/*
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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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// Contains the implementation of the operations.
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#define LOG_TAG "Operations"
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#include "CpuOperationUtils.h"
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#include "OperationResolver.h"
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#include "tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/reference/legacy_reference_ops.h"
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#include "Tracing.h"
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#include <algorithm>
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namespace android {
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namespace nn {
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namespace broadcast {
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constexpr uint32_t kNumInputs = 3;
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constexpr uint32_t kInputTensor1 = 0;
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constexpr uint32_t kInputTensor2 = 1;
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constexpr uint32_t kActivationScalar = 2;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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namespace {
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#define ANDROID_NN_MACRO_DISPATCH(macro) \
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switch (activation) { \
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case (int32_t)FusedActivationFunc::NONE: \
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macro(kNone); \
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break; \
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case (int32_t)FusedActivationFunc::RELU: \
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macro(kRelu); \
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break; \
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case (int32_t)FusedActivationFunc::RELU1: \
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macro(kRelu1); \
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break; \
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case (int32_t)FusedActivationFunc::RELU6: \
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macro(kRelu6); \
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break; \
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default: \
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LOG(ERROR) << "Unsupported fused activation function type"; \
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return false; \
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}
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using binaryFunctionFloat32 = std::function<bool(
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const float* in1, const Shape& shape1, const float* in2, const Shape& shape2,
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int32_t activation, float* out, const Shape& shapeOut)>;
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bool binaryOperationFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2,
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const Shape& shape2, int32_t activation, _Float16* out,
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const Shape& shapeOut, binaryFunctionFloat32 operationFloat32) {
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std::vector<float> in1_float32(getNumberOfElements(shape1));
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convertFloat16ToFloat32(in1, &in1_float32);
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std::vector<float> in2_float32(getNumberOfElements(shape2));
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convertFloat16ToFloat32(in2, &in2_float32);
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std::vector<float> out_float32(getNumberOfElements(shapeOut));
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operationFloat32(in1_float32.data(), shape1, in2_float32.data(), shape2, activation,
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out_float32.data(), shapeOut);
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convertFloat32ToFloat16(out_float32, out);
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return true;
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}
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bool addFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2,
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int32_t activation, float* out, const Shape& shapeOut) {
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NNTRACE_TRANS("addFloat32");
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bool needBroadcast = !SameShape(shape1, shape2);
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if (needBroadcast) {
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastAdd");
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#define ANDROID_NN_BROADCAST_ADD(activation) \
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tflite::optimized_ops::BroadcastAdd<tflite::FusedActivationFunctionType::activation>( \
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in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2), out, \
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convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_BROADCAST_ADD)
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#undef ANDROID_NN_BROADCAST_ADD
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} else {
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NNTRACE_COMP_SWITCH("optimized_ops::Add");
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#define ANDROID_NN_ADD(activation) \
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tflite::optimized_ops::Add<tflite::FusedActivationFunctionType::activation>( \
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in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2), out, \
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convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_ADD)
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#undef ANDROID_NN_ADD
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}
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return true;
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}
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bool addFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2,
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int32_t activation, _Float16* out, const Shape& shapeOut) {
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NNTRACE_TRANS("addFloat16");
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return binaryOperationFloat16(in1, shape1, in2, shape2, activation, out, shapeOut, &addFloat32);
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}
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bool addQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2,
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int32_t activation, uint8_t* out, const Shape& shapeOut) {
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NNTRACE_TRANS("addQuant8");
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bool needBroadcast = !SameShape(shape1, shape2);
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const int32_t input1_offset = -shape1.offset;
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const int32_t input2_offset = -shape2.offset;
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const int32_t output_offset = shapeOut.offset;
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const int left_shift = 20;
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const double twice_max_input_scale = 2 * std::max(shape1.scale, shape2.scale);
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const double real_input1_multiplier = shape1.scale / twice_max_input_scale;
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const double real_input2_multiplier = shape2.scale / twice_max_input_scale;
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const double real_output_multiplier =
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twice_max_input_scale / ((1 << left_shift) * shapeOut.scale);
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int32_t input1_multiplier;
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int32_t input1_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_input1_multiplier, &input1_multiplier,
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&input1_shift)) {
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return false;
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}
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int32_t input2_multiplier;
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int32_t input2_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_input2_multiplier, &input2_multiplier,
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&input2_shift)) {
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return false;
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}
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int32_t output_multiplier;
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int32_t output_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_output_multiplier, &output_multiplier,
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&output_shift)) {
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return false;
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}
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int32_t output_activation_min;
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int32_t output_activation_max;
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CalculateActivationRangeUint8(activation, shapeOut, &output_activation_min,
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&output_activation_max);
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if (needBroadcast) {
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastAdd");
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#define ANDROID_NN_BROADCAST_ADD(activation) \
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tflite::optimized_ops::BroadcastAdd<tflite::FusedActivationFunctionType::activation>( \
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left_shift, in1, convertShapeToDims(shape1), input1_offset, input1_multiplier, \
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input1_shift, in2, convertShapeToDims(shape2), input2_offset, input2_multiplier, \
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input2_shift, output_offset, output_multiplier, output_shift, output_activation_min, \
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output_activation_max, out, convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_BROADCAST_ADD)
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#undef ANDROID_NN_BROADCAST_ADD
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} else {
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NNTRACE_COMP_SWITCH("optimized_ops::Add");
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#define ANDROID_NN_NORMAL_ADD(activation) \
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tflite::optimized_ops::Add<tflite::FusedActivationFunctionType::activation>( \
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left_shift, in1, convertShapeToDims(shape1), input1_offset, input1_multiplier, \
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input1_shift, in2, convertShapeToDims(shape2), input2_offset, input2_multiplier, \
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input2_shift, output_offset, output_multiplier, output_shift, output_activation_min, \
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output_activation_max, out, convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_NORMAL_ADD)
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#undef ANDROID_NN_NORMAL_ADD
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}
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return true;
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}
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bool mulFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2,
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int32_t activation, float* out, const Shape& shapeOut) {
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NNTRACE_TRANS("mulFloat32");
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bool needBroadcast = !SameShape(shape1, shape2);
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if (needBroadcast) {
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastMul");
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#define ANDROID_NN_BROADCAST_MUL(activation) \
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tflite::optimized_ops::BroadcastMul<tflite::FusedActivationFunctionType::activation>( \
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in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2), out, \
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convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_BROADCAST_MUL)
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#undef ANDROID_NN_BROADCAST_MUL
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} else {
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float output_activation_min, output_activation_max;
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CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max);
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NNTRACE_COMP_SWITCH("optimized_ops::Mul");
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tflite::optimized_ops::Mul(in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2),
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output_activation_min, output_activation_max, out,
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convertShapeToDims(shapeOut));
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}
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return true;
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}
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bool mulFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2,
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int32_t activation, _Float16* out, const Shape& shapeOut) {
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NNTRACE_TRANS("mulFloat16");
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return binaryOperationFloat16(in1, shape1, in2, shape2, activation, out, shapeOut, &mulFloat32);
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}
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bool mulQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2,
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int32_t activation, uint8_t* out, const Shape& shapeOut) {
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NNTRACE_TRANS("mulQuant8");
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const int32_t input1_offset = -shape1.offset;
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const int32_t input2_offset = -shape2.offset;
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const int32_t output_offset = shapeOut.offset;
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const double input_product_scale = shape1.scale * shape2.scale;
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const double real_multiplier = input_product_scale / shapeOut.scale;
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int32 output_multiplier;
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int output_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier, &output_shift)) {
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return false;
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}
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int32_t output_activation_min;
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int32_t output_activation_max;
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CalculateActivationRangeUint8(activation, shapeOut, &output_activation_min,
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&output_activation_max);
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// Use BROADCAST version to handle the normal case.
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastMul");
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tflite::optimized_ops::BroadcastMul(in1, convertShapeToDims(shape1), input1_offset, in2,
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convertShapeToDims(shape2), input2_offset, output_offset,
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output_multiplier, output_shift, output_activation_min,
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output_activation_max, out, convertShapeToDims(shapeOut));
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return true;
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}
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bool subFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2,
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int32_t activation, float* out, const Shape& shapeOut) {
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NNTRACE_TRANS("subFloat32");
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NNTRACE_COMP_SWITCH("optimized_ops::Sub");
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tflite::optimized_ops::Sub(in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2),
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out, convertShapeToDims(shapeOut));
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// TFLite does not apply activation to broadcast sub.
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float output_activation_min, output_activation_max;
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CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max);
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uint32_t numOutputElements = getNumberOfElements(shapeOut);
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for (uint32_t i = 0; i < numOutputElements; i++) {
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out[i] = std::min(std::max(out[i], output_activation_min), output_activation_max);
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}
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return true;
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}
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bool subFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2,
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int32_t activation, _Float16* out, const Shape& shapeOut) {
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NNTRACE_TRANS("subFloat16");
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return binaryOperationFloat16(in1, shape1, in2, shape2, activation, out, shapeOut, &subFloat32);
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}
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bool subQuant8(const uint8_t* in1, const Shape& shape1, const uint8_t* in2, const Shape& shape2,
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int32_t activation, uint8_t* out, const Shape& shapeOut) {
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NNTRACE_TRANS("subQuant8");
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const int32_t input1_offset = -shape1.offset;
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const int32_t input2_offset = -shape2.offset;
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const int32_t output_offset = shapeOut.offset;
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const int left_shift = 20;
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const double twice_max_input_scale = 2 * std::max(shape1.scale, shape2.scale);
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const double real_input1_multiplier = shape1.scale / twice_max_input_scale;
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const double real_input2_multiplier = shape2.scale / twice_max_input_scale;
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const double real_output_multiplier =
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twice_max_input_scale / ((1 << left_shift) * shapeOut.scale);
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int32_t input1_multiplier;
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int32_t input1_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_input1_multiplier, &input1_multiplier,
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&input1_shift)) {
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return false;
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}
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int32_t input2_multiplier;
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int32_t input2_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_input2_multiplier, &input2_multiplier,
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&input2_shift)) {
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return false;
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}
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input2_multiplier *= -1;
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int32_t output_multiplier;
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int32_t output_shift;
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if (!QuantizeMultiplierSmallerThanOne(real_output_multiplier, &output_multiplier,
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&output_shift)) {
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return false;
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}
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int32_t output_activation_min;
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int32_t output_activation_max;
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CalculateActivationRangeUint8(activation, shapeOut, &output_activation_min,
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&output_activation_max);
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// We are using tflite::optimized_ops::BroadcastAdd unconditionally here
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// because tflite::optimized_ops::Add fails to pass some of the
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// sub_quantized_different_scales tests.
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastAdd");
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#define ANDROID_NN_BROADCAST_ADD(activation) \
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tflite::optimized_ops::BroadcastAdd<tflite::FusedActivationFunctionType::activation>( \
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left_shift, in1, convertShapeToDims(shape1), input1_offset, input1_multiplier, \
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input1_shift, in2, convertShapeToDims(shape2), input2_offset, input2_multiplier, \
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input2_shift, output_offset, output_multiplier, output_shift, output_activation_min, \
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output_activation_max, out, convertShapeToDims(shapeOut))
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ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_BROADCAST_ADD)
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#undef ANDROID_NN_BROADCAST_ADD
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return true;
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}
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bool divFloat32(const float* in1, const Shape& shape1, const float* in2, const Shape& shape2,
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int32_t activation, float* out, const Shape& shapeOut) {
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NNTRACE_TRANS("divFloat32");
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float output_activation_min, output_activation_max;
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CalculateActivationRangeFloat(activation, &output_activation_min, &output_activation_max);
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bool needBroadcast = !SameShape(shape1, shape2);
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if (needBroadcast) {
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NNTRACE_COMP_SWITCH("optimized_ops::BroadcastDiv");
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tflite::optimized_ops::BroadcastDiv(
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in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2),
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output_activation_min, output_activation_max, out, convertShapeToDims(shapeOut));
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} else {
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NNTRACE_COMP_SWITCH("optimized_ops::Div");
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tflite::optimized_ops::Div(in1, convertShapeToDims(shape1), in2, convertShapeToDims(shape2),
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output_activation_min, output_activation_max, out,
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convertShapeToDims(shapeOut));
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}
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return true;
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}
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bool divFloat16(const _Float16* in1, const Shape& shape1, const _Float16* in2, const Shape& shape2,
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int32_t activation, _Float16* out, const Shape& shapeOut) {
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NNTRACE_TRANS("divFloat16");
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return binaryOperationFloat16(in1, shape1, in2, shape2, activation, out, shapeOut, &divFloat32);
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}
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} // namespace
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bool validate(OperationType opType, const IOperationValidationContext* context) {
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const HalVersion opIntroducedAt = (opType == OperationType::DIV || opType == OperationType::SUB)
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? HalVersion::V1_1
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: HalVersion::V1_0;
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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auto inputType = context->getInputType(kInputTensor1);
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if (inputType == OperandType::TENSOR_FLOAT32) {
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NN_RET_CHECK(validateHalVersion(context, std::max(HalVersion::V1_0, opIntroducedAt)));
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} else if (inputType == OperandType::TENSOR_FLOAT16) {
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NN_RET_CHECK(validateHalVersion(context, std::max(HalVersion::V1_2, opIntroducedAt)));
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} else if (inputType == OperandType::TENSOR_QUANT8_ASYMM) {
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if (opType == OperationType::SUB) {
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NN_RET_CHECK(validateHalVersion(context, std::max(HalVersion::V1_2, opIntroducedAt)));
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} else if (opType == OperationType::DIV) {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation DIV";
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} else if (opType == OperationType::MUL) {
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Shape output = context->getOutputShape(kOutputTensor);
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Shape input1 = context->getInputShape(kInputTensor1);
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Shape input2 = context->getInputShape(kInputTensor2);
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NN_RET_CHECK_GT(output.scale, input1.scale * input2.scale);
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NN_RET_CHECK(validateHalVersion(context, std::max(HalVersion::V1_0, opIntroducedAt)));
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} else {
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NN_RET_CHECK(validateHalVersion(context, std::max(HalVersion::V1_0, opIntroducedAt)));
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}
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} else {
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << getOperationName(opType);
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}
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return validateInputTypes(context, {inputType, inputType, OperandType::INT32}) &&
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validateOutputTypes(context, {inputType});
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape input1 = context->getInputShape(kInputTensor1);
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Shape input2 = context->getInputShape(kInputTensor2);
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Shape output = context->getOutputShape(kOutputTensor);
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NN_RET_CHECK_LE(getNumberOfDimensions(input1), 4);
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NN_RET_CHECK_LE(getNumberOfDimensions(input2), 4);
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NN_RET_CHECK(calculateBroadcastedShape(input1, input2, &output));
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return context->setOutputShape(kOutputTensor, output);
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}
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bool executeAdd(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return addFloat16(context->getInputBuffer<_Float16>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<_Float16>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return addFloat32(context->getInputBuffer<float>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<float>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return addQuant8(context->getInputBuffer<uint8_t>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<uint8_t>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<uint8_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 ADD";
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}
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}
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bool executeMul(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return mulFloat16(context->getInputBuffer<_Float16>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<_Float16>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return mulFloat32(context->getInputBuffer<float>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<float>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return mulQuant8(context->getInputBuffer<uint8_t>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<uint8_t>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<uint8_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 MUL";
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}
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}
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bool executeSub(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor1)) {
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case OperandType::TENSOR_FLOAT16:
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return subFloat16(context->getInputBuffer<_Float16>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<_Float16>(kInputTensor2),
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context->getInputShape(kInputTensor2),
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context->getInputValue<int32_t>(kActivationScalar),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return subFloat32(context->getInputBuffer<float>(kInputTensor1),
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context->getInputShape(kInputTensor1),
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context->getInputBuffer<float>(kInputTensor2),
|
context->getInputShape(kInputTensor2),
|
context->getInputValue<int32_t>(kActivationScalar),
|
context->getOutputBuffer<float>(kOutputTensor),
|
context->getOutputShape(kOutputTensor));
|
case OperandType::TENSOR_QUANT8_ASYMM:
|
return subQuant8(context->getInputBuffer<uint8_t>(kInputTensor1),
|
context->getInputShape(kInputTensor1),
|
context->getInputBuffer<uint8_t>(kInputTensor2),
|
context->getInputShape(kInputTensor2),
|
context->getInputValue<int32_t>(kActivationScalar),
|
context->getOutputBuffer<uint8_t>(kOutputTensor),
|
context->getOutputShape(kOutputTensor));
|
default:
|
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation SUB";
|
}
|
}
|
|
bool executeDiv(IOperationExecutionContext* context) {
|
// Bypass execution in the case of zero-sized input.
|
if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
|
switch (context->getInputType(kInputTensor1)) {
|
case OperandType::TENSOR_FLOAT16:
|
return divFloat16(context->getInputBuffer<_Float16>(kInputTensor1),
|
context->getInputShape(kInputTensor1),
|
context->getInputBuffer<_Float16>(kInputTensor2),
|
context->getInputShape(kInputTensor2),
|
context->getInputValue<int32_t>(kActivationScalar),
|
context->getOutputBuffer<_Float16>(kOutputTensor),
|
context->getOutputShape(kOutputTensor));
|
case OperandType::TENSOR_FLOAT32:
|
return divFloat32(context->getInputBuffer<float>(kInputTensor1),
|
context->getInputShape(kInputTensor1),
|
context->getInputBuffer<float>(kInputTensor2),
|
context->getInputShape(kInputTensor2),
|
context->getInputValue<int32_t>(kActivationScalar),
|
context->getOutputBuffer<float>(kOutputTensor),
|
context->getOutputShape(kOutputTensor));
|
default:
|
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation DIV";
|
}
|
}
|
|
} // namespace broadcast
|
|
using std::placeholders::_1;
|
NN_REGISTER_OPERATION(ADD, "ADD", std::bind(broadcast::validate, OperationType::ADD, _1),
|
broadcast::prepare, broadcast::executeAdd, .allowZeroSizedInput = true);
|
NN_REGISTER_OPERATION(MUL, "MUL", std::bind(broadcast::validate, OperationType::MUL, _1),
|
broadcast::prepare, broadcast::executeMul, .allowZeroSizedInput = true);
|
NN_REGISTER_OPERATION(SUB, "SUB", std::bind(broadcast::validate, OperationType::SUB, _1),
|
broadcast::prepare, broadcast::executeSub, .allowZeroSizedInput = true);
|
NN_REGISTER_OPERATION(DIV, "DIV", std::bind(broadcast::validate, OperationType::DIV, _1),
|
broadcast::prepare, broadcast::executeDiv, .allowZeroSizedInput = true);
|
|
} // namespace nn
|
} // namespace android
|
