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301 lines
14 KiB
301 lines
14 KiB
/*
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* Copyright (C) 2019 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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#define LOG_TAG "Operations"
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#include <algorithm>
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#include <vector>
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#include "OperationResolver.h"
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#include "Tracing.h"
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#include <tensorflow/lite/kernels/internal/optimized/optimized_ops.h>
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#include <tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h>
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#include "CpuOperationUtils.h"
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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namespace android {
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namespace nn {
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namespace l2_norm {
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constexpr char kOperationName[] = "L2_NORMALIZATION";
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constexpr uint32_t kNumInputs = 2;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kAxisScalar = 1;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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namespace {
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inline bool l2normFloat32Impl(const float* inputData, const Shape& inputShape, int32_t axis,
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float* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("l2normFloat32");
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constexpr float kEpsilon = 1e-6f;
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const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
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const uint32_t innerSize =
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getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
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for (uint32_t outer = 0; outer < outerSize; ++outer) {
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const float* inputBeg = inputData + outer * axisSize * innerSize;
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const float* inputEnd = inputBeg + axisSize * innerSize;
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float* outputBeg = outputData + outer * axisSize * innerSize;
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for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBeg, ++inputEnd, ++outputBeg) {
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float sum = 0.0f;
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for (const float* p = inputBeg; p < inputEnd; p += innerSize) {
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float val = *p;
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sum += val * val;
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}
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float l2_norm = std::max(std::sqrt(sum), kEpsilon);
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float* pOut = outputBeg;
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for (const float* p = inputBeg; p < inputEnd; p += innerSize, pOut += innerSize) {
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*pOut = *p / l2_norm;
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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 l2normQuant8Impl(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
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uint8_t* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("l2normQuant8");
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const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
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const uint32_t innerSize =
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getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
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for (uint32_t outer = 0; outer < outerSize; ++outer) {
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const uint8_t* inputBeg = inputData + outer * axisSize * innerSize;
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const uint8_t* inputEnd = inputBeg + axisSize * innerSize;
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uint8_t* outputBeg = outputData + outer * axisSize * innerSize;
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for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBeg, ++inputEnd, ++outputBeg) {
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int32_t sum = 0;
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for (const uint8_t* p = inputBeg; p < inputEnd; p += innerSize) {
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int32_t val = static_cast<int32_t>(*p) - inputShape.offset;
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sum += val * val;
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}
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int32_t invMultiplier, invShift;
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tflite::GetInvSqrtQuantizedMultiplierExp(sum, -1, &invMultiplier, &invShift);
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uint8_t* pOut = outputBeg;
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for (const uint8_t* p = inputBeg; p < inputEnd; p += innerSize, pOut += innerSize) {
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int32_t val = static_cast<int32_t>(*p) - inputShape.offset;
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int32_t scaledVal = tflite::MultiplyByQuantizedMultiplierSmallerThanOneExp(
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val * 128, invMultiplier, invShift) +
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128;
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*pOut = static_cast<uint8_t>(std::min(std::max(scaledVal, 0), 255));
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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 l2normQuant8SignedImpl(const int8_t* inputData, const Shape& inputShape, int32_t axis,
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int8_t* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("l2normQuant8Signed");
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const uint32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const uint32_t axisSize = getSizeOfDimension(inputShape, axis);
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const uint32_t innerSize =
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getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
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for (uint32_t outer = 0; outer < outerSize; ++outer) {
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const int8_t* inputBeg = inputData + outer * axisSize * innerSize;
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const int8_t* inputEnd = inputBeg + axisSize * innerSize;
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int8_t* outputBeg = outputData + outer * axisSize * innerSize;
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for (uint32_t inner = 0; inner < innerSize; ++inner, ++inputBeg, ++inputEnd, ++outputBeg) {
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int32_t sum = 0;
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for (const int8_t* p = inputBeg; p < inputEnd; p += innerSize) {
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int32_t val = static_cast<int32_t>(*p) - inputShape.offset;
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sum += val * val;
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}
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int32_t invMultiplier, invShift;
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tflite::GetInvSqrtQuantizedMultiplierExp(sum, -1, &invMultiplier, &invShift);
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int8_t* pOut = outputBeg;
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for (const int8_t* p = inputBeg; p < inputEnd; p += innerSize, pOut += innerSize) {
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int32_t val = static_cast<int32_t>(*p) - inputShape.offset;
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int32_t scaledVal = tflite::MultiplyByQuantizedMultiplierSmallerThanOneExp(
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val * 128, invMultiplier, invShift);
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*pOut = static_cast<int8_t>(std::min(std::max(scaledVal, -128), 127));
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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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bool l2normFloat32(const float* inputData, const Shape& inputShape, int32_t axis, float* outputData,
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const Shape& outputShape) {
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int32_t ndim = getNumberOfDimensions(inputShape);
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NN_CHECK(handleNegativeAxis(inputShape, &axis));
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// TFLite optimized implementation only supports computation along the last axis
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if (axis == ndim - 1) {
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NNTRACE_COMP("optimized_ops::L2Normalization::float");
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tflite::L2NormalizationParams param = {.input_zero_point = 0};
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tflite::optimized_ops::L2Normalization(param, convertShapeToTflshape(inputShape), inputData,
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convertShapeToTflshape(outputShape), outputData);
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return true;
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} else {
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return l2normFloat32Impl(inputData, inputShape, axis, outputData, outputShape);
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}
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}
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bool l2normFloat16(const _Float16* inputData, const Shape& inputShape, int32_t axis,
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_Float16* outputData, const Shape& outputShape) {
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NNTRACE_TRANS("l2normFloat16");
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std::vector<float> inputDataFloat32(getNumberOfElements(inputShape));
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convertFloat16ToFloat32(inputData, &inputDataFloat32);
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std::vector<float> outputDataFloat32(getNumberOfElements(outputShape));
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l2normFloat32(inputDataFloat32.data(), inputShape, axis, outputDataFloat32.data(), outputShape);
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convertFloat32ToFloat16(outputDataFloat32, outputData);
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return true;
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}
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bool l2normQuant8(const uint8_t* inputData, const Shape& inputShape, int32_t axis,
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uint8_t* outputData, const Shape& outputShape) {
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int32_t ndim = getNumberOfDimensions(inputShape);
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NN_CHECK(handleNegativeAxis(inputShape, &axis));
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// TFLite optimized implementation only supports computation along the last axis
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if (axis == ndim - 1) {
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NNTRACE_COMP("optimized_ops::L2Normalization::uint8");
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tflite::L2NormalizationParams param = {.input_zero_point = inputShape.offset};
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tflite::optimized_ops::L2Normalization(param, convertShapeToTflshape(inputShape), inputData,
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convertShapeToTflshape(outputShape), outputData);
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return true;
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} else {
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return l2normQuant8Impl(inputData, inputShape, axis, outputData, outputShape);
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}
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}
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bool l2normQuant8Signed(const int8_t* inputData, const Shape& inputShape, int32_t axis,
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int8_t* outputData, const Shape& outputShape) {
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int32_t ndim = getNumberOfDimensions(inputShape);
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NN_CHECK(handleNegativeAxis(inputShape, &axis));
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// TFLite implementation only supports computation along the last axis
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if (axis == ndim - 1) {
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NNTRACE_COMP("reference_integer_ops::L2Normalization");
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const int32_t outerSize = getNumberOfElements(inputShape, 0, axis);
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const int32_t axisSize = getSizeOfDimension(inputShape, axis);
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tflite::reference_integer_ops::L2Normalization(inputShape.offset, outerSize, axisSize,
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inputData, outputData);
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return true;
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} else {
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return l2normQuant8SignedImpl(inputData, inputShape, axis, outputData, outputShape);
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}
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}
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} // namespace
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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Result<Version> validate(const IOperationValidationContext* context) {
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NN_RET_CHECK(context->getNumInputs() == kNumInputs ||
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context->getNumInputs() == kNumInputs - 1);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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const OperandType inputType = context->getInputType(kInputTensor);
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std::vector<OperandType> inExpectedTypes = {inputType};
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auto minSupportedVersion = Version::ANDROID_OC_MR1;
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if (inputType == OperandType::TENSOR_FLOAT16 || inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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minSupportedVersion = Version::ANDROID_Q;
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} else if (inputType == OperandType::TENSOR_FLOAT32) {
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minSupportedVersion = Version::ANDROID_OC_MR1;
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} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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minSupportedVersion = Version::ANDROID_R;
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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if (context->getNumInputs() == kNumInputs) {
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inExpectedTypes.push_back(OperandType::INT32);
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minSupportedVersion = Version::ANDROID_Q;
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} else if (context->getInputShape(kInputTensor).dimensions.size() != 4) {
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minSupportedVersion = Version::ANDROID_Q;
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}
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const Shape& input = context->getInputShape(kInputTensor);
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if (hasKnownRank(input)) {
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NN_RET_CHECK_LE(getNumberOfDimensions(input), 4);
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}
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NN_RET_CHECK(validateInputTypes(context, inExpectedTypes));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return minSupportedVersion;
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}
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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bool prepare(IOperationExecutionContext* context) {
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const Shape& input = context->getInputShape(kInputTensor);
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int32_t numDimensions = getNumberOfDimensions(input);
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int32_t axis = context->getNumInputs() == kNumInputs
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? context->getInputValue<int32_t>(kAxisScalar)
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: -1;
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NN_RET_CHECK_LE(numDimensions, 4);
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NN_RET_CHECK_GE(axis, -numDimensions);
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NN_RET_CHECK_LT(axis, numDimensions);
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Shape output = context->getOutputShape(kOutputTensor);
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output.type = input.type;
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output.dimensions = input.dimensions;
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if (output.type == OperandType::TENSOR_QUANT8_ASYMM) {
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output.scale = 1.0f / 128.0f;
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output.offset = 128;
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} else if (output.type == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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output.scale = 1.0f / 128.0f;
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output.offset = 0;
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} else {
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output.scale = 0;
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output.offset = 0;
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}
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return context->setOutputShape(kOutputTensor, output);
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}
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bool execute(IOperationExecutionContext* context) {
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int32_t axis = context->getNumInputs() == kNumInputs
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? context->getInputValue<int32_t>(kAxisScalar)
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: -1;
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NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT32:
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return l2normFloat32(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor), axis,
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT16:
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return l2normFloat16(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor), axis,
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return l2normQuant8(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor), axis,
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return l2normQuant8Signed(context->getInputBuffer<int8_t>(kInputTensor),
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context->getInputShape(kInputTensor), axis,
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context->getOutputBuffer<int8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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} // namespace l2_norm
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NN_REGISTER_OPERATION(L2_NORMALIZATION, l2_norm::kOperationName, l2_norm::validate,
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l2_norm::prepare, l2_norm::execute);
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} // namespace nn
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} // namespace android
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