@@ -65,8 +65,6 @@ def is_floating_point(obj):
     return obj.is_floating_point()
 
 
-strided = False

@@ -207,3 +210,2 @@
             else:
-                warning("If you want to obtain `smallest_normal` in finfo, " \
-                        "NumPy version must be greater or equal 1.23.0.")
+                warning_only_once("If you want to obtain `smallest_normal` in finfo, " \

@@ -1478,2 +1479,3 @@
 <span id="jump13">Storage统一约束:</span>
-- 当前Storage相关对象并未实现存储区控制功能，仅支持size，nbytes，len，element_size，tolist，type及__getitem__等基础功能对标。
+- 由于存储机制差异，无法支持share_memory_，pin_memory等涉及数据存储区控制的功能，暂不支持两个Tensor共享同一个Storage对象的相关场景。
+- 当前基于Numpy实现，性能存在较大优化空间，不建议频繁调用。

@@ -75,6 +77,11 @@ def warning(msg, *args, **kwargs):
     _get_logger().warning(msg, *args, **kwargs)
 
 
+@lru_cache(_GLOBAL_LRU_CACHE_SIZE_NN)

@@ -7,3 +8,3 @@
 from mindspore.ops.primitive import _primexpr
 from mindspore._c_expression import typing
-from mindtorch.torch.logging import warning
+from mindtorch.torch.logging import warning_only_once

@@ -54,3 +61,1 @@
-            return self.inner_data.flatten()[idx].item()
-        else:
-            return self.inner_data.flatten()[idx]
+        return self.inner_data[idx]

@@ -1188,0 +1213,4 @@
+                target_shape = ori_shape[:-1] + target_shape
+            out = np.frombuffer(self.numpy(), _TypeDict.get(dtype, np.float32))
+            if dtype == mindtorch_dtype.bfloat16:
+                return tensor(out.astype(np.float32), dtype=dtype).reshape(target_shape)

@@ -123,0 +155,4 @@
+        else:
+            append_data = np.random.randint(0, 255, size=size - self.size(), dtype=np.uint8)
+            self.inner_data = np.concatenate((self.inner_data, append_data), axis=0)
+        if self.referenced_tensor is not None:

@@ -493,0 +554,4 @@
+        elif not isinstance(idx, int):
+            raise RuntimeError(f"can't index a {type(self)} with {type(idx)}")
+        idx_wrapped = self._maybe_wrap_index(idx)
+        tmp_np_data = np.frombuffer(self._storage.inner_data, _TypeDict.get(self.dtype, np.float32))

@@ -621,2 +687,3 @@
             raise TypeError(f"Argument 'dtype' must be torch.dtype, not {type(dtype)}")
-        storage = self._storage.inner_data.to(dtype).storage()
+        from mindtorch.torch.tensor import tensor # pylint: disable=R0401, C0415
+        np_data = np.frombuffer(self._storage.inner_data, _TypeDict.get(self.dtype))