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 +1,62 @@
+//
+//  ShapeSliceTf.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 SliceTfComputer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        MNN_ASSERT(inputs.size() == 3);
+        MNN_ASSERT(outputs.size() == 1);
+
+        auto input = inputs[0];
+        // these two inputs should be const
+        auto begin_tensor = inputs[1];
+        auto size_tensor  = inputs[2];
+
+        MNN_ASSERT(begin_tensor->buffer().dimensions == 1);
+        MNN_ASSERT(size_tensor->buffer().dimensions == 1);
+        MNN_ASSERT(input->buffer().dimensions >= 1);
+        MNN_ASSERT(input->buffer().dimensions == begin_tensor->buffer().dim[0].extent);
+        MNN_ASSERT(input->buffer().dimensions == size_tensor->buffer().dim[0].extent);
+
+        auto output                 = outputs[0];
+        output->buffer().dimensions = input->buffer().dimensions;
+        output->buffer().type       = input->buffer().type;
+        int dim                     = 0;
+        auto sizePtr = size_tensor->host<int32_t>();
+        auto beginPtr = begin_tensor->host<int32_t>();
+        for (int i = 0; i < input->buffer().dimensions; i++) {
+            dim = sizePtr[i];
+            auto begin = beginPtr[i];
+            if (begin < 0) {
+                begin += input->length(i);
+            }
+            if (begin < 0 || begin > input->length(i)) {
+                return false;
+            }
+            if (dim == -1 ) {
+                dim = input->buffer().dim[i].extent - begin;
+            }
+            if (dim < 0 || begin + dim > input->length(i)) {
+                return false;
+            }
+            output->buffer().dim[i].extent = dim;
+        }
+        for (int i=0; i<outputs.size(); ++i) {
+            TensorUtils::getDescribe(outputs[i])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        }
+        return true;
+    }
+};
+
+REGISTER_SHAPE_INPUTS(SliceTfComputer, OpType_SliceTf, (std::vector<int>{1, 2}));
+} // namespace MNN