[2.0API]Bind method for tensor and Variable (#26416)

* binding tensor method

* binding tensor method

* binding tensor method

* Binding methods for class Tensor and Variable

python/paddle/__init__.py

@@ -31,6 +31,10 @@ import paddle.reader
 import paddle.dataset
 import paddle.batch
 batch = batch.batch
+from .fluid import monkey_patch_variable
+from .fluid.dygraph import monkey_patch_math_varbase
+monkey_patch_variable()
+monkey_patch_math_varbase()
 import paddle.framework
 from .framework import VarBase as Tensor
 from .framework import ComplexVariable as ComplexTensor

python/paddle/fluid/dygraph/__init__.py

@@ -59,6 +59,8 @@ from .rnn import *
 from . import amp
 from .amp import *
 
+from .math_op_patch import monkey_patch_math_varbase
+
 __all__ = []
 __all__ += layers.__all__
 __all__ += base.__all__

python/paddle/fluid/dygraph/math_op_patch.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 from .. import core
 from ..framework import Variable, convert_np_dtype_to_dtype_, _varbase_creator
 from ..layers.layer_function_generator import OpProtoHolder
+from ..layers import common_methods
 from . import to_variable, no_grad
 
 import numpy as np
@@ -30,6 +31,8 @@ _supported_int_dtype_ = [
     core.VarDesc.VarType.INT64,
 ]
 
+_already_patch_varbase = False
+
 
 def monkey_patch_math_varbase():
     """
@@ -140,25 +143,30 @@ def monkey_patch_math_varbase():
         else:
             return int(var.numpy().flatten()[0])
 
-    def _scalar_elementwise_add_(var, value):
+    @property
+    def _ndim_(var):
+        return len(var.shape)
+
+    def _scalar_add_(var, value):
         return _scalar_elementwise_op_(var, 1.0, value)
 
-    def _scalar_elementwise_sub_(var, value):
+    def _scalar_sub_(var, value):
         return _scalar_elementwise_op_(var, 1.0, -value)
 
-    def _scalar_elementwise_rsub_(var, value):
+    def _scalar_rsub_(var, value):
         return _scalar_elementwise_op_(var, -1.0, value)
 
-    def _scalar_elementwise_mul_(var, value):
+    def _scalar_mul_(var, value):
         return _scalar_elementwise_op_(var, value, 0.0)
 
-    def _scalar_elementwise_div_(var, value):
+    def _scalar_div_(var, value):
         return _scalar_elementwise_op_(var, 1.0 / value, 0.0)
 
-    def _elemwise_method_creator_(method_name,
-                                  op_type,
-                                  reverse=False,
-                                  scalar_method=None):
+    # for binary operator such as elementwise, compare
+    def _binary_creator_(method_name,
+                         op_type,
+                         reverse=False,
+                         scalar_method=None):
         def __impl__(self, other_var):
             # FIXME(zjl): elementwise_div between integers cannot be converted to scale,
             # which may lose accuracy. This is a hot fix for release 1.6.
@@ -200,60 +208,119 @@ def monkey_patch_math_varbase():
         __impl__.__doc__ = """
         {0}
         Args:
-            self(Variable): left hand variable
-            other_var(Variable|float|int): right hand variable
+            self(Tensor): left hand Tensor
+            other_var(Tensor|float|int): right hand Tensor
 
         Returns:
-            Variable
+            Tensor
         """.format(comment)
         __impl__.__name__ = method_name
         return __impl__
 
-    # inject methods
-    for method_name, op_type, reverse, scalar_method in (
-        ("__add__", "elementwise_add", False, _scalar_elementwise_add_),
-            # a+b == b+a. Do not need to reverse explicitly
-        ("__radd__", "elementwise_add", False, _scalar_elementwise_add_),
-        ("__sub__", "elementwise_sub", False, _scalar_elementwise_sub_),
-        ("__rsub__", "elementwise_sub", True, _scalar_elementwise_rsub_),
-        ("__mul__", "elementwise_mul", False, _scalar_elementwise_mul_),
-            # a*b == b*a. Do not need to reverse explicitly
-        ("__rmul__", "elementwise_mul", False, _scalar_elementwise_mul_),
-        ("__div__", "elementwise_div", False, _scalar_elementwise_div_),
-        ("__truediv__", "elementwise_div", False, _scalar_elementwise_div_),
-        ("__rdiv__", "elementwise_div", True, None),
-        ("__rtruediv__", "elementwise_div", True, None),
-        ("__pow__", "elementwise_pow", False, None),
-        ("__rpow__", "elementwise_pow", True, None),
-        ("__floordiv__", "elementwise_floordiv", False, None),
-        ("__mod__", "elementwise_mod", False, None),
-            # for logical compare
-        ("__eq__", "equal", False, None),
-        ("__ne__", "not_equal", False, None),
-        ("__lt__", "less_than", False, None),
-        ("__le__", "less_equal", False, None),
-        ("__gt__", "greater_than", False, None),
-        ("__ge__", "greater_equal", False, None)):
-
-        setattr(core.VarBase, method_name,
-                _elemwise_method_creator_(method_name, op_type, reverse,
-                                          scalar_method))
-
-    # b = -a
-    core.VarBase.__neg__ = _neg_
-    core.VarBase.__float__ = _float_
-    core.VarBase.__long__ = _long_
-    core.VarBase.__int__ = _int_
-    core.VarBase.__len__ = _len_
-    core.VarBase.__index__ = _index_
-    core.VarBase.astype = astype
-    """
-    When code is written like this
-    y = np.pi * var
-    ndarray.__mul__(self, var) is called, var will be traced as an array(by using __len__, __getitem__), which is not right.
-    when var.__array_ufunc__  is set to None, var.__rmul__(self,  np) will be called.
+    # Todo(zhouwei): implement dygraph template to adapt to any function, receive('op_type', 'arg_template')
+    #  Such as _method_creator_('addmm', 'x, y, alpha=1.0, beta=1.0, name=None'). It can reduce call time.
+    def _method_creator_(op_type, arg_template=None):
+        def __impl__(self):
+            op = getattr(core.ops, op_type)
+            return op(self)
 
-    The details can be seen bellow:
-    https://docs.scipy.org/doc/numpy-1.13.0/neps/ufunc-overrides.html#behavior-in-combination-with-python-s-binary-operations
-    """
-    core.VarBase.__array_ufunc__ = None
+        __impl__.__doc__ = """
+
+        See paddle.{}""".format(op_type)
+        __impl__.__name__ = op_type
+
+        return __impl__
+
+    varbase_methods = [
+        # Type1: From custom fun or lambda
+        ##   b=-a
+        ('__neg__', _neg_),
+        ('__float__', _float_),
+        ('__long__', _long_),
+        ('__int__', _int_),
+        ('__len__', _len_),
+        ('__index__', _index_),
+        ('astype', astype),
+        ('dim', lambda x: len(x.shape)),
+        ('ndimension', lambda x: len(x.shape)),
+        ('ndim', _ndim_),
+        ('size', lambda x: x.shape),
+        # Type2: From Template that create core.ops automatically. It's recommended.
+        ('__add__',
+         _binary_creator_('__add__', 'elementwise_add', False, _scalar_add_)),
+        ##  a+b == b+a. Do not need to reverse explicitly
+        ('__radd__',
+         _binary_creator_('__radd__', 'elementwise_add', False, _scalar_add_)),
+        ('__sub__', _binary_creator_('__sub__', 'elementwise_sub', False,
+                                     _scalar_sub_)),
+        ('__rsub__', _binary_creator_('__rsub__', 'elementwise_sub', True,
+                                      _scalar_rsub_)),
+        ('__mul__', _binary_creator_('__mul__', 'elementwise_mul', False,
+                                     _scalar_mul_)),
+        ## a*b == b*a. Do not need to reverse explicitly
+        ('__rmul__',
+         _binary_creator_('__rmul__', 'elementwise_mul', False, _scalar_mul_)),
+        ('__div__', _binary_creator_('__div__', 'elementwise_div', False,
+                                     _scalar_div_)),
+        ('__truediv__', _binary_creator_('__truediv__', 'elementwise_div',
+                                         False, _scalar_div_)),
+        ('__rdiv__', _binary_creator_('__rdiv__', 'elementwise_div', True,
+                                      None)),
+        ('__rtruediv__', _binary_creator_('rtruediv__', 'elementwise_div', True,
+                                          None)),
+        ('__pow__', _binary_creator_('__pow__', 'elementwise_pow', False,
+                                     None)),
+        ('__rpow__', _binary_creator_('__rpow__', 'elementwise_pow', True,
+                                      None)),
+        ('__floordiv__', _binary_creator_('__floordiv__',
+                                          'elementwise_floordiv', False, None)),
+        ('__mod__', _binary_creator_('__mod__', 'elementwise_mod', False,
+                                     None)),
+        ## for logical compare
+        ('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
+        ('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
+        ('__lt__', _binary_creator_('__lt__', 'less_than', False, None)),
+        ('__le__', _binary_creator_('__le__', 'less_equal', False, None)),
+        ('__gt__', _binary_creator_('__gt__', 'greater_than', False, None)),
+        ('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None)),
+        ('__array_ufunc__', None),
+        ('sigmoid', _method_creator_('sigmoid', 'name=None')),
+        ('logsigmoid', _method_creator_('logsigmoid', 'name=None')),
+        ('exp', _method_creator_('exp', 'name=None')),
+        ('tanh', _method_creator_('tanh', 'name=None')),
+        ('atan', _method_creator_('atan', 'name=None')),
+        ('tanh_shrink', _method_creator_('tanh_shrink', 'name=None')),
+        ('sqrt', _method_creator_('sqrt', 'name=None')),
+        ('rsqrt', _method_creator_('rsqrt', 'name=None')),
+        ('abs', _method_creator_('abs', 'name=None')),
+        ('ceil', _method_creator_('ceil', 'name=None')),
+        ('floor', _method_creator_('floor', 'name=None')),
+        ('cos', _method_creator_('cos', 'name=None')),
+        ('acos', _method_creator_('acos', 'name=None')),
+        ('asin', _method_creator_('asin', 'name=None')),
+        ('sin', _method_creator_('sin', 'name=None')),
+        ('sinh', _method_creator_('sinh', 'name=None')),
+        ('cosh', _method_creator_('cosh', 'name=None')),
+        ('round', _method_creator_('round', 'name=None')),
+        ('reciprocal', _method_creator_('reciprocal', 'name=None')),
+        ('square', _method_creator_('square', 'name=None')),
+        ('softplus', _method_creator_('softplus', 'name=None')),
+        ('softsign', _method_creator_('softsign', 'name=None')),
+        # Type3: Form module 'paddle.tensor' defaultly.
+        #   It's not a goodway, because it will increase call time.
+    ]
+
+    global _already_patch_varbase
+    if not _already_patch_varbase:
+        for method in varbase_methods:
+            method_name = method[0]
+            method_impl = method[1]
+            setattr(core.VarBase, method_name, method_impl)
+    else:
+        import paddle.tensor
+        for method_name in common_methods:
+            if hasattr(core.VarBase, method_name): continue
+            method_impl = getattr(paddle.tensor, method_name, None)
+            if method_impl: setattr(core.VarBase, method_name, method_impl)
+
+    _already_patch_varbase = True

python/paddle/fluid/layers/math_op_patch.py

@@ -54,6 +54,31 @@ EXPRESSION_MAP = {
     "__ge__": "A >= B"
 }
 
+# method for Tensor from paddle.tensor
+# edit it when paddle.tensor has new method about Tensor operation
+common_methods = [
+    'exp', 'tanh', 'atan', 'sqrt', 'rsqrt', 'abs', 'ceil', 'floor', 'cos',
+    'acos', 'asin', 'sin', 'sinh', 'cosh', 'round', 'reciprocal', 'square',
+    'rank', 'matmul', 'dot', 'norm', 'transpose', 'dist', 't', 'cross',
+    'cholesky', 'bmm', 'histogram', 'equal', 'greater_equal', 'greater_than',
+    'is_empty', 'isfinite', 'less_equal', 'less_than', 'logical_and',
+    'logical_not', 'logical_or', 'logical_xor', 'not_equal', 'reduce_all',
+    'reduce_any', 'allclose', 'equal_all', 'cast', 'expand', 'expand_as',
+    'tile', 'flatten', 'gather', 'gather_nd', 'reshape', 'reverse', 'scatter',
+    'scatter_nd_add', 'scatter_nd', 'shard_index', 'slice', 'split', 'squeeze',
+    'strided_slice', 'unique', 'unique_with_counts', 'unsqueeze', 'flip',
+    'unbind', 'roll', 'cumsum', 'increment', 'log', 'pow', 'reciprocal',
+    'round', 'rsqrt', 'scale', 'sign', 'stanh', 'sum', 'reduce_prod', 'max',
+    'min', 'mm', 'div', 'multiply', 'add', 'logsumexp', 'log1p', 'erf',
+    'addcmul', 'addmm', 'clamp', 'trace', 'kron', 'argmax', 'argmin', 'argsort',
+    'has_inf', 'has_nan', 'topk', 'index_select', 'nonzero', 'sort',
+    'index_sample', 'mean', 'std', 'var', 'elementwise_add', 'elementwise_div',
+    'elementwise_floordiv', 'elementwise_mod', 'elementwise_pow',
+    'elementwise_sub'
+]
+
+_already_patch_variable = False
+
 
 def monkey_patch_variable():
     def unique_tmp_name():
@@ -179,7 +204,7 @@ def monkey_patch_variable():
                    "out_dtype": out.dtype})
         return out
 
-    def _scalar_elementwise_op_(var, scale, bias):
+    def _scalar_op_(var, scale, bias):
         block = current_block(var)
         out = create_new_tmp_var(block, var.dtype)
         block.append_op(
@@ -191,27 +216,27 @@ def monkey_patch_variable():
         return out
 
     def _neg_(var):
-        return _scalar_elementwise_op_(var, -1.0, 0.0)
+        return _scalar_op_(var, -1.0, 0.0)
 
-    def _scalar_elementwise_add_(var, value):
-        return _scalar_elementwise_op_(var, 1.0, value)
+    def _scalar_add_(var, value):
+        return _scalar_op_(var, 1.0, value)
 
-    def _scalar_elementwise_sub_(var, value):
-        return _scalar_elementwise_op_(var, 1.0, -value)
+    def _scalar_sub_(var, value):
+        return _scalar_op_(var, 1.0, -value)
 
-    def _scalar_elementwise_rsub_(var, value):
-        return _scalar_elementwise_op_(var, -1.0, value)
+    def _scalar_rsub_(var, value):
+        return _scalar_op_(var, -1.0, value)
 
-    def _scalar_elementwise_mul_(var, value):
-        return _scalar_elementwise_op_(var, value, 0.0)
+    def _scalar_mul_(var, value):
+        return _scalar_op_(var, value, 0.0)
 
-    def _scalar_elementwise_div_(var, value):
-        return _scalar_elementwise_op_(var, 1.0 / value, 0.0)
+    def _scalar_div_(var, value):
+        return _scalar_op_(var, 1.0 / value, 0.0)
 
-    def _elemwise_method_creator_(method_name,
-                                  op_type,
-                                  reverse=False,
-                                  scalar_method=None):
+    def _binary_creator_(method_name,
+                         op_type,
+                         reverse=False,
+                         scalar_method=None):
         def __impl__(self, other_var):
             # FIXME(zjl): elementwise_div between integers cannot be converted to scale,
             # which may lose accuracy. This is a hot fix for release 1.6.
@@ -296,35 +321,60 @@ def monkey_patch_variable():
         __impl__.__name__ = method_name
         return __impl__
 
-    # inject methods
-    for method_name, op_type, reverse, scalar_method in (
-        ("__add__", "elementwise_add", False, _scalar_elementwise_add_),
-            # a+b == b+a. Do not need to reverse explicitly
-        ("__radd__", "elementwise_add", False, _scalar_elementwise_add_),
-        ("__sub__", "elementwise_sub", False, _scalar_elementwise_sub_),
-        ("__rsub__", "elementwise_sub", True, _scalar_elementwise_rsub_),
-        ("__mul__", "elementwise_mul", False, _scalar_elementwise_mul_),
-            # a*b == b*a. Do not need to reverse explicitly
-        ("__rmul__", "elementwise_mul", False, _scalar_elementwise_mul_),
-        ("__div__", "elementwise_div", False, _scalar_elementwise_div_),
-        ("__truediv__", "elementwise_div", False, _scalar_elementwise_div_),
-        ("__rdiv__", "elementwise_div", True, None),
-        ("__rtruediv__", "elementwise_div", True, None),
-        ("__pow__", "elementwise_pow", False, None),
-        ("__rpow__", "elementwise_pow", True, None),
-        ("__floordiv__", "elementwise_floordiv", False, None),
-        ("__mod__", "elementwise_mod", False, None),
-            # for logical compare
-        ("__eq__", "equal", False, None),
-        ("__ne__", "not_equal", False, None),
-        ("__lt__", "less_than", False, None),
-        ("__le__", "less_equal", False, None),
-        ("__gt__", "greater_than", False, None),
-        ("__ge__", "greater_equal", False, None)):
-        setattr(Variable, method_name,
-                _elemwise_method_creator_(method_name, op_type, reverse,
-                                          scalar_method))
-
-    # b = -a
-    Variable.__neg__ = _neg_
-    Variable.astype = astype
+    variable_methods = [
+        #   b=-a
+        ('__neg__', _neg_),
+        ('astype', astype),
+        ('__add__', _binary_creator_('__add__', 'elementwise_add', False,
+                                     _scalar_add_)),
+        #  a+b == b+a. Do not need to reverse explicitly
+        ('__radd__',
+         _binary_creator_('__radd__', 'elementwise_add', False, _scalar_add_)),
+        ('__sub__', _binary_creator_('__sub__', 'elementwise_sub', False,
+                                     _scalar_sub_)),
+        ('__rsub__', _binary_creator_('__rsub__', 'elementwise_sub', True,
+                                      _scalar_rsub_)),
+        ('__mul__', _binary_creator_('__mul__', 'elementwise_mul', False,
+                                     _scalar_mul_)),
+        #  a*b == b*a. Do not need to reverse explicitly
+        ('__rmul__',
+         _binary_creator_('__rmul__', 'elementwise_mul', False, _scalar_mul_)),
+        ('__div__', _binary_creator_('__div__', 'elementwise_div', False,
+                                     _scalar_div_)),
+        ('__truediv__', _binary_creator_('__truediv__', 'elementwise_div',
+                                         False, _scalar_div_)),
+        ('__rdiv__', _binary_creator_('__rdiv__', 'elementwise_div', True,
+                                      None)),
+        ('__rtruediv__', _binary_creator_('__rtruediv__', 'elementwise_div',
+                                          True, None)),
+        ('__pow__', _binary_creator_('__pow__', 'elementwise_pow', False,
+                                     None)),
+        ('__rpow__', _binary_creator_('__rpow__', 'elementwise_pow', True,
+                                      None)),
+        ('__floordiv__', _binary_creator_('__floordiv__',
+                                          'elementwise_floordiv', False, None)),
+        ('__mod__', _binary_creator_('__mod__', 'elementwise_mod', False,
+                                     None)),
+        #  for logical compare
+        ('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
+        ('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
+        ('__lt__', _binary_creator_('__lt__', 'less_than', False, None)),
+        ('__le__', _binary_creator_('__le__', 'less_equal', False, None)),
+        ('__gt__', _binary_creator_('__gt__', 'greater_than', False, None)),
+        ('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None))
+    ]
+
+    global _already_patch_variable
+    if not _already_patch_variable:
+        for method in variable_methods:
+            method_name = method[0]
+            method_impl = method[1]
+            setattr(Variable, method_name, method_impl)
+    else:
+        import paddle.tensor
+        for method_name in common_methods:
+            if hasattr(Variable, method_name): continue
+            method_impl = getattr(paddle.tensor, method_name, None)
+            if method_impl: setattr(Variable, method_name, method_impl)
+
+    _already_patch_variable = True

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

@@ -15,6 +15,7 @@
 from __future__ import print_function
 
 import unittest
+import paddle
 import paddle.fluid as fluid
 import numpy as np
 import six
@@ -284,6 +285,223 @@ class TestMathOpPatchesVarBase(unittest.TestCase):
             self.assertEqual((a != b).dtype, fluid.core.VarDesc.VarType.BOOL)
             self.assertTrue(np.array_equal((a != b).numpy(), a_np != b_np))
 
+    def test_tensor_patch_method(self):
+        paddle.disable_static()
+        x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
+        y_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
+        z_np = np.random.uniform(-1, 1, [6, 9]).astype(self.dtype)
+
+        x = paddle.to_tensor(x_np)
+        y = paddle.to_tensor(y_np)
+        z = paddle.to_tensor(z_np)
+
+        a = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
+        b = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
+
+        # 1. Unary operation for Tensor
+        self.assertEqual(x.dim(), 2)
+        self.assertEqual(x.ndimension(), 2)
+        self.assertEqual(x.ndim, 2)
+        self.assertEqual(x.size(), [2, 3])
+        self.assertTrue(
+            np.array_equal(x.sigmoid().numpy(), fluid.layers.sigmoid(x).numpy(
+            )))
+        self.assertTrue(
+            np.array_equal(x.logsigmoid().numpy(),
+                           fluid.layers.logsigmoid(x).numpy()))
+        self.assertTrue(np.array_equal(x.exp().numpy(), paddle.exp(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.tanh().numpy(), paddle.tanh(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.atan().numpy(), paddle.atan(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.tanh_shrink().numpy(),
+                           fluid.layers.tanh_shrink(x).numpy()))
+        self.assertTrue(np.array_equal(x.abs().numpy(), paddle.abs(x).numpy()))
+        m = x.abs()
+        self.assertTrue(
+            np.array_equal(m.sqrt().numpy(), paddle.sqrt(m).numpy()))
+        self.assertTrue(
+            np.array_equal(m.rsqrt().numpy(), paddle.rsqrt(m).numpy()))
+        self.assertTrue(
+            np.array_equal(x.ceil().numpy(), paddle.ceil(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.floor().numpy(), paddle.floor(x).numpy()))
+        self.assertTrue(np.array_equal(x.cos().numpy(), paddle.cos(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.acos().numpy(), paddle.acos(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.asin().numpy(), paddle.asin(x).numpy()))
+        self.assertTrue(np.array_equal(x.sin().numpy(), paddle.sin(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.sinh().numpy(), paddle.sinh(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.cosh().numpy(), paddle.cosh(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.round().numpy(), paddle.round(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.reciprocal().numpy(), paddle.reciprocal(x).numpy(
+            )))
+        self.assertTrue(
+            np.array_equal(x.square().numpy(), paddle.square(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.softplus().numpy(),
+                           fluid.layers.softplus(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.softsign().numpy(),
+                           fluid.layers.softsign(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.rank().numpy(), paddle.rank(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x[0].t().numpy(), paddle.t(x[0]).numpy()))
+        m = paddle.to_tensor(np.random.uniform(1, 2, [3, 3]), 'float32')
+        m = m.matmul(m.t())
+        self.assertTrue(
+            np.array_equal(m.cholesky().numpy(), paddle.cholesky(m).numpy()))
+
+        self.assertTrue(
+            np.array_equal(x.is_empty().numpy(), paddle.is_empty(x).numpy()))
+        self.assertTrue(
+            np.array_equal(x.isfinite().numpy(), paddle.isfinite(x).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.cast('int32').numpy(), paddle.cast(x, 'int32').numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.expand([3, 2, 3]).numpy(),
+                paddle.expand(x, [3, 2, 3]).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.tile([2, 2]).numpy(), paddle.tile(x, [2, 2]).numpy()))
+        self.assertTrue(
+            np.array_equal(x.flatten().numpy(), paddle.flatten(x).numpy()))
+        index = paddle.to_tensor([0, 1])
+        self.assertTrue(
+            np.array_equal(
+                x.gather(index).numpy(), paddle.gather(x, index).numpy()))
+        index = paddle.to_tensor([[0, 1], [1, 2]])
+        self.assertTrue(
+            np.array_equal(
+                x.gather_nd(index).numpy(), paddle.gather_nd(x, index).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.reverse([0, 1]).numpy(), paddle.reverse(x, [0, 1]).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                a.reshape([3, 2]).numpy(), paddle.reshape(a, [3, 2]).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.slice([0, 1], [0, 0], [1, 2]).numpy(),
+                paddle.slice(x, [0, 1], [0, 0], [1, 2]).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.split(2)[0].numpy(), paddle.split(x, 2)[0].numpy()))
+        m = paddle.to_tensor(
+            np.random.uniform(-1, 1, [1, 6, 1, 1]).astype(self.dtype))
+        self.assertTrue(
+            np.array_equal(
+                m.squeeze([]).numpy(), paddle.squeeze(m, []).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                m.squeeze([1, 2]).numpy(), paddle.squeeze(m, [1, 2]).numpy()))
+        m = paddle.to_tensor([2, 3, 3, 1, 5, 3], 'float32')
+        self.assertTrue(
+            np.array_equal(m.unique()[0].numpy(), paddle.unique(m)[0].numpy()))
+        self.assertTrue(
+            np.array_equal(m.unique_with_counts()[2],
+                           paddle.unique_with_counts(m)[2]))
+        self.assertTrue(np.array_equal(x.flip([0]), paddle.flip(x, [0])))
+        self.assertTrue(np.array_equal(x.unbind(0), paddle.unbind(x, 0)))
+        self.assertTrue(np.array_equal(x.roll(1), paddle.roll(x, 1)))
+        self.assertTrue(np.array_equal(x.cumsum(1), paddle.cumsum(x, 1)))
+        m = paddle.to_tensor(1)
+        self.assertTrue(np.array_equal(m.increment(), paddle.increment(m)))
+        m = x.abs()
+        self.assertTrue(np.array_equal(m.log(), paddle.log(m)))
+        self.assertTrue(np.array_equal(x.pow(2), paddle.pow(x, 2)))
+        self.assertTrue(np.array_equal(x.reciprocal(), paddle.reciprocal(x)))
+
+        # 2. Binary operation
+        self.assertTrue(
+            np.array_equal(
+                x.matmul(y, True, False).numpy(),
+                paddle.matmul(x, y, True, False).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.norm(
+                    p='fro', axis=[0, 1]).numpy(),
+                paddle.norm(
+                    x, p='fro', axis=[0, 1]).numpy()))
+        self.assertTrue(
+            np.array_equal(x.dist(y).numpy(), paddle.dist(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(x.cross(y).numpy(), paddle.cross(x, y).numpy()))
+        m = x.expand([2, 2, 3])
+        n = y.expand([2, 2, 3]).transpose([0, 2, 1])
+        self.assertTrue(
+            np.array_equal(m.bmm(n).numpy(), paddle.bmm(m, n).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.histogram(5, -1, 1).numpy(),
+                paddle.histogram(x, 5, -1, 1).numpy()))
+        self.assertTrue(
+            np.array_equal(x.equal(y).numpy(), paddle.equal(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.greater_equal(y).numpy(), paddle.greater_equal(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.greater_than(y).numpy(), paddle.greater_than(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.less_equal(y).numpy(), paddle.less_equal(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.less_than(y).numpy(), paddle.less_than(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.not_equal(y).numpy(), paddle.not_equal(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.equal_all(y).numpy(), paddle.equal_all(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.allclose(y).numpy(), paddle.allclose(x, y).numpy()))
+        m = x.expand([2, 2, 3])
+        self.assertTrue(
+            np.array_equal(
+                x.expand_as(m).numpy(), paddle.expand_as(x, m).numpy()))
+        index = paddle.to_tensor([2, 1, 0])
+        self.assertTrue(
+            np.array_equal(
+                a.scatter(index, b).numpy(),
+                paddle.scatter(a, index, b).numpy()))
+
+        # 3. Bool tensor operation
+        x = paddle.to_tensor([[True, False], [True, False]])
+        y = paddle.to_tensor([[False, False], [False, True]])
+        self.assertTrue(
+            np.array_equal(x.reduce_all().numpy(), paddle.reduce_all(x).numpy(
+            )))
+        self.assertTrue(
+            np.array_equal(x.reduce_any().numpy(), paddle.reduce_any(x).numpy(
+            )))
+        self.assertTrue(
+            np.array_equal(
+                x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.logical_not(y).numpy(), paddle.logical_not(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.logical_or(y).numpy(), paddle.logical_or(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.logical_xor(y).numpy(), paddle.logical_xor(x, y).numpy()))
+        self.assertTrue(
+            np.array_equal(
+                x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()))
+
 
 if __name__ == '__main__':
     unittest.main()