Ones op for API 2.0: remove the device and out parameters (#25497)

python/paddle/fluid/layers/tensor.py

@@ -650,9 +650,10 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
     Returns:
         Variable: Tensor which is created according to shape and dtype.
 
-    Raise:
+    Raises:
         TypeError: The dtype must be one of bool, float16, float32, float64, int32 and int64
         and the data type of out Tensor must be the same as the dtype. 
+        TypeError: The shape must be one of list, tuple and Variable.
 
     Examples:
         .. code-block:: python
@@ -665,7 +666,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
 
           # attr shape is a list which contains Variable Tensor.
           positive_2 = fluid.layers.fill_constant([1], "int32", 2)
-          data3 = fluid.layers.fill_constant(shape=[1, positive_2], dtype='float32', value=1.5) # data3=[1.5, 1.5]
+          data3 = fluid.layers.fill_constant(shape=[1, positive_2], dtype='float32', value=1.5) # data3=[[1.5, 1.5]]
 
           # attr shape is an Variable Tensor.
           shape = fluid.layers.fill_constant([2], "int32", 2) # shape=[2,2]
@@ -1424,6 +1425,12 @@ def linspace(start, stop, num, dtype=None, name=None):
         the data shape of this tensor is :math:`[num]` . If the :attr:`num` is set 1, the output tensor just has \
         the value with input :attr:`start`. 
 
+    Raises:
+        TypeError: The dtype must be one of float32 and float64.
+        TypeError: The dtype of `start` and `stop`  must be one of float32 and float64.
+        TypeError: The dtype of `num` must be one of int32 and int64.
+
+
     Examples:
         .. code-block:: python
 

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

@@ -83,26 +83,6 @@ class TestFillConstantOp4(OpTest):
         self.check_output()
 
 
-class TestFillConstantOp5(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program()):
-            out_np = np.zeros(shape=(1), dtype='float32')
-            out = paddle.zeros(shape=[1], dtype="float32")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            result = exe.run(fetch_list=[out])
-            self.assertEqual((result == out_np).all(), True)
-        with program_guard(Program()):
-            data = fluid.data(name="X", shape=[1], dtype="float32")
-            out = paddle.ones(shape=[1], out=data, dtype="float32")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
-            result = exe.run(feed={"X": np.array(
-                [0.1], dtype="float32")},
-                             fetch_list=[data, out])
-            self.assertEqual(result[0], result[1])
-
-
 class TestFillConstantOpWithSelectedRows(unittest.TestCase):
     def check_with_place(self, place):
         scope = core.Scope()

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

@@ -26,27 +26,36 @@ import numpy as np
 
 
 class ApiOnesTest(unittest.TestCase):
-    def test_out(self):
-        with fluid.program_guard(fluid.Program()):
+    def test_paddle_ones(self):
+        with paddle.program_guard(paddle.Program()):
             ones = paddle.ones(shape=[10], dtype="float64")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[ones])
             expected_result = np.ones(10, dtype="float64")
         self.assertEqual((result == expected_result).all(), True)
 
-        with fluid.program_guard(fluid.Program()):
+        with paddle.program_guard(paddle.Program()):
+            ones = paddle.ones(shape=[10], dtype="float64")
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
+            result, = exe.run(fetch_list=[ones])
+            expected_result = np.ones(10, dtype="float64")
+        self.assertEqual((result == expected_result).all(), True)
+
+        with paddle.program_guard(paddle.Program()):
             ones = paddle.ones(shape=[10], dtype="int64")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[ones])
             expected_result = np.ones(10, dtype="int64")
         self.assertEqual((result == expected_result).all(), True)
 
-        with fluid.program_guard(fluid.Program()):
-            ones = paddle.ones(shape=[10], dtype="int64", device="cpu")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+    def test_fluid_ones(self):
+        with paddle.program_guard(paddle.Program()):
+            ones = fluid.layers.ones(shape=[10], dtype="int64")
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[ones])
             expected_result = np.ones(10, dtype="int64")
         self.assertEqual((result == expected_result).all(), True)
@@ -55,25 +64,25 @@ class ApiOnesTest(unittest.TestCase):
 class ApiOnesZerosError(unittest.TestCase):
     def test_errors(self):
         def test_error1():
-            with fluid.program_guard(fluid.Program()):
-                ones = paddle.ones(shape=10, dtype="int64", device="opu")
+            with paddle.program_guard(paddle.Program()):
+                ones = paddle.ones(shape=10, dtype="int64")
 
-        self.assertRaises(ValueError, test_error1)
+        self.assertRaises(TypeError, test_error1)
 
         def test_error2():
-            with fluid.program_guard(fluid.Program()):
-                ones = paddle.ones(shape=10, dtype="int64", device="opu")
+            with paddle.program_guard(paddle.Program()):
+                ones = paddle.ones(shape=10)
 
-        self.assertRaises(ValueError, test_error2)
+        self.assertRaises(TypeError, test_error2)
 
         def test_error3():
-            with fluid.program_guard(fluid.Program()):
+            with paddle.program_guard(paddle.Program()):
                 ones = fluid.layers.ones(shape=10, dtype="int64")
 
         self.assertRaises(TypeError, test_error3)
 
         def test_error4():
-            with fluid.program_guard(fluid.Program()):
+            with paddle.program_guard(paddle.Program()):
                 ones = fluid.layers.ones(shape=[10], dtype="int8")
 
         self.assertRaises(TypeError, test_error4)

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

@@ -36,26 +36,43 @@ class TestZerosOpError(unittest.TestCase):
 
 class ApiZerosTest(unittest.TestCase):
     def test_out(self):
-        with paddle.program_guard(fluid.Program()):
+        with program_guard(Program()):
             zeros = paddle.zeros(shape=[10], dtype="float64")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[zeros])
             expected_result = np.zeros(10, dtype="float64")
         self.assertEqual((result == expected_result).all(), True)
 
-        with paddle.program_guard(fluid.Program()):
+        with paddle.program_guard(Program()):
             zeros = paddle.zeros(shape=[10], dtype="int64")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[zeros])
             expected_result = np.zeros(10, dtype="int64")
         self.assertEqual((result == expected_result).all(), True)
 
-        with paddle.program_guard(fluid.Program()):
+        with program_guard(Program()):
             zeros = paddle.zeros(shape=[10], dtype="int64")
-            place = fluid.CPUPlace()
-            exe = fluid.Executor(place)
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
+            result, = exe.run(fetch_list=[zeros])
+            expected_result = np.zeros(10, dtype="int64")
+        self.assertEqual((result == expected_result).all(), True)
+
+        with program_guard(Program()):
+            out_np = np.zeros(shape=(1), dtype='float32')
+            out = paddle.zeros(shape=[1], dtype="float32")
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
+            result = exe.run(fetch_list=[out])
+            self.assertEqual((result == out_np).all(), True)
+
+    def test_fluid_out(self):
+        with program_guard(Program()):
+            zeros = fluid.layers.zeros(shape=[10], dtype="int64")
+            place = paddle.CPUPlace()
+            exe = paddle.Executor(place)
             result, = exe.run(fetch_list=[zeros])
             expected_result = np.zeros(10, dtype="int64")
         self.assertEqual((result == expected_result).all(), True)

python/paddle/tensor/creation.py

@@ -75,6 +75,9 @@ def full_like(x, fill_value, dtype=None, name=None):
     Returns:
         out(Variable): The Tensor variable storing the output.
     
+    Raises:
+        TypeError: The dtype must be one of bool, float16, float32, float64, int32, int64 and None.
+    
     Examples:
         .. code-block:: python
 
@@ -84,7 +87,8 @@ def full_like(x, fill_value, dtype=None, name=None):
           paddle.enable_imperative()  # Now we are in imperative mode 
           input = paddle.full(shape=[2, 3], fill_value=0.0, dtype='float32', name='input')
           output = paddle.full_like(input, 2.0)
-          #output result : [array([[2., 2., 2.], [2., 2., 2.]], dtype=float32)]
+          # [[2. 2. 2.]
+          #  [2. 2. 2.]]
     """
 
     if dtype is None:
@@ -99,7 +103,7 @@ def full_like(x, fill_value, dtype=None, name=None):
     helper = LayerHelper("full_like", **locals())
     check_dtype(dtype, 'dtype',
                 ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
-                'full_like/zeros_like')
+                'full_like')
     out = helper.create_variable_for_type_inference(dtype=dtype)
 
     helper.append_op(
@@ -112,7 +116,7 @@ def full_like(x, fill_value, dtype=None, name=None):
     return out
 
 
-def ones(shape, dtype=None, out=None, device=None):
+def ones(shape, dtype=None, name=None):
     """
 	:alias_main: paddle.ones
 	:alias: paddle.ones,paddle.tensor.ones,paddle.tensor.creation.ones
@@ -120,38 +124,44 @@ def ones(shape, dtype=None, out=None, device=None):
     The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 1.
 
     Args:
-        shape(tuple|list): Shape of output tensor.
-        dtype(np.dtype|core.VarDesc.VarType|str): Data type of output tensor, it supports
-            bool, float16, float32, float64, int32 and int64.
-        out(Variable, optional): 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.
-        device(str, optional): Which device to run the operator. The :attr:`device` must be
-            None,'cpu', 'gpu'. If :attr:`device` is None, it will be choose the device that the user set in 
-            the paddle program. Default value is False.
-
+        shape(tuple|list|Variable): Shape of output tensor, the data type of shape is int32 or int64.
+        dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of output tensor, it supports
+            bool, float16, float32, float64, int32 and int64. Default: if None, the data type is 'float32'.
+        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:
         Variable: A tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements set to 1.
 
+    Raises:
+        TypeError: The dtype must be one of bool, float16, float32, float64, int32, int64 and None
+        and the data type of out Tensor must be the same as the dtype. 
+        TypeError: The `shape` must be one of list, tuple and Variable.
+    
     Examples:
         .. code-block:: python
 
-          import paddle
-          data = paddle.ones(shape=[3, 2], dtype='float32') # [[1., 1.], [1., 1.], [1., 1.]]
-          data = paddle.ones(shape=[2, 2], dtype='float32', device='cpu') # [[1., 1.], [1., 1.]]
+          import paddle 
+          paddle.enable_imperative()
+          
+          #default dtype for ones OP
+          data1 = paddle.ones(shape=[3, 2]) 
+          # [[1. 1.]
+          #  [1. 1.]
+          #  [1. 1.]]
+          
+          data2 = paddle.ones(shape=[2, 2], dtype='int32') 
+          # [[1 1]
+          #  [1 1]]
+          
+          #shape is a Variable
+          shape = paddle.fill_constant(shape=[2], dtype='int32', value=2)
+          data3 = paddle.ones(shape=shape, dtype='int32') 
+          # [[1 1]
+          #  [1 1]]
     """
-    check_dtype(dtype, 'create data type',
-                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
-                'zeros')
-
-    if device is not None:
-        if device not in ['cpu', 'gpu']:
-            raise ValueError(
-                "The value of 'device' in zeros_op must be cpu or gpu, but received %s."
-                % (device))
-        with fluid.device_guard(device):
-            return fill_constant(value=1.0, shape=shape, dtype=dtype, out=out)
-    return fill_constant(value=1.0, shape=shape, dtype=dtype, out=out)
+    if dtype is None:
+        dtype = 'float32'
+    return fill_constant(value=1.0, shape=shape, dtype=dtype, name=name)
 
 
 def ones_like(input, dtype=None, device=None, name=None):
@@ -366,7 +376,7 @@ def full(shape, fill_value, dtype=None, name=None):
 
     Raises:
         TypeError: The `dtype` must be one of None, bool, float16, float32, float64, int32 and int64.
-        TypeError: The `shape` must be one of Variable, list tuple.
+        TypeError: The `shape` must be one of Variable, list and tuple.
     
     Examples:
         .. code-block:: python
@@ -374,23 +384,28 @@ def full(shape, fill_value, dtype=None, name=None):
           import paddle
 
           paddle.enable_imperative()  # Now we are in imperative mode
-          data1 = paddle.full(shape=[2,1], fill_value=0, dtype='int64') # data1=[[0],[0]]
+          data1 = paddle.full(shape=[2,1], fill_value=0, dtype='int64') 
+          #[[0]
+          # [0]]
 
           # attr shape is a list which contains Variable Tensor.
           positive_2 = paddle.fill_constant([1], "int32", 2)
-          data3 = paddle.full(shape=[1, positive_2], dtype='float32', fill_value=1.5) # data3=[1.5, 1.5]
+          data3 = paddle.full(shape=[1, positive_2], dtype='float32', fill_value=1.5)
+          # [[1.5 1.5]]
 
           # attr shape is an Variable Tensor.
-          shape = paddle.fill_constant([2], "int32", 2) # shape=[2,2]
-          data4 = paddle.full(shape=shape, dtype='bool', fill_value=True) # data4=[[True,True],[True,True]]
+          shape = paddle.fill_constant([2], "int32", 2)
+          data4 = paddle.full(shape=shape, dtype='bool', fill_value=True) 
+          # [[True True] 
+          #  [True True]]
           
-          # attr value is an Variable Tensor.
-          val = paddle.fill_constant([1], "float32", 2.0) # val=[2.0]
-          data5 = paddle.full(shape=[2,1], fill_value=val, dtype='float32') #data5=[[2.0],[2.0]]
+          # attr fill_value is an Variable Tensor.
+          val = paddle.fill_constant([1], "float32", 2.0)
+          data5 = paddle.full(shape=[2,1], fill_value=val, dtype='float32')
+          # [[2.0] 
+          #  [2.0]]
     """
 
-    helper = LayerHelper("full", **locals())
-
     if dtype is None:
         dtype = 'float32'