chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
@@ -0,0 +1,110 @@
+//
+//  ReduceGradTest.cpp
+//  MNNTests
+//
+//  Created by MNN on 2022/07/12.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include <MNN/expr/Expr.hpp>
+#include <MNN/expr/ExprCreator.hpp>
+#include "MNNTestSuite.h"
+#include "TestUtils.h"
+#include "../tools/train/source/grad/OpGrad.hpp"
+
+using namespace MNN;
+using namespace MNN::Express;
+
+class ReduceGradTest : public MNNTestCase {
+public:
+    char name[20] = "Reduce";
+    virtual ~ReduceGradTest() = default;
+
+    bool checkResult(VARP output, VARP outputDiff, std::vector<float> expectedOutput, const char* subname) {
+        const int len = expectedOutput.size();
+        auto opExpr = output->expr().first;
+        auto grad = OpGrad::get(opExpr->get()->type());
+        if (grad == nullptr) {
+            MNN_ERROR("no grad defined for: %s %s\n", name, subname);
+        }
+        auto inputGrad = grad->onGrad(opExpr, {outputDiff});
+        auto gotOutput = inputGrad[0]->readMap<float>();
+
+        for (int i = 0; i < len; ++i) {
+            auto diff = ::fabsf(gotOutput[i] - expectedOutput[i]);
+            if (diff > 0.001) {
+                MNN_ERROR("%s %s grad test failed, expected: %f, but got: %f!\n", name, subname, expectedOutput[i], gotOutput[i]);
+                return false;
+            }
+        }
+        return true;
+    }
+
+    virtual bool run(int precision) {
+        const int len = 5;
+        auto input = _Input({len}, NCHW);
+        const float inpudata[] = {-1.0, -2.0, 0.0, 4.0, -5.0};
+        auto inputPtr          = input->writeMap<float>();
+        memcpy(inputPtr, inpudata, len * sizeof(float));
+        std::vector<float> outputDiffVec = {0.1};
+
+        {
+            auto output = _ReduceSum(input);
+            auto ptr = output->readMap<float>();
+            const std::vector<float> expectedOutput = {0.1, 0.1, 0.1, 0.1, 0.1};
+            auto outputDiff = _Const(outputDiffVec.data(), {});
+            if (!checkResult(output, outputDiff, expectedOutput, "ReduceSum")) {
+                return false;
+            }
+        }
+
+        {
+            auto output = _ReduceMean(input);
+            const std::vector<float> expectedOutput = {0.0200, 0.0200, 0.0200, 0.0200, 0.0200};
+            auto outputDiff = _Const(outputDiffVec.data(), {});
+            if (!checkResult(output, outputDiff, expectedOutput, "ReduceMean")) {
+                return false;
+            }
+        }
+
+        {
+            const float inpudata[] = {-1.0, -2.0, 0.0, 4.0, 4.0};
+            auto inputPtr          = input->writeMap<float>();
+            memcpy(inputPtr, inpudata, len * sizeof(float));
+            auto output = _ReduceMax(input);
+            const std::vector<float> expectedOutput = {0.0, 0.0, 0.0, 0.05, 0.05};
+            auto outputDiff = _Const(outputDiffVec.data(), {});
+            if (!checkResult(output, outputDiff, expectedOutput, "ReduceMax")) {
+                return false;
+            }
+        }
+
+        {
+            const float inpudata[] = {-2.0, -2.0, 0.0, 4.0, 4.0};
+            auto inputPtr          = input->writeMap<float>();
+            memcpy(inputPtr, inpudata, len * sizeof(float));
+            auto output = _ReduceMin(input);
+            const std::vector<float> expectedOutput = {0.05, 0.05, 0.0, 0.0, 0.0};
+            auto outputDiff = _Const(outputDiffVec.data(), {});
+            if (!checkResult(output, outputDiff, expectedOutput, "ReduceMin")) {
+                return false;
+            }
+        }
+
+        {
+            const float inpudata[] = {-1.0, -2.0, 1.0, 4.0, -5.0};
+            auto inputPtr          = input->writeMap<float>();
+            memcpy(inputPtr, inpudata, len * sizeof(float));
+            auto output = _ReduceProd(input);
+            const std::vector<float> expectedOutput = {4.0000, 2.0000, -4.0000, -1.0000, 0.8000};
+            auto outputDiff = _Const(outputDiffVec.data(), {});
+            if (!checkResult(output, outputDiff, expectedOutput, "ReduceProd")) {
+                return false;
+            }
+        }
+
+        return true;
+    }
+};
+
+MNNTestSuiteRegister(ReduceGradTest, "grad/reduce");