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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 <algorithm>
#include <vector>
#include "fuzzing/operation_signatures/OperationSignatureUtils.h"
namespace android {
namespace nn {
namespace fuzzing_test {
static void spaceToDepthConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw = false;
if (op->inputs.size() > 2) useNchw = op->inputs[2]->value<bool8>();
int heightIndex = useNchw ? 2 : 1;
int widthIndex = useNchw ? 3 : 2;
int depthIndex = useNchw ? 1 : 3;
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE,
RandomVariableType::FREE, RandomVariableType::FREE};
int32_t blockSize = op->inputs[1]->value<int32_t>();
auto outHeight = op->inputs[0]->dimensions[heightIndex].exactDiv(blockSize);
auto outWidth = op->inputs[0]->dimensions[widthIndex].exactDiv(blockSize);
auto outDepth = op->inputs[0]->dimensions[depthIndex] * (blockSize * blockSize);
if (useNchw) {
op->outputs[0]->dimensions = {op->inputs[0]->dimensions[0], outDepth, outHeight, outWidth};
} else {
op->outputs[0]->dimensions = {op->inputs[0]->dimensions[0], outHeight, outWidth, outDepth};
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_SPACE_TO_DEPTH_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(SPACE_TO_DEPTH_##ver){ \
.opType = TestOperationType::SPACE_TO_DEPTH, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_RANGE(TestOperandType::INT32, 1, 5)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = spaceToDepthConstructor};
DEFINE_SPACE_TO_DEPTH_SIGNATURE(V1_0, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_SPACE_TO_DEPTH_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_SPACE_TO_DEPTH_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_SPACE_TO_DEPTH_WITH_LAYOUT_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(SPACE_TO_DEPTH_layout_##ver){ \
.opType = TestOperationType::SPACE_TO_DEPTH, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_RANGE(TestOperandType::INT32, 1, 5), \
PARAMETER_CHOICE(TestOperandType::BOOL, true, false)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = spaceToDepthConstructor};
DEFINE_SPACE_TO_DEPTH_WITH_LAYOUT_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_SPACE_TO_DEPTH_WITH_LAYOUT_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void depthToSpaceConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw = false;
if (op->inputs.size() > 2) useNchw = op->inputs[2]->value<bool8>();
int heightIndex = useNchw ? 2 : 1;
int widthIndex = useNchw ? 3 : 2;
int depthIndex = useNchw ? 1 : 3;
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE,
RandomVariableType::FREE, RandomVariableType::FREE};
int32_t blockSize = op->inputs[1]->value<int32_t>();
auto outHeight = op->inputs[0]->dimensions[heightIndex] * blockSize;
auto outWidth = op->inputs[0]->dimensions[widthIndex] * blockSize;
auto outDepth = op->inputs[0]->dimensions[depthIndex].exactDiv(blockSize * blockSize);
if (useNchw) {
op->outputs[0]->dimensions = {op->inputs[0]->dimensions[0], outDepth, outHeight, outWidth};
} else {
op->outputs[0]->dimensions = {op->inputs[0]->dimensions[0], outHeight, outWidth, outDepth};
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_DEPTH_TO_SPACE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(DEPTH_TO_SPACE_##ver){ \
.opType = TestOperationType::DEPTH_TO_SPACE, \
.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, \
TestOperandType::TENSOR_QUANT8_ASYMM}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_RANGE(TestOperandType::INT32, 1, 3)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = depthToSpaceConstructor};
DEFINE_DEPTH_TO_SPACE_SIGNATURE(V1_0, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_DEPTH_TO_SPACE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_DEPTH_TO_SPACE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_DEPTH_TO_SPACE_WITH_LAYOUT_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(DEPTH_TO_SPACE_layout_##ver){ \
.opType = TestOperationType::DEPTH_TO_SPACE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_RANGE(TestOperandType::INT32, 1, 3), \
PARAMETER_CHOICE(TestOperandType::BOOL, true, false)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = depthToSpaceConstructor};
DEFINE_DEPTH_TO_SPACE_WITH_LAYOUT_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_DEPTH_TO_SPACE_WITH_LAYOUT_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void reshapeConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
op->inputs[1]->dimensions = {rank};
op->inputs[1]->randomBuffer.resize(rank);
RandomVariable numInputElements = 1;
RandomVariable numOutputElements = 1;
for (uint32_t i = 0; i < rank; i++) {
op->inputs[1]->randomBuffer[i] = RandomVariableType::FREE;
numInputElements = numInputElements * op->inputs[0]->dimensions[i];
numOutputElements = numOutputElements * op->inputs[1]->randomBuffer[i];
}
numInputElements.setEqual(numOutputElements);
op->outputs[0]->dimensions = op->inputs[1]->randomBuffer;
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_RESHAPE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(RESHAPE_##ver){ \
.opType = TestOperationType::RESHAPE, \
.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, \
TestOperandType::TENSOR_QUANT8_ASYMM}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = reshapeConstructor};
DEFINE_RESHAPE_SIGNATURE(V1_0, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_RESHAPE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_RESHAPE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void batchToSpaceConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw = false;
if (op->inputs.size() > 2) useNchw = op->inputs[2]->value<bool8>();
int heightIndex = useNchw ? 2 : 1;
int widthIndex = useNchw ? 3 : 2;
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE,
RandomVariableType::FREE, RandomVariableType::FREE};
int32_t blockHeight = op->inputs[1]->value<int32_t>(0);
int32_t blockWidth = op->inputs[1]->value<int32_t>(1);
auto outBatch = op->inputs[0]->dimensions[0].exactDiv(blockHeight * blockWidth);
auto outHeight = op->inputs[0]->dimensions[heightIndex] * blockHeight;
auto outWidth = op->inputs[0]->dimensions[widthIndex] * blockWidth;
if (useNchw) {
op->outputs[0]->dimensions = {outBatch, op->inputs[0]->dimensions[1], outHeight, outWidth};
} else {
op->outputs[0]->dimensions = {outBatch, outHeight, outWidth, op->inputs[0]->dimensions[3]};
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_BATCH_TO_SPACE_ND_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(BATCH_TO_SPACE_ND_##ver){ \
.opType = TestOperationType::BATCH_TO_SPACE_ND, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_VEC_RANGE(TestOperandType::TENSOR_INT32, \
/*len=*/2, /*range=*/1, 3)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = batchToSpaceConstructor};
DEFINE_BATCH_TO_SPACE_ND_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_BATCH_TO_SPACE_ND_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_BATCH_TO_SPACE_ND_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_BATCH_TO_SPACE_ND_WITH_LAYOUT_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(BATCH_TO_SPACE_ND_layout_##ver){ \
.opType = TestOperationType::BATCH_TO_SPACE_ND, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, \
PARAMETER_VEC_RANGE(TestOperandType::TENSOR_INT32, /*len=*/2, /*range=*/1, \
3), \
PARAMETER_CHOICE(TestOperandType::BOOL, true, false)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = batchToSpaceConstructor};
DEFINE_BATCH_TO_SPACE_ND_WITH_LAYOUT_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_BATCH_TO_SPACE_ND_WITH_LAYOUT_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void spaceToBatchConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 4);
bool useNchw = false;
if (op->inputs.size() > 3) useNchw = op->inputs[3]->value<bool8>();
int heightIndex = useNchw ? 2 : 1;
int widthIndex = useNchw ? 3 : 2;
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE,
RandomVariableType::FREE, RandomVariableType::FREE};
// Compute padded height and width.
auto paddedHeight = op->inputs[0]->dimensions[heightIndex] +
(op->inputs[2]->value<int32_t>(0) + op->inputs[2]->value<int32_t>(1));
auto paddedWidth = op->inputs[0]->dimensions[widthIndex] +
(op->inputs[2]->value<int32_t>(2) + op->inputs[2]->value<int32_t>(3));
// blockHeight/blockWidth must be a divisor of padded height/width
int32_t blockHeight = op->inputs[1]->value<int32_t>(0);
int32_t blockWidth = op->inputs[1]->value<int32_t>(1);
auto outBatch = op->inputs[0]->dimensions[0] * (blockHeight * blockWidth);
auto outHeight = paddedHeight.exactDiv(blockHeight);
auto outWidth = paddedWidth.exactDiv(blockWidth);
if (useNchw) {
op->outputs[0]->dimensions = {outBatch, op->inputs[0]->dimensions[1], outHeight, outWidth};
} else {
op->outputs[0]->dimensions = {outBatch, outHeight, outWidth, op->inputs[0]->dimensions[3]};
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
// The paddings tensor in SPACE_TOBATCH_ND, a [2, 2] tensor with value selected from [0, 10].
static const OperandSignature paddingTensor_SPACE_TO_BATCH_ND = {
.type = RandomOperandType::CONST,
.constructor = [](TestOperandType, uint32_t, RandomOperand* op) {
op->dataType = TestOperandType::TENSOR_INT32;
op->dimensions = {2, 2};
op->resizeBuffer<int32_t>(4);
for (int i = 0; i < 4; i++) op->value<int32_t>(i) = getUniform<int32_t>(0, 10);
}};
#define DEFINE_SPACE_TO_BATCH_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(SPACE_TO_BATCH_ND_##ver){ \
.opType = TestOperationType::SPACE_TO_BATCH_ND, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, \
PARAMETER_VEC_RANGE(TestOperandType::TENSOR_INT32, /*len=*/2, /*range=*/1, \
5), \
paddingTensor_SPACE_TO_BATCH_ND}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = spaceToBatchConstructor};
DEFINE_SPACE_TO_BATCH_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_SPACE_TO_BATCH_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_SPACE_TO_BATCH_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_SPACE_TO_BATCH_WITH_LAYOUT_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(SPACE_TO_BATCH_ND_layout_##ver){ \
.opType = TestOperationType::SPACE_TO_BATCH_ND, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, \
PARAMETER_VEC_RANGE(TestOperandType::TENSOR_INT32, /*len=*/2, /*range=*/1, \
5), \
paddingTensor_SPACE_TO_BATCH_ND, \
PARAMETER_CHOICE(TestOperandType::BOOL, true, false)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = spaceToBatchConstructor};
DEFINE_SPACE_TO_BATCH_WITH_LAYOUT_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_SPACE_TO_BATCH_WITH_LAYOUT_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void padConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
op->inputs[1]->dimensions = {rank, 2};
op->inputs[1]->resizeBuffer<int32_t>(rank * 2);
op->outputs[0]->dimensions.resize(rank);
for (uint32_t i = 0; i < rank; i++) {
int32_t left = getUniform<int32_t>(0, 5), right = getUniform<int32_t>(0, 5);
op->inputs[1]->value<int32_t>(i * 2) = left;
op->inputs[1]->value<int32_t>(i * 2 + 1) = right;
op->outputs[0]->dimensions[i] = op->inputs[0]->dimensions[i] + (left + right);
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
static const OperandSignature paddingScalar_PAD_V2 = {
.type = RandomOperandType::CONST,
.constructor = [](TestOperandType dataType, uint32_t, RandomOperand* op) {
switch (dataType) {
case TestOperandType::TENSOR_FLOAT32:
op->dataType = TestOperandType::FLOAT32;
op->setScalarValue<float>(getUniform<float>(-10.0f, 10.0f));
break;
case TestOperandType::TENSOR_FLOAT16:
op->dataType = TestOperandType::FLOAT16;
op->setScalarValue<_Float16>(getUniform<_Float16>(-10.0f, 10.0f));
break;
case TestOperandType::TENSOR_QUANT8_ASYMM:
op->dataType = TestOperandType::INT32;
op->setScalarValue<int32_t>(getUniform<int32_t>(0, 255));
break;
case TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED:
op->dataType = TestOperandType::INT32;
op->setScalarValue<int32_t>(getUniform<int32_t>(-128, 127));
break;
default:
NN_FUZZER_CHECK(false) << "Unsupported data type for PAD_V2";
}
}};
#define DEFINE_PAD_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(PAD_##ver){ \
.opType = TestOperationType::PAD, \
.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, \
TestOperandType::TENSOR_QUANT8_ASYMM}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = padConstructor};
DEFINE_PAD_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_PAD_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_PAD_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
#define DEFINE_PAD_V2_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(PAD_V2_##ver){ \
.opType = TestOperationType::PAD_V2, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32), \
paddingScalar_PAD_V2}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = padConstructor};
DEFINE_PAD_V2_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_PAD_V2_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void transposeConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
// Create the permutation value by randomly shuffling a sequential array.
std::vector<int32_t> permutation(rank);
std::iota(permutation.begin(), permutation.end(), 0);
randomShuffle(&permutation);
op->inputs[1]->resizeBuffer<int32_t>(rank);
std::copy(permutation.begin(), permutation.end(),
reinterpret_cast<int32_t*>(op->inputs[1]->buffer.data()));
setFreeDimensions(op->inputs[0], rank);
op->inputs[1]->dimensions = {rank};
op->outputs[0]->dimensions.resize(rank);
for (uint32_t i = 0; i < rank; i++) {
op->outputs[0]->dimensions[i] = op->inputs[0]->dimensions[permutation[i]];
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
static void transposeOmittedConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
NN_FUZZER_CHECK(rank == 2);
op->inputs[0]->dimensions = {RandomVariableType::FREE, RandomVariableType::FREE};
op->inputs[1]->dimensions = {2};
op->outputs[0]->dimensions = {op->inputs[0]->dimensions[1], op->inputs[0]->dimensions[0]};
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_TRANSPOSE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(TRANSPOSE_##ver){ \
.opType = TestOperationType::TRANSPOSE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = transposeConstructor}; \
DEFINE_OPERATION_SIGNATURE(TRANSPOSE_omitted_##ver){ \
.opType = TestOperationType::TRANSPOSE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {2}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NO_VALUE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = transposeOmittedConstructor};
DEFINE_TRANSPOSE_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_TRANSPOSE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_TRANSPOSE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void channelShuffleConstructor(TestOperandType dataType, uint32_t rank,
RandomOperation* op) {
sameShapeOpConstructor(dataType, rank, op);
// The number of groups must be a divisor of the target axis size.
int32_t axis = getUniform<int32_t>(-rank, rank - 1);
op->inputs[2]->setScalarValue<int32_t>(axis);
int32_t numGroups = op->inputs[1]->value<int32_t>();
if (axis < 0) axis += rank;
(op->inputs[0]->dimensions[axis] % numGroups).setEqual(0);
}
#define DEFINE_CHANNEL_SHUFFLE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(CHANNEL_SHUFFLE_##ver){ \
.opType = TestOperationType::CHANNEL_SHUFFLE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_RANGE(TestOperandType::INT32, 1, 5), \
PARAMETER_NONE(TestOperandType::INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = channelShuffleConstructor};
DEFINE_CHANNEL_SHUFFLE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_FLOAT16);
DEFINE_CHANNEL_SHUFFLE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void squeezeConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
// A boolean array indicating whether each dimension is selected to be squeezed.
bool squeeze[4] = {false, false, false, false};
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++) {
// Generate values for the "axis" tensor.
int32_t dim = getUniform<int32_t>(0, rank - 1);
op->inputs[1]->value<int32_t>(i) = dim;
squeeze[dim] = true;
}
op->inputs[0]->dimensions.resize(rank);
for (uint32_t i = 0; i < rank; i++) {
if (squeeze[i]) {
op->inputs[0]->dimensions[i] = 1;
} else {
op->inputs[0]->dimensions[i] = RandomVariableType::FREE;
op->outputs[0]->dimensions.emplace_back(op->inputs[0]->dimensions[i]);
}
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
static void squeezeOmittedConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
// A boolean array indicating whether each dimension is selected to be squeezed.
std::vector<bool> squeeze(rank, false);
for (uint32_t i = 0; i < rank; i++) {
squeeze[i] = getBernoulli(0.5f);
}
op->inputs[0]->dimensions.resize(rank);
op->inputs[1]->dimensions = {0};
for (uint32_t i = 0; i < rank; i++) {
if (squeeze[i]) {
op->inputs[0]->dimensions[i] = 1;
} else {
// Set the dimension to any value greater than 1 to prevent from getting sqeezed.
op->inputs[0]->dimensions[i] = RandomVariableType::FREE;
op->inputs[0]->dimensions[i].setGreaterThan(1);
op->outputs[0]->dimensions.emplace_back(op->inputs[0]->dimensions[i]);
}
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_SQUEEZE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(SQUEEZE_##ver){ \
.opType = TestOperationType::SQUEEZE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = squeezeConstructor}; \
DEFINE_OPERATION_SIGNATURE(SQUEEZE_omitted_##ver){ \
.opType = TestOperationType::SQUEEZE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NO_VALUE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = squeezeOmittedConstructor};
DEFINE_SQUEEZE_SIGNATURE(V1_1, TestOperandType::TENSOR_FLOAT32,
TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_SQUEEZE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT16);
DEFINE_SQUEEZE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void expandDimsConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
// Generate values for the "axis" tensor.
int32_t axis = getUniform<int32_t>(-rank - 1, rank);
op->inputs[1]->setScalarValue<int32_t>(axis);
if (axis < 0) axis += rank + 1;
setFreeDimensions(op->inputs[0], rank);
for (uint32_t i = 0; i < rank; i++) {
if (i == static_cast<uint32_t>(axis)) {
op->outputs[0]->dimensions.push_back(1);
}
op->outputs[0]->dimensions.push_back(op->inputs[0]->dimensions[i]);
}
if (rank == static_cast<uint32_t>(axis)) op->outputs[0]->dimensions.push_back(1);
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_EXPAND_DIMS_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(EXPAND_DIMS_##ver){ \
.opType = TestOperationType::EXPAND_DIMS, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4, 5}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = expandDimsConstructor};
DEFINE_EXPAND_DIMS_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
TestOperandType::TENSOR_INT32, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_EXPAND_DIMS_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void tileConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
op->outputs[0]->dimensions.resize(rank);
op->inputs[1]->dimensions = {rank};
op->inputs[1]->resizeBuffer<int32_t>(rank);
for (uint32_t i = 0; i < rank; i++) {
int32_t multiple = getUniform<int32_t>(1, 5);
op->inputs[1]->value<int32_t>(i) = multiple;
op->outputs[0]->dimensions[i] = op->inputs[0]->dimensions[i] * multiple;
}
setSameQuantization(op->outputs[0], op->inputs[0]);
}
#define DEFINE_TILE_SIGNATURE(ver, ...) \
DEFINE_OPERATION_SIGNATURE(TILE_##ver){ \
.opType = TestOperationType::TILE, \
.supportedDataTypes = {__VA_ARGS__}, \
.supportedRanks = {1, 2, 3, 4, 5}, \
.version = TestHalVersion::ver, \
.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::TENSOR_INT32)}, \
.outputs = {OUTPUT_DEFAULT}, \
.constructor = tileConstructor};
DEFINE_TILE_SIGNATURE(V1_2, TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
TestOperandType::TENSOR_INT32, TestOperandType::TENSOR_QUANT8_ASYMM);
DEFINE_TILE_SIGNATURE(V1_3, TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED);
static void fillConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
op->inputs[0]->dimensions = {rank};
setFreeDimensions(op->outputs[0], rank);
op->inputs[0]->randomBuffer = op->outputs[0]->dimensions;
}
DEFINE_OPERATION_SIGNATURE(FILL_V1_3){
.opType = TestOperationType::FILL,
.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
TestOperandType::TENSOR_INT32},
.supportedRanks = {1, 2, 3, 4, 5},
.version = TestHalVersion::V1_3,
.inputs = {PARAMETER_NONE(TestOperandType::TENSOR_INT32), INPUT_SCALAR},
.outputs = {OUTPUT_DEFAULT},
.constructor = fillConstructor};
static void rankConstructor(TestOperandType, uint32_t rank, RandomOperation* op) {
setFreeDimensions(op->inputs[0], rank);
}
DEFINE_OPERATION_SIGNATURE(RANK_V1_3){
.opType = TestOperationType::RANK,
.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
TestOperandType::TENSOR_INT32, TestOperandType::TENSOR_QUANT8_ASYMM,
TestOperandType::TENSOR_BOOL8},
.supportedRanks = {1, 2, 3, 4, 5},
.version = TestHalVersion::V1_3,
.inputs = {INPUT_DEFAULT},
.outputs = {OUTPUT_TYPED(TestOperandType::INT32)},
.constructor = rankConstructor};
} // namespace fuzzing_test
} // namespace nn
} // namespace android
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