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 +1,98 @@
+//
+//  ReluGrad.cpp
+//  MNN
+//
+//  Created by MNN on 2019/04/22.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "ReluGrad.hpp"
+#include "core/Macro.h"
+#include <string.h>
+using namespace std;
+namespace MNN {
+using namespace MNN::Express;
+class PReluGrad : public OpGrad {
+public:
+    virtual std::vector<Express::VARP> onGrad(Express::EXPRP expr,
+                                              const std::vector<Express::VARP>& backwardOutput) override {
+        std::vector<Express::VARP> result(1, nullptr);
+        auto op = expr->get();
+        auto input = expr->inputs()[0];
+        auto mask = _Relu(_Sign(input));
+        auto prelu = op->main_as_PRelu();
+        if (prelu->slope()->size() == 1) {
+            auto slope = prelu->slope()->data()[0];
+            result[0] = (mask + (_Scalar<float>(1.0f) - mask) * _Scalar<float>(slope)) * backwardOutput[0];
+            return result;
+        }
+        auto channel = prelu->slope()->size();
+        std::vector<float> scale(channel);
+        ::memcpy(scale.data(), prelu->slope()->data(), channel * sizeof(float));
+        std::vector<float> bias(channel, 0.0f);
+        auto outputSecond = _Scale(backwardOutput[0], channel, std::move(scale), std::move(bias));
+        result[0] = mask * backwardOutput[0] + (_Scalar<float>(1.0f) - mask) * outputSecond;
+//        auto diffInfo = result[0]->getInfo();
+//        auto inputInfo = input->getInfo();
+//        for (int i=0; i<diffInfo->dim.size(); ++i) {
+//            MNN_ASSERT(diffInfo->dim[i] == inputInfo->dim[i]);
+//            MNN_PRINT("%s, %d, %d - %d\n", expr->name().c_str(), i, diffInfo->dim[i], inputInfo->dim[i]);
+//        }
+//        MNN_ASSERT(diffInfo->order == inputInfo->order);
+        return result;
+    }
+
+};
+class ReluGrad : public OpGrad {
+public:
+    ReluGrad() {
+        mType = SEMI_LINEAR;
+    }
+    virtual std::vector<Express::VARP> onGrad(Express::EXPRP expr,
+                                              const std::vector<Express::VARP>& backwardOutput) override {
+        std::vector<Express::VARP> result(1, nullptr);
+        auto op = expr->get();
+        auto input = expr->inputs()[0];
+        auto mask = _Relu(_Sign(input));
+        if (nullptr != op->main_as_Relu() && op->main_as_Relu()->slope() != 0.0f) {
+            auto mask2 = _Cast<float>(_Less(input, _Scalar(0.0f)));
+            result[0] = (mask + mask2 * _Scalar<float>(op->main_as_Relu()->slope())) * backwardOutput[0];
+            return result;
+        }
+        result[0] = mask * backwardOutput[0];
+        return result;
+    }
+};
+class Relu6Grad : public OpGrad {
+public:
+    Relu6Grad() {
+        mType = SEMI_LINEAR;
+    }
+    virtual std::vector<Express::VARP> onGrad(Express::EXPRP expr,
+                                              const std::vector<Express::VARP>& backwardOutput) override {
+        std::vector<Express::VARP> result{nullptr};
+        auto op = expr->get();
+        MNN_ASSERT(nullptr != op);
+        auto relu6 = op->main_as_Relu6();
+        MNN_ASSERT(nullptr != relu6);
+        auto input = expr->inputs()[0];
+        auto mask0 = _Cast<float>(_Greater(input, _Scalar(relu6->minValue())));
+        auto mask1 = _Cast<float>(_Less(input, _Scalar(relu6->maxValue())));
+
+        result[0] = mask0 * mask1 * backwardOutput[0];
+        return result;
+    }
+};
+static void _create() {
+    static ReluGrad _c;
+    OpGrad::insert(OpType_ReLU, &_c);
+    static Relu6Grad _d;
+    OpGrad::insert(OpType_ReLU6, &_d);
+    static PReluGrad _e;
+    OpGrad::insert(OpType_PReLU, &_e);
+
+}
+
+REGISTER_GRAD(ReluGrad_cpp, _create);
+};
+