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 +1,94 @@
+//
+//  ShapeGatherV2.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 GatherV2Computer : public SizeComputer {
+    virtual bool onComputeSize(const MNN::Op* op, const std::vector<Tensor*>& inputs,
+                               const std::vector<Tensor*>& outputs) const override {
+        Tensor* indices = nullptr;
+        int32_t paramShape[MNN_MAX_TENSOR_DIM];
+        int32_t paramDim = 0;
+        if (inputs.size() == 1) {
+            if (nullptr == op->main_as_Input()) {
+                MNN_ERROR("One input GatherV2 should has blob parameter\n");
+                return false;
+            }
+            indices = inputs[0];
+            auto blob = op->main_as_Input();
+            outputs[0]->setType(blob->dtype());
+            if (nullptr != blob->dims()) {
+                paramDim = blob->dims()->size();
+                ::memcpy(paramShape, blob->dims()->data(), paramDim * sizeof(int));
+            }
+            TensorUtils::getDescribe(outputs[0])->dimensionFormat = blob->dformat();
+        } else {
+            auto params = inputs[0];
+            indices = inputs[1];
+            paramDim = params->dimensions();
+            for (int i = 0; i < params->dimensions(); ++i) {
+                paramShape[i] = params->length(i);
+            }
+            outputs[0]->buffer().type = params->buffer().type;
+            TensorUtils::getDescribe(outputs[0])->dimensionFormat = TensorUtils::getDescribe(inputs[0])->dimensionFormat;
+        }
+        if (indices->getType().code != halide_type_int) {
+            return false;
+        }
+        int axis = 0;
+        if (inputs.size() == 3) {
+            auto axis_tensor = inputs[2];
+            axis = axis_tensor->host<int32_t>()[0];
+        }
+        if (op->main_type() == OpParameter_Axis && op->main_as_Axis()) {
+            axis = op->main_as_Axis()->axis();
+        }
+
+        if( axis <= -paramDim || axis >= paramDim) {
+            return false;
+        }
+
+        if (axis < 0) {
+            axis = paramDim + axis;
+        }
+
+        const int gather_dim_size = paramShape[axis];
+        MNN_ASSERT(gather_dim_size <= std::numeric_limits<int32_t>::max());
+
+        const int numDimensions = paramDim + indices->buffer().dimensions - 1;
+        MNN_ASSERT(axis <= numDimensions);
+
+        std::vector<int> result_shape;
+
+        for (int i = 0; i < axis; i++) {
+            result_shape.push_back(paramShape[i]);
+        }
+
+        for (int i = 0; i < indices->buffer().dimensions; i++) {
+            result_shape.push_back(indices->buffer().dim[i].extent);
+        }
+
+        for (int i = axis + 1; i < paramDim; i++) {
+            result_shape.push_back(paramShape[i]);
+        }
+
+        outputs[0]->buffer().dimensions = (int)result_shape.size();
+        for (int i = 0; i < result_shape.size(); i++) {
+            outputs[0]->buffer().dim[i].extent = result_shape.at(i);
+        }
+
+        return true;
+    }
+};
+
+REGISTER_SHAPE_INPUTS(GatherV2Computer, OpType_GatherV2, (std::vector<int>{2}));
+REGISTER_SHAPE(GatherV2Computer, OpType_Gather);
+} // namespace MNN