perf(lite): optimized lite tensor get data by share

GitOrigin-RevId: 62e48ca53926514b87df35e5e08df904949518be

lite/lite-c/include/lite-c/global_c.h

@@ -173,7 +173,7 @@ LITE_API int LITE_register_memory_pair(
  * clear the physical and virtual address pair in mge.
  */
 LITE_API int LITE_clear_memory_pair(
-        void* phy_ptr, void* vir_ptr, LiteDeviceType device, LiteBackend backend);
+        void* vir_ptr, void* phy_ptr, LiteDeviceType device, LiteBackend backend);
 
 #ifdef __cplusplus
 }

lite/lite-c/src/global.cpp

@@ -198,7 +198,7 @@ int LITE_register_memory_pair(
 }
 
 int LITE_clear_memory_pair(
-        void* phy_ptr, void* vir_ptr, LiteDeviceType device, LiteBackend backend) {
+        void* vir_ptr, void* phy_ptr, LiteDeviceType device, LiteBackend backend) {
     LITE_CAPI_BEGIN();
     lite::clear_memory_pair(vir_ptr, phy_ptr, device, backend);
     LITE_CAPI_END();

lite/pylite/megenginelite/tensor.py

@@ -225,6 +225,7 @@ class LiteTensor(object):
         tensor_desc.device_id = device_id
         tensor_desc.is_pinned_host = is_pinned_host
         self._api.LITE_make_tensor(tensor_desc, byref(self._tensor))
+        self.update()
 
     def __del__(self):
         self._api.LITE_destroy_tensor(self._tensor)
@@ -318,6 +319,11 @@ class LiteTensor(object):
         self._device_type = device_type
         self._api.LITE_get_tensor_layout(self._tensor, byref(self._layout))
 
+        c_types = _lite_dtypes_to_ctype[self._layout.data_type]
+        self.np_array_type = np.ctypeslib._ctype_ndarray(
+            c_types, list(self._layout.shapes)[0 : self._layout.ndim]
+        )
+
     def copy_from(self, src_tensor):
         """
         copy memory form the src_tensor
@@ -447,15 +453,11 @@ class LiteTensor(object):
         return the numpy arrray, be careful, the data in numpy is valid before
         the tensor memory is write again, such as LiteNetwok forward next time.
         """
-        assert self.is_continue, "get_data_by_share can only apply in continue tensor."
-        assert (
-            self.is_pinned_host or self.device_type == LiteDeviceType.LITE_CPU
-        ), "get_data_by_share can only apply in CPU tensor or cpu pinned tensor."
 
-        memory = self.get_ctypes_memory()
-        c_type = _lite_dtypes_to_ctype[LiteDataType(self._layout.data_type)]
-        pnt = cast(memory, POINTER(c_type))
-        return np.ctypeslib.as_array(pnt, self._layout.shapes)
+        buffer = c_void_p()
+        self._api.LITE_get_tensor_memory(self._tensor, byref(buffer))
+        buffer = self.np_array_type.from_address(buffer.value)
+        return np.ctypeslib.as_array(buffer)
 
     def to_numpy(self):
         """