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 +1,69 @@
+//
+//  ShapeConcat.cpp
+//  MNN
+//
+//  Created by MNN on 2019/01/10.
+//  Copyright © 2018, Alibaba Group Holding Limited
+//
+
+#include "shape/SizeComputer.hpp"
+#include "core/Macro.h"
+
+namespace MNN {
+class ConcatSizeComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(1 == outputs.size());
+        // MNN_ASSERT(inputs.size() >= 2);
+        auto& ob      = outputs[0]->buffer();
+        int basicAxis = 0;
+        if (op->type() == OpType_Concat) {
+            if (op->main_as_Axis() != nullptr) {
+                basicAxis = op->main_as_Axis()->axis();
+            } else {
+                MNN_ERROR("Concat op axis is nullptr, set to 0 as default\n");
+            }
+        } else if (op->type() == OpType_QuantizedConcat) {
+            basicAxis = op->main_as_QuantizedConcat()->axis();
+        }
+        int axis = basicAxis;
+        // Concat-inputs may have scalar which should be delete
+        for (const auto& input : inputs) {
+            auto inputDimensions = input->buffer().dimensions;
+
+            //  Tensor might be zeros size, but some dims may not be zero. should concat as usual.
+
+            ::memcpy(ob.dim, input->buffer().dim, sizeof(halide_dimension_t) * inputDimensions);
+            ob.dimensions = inputDimensions;
+            ob.type       = input->buffer().type;
+            if (axis < 0) {
+                axis = inputDimensions + axis;
+            }
+            break;
+        }
+
+
+        int sum = 0;
+        for (auto t : inputs) {
+            sum += t->buffer().dim[axis].extent;
+            ob.type = t->buffer().type;
+            for (int i = 0; i < t->dimensions(); ++i) {
+                if (axis == i) {
+                    continue;
+                }
+                if (t->length(i) != outputs[0]->length(i)) {
+                    auto name = op->name() ? op->name()->c_str() : "";
+                    MNN_PRINT("Error for concat size of op [ %s ], the %d input not match output\n", name, i);
+                    return false;
+                }
+            }
+        }
+        ob.dim[axis].extent                                   = sum;
+        TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        return true;
+    }
+};
+
+REGISTER_SHAPE(ConcatSizeComputer, OpType_Concat);
+REGISTER_SHAPE(ConcatSizeComputer, OpType_QuantizedConcat);
+} // namespace MNN