remove new linalg api in paddle.__init__ (#36151)

remove recent linalg api in paddle.init;
add args 'name' in some new linalg api interface
same change in develop branch to #36112

python/paddle/__init__.py

@@ -94,18 +94,12 @@ from .tensor.linalg import dot  # noqa: F401
 from .tensor.linalg import norm  # noqa: F401
 from .tensor.linalg import transpose  # noqa: F401
 from .tensor.linalg import dist  # noqa: F401
-from .tensor.linalg import cond  # noqa: F401
 from .tensor.linalg import t  # noqa: F401
 from .tensor.linalg import cross  # noqa: F401
 from .tensor.linalg import cholesky  # noqa: F401
 from .tensor.linalg import bmm  # noqa: F401
 from .tensor.linalg import histogram  # noqa: F401
 from .tensor.linalg import mv  # noqa: F401
-from .tensor.linalg import det  # noqa: F401
-from .tensor.linalg import slogdet  # noqa: F401
-from .tensor.linalg import matrix_power  # noqa: F401
-from .tensor.linalg import svd  # noqa: F401
-from .tensor.linalg import solve  # noqa: F401
 from .tensor.logic import equal  # noqa: F401
 from .tensor.logic import greater_equal  # noqa: F401
 from .tensor.logic import greater_than  # noqa: F401
@@ -504,7 +498,6 @@ __all__ = [  # noqa
            'stack',
            'sqrt',
            'cholesky',
-           'matrix_power',
            'randperm',
            'linspace',
            'reshape',

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

@@ -28,7 +28,7 @@ def test_static_assert_true(self, x_list, p_list):
         for x in x_list:
             with static.program_guard(static.Program(), static.Program()):
                 input_data = static.data("X", shape=x.shape, dtype=x.dtype)
-                output = paddle.cond(input_data, p)
+                output = paddle.linalg.cond(input_data, p)
                 exe = static.Executor()
                 result = exe.run(feed={"X": x}, fetch_list=[output])
                 expected_output = np.linalg.cond(x, p)
@@ -39,7 +39,7 @@ def test_dygraph_assert_true(self, x_list, p_list):
     for p in p_list:
         for x in x_list:
             input_tensor = paddle.to_tensor(x)
-            output = paddle.cond(input_tensor, p)
+            output = paddle.linalg.cond(input_tensor, p)
             expected_output = np.linalg.cond(x, p)
             self.assertTrue(np.allclose(output, expected_output))
 
@@ -103,12 +103,12 @@ class TestCondAPIError(unittest.TestCase):
         for p in p_list_error:
             for x in (x_list_n_n + x_list_m_n):
                 x_tensor = paddle.to_tensor(x)
-                self.assertRaises(ValueError, paddle.cond, x_tensor, p)
+                self.assertRaises(ValueError, paddle.linalg.cond, x_tensor, p)
 
         for p in p_list_n_n:
             for x in x_list_m_n:
                 x_tensor = paddle.to_tensor(x)
-                self.assertRaises(ValueError, paddle.cond, x_tensor, p)
+                self.assertRaises(ValueError, paddle.linalg.cond, x_tensor, p)
 
     def test_static_api_error(self):
         paddle.enable_static()
@@ -119,13 +119,13 @@ class TestCondAPIError(unittest.TestCase):
             for x in (x_list_n_n + x_list_m_n):
                 with static.program_guard(static.Program(), static.Program()):
                     x_data = static.data("X", shape=x.shape, dtype=x.dtype)
-                    self.assertRaises(ValueError, paddle.cond, x_data, p)
+                    self.assertRaises(ValueError, paddle.linalg.cond, x_data, p)
 
         for p in p_list_n_n:
             for x in x_list_m_n:
                 with static.program_guard(static.Program(), static.Program()):
                     x_data = static.data("X", shape=x.shape, dtype=x.dtype)
-                    self.assertRaises(ValueError, paddle.cond, x_data, p)
+                    self.assertRaises(ValueError, paddle.linalg.cond, x_data, p)
 
     # it's not supported when input is an empty tensor in static mode
     def test_static_empty_input_error(self):
@@ -136,13 +136,13 @@ class TestCondAPIError(unittest.TestCase):
             for x in x_list_n_n:
                 with static.program_guard(static.Program(), static.Program()):
                     x_data = static.data("X", shape=x.shape, dtype=x.dtype)
-                    self.assertRaises(ValueError, paddle.cond, x_data, p)
+                    self.assertRaises(ValueError, paddle.linalg.cond, x_data, p)
 
         for p in (p_list_n_n + p_list_m_n):
             for x in x_list_n_n:
                 with static.program_guard(static.Program(), static.Program()):
                     x_data = static.data("X", shape=x.shape, dtype=x.dtype)
-                    self.assertRaises(ValueError, paddle.cond, x_data, p)
+                    self.assertRaises(ValueError, paddle.linalg.cond, x_data, p)
 
 
 class TestCondEmptyTensorInput(unittest.TestCase):

python/paddle/tensor/linalg.py

@@ -448,7 +448,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
             format(axis))
 
 
-def dist(x, y, p=2):
+def dist(x, y, p=2, name=None):
     r"""
 
     This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure
@@ -1251,7 +1251,7 @@ def bmm(x, y, name=None):
     return out
 
 
-def histogram(input, bins=100, min=0, max=0):
+def histogram(input, bins=100, min=0, max=0, name=None):
     """
     Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.
     If min and max are both zero, the minimum and maximum values of the data are used.
@@ -1351,7 +1351,7 @@ def mv(x, vec, name=None):
     return out
 
 
-def det(x):
+def det(x, name=None):
     """
     Calculates determinant value of a square matrix or batches of square matrices.
     Args:
@@ -1367,7 +1367,7 @@ def det(x):
 
         x =  paddle.randn([3,3,3])
 
-        A = paddle.det(x)
+        A = paddle.linalg.det(x)
 
         print(A)
 
@@ -1399,7 +1399,7 @@ def det(x):
     return out
 
 
-def slogdet(x):
+def slogdet(x, name=None):
     """
     Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.
     The determinant can be computed with ``sign * exp(logabsdet)
@@ -1422,7 +1422,7 @@ def slogdet(x):
 
         x =  paddle.randn([3,3,3])
 
-        A = paddle.slogdet(x)
+        A = paddle.linalg.slogdet(x)
 
         print(A)
 
@@ -1563,17 +1563,17 @@ def matrix_power(x, n, name=None):
             x = paddle.to_tensor([[1, 2, 3],
                                   [1, 4, 9],
                                   [1, 8, 27]], dtype='float64')
-            print(paddle.matrix_power(x, 2))
+            print(paddle.linalg.matrix_power(x, 2))
             # [[6.  , 34. , 102.],
             #  [14. , 90. , 282.],
             #  [36. , 250., 804.]]
 
-            print(paddle.matrix_power(x, 0))
+            print(paddle.linalg.matrix_power(x, 0))
             # [[1., 0., 0.],
             #  [0., 1., 0.],
             #  [0., 0., 1.]]
 
-            print(paddle.matrix_power(x, -2))
+            print(paddle.linalg.matrix_power(x, -2))
             # [[ 12.91666667, -12.75000000,  2.83333333 ],
             #  [-7.66666667 ,  8.         , -1.83333333 ],
             #  [ 1.80555556 , -1.91666667 ,  0.44444444 ]]