fix the doc of inverse, dot, cholesky (#25860)

* fix the doc of inverse, dot, cholesky

python/paddle/fluid/tests/unittests/test_cholesky_op.py

@@ -90,5 +90,15 @@ class TestCholeskyOp2D(TestCholeskyOp):
         self._input_shape = (64, 64)
 
 
+class TestDygraph(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+        a = np.random.rand(3, 3)
+        a_t = np.transpose(a, [1, 0])
+        x_data = np.matmul(a, a_t) + 1e-03
+        x = paddle.to_variable(x_data)
+        out = paddle.cholesky(x, upper=False)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_inverse_op.py

@@ -89,8 +89,7 @@ class TestInverseAPI(unittest.TestCase):
     def check_static_result(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(name="input", shape=[4, 4], dtype="float64")
-            result = paddle.inverse(input=input)
-
+            result = paddle.inverse(x=input)
             input_np = np.random.random([4, 4]).astype("float64")
             result_np = np.linalg.inv(input_np)
 
@@ -145,7 +144,7 @@ class TestInverseSingularAPI(unittest.TestCase):
     def check_static_result(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(name="input", shape=[4, 4], dtype="float64")
-            result = paddle.inverse(input=input)
+            result = paddle.inverse(x=input)
 
             input_np = np.zeros([4, 4]).astype("float64")
 

python/paddle/tensor/linalg.py

@@ -452,21 +452,18 @@ def dist(x, y, p=2):
 
 def dot(x, y, name=None):
     """
-	:alias_main: paddle.dot
-	:alias: paddle.dot,paddle.tensor.dot,paddle.tensor.linalg.dot
-
     This operator calculates inner product for vectors.
    
     .. note::
        Only support 1-d Tensor(vector).
 
     Parameters:
-        x(Variable): 1-D ``Tensor`` or ``LoDTensor``. Its datatype should be ``float32``, ``float64``, ``int32``, ``int64``
-        y(Variable): 1-D ``Tensor`` or ``LoDTensor``. Its datatype soulde be ``float32``, ``float64``, ``int32``, ``int64``
+        x(Tensor): 1-D ``Tensor``. Its datatype should be ``float32``, ``float64``, ``int32``, ``int64``
+        y(Tensor): 1-D ``Tensor``. Its datatype soulde be ``float32``, ``float64``, ``int32``, ``int64``
         name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`
 
     Returns:
-        Variable: the calculated result Tensor/LoDTensor.
+        Variable: the calculated result Tensor.
 
     Examples:
 
@@ -475,12 +472,14 @@ def dot(x, y, name=None):
         import paddle
         import paddle.fluid as fluid
         import numpy as np
-        
-        with fluid.dygraph.guard():
-          x = fluid.dygraph.to_variable(np.random.uniform(0.1, 1, [10]).astype(np.float32))
-          y = fluid.dygraph.to_variable(np.random.uniform(1, 3, [10]).astype(np.float32))
-          z = paddle.dot(x, y)
-          print(z.numpy())
+
+        paddle.disable_static()
+        x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)
+        y_data = np.random.uniform(1, 3, [10]).astype(np.float32)
+        x = paddle.to_variable(x_data)
+        y = paddle.to_variable(y_data)
+        z = paddle.dot(x, y)
+        print(z.numpy())
 
     """
     op_type = 'dot'
@@ -651,11 +650,8 @@ def cross(x, y, axis=None, name=None):
     return out
 
 
-def cholesky(x, upper=False):
+def cholesky(x, upper=False, name=None):
     """
-	:alias_main: paddle.cholesky
-	:alias: paddle.cholesky,paddle.tensor.cholesky,paddle.tensor.linalg.cholesky
-
     Computes the Cholesky decomposition of one symmetric positive-definite
     matrix or batches of symmetric positive-definite matrice. 
     
@@ -680,21 +676,22 @@ def cholesky(x, upper=False):
         .. code-block:: python
 
             import paddle
-            import paddle.fluid as fluid
             import numpy as np
 
-            with fluid.dygraph.guard():
-                a = np.random.rand(3, 3)
-                a_t = np.transpose(a, [1, 0])
-                x = np.matmul(a, a_t) + 1e-03
-                x = fluid.dygraph.to_variable(x)
-                out = paddle.cholesky(x, upper=False)
-                print(out.numpy())
-                # [[1.190523   0.         0.        ]
-                #  [0.9906703  0.27676893 0.        ]
-                #  [1.25450498 0.05600871 0.06400121]]
+            paddle.disable_static()
+            a = np.random.rand(3, 3)
+            a_t = np.transpose(a, [1, 0])
+            x_data = np.matmul(a, a_t) + 1e-03
+            x = paddle.to_variable(x_data)
+            out = paddle.cholesky(x, upper=False)
+            print(out.numpy())
+            # [[1.190523   0.         0.        ]
+            #  [0.9906703  0.27676893 0.        ]
+            #  [1.25450498 0.05600871 0.06400121]]
 
     """
+    if in_dygraph_mode():
+        return core.ops.cholesky(x, "upper", upper)
     check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')
     check_type(upper, 'upper', bool, 'cholesky')
     helper = LayerHelper('cholesky', **locals())

python/paddle/tensor/math.py

@@ -1099,17 +1099,15 @@ def logsumexp(x, dim=None, keepdim=False, name=None):
     return layers.log(sum_out, name)
 
 
-def inverse(input, name=None):
-    """
-	:alias_main: paddle.inverse
-	:alias: paddle.inverse,paddle.tensor.inverse,paddle.tensor.math.inverse
 
+def inverse(x, name=None):
+    """
     Takes the inverse of the square matrix. A square matrix is a matrix with
     the same number of rows and columns. The input can be a square matrix
     (2-D Tensor) or batches of square matrices.
 
     Args:
-        input (Variable): The input Variable which holds a Tensor. The last two
+        x (Variable): The input tensor. The last two
             dimensions should be equal. When the number of dimensions is
             greater than 2, it is treated as batches of square matrix. The data
             type can be float32 and float64.
@@ -1118,52 +1116,38 @@ def inverse(input, name=None):
             please refer to :ref:`api_guide_Name`
 
     Returns:
-        Variable: A Tensor holds the inverse of input. The shape and data type
-            is the same as input.
+        Variable: A Tensor holds the inverse of x. The shape and data type
+                        is the same as x.
 
     Examples:
         .. code-block:: python
 
             import numpy as np
             import paddle
-            import paddle.fluid as fluid
 
             mat_np = np.array([[2, 0], [0, 2]]).astype("float32")
+            paddle.disable_static()
+            mat = paddle.to_variable(mat_np)
+            inv = paddle.inverse(mat)
+            print(inv) # [[0.5, 0], [0, 0.5]]
 
-            # example for static graph
-            input = fluid.data("input", shape=[2, 2], dtype="float32")
-            out = paddle.inverse(input)
-
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            results = exe.run(feed={"input": mat_np },
-                              fetch_list=[out.name])
-            print(results[0]) # [[0.5, 0], [0, 0.5]]
-
-            # example for dynamic graph
-            with fluid.dygraph.guard():
-                mat = fluid.dygraph.to_variable(mat_np)
-                inv = paddle.inverse(mat)
-                print(inv) # [[0.5, 0], [0, 0.5]]
     """
     if in_dygraph_mode():
-        return core.ops.inverse(input)
+        return core.ops.inverse(x)
 
-    def _check_input(input):
-        check_variable_and_dtype(input, 'input',
+    def _check_input(x):
+        check_variable_and_dtype(x, 'x',
                                  ['float32', 'float64'], 'inverse')
-        if len(input.shape) < 2:
+        if len(x.shape) < 2:
             raise ValueError(
                 "The input of inverse is expected to be a Tensor whose number "
                 "of dimensions is no less than 2. But reviced: %d, "
-                "input's shape: %s." % (len(input.shape), input.shape))
-
-    _check_input(input)
-
+                "x's shape: %s." % (len(x.shape), x.shape))
+    _check_input(x)
     helper = LayerHelper('inverse', **locals())
-    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    out = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
-        type='inverse', inputs={'Input': [input] }, outputs={'Output': [out]})
+        type='inverse', inputs={'Input': [x] }, outputs={'Output': [out]})
     return out