Fix the argsort and sort api for the paddle api2.0 (#25514)

Fix the argsort and sort op for the api2.0, and update the api 

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import paddle
 import paddle.fluid as fluid
+import paddle.imperative as imperative
 import paddle.fluid.layers as layers
 import numpy as np
 import six
@@ -321,58 +322,83 @@ class TestArgsortOpDescendingAxisNeg2GPU(TestArgsortOpAxisNeg2GPU):
         self.descending = True
 
 
-class TestSortOnCPU(TestArgsortOpCPU):
-    def init_place(self):
+class TestArgsortErrorOnCPU(unittest.TestCase):
+    def setUp(self):
         self.place = core.CPUPlace()
 
-    def test_out(self):
-        self.init_place()
-        with fluid.program_guard(fluid.Program()):
-            input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
-
-            res = fluid.data(name="output", shape=[2, 3, 4], dtype="float32")
-            output = paddle.tensor.sort(input=input, out=res)
-
-            exe = fluid.Executor(self.place)
-            data = np.array(
-                [[[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
-                 [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]]],
-                dtype='float32')
-            result = exe.run(feed={'input': data}, fetch_list=[res, output[0]])
+    def test_error(self):
+        def test_fluid_var_type():
+            with fluid.program_guard(fluid.Program()):
+                x = [1]
+                output = fluid.layers.argsort(input=x)
+            self.assertRaises(TypeError, test_fluid_var_type)
 
-            self.assertEqual((result[0] == result[1]).all(), True)
+        def test_paddle_var_type():
+            with fluid.program_guard(fluid.Program()):
+                x = [1]
+                output = paddle.argsort(input=x)
+            self.assertRaises(TypeError, test_paddle_var_type)
 
 
-class TestSortOnGPU(TestSortOnCPU):
-    def init_place(self):
+class TestArgsortErrorOnGPU(TestArgsortErrorOnCPU):
+    def setUp(self):
         if core.is_compiled_with_cuda():
             self.place = core.CUDAPlace(0)
         else:
             self.place = core.CPUPlace()
 
 
-class TestArgsortErrorOnCPU(unittest.TestCase):
-    def init_place(self):
-        self.place = core.CPUPlace()
+class TestArgsort(unittest.TestCase):
+    def setUp(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+        self.data = np.random.rand(2, 3, 4).astype("float32")
 
-    def test_error(self):
-        self.init_place()
+    def test_api_0(self):
         with fluid.program_guard(fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
+            output = paddle.argsort(x=input)
+            exe = fluid.Executor(self.place)
+            result, = exe.run(feed={'input': self.data}, fetch_list=[output])
+            np_result = np.argsort(self.data)
+            self.assertEqual((result == np_result).all(), True)
 
-            def test_input_type():
-                x = [1]
-                output = fluid.layers.argsort(input=x)
-
-            self.assertRaises(TypeError, test_input_type)
+    def test_api_1(self):
+        with fluid.program_guard(fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
+            output = paddle.argsort(x=input, axis=1)
+            exe = fluid.Executor(self.place)
+            result, = exe.run(feed={'input': self.data}, fetch_list=[output])
+            np_result = np.argsort(self.data, axis=1)
+            self.assertEqual((result == np_result).all(), True)
 
 
-class TestArgsortErrorOnGPU(TestArgsortErrorOnCPU):
-    def init_place(self):
+class TestArgsortDygraph(unittest.TestCase):
+    def setUp(self):
+        self.input_data = np.random.rand(10, 10)
         if core.is_compiled_with_cuda():
             self.place = core.CUDAPlace(0)
         else:
             self.place = core.CPUPlace()
 
+    def test_api_0(self):
+        with imperative.guard(self.place):
+            var_x = imperative.to_variable(self.input_data)
+            out = paddle.argsort(var_x)
+            self.assertEqual((np.argsort(self.input_data) == out.numpy()).all(),
+                             True)
+
+    def test_api_1(self):
+        with imperative.guard(self.place):
+            var_x = imperative.to_variable(self.input_data)
+            out = paddle.argsort(var_x, axis=-1)
+            self.assertEqual(
+                (np.argsort(
+                    self.input_data, axis=-1) == out.numpy()).all(),
+                True)
+
 
 if __name__ == "__main__":
     unittest.main()

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

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import paddle
+import paddle.fluid as fluid
+import paddle.imperative as imperative
+import paddle.fluid.layers as layers
+import numpy as np
+import six
+import paddle.fluid.core as core
+
+
+class TestSortOnCPU(unittest.TestCase):
+    def setUp(self):
+        self.place = core.CPUPlace()
+
+    def test_api_0(self):
+        with fluid.program_guard(fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
+            output = paddle.sort(x=input)
+            exe = fluid.Executor(self.place)
+            data = np.array(
+                [[[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
+                 [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]]],
+                dtype='float32')
+            result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
+            np_result = np.sort(result)
+            self.assertEqual((result == np_result).all(), True)
+
+    def test_api_1(self):
+        with fluid.program_guard(fluid.Program()):
+            input = fluid.data(name="input", shape=[2, 3, 4], dtype="float32")
+            output = paddle.sort(x=input, axis=1)
+            exe = fluid.Executor(self.place)
+            data = np.array(
+                [[[5, 8, 9, 5], [0, 0, 1, 7], [6, 9, 2, 4]],
+                 [[5, 2, 4, 2], [4, 7, 7, 9], [1, 7, 0, 6]]],
+                dtype='float32')
+            result, = exe.run(feed={'input': data}, fetch_list=[output[0]])
+            np_result = np.sort(result, axis=1)
+            self.assertEqual((result == np_result).all(), True)
+
+
+class TestSortOnGPU(TestSortOnCPU):
+    def init_place(self):
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+
+class TestSortDygraph(unittest.TestCase):
+    def setUp(self):
+        self.input_data = np.random.rand(10, 10)
+        if core.is_compiled_with_cuda():
+            self.place = core.CUDAPlace(0)
+        else:
+            self.place = core.CPUPlace()
+
+    def test_api_0(self):
+        with imperative.guard(self.place):
+            var_x = imperative.to_variable(self.input_data)
+            out = paddle.sort(var_x)
+            self.assertEqual((np.sort(self.input_data) == out[0].numpy()).all(),
+                             True)
+
+    def test_api_1(self):
+        with imperative.guard(self.place):
+            var_x = imperative.to_variable(self.input_data)
+            out = paddle.sort(var_x, axis=-1)
+            self.assertEqual(
+                (np.sort(
+                    self.input_data, axis=-1) == out[0].numpy()).all(),
+                True)

python/paddle/tensor/search.py

@@ -19,7 +19,6 @@ from ..fluid import core, layers
 
 # TODO: define searching & indexing functions of a tensor  
 from ..fluid.layers import argmin  #DEFINE_ALIAS
-from ..fluid.layers import argsort  #DEFINE_ALIAS
 from ..fluid.layers import has_inf  #DEFINE_ALIAS
 from ..fluid.layers import has_nan  #DEFINE_ALIAS
 from ..fluid.layers import topk  #DEFINE_ALIAS
@@ -42,6 +41,92 @@ __all__ = [
 from paddle.common_ops_import import *
 
 
+def argsort(x, axis=-1, descending=False, name=None):
+    """
+	:alias_main: paddle.argsort
+	:alias: paddle.argsort,paddle.tensor.argsort,paddle.tensor.search.argsort
+
+    This OP sorts the input along the given axis, and returns sorted output
+    data Varibale and its corresponding index Variable with the same shape as ``x``.
+
+    Args:
+        x(Tensor): An input N-D Tensor with type float32, float64, int16,
+            int32, int64, uint8.
+        axis(int, optional): Axis to compute indices along. The effective range
+            is [-R, R), where R is Rank(x). when axis<0, it works the same way
+            as axis+R. Default is 0.
+        descending(bool, optional) : Descending is a flag, if set to true,
+            algorithm will sort by descending order, else sort by
+            ascending order. Default is false.
+        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:
+        Tensor: sorted indices(with the same shape as ``x``
+        and with data type int64).
+
+    Examples:
+        .. code-block:: python
+            import paddle
+            import paddle.imperative as imperative 
+            import numpy as np
+            
+            paddle.enable_imperative()
+            input_array = np.array([[[5,8,9,5],
+                            [0,0,1,7],
+                            [6,9,2,4]],
+                            [[5,2,4,2],
+                            [4,7,7,9],
+                            [1,7,0,6]]]).astype(np.float32)
+            x = imperative.to_variable(input_array)
+            out1 = paddle.argsort(x=x, axis=-1)
+            out2 = paddle.argsort(x=x, axis=0)
+            out3 = paddle.argsort(x=x, axis=1)
+            print(out1.numpy())
+	    #[[[0 3 1 2]
+	    #  [0 1 2 3]
+	    #  [2 3 0 1]]
+            # [[1 3 2 0]
+	    #  [0 1 2 3]
+	    #  [2 0 3 1]]]
+            print(out2.numpy())
+	    #[[[0 1 1 1]
+	    #  [0 0 0 0]
+	    #  [1 1 1 0]]
+	    # [[1 0 0 0]
+	    #  [1 1 1 1]
+	    #  [0 0 0 1]]]
+            print(out3.numpy())
+	    #[[[1 1 1 2]
+	    #  [0 0 2 0]
+	    #  [2 2 0 1]]
+	    # [[2 0 2 0]
+	    #  [1 1 0 2]
+	    #  [0 2 1 1]]]
+    """
+    if in_dygraph_mode():
+        _, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
+        return ids
+    check_variable_and_dtype(
+        x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
+        'argsort')
+
+    helper = LayerHelper("argsort", **locals())
+    out = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=True)
+    ids = helper.create_variable_for_type_inference(
+        VarDesc.VarType.INT64, stop_gradient=True)
+    helper.append_op(
+        type='argsort',
+        inputs={'X': x},
+        outputs={'Out': out,
+                 'Indices': ids},
+        attrs={'axis': axis,
+               'descending': descending})
+    return ids
+
+
 def argmax(input, axis=None, dtype=None, out=None, keepdims=False, name=None):
     """
 	:alias_main: paddle.argmax
@@ -291,19 +376,16 @@ def nonzero(input, as_tuple=False):
         return tuple(list_out)
 
 
-def sort(input, axis=-1, descending=False, out=None, name=None):
+def sort(x, axis=-1, descending=False, name=None):
     """
 	:alias_main: paddle.sort
 	:alias: paddle.sort,paddle.tensor.sort,paddle.tensor.search.sort
 
     This OP sorts the input along the given axis, and returns sorted output
-    data Varibale and its corresponding index Variable with the same shape as
-    :attr:`input`.
+    data Tensor and its corresponding index Tensor with the same shape as ``x``.
 
-    **NOTICE**: The Variable in the output of this OP has gradient. You could\
-        set Variable :attr:`stop_gradient`.
     Args:
-        input(Variable): An input N-D Tensor with type float32, float64, int16,
+        x(Tensor): An input N-D Tensor with type float32, float64, int16,
             int32, int64, uint8.
         axis(int, optional): Axis to compute indices along. The effective range
             is [-R, R), where R is Rank(x). when axis<0, it works the same way
@@ -311,71 +393,70 @@ def sort(input, axis=-1, descending=False, out=None, name=None):
         descending(bool, optional) : Descending is a flag, if set to true,
             algorithm will sort by descending order, else sort by
             ascending order. Default is false.
-        out(Variable, optional): The default value is None. Optional output 
-            which can be any created Variable that meets the requirements to
-            store the result of operation. if out is None, a new Varibale will
-            be create to store the result.
         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:
-        tuple: A tuple of sorted data Variable(with the same shape and data
-        type as input) and the sorted indices(with the same shape as input's
+        tuple: A tuple of sorted data tensor(with the same shape and data
+        type as ``x``) and the sorted indices(with the same shape as ``x``
         and with data type int64).
     Examples:
         .. code-block:: python
             import paddle
-            import paddle.fluid as fluid
+            import paddle.imperative as imperative 
             import numpy as np
-            in1 = np.array([[[5,8,9,5],
+            
+            paddle.enable_imperative()
+            input_array = np.array([[[5,8,9,5],
                             [0,0,1,7],
                             [6,9,2,4]],
                             [[5,2,4,2],
                             [4,7,7,9],
                             [1,7,0,6]]]).astype(np.float32)
-            with fluid.dygraph.guard():
-                x = fluid.dygraph.to_variable(in1)
-                out1 = paddle.sort(input=x, axis=-1)
-                out2 = paddle.sort(input=x, axis=0)
-                out3 = paddle.sort(input=x, axis=1)
-                print(out1[0].numpy())
-                # [[[5. 5. 8. 9.]
-                #   [0. 0. 1. 7.]
-                #   [2. 4. 6. 9.]]
-                #  [[2. 2. 4. 5.]
-                #   [4. 7. 7. 9.]
-                #   [0. 1. 6. 7.]]]
-                print(out1[1].numpy())
-                # [[[0 3 1 2]
-                #   [0 1 2 3]
-                #   [2 3 0 1]]
-                #  [[1 3 2 0]
-                #   [0 1 2 3]
-                #   [2 0 3 1]]]
-                print(out2[0].numpy())
-                # [[[5. 2. 4. 2.]
-                #   [0. 0. 1. 7.]
-                #   [1. 7. 0. 4.]]
-                #  [[5. 8. 9. 5.]
-                #   [4. 7. 7. 9.]
-                #   [6. 9. 2. 6.]]]
-                print(out3[0].numpy())
-                # [[[0. 0. 1. 4.]
-                #   [5. 8. 2. 5.]
-                #   [6. 9. 9. 7.]]
-                #  [[1. 2. 0. 2.]
-                #   [4. 7. 4. 6.]
-                #   [5. 7. 7. 9.]]]
+            x = imperative.to_variable(input_array)
+            out1 = paddle.sort(x=x, axis=-1)
+            out2 = paddle.sort(x=x, axis=0)
+            out3 = paddle.sort(x=x, axis=1)
+            print(out1[0].numpy())
+	    #[[[5. 5. 8. 9.]
+	    #  [0. 0. 1. 7.]
+	    #  [2. 4. 6. 9.]]
+	    # [[2. 2. 4. 5.]
+	    #  [4. 7. 7. 9.]
+	    #  [0. 1. 6. 7.]]]
+            print(out1[1].numpy())
+	    #[[[0 3 1 2]
+	    #  [0 1 2 3]
+	    #  [2 3 0 1]]
+            # [[1 3 2 0]
+	    #  [0 1 2 3]
+	    #  [2 0 3 1]]]
+            print(out2[0].numpy())
+            #[[[5. 2. 4. 2.]
+	    #  [0. 0. 1. 7.]
+	    #  [1. 7. 0. 4.]]
+	    # [[5. 8. 9. 5.]
+	    #  [4. 7. 7. 9.]
+	    #  [6. 9. 2. 6.]]]
+            print(out3[0].numpy())
+            #[[[0. 0. 1. 4.]
+	    #  [5. 8. 2. 5.]
+	    #  [6. 9. 9. 7.]]
+	    # [[1. 2. 0. 2.]
+	    #  [4. 7. 4. 6.]
+	    #  [5. 7. 7. 9.]]]
     """
+    if in_dygraph_mode():
+        out, ids = core.ops.argsort(x, 'axis', axis, 'descending', descending)
+        return out, ids
     helper = LayerHelper("sort", **locals())
-    if out is None:
-        out = helper.create_variable_for_type_inference(
-            dtype=input.dtype, stop_gradient=False)
+    out = helper.create_variable_for_type_inference(
+        dtype=x.dtype, stop_gradient=False)
     ids = helper.create_variable_for_type_inference(
         VarDesc.VarType.INT64, stop_gradient=True)
     helper.append_op(
         type='argsort',
-        inputs={'X': input},
+        inputs={'X': x},
         outputs={'Out': out,
                  'Indices': ids},
         attrs={'axis': axis,