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

Author: wangchaochaohu

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 @@ def test_check_output(self):
  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 @@
 
 
 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 @@ def test_out(self):
 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 @@ def test_errors(self):
 
 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(
@@ -112,46 +116,52 @@ 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
 
  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):
@@ -366,31 +376,36 @@ 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
 
  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'