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 +1,72 @@
+//
+//  ShapeBinaryOp.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+#include <vector>
+namespace MNN {
+class BinaryOpComputer : public SizeComputer {
+public:
+    static bool outputBool(int operation) {
+        if (operation == BinaryOpOperation_GREATER_EQUAL) {
+            return true;
+        }
+        if (operation == BinaryOpOperation_GREATER) {
+            return true;
+        }
+        if (operation == BinaryOpOperation_LESS) {
+            return true;
+        }
+        if (operation == BinaryOpOperation_LESS_EQUAL) {
+            return true;
+        }
+        if (operation == BinaryOpOperation_EQUAL) {
+            return true;
+        }
+        if (operation == BinaryOpOperation_NOTEQUAL) {
+            return true;
+        }
+        return false;
+    }
+    virtual bool onComputeSize(const Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(2 == inputs.size());
+        MNN_ASSERT(1 == outputs.size());
+        // set output type & format
+        auto input0 = inputs[0], input1 = inputs[1], output = outputs[0];
+        auto &buffer = output->buffer();
+        const auto opType = op->main_as_BinaryOp()->opType();
+        if (outputBool(opType)) {
+            buffer.type = halide_type_of<int32_t>();
+        } else {
+            buffer.type = input0->getType();
+        }
+
+        if (input0->getType().code != input1->getType().code) {
+#ifdef DEBUG
+            MNN_PRINT("Error for binary op: input0's type != input1's type, %d != %d, optype:%d, ", input0->getType().code, input1->getType().code, opType);
+            if (nullptr != op->name()) {
+                MNN_PRINT("op name: %s", op->name()->c_str());
+            }
+            MNN_PRINT("\n");
+#endif
+            return false;
+        }
+
+        if (input0->dimensions() < input1->dimensions()) {
+            auto temp = input0;
+            input0 = input1;
+            input1 = temp;
+        }
+        TensorUtils::getDescribe(output)->dimensionFormat = TensorUtils::getDescribe(input0)->dimensionFormat;
+        return SizeComputer::computeBroadCastDims(inputs, outputs);
+    }
+};
+
+REGISTER_SHAPE(BinaryOpComputer, OpType_BinaryOp);
+} // namespace MNN