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/*
* Copyright (C) 2019 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.
*/
#include "fuzzing/operation_signatures/OperationSignatureUtils.h"
namespace android {
namespace nn {
namespace fuzzing_test {
static void reduceOpConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
// A boolean array indicating whether each dimension is selected to be reduced.
bool reduce[4] = {false, false, false, false};
// Generate values for the "axis" tensor.
uint32_t numAxis = getUniform<int32_t>(1, 10);
op->inputs[1]->dimensions = {numAxis};
op->inputs[1]->resizeBuffer<int32_t>(numAxis);
for (uint32_t i = 0; i < numAxis; i++) {
int32_t dim = getUniform<int32_t>(-rank, rank - 1);
op->inputs[1]->value<int32_t>(i) = dim;
reduce[dim < 0 ? dim + rank : dim] = true;
}
// This scalar may have two types: in MEAN it is INT32, in REDUCE_* it is BOOL
bool keepDims;
if (op->inputs[2]->dataType == TestOperandType::BOOL) {
keepDims = op->inputs[2]->value<bool8>();
} else {
keepDims = op->inputs[2]->value<int32_t>() > 0;
}
for (uint32_t i = 0; i < rank; i++) {
if (!reduce[i]) {
op->outputs[0]->dimensions.emplace_back(op->inputs[0]->dimensions[i]);
} else if (keepDims) {
op->outputs[0]->dimensions.emplace_back(1);
}
}
setSameQuantization(op->outputs[0], op->inputs[0]);
// REDUCE_PROD may produce Inf output values. We should not connect the output tensor to the
// input of another operation.
if (op->opType == TestOperationType::REDUCE_PROD) {
op->outputs[0]->doNotConnect = true;
}
}
#define DEFINE_MEAN_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(MEAN_##ver){ \
.opType = TestOperationType::MEAN, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32), \
PARAMETER_CHOICE(TestOperandType::INT32, -100, 100)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = reduceOpConstructor};
DEFINE_MEAN_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_MEAN_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_MEAN_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_REDUCE_SIGNATURE(op, ver, ...) \
DEFINE_OPERATION_SIGNATURE(op##_##ver){ \
.opType = TestOperationType::op, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32), \
PARAMETER_CHOICE(TestOperandType::BOOL, true, false)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = reduceOpConstructor};
DEFINE_REDUCE_SIGNATURE(REDUCE_ALL, V1_2, TestOperandType::TENSOR_BOOL8);
DEFINE_REDUCE_SIGNATURE(REDUCE_ANY, V1_2, TestOperandType::TENSOR_BOOL8);
DEFINE_REDUCE_SIGNATURE(REDUCE_PROD, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16);
DEFINE_REDUCE_SIGNATURE(REDUCE_SUM, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16);
DEFINE_REDUCE_SIGNATURE(REDUCE_MAX, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_REDUCE_SIGNATURE(REDUCE_MIN, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_REDUCE_SIGNATURE(REDUCE_MAX, V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
DEFINE_REDUCE_SIGNATURE(REDUCE_MIN, V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void singleAxisReduceOpConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
// "axis" must be in the range [-rank, rank).
// Negative "axis" is used to specify axis from the end.
int32_t axis = getUniform<int32_t>(-rank, rank - 1);
op->inputs[1]->setScalarValue<int32_t>(axis);
for (uint32_t i = 0; i < rank; i++) {
if (i != static_cast<uint32_t>(axis) && i != axis + rank) {
op->outputs[0]->dimensions.emplace_back(op->inputs[0]->dimensions[i]);
}
}
}
#define DEFINE_ARGMIN_MAX_SIGNATURE(op, ver, ...) \
DEFINE_OPERATION_SIGNATURE(op##_##ver){ \
.opType = TestOperationType::op, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4, 5}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32)}, \
.outputs = {OUTPUT_TYPED(TestOperandType::TENSOR_INT32)}, \
.constructor = singleAxisReduceOpConstructor};
DEFINE_ARGMIN_MAX_SIGNATURE(ARGMAX, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_INT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_ARGMIN_MAX_SIGNATURE(ARGMIN, V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_FLOAT16, TestOperandType::TENSOR_INT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_ARGMIN_MAX_SIGNATURE(ARGMAX, V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
DEFINE_ARGMIN_MAX_SIGNATURE(ARGMIN, V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
} // namespace fuzzing_test
} // namespace nn
} // namespace android
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