Introduce torch::executor::TensorAccessor

Summary: Replicate the TensorAccessor template from https://github.com/pytorch/pytorch/blob/fc813df1200b530d246eacc710781241c5a9dedf/aten/src/ATen/core/TensorAccessor.h#L73.

Differential Revision: D66033489

Author: sxu

runtime/core/exec_aten/exec_aten.h

@@ -140,6 +140,63 @@ using torch::executor::compute_numel;
 
 #endif // Use ExecuTorch types
 
+template <typename T, size_t N>
+class TensorAccessor {
+ public:
+  static TensorAccessor from_tensor(const Tensor& t) {
+    T* ptr = nullptr;
+    if constexpr (std::is_const_v<T>) {
+      ptr = t.const_data_ptr<T>();
+    } else {
+      ptr = t.mutable_data_ptr<T>();
+    }
+    return TensorAccessor(ptr, t.sizes().data(), t.strides().data());
+  }
+
+  TensorAccessor(
+      T* data,
+      const SizesType* sizes,
+      const StridesType* strides)
+      : data_(data), sizes_(sizes), strides_(strides) {}
+
+  ArrayRef<SizesType> sizes() const {
+    return ArrayRef<SizesType>(sizes_, N);
+  }
+
+  ArrayRef<StridesType> strides() const {
+    return ArrayRef<StridesType>(strides_, N);
+  }
+
+  SizesType size(size_t i) const {
+    return sizes_[i];
+  }
+
+  StridesType stride(size_t i) const {
+    return strides_[i];
+  }
+
+  T* data() {
+    return data_;
+  }
+
+  const T* data() const {
+    return data_;
+  }
+
+  TensorAccessor<T, N - 1> operator[](size_t i) {
+    return TensorAccessor<T, N - 1>(data_ + strides_[0] * i, sizes_ + 1, strides_ + 1);
+  }
+
+  const TensorAccessor<T, N - 1> operator[](size_t i) const {
+    return TensorAccessor<T, N - 1>(data_ + strides_[0] * i, sizes_ + 1, strides_ + 1);
+  }
+
+ private:
+  T* data_;
+  const SizesType* sizes_;
+  const StridesType* strides_;
+};
+
 } // namespace aten
 } // namespace executorch