198 lines
8.9 KiB
C++
198 lines
8.9 KiB
C++
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/*
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* Copyright (C) 2019 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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#include "fuzzing/operation_signatures/OperationSignatureUtils.h"
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namespace android {
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namespace nn {
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namespace fuzzing_test {
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// CONCATENATION with fixed number of input tensors.
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static void concatConstructor(uint32_t numInputs, bool isV1_0, uint32_t rank, RandomOperation* op) {
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for (uint32_t i = 0; i < numInputs; i++) {
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op->inputs[i]->dimensions.resize(rank);
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if (isV1_0) setSameQuantization(op->inputs[i], op->inputs[0]);
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}
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op->outputs[0]->dimensions.resize(rank);
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int32_t axis = getUniform<int32_t>(0, rank - 1);
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op->inputs[numInputs]->setScalarValue<int32_t>(axis);
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for (uint32_t i = 0; i < rank; i++) {
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op->inputs[0]->dimensions[i] = RandomVariableType::FREE;
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op->outputs[0]->dimensions[i] = op->inputs[0]->dimensions[i];
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for (uint32_t j = 1; j < numInputs; j++) {
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if (axis == static_cast<int32_t>(i)) {
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op->inputs[j]->dimensions[i] = RandomVariableType::FREE;
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op->outputs[0]->dimensions[i] =
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op->outputs[0]->dimensions[i] + op->inputs[j]->dimensions[i];
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} else {
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op->inputs[j]->dimensions[i] = op->inputs[0]->dimensions[i];
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}
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}
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}
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}
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DEFINE_OPERATION_SIGNATURE(CONCAT_2_V1_0){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32,
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TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_0,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/2, /*isV1_0=*/true, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(CONCAT_3_V1_0){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32,
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TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_0,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, INPUT_DEFAULT,
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PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/3, /*isV1_0=*/true, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(CONCAT_2_V1_2){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT16,
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TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_2,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/2, /*isV1_0=*/false, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(CONCAT_3_V1_2){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT16,
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TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_2,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, INPUT_DEFAULT,
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PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/3, /*isV1_0=*/false, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(CONCAT_2_V1_3){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_3,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/2, /*isV1_0=*/false, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(CONCAT_3_V1_3){
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.opType = TestOperationType::CONCATENATION,
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.supportedDataTypes = {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_3,
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.inputs = {INPUT_DEFAULT, INPUT_DEFAULT, INPUT_DEFAULT,
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PARAMETER_NONE(TestOperandType::INT32)},
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.outputs = {OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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concatConstructor(/*numInputs=*/3, /*isV1_0=*/false, rank, op);
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}};
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// SPLIT with fixed number of splits.
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static void splitConstructor(uint32_t numSplits, uint32_t rank, RandomOperation* op) {
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int32_t axis = getRandomAxis(rank);
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op->inputs[1]->setScalarValue<int32_t>(axis);
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axis = toPositiveAxis(axis, rank);
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op->inputs[0]->dimensions.resize(rank);
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for (uint32_t i = 0; i < numSplits; i++) {
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op->outputs[i]->dimensions.resize(rank);
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setSameQuantization(op->outputs[i], op->inputs[0]);
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}
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for (uint32_t i = 0; i < rank; i++) {
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op->inputs[0]->dimensions[i] = RandomVariableType::FREE;
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RandomVariable outDim;
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if (axis == static_cast<int32_t>(i)) {
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outDim = op->inputs[0]->dimensions[i].exactDiv(numSplits);
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} else {
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outDim = op->inputs[0]->dimensions[i];
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}
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for (uint32_t j = 0; j < numSplits; j++) op->outputs[j]->dimensions[i] = outDim;
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}
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}
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DEFINE_OPERATION_SIGNATURE(SPLIT_2_V1_2){
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.opType = TestOperationType::SPLIT,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
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TestOperandType::TENSOR_INT32, TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_2,
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.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32),
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PARAMETER_CHOICE(TestOperandType::INT32, 2)},
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.outputs = {OUTPUT_DEFAULT, OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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splitConstructor(/*numSplits=*/2, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(SPLIT_3_V1_2){
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.opType = TestOperationType::SPLIT,
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.supportedDataTypes = {TestOperandType::TENSOR_FLOAT32, TestOperandType::TENSOR_FLOAT16,
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TestOperandType::TENSOR_INT32, TestOperandType::TENSOR_QUANT8_ASYMM},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_2,
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.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32),
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PARAMETER_CHOICE(TestOperandType::INT32, 3)},
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.outputs = {OUTPUT_DEFAULT, OUTPUT_DEFAULT, OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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splitConstructor(/*numSplits=*/3, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(SPLIT_2_V1_3){
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.opType = TestOperationType::SPLIT,
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.supportedDataTypes = {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_3,
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.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32),
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PARAMETER_CHOICE(TestOperandType::INT32, 2)},
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.outputs = {OUTPUT_DEFAULT, OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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splitConstructor(/*numSplits=*/2, rank, op);
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}};
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DEFINE_OPERATION_SIGNATURE(SPLIT_3_V1_3){
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.opType = TestOperationType::SPLIT,
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.supportedDataTypes = {TestOperandType::TENSOR_QUANT8_ASYMM_SIGNED},
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.supportedRanks = {1, 2, 3, 4},
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.version = TestHalVersion::V1_3,
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.inputs = {INPUT_DEFAULT, PARAMETER_NONE(TestOperandType::INT32),
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PARAMETER_CHOICE(TestOperandType::INT32, 3)},
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.outputs = {OUTPUT_DEFAULT, OUTPUT_DEFAULT, OUTPUT_DEFAULT},
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.constructor = [](TestOperandType, uint32_t rank, RandomOperation* op) {
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splitConstructor(/*numSplits=*/3, rank, op);
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}};
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} // namespace fuzzing_test
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} // namespace nn
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} // namespace android
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