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chore: import upstream snapshot with attribution

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2026-07-13 13:33:03 +08:00
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test/op/ReductionTest.cpp
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//
// ReductionTest.cpp
// MNNTests
//
// Created by MNN on 2019/01/15.
// Copyright © 2018, Alibaba Group Holding Limited
//
#include <MNN/expr/Expr.hpp>
#include <MNN/expr/ExprCreator.hpp>
#include "MNNTestSuite.h"
#include "TestUtils.h"
#include <random>
using namespace MNN::Express;
class ReduceSumTest : public MNNTestCase {
public:
virtual ~ReduceSumTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _ReduceSum(input);
const std::vector<float> expectedOutput = {4.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, 0.01)) {
MNN_ERROR("ReduceSumTest test failed!\n");
return false;
}
return true;
}
};
class ReduceSumMultiTest : public MNNTestCase {
public:
virtual ~ReduceSumMultiTest() = default;
virtual bool run(int precision) {
float threshold = 0.01;
if (precision == 2) {
threshold = 0.1;
}
{
auto input = _Input({4, 10, 1, 4}, NCHW, halide_type_of<float>());
// set input data
auto inputPtr = input->writeMap<float>();
auto inputInfo = input->getInfo();
std::vector<float> inputData(inputInfo->size);
for (int i = 0; i < inputData.size(); ++i) {
if (precision == 2) {
inputData[i] = (float)((10.3 - i) * 0.002);
} else {
inputData[i] = (float)((10.3 - i) * (i + 0.2));
}
}
memcpy(inputPtr, inputData.data(), inputData.size() * sizeof(float));
input->unMap();
auto output = _ReduceSum(input, {0, 2, 3});
std::vector<float> expectedOutput(10);
auto func = FP32Converter[precision];
for (int i = 0; i < 10; ++i) {
float sumValue = 0.0f;
for (int j = 0; j < 4; ++j) {
for (int k = 0; k < 4; ++k) {
sumValue = func(func(inputData[i * 4 + k + j * 40]) + sumValue);
}
}
expectedOutput[i] = sumValue;
}
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, threshold)) {
MNN_ERROR("ReduceSumMultiTest test failed!\n");
return false;
}
}
{
std::mt19937 gen(42);
std::uniform_real_distribution<> dis(0.0, 1.0);
std::vector<int> inputShape = {3136, 16};
auto input = _Input({inputShape[0], inputShape[1]}, NCHW, halide_type_of<float>());
// set input data
auto inputPtr = input->writeMap<float>();
auto inputInfo = input->getInfo();
std::vector<float> inputData(inputInfo->size);
for (int i = 0; i < inputData.size(); ++i) {
if (precision == 2) {
inputData[i] = (float)((i % 10) * 0.002);
} else {
float randomValue = dis(gen);
inputData[i] = randomValue;
}
}
memcpy(inputPtr, inputData.data(), inputData.size() * sizeof(float));
input->unMap();
auto output = _ReduceSum(input, {0});
std::vector<float> expectedOutput(inputShape[1]);
auto func = FP32Converter[precision];
for (int i = 0; i < inputShape[1]; ++i) {
float sumValue = 0.0f;
for (int j = 0; j < inputShape[0]; ++j) {
sumValue = func(func(inputData[i + j * inputShape[1]]) + sumValue);
}
expectedOutput[i] = sumValue;
}
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, threshold)) {
MNN_ERROR("ReduceSumMultiTest test failed!\n");
return false;
}
}
return true;
}
};
class ReduceMeanTest : public MNNTestCase {
public:
virtual ~ReduceMeanTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _ReduceMean(input);
const std::vector<float> expectedOutput = {1.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, 0.01)) {
MNN_ERROR("ReduceMeanTest test failed!\n");
return false;
}
return true;
}
};
class ReduceMaxTest : public MNNTestCase {
public:
virtual ~ReduceMaxTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _ReduceMax(input);
const std::vector<float> expectedOutput = {4.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, 0.01)) {
MNN_ERROR("ReduceMaxTest test failed!\n");
return false;
}
return true;
}
};
class ReduceMinTest : public MNNTestCase {
public:
virtual ~ReduceMinTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _ReduceMin(input);
const std::vector<float> expectedOutput = {-2.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, 0.01)) {
MNN_ERROR("ReduceMinTest test failed!\n");
return false;
}
return true;
}
};
class ReduceProdTest : public MNNTestCase {
public:
virtual ~ReduceProdTest() = default;
virtual bool run(int precision) {
auto input = _Input(
{
4,
},
NCHW);
input->setName("input_tensor");
// set input data
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
auto inputPtr = input->writeMap<float>();
memcpy(inputPtr, inpudata, 4 * sizeof(float));
input->unMap();
auto output = _ReduceProd(input);
const std::vector<float> expectedOutput = {24.0};
auto gotOutput = output->readMap<float>();
if (!checkVector<float>(gotOutput, expectedOutput.data(), 1, 0.01)) {
MNN_ERROR("ReduceProdTest test failed!\n");
return false;
}
return true;
}
};
MNNTestSuiteRegister(ReduceSumTest, "op/reduction/reduce_sum");
MNNTestSuiteRegister(ReduceSumMultiTest, "op/reduction/reduce_sum_multi");
MNNTestSuiteRegister(ReduceMeanTest, "op/reduction/reduce_mean");
MNNTestSuiteRegister(ReduceMaxTest, "op/reduction/reduce_max");
MNNTestSuiteRegister(ReduceMinTest, "op/reduction/reduce_min");
MNNTestSuiteRegister(ReduceProdTest, "op/reduction/reduce_prod");