutput(const Operation &operation,
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std::vector<RunTimeOperandInfo> &operands,
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int index) {
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return &operands[operation.outputs[index]];
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}
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} // anonymous namespace
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} // namespace nn
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} // namespace android
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#endif // ANDROID_ML_NN_COMMON_CPU_EXECUTOR_H
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