/*
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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_OPERATIONS_UTILS_H
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#define ANDROID_ML_NN_COMMON_OPERATIONS_UTILS_H
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#include "Utils.h"
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#include <cstdint>
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#include <vector>
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namespace android {
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namespace nn {
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// DEPRECATED. Use NN_RET_CHECK instead.
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#define NN_CHECK(x) NN_RET_CHECK(x)
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#define NN_OPS_CHECK(x) NN_RET_CHECK(x)
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// DEPRECATED. Use NN_RET_CHECK_EQ instead.
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#define NN_CHECK_EQ(x, y) NN_RET_CHECK_EQ(x, y)
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// An 8-bit boolean type (sizeof(bool) is implementation-defined).
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typedef uint8_t bool8;
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enum PaddingScheme {
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kPaddingUnknown = 0,
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kPaddingSame = 1,
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kPaddingValid = 2,
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};
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// Stores operand type information. "Shape" is a historical name.
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struct Shape {
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OperandType type;
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std::vector<uint32_t> dimensions;
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float scale;
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int32_t offset;
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Operand::ExtraParams extraParams;
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};
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// Provides information available during graph creation to validate an operation.
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class IOperationValidationContext {
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public:
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virtual ~IOperationValidationContext() {}
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// The HAL version of the environment in which the operation is to be
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// executed.
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//
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// Operation validation logic needs to handle all HAL versions to support
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// the following use cases (assume in these examples that the latest HAL
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// version is V1_2):
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// 1. Our runtime wants to distribute work to a driver implementing an older
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// HAL version and calls, for example,
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// compliantWithV1_0(const V1_2::Model&).
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// 2. A driver implements an older HAL version and delegates model
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// validation to, for example, validateModel(const V1_0::Model&).
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//
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// If getHalVersion() returns HalVersion::V1_0 and the operation
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// is only supported since HalVersion::V1_1, validation will fail.
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virtual HalVersion getHalVersion() const = 0;
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virtual uint32_t getNumInputs() const = 0;
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virtual OperandType getInputType(uint32_t index) const = 0;
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virtual Shape getInputShape(uint32_t index) const = 0;
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virtual const Operand::ExtraParams getInputExtraParams(uint32_t index) const = 0;
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virtual uint32_t getNumOutputs() const = 0;
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virtual OperandType getOutputType(uint32_t index) const = 0;
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virtual Shape getOutputShape(uint32_t index) const = 0;
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};
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// Provides inputs and outputs during operation execution.
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class IOperationExecutionContext {
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public:
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virtual ~IOperationExecutionContext() {}
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virtual uint32_t getNumInputs() const = 0;
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virtual OperandType getInputType(uint32_t index) const = 0;
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virtual Shape getInputShape(uint32_t index) const = 0;
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virtual const void* getInputBuffer(uint32_t index) const = 0;
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virtual const Operand::ExtraParams getInputExtraParams(uint32_t index) const = 0;
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virtual uint32_t getNumOutputs() const = 0;
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virtual OperandType getOutputType(uint32_t index) const = 0;
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virtual Shape getOutputShape(uint32_t index) const = 0;
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virtual void* getOutputBuffer(uint32_t index) = 0;
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// Updates the output shape, allocating the buffer if necessary.
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virtual bool setOutputShape(uint32_t index, const Shape& shape) = 0;
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virtual bool isOmittedInput(uint32_t index) const = 0;
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virtual bool isOmittedOutput(uint32_t index) const = 0;
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template <typename T>
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const T* getInputBuffer(uint32_t index) const {
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return reinterpret_cast<const T*>(getInputBuffer(index));
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}
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template <typename T>
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T* getOutputBuffer(uint32_t index) {
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return reinterpret_cast<T*>(getOutputBuffer(index));
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}
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template <typename T>
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T getInputValue(uint32_t index) const {
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return getInputBuffer<T>(index)[0];
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}
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};
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// Verifies that the number and types of operation inputs are as expected.
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bool validateInputTypes(const IOperationValidationContext* context,
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const std::vector<OperandType>& expectedTypes);
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// Verifies that the number and types of operation outputs are as expected.
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bool validateOutputTypes(const IOperationValidationContext* context,
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const std::vector<OperandType>& expectedTypes);
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// Verifies that the HAL version specified in the context is greater or equal
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// than the minimal supported HAL version.
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bool validateHalVersion(const IOperationValidationContext* context,
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HalVersion minSupportedHalVersion);
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// Verifies that the two shapes are the same.
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bool SameShape(const Shape& in1, const Shape& in2);
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// Sets out to the same shape as in.
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bool SetShape(const Shape& in, Shape* out);
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// Combine two tensor dimensions, both can have unspecified dimensions.
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bool combineDimensions(const std::vector<uint32_t>& lhs, const std::vector<uint32_t>& rhs,
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std::vector<uint32_t>* combined);
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// Return the total number of elements, i.e. all the dimensions multiplied
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// together. For a scalar, returns one.
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uint32_t getNumberOfElements(const Shape& shape);
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uint32_t getNumberOfElements(const Shape& shape,
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size_t firstAxisInclusive,
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size_t lastAxisExclusive);
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uint32_t getNumberOfDimensions(const Shape& shape);
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uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx);
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// Converts an axis index from the range [-dims, dims) into the range [0, dims).
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bool handleNegativeAxis(int32_t numberOfDimensions, int32_t* axis);
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inline bool handleNegativeAxis(const Shape& shape, int32_t* axis) {
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return handleNegativeAxis(getNumberOfDimensions(shape), axis);
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}
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inline int32_t computeOutSize(int32_t imageSize, int32_t filterSize, int32_t stride,
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int32_t paddingHead, int32_t paddingTail) {
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return (imageSize - filterSize + stride + paddingHead + paddingTail) / stride;
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}
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inline int32_t computeOutSize(int32_t imageSize, int32_t filterSize, int32_t stride,
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int32_t dilationRate, int32_t paddingHead, int32_t paddingTail) {
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int32_t effectiveFilterSize = ((filterSize - 1) * dilationRate + 1);
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return (imageSize - effectiveFilterSize + stride + paddingHead + paddingTail) / stride;
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}
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inline int32_t computeOutSizeTransposeConv(int32_t imageSize, int32_t filterSize, int32_t stride,
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int32_t paddingHead, int32_t paddingTail) {
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return imageSize * stride + filterSize - stride - paddingHead - paddingTail;
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}
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__wur bool QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, int* shift);
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__wur
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bool QuantizeMultiplierSmallerThanOne(double double_multiplier,
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int32_t* quantized_multiplier,
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int32_t* right_shift);
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__wur
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bool QuantizeMultiplierGreaterThanOne(double double_multiplier,
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int32_t* quantized_multiplier,
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int* left_shift);
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__wur bool GetQuantizedConvolutionMultipler(const Shape& inputShape, const Shape& filterShape,
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const Shape& biasShape, const Shape& outputShape,
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double* multiplier);
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void CalculateActivationRangeUint8(int32_t activation,
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const Shape& outputShape,
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int32_t* act_min,
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int32_t* act_max);
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void CalculateActivationRangeFloat(int32_t activation,
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float* activation_min,
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float* activation_max);
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int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
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void calculateExplicitPaddingImpl(int32_t in_size, int32_t stride, int32_t dilation_factor,
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int32_t filter_size, int32_t padding_implicit,
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bool isTransposeConv, int32_t* padding_head,
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int32_t* padding_tail);
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inline void calculateExplicitPadding(int32_t in_size, int32_t stride, int32_t dilation_factor,
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int32_t filter_size, int32_t padding_implicit,
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int32_t* padding_head, int32_t* padding_tail) {
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calculateExplicitPaddingImpl(in_size, stride, dilation_factor, filter_size, padding_implicit,
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/*isTransposeConv=*/false, padding_head, padding_tail);
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}
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inline void calculateExplicitPadding(int32_t in_size, int32_t stride, int32_t filter_size,
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int32_t padding_implicit, int32_t* padding_head,
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int32_t* padding_tail) {
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calculateExplicitPadding(in_size, stride, 1, filter_size, padding_implicit, padding_head,
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padding_tail);
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}
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inline void calculateExplicitPaddingTransposeConv(int32_t in_size, int32_t stride,
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int32_t filter_size, int32_t padding_implicit,
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int32_t* padding_head, int32_t* padding_tail) {
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calculateExplicitPaddingImpl(in_size, stride, /*dilation_factor=*/1, filter_size,
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padding_implicit, /*isTransposeConv=*/true, padding_head,
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padding_tail);
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}
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inline PaddingScheme getPaddingScheme(int32_t inWidth, int32_t inHeight,
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int32_t strideWidth, int32_t strideHeight,
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int32_t filterWidth, int32_t filterHeight,
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int32_t paddingLeft, int32_t paddingRight,
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int32_t paddingTop, int32_t paddingBottom) {
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if (paddingLeft == 0 && paddingRight == 0 && paddingTop == 0 && paddingBottom == 0) {
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return kPaddingValid;
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}
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int32_t expectedPaddingLeft, expectedPaddingRight;
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int32_t expectedPaddingTop, expectedPaddingBottom;
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calculateExplicitPadding(inWidth, strideWidth, filterWidth, kPaddingSame,
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&expectedPaddingLeft, &expectedPaddingRight);
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calculateExplicitPadding(inHeight, strideHeight, filterHeight, kPaddingSame,
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&expectedPaddingTop, &expectedPaddingBottom);
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if (expectedPaddingLeft == paddingLeft && expectedPaddingRight == paddingRight &&
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expectedPaddingTop == paddingTop && expectedPaddingBottom == paddingBottom) {
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return kPaddingSame;
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} else {
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return kPaddingUnknown;
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}
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}
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// TODO: add more documentation from upstream.
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// Reverse order of bits in the mask to match the expected order in kernel
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inline int ReverseMaskBits(int mask, int num_dimensions) {
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int out = 0;
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for (int dim = 0; dim < num_dimensions; dim++) {
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out <<= 1;
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out += (mask & 1);
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mask >>= 1;
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}
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return out;
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}
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// TODO: add more documentation from upstream.
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inline int32_t PositiveRemainder(int32_t dividend, int32_t divisor) {
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return (divisor + (dividend % divisor)) % divisor;
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}
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// TODO: add more documentation from upstream.
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inline int32_t ClampedIndex(int32_t index, int dim, bool pos_stride) {
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return pos_stride
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? (index >= dim ? dim
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: PositiveRemainder(
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std::min(std::max(index, -dim), dim), dim))
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: (index < -dim
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? -1
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: PositiveRemainder(
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std::min(std::max(index, -dim), dim - 1), dim));
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}
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// Broadcasts input shape against one another and puts the result into output
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// shape. Returns true on success and false on error.
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bool calculateBroadcastedShape(const Shape& in1, const Shape& in2, Shape* out);
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// Dequantizes a value and quantizes it back using new scale and offset.
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uint8_t requantize(uint8_t value, const Shape& oldShape, const Shape& newShape);
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// Preparation functions for the corresponding ops
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bool floorPrepare(const Shape& input, Shape* output);
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bool depthwiseConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
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int32_t padding_left, int32_t padding_right, int32_t padding_top,
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int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
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int32_t depth_multiplier, int32_t dilation_width_factor,
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int32_t dilation_height_factor, Shape* output);
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bool genericActivationPrepare(const Shape& input, Shape* output);
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bool genericNormalizationPrepare(const Shape& input, Shape* output);
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bool reshapePrepare(const Shape& input,
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const int32_t* targetDims,
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const int32_t targetDimsSize,
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Shape* output);
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bool depthToSpacePrepare(const Shape& input,
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int32_t blockSize,
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Shape* output);
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bool spaceToDepthPrepare(const Shape& input,
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int32_t blockSize,
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Shape* output);
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bool embeddingLookupPrepare(const Shape &valueShape,
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const Shape &lookupShape,
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Shape *outputShape);
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bool hashtableLookupPrepare(const Shape &lookupShape,
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const Shape &keyShape,
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const Shape &valueShape,
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Shape *outputShape,
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Shape *hitShape);
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bool padPrepare(const Shape& input,
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const int32_t* paddingsData,
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const Shape& paddingsShape,
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Shape* output);
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bool batchToSpacePrepare(const Shape& input,
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const int32_t* blockSizeData,
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const Shape& blockSizeShape,
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Shape* output);
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bool spaceToBatchPrepare(const Shape& input,
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const int32_t* blockSizeData,
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const Shape& blockSizeShape,
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const int32_t* paddingsData,
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const Shape& paddingsShape,
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Shape* output);
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bool squeezePrepare(const Shape& input,
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const int32_t* squeezeDims,
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const Shape& squeezeDimsShape,
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Shape* output);
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bool meanPrepare(const Shape& input,
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const int32_t* axisData,
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const Shape& axisShape,
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bool keepDims,
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Shape* output);
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bool stridedSlicePrepare(const Shape& input,
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const int32_t* beginData, const Shape& beginShape,
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const int32_t* endData, const Shape& endShape,
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const int32_t* stridesData, const Shape& stridesShape,
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int32_t beginMask, int32_t endMask, int32_t shrinkAxisMask,
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Shape* output);
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bool argMinMaxPrepare(const Shape& input, int32_t axis, Shape* output);
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bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs, std::vector<Shape>* output);
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bool groupedConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
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int32_t padding_left, int32_t padding_right, int32_t padding_top,
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int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
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int32_t numGroups, Shape* output);
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// Transposes the first two dimensions.
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template <typename T>
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inline bool transposeFirstTwoDimensions(const T* buffer, const Shape& shape, T* transposedBuffer) {
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const int numDims = getNumberOfDimensions(shape);
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NN_RET_CHECK(numDims >= 2);
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const int firstDim = getSizeOfDimension(shape, 0);
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const int secondDim = getSizeOfDimension(shape, 1);
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int blockSize = 1;
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for (int i = 2; i < numDims; ++i) {
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blockSize *= getSizeOfDimension(shape, i);
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}
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for (int i = 0; i < firstDim; ++i) {
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for (int j = 0; j < secondDim; ++j) {
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for (int k = 0; k < blockSize; ++k) {
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transposedBuffer[(j * firstDim + i) * blockSize + k] =
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buffer[(i * secondDim + j) * blockSize + k];
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}
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}
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}
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return true;
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}
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inline bool transposeFirstTwoDimensions(const Shape& shape, Shape* transposedShape) {
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NN_RET_CHECK(getNumberOfDimensions(shape) >= 2);
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*transposedShape = shape;
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transposedShape->dimensions[0] = shape.dimensions[1];
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transposedShape->dimensions[1] = shape.dimensions[0];
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return true;
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}
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// Given two 3-dimensional tensors, merge them into one 3-dimensional tensor
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// at the third dimension. The merged tensor's third dimension size will be
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// sum of that of the two inputs.
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template <typename T>
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inline bool mergeThirdDimension(const T* bufferA, const std::vector<uint32_t>& dimsA,
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const T* bufferB, const std::vector<uint32_t>& dimsB, T* merged) {
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NN_RET_CHECK_EQ(dimsA.size(), 3u);
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NN_RET_CHECK_EQ(dimsB.size(), 3u);
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NN_RET_CHECK_EQ(dimsA[0], dimsB[0]);
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NN_RET_CHECK_EQ(dimsA[1], dimsB[1]);
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for (unsigned int i = 0; i < dimsA[0]; ++i) {
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for (unsigned int j = 0; j < dimsA[1]; ++j) {
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for (unsigned int k = 0; k < dimsA[2]; ++k) {
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merged[(i * dimsA[1] + j) * (dimsA[2] + dimsB[2]) + k] =
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bufferA[(i * dimsA[1] + j) * dimsA[2] + k];
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}
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for (unsigned int k = 0; k < dimsB[2]; ++k) {
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merged[(i * dimsA[1] + j) * (dimsA[2] + dimsB[2]) + dimsA[2] + k] =
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bufferB[(i * dimsB[1] + j) * dimsB[2] + k];
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}
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}
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}
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return true;
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}
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} // namespace nn
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} // namespace android
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#endif // ANDROID_ML_NN_COMMON_OPERATIONS_UTILS_H
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