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/*
* Copyright (C) 2018 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.
*/
#define LOG_TAG "Operations"
#include "IndexedShapeWrapper.h"
#include "OperationResolver.h"
#include "OperationsUtils.h"
namespace android {
namespace nn {
namespace select_op {
constexpr uint32_t kNumInputs = 3;
constexpr uint32_t kInputCondition = 0;
constexpr uint32_t kInputTensor1 = 1;
constexpr uint32_t kInputTensor2 = 2;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
template <typename T>
bool compute(const bool8* conditionData, const Shape& conditionShape, const T* aData,
const Shape& aShape, const T* bData, const Shape& bShape, T* outputData,
const Shape& outputShape) {
// The code assumes that condition has the same shape as all other tensors.
// This should be checked during preparation stage.
uint32_t size = getNumberOfElements(conditionShape);
for (uint32_t i = 0; i < size; ++i) {
T a = aData[i];
T b = bData[i];
if constexpr (std::is_same_v<T, uint8_t> || std::is_same_v<T, int8_t>) {
a = requantize<T>(a, aShape, outputShape);
b = requantize<T>(b, bShape, outputShape);
}
outputData[i] = conditionData[i] ? a : b;
}
return true;
}
template <typename T>
bool executeTyped(IOperationExecutionContext* context) {
return compute<T>(
context->getInputBuffer<bool8>(kInputCondition),
context->getInputShape(kInputCondition), context->getInputBuffer<T>(kInputTensor1),
context->getInputShape(kInputTensor1), context->getInputBuffer<T>(kInputTensor2),
context->getInputShape(kInputTensor2), context->getOutputBuffer<T>(kOutputTensor),
context->getOutputShape(kOutputTensor));
}
} // namespace
bool validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
OperandType inputType = context->getInputType(kInputTensor1);
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
inputType == OperandType::TENSOR_FLOAT32 ||
inputType == OperandType::TENSOR_INT32 ||
inputType == OperandType::TENSOR_QUANT8_ASYMM ||
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
<< "Unsupported input operand type for select op: " << inputType;
NN_RET_CHECK(validateInputTypes(context, {OperandType::TENSOR_BOOL8, inputType, inputType}));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
return validateVersion(context, Version::ANDROID_Q);
}
bool prepare(IOperationExecutionContext* context) {
Shape inputCondition = context->getInputShape(kInputCondition);
Shape input1 = context->getInputShape(kInputTensor1);
if (inputCondition.dimensions.size() != input1.dimensions.size()) {
LOG(ERROR) << "Condition and input tensor dimensions are not equal";
return false;
}
for (int i = 0; i < inputCondition.dimensions.size(); ++i) {
if (inputCondition.dimensions[i] != input1.dimensions[i]) {
LOG(ERROR) << "Condition and input tensor dimensions are not equal";
return false;
}
}
Shape input2 = context->getInputShape(kInputTensor2);
NN_RET_CHECK(SameShape(input1, input2));
Shape output = context->getOutputShape(kOutputTensor);
NN_RET_CHECK(SetShape(input1, &output));
return context->setOutputShape(kOutputTensor, output);
}
bool execute(IOperationExecutionContext* context) {
switch (context->getInputType(kInputTensor1)) {
case OperandType::TENSOR_FLOAT16:
return executeTyped<_Float16>(context);
case OperandType::TENSOR_FLOAT32:
return executeTyped<float>(context);
case OperandType::TENSOR_INT32:
return executeTyped<int32_t>(context);
case OperandType::TENSOR_QUANT8_ASYMM:
return executeTyped<uint8_t>(context);
case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
return executeTyped<int8_t>(context);
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for SELECT op.";
}
}
} // namespace select_op
NN_REGISTER_OPERATION(SELECT, "SELECT", select_op::validate, select_op::prepare,
select_op::execute);
} // namespace nn
} // namespace android
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