[2.2]add API Tensor.T for reverse dim of Tensor

python/paddle/fluid/dygraph/math_op_patch.py

@@ -148,6 +148,16 @@ def _ndim_(var):
     def _size_(var):
         return np.prod(var.shape)
 
+    @property
+    def _T_(var):
+        if len(var.shape) == 1:
+            return var
+        perm = []
+        for i in range(len(var.shape)):
+            perm.insert(0, i)
+        out, _ = _C_ops.transpose2(var, 'axis', perm)
+        return out
+
     def _scalar_add_(var, value):
         return _scalar_elementwise_op_(var, 1.0, value)
 
@@ -271,6 +281,7 @@ def __impl__(self, other_var):
         ('ndimension', lambda x: len(x.shape)),
         ('ndim', _ndim_),
         ('size', _size_),
+        ('T', _T_),
         ('__add__',
          _binary_creator_('__add__', 'elementwise_add', False, _scalar_add_)),
         ##  a+b == b+a. Do not need to reverse explicitly

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -88,17 +88,17 @@ def _to_static_var(self, to_parameter=False, **kwargs):
         """
 
         # Note: getattr(self, attr, None) will call x.grad=x.gradient(), but gradient() only available in dygraph.
-        # It will fail. So, for propery in dygraph only, should not let it getattr(self, attr, None).
-        attr_not_need_keys = ['grad']
+        # It will fail. So, for propery that different between dynamic and static graph, should not getattr(self, attr, None).
+        attr_not_need_keys = ['grad', 'T']
         if isinstance(self, ParamBase):
             attr_kwargs = self.__dict__.copy()
         else:
             attr_names = []
             for name in dir(self):
-                if name not in attr_not_need_keys and not (
-                        inspect.ismethod(getattr(self, name)) or
-                        name.startswith('_')):
-                    attr_names.append(name)
+                if name not in attr_not_need_keys:
+                    if not inspect.ismethod(getattr(
+                            self, name)) and not name.startswith('_'):
+                        attr_names.append(name)
             attr_kwargs = {name: getattr(self, name) for name in attr_names}
 
         attr_keys = ['block', 'shape', 'dtype', 'type', 'name', 'persistable']

python/paddle/fluid/framework.py

@@ -1503,6 +1503,55 @@ def type(self):
         """
         return self.desc.type()
 
+    @property
+    def T(self):
+        """
+        Permute current Variable with its dimensions reversed.
+
+        If `n` is the dimensions of `x` , `x.T` is equivalent to `x.transpose([n-1, n-2, ..., 0])`.
+
+        Examples:
+
+            .. code-block:: python
+
+                import paddle
+                paddle.enable_static()
+
+                x = paddle.ones(shape=[2, 3, 5])
+                x_T = x.T
+
+                exe = paddle.static.Executor()
+                x_T_np = exe.run(paddle.static.default_main_program(), fetch_list=[x_T])[0]
+                print(x_T_np.shape)
+                # (5, 3, 2)
+        """
+        if len(self.shape) == 1:
+            return self
+        perm = []
+        for i in range(len(self.shape)):
+            perm.insert(0, i)
+
+        out = self.block.create_var(
+            name=unique_name.generate_with_ignorable_key(self.name + '.tmp'),
+            dtype=self.dtype,
+            type=self.type,
+            persistable=False,
+            stop_gradient=False)
+        input_shape = self.block.create_var(
+            name=unique_name.generate_with_ignorable_key(self.name + '.tmp'),
+            dtype=self.dtype,
+            type=core.VarDesc.VarType.LOD_TENSOR,
+            persistable=False,
+            stop_gradient=False)
+
+        self.block.append_op(
+            type='transpose2',
+            inputs={'X': [self]},
+            outputs={'Out': [out],
+                     'XShape': [input_shape]},
+            attrs={'axis': perm})
+        return out
+
     def clone(self):
         """
         Returns a new static Variable, which is the clone of the original static

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

@@ -335,6 +335,20 @@ def test_bitwise_not(self):
                       fetch_list=[z])
         self.assertTrue(np.array_equal(out[0], out_np))
 
+    @prog_scope()
+    def test_T(self):
+        x_np = np.random.randint(-100, 100, [2, 8, 5, 3]).astype("int32")
+        out_np = x_np.T
+
+        x = paddle.static.data(name="x", shape=[2, 8, 5, 3], dtype="int32")
+        z = x.T
+
+        exe = fluid.Executor()
+        out = exe.run(fluid.default_main_program(),
+                      feed={"x": x_np},
+                      fetch_list=[z])
+        self.assertTrue(np.array_equal(out[0], out_np))
+
     @prog_scope()
     def test_ndim(self):
         a = paddle.static.data(name="a", shape=[10, 1])

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

@@ -527,6 +527,12 @@ def test_tensor_patch_method(self):
             np.array_equal(
                 x.where(a, b).numpy(), paddle.where(x, a, b).numpy()))
 
+        x_np = np.random.randn(3, 6, 9, 7)
+        x = paddle.to_tensor(x_np)
+        x_T = x.T
+        self.assertTrue(x_T.shape, [7, 9, 6, 3])
+        self.assertTrue(np.array_equal(x_T.numpy(), x_np.T))
+
         self.assertTrue(inspect.ismethod(a.dot))
         self.assertTrue(inspect.ismethod(a.logsumexp))
         self.assertTrue(inspect.ismethod(a.multiplex))

python/paddle/tensor/__init__.py

@@ -367,8 +367,8 @@
            'real',
            'imag',
            'digamma',
-           'diagonal'
-           'trunc'
+           'diagonal',
+           'trunc',
            'bitwise_and',
            'bitwise_or',
            'bitwise_xor',