add gather split squeeze stack unsqueeze api (#24035)

* add gather split squeeze stack unsqueeze api test=develop

* add gather split squeeze stack unsqueeze api test=develop

* fix bug test=develop

* fix bug test=develop

* fix bug test=develop

* fix bug test=develop

* fix bug test=develop

python/paddle/__init__.py

@@ -166,7 +166,7 @@ from .tensor.linalg import cross  #DEFINE_ALIAS
 # from .tensor.manipulation import expand   #DEFINE_ALIAS
 # from .tensor.manipulation import expand_as   #DEFINE_ALIAS
 # from .tensor.manipulation import flatten   #DEFINE_ALIAS
-# from .tensor.manipulation import gather   #DEFINE_ALIAS
+from .tensor.manipulation import gather  #DEFINE_ALIAS
 # from .tensor.manipulation import gather_nd   #DEFINE_ALIAS
 # from .tensor.manipulation import reshape   #DEFINE_ALIAS
 # from .tensor.manipulation import reverse   #DEFINE_ALIAS
@@ -175,14 +175,14 @@ from .tensor.linalg import cross  #DEFINE_ALIAS
 # from .tensor.manipulation import scatter_nd   #DEFINE_ALIAS
 # from .tensor.manipulation import shard_index   #DEFINE_ALIAS
 # from .tensor.manipulation import slice   #DEFINE_ALIAS
-# from .tensor.manipulation import split   #DEFINE_ALIAS
-# from .tensor.manipulation import squeeze   #DEFINE_ALIAS
-# from .tensor.manipulation import stack   #DEFINE_ALIAS
+from .tensor.manipulation import split  #DEFINE_ALIAS
+from .tensor.manipulation import squeeze  #DEFINE_ALIAS
+from .tensor.manipulation import stack  #DEFINE_ALIAS
 # from .tensor.manipulation import strided_slice   #DEFINE_ALIAS
 # from .tensor.manipulation import transpose   #DEFINE_ALIAS
 # from .tensor.manipulation import unique   #DEFINE_ALIAS
 # from .tensor.manipulation import unique_with_counts   #DEFINE_ALIAS
-# from .tensor.manipulation import unsqueeze   #DEFINE_ALIAS
+from .tensor.manipulation import unsqueeze  #DEFINE_ALIAS
 # from .tensor.manipulation import unstack   #DEFINE_ALIAS
 from .tensor.manipulation import flip  #DEFINE_ALIAS
 # from .tensor.manipulation import unbind   #DEFINE_ALIAS

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

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
 
 
 class TestGatherOp(OpTest):
@@ -106,5 +108,35 @@ class TestCase6(TestGatherOp):
         self.index_type = "int32"
 
 
+class API_TestGather(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[-1, 2], dtype='float64')
+            index = fluid.layers.data('index', shape=[-1, 1], dtype='float64')
+            out = paddle.gather(data1, index)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input = np.array([[1, 2], [3, 4], [5, 6]])
+            index_1 = np.array([1, 2])
+            result, = exe.run(feed={"data1": input,
+                                    "index": index_1},
+                              fetch_list=[out])
+            expected_output = np.array([[3, 4], [5, 6]])
+        self.assertTrue(np.allclose(result, expected_output))
+
+
+class API_TestDygraphGather(unittest.TestCase):
+    def test_out(self):
+        with fluid.dygraph.guard():
+            input_1 = np.array([[1, 2], [3, 4], [5, 6]])
+            index_1 = np.array([1, 2])
+            input = fluid.dygraph.to_variable(input_1)
+            index = fluid.dygraph.to_variable(index_1)
+            output = paddle.fluid.layers.gather(input, index)
+            output_np = output.numpy()
+            expected_output = np.array([[3, 4], [5, 6]])
+        self.assertTrue(np.allclose(output_np, expected_output))
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from __future__ import print_function
-
+import paddle
 import unittest
 import numpy as np
 from op_test import OpTest
@@ -278,6 +278,101 @@ class TestSplitOpError(unittest.TestCase):
 
             self.assertRaises(TypeError, test_num_or_sections_type)
 
+            def test_num_or_sections_type_tensor():
+                x7 = fluid.layers.data(shape=[4], dtype='float16', name='x5')
+                paddle.split(input=x7, num_or_sections=2.1, dim=3)
+
+            self.assertRaises(TypeError, test_num_or_sections_type_tensor)
+
+            def test_axis_type_tensor():
+                x8 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
+                paddle.split(input=x8, num_or_sections=2, dim=3.2)
+
+            self.assertRaises(TypeError, test_axis_type_tensor)
+
+
+class API_TestSplit(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[4, 6, 6], dtype='float64')
+            data2 = fluid.layers.data('data2', shape=[1], dtype='int32')
+            x0, x1, x2 = paddle.split(data1, num_or_sections=3, dim=data2)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([4, 6, 6]).astype('float64')
+            input2 = np.array([2]).astype('int32')
+            r0, r1, r2, = exe.run(feed={"data1": input1,
+                                        "data2": input2},
+                                  fetch_list=[x0, x1, x2])
+            ex_x0, ex_x1, ex_x2 = np.split(input1, 3, axis=2)
+            self.assertTrue(np.allclose(ex_x0, r0))
+            self.assertTrue(np.allclose(ex_x1, r1))
+            self.assertTrue(np.allclose(ex_x2, r2))
+
+
+class API_TestSplit2(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[4, 6, 6], dtype='float64')
+            x0, x1, x2 = paddle.split(data1, num_or_sections=3, dim=2)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([4, 6, 6]).astype('float64')
+            r0, r1, r2, = exe.run(feed={"data1": input1},
+                                  fetch_list=[x0, x1, x2])
+            ex_x0, ex_x1, ex_x2 = np.split(input1, 3, axis=2)
+            self.assertTrue(np.allclose(ex_x0, r0))
+            self.assertTrue(np.allclose(ex_x1, r1))
+            self.assertTrue(np.allclose(ex_x2, r2))
+
+
+class API_TestSplit3(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data = fluid.layers.data('data', shape=[-1, 10], dtype='float64')
+            x0, x1 = paddle.split(data, num_or_sections=(3, 7), dim=1)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([1, 10]).astype('float64')
+            r0, r1 = exe.run(feed={"data": input1}, fetch_list=[x0, x1])
+            ex_x0, ex_x1 = np.split(input1, (3, ), axis=1)
+            self.assertTrue(np.allclose(ex_x0, r0))
+            self.assertTrue(np.allclose(ex_x1, r1))
+
+
+class API_TestSplit4(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data = fluid.layers.data('data', shape=[-1, 10], dtype='float64')
+            index = fluid.layers.data('index', shape=[1], dtype='int32')
+            x0, x1 = paddle.split(data, num_or_sections=(3, index), dim=1)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([1, 10]).astype('float64')
+            input2 = np.array([7]).astype('int32')
+            r0, r1 = exe.run(feed={"data": input1,
+                                   "index": input2},
+                             fetch_list=[x0, x1])
+            ex_x0, ex_x1 = np.split(input1, (3, ), axis=1)
+            self.assertTrue(np.allclose(ex_x0, r0))
+            self.assertTrue(np.allclose(ex_x1, r1))
+
+
+class API_TestDygraphSplit(unittest.TestCase):
+    def test_out(self):
+        with fluid.dygraph.guard():
+            input_1 = np.random.random([4, 6, 6]).astype("int32")
+            # input is a variable which shape is [4, 6, 6]
+            input = fluid.dygraph.to_variable(input_1)
+            x0, x1, x2 = paddle.split(input, num_or_sections=3, dim=1)
+            x0_out = x0.numpy()
+            x1_out = x1.numpy()
+            x2_out = x2.numpy()
+            ex_x0, ex_x1, ex_x2 = np.split(input_1, 3, axis=1)
+        self.assertTrue(np.allclose(ex_x0, x0_out))
+        self.assertTrue(np.allclose(ex_x1, x1_out))
+        self.assertTrue(np.allclose(ex_x2, x2_out))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid import compiler, Program, program_guard
-
+import paddle
 from op_test import OpTest
 
 
@@ -85,5 +85,31 @@ class TestSqueezeOpError(unittest.TestCase):
             self.assertRaises(TypeError, fluid.layers.squeeze, x3, axes=0)
 
 
+class API_TestSqueeze(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data(
+                'data1', shape=[-1, 1, 10], dtype='float64')
+            result_squeeze = paddle.squeeze(data1, axes=[1])
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([5, 1, 10]).astype('float64')
+            result, = exe.run(feed={"data1": input1},
+                              fetch_list=[result_squeeze])
+            expected_result = np.squeeze(input1, axis=1)
+            self.assertTrue(np.allclose(expected_result, result))
+
+
+class API_TestDygraphSqueeze(unittest.TestCase):
+    def test_out(self):
+        with fluid.dygraph.guard():
+            input_1 = np.random.random([5, 1, 10]).astype("int32")
+            input = fluid.dygraph.to_variable(input_1)
+            output = paddle.squeeze(input, axes=[1])
+            out_np = output.numpy()
+            expected_out = np.squeeze(input_1, axis=1)
+            self.assertTrue(np.allclose(expected_out, out_np))
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -14,6 +14,7 @@
 
 import numpy as np
 import unittest
+import paddle
 import paddle.fluid as fluid
 from op_test import OpTest
 
@@ -125,5 +126,84 @@ class TestStackAPIWithLoDTensorArray(unittest.TestCase):
                     [self.x] * self.iter_num, axis=self.axis)))
 
 
+class TestTensorStackAPIWithLoDTensorArray(unittest.TestCase):
+    """
+    Test stack api when the input(x) is a LoDTensorArray.
+    """
+
+    def setUp(self):
+        self.axis = 1
+        self.iter_num = 3
+        self.input_shape = [2, 3]
+        self.x = np.random.random(self.input_shape).astype("float32")
+        self.place = fluid.CUDAPlace(0) \
+            if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
+        self.set_program()
+
+    def set_program(self):
+        self.program = fluid.Program()
+        with fluid.program_guard(self.program):
+            input = fluid.layers.assign(self.x)
+            tensor_array = fluid.layers.create_array(dtype='float32')
+            zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
+
+            for i in range(self.iter_num):
+                fluid.layers.array_write(input, zero + i, tensor_array)
+
+            self.out_var = paddle.stack(tensor_array, axis=self.axis)
+
+    def test_case(self):
+        self.assertTrue(self.out_var.shape[self.axis] == -1)
+        exe = fluid.Executor(self.place)
+        res = exe.run(self.program, fetch_list=self.out_var)
+        self.assertTrue(
+            np.array_equal(
+                res[0], np.stack(
+                    [self.x] * self.iter_num, axis=self.axis)))
+
+
+class API_test(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[1, 2], dtype='float64')
+            data2 = fluid.layers.data('data2', shape=[1, 2], dtype='float64')
+            data3 = fluid.layers.data('data3', shape=[1, 2], dtype='float64')
+            result_stack = paddle.stack([data1, data2, data3], axis=0)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([1, 2]).astype('float64')
+            input2 = np.random.random([1, 2]).astype('float64')
+            input3 = np.random.random([1, 2]).astype('float64')
+            result, = exe.run(
+                feed={"data1": input1,
+                      "data2": input2,
+                      "data3": input3},
+                fetch_list=[result_stack])
+            expected_result = np.stack([input1, input2, input3], axis=0)
+            self.assertTrue(np.allclose(expected_result, result))
+
+
+class API_DygraphTest(unittest.TestCase):
+    def test_out(self):
+        data1 = np.array([[1.0, 2.0]])
+        data2 = np.array([[3.0, 4.0]])
+        data3 = np.array([[5.0, 6.0]])
+        with fluid.dygraph.guard():
+            x1 = fluid.dygraph.to_variable(data1)
+            x2 = fluid.dygraph.to_variable(data2)
+            x3 = fluid.dygraph.to_variable(data3)
+            result = paddle.stack([x1, x2, x3], axis=0)
+            result_np = result.numpy()
+        expected_result = np.stack([data1, data2, data3], axis=0)
+        self.assertTrue(np.allclose(expected_result, result_np))
+
+        with fluid.dygraph.guard():
+            y1 = fluid.dygraph.to_variable(data1)
+            result = paddle.stack(y1, axis=0)
+            result_np_2 = result.numpy()
+        expected_result_2 = np.stack(data1, axis=0)
+        self.assertTrue(np.allclose(expected_result_2, result_np_2))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -16,7 +16,8 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
-
+import paddle
+import paddle.fluid as fluid
 from op_test import OpTest
 
 
@@ -76,5 +77,87 @@ class TestUnsqueezeOp4(TestUnsqueezeOp):
         self.new_shape = (10, 1, 1, 2, 5, 1)
 
 
+class API_TestUnsqueeze(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.layers.data('data1', shape=[-1, 10], dtype='float64')
+            result_squeeze = paddle.unsqueeze(data1, axes=[1])
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([5, 1, 10]).astype('float64')
+            input = np.squeeze(input1, axis=1)
+            result, = exe.run(feed={"data1": input},
+                              fetch_list=[result_squeeze])
+            self.assertTrue(np.allclose(input1, result))
+
+
+class TestUnsqueezeOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            # The type of axis in split_op should be int or Variable.
+            def test_axes_type():
+                x6 = fluid.layers.data(
+                    shape=[-1, 10], dtype='float16', name='x3')
+                paddle.unsqueeze(x6, axes=3.2)
+
+            self.assertRaises(TypeError, test_axes_type)
+
+
+class API_TestUnsqueeze2(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.data('data1', shape=[-1, 10], dtype='float64')
+            data2 = fluid.data('data2', shape=[1], dtype='int32')
+            result_squeeze = paddle.unsqueeze(data1, axes=data2)
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([5, 1, 10]).astype('float64')
+            input2 = np.array([1]).astype('int32')
+            input = np.squeeze(input1, axis=1)
+            result1, = exe.run(feed={"data1": input,
+                                     "data2": input2},
+                               fetch_list=[result_squeeze])
+            self.assertTrue(np.allclose(input1, result1))
+
+
+class API_TestUnsqueeze3(unittest.TestCase):
+    def test_out(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            data1 = fluid.data('data1', shape=[-1, 10], dtype='float64')
+            data2 = fluid.data('data2', shape=[1], dtype='int32')
+            result_squeeze = paddle.unsqueeze(data1, axes=[data2, 3])
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            input1 = np.random.random([5, 1, 10, 1]).astype('float64')
+            input2 = np.array([1]).astype('int32')
+            input = np.squeeze(input1)
+            result1, = exe.run(feed={"data1": input,
+                                     "data2": input2},
+                               fetch_list=[result_squeeze])
+            self.assertTrue(np.allclose(input1, result1))
+
+
+class API_TestDyUnsqueeze(unittest.TestCase):
+    def test_out(self):
+        with fluid.dygraph.guard():
+            input_1 = np.random.random([5, 1, 10]).astype("int32")
+            input1 = np.squeeze(input_1, axis=1)
+            input = fluid.dygraph.to_variable(input_1)
+            output = paddle.unsqueeze(input, axes=[1])
+            out_np = output.numpy()
+            self.assertTrue(np.allclose(input1, out_np))
+
+
+class API_TestDyUnsqueeze2(unittest.TestCase):
+    def test_out(self):
+        with fluid.dygraph.guard():
+            input_1 = np.random.random([5, 1, 10]).astype("int32")
+            input1 = np.squeeze(input_1, axis=1)
+            input = fluid.dygraph.to_variable(input_1)
+            output = paddle.unsqueeze(input, axes=1)
+            out_np = output.numpy()
+            self.assertTrue(np.allclose(input1, out_np))
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/__init__.py

@@ -145,7 +145,7 @@ from .linalg import bmm  #DEFINE_ALIAS
 # from .manipulation import expand   #DEFINE_ALIAS
 # from .manipulation import expand_as   #DEFINE_ALIAS
 # from .manipulation import flatten   #DEFINE_ALIAS
-# from .manipulation import gather   #DEFINE_ALIAS
+from .manipulation import gather  #DEFINE_ALIAS
 # from .manipulation import gather_nd   #DEFINE_ALIAS
 # from .manipulation import reshape   #DEFINE_ALIAS
 # from .manipulation import reverse   #DEFINE_ALIAS
@@ -154,14 +154,14 @@ from .linalg import bmm  #DEFINE_ALIAS
 # from .manipulation import scatter_nd   #DEFINE_ALIAS
 # from .manipulation import shard_index   #DEFINE_ALIAS
 # from .manipulation import slice   #DEFINE_ALIAS
-# from .manipulation import split   #DEFINE_ALIAS
-# from .manipulation import squeeze   #DEFINE_ALIAS
-# from .manipulation import stack  #DEFINE_ALIAS
+from .manipulation import split  #DEFINE_ALIAS
+from .manipulation import squeeze  #DEFINE_ALIAS
+from .manipulation import stack  #DEFINE_ALIAS
 # from .manipulation import strided_slice   #DEFINE_ALIAS
 # from .manipulation import transpose   #DEFINE_ALIAS
 # from .manipulation import unique   #DEFINE_ALIAS
 # from .manipulation import unique_with_counts   #DEFINE_ALIAS
-# from .manipulation import unsqueeze   #DEFINE_ALIAS
+from .manipulation import unsqueeze  #DEFINE_ALIAS
 # from .manipulation import unstack    #DEFINE_ALIAS
 from .manipulation import flip  #DEFINE_ALIAS
 # from .manipulation import unbind   #DEFINE_ALIAS

python/paddle/tensor/manipulation.py

@@ -18,7 +18,8 @@ from ..fluid.layers import core, reshape
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.framework import Variable, OpProtoHolder, in_dygraph_mode, convert_np_dtype_to_dtype_
 from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
-
+from ..fluid.layers.tensor import fill_constant
+from ..fluid.layers import utils
 # TODO: define functions to manipulate a tensor  
 __all__ = [
     #            'cast',
@@ -26,7 +27,7 @@ __all__ = [
     #            'expand',
     #            'expand_as',
     #            'flatten',
-    #            'gather',
+    'gather',
     #            'gather_nd',
     #            'reshape',
     #            'reverse',
@@ -35,14 +36,14 @@ __all__ = [
     #            'scatter_nd',
     #            'shard_index',
     #            'slice',
-    #            'split',
-    #            'squeeze',
-    #            'stack',
+    'split',
+    'squeeze',
+    'stack',
     #            'strided_slice',
     #            'transpose',
     #            'unique',
     #            'unique_with_counts',
-    #            'unsqueeze',
+    'unsqueeze',
     #            'unstack',
     'flip',
     #            'unbind',
@@ -169,3 +170,476 @@ def roll(input, shifts, dims=None):
                'shifts': shifts})
     out = reshape(out, shape=origin_shape, inplace=True)
     return out
+
+
+def stack(x, axis=0, out=None, name=None):
+    """
+
+    This OP stacks all the inputs :code:`x` along axis.
+
+    .. code-block:: text
+
+        Case 1:
+
+          Input:
+            x[0].shape = [1, 2]
+            x[0].data = [ [1.0 , 2.0 ] ]
+            x[1].shape = [1, 2]
+            x[1].data = [ [3.0 , 4.0 ] ]
+            x[2].shape = [1, 2]
+            x[2].data = [ [5.0 , 6.0 ] ]
+
+          Attrs:
+            axis = 0
+
+          Output:
+            Out.dims = [3, 1, 2]
+            Out.data =[ [ [1.0, 2.0] ],
+                        [ [3.0, 4.0] ],
+                        [ [5.0, 6.0] ] ]
+
+
+        Case 2:
+
+
+          Input:
+            x[0].shape = [1, 2]
+            x[0].data = [ [1.0 , 2.0 ] ]
+            x[1].shape = [1, 2]
+            x[1].data = [ [3.0 , 4.0 ] ]
+            x[2].shape = [1, 2]
+            x[2].data = [ [5.0 , 6.0 ] ]
+
+
+          Attrs:
+            axis = 1 or axis = -2
+
+          Output:
+            Out.shape = [1, 3, 2]
+            Out.data =[ [ [1.0, 2.0]
+                          [3.0, 4.0]
+                          [5.0, 6.0] ] ]
+
+    Args:
+        x (Variable|list(Variable)): Input :code:`x` can be a single Tensor, a :code:`list` of Tensors.
+                                     If :code:`x` is a :code:`list`, the shapes of all these Tensors
+                                     must be the same. Supposing input is N dims
+                                     Tensors :math:`[d_0, d_1, ..., d_{n-1}]`, the output is N+1 dims
+                                     Tensor :math:`[d_0, d_1, d_{axis-1}, len(x), d_{axis}, ..., d_{n-1}]`.
+                                     Support data types: float32, float64, int32, int64.
+        axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is :math:`[-(R+1), R+1)`.
+                              R is the first tensor of inputs. If ``axis`` < 0, :math:`axis=axis+rank(x[0])+1`.
+                              The default value of axis is 0.
+
+    Returns:
+        Variable: The stacked Tensor, has same data type with input Tensors. Output dim is :math:`rank(x[0])+1`.
+
+    Example:    
+        .. code-block:: python
+            import numpy as np
+            import paddle
+            import paddle.fluid as fluid
+
+            data1 = np.array([[1.0, 2.0]])
+            data2 = np.array([[3.0, 4.0]])
+            data3 = np.array([[5.0, 6.0]])
+            with fluid.dygraph.guard():
+                x1 = fluid.dygraph.to_variable(data1)
+                x2 = fluid.dygraph.to_variable(data2)
+                x3 = fluid.dygraph.to_variable(data3)
+                result = paddle.stack([x1, x2, x3], axis=0)
+                # result shape: [3, 1, 2]
+                # result value: [[[1.0, 2.0]],
+                #                [[3.0, 4.0]],
+                #                [[5.0, 6.0]]]
+    """
+
+    helper = LayerHelper('stack', **locals())
+    axis = 0 if axis is None else axis
+
+    if not isinstance(x, list) and not isinstance(x, tuple):
+        x = [x]
+    out = helper.create_variable_for_type_inference(x[0].dtype)
+    if not in_dygraph_mode() and \
+            x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+        assert len(x) == 1, "If the elements of 'x' in stack are Variable(LoDTensorArray), " \
+                            "number of the elements must be 1, but received %s." % len(x)
+        out_index = helper.create_variable_for_type_inference(dtype="int32")
+        helper.append_op(
+            type='tensor_array_to_tensor',
+            inputs={'X': x[0]},
+            outputs={'Out': [out],
+                     'OutIndex': [out_index]},
+            attrs={'axis': axis,
+                   'use_stack': True})
+    else:
+        helper.append_op(
+            type='stack',
+            inputs={'X': x},
+            outputs={'Y': out},
+            attrs={'axis': axis})
+
+    return out
+
+
+def split(input, num_or_sections, dim=-1, name=None):
+    """
+    Split the input tensor into multiple sub-Tensors.
+    Args:
+        input (Variable): The input variable which is an N-D Tensor or LoDTensor, data type being float32, float64, int32 or int64.
+        num_or_sections (int|list|tuple): If :attr:`num_or_sections` is an integer,
+            then the integer indicates the number of equal sized sub-Tensors
+            that the Tensor will be divided into. If :attr:`num_or_sections`
+            is a list or tuple, the length of it indicates the number of
+            sub-Tensors and the elements in it indicate the sizes of sub-Tensors'
+            :attr:`dim` dimension orderly. The length of the list mustn't be larger than the Tensor's size of :attr:`dim` .
+        dim (int32|Varible, optional): A scalar with type ``int32`` or a ``Tensor`` with shape [1] and type ``int32``. The dimension along which to split. If :math:`dim < 0`, the
+            dimension to split along is :math:`rank(input) + dim`. Default is -1.
+        name(str, optional): The default value is None.  Normally there is no need for user to set this property.  For more information, please refer to :ref:`api_guide_Name` .
+    Returns:
+        list(Variable): The list of segmented Tensor variables.
+    Raises:
+        TypeError: num_or_sections is not int, list or tuple.
+        TypeError: dim is not int or Variable.
+    Example:
+        .. code-block:: python
+            import numpy as np
+            import paddle
+            import paddle.fluid as fluid
+            
+            with fluid.dygraph.guard():
+                input_1 = np.random.random([4, 6, 6]).astype("int32")
+                # input is a variable which shape is [4, 6, 6]
+                input = fluid.dygraph.to_variable(input_1)
+
+                x0, x1, x2 = paddle.split(input, num_or_sections=3, dim=1)
+                # x0.shape [4, 2, 6]
+                # x1.shape [4, 2, 6]
+                # x2.shape [4, 2, 6]
+    """
+    if in_dygraph_mode():
+        num = None
+        attrs = ()
+
+        if isinstance(dim, Variable):
+            dim = dim.numpy()
+            assert dim.shape == (1,
+                                 ), "dim of type Variable should have shape [1]"
+            dim = dim[0]
+        dim = (len(input.shape) + dim) if dim < 0 else dim
+        attrs += ('axis', dim)
+
+        if isinstance(num_or_sections, int):
+            num = num_or_sections
+            attrs += ('num', num_or_sections)
+        elif isinstance(num_or_sections, (list, tuple)):
+            num = len(num_or_sections)
+            if utils._contain_var(num_or_sections):
+                raise TypeError(
+                    "The type of 'num_or_sections' in split must be int or list[int] or tuple[int] in Dygraph mode, but "
+                    "received %s, which contains Variable." %
+                    (type(num_or_sections)))
+            else:
+                attrs += ('sections', list(num_or_sections))
+        else:
+            raise TypeError(
+                "The type of 'num_or_sections' in split must be int or list in Dygraph mode, but "
+                "received %s." % (type(num_or_sections)))
+        return core.ops.split(input, num, *attrs)
+
+    if not isinstance(num_or_sections, (int, list, tuple)):
+        raise TypeError(
+            "The type of 'num_or_sections' in split must be int, list or "
+            "tuple, but received %s." % (type(num_or_sections)))
+    if not isinstance(dim, (int, Variable)):
+        raise TypeError(
+            "The type of 'dim' in split must be int or Variable, but "
+            "received %s." % (type(dim)))
+
+    helper = LayerHelper('split', **locals())
+    input_shape = input.shape
+    inputs = {'X': input}
+    attrs = {'num': num_or_sections if isinstance(num_or_sections, int) else 0}
+
+    def _get_SectionsTensorList(one_list):
+        tensor_list = []
+        unk_dim_idx = -1
+        for idx, dim_size in enumerate(one_list):
+            if isinstance(dim_size, Variable):
+                dim_size.stop_gradient = True
+                tensor_list.append(dim_size)
+            else:
+                assert (isinstance(dim_size, int))
+                if dim_size == -1:
+                    assert unk_dim_idx == -1, (
+                        "Only one value of 'num_or_section' in split can "
+                        "be -1. But received num_or_section[%d] is also -1." %
+                        idx)
+                    unk_dim_idx = idx
+                temp_out = helper.create_variable_for_type_inference('int32')
+                fill_constant(
+                    [1], 'int32', dim_size, force_cpu=True, out=temp_out)
+                tensor_list.append(temp_out)
+        return tensor_list
+
+    if isinstance(dim, Variable):
+        dim.stop_gradient = True
+        inputs['AxisTensor'] = dim
+    else:
+        dim = (len(input_shape) + dim) if dim < 0 else dim
+        attrs['axis'] = dim
+
+    if isinstance(num_or_sections, int):
+        assert num_or_sections > 1, 'num_or_sections must be more than 1.'
+        if isinstance(dim, int) and input_shape[dim] > 0:
+            assert input_shape[dim] % num_or_sections ==0, \
+                "The input's size along the split dimension " \
+                "must be evenly divisible by Attr(num_or_sections). " \
+                "But %d is not evenly divisible by %d. " % (num_or_sections,input_shape[dim])
+        num = num_or_sections
+    else:
+        if isinstance(dim, int) and input_shape[dim] > 0:
+            assert len(num_or_sections) <= input_shape[
+                dim], 'len(num_or_sections) must not be more than input.shape[dim].'
+        num = len(num_or_sections)
+        attrs['sections'] = list(
+            map(lambda ele: -1 if isinstance(ele, Variable) else ele,
+                num_or_sections))
+        if utils._contain_var(num_or_sections):
+            inputs['SectionsTensorList'] = _get_SectionsTensorList(
+                num_or_sections)
+
+    outs = [
+        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+        for i in range(num)
+    ]
+    helper.append_op(
+        type='split', inputs=inputs, outputs={'Out': outs}, attrs=attrs)
+    return outs
+
+
+def squeeze(input, axes, out=None, name=None):
+    """
+    This OP will squeeze single-dimensional entries of input tensor's shape. If axes is provided, will
+    remove the dims by axes, the dims selected by axes should be one. If not provide axes, all dims equal
+    to one will be deleted.
+
+
+    .. code-block:: text
+
+        Case1:
+
+          Input:
+            X.shape = (1, 3, 1, 5)
+            axes = [0]
+          Output:
+            Out.shape = (3, 1, 5)
+
+        Case2:
+
+          Input:
+            X.shape = (1, 3, 1, 5)
+            axes = []
+          Output:
+            Out.shape = (3, 5)
+
+        Case3:
+
+          Input:
+            X.shape = [1,3,1,5]
+            axes = [-2]
+          Output:
+            Out.shape = [1,3,5]
+
+    Args:
+        input (Variable): The input Tensor. Support data type: float32, float64, int8, int32, int64.
+                          axes (list): One integer or List of integers, indicating the dimensions to be squeezed.
+                          Axes range is :math:`[-rank(input), rank(input))`.
+                          If axes is negative, :math:`axes=axes+rank(input)`.
+        name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
+
+    Returns:
+        Variable: Output squeezed Tensor. Data type is same as input Tensor.
+
+    Examples:
+        .. code-block:: python
+            import numpy as np
+            import paddle
+            import paddle.fluid as fluid
+
+            with fluid.dygraph.guard():
+                input_1 = np.random.random([5, 1, 10]).astype("int32")
+                # input is a variable which shape is [5, 1, 10]
+                input = fluid.dygraph.to_variable(input_1)
+
+                output = paddle.squeeze(input, axes=[1])
+                # output.shape [5, 10]
+
+    """
+
+    helper = LayerHelper("squeeze", **locals())
+    check_variable_and_dtype(input, 'input',
+                             ['float32', 'float64', 'int8', 'int32', 'int64'],
+                             'squeeze')
+    check_type(axes, 'axes', list, 'squeeze')
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
+    helper.append_op(
+        type="squeeze2",
+        inputs={"X": input},
+        attrs={"axes": axes},
+        outputs={"Out": out,
+                 "XShape": x_shape})
+
+    return out
+
+
+def unsqueeze(input, axes, out=None, name=None):
+    """
+    Insert single-dimensional entries to the shape of a Tensor. Takes one
+    required argument axes, a list of dimensions that will be inserted.
+    Dimension indices in axes are as seen in the output tensor.
+
+    For example:
+
+    .. code-block:: text
+
+      Given a tensor such that tensor with shape [3, 4, 5],
+      then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
+
+    Args:
+        input (Variable): The input Tensor to be unsqueezed. It is a N-D Tensor of data types float32, float64, int32.
+        axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor .
+        name (str|None): Name for this layer.
+
+    Returns:
+        Variable: Output unsqueezed Tensor, with data type being float32, float64, int32, int64.
+
+    Examples:
+        .. code-block:: python
+            import numpy as np
+            import paddle
+            import paddle.fluid as fluid
+
+            with fluid.dygraph.guard():
+                input_1 = np.random.random([5, 10]).astype("int32")
+                # input is a variable which shape is [5, 10]
+                input = fluid.dygraph.to_variable(input_1)
+
+                output = paddle.unsqueeze(input, axes=[1])
+                # output.shape [5, 1, 10]
+    """
+    if not isinstance(axes, (int, list, tuple, Variable)):
+        raise TypeError(
+            "The type of 'axes' in unsqueeze must be int, list, tuple or Variable, but "
+            "received %s." % (type(axes)))
+    helper = LayerHelper("unsqueeze2", **locals())
+    inputs = {"X": input}
+    attrs = {}
+
+    def _to_Variable_list(one_list):
+        Variable_list = []
+        for ele in one_list:
+            if isinstance(ele, Variable):
+                ele.stop_gradient = True
+                Variable_list.append(ele)
+            else:
+                assert (isinstance(ele, int))
+                temp_out = helper.create_variable_for_type_inference('int32')
+                fill_constant([1], 'int32', ele, force_cpu=True, out=temp_out)
+                Variable_list.append(temp_out)
+        return Variable_list
+
+    if isinstance(axes, int):
+        axes = [axes]
+    if isinstance(axes, Variable):
+        axes.stop_gradient = True
+        inputs["AxesTensor"] = axes
+    elif isinstance(axes, (list, tuple)):
+        contain_var = not all(not isinstance(ele, Variable) for ele in axes)
+        if contain_var:
+            inputs["AxesTensorList"] = _to_Variable_list(axes)
+        else:
+            attrs["axes"] = axes
+
+    out = helper.create_variable_for_type_inference(dtype=input.dtype)
+    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
+    helper.append_op(
+        type="unsqueeze2",
+        inputs=inputs,
+        attrs=attrs,
+        outputs={"Out": out,
+                 "XShape": x_shape})
+
+    return out
+
+
+def gather(input, index, overwrite=True):
+    """
+    **Gather Layer**
+
+    Output is obtained by gathering entries of the outer-most dimension
+    of X indexed by `index` and concatenate them together.
+
+    .. math::
+
+        Out = X[Index]
+
+
+    .. code-block:: text
+
+
+                Given:
+
+                X = [[1, 2],
+                     [3, 4],
+                     [5, 6]]
+
+                Index = [1, 2]
+
+                Then:
+
+                Out = [[3, 4],
+                       [5, 6]]
+    Args:
+        input (Variable): The source input tensor with rank>=1. Supported data type is
+            int32, int64, float32, float64 and uint8 (only for CPU),
+            float16 (only for GPU).
+        index (Variable): The index input tensor with rank=1. Data type is int32 or int64.
+        overwrite (bool, optional): The mode that updating the grad when has same index.
+            If True, use the overwrite mode to update the grad of the same index,
+            if False, use the accumulate mode to update the grad of the same index.
+            Default value is True.
+
+
+
+    Returns:
+        output (Variable): The output is a tensor with the same rank as input.
+
+    Examples:
+
+        .. code-block:: python
+
+            import numpy as np
+            import paddle
+            import paddle.fluid as fluid
+
+
+            with fluid.dygraph.guard():
+                input_1 = np.array([[1,2],[3,4],[5,6]])
+                index_1 = np.array([0,1])
+                input = fluid.dygraph.to_variable(input_1)
+                index = fluid.dygraph.to_variable(index_1)
+                output = paddle.gather(input, index)
+                # expected output: [[1,2],[3,4]]
+    """
+    helper = LayerHelper('gather', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type="gather",
+        inputs={"X": input,
+                "Index": index},
+        outputs={"Out": out},
+        attrs={'overwrite': overwrite})
+    return out