this is the first PR of WWZ team!

mindspore/numpy/dlpack.py

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+# https://github.com/vadimkantorov/pydlpack/blob/master/dlpack.py
+import ctypes
+
+class DLDeviceType(ctypes.c_int):
+	kDLCPU = 1
+	kDLGPU = 2
+	kDLCPUPinned = 3
+	kDLOpenCL = 4
+	kDLVulkan = 7
+	kDLMetal = 8
+	kDLVPI = 9
+	kDLROCM = 10
+	kDLExtDev = 12
+
+class DLDataTypeCode(ctypes.c_uint8):
+	kDLInt = 0
+	kDLUInt = 1
+	kDLFloat = 2
+	kDLBfloat = 4
+
+	def __str__(self):
+		return {self.kDLInt : 'int', self.kDLUInt : 'uint', self.kDLFloat : 'float', self.kDLBfloat : 'bfloat'}[self.value]
+
+class DLDataType(ctypes.Structure):
+	_fields_ = [
+		('type_code', DLDataTypeCode),
+		('bits', ctypes.c_uint8),
+		('lanes', ctypes.c_uint16)
+	]
+
+	@property
+	def descr(self):
+		typestr = str(self.type_code) + str(self.bits)
+		return [('f' + str(l), typestr) for l in range(self.lanes)]
+
+	def __str__(self):
+		return repr(self.descr)
+
+class DLContext(ctypes.Structure):
+	_fields_ = [
+		('device_type', DLDeviceType),
+		('device_id', ctypes.c_int)
+	]
+
+class DLTensor(ctypes.Structure):
+	_fields_ = [
+		('data', ctypes.c_void_p),
+		('ctx', DLContext),
+		('ndim', ctypes.c_int),
+		('dtype', DLDataType),
+		('shape', ctypes.POINTER(ctypes.c_int64)),
+		('strides', ctypes.POINTER(ctypes.c_int64)),
+		('byte_offset', ctypes.c_uint64)
+	]
+
+	@property
+	def size(self):
+		prod = 1
+		for i in range(self.ndim):
+			prod *= self.shape[i]
+		return prod
+
+	@property
+	def itemsize(self):
+		return self.dtype.lanes * self.dtype.bits // 8;
+
+	@property
+	def nbytes(self):
+		return self.size * self.itemsize 
+
+	@property
+	def __array_interface__(self):
+		shape = tuple(self.shape[dim] for dim in range(self.ndim))
+		strides = tuple(self.strides[dim] * self.itemsize for dim in range(self.ndim))
+		typestr = '|' + str(self.dtype.type_code)[0] + str(self.itemsize)
+		return dict(version = 3, shape = shape, strides = strides, data = (self.data, True), offset = self.byte_offset, typestr = typestr)
+
+	def __str__(self):
+		return 'dtype={dtype}, ndim={ndim}, shape={shape}, strides={strides}, byte_offset={byte_offset}'.format(dtype = self.dtype, ndim = self.ndim, shape = tuple(self.shape[i] for i in range(self.ndim)), strides = tuple(self.strides[i] for i in range(self.ndim)), byte_offset = self.byte_offset)
+
+class DLManagedTensor(ctypes.Structure):
+	_fields_ = [
+		('dl_tensor', DLTensor),
+		('manager_ctx', ctypes.c_void_p),
+		('deleter', ctypes.CFUNCTYPE(None, ctypes.c_void_p))
+	]
+
+PyCapsule_Destructor = ctypes.CFUNCTYPE(None, ctypes.py_object)
+PyCapsule_New = ctypes.pythonapi.PyCapsule_New
+PyCapsule_New.restype = ctypes.py_object
+PyCapsule_New.argtypes = (ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p)
+PyCapsule_GetPointer = ctypes.pythonapi.PyCapsule_GetPointer
+PyCapsule_GetPointer.restype = ctypes.c_void_p
+PyCapsule_GetPointer.argtypes = (ctypes.py_object, ctypes.c_char_p)
+
+
+###  mindspore.tensor to numpy.array
+from mindspore import Tensor
+import numpy as np 
+def tensor_from_numpy(data_numpy):
+    if not isinstance(data_numpy,np.array):
+	panic()
+    return Tensor(data_numpy)
+
+## dlpack to numpy.array
+def from_dlpack(pycapsule):
+	dl_managed_tensor = ctypes.cast(PyCapsule_GetPointer(pycapsule, b'dltensor'), ctypes.POINTER(DLManagedTensor)).contents
+	wrapped = type('', (), dict(__array_interface__ = dl_managed_tensor.dl_tensor.__array_interface__, __del__ = lambda self: dl_managed_tensor.deleter(ctypes.byref(dl_managed_tensor))))()
+	tensor_mindspore = tensor_from_numpy(numpy.asarray(wrapped))
+	return tensor_mindspore
+
+# def to_dlpack(tensor_mindspore):
+# 	if not isinstance(tensor_mindspore, mindspore.Tensor):
+# 		panic()
+# 	data_numpy = tensor_mindspore.asnumpy()	
+# 	return PyCapsule_New(ctypes.byref(dl_managed_tensor), b'dltensor', None)
+
+
+#