/*
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* Copyright (C) 2018 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 "tensorflow/lite/kernels/internal/reference/reference_ops.h"
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#include "HalInterfaces.h"
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#include "OperationResolver.h"
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#include "OperationsUtils.h"
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#include "Tracing.h"
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namespace android {
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namespace nn {
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namespace reduce {
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constexpr uint32_t kNumInputs = 3;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kInputAxes = 1;
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constexpr uint32_t kInputKeepDims = 2;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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// Values from
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// https://en.wikipedia.org/wiki/Half-precision_floating-point_format#IEEE_754_half-precision_binary_floating-point_format:_binary16
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constexpr _Float16 kFloat16Max = 65504;
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constexpr _Float16 kFloat16Lowest = -kFloat16Max;
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namespace {
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template <typename T>
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inline bool compute(IOperationExecutionContext* context, T init, T func(T, T)) {
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const Shape inputShape = context->getInputShape(kInputTensor);
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const Shape axesShape = context->getInputShape(kInputAxes);
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const Shape outputShape = context->getOutputShape(kOutputTensor);
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const uint32_t inputRank = getNumberOfDimensions(inputShape);
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const uint32_t numAxes = getNumberOfElements(axesShape);
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std::vector<int> tempIndex(inputShape.dimensions.size());
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std::vector<int> tempAxes(numAxes);
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return tflite::reference_ops::ReduceGeneric<T>(
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context->getInputBuffer<T>(kInputTensor),
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reinterpret_cast<const int32_t*>(inputShape.dimensions.data()), inputRank,
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context->getOutputBuffer<T>(kOutputTensor),
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reinterpret_cast<const int32_t*>(outputShape.dimensions.data()),
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outputShape.dimensions.size(), context->getInputBuffer<int32_t>(kInputAxes), numAxes,
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context->getInputValue<bool8>(kInputKeepDims), tempIndex.data(), tempAxes.data(), init,
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func);
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}
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} // namespace
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bool validateProdSum(const IOperationValidationContext* context) {
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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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OperandType inputType = context->getInputType(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32)
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<< "Unsupported tensor type for REDUCE_PROD or REDUCE_SUM";
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NN_RET_CHECK(
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validateInputTypes(context, {inputType, OperandType::TENSOR_INT32, OperandType::BOOL}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return validateHalVersion(context, HalVersion::V1_2);
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}
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bool validateMaxMin(const IOperationValidationContext* context) {
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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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OperandType inputType = context->getInputType(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32 ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM)
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<< "Unsupported tensor type for REDUCE_MAX or REDUCE_MIN";
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NN_RET_CHECK(
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validateInputTypes(context, {inputType, OperandType::TENSOR_INT32, OperandType::BOOL}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return validateHalVersion(context, HalVersion::V1_2);
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}
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bool validateLogical(const IOperationValidationContext* context) {
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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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OperandType inputType = context->getInputType(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_BOOL8)
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<< "Unsupported tensor type for REDUCE_ANY or REDUCE_ALL";
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NN_RET_CHECK(
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validateInputTypes(context, {inputType, OperandType::TENSOR_INT32, OperandType::BOOL}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return validateHalVersion(context, HalVersion::V1_2);
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape inputShape = context->getInputShape(kInputTensor);
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const uint32_t inputRank = getNumberOfDimensions(inputShape);
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std::vector<bool> shouldReduce(inputRank);
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const int32_t* axes = context->getInputBuffer<int32_t>(kInputAxes);
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Shape axesShape = context->getInputShape(kInputAxes);
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NN_RET_CHECK_EQ(getNumberOfDimensions(axesShape), 1u);
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const uint32_t numAxes = getNumberOfElements(axesShape);
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for (uint32_t i = 0; i < numAxes; ++i) {
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int32_t axis = axes[i];
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NN_RET_CHECK(handleNegativeAxis(inputRank, &axis));
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shouldReduce[axis] = true;
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}
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// Input and output must have the same quantization parameters, etc.
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Shape outputShape = inputShape;
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outputShape.dimensions.clear();
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bool keepDims = context->getInputValue<bool8>(kInputKeepDims);
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for (uint32_t axis = 0; axis < inputRank; ++axis) {
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if (shouldReduce[axis]) {
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if (keepDims) {
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outputShape.dimensions.push_back(1);
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}
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} else {
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outputShape.dimensions.push_back(getSizeOfDimension(inputShape, axis));
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}
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}
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return context->setOutputShape(kOutputTensor, outputShape);
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}
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bool executeProd(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return compute<_Float16>(context, 1, [](_Float16 a, _Float16 b) { return a * b; });
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case OperandType::TENSOR_FLOAT32:
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return compute<float>(context, 1, [](float a, float b) { return a * b; });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_PROD";
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}
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}
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bool executeSum(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return compute<_Float16>(context, 0, [](_Float16 a, _Float16 b) { return a + b; });
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case OperandType::TENSOR_FLOAT32:
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return compute<float>(context, 0, [](float a, float b) { return a + b; });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_SUM";
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}
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}
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bool executeMax(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return compute<_Float16>(context, kFloat16Lowest,
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[](_Float16 a, _Float16 b) { return std::max(a, b); });
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case OperandType::TENSOR_FLOAT32:
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return compute<float>(context, std::numeric_limits<float>::lowest(),
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[](float a, float b) { return std::max(a, b); });
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case OperandType::TENSOR_QUANT8_ASYMM:
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return compute<uint8_t>(context, std::numeric_limits<uint8_t>::lowest(),
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[](uint8_t a, uint8_t b) { return std::max(a, b); });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_MAX";
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}
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}
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bool executeMin(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return compute<_Float16>(context, kFloat16Max,
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[](_Float16 a, _Float16 b) { return std::min(a, b); });
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case OperandType::TENSOR_FLOAT32:
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return compute<float>(context, std::numeric_limits<float>::max(),
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[](float a, float b) { return std::min(a, b); });
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case OperandType::TENSOR_QUANT8_ASYMM:
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return compute<uint8_t>(context, std::numeric_limits<uint8_t>::max(),
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[](uint8_t a, uint8_t b) { return std::min(a, b); });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_MIN";
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}
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}
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bool executeAny(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_BOOL8:
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return compute<bool8>(context, false,
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[](bool8 a, bool8 b) { return static_cast<bool8>(a || b); });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_ANY";
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}
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}
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bool executeAll(IOperationExecutionContext* context) {
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_BOOL8:
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return compute<bool8>(context, true,
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[](bool8 a, bool8 b) { return static_cast<bool8>(a && b); });
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation REDUCE_ALL";
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}
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}
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} // namespace reduce
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NN_REGISTER_OPERATION(REDUCE_PROD, "REDUCE_PROD", reduce::validateProdSum, reduce::prepare,
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reduce::executeProd);
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NN_REGISTER_OPERATION(REDUCE_SUM, "REDUCE_SUM", reduce::validateProdSum, reduce::prepare,
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reduce::executeSum);
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NN_REGISTER_OPERATION(REDUCE_MAX, "REDUCE_MAX", reduce::validateMaxMin, reduce::prepare,
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reduce::executeMax);
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NN_REGISTER_OPERATION(REDUCE_MIN, "REDUCE_MIN", reduce::validateMaxMin, reduce::prepare,
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reduce::executeMin);
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NN_REGISTER_OPERATION(REDUCE_ANY, "REDUCE_ANY", reduce::validateLogical, reduce::prepare,
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reduce::executeAny);
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NN_REGISTER_OPERATION(REDUCE_ALL, "REDUCE_ALL", reduce::validateLogical, reduce::prepare,
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reduce::executeAll);
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} // namespace nn
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} // namespace android
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