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rand API: remove out, device, stop_gradient; add name (#25246)

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fix_copy_if_different
zhupengyang 5 years ago committed by GitHub
parent 22720a1535
commit eb3173e2b6
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6 changed files with 134 additions and 182 deletions
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2
paddle/fluid/operators/gaussian_random_op.cc
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@ -98,7 +98,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
return;
}
if (!(ctx->HasInput("ShapeTensor") && !ctx->HasInputs("ShapeTensorList"))) {
if (!ctx->HasInput("ShapeTensor") && !ctx->HasInputs("ShapeTensorList")) {
PADDLE_ENFORCE_GT(
shape.size(), 0UL,
platform::errors::InvalidArgument(

112
python/paddle/fluid/layers/nn.py
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@ -10487,29 +10487,24 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_variable_for_type_inference(dtype)
if not isinstance(shape, (list, tuple, Variable)):
raise TypeError(
"The type of 'shape' in fill_constant must be Variable, list or tuple, but "
"received %s." % (type(shape)))
c_dtype = convert_np_dtype_to_dtype_(dtype)
check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random')
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random')
inputs = {}
attrs = {
'mean': mean,
'std': std,
'seed': seed,
'dtype': c_dtype,
'dtype': dtype,
'use_mkldnn': False
}
inputs = {}
utils._get_shape_tensor_inputs(
inputs=inputs,
helper=helper,
attrs=attrs,
shape=shape,
op_type='gaussian_random')
inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random')
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='gaussian_random',
inputs=inputs,
@ -14937,7 +14932,8 @@ def gather_tree(ids, parents):
@templatedoc()
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
name=None):
"""
This OP initializes a variable with random values sampled from a
uniform distribution in the range [min, max).
@ -14952,18 +14948,24 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
result=[[0.8505902, 0.8397286]]
Args:
shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple,
its elements can be an integer
or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64.
If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be int32 or int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
Default: float32.
min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means use a
seed generated by the system. Note that if seed is not 0, this
operator will always generate the same random numbers every time.
Default 0.
shape (list|tuple|Variable): The shape of the output Tensor, if the
shape is a list or tuple, its elements can be an integer or a
Tensor with the shape [1], and the type of the Tensor must be
int32 or int64. If the shape is a Variable, it is a 1-D Tensor, and
the type of the Tensor must be int32 or int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the
output Tensor. Supported data types: float32, float64. Default: float32.
min (float, optional): The lower bound on the range of random values
to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values
to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means
use a seed generated by the system. Note that if seed is not 0,
this operator will always generate the same random numbers every
time. Default 0.
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 the specified shape filled with uniform_random values.
@ -14993,62 +14995,30 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.uniform_random(var_shape_int32)
"""
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
def get_new_shape_tensor(list_shape):
new_shape_tensor = []
for dim in list_shape:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_shape_tensor.append(dim)
else:
assert (isinstance(dim, int))
temp_out = helper.create_variable_for_type_inference('int64')
fill_constant([1], 'int64', dim, force_cpu=True, out=temp_out)
new_shape_tensor.append(temp_out)
return new_shape_tensor
if in_dygraph_mode():
shape = utils._convert_shape_to_list(shape)
return core.ops.uniform_random('shape', shape, 'min',
float(min), 'max',
float(max), 'seed', seed, 'dtype', dtype)
def get_attr_shape(list_shape):
unk_dim_idx = -1
attrs_shape = []
for dim_idx, dim_size in enumerate(list_shape):
if isinstance(dim_size, Variable):
attrs_shape.append(-1)
else:
attrs_shape.append(dim_size)
assert dim_size > 0, (
"Each dimension size given in shape must not be negative "
"except one unknown dimension.")
return attrs_shape
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
helper = LayerHelper("uniform_random", **locals())
inputs = dict()
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
if in_dygraph_mode():
attrs['shape'] = shape
else:
if isinstance(shape, Variable):
shape.stop_gradient = True
inputs["ShapeTensor"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, (
"The size of argument(shape) can't be zero.")
attrs["shape"] = get_attr_shape(shape)
if utils._contain_var(shape):
inputs['ShapeTensorList'] = get_new_shape_tensor(shape)
utils._get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random')
helper = LayerHelper("uniform_random", **locals())
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="uniform_random", inputs=inputs, attrs=attrs,
outputs={"Out": out})
return helper.append_activation(out)
return out
def unbind(input, axis=0):

15
python/paddle/fluid/layers/tensor.py
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@ -685,12 +685,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
attrs['str_value'] = str(float(value))
if in_dygraph_mode():
if isinstance(shape, (list, tuple)):
shape = list(
map(lambda x: x.numpy()[0] if isinstance(x, Variable) else x,
shape))
else:
shape = list(shape.numpy().astype(int))
shape = utils._convert_shape_to_list(shape)
if out is None:
out = _varbase_creator(dtype=dtype)
@ -719,12 +714,8 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
'fill_constant')
helper = LayerHelper("fill_constant", **locals())
inputs = utils._get_shape_tensor_inputs(
inputs=inputs,
helper=helper,
attrs=attrs,
shape=shape,
op_type='fill_constant')
utils._get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='fill_constant')
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)

27
python/paddle/fluid/layers/utils.py
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@ -282,7 +282,7 @@ def _contain_var(list_or_tuple):
return False
def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
def _get_shape_tensor_inputs(inputs, attrs, shape, op_type):
from .tensor import fill_constant, cast
def _get_attr_shape(list_shape):
@ -295,7 +295,7 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
return attr_shape
def _get_shape_tensor(list_shape):
new_shape_tensor = []
shape_tensor_list = []
for idx, dim in enumerate(list_shape):
if isinstance(dim, Variable):
dim.stop_gradient = True
@ -305,11 +305,11 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
'(When type of shape in' + op_type + 'is list or tuple.)')
if convert_dtype(dim.dtype) == 'int64':
dim = cast(x=dim, dtype='int32')
new_shape_tensor.append(dim)
shape_tensor_list.append(dim)
else:
temp_out = fill_constant([1], 'int32', dim, force_cpu=True)
new_shape_tensor.append(temp_out)
return new_shape_tensor
shape_tensor_list.append(temp_out)
return shape_tensor_list
if isinstance(shape, Variable):
shape.stop_gradient = True
@ -325,8 +325,8 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
attrs["shape"] = _get_attr_shape(shape)
if _contain_var(shape):
inputs['ShapeTensorList'] = _get_shape_tensor(shape)
return inputs
else:
raise TypeError("Shape only supports Variable, or list, or tuple.")
def _convert_to_tensor_list(old_list, dtype="int32"):
@ -345,3 +345,16 @@ def _convert_to_tensor_list(old_list, dtype="int32"):
temp_out = fill_constant([1], dtype, ele, force_cpu=True)
new_list_tensor.append(temp_out)
return new_list_tensor
def _convert_shape_to_list(shape):
"""
Convert shape(list, tuple, variable) to list in imperative mode
"""
if isinstance(shape, (list, tuple)):
shape = list(
map(lambda x: x.numpy()[0] if isinstance(x, Variable) else x,
shape))
else:
shape = list(shape.numpy().astype(int))
return shape

58
python/paddle/fluid/tests/unittests/test_rand_op.py
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@ -47,71 +47,73 @@ class TestRandOpError(unittest.TestCase):
self.assertRaises(TypeError, test_dtype)
def test_shape_list():
rand(shape=[2.])
self.assertRaises(TypeError, test_shape_list)
def test_shape_list2():
rand(shape=[2, 3.])
self.assertRaises(TypeError, test_shape_list2)
def test_device():
rand(shape=[3, 4], device='device')
self.assertRaises(ValueError, test_device)
class TestRandOp(unittest.TestCase):
"""
This class test the common usages of randop.
"""
def test_run(self):
use_cuda = False
def run_net(self, use_cuda=False):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
result_1 = rand(shape=[3, 4])
result_0 = rand([3, 4])
result_1 = rand([3, 4], 'float64')
dim_1 = fluid.layers.fill_constant([1], "int64", 3)
dim_2 = fluid.layers.fill_constant([1], "int32", 5)
result_2 = rand(shape=[dim_1, dim_2])
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = rand(var_shape)
var_shape_int32 = fluid.data(
name='var_shape_int32', shape=[2], dtype="int32")
result_4 = rand(var_shape_int32)
exe.run(startup_program)
x1 = np.array([3, 2]).astype('int64')
x2 = np.array([4, 3]).astype('int32')
ret = exe.run(train_program,
feed={"var_shape": x1,
"var_shape_int32": x2},
fetch_list=[result_1, result_2, result_3, result_4])
ret = exe.run(
train_program,
feed={"var_shape": x1,
"var_shape_int32": x2},
fetch_list=[result_1, result_1, result_2, result_3, result_4])
def test_run(self):
self.run_net(False)
if core.is_compiled_with_cuda():
self.run_net(True)
class TestRandOpForDygraph(unittest.TestCase):
"""
This class test the common usages of randop.
"""
def test_run(self):
use_cuda = False
with fluid.dygraph.guard():
rand(shape=[3, 4])
def run_net(self, use_cuda=False):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
with fluid.dygraph.guard(place):
rand([3, 4])
rand([3, 4], 'float64')
dim_1 = fluid.layers.fill_constant([1], "int64", 3)
dim_2 = fluid.layers.fill_constant([1], "int32", 5)
rand(shape=[dim_1, dim_2])
var_shape = fluid.dygraph.to_variable(np.array([3, 4]))
rand(var_shape)
def test_run(self):
self.run_net(False)
if core.is_compiled_with_cuda():
self.run_net(True)
if __name__ == "__main__":
unittest.main()

102
python/paddle/tensor/random.py
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@ -406,7 +406,7 @@ def randperm(n,
return out
def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
def rand(shape, dtype=None, name=None):
"""
:alias_main: paddle.rand
:alias: paddle.rand,paddle.tensor.rand,paddle.tensor.random.rand
@ -424,22 +424,19 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
result=[[0.8505902, 0.8397286]]
Args:
shape(list|tuple|Variable): Shape of the Tensor to be created.
The data type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1].
If ``shape`` is a Variable, it should 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 float32, float64, if dytpe is `None`, the data
type of created tensor is `float32`
device(str, optional): This parameter specifies that the Tensor is created
on the GPU or CPU.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is True.
shape(list|tuple|Variable): Shape of the Tensor to be created. The data
type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1]. If
``shape`` is a Variable, it should be an 1-D Tensor .
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the
output tensor which can be float32, float64, if dytpe is `None`,
the data type of created tensor 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 the specified shape filled with random numbers from a uniform distribution on the interval [0, 1).
Variable: A Tensor of the specified shape filled with random numbers
from a uniform distribution on the interval [0, 1).
Raises:
TypeError: The shape type should be list or tupple or Variable.
@ -447,54 +444,33 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
result_1 = paddle.rand(shape=[3, 4])
# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
result_2 = paddle.rand(shape=[dim_1, dim_2])
import paddle
import numpy as np
paddle.enable_imperative()
# example 1: attr shape is a list which doesn't contain tensor Variable.
result_1 = paddle.rand(shape=[2, 3])
# [[0.451152 , 0.55825245, 0.403311 ],
# [0.22550228, 0.22106001, 0.7877319 ]]
# example 2: attr shape is a list which contains tensor Variable.
dim_1 = paddle.fill_constant([1], "int64", 2)
dim_2 = paddle.fill_constant([1], "int32", 3)
result_2 = paddle.rand(shape=[dim_1, dim_2, 2])
# [[[0.8879919 0.25788337]
# [0.28826773 0.9712097 ]
# [0.26438272 0.01796806]]
# [[0.33633623 0.28654453]
# [0.79109055 0.7305809 ]
# [0.870881 0.2984597 ]]]
# example 3: attr shape is a Variable, the data type must be int64 or int32.
var_shape = paddle.imperative.to_variable(np.array([2, 3]))
result_3 = paddle.rand(var_shape)
# [[0.22920267 0.841956 0.05981819]
# [0.4836288 0.24573246 0.7516129 ]]
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = paddle.rand(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = paddle.rand(var_shape_int32)
"""
if dtype is None:
dtype = 'float32'
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'rand')
check_type(shape, 'shape', (Variable, list, tuple), 'rand')
if isinstance(shape, Variable):
check_variable_and_dtype(shape, 'shape', ['int32', 'int64'], 'rand')
elif isinstance(shape, (list, tuple)):
for i, _shape in enumerate(shape):
if not isinstance(_shape, Variable):
check_type(_shape, '_shape', (int), 'rand')
else:
check_variable_and_dtype(_shape, 'shape[' + str(i) + ']',
['int32', 'int64'], 'rand')
if device not in [None, 'cpu', 'gpu']:
raise ValueError(
"The input device should in [None, 'cpu', 'gpu'], but received {}".
format(device))
helper = LayerHelper("rand", **locals())
if out is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
else:
check_variable_and_dtype(out, 'out', [dtype], 'rand')
out.stop_gradient = stop_gradient
with device_guard(device):
out = uniform_random(shape, dtype, min=0., max=1.0)
return out
return uniform_random(shape, dtype, min=0.0, max=1.0, name=name)

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