Cythonize the shape property

python/tvm/_ffi/_ctypes/ndarray.py

@@ -90,6 +90,11 @@ def _copyto(self, target_nd):
         check_call(_LIB.TVMArrayCopyFromTo(self.handle, target_nd.handle, None))
         return target_nd
 
+    @property
+    def shape(self):
+        """Shape of this array"""
+        return tuple(self.handle.contents.shape[i] for i in range(self.handle.contents.ndim))
+
     def to_dlpack(self):
         """Produce an array from a DLPack Tensor without copying memory
 

python/tvm/_ffi/_cython/ndarray.pxi

@@ -68,6 +68,11 @@ cdef class NDArrayBase:
         def __set__(self, value):
             self._set_handle(value)
 
+    property shape:
+        """Shape of this array"""
+        def __get__(self):
+            return tuple(self.chandle.shape[i] for i in range(self.chandle.ndim))
+
     def __init__(self, handle, is_view):
         self._set_handle(handle)
         self.c_is_view = is_view

python/tvm/_ffi/ndarray.py

@@ -157,10 +157,6 @@ def from_dlpack(dltensor):
 
 class NDArrayBase(_NDArrayBase):
     """A simple Device/CPU Array object in runtime."""
-    @property
-    def shape(self):
-        """Shape of this array"""
-        return tuple(self.handle.contents.shape[i] for i in range(self.handle.contents.ndim))
 
     @property
     def dtype(self):
@@ -240,6 +236,7 @@ def copyfrom(self, source_array):
             except:
                 raise TypeError('array must be an array_like data,' +
                                 'type %s is not supported' % str(type(source_array)))
+
         t = TVMType(self.dtype)
         shape, dtype = self.shape, self.dtype
         if t.lanes > 1: