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full Op:remove device, out and stop_gradient parameter for API 2.0 test=develop (#25257)

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fix_copy_if_different
wangchaochaohu 5 years ago committed by GitHub
parent 548cdbc544
commit bdc2c2dba8
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6 changed files with 174 additions and 212 deletions
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33
python/paddle/fluid/layers/tensor.py
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@ -620,7 +620,7 @@ def assign(input, output=None):
return output
def fill_constant(shape, dtype, value, force_cpu=False, out=None):
def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
"""
:alias_main: paddle.fill_constant
:alias: paddle.fill_constant,paddle.tensor.fill_constant,paddle.tensor.creation.fill_constant
@ -638,12 +638,14 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
If ``shape`` is an Variable, it should be an 1-D Tensor .
dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
be float16, float32, float64, int32, int64.
value(float16|float32|float64|int32|int64|Variable): The constant value used to initialize
value(bool|float|int|Variable): The constant value used to initialize
the Tensor to be created. If value is an Variable, it should be an 1-D Tensor.
force_cpu(bool): data should be on CPU if it's true, default value is False.
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.
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: Tensor which is created according to shape and dtype.
@ -666,19 +668,16 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
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([1,2], "int32", 2) # shape=[2,2]
shape = fluid.layers.fill_constant([2], "int32", 2) # shape=[2,2]
data4 = fluid.layers.fill_constant(shape=shape, dtype='bool', value=True) # data4=[[True,True],[True,True]]
# attr value is an Variable Tensor.
val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
data5 = fluid.layers.fill_constant(shape=[2,1], value=val, dtype='float32') #data5=[[2.0],[2.0]]
"""
inputs = {}
attrs = {'force_cpu': force_cpu}
if isinstance(value, Variable):
inputs['ValueTensor'] = value
else:
attrs['value'] = float(value)
if not isinstance(value, Variable):
if convert_dtype(dtype) in ['int64', 'int32']:
attrs['str_value'] = str(int(value))
else:
@ -702,13 +701,19 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
out.stop_gradient = True
return out
helper = LayerHelper("fill_constant", **locals())
inputs = {}
if isinstance(value, Variable):
inputs['ValueTensor'] = value
check_dtype(dtype, 'dtype',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'fill_constant')
check_type(shape, 'shape', (Variable, list, tuple), 'fill_constant')
if isinstance(shape, Variable):
check_variable_and_dtype(shape, 'shape', ['int32', 'int64'],
'fill_constant')
check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'fill_constant')
if out is not None:
check_variable_and_dtype(out, 'out', [convert_dtype(dtype)],
'fill_constant')
@ -1048,7 +1053,7 @@ def ones(shape, dtype, force_cpu=False):
return fill_constant(value=1.0, **locals())
def zeros(shape, dtype, force_cpu=False):
def zeros(shape, dtype, force_cpu=False, name=None):
"""
The OP creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0.
Its :attr:`stop_gradient` will be set to True to stop gradient computation.
@ -1060,6 +1065,8 @@ def zeros(shape, dtype, force_cpu=False):
force_cpu (bool, optional): Whether force to store the output tensor in CPU memory.
If :attr:`force_cpu` is False, the output tensor will be stored in running device memory.
Default: 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:
Variable: A tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements set to 0.
@ -1070,10 +1077,6 @@ def zeros(shape, dtype, force_cpu=False):
import paddle.fluid as fluid
data = fluid.layers.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
"""
check_type(shape, 'shape', (list, tuple), 'zeros')
check_dtype(dtype, 'create data type',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'zeros')
return fill_constant(value=0.0, **locals())

109
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
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@ -22,8 +22,8 @@ import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
import numpy as np
from paddle.fluid import compiler, Program, program_guard
# Situation 1: Attr(shape) is a list(without tensor)
@ -85,16 +85,14 @@ class TestFillConstantOp4(OpTest):
class TestFillConstantOp5(unittest.TestCase):
def test_errors(self):
with fluid.program_guard(fluid.Program()):
data = fluid.data(name="X", shape=[1], dtype="float32")
out = paddle.zeros(shape=[1], out=data, dtype="float32")
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(feed={"X": np.array(
[0.1], dtype="float32")},
fetch_list=[data, out])
self.assertEqual(result[0], result[1])
with fluid.program_guard(fluid.Program()):
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()
@ -389,98 +387,5 @@ class TestFillConstantOpError(unittest.TestCase):
self.assertRaises(TypeError, test_shape_tensor_list_dtype)
class ApiZerosTest(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
zeros = paddle.zeros(shape=[10], dtype="float64")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="float64")
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
zeros = paddle.zeros(shape=[10], dtype="int64")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
with fluid.program_guard(fluid.Program()):
zeros = paddle.zeros(shape=[10], dtype="int64", device="cpu")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
class ApiOnesTest(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
ones = paddle.ones(shape=[10], dtype="float64")
place = fluid.CPUPlace()
exe = fluid.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()):
ones = paddle.ones(shape=[10], dtype="int64")
place = fluid.CPUPlace()
exe = fluid.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)
result, = exe.run(fetch_list=[ones])
expected_result = np.ones(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
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")
self.assertRaises(ValueError, test_error1)
def test_error2():
with fluid.program_guard(fluid.Program()):
ones = paddle.ones(shape=10, dtype="int64", device="opu")
self.assertRaises(ValueError, test_error2)
def test_error3():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.ones(shape=10, dtype="int64")
self.assertRaises(TypeError, test_error3)
def test_error4():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.ones(shape=[10], dtype="int8")
self.assertRaises(TypeError, test_error4)
def test_error5():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=10, dtype="int64")
self.assertRaises(TypeError, test_error5)
def test_error6():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=[10], dtype="int8")
self.assertRaises(TypeError, test_error6)
if __name__ == "__main__":
unittest.main()

43
python/paddle/fluid/tests/unittests/test_full_op.py
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@ -37,33 +37,19 @@ class TestFullAPI(unittest.TestCase):
shape_tensor_int64 = fluid.data(
name="shape_tensor_int64", shape=[2], dtype="int64")
out_1 = paddle.full(
shape=[1, 2], dtype="float32", fill_value=1.1, device='gpu')
out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)
out_2 = paddle.full(
shape=[1, positive_2_int32],
dtype="float32",
fill_value=1.1,
device='cpu')
shape=[1, positive_2_int32], dtype="float32", fill_value=1.1)
out_3 = paddle.full(
shape=[1, positive_2_int64],
dtype="float32",
fill_value=1.1,
device='gpu')
shape=[1, positive_2_int64], dtype="float32", fill_value=1.1)
out_4 = paddle.full(
shape=shape_tensor_int32,
dtype="float32",
fill_value=1.2,
out=out_3)
shape=shape_tensor_int32, dtype="float32", fill_value=1.2)
out_5 = paddle.full(
shape=shape_tensor_int64,
dtype="float32",
fill_value=1.1,
device='gpu',
stop_gradient=False)
shape=shape_tensor_int64, dtype="float32", fill_value=1.1)
out_6 = paddle.full(
shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1)
@ -83,7 +69,7 @@ class TestFullAPI(unittest.TestCase):
assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_3, np.full([1, 2], 1.2, dtype="float32"))
assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_4, np.full([1, 2], 1.2, dtype="float32"))
assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
@ -94,28 +80,11 @@ class TestFullOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
#for ci coverage
x1 = fluid.layers.data(name='x1', shape=[1], dtype="int16")
x2 = np.random.randn(1, 2).astype('int32')
self.assertRaises(
ValueError, paddle.full, shape=[1], fill_value=5, dtype='uint4')
self.assertRaises(
TypeError,
paddle.full,
shape=[1],
fill_value=5,
dtype='int32',
out=x2)
self.assertRaises(
TypeError,
paddle.full,
shape=[1],
fill_value=5,
dtype='int16',
out=x1)
# The argument dtype of full must be one of bool, float16,
#float32, float64, int32 or int64
x2 = fluid.layers.data(name='x2', shape=[1], dtype="int32")
self.assertRaises(
TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint8')

83
python/paddle/fluid/tests/unittests/test_ones_op.py
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@ -0,0 +1,83 @@
# Copyright (c) 2018 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 numpy as np
from op_test import OpTest
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
import numpy as np
class ApiOnesTest(unittest.TestCase):
def test_out(self):
with fluid.program_guard(fluid.Program()):
ones = paddle.ones(shape=[10], dtype="float64")
place = fluid.CPUPlace()
exe = fluid.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()):
ones = paddle.ones(shape=[10], dtype="int64")
place = fluid.CPUPlace()
exe = fluid.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)
result, = exe.run(fetch_list=[ones])
expected_result = np.ones(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
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")
self.assertRaises(ValueError, test_error1)
def test_error2():
with fluid.program_guard(fluid.Program()):
ones = paddle.ones(shape=10, dtype="int64", device="opu")
self.assertRaises(ValueError, test_error2)
def test_error3():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.ones(shape=10, dtype="int64")
self.assertRaises(TypeError, test_error3)
def test_error4():
with fluid.program_guard(fluid.Program()):
ones = fluid.layers.ones(shape=[10], dtype="int8")
self.assertRaises(TypeError, test_error4)
if __name__ == "__main__":
unittest.main()

43
python/paddle/fluid/tests/unittests/test_zeros_op.py
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@ -18,6 +18,7 @@ import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
@ -33,5 +34,47 @@ class TestZerosOpError(unittest.TestCase):
self.assertRaises(TypeError, fluid.layers.zeros, shape, dtype)
class ApiZerosTest(unittest.TestCase):
def test_out(self):
with paddle.program_guard(fluid.Program()):
zeros = paddle.zeros(shape=[10], dtype="float64")
place = fluid.CPUPlace()
exe = fluid.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()):
zeros = paddle.zeros(shape=[10], dtype="int64")
place = fluid.CPUPlace()
exe = fluid.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()):
zeros = paddle.zeros(shape=[10], dtype="int64")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
self.assertEqual((result == expected_result).all(), True)
class ApiZerosError(unittest.TestCase):
def test_errors(self):
def test_error1():
with paddle.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=10, dtype="int64")
self.assertRaises(TypeError, test_error1)
def test_error2():
with paddle.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=[10], dtype="int8")
self.assertRaises(TypeError, test_error2)
if __name__ == "__main__":
unittest.main()

75
python/paddle/tensor/creation.py
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@ -223,7 +223,7 @@ def ones_like(input, dtype=None, device=None, name=None):
return out
def zeros(shape, dtype, out=None, device=None):
def zeros(shape, dtype=None, name=None):
"""
:alias_main: paddle.zeros
:alias: paddle.zeros,paddle.tensor.zeros,paddle.tensor.creation.zeros
@ -232,14 +232,10 @@ def zeros(shape, dtype, out=None, device=None):
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.
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 date 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 0.
@ -248,21 +244,14 @@ def zeros(shape, dtype, out=None, device=None):
.. code-block:: python
import paddle
paddle.enable_imperative() # Now we are in imperative mode
data = paddle.zeros(shape=[3, 2], dtype='float32') # [[0., 0.], [0., 0.], [0., 0.]]
data = paddle.zeros(shape=[2, 2], dtype='float32', device='cpu') # [[0., 0.], [0., 0.]]
data = paddle.zeros(shape=[2, 2], dtype='int32', name='zeros') # [[0, 0], [0, 0]]
"""
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=0.0, shape=shape, dtype=dtype, out=out)
return fill_constant(value=0.0, shape=shape, dtype=dtype, out=out)
if dtype is None:
dtype = 'float32'
return fill_constant(value=0.0, shape=shape, dtype=dtype, name=name)
def zeros_like(input, dtype=None, device=None, name=None):
@ -398,13 +387,7 @@ def eye(num_rows,
return out
def full(shape,
fill_value,
out=None,
dtype=None,
device=None,
stop_gradient=True,
name=None):
def full(shape, fill_value, dtype=None, name=None):
"""
:alias_main: paddle.full
:alias: paddle.full,paddle.tensor.full,paddle.tensor.creation.full
@ -418,17 +401,9 @@ def full(shape,
If ``shape`` is an Variable, it should be an 1-D Tensor .
fill_value(bool|float16|float32|float64|int32|int64|Variable): The constant value
used to initialize the Tensor to be created. If fill_value is an Variable, it must be an 1-D Tensor.
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.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output tensor
which can be float16, float32, float64, int32, int64, if dytpe is `None`, the data
type of created tensor is `float32`
device(str, optional): On which device to run this Op. The :attr:`device` must be
None, 'cpu' or 'gpu'. If :attr:`device` is None, the device that the user set in
the paddle program will be chosen. Default value is None.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is True.
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`.
@ -437,28 +412,26 @@ def full(shape,
Raises:
TypeError: The `dtype` must be one of None, bool, float16, float32, float64, int32 and int64.
TypeError: The `out` must be a Variable.
TypeError: The `shape` must be one of Variable, list tuple.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_imperative() # Now we are in imperative mode
data1 = paddle.full(shape=[2,1], fill_value=0, dtype='int64') # data1=[[0],[0]]
data2 = paddle.full(shape=[2,1], fill_value=5, dtype='int64', device='gpu') # data2=[[5],[5]]
# attr shape is a list which contains Variable Tensor.
positive_2 = fluid.layers.fill_constant([1], "int32", 2)
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]
# attr shape is an Variable Tensor.
shape = fluid.layers.fill_constant([1,2], "int32", 2) # shape=[2,2]
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]]
# attr value is an Variable Tensor.
val = fluid.layers.fill_constant([1], "float32", 2.0) # val=[2.0]
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]]
"""
@ -467,21 +440,7 @@ def full(shape,
if dtype is None:
dtype = 'float32'
check_dtype(dtype, 'create data type',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
'full')
check_type(shape, 'shape', (Variable, list, tuple), 'full')
if out is not None:
check_type(out, 'out', (Variable), 'full')
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
out.stop_gradient = stop_gradient
with device_guard(device):
out = fill_constant(shape=shape, dtype=dtype, value=fill_value, out=out)
return out
return fill_constant(shape=shape, dtype=dtype, value=fill_value, name=name)
def arange(start, end, step=1, dtype=None, name=None):

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