!9245 Fixes some typos and formatting errors in docstring.

From: @Peilin-wang
Reviewed-by: @robingrosman,@robingrosman,@tom__chen
Signed-off-by: @tom__chen

mindspore/nn/loss/loss.py

@@ -290,8 +290,8 @@ class SampledSoftmaxLoss(_Loss):
         num_classes (int): The number of possible classes.
         num_true (int): The number of target classes per training example.
         sampled_values (Tuple):  Tuple of (`sampled_candidates`, `true_expected_count`,
-            `sampled_expected_count`) returned by a `*_candidate_sampler` function.
-            Default to None, `log_uniform_candidate_sampler` is applied.
+            `sampled_expected_count`) returned by a `*CandidateSampler` function.
+            Default to None, `UniformCandidateSampler` is applied.
         remove_accidental_hits (bool): Whether to remove "accidental hits"
             where a sampled class equals one of the target classes.  Default is True.
         seed (int): Random seed for candidate sampling. Default: 0
@@ -301,7 +301,7 @@ class SampledSoftmaxLoss(_Loss):
     Inputs:
         - **weights** (Tensor) - Tensor of shape (C, dim).
         - **bias** (Tensor) - Tensor of shape (C).  The class biases.
-        - **labels** (Tensor) - Tensor of shape (N, num_true), type `int64`. The
+        - **labels** (Tensor) - Tensor of shape (N, num_true), type `int64, int32`. The
             target classes.
         - **inputs** (Tensor) - Tensor of shape (N, dim).  The forward activations of
             the input network.
@@ -414,7 +414,7 @@ def _compute_sampled_logits(self, weights,
                 activations of the input network.
             num_true (int): The number of target classes per training example.
             sampled_values: a tuple of (`sampled_candidates`, `true_expected_count`,
-                `sampled_expected_count`) returned by a `UniformSampler` function.
+                `sampled_expected_count`) returned by a `UniformCandidateSampler` function.
             subtract_log_q: A `bool`.  whether to subtract the log expected count of
                 the labels in the sample to get the logits of the true labels.
                 Default is True.

mindspore/ops/composite/__init__.py

@@ -27,7 +27,7 @@
 from .multitype_ops.ones_like_impl import ones_like
 from .multitype_ops.zeros_like_impl import zeros_like
 from .random_ops import normal, laplace, uniform, gamma, poisson, multinomial
-from .math_ops import count_nonzero, TensorDot
+from .math_ops import count_nonzero, tensor_dot
 from .array_ops import repeat_elements
 
 
@@ -52,5 +52,5 @@
     'clip_by_value',
     'clip_by_global_norm',
     'count_nonzero',
-    'TensorDot',
+    'tensor_dot',
     'repeat_elements']

mindspore/ops/composite/array_ops.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""math Operations."""
+"""array Operations."""
 from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
 from mindspore.common import dtype as mstype
 from mindspore._checkparam import Validator as validator
@@ -69,7 +69,7 @@ def repeat_elements(x, rep, axis=0):
 
     Examples:
         >>> x = Tensor(np.array([[0, 1, 2], [3, 4, 5]]), mindspore.int32)
-        >>> output = C.RepeatElements(x, rep = 2, axis = 0)
+        >>> output = C.repeat_elements(x, rep = 2, axis = 0)
         >>> print(output)
         [[0, 1, 2],
          [0, 1, 2],

mindspore/ops/composite/math_ops.py

@@ -75,7 +75,7 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
 
     return nonzero_num
 
-# TensorDot
+# tensor dot
 @constexpr
 def _int_to_tuple_conv(axes):
     """
@@ -92,7 +92,7 @@ def _check_axes(axes):
     """
     Check for validity and type of axes passed to function.
     """
-    validator.check_value_type('axes', axes, [int, tuple, list], "TensorDot")
+    validator.check_value_type('axes', axes, [int, tuple, list], "tensor dot")
     if not isinstance(axes, int):
         axes = list(axes) # to avoid immutability issues
         if len(axes) != 2:
@@ -156,7 +156,7 @@ def _calc_new_shape(shape, axes, position=0):
     return new_shape, transpose_perm, free_dims
 
 
-def TensorDot(x1, x2, axes):
+def tensor_dot(x1, x2, axes):
     """
     Computation of Tensor contraction on arbitrary axes between tensors `a` and `b`.
 
@@ -171,8 +171,8 @@ def TensorDot(x1, x2, axes):
     axes = 2 is the same as axes = ((0,1),(1,2)) where length of input shape is 3 for both `a` and `b`
 
     Inputs:
-        - **x1** (Tensor) - First tensor in TensorDot op with datatype float16 or float32
-        - **x2** (Tensor) - Second tensor in TensorDot op with datatype float16 or float32
+        - **x1** (Tensor) - First tensor in tensor_dot with datatype float16 or float32
+        - **x2** (Tensor) - Second tensor in tensor_dot with datatype float16 or float32
         - **axes** (Union[int, tuple(int), tuple(tuple(int)), list(list(int))]) - Single value or
           tuple/list of length 2 with dimensions specified for `a` and `b` each. If single value `N` passed,
           automatically picks up first N dims from `a` input shape and last N dims from `b` input shape.
@@ -184,7 +184,7 @@ def TensorDot(x1, x2, axes):
     Examples:
         >>> input_x1 = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
         >>> input_x2 = Tensor(np.ones(shape=[3, 1, 2]), mindspore.float32)
-        >>> output = C.TensorDot(input_x1, input_x2, ((0,1),(1,2)))
+        >>> output = C.tensor_dot(input_x1, input_x2, ((0,1),(1,2)))
         >>> print(output)
         [[2,2,2],
          [2,2,2],
@@ -206,7 +206,7 @@ def TensorDot(x1, x2, axes):
     x1_reshape_fwd, x1_transpose_fwd, x1_ret = _calc_new_shape(x1_shape, axes, 0)
     x2_reshape_fwd, x2_transpose_fwd, x2_ret = _calc_new_shape(x2_shape, axes, 1)
     output_shape = x1_ret + x2_ret  # combine free axes from both inputs
-    # run TensorDot op
+    # run tensor_dot op
     x1_transposed = transpose_op(x1, x1_transpose_fwd)
     x2_transposed = transpose_op(x2, x2_transpose_fwd)
     x1_reshaped = reshape_op(x1_transposed, x1_reshape_fwd)

mindspore/ops/operations/array_ops.py

@@ -723,8 +723,10 @@ class Unique(Primitive):
         - **x** (Tensor) - The input tensor.
 
     Outputs:
-        Tuple, containing Tensor objects `(y, idx)`, `y` is a tensor has the same type as `x`, `idx` is a tensor
-        containing indices of elements in the input coressponding to the output tensor.
+        Tuple, containing Tensor objects `(y, idx)., `y` is a tensor with the
+        same type as `x`, and contains the unique elements in `x`, sorted in
+        ascending order. `idx` is a tensor containing indices of elements in
+        the input corresponding to the output tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -734,6 +736,23 @@ class Unique(Primitive):
         >>> output = ops.Unique()(x)
         >>> print(output)
         (Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1]))
+        >>>
+        >>> # note that for GPU, this operator must be wrapped inside a model, and executed in graph mode.
+        >>> class UniqueNet(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(UniqueNet, self).__init__()
+        >>>         self.unique_op = P.Unique()
+        >>>
+        >>>     def construct(self, x):
+        >>>         output, indices = self.unique_op(x)
+        >>>         return output, indices
+        >>>
+        >>> x = Tensor(np.array([1, 2, 5, 2]), mindspore.int32)
+        >>> context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+        >>> net = UniqueNet()
+        >>> output = net(x)
+        >>> print(output)
+        (Tensor(shape=[3], dtype=Int32, value= [1, 2, 5]), Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 1]))
     """
 
     @prim_attr_register

tests/st/ops/gpu/test_tensordot_op.py

@@ -29,7 +29,7 @@ def __init__(self, axes):
         self.axes = axes
 
     def construct(self, x, y):
-        return C.TensorDot(x, y, self.axes)
+        return C.tensor_dot(x, y, self.axes)
 
 
 class GradNetwork(nn.Cell):