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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/expr/GatherTest.cpp
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//
// GatherTest.cpp
// MNNTests
//
// Created by MNN on 2019/09/17.
// Copyright © 2018, Alibaba Group Holding Limited
//
/*
Test Case From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/gather-nd
*/
#include <math.h>
#include <MNN/expr/ExprCreator.hpp>
#include <MNN/expr/Module.hpp>
#include "MNNTestSuite.h"
#include "MNN_generated.h"
#include "TestUtils.h"
using namespace MNN;
using namespace MNN::Express;
class GatherExprTest : public MNNTestCase {
public:
virtual bool run(int precision) {
auto executor = cloneCurrentExecutor();
ExecutorScope scope(executor);
std::unique_ptr<MNN::OpT> gatherOp(new MNN::OpT);
gatherOp->type = MNN::OpType_GatherND;
auto parameter = _Input({2, 2}, NHWC, halide_type_of<int32_t>());
parameter->setName("param");
auto indice = _Input({2, 2}, NHWC, halide_type_of<int32_t>());
indice->setName("indice");
auto y = Variable::create(Expr::create(gatherOp.get(), {parameter, indice}));
y->setName("y");
{
parameter->resize({2, 2});
auto ptr = parameter->writeMap<float>();
ptr[0] = 7.0;
ptr[1] = 2.0;
ptr[2] = 4.0;
ptr[3] = 6.0;
}
{
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 0;
indicePtr[1] = 0;
indicePtr[2] = 1;
indicePtr[3] = 1;
auto size = y->getInfo()->size;
if (size != 2) {
return false;
}
auto yPtr = y->readMap<float>();
if (fabs(yPtr[0] - 7.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001) {
return false;
}
}
{
indice->resize({2, 1});
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 1;
indicePtr[1] = 0;
auto size = y->getInfo()->size;
if (4 != size) {
return false;
}
auto yPtr = y->readMap<float>();
if (fabs(yPtr[0] - 4.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001 || fabs(yPtr[2] - 7.0) > 0.001 ||
fabs(yPtr[3] - 2.0) > 0.001) {
return false;
}
}
{
indice->resize({1, 1});
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 1;
parameter->resize({2, 2, 2});
auto parameterPtr = parameter->writeMap<float>();
for (int i = 0; i < parameter->getInfo()->size; ++i) {
parameterPtr[i] = 1.0 * i;
}
auto size = y->getInfo()->size;
if (4 != size) {
return false;
}
auto yPtr = y->readMap<float>();
for (int i = 0; i < size; ++i) {
if (fabs(yPtr[i] - 4.0 - i) > 0.001) {
return false;
}
}
}
// Run as Module
flatbuffers::FlatBufferBuilder builderOutput(1024);
{
std::unique_ptr<MNN::NetT> net(new NetT);
Variable::save({y}, net.get());
y = nullptr;
auto len = MNN::Net::Pack(builderOutput, net.get());
builderOutput.Finish(len);
}
int sizeOutput = builderOutput.GetSize();
auto bufferOutput = builderOutput.GetBufferPointer();
std::shared_ptr<MNN::Express::Module> module(Module::load(std::vector<std::string>{"param", "indice"}, std::vector<std::string>{"y"}, bufferOutput, sizeOutput));
{
{
parameter->resize({2, 2});
auto ptr = parameter->writeMap<float>();
ptr[0] = 7.0;
ptr[1] = 2.0;
ptr[2] = 4.0;
ptr[3] = 6.0;
}
{
indice->resize({2, 2});
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 0;
indicePtr[1] = 0;
indicePtr[2] = 1;
indicePtr[3] = 1;
auto y2 = module->onForward({parameter, indice})[0];
auto size = y2->getInfo()->size;
if (size != 2) {
return false;
}
auto yPtr = y2->readMap<float>();
if (fabs(yPtr[0] - 7.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001) {
return false;
}
}
{
indice->resize({2, 1});
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 1;
indicePtr[1] = 0;
auto y2 = module->onForward({parameter, indice})[0];
auto size = y2->getInfo()->size;
if (4 != size) {
return false;
}
auto yPtr = y2->readMap<float>();
if (fabs(yPtr[0] - 4.0) > 0.001 || fabs(yPtr[1] - 6.0) > 0.001 || fabs(yPtr[2] - 7.0) > 0.001 ||
fabs(yPtr[3] - 2.0) > 0.001) {
return false;
}
}
{
indice->resize({1, 1});
auto indicePtr = indice->writeMap<int32_t>();
indicePtr[0] = 1;
parameter->resize({2, 2, 2});
auto parameterPtr = parameter->writeMap<float>();
for (int i = 0; i < parameter->getInfo()->size; ++i) {
parameterPtr[i] = 1.0 * i;
}
auto y2 = module->onForward({parameter, indice})[0];
auto yPtr = y2->readMap<float>();
auto size = y2->getInfo()->size;
if (4 != size) {
return false;
}
for (int i = 0; i < size; ++i) {
if (fabs(yPtr[i] - 4.0 - i) > 0.001) {
return false;
}
}
}
{
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
const int indices_data[] = {0, 0, 1, 1};
auto params = _Const(inpudata, {2, 2}, NHWC, halide_type_of<float>());
auto indices = _Const(indices_data, {2, 2}, NHWC, halide_type_of<int>());
auto x1 = _GatherND(params, indices);
x1->setName("input1");
auto shape = x1->getInfo()->dim;
auto x0 = _Input(shape, NHWC, halide_type_of<float>());
float x0data[] = {1.0f, 2.0f};
::memcpy(x0->writeMap<float>(), x0data, 2 * sizeof(float));
x0->setName("input0");
auto res = _Add(x0, x1);
res->setName("GatherNd_output_0");
flatbuffers::FlatBufferBuilder builderOutput(1024);
{
std::unique_ptr<MNN::NetT> net(new NetT);
Variable::save({res}, net.get());
y = nullptr;
auto len = MNN::Net::Pack(builderOutput, net.get());
builderOutput.Finish(len);
}
int sizeOutput = builderOutput.GetSize();
auto bufferOutput = builderOutput.GetBufferPointer();
const char* cacheFileName = ".tempcache";
MNN::ScheduleConfig config;
config.numThread = 1;
BackendConfig bnConfig;
bnConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
config.backendConfig = &bnConfig;
std::shared_ptr<Executor::RuntimeManager> rtmgr(Executor::RuntimeManager::createRuntimeManager(config));
rtmgr->setCache(cacheFileName);
MNN::Express::Module::Config mConfig;
/*
ScheduleConfig config;
BackendConfig bnConfig;
bnConfig.precision = (MNN::BackendConfig::PrecisionMode)precision;
config.numThread = 1;
config.type = ExecutorScope::Current()->getAttr()->firstType.first;
config.backendConfig = &bnConfig;
*/
std::shared_ptr<MNN::Express::Module> module_2(Module::load(std::vector<std::string>{"input0"}, std::vector<std::string>{"GatherNd_output_0"}, bufferOutput, sizeOutput, rtmgr, &mConfig));
auto y2 = module_2->onForward({x0})[0];
const float inpudata1[] = {-5.0, -6.0, 7.0, 8.0};
x0->resize({2, 2});
auto parameterPtr = params->writeMap<float>();
::memcpy(parameterPtr, inpudata1, 4*sizeof(float));
y2 = module_2->onForward({x0})[0];
}
}
return true;
}
};
class GatherNdReComputeTest : public MNNTestCase {
public:
virtual bool run(int precision) {
auto executor = cloneCurrentExecutor();
ExecutorScope scope(executor);
const float inpudata[] = {-1.0, -2.0, 3.0, 4.0};
const int indices_data[] = {0, 0, 1, 1};
auto params = _Const(inpudata, {2, 2}, NHWC, halide_type_of<float>());
auto indices = _Const(indices_data, {2, 2}, NHWC, halide_type_of<int>());
auto x1 = _GatherND(params, indices);
x1->setName("input1");
auto shape = x1->getInfo()->dim;
auto x0 = _Input(shape, NHWC, halide_type_of<float>());
x0->setName("input0");
auto res = _Add(x0, x1);
res->setName("GatherNd_output_0");
flatbuffers::FlatBufferBuilder builderOutput(1024);
{
std::unique_ptr<MNN::NetT> net(new NetT);
Variable::save({res}, net.get());
auto len = MNN::Net::Pack(builderOutput, net.get());
builderOutput.Finish(len);
}
int sizeOutput = builderOutput.GetSize();
auto bufferOutput = builderOutput.GetBufferPointer();
std::shared_ptr<MNN::Express::Module> module(Module::load(std::vector<std::string>{"input0"}, std::vector<std::string>{"GatherNd_output_0"}, bufferOutput, sizeOutput));
// first run, call GatherNd compute function when resize.
float data0[] = {1.0f, 2.0f};
::memcpy(x0->writeMap<float>(), data0, 2 * sizeof(float));
auto y = module->onForward({x0});
// resize input and test GatherNd recompute function when risize.
const float data1[] = {-5.0, -6.0, 7.0, 8.0};
x0->resize({2, 2});
::memcpy(x0->writeMap<float>(), data1, 4 * sizeof(float));
y = module->onForward({x0});
return true;
}
};
MNNTestSuiteRegister(GatherExprTest, "expr/Gather");
MNNTestSuiteRegister(GatherNdReComputeTest, "expr/GatherNdRecomputeTest");