blob: 00ae85517e159bf11d56af876e149e0322b42e22 [file] [log] [blame]
/*
* 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.
*/
#ifndef ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_UTILS_H
#define ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_UTILS_H
#include <algorithm>
#include <cstdint>
#include <vector>
#include "HalInterfaces.h"
#include "Utils.h"
namespace android {
namespace nn {
// DEPRECATED. Use NN_RET_CHECK instead.
#define NN_CHECK(x) NN_RET_CHECK(x)
#define NN_OPS_CHECK(x) NN_RET_CHECK(x)
// DEPRECATED. Use NN_RET_CHECK_EQ instead.
#define NN_CHECK_EQ(x, y) NN_RET_CHECK_EQ(x, y)
// An 8-bit boolean type (sizeof(bool) is implementation-defined).
typedef uint8_t bool8;
enum PaddingScheme {
kPaddingUnknown = 0,
kPaddingSame = 1,
kPaddingValid = 2,
};
// Stores operand type information. "Shape" is a historical name.
struct Shape {
hal::OperandType type;
std::vector<uint32_t> dimensions;
float scale;
int32_t offset;
hal::Operand::ExtraParams extraParams;
};
// Provides information available during graph creation to validate an operation.
class IOperationValidationContext {
public:
virtual ~IOperationValidationContext() {}
virtual const char* getOperationName() const = 0;
// The HAL version of the environment in which the operation is to be
// executed.
//
// Operation validation logic needs to handle all HAL versions to support
// the following use cases (assume in these examples that the latest HAL
// version is V1_2):
// 1. Our runtime wants to distribute work to a driver implementing an older
// HAL version and calls, for example,
// compliantWithV1_0(const V1_2::Model&).
// 2. A driver implements an older HAL version and delegates model
// validation to, for example, validateModel(const V1_0::Model&).
//
// If getHalVersion() returns HalVersion::V1_0 and the operation
// is only supported since HalVersion::V1_1, validation will fail.
virtual HalVersion getHalVersion() const = 0;
virtual uint32_t getNumInputs() const = 0;
virtual hal::OperandType getInputType(uint32_t index) const = 0;
virtual Shape getInputShape(uint32_t index) const = 0;
virtual const hal::Operand::ExtraParams getInputExtraParams(uint32_t index) const = 0;
virtual uint32_t getNumOutputs() const = 0;
virtual hal::OperandType getOutputType(uint32_t index) const = 0;
virtual Shape getOutputShape(uint32_t index) const = 0;
};
// Provides inputs and outputs during operation execution.
class IOperationExecutionContext {
public:
virtual ~IOperationExecutionContext() {}
virtual uint32_t getNumInputs() const = 0;
virtual hal::OperandType getInputType(uint32_t index) const = 0;
virtual Shape getInputShape(uint32_t index) const = 0;
virtual const void* getInputBuffer(uint32_t index) const = 0;
virtual const hal::Operand::ExtraParams getInputExtraParams(uint32_t index) const = 0;
virtual uint32_t getNumOutputs() const = 0;
virtual hal::OperandType getOutputType(uint32_t index) const = 0;
virtual Shape getOutputShape(uint32_t index) const = 0;
virtual void* getOutputBuffer(uint32_t index) = 0;
// Updates the output shape, allocating the buffer if necessary.
virtual bool setOutputShape(uint32_t index, const Shape& shape) = 0;
virtual bool isOmittedInput(uint32_t index) const = 0;
virtual bool isOmittedOutput(uint32_t index) const = 0;
template <typename T>
const T* getInputBuffer(uint32_t index) const {
return reinterpret_cast<const T*>(getInputBuffer(index));
}
template <typename T>
T* getOutputBuffer(uint32_t index) {
return reinterpret_cast<T*>(getOutputBuffer(index));
}
template <typename T>
T getInputValue(uint32_t index) const {
return getInputBuffer<T>(index)[0];
}
};
// Verifies that the number and types of operation inputs are as expected.
bool validateInputTypes(const IOperationValidationContext* context,
const std::vector<hal::OperandType>& expectedTypes);
// Verifies that the number and types of operation outputs are as expected.
bool validateOutputTypes(const IOperationValidationContext* context,
const std::vector<hal::OperandType>& expectedTypes);
// Verifies that the HAL version specified in the context is greater or equal
// than the minimal supported HAL version.
bool validateHalVersion(const IOperationValidationContext* context,
HalVersion minSupportedHalVersion);
// Verifies that the two shapes are the same.
bool SameShape(const Shape& in1, const Shape& in2);
// Sets out to the same shape as in.
bool SetShape(const Shape& in, Shape* out);
// Combine two tensor dimensions, both can have unspecified dimensions.
bool combineDimensions(const std::vector<uint32_t>& lhs, const std::vector<uint32_t>& rhs,
std::vector<uint32_t>* combined);
// Return the total number of elements, i.e. all the dimensions multiplied
// together. For a scalar, returns one.
uint32_t getNumberOfElements(const Shape& shape);
uint32_t getNumberOfElements(const Shape& shape, size_t firstAxisInclusive,
size_t lastAxisExclusive);
uint32_t getNumberOfDimensions(const Shape& shape);
uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx);
// Converts an axis index from the range [-dims, dims) into the range [0, dims).
bool handleNegativeAxis(int32_t numberOfDimensions, int32_t* axis);
inline bool handleNegativeAxis(const Shape& shape, int32_t* axis) {
return handleNegativeAxis(getNumberOfDimensions(shape), axis);
}
inline int32_t computeOutSize(int32_t imageSize, int32_t filterSize, int32_t stride,
int32_t paddingHead, int32_t paddingTail) {
return (imageSize - filterSize + stride + paddingHead + paddingTail) / stride;
}
inline int32_t computeOutSize(int32_t imageSize, int32_t filterSize, int32_t stride,
int32_t dilationRate, int32_t paddingHead, int32_t paddingTail) {
int32_t effectiveFilterSize = ((filterSize - 1) * dilationRate + 1);
return (imageSize - effectiveFilterSize + stride + paddingHead + paddingTail) / stride;
}
inline int32_t computeOutSizeTransposeConv(int32_t imageSize, int32_t filterSize, int32_t stride,
int32_t paddingHead, int32_t paddingTail) {
return imageSize * stride + filterSize - stride - paddingHead - paddingTail;
}
__wur bool QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier,
int32_t* shift);
__wur bool QuantizeMultiplierSmallerThanOne(double double_multiplier, int32_t* quantized_multiplier,
int32_t* right_shift);
// Same as QuantizeMultiplierSmallerThanOne but returns left shift (i.e. negated
// right shift), so that it has the same interface as
// QuantizeMultiplierGreaterThanOne and QuantizeMultiplier functions.
__wur bool QuantizeMultiplierSmallerThanOneExp(double double_multiplier,
int32_t* quantized_multiplier, int32_t* left_shift);
__wur bool QuantizeMultiplierGreaterThanOne(double double_multiplier, int32_t* quantized_multiplier,
int* left_shift);
__wur bool GetQuantizedConvolutionMultipler(const Shape& inputShape, const Shape& filterShape,
const Shape& biasShape, const Shape& outputShape,
double* multiplier);
void CalculateActivationRangeUint8(int32_t activation, const Shape& outputShape, int32_t* act_min,
int32_t* act_max);
void CalculateActivationRangeInt8(int32_t activation, const Shape& outputShape, int32_t* act_min,
int32_t* act_max);
void CalculateActivationRangeFloat(int32_t activation, float* activation_min,
float* activation_max);
int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift);
void calculateExplicitPaddingImpl(int32_t in_size, int32_t stride, int32_t dilation_factor,
int32_t filter_size, int32_t padding_implicit,
bool isTransposeConv, int32_t* padding_head,
int32_t* padding_tail);
inline void calculateExplicitPadding(int32_t in_size, int32_t stride, int32_t dilation_factor,
int32_t filter_size, int32_t padding_implicit,
int32_t* padding_head, int32_t* padding_tail) {
calculateExplicitPaddingImpl(in_size, stride, dilation_factor, filter_size, padding_implicit,
/*isTransposeConv=*/false, padding_head, padding_tail);
}
inline void calculateExplicitPadding(int32_t in_size, int32_t stride, int32_t filter_size,
int32_t padding_implicit, int32_t* padding_head,
int32_t* padding_tail) {
calculateExplicitPadding(in_size, stride, 1, filter_size, padding_implicit, padding_head,
padding_tail);
}
inline void calculateExplicitPaddingTransposeConv(int32_t in_size, int32_t stride,
int32_t filter_size, int32_t padding_implicit,
int32_t* padding_head, int32_t* padding_tail) {
calculateExplicitPaddingImpl(in_size, stride, /*dilation_factor=*/1, filter_size,
padding_implicit, /*isTransposeConv=*/true, padding_head,
padding_tail);
}
inline PaddingScheme getPaddingScheme(int32_t inWidth, int32_t inHeight, int32_t strideWidth,
int32_t strideHeight, int32_t filterWidth,
int32_t filterHeight, int32_t paddingLeft,
int32_t paddingRight, int32_t paddingTop,
int32_t paddingBottom) {
if (paddingLeft == 0 && paddingRight == 0 && paddingTop == 0 && paddingBottom == 0) {
return kPaddingValid;
}
int32_t expectedPaddingLeft, expectedPaddingRight;
int32_t expectedPaddingTop, expectedPaddingBottom;
calculateExplicitPadding(inWidth, strideWidth, filterWidth, kPaddingSame, &expectedPaddingLeft,
&expectedPaddingRight);
calculateExplicitPadding(inHeight, strideHeight, filterHeight, kPaddingSame,
&expectedPaddingTop, &expectedPaddingBottom);
if (expectedPaddingLeft == paddingLeft && expectedPaddingRight == paddingRight &&
expectedPaddingTop == paddingTop && expectedPaddingBottom == paddingBottom) {
return kPaddingSame;
} else {
return kPaddingUnknown;
}
}
// Reverse order of bits in the mask to match the expected order in kernel
inline int ReverseMaskBits(int mask, int num_dimensions) {
int out = 0;
for (int dim = 0; dim < num_dimensions; dim++) {
out <<= 1;
out += (mask & 1);
mask >>= 1;
}
return out;
}
// Compute the positive remainder.
inline int32_t PositiveRemainder(int32_t dividend, int32_t divisor) {
return (divisor + (dividend % divisor)) % divisor;
}
// Compute clamped index.
inline int32_t ClampedIndex(int32_t index, int dim, bool pos_stride) {
return pos_stride
? (index >= dim ? dim
: PositiveRemainder(std::min(std::max(index, -dim), dim), dim))
: (index < -dim
? -1
: PositiveRemainder(std::min(std::max(index, -dim), dim - 1), dim));
}
// Broadcasts input shape against one another and puts the result into output
// shape. Returns true on success and false on error.
bool calculateBroadcastedShape(const Shape& in1, const Shape& in2, Shape* out);
// Dequantizes a value and quantizes it back using new scale and offset.
uint8_t requantize(uint8_t value, const Shape& oldShape, const Shape& newShape);
// Preparation functions for the corresponding ops
bool floorPrepare(const Shape& input, Shape* output);
bool depthwiseConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
int32_t padding_left, int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
int32_t depth_multiplier, int32_t dilation_width_factor,
int32_t dilation_height_factor, Shape* output);
bool genericActivationPrepare(const Shape& input, Shape* output);
bool genericNormalizationPrepare(const Shape& input, Shape* output);
bool reshapePrepare(const Shape& input, const int32_t* targetDims, const int32_t targetDimsSize,
Shape* output);
bool depthToSpacePrepare(const Shape& input, int32_t blockSize, Shape* output);
bool spaceToDepthPrepare(const Shape& input, int32_t blockSize, Shape* output);
bool embeddingLookupPrepare(const Shape& valueShape, const Shape& lookupShape, Shape* outputShape);
bool hashtableLookupPrepare(const Shape& lookupShape, const Shape& keyShape,
const Shape& valueShape, Shape* outputShape, Shape* hitShape);
bool padPrepare(const Shape& input, const int32_t* paddingsData, const Shape& paddingsShape,
Shape* output);
bool batchToSpacePrepare(const Shape& input, const int32_t* blockSizeData,
const Shape& blockSizeShape, Shape* output);
bool spaceToBatchPrepare(const Shape& input, const int32_t* blockSizeData,
const Shape& blockSizeShape, const int32_t* paddingsData,
const Shape& paddingsShape, Shape* output);
bool meanPrepare(const Shape& input, const int32_t* axisData, const Shape& axisShape, bool keepDims,
Shape* output);
bool argMinMaxPrepare(const Shape& input, int32_t axis, Shape* output);
bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs, std::vector<Shape>* output);
bool groupedConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
int32_t padding_left, int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
int32_t numGroups, Shape* output);
// Transposes the first two dimensions.
template <typename T>
inline bool transposeFirstTwoDimensions(const T* buffer, const Shape& shape, T* transposedBuffer) {
const int numDims = getNumberOfDimensions(shape);
NN_RET_CHECK(numDims >= 2);
const int firstDim = getSizeOfDimension(shape, 0);
const int secondDim = getSizeOfDimension(shape, 1);
int blockSize = 1;
for (int i = 2; i < numDims; ++i) {
blockSize *= getSizeOfDimension(shape, i);
}
for (int i = 0; i < firstDim; ++i) {
for (int j = 0; j < secondDim; ++j) {
for (int k = 0; k < blockSize; ++k) {
transposedBuffer[(j * firstDim + i) * blockSize + k] =
buffer[(i * secondDim + j) * blockSize + k];
}
}
}
return true;
}
inline bool transposeFirstTwoDimensions(const Shape& shape, Shape* transposedShape) {
NN_RET_CHECK(getNumberOfDimensions(shape) >= 2);
*transposedShape = shape;
transposedShape->dimensions[0] = shape.dimensions[1];
transposedShape->dimensions[1] = shape.dimensions[0];
return true;
}
// Given two 3-dimensional tensors, merge them into one 3-dimensional tensor
// at the third dimension. The merged tensor's third dimension size will be
// sum of that of the two inputs.
template <typename T>
inline bool mergeThirdDimension(const T* bufferA, const std::vector<uint32_t>& dimsA,
const T* bufferB, const std::vector<uint32_t>& dimsB, T* merged) {
NN_RET_CHECK_EQ(dimsA.size(), 3u);
NN_RET_CHECK_EQ(dimsB.size(), 3u);
NN_RET_CHECK_EQ(dimsA[0], dimsB[0]);
NN_RET_CHECK_EQ(dimsA[1], dimsB[1]);
for (unsigned int i = 0; i < dimsA[0]; ++i) {
for (unsigned int j = 0; j < dimsA[1]; ++j) {
for (unsigned int k = 0; k < dimsA[2]; ++k) {
merged[(i * dimsA[1] + j) * (dimsA[2] + dimsB[2]) + k] =
bufferA[(i * dimsA[1] + j) * dimsA[2] + k];
}
for (unsigned int k = 0; k < dimsB[2]; ++k) {
merged[(i * dimsA[1] + j) * (dimsA[2] + dimsB[2]) + dimsA[2] + k] =
bufferB[(i * dimsB[1] + j) * dimsB[2] + k];
}
}
}
return true;
}
} // namespace nn
} // namespace android
#endif // ANDROID_FRAMEWORKS_ML_NN_COMMON_OPERATIONS_UTILS_H