!31723 code_check_clean

Merge pull request !31723 from liuyang/tensor_ms

docs/api/api_python/ops/mindspore.ops.func_arange.rst

@@ -10,7 +10,7 @@ mindspore.ops.arange
     - **start** (Union[int, float]) - 指定范围的起始值，范围包含该值。类型为int或float。
     - **stop** (Union[int, float]) - 指定范围的结束值，范围不包含该值。类型为int或float。
     - **step** (Union[int, float]) - 指定取值的间隔。类型为int或float。
-    - **rtype** (Union[mindspore.dtype，str]) - 指定返回数据的类型，如果不指定，则会根据 `start` 、 `stop` 、 `step` 的值推断类型。
+    - **rtype** (Union[mindspore.dtype, str]) - 指定返回数据的类型，如果不指定，则会根据 `start` 、 `stop` 、 `step` 的值推断类型。
 
     **返回：**
 

mindspore/ccsrc/transform/express_ir/onnx_exporter.cc

@@ -236,8 +236,11 @@ uint32_t Fp32ToFp16(float value) {
   const unsigned int FP16_E = 16 - 1 - FP16_M;
 
   uint32_t fp32_bits;
-  memcpy_s(reinterpret_cast<std::byte *>(&fp32_bits), sizeof(fp32_bits), reinterpret_cast<std::byte *>(&value),
-           sizeof(value));
+  auto ret = memcpy_s(reinterpret_cast<std::byte *>(&fp32_bits), sizeof(fp32_bits),
+                      reinterpret_cast<std::byte *>(&value), sizeof(value));
+  if (ret != 0) {
+    MS_LOG(ERROR) << "Set data memcpy_s failed, ret = " << ret;
+  }
 
   uint32_t mantissa = fp32_bits & FieldMask(FP32_M);
   uint32_t fp32_exp_mask = FieldMask(FP32_E);

mindspore/ccsrc/transform/graph_ir/convert.cc

@@ -432,11 +432,11 @@ void DfGraphConvertor::BuildSaveCheckpointGraph() {
   size_t index = 0;
   string name;
 
-  size_t count_size = std::count_if(vars_.begin(), vars_.end(), [](const auto &it) {
+  auto count_size = std::count_if(vars_.begin(), vars_.end(), [](const auto &it) {
     return LongToUlong(it.second == nullptr || it.first.find("/") != std::string::npos);
   });
 
-  (void)save_op.create_dynamic_input_tensors(static_cast<uint32_t>(vars_.size() - count_size));
+  (void)save_op.create_dynamic_input_tensors(static_cast<uint32_t>(vars_.size() - static_cast<size_t>(count_size)));
 
   // for each "parameter" in anf graph excluding "input"
   for (const auto &it : vars_) {

mindspore/python/mindspore/common/tensor.py

@@ -1028,8 +1028,8 @@ class Tensor(Tensor_):
         Return a copy of the tensor, cast to a specified type.
 
         Args:
-            dtype (Union[:class:`mindspore.dtype`, str]): Designated tensor dtype, can be in format
-                of :class:`mindspore.dtype.float32` or `float32`.
+            dtype (Union[:class:`mindspore.dtype`, :class:`numpy.dtype`, str]): Designated tensor dtype, can be in
+                format of :class:`mindspore.dtype.float32` or :class:`numpy.float32` or `float32`.
             copy (bool, optional): By default, astype always returns a newly allocated
                 tensor. If this is set to false, the input tensor is returned instead
                 of a copy. Default: True.
@@ -1998,9 +1998,13 @@ class Tensor(Tensor_):
             v = tensor_operator_registry.get('make_tensor')(v)
         shape = v.shape
         if sorter is not None:
+            if not isinstance(sorter, (int, float, bool, list, tuple, Tensor)):
+                raise TypeError("For Tensor.searchsorted, the type of the argument 'sorter' must be one of 'int', "
+                                "'float', 'bool', 'list', 'tuple', 'Tensor', but got {}.".format(type(sorter)))
+            if not isinstance(sorter, Tensor):
+                sorter = tensor_operator_registry.get('make_tensor')(sorter)
             if sorter.ndim != 1 or sorter.size != a.size:
                 raise ValueError('sorter must be 1-D array with the same size as the Tensor')
-            sorter = tensor_operator_registry.get('make_tensor')(sorter)
             sorter = sorter.reshape(sorter.shape + (1,))
             a = tensor_operator_registry.get('gather_nd')(a, sorter)
         less_op = tensor_operator_registry.get('__le__') if side == 'left' else tensor_operator_registry.get('__lt__')

mindspore/python/mindspore/numpy/array_creations.py

@@ -430,7 +430,7 @@ def randn(*shape, dtype=mstype.float32):
 
     Returns:
         Tensor, with the designated shape and dtype, filled with a sample (or samples)
-            from the "standard normal" distribution.
+        from the "standard normal" distribution.
 
     Raises:
         TypeError: If input arguments have types not specified above.
@@ -470,7 +470,7 @@ def rand(*shape, dtype=mstype.float32):
 
     Returns:
         Tensor, with the designated shape and dtype, filled with random numbers from the
-    uniform distribution on the interval :math:`[0, 1)`.
+        uniform distribution on the interval :math:`[0, 1)`.
 
     Raises:
         TypeError: If input arguments have types not specified above.
@@ -501,7 +501,7 @@ def randint(minval, maxval=None, shape=None, dtype=mstype.int32):
     """
     Return random integers from minval (inclusive) to maxval (exclusive). Return random integers from the
     discrete uniform distribution of the specified dtype in the “half-open” interval :math:`[minval, maxval)`.
-    If maxval is None (the default), then results are from [0, maxval).
+    If maxval is None (the default), the value range will be [0, minval), in this case, minval must be greater than 0.
 
     Args:
         minval(Union[int]): Start value of interval. The interval includes this value. When `maxval`

mindspore/python/mindspore/train/callback/_history.py

@@ -18,6 +18,7 @@ import numpy as np
 from mindspore.common.tensor import Tensor
 from ._callback import Callback
 
+
 class History(Callback):
     """
     Records the network outputs information into a `History` object.

mindspore/python/mindspore/train/callback/_lambda_callback.py

@@ -16,6 +16,7 @@
 
 from ._callback import Callback
 
+
 class LambdaCallback(Callback):
     """
     Callback for creating simple, custom callbacks.

mindspore/python/mindspore/train/dataset_helper.py

@@ -404,8 +404,10 @@ class _DatasetIter:
         return self.op()
 
     def types_shapes(self):
-        """Return the types and shapes of the dataset. The type and shape of each data in the dataset
-        should be consistent."""
+        """
+        Return the types and shapes of the dataset. The type and shape of each data in the dataset
+        should be consistent.
+        """
         return self.dataset_types, self.dataset_shapes
 
     def get_sink_count(self, dataset):