!9380 Codedex change and comments change

From: @liangzhibo
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

mindspore/ccsrc/pipeline/jit/pipeline_split.cc

@@ -49,6 +49,9 @@ static int64_t GetRank() {
 }
 
 static int64_t InferStage(int64_t rank_id, int64_t stage_num, int64_t device_num) {
+  if (stage_num == 0) {
+    MS_LOG(EXCEPTION) << "stage_num is zero";
+  }
   if (device_num % stage_num != 0) {
     MS_LOG(EXCEPTION) << "Device_num must be divisible by the stage_num, got device_num: " << device_num
                       << "stage_num: " << stage_num;

mindspore/common/parameter.py

@@ -72,21 +72,22 @@ class Parameter(MetaTensor_):
         >>> from mindspore import context
         >>>
         >>> class Net(Cell):
-        >>>     def __init__(self):
-        >>>         super(Net, self).__init__()
-        >>>         self.matmul = P.MatMul()
-        >>>         self.weight = Parameter(Tensor(np.ones((1,2))), requires_grad=True)
-        >>>
-        >>>     def construct(self, x):
-        >>>         out = self.matmul(self.weight, x)
-        >>>         return out
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self.matmul = P.MatMul()
+        ...         self.weight = Parameter(Tensor(np.ones((1,2))), name="w", requires_grad=True)
+        ...
+        ...     def construct(self, x):
+        ...         out = self.matmul(self.weight, x)
+        ...         return out
         >>> context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
         >>> net = Net()
         >>> x = Tensor(np.ones((2,1)))
-        >>> net(x)
+        >>> print(net(x))
         [[2.]]
         >>> net.weight.set_data(Tensor(np.zeros((1,2))))
-        >>> net(x)
+        Parameter (name=w)
+        >>> print(net(x))
         [[0.]]
     """
     __base_type__ = {}

mindspore/common/tensor.py

@@ -46,6 +46,8 @@ class Tensor(Tensor_):
         Tensor, with the same shape as `input_data`.
 
     Examples:
+        >>> import mindspore as ms
+        >>> import mindspore.nn as nn
         >>> # initialize a tensor with input data
         >>> t1 = Tensor(np.zeros([1, 2, 3]), mindspore.float32)
         >>> assert isinstance(t1, Tensor)
@@ -381,17 +383,25 @@ class RowTensor:
         RowTensor, composed of `indices`, `values`, and `dense_shape`.
 
     Examples:
+        >>> import mindspore as ms
+        >>> import mindspore.nn as nn
         >>> class Net(nn.Cell):
-        >>>     def __init__(self, dense_shape):
-        >>>         super(Net, self).__init__()
-        >>>         self.dense_shape = dense_shape
-        >>>     def construct(self, indices, values):
-        >>>         x = RowTensor(indices, values, self.dense_shape)
-        >>>         return x.values, x.indices, x.dense_shape
+        ...     def __init__(self, dense_shape):
+        ...         super(Net, self).__init__()
+        ...         self.dense_shape = dense_shape
+        ...     def construct(self, indices, values):
+        ...         x = RowTensor(indices, values, self.dense_shape)
+        ...         return x.values, x.indices, x.dense_shape
         >>>
         >>> indices = Tensor([0])
         >>> values = Tensor([[1, 2]], dtype=ms.float32)
-        >>> Net((3, 2))(indices, values)
+        >>> out = Net((3, 2))(indices, values)
+        >>> print(out[0])
+        [[1. 2.]]
+        >>> print(out[1])
+        [0]
+        >>> print(out[2])
+        (3, 2)
     """
 
     def __init__(self, indices, values, dense_shape):
@@ -437,17 +447,26 @@ class SparseTensor:
         SparseTensor, composed of `indices`, `values`, and `dense_shape`.
 
     Examples:
+        >>> import mindspore as ms
+        >>> import mindspore.nn as nn
         >>> class Net(nn.Cell):
-        >>>     def __init__(self, dense_shape):
-        >>>         super(Net, self).__init__()
-        >>>         self.dense_shape = dense_shape
-        >>>     def construct(self, indices, values):
-        >>>         x = SparseTensor(indices, values, self.dense_shape)
-        >>>         return x.values, x.indices, x.dense_shape
+        ...     def __init__(self, dense_shape):
+        ...         super(Net, self).__init__()
+        ...         self.dense_shape = dense_shape
+        ...     def construct(self, indices, values):
+        ...         x = SparseTensor(indices, values, self.dense_shape)
+        ...         return x.values, x.indices, x.dense_shape
         >>>
         >>> indices = Tensor([[0, 1], [1, 2]])
         >>> values = Tensor([1, 2], dtype=ms.float32)
-        >>> Net((3, 4))(indices, values)
+        >>> out = Net((3, 4))(indices, values)
+        >>> print(out[0])
+        [1. 2.]
+        >>> print(out[1])
+        [[0 1]
+         [1 2]]
+        >>> print(out[2])
+        (3, 4)
     """
 
     def __init__(self, indices, values, dense_shape):

mindspore/core/ir/pattern_matcher.h

@@ -811,6 +811,9 @@ class PConstant : public PBase<PConstant<T> > {
   template <typename TM>
   void CalcByOperator(void *in_data_1, int in_data_1_size, void *in_data_2, int in_data_2_size, void **out_data,
                       int out_data_size, BinOperator bin_operator) const {
+    if (out_data_size <= 0) {
+      MS_EXCEPTION(ValueError) << "out_data_size should be greater than zeros";
+    }
     TM *data_1 = reinterpret_cast<TM *>(in_data_1);
     TM *data_2 = reinterpret_cast<TM *>(in_data_2);
     TM *data_out = new TM[out_data_size];

mindspore/nn/graph_kernels/graph_kernels.py

@@ -41,9 +41,9 @@ class InplaceAssign(PrimitiveWithInfer):
 
     Examples:
     >>> def construct(self, x):
-    >>> val = x - 1.0
-    >>> ret = x + 2.0
-    >>> return InplaceAssign()(x, val, ret)
+    ...     val = x - 1.0
+    ...     ret = x + 2.0
+    ...     return InplaceAssign()(x, val, ret)
     >>> x = Tensor([2.0], mindspore.float32)
     >>> net = Net()
     >>> net(x)
@@ -225,8 +225,8 @@ class BiasAdd(GraphKernel):
         Tensor, the sum of x and bias.
 
     Example:
-    >>> layer = BiasGrad()
-    >>> output = BiasAdd(Tensor([1, 2, 3]), Tensor([1,]))
+    >>> layer = BiasAdd()
+    >>> output = layer(Tensor([1, 2, 3]), Tensor([1,]))
     """
 
     def __init__(self):
@@ -286,7 +286,8 @@ class EqualCount(GraphKernel):
         >>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
         >>> y = Tensor(np.array([1, 2, 4]), mindspore.int32)
         >>> equal_count = EqualCount()
-        >>> equal_count(x, y)
+        >>> print(equal_count(x, y))
+        2
     """
     def __init__(self):
         super(EqualCount, self).__init__()
@@ -744,7 +745,7 @@ class Tanh(GraphKernel):
         >>> tanh = Tanh()
         >>> result = tanh(input_x)
         >>> print(result)
-        [0.7615941  0.9640276  0.9950548  0.9993293  0.99990916]
+        [0.7615941 0.9640276 0.9950548 0.9993293 0.99990916]
     """
     def __init__(self):
         super(Tanh, self).__init__()

mindspore/nn/layer/basic.py

@@ -570,14 +570,20 @@ class Pad(Cell):
 
 
 @constexpr
-def interpolate(shape, size, scale):
+def interpolate(shape, size, scale, align_corners):
     """Check input and calculate shape"""
+    if not isinstance(align_corners, bool):
+        raise TypeError("align_corners should be type boolean")
     if size is None and scale is None:
         raise ValueError("size and scale both none")
     if size is not None and scale is not None:
         raise ValueError("size and scale both not none")
     if size is not None:
+        if not isinstance(size, (tuple, list)):
+            raise ValueError("size must be tuple or list")
         Validator.check_int(len(size), 2, Rel.EQ, "size", "interpolate")
+        Validator.check_int(size[0], 1, Rel.GE, "size[0]", "interpolate")
+        Validator.check_int(size[1], 1, Rel.GE, "size[1]", "interpolate")
         return size
     Validator.check_int(scale, 1, Rel.GE, "scale factor", "interpolate")
     ret = (scale * shape[2], scale * shape[3])
@@ -620,7 +626,7 @@ class Interpolate(Cell):
         super(Interpolate, self).__init__()
 
     def construct(self, x, size=None, scale_factor=None, align_corners=False):
-        shape = interpolate(x.shape, size, scale_factor)
+        shape = interpolate(x.shape, size, scale_factor, align_corners)
         resize_bilinear = P.ResizeBilinear(shape, align_corners)
         return resize_bilinear(x)
 
@@ -715,10 +721,12 @@ class Tril(Cell):
         super(Tril, self).__init__()
         self.dtype = P.DType()
         self.mul = P.Mul()
+        self.cast = P.Cast()
 
     def construct(self, x, k=0):
         assist = tril(x.shape, self.dtype(x), k)
-        return self.mul(x, assist)
+        result = self.mul(self.cast(x, mstype.int32), self.cast(assist, mstype.int32))
+        return self.cast(result, self.dtype(x))
 
 
 @constexpr
@@ -755,10 +763,12 @@ class Triu(Cell):
         super(Triu, self).__init__()
         self.dtype = P.DType()
         self.mul = P.Mul()
+        self.cast = P.Cast()
 
     def construct(self, x, k=0):
         assist = triu(x.shape, self.dtype(x), k)
-        return self.mul(x, assist)
+        result = self.mul(self.cast(x, mstype.int32), self.cast(assist, mstype.int32))
+        return self.cast(result, self.dtype(x))
 
 
 @constexpr