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framework.py
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framework.py
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# 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 collections
from collections import defaultdict
from collections import Iterable
import contextlib
from .wrapped_decorator import signature_safe_contextmanager, wrap_decorator
import os
import re
import traceback
import six
import numpy as np
import subprocess
import multiprocessing
import sys
import logging
from .. import compat as cpt
from .proto import framework_pb2
from . import core
from . import unique_name
import paddle.version as fluid_version
import warnings
__all__ = [
'Program',
'default_startup_program',
'default_main_program',
'program_guard',
'name_scope',
'cuda_places',
'cpu_places',
'cuda_pinned_places',
'in_dygraph_mode',
'is_compiled_with_cuda',
'Variable',
'load_op_library',
'require_version',
]
EMPTY_VAR_NAME = core.kEmptyVarName()
TEMP_VAR_NAME = core.kTempVarName()
GRAD_VAR_SUFFIX = core.kGradVarSuffix()
ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
_dygraph_tracer_ = None
_dygraph_current_expected_place_ = None
def require_version(min_version, max_version=None):
"""
Check if the installed version of PaddlePaddle is in [min_version, max_version],
if the installed version is lower than ``min_version`` or higher than ``max_version``,
an exception will be thrown, NO returns if the installed version is satisfied.
Args:
min_version (str): the minimum version required (like '1.4.0').
max_version (str, optional): the max version required (like '1.6.0'), default is None,
meaning any version equal or higher than ``min_version`` is acceptable.
Returns:
None.
Raises:
TypeError: if the type of ``min_version`` is not str.
TypeError: if the type of ``max_version`` is not str or type(None).
ValueError: if the value of ``min_version`` is not in version format.
ValueError: if the value of ``max_version`` is not in version format or None.
Exception: if the installed version is lower than ``min_version`` or higher than ``max_version``.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# any version >= 0.1.0 is acceptable.
fluid.require_version('0.1.0')
# if 0.1.0 <= version <= 10.0.0, it is acceptable.
fluid.require_version(min_version='0.1.0', max_version='10.0.0')
"""
if not isinstance(min_version, str):
raise TypeError(
"The type of 'min_version' in require_version must be str, but received %s."
% (type(min_version)))
if not isinstance(max_version, (str, type(None))):
raise TypeError(
"The type of 'max_version' in require_version must be str or type(None), but received %s."
% (type(max_version)))
check_format = re.match(r'\d+(\.\d+){0,3}', min_version)
if check_format is None or check_format.group() != min_version:
raise ValueError(
"The value of 'min_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
"like '1.5.2.0', but received %s" % min_version)
if max_version is not None:
check_format = re.match(r'\d+(\.\d+){0,3}', max_version)
if check_format is None or check_format.group() != max_version:
raise ValueError(
"The value of 'max_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
"like '1.5.2.0', but received %s" % max_version)
version_installed = [
fluid_version.major, fluid_version.minor, fluid_version.patch,
fluid_version.rc
]
zero_version = ['0', '0', '0', '0']
def version_cmp(ver_a, ver_b):
for i in six.moves.range(len(ver_a)):
if int(ver_a[i]) > int(ver_b[i]):
return 1
elif int(ver_a[i]) < int(ver_b[i]):
return -1
return 0
if version_cmp(version_installed, zero_version) == 0:
if max_version is not None:
warnings.warn(
"PaddlePaddle version in [%s, %s] required, but %s installed. "
"Maybe you are using a develop version, "
"please make sure the version is good with your code." %
(min_version, max_version, fluid_version.full_version))
else:
warnings.warn(
"PaddlePaddle version %s or higher is required, but %s installed, "
"Maybe you are using a develop version, "
"please make sure the version is good with your code." %
(min_version, fluid_version.full_version))
return
min_version_split = min_version.split('.')
min_version_to_check = min_version_split + zero_version[len(
min_version_split):]
if max_version is not None:
max_version_split = max_version.split('.')
max_version_to_check = max_version_split + zero_version[len(
max_version_split):]
if version_cmp(version_installed,
max_version_to_check) > 0 or version_cmp(
version_installed, min_version_to_check) < 0:
raise Exception(
"VersionError: PaddlePaddle version in [%s, %s] required, but %s installed."
% (min_version, max_version, fluid_version.full_version))
else:
if version_cmp(version_installed, min_version_to_check) < 0:
raise Exception(
"VersionError: PaddlePaddle version %s or higher is required, but %s installed, "
"please upgrade your PaddlePaddle to %s or other higher version."
% (min_version, fluid_version.full_version, min_version))
def in_dygraph_mode():
"""
This function checks whether the program runs in dynamic graph mode or not.
You can turn on dynamic graph mode with :ref:`api_fluid_dygraph_guard` api.
Returns:
bool: Whether the program is running in dynamic graph mode.
Examples:
.. code-block:: python
import paddle.fluid as fluid
if fluid.in_dygraph_mode():
print('running in dygraph mode')
else:
print('not running in dygraph mode')
"""
return _dygraph_tracer_ is not None
def _dygraph_not_support_(func):
def __impl__(*args, **kwargs):
assert not in_dygraph_mode(
), "We don't support %s in Dygraph mode" % func.__name__
return func(*args, **kwargs)
return __impl__
def _dygraph_only_(func):
def __impl__(*args, **kwargs):
assert in_dygraph_mode(
), "We Only support %s in Dygraph mode, please use fluid.dygraph.guard() as context to run it in Dygraph Mode" % func.__name__
return func(*args, **kwargs)
return __impl__
dygraph_not_support = wrap_decorator(_dygraph_not_support_)
dygraph_only = wrap_decorator(_dygraph_only_)
def _dygraph_tracer():
return _dygraph_tracer_
def _current_expected_place():
return _dygraph_current_expected_place_
# TODO(zhiqiu): remove this function.
def _var_base_to_np(var_base):
"""
convert VarBase tp numpy
Args:
var_base(VarBase) : the VarBase to convert
Returns (np.ndarray): the np.ndarray contain the value of VarBase
"""
warnings.warn(
"paddle.fluid.framework._var_base_to_np is deprecated, please use var_base.numpy() instead of _var_base_to_np(var_base)."
)
return var_base.numpy()
def _cpu_num():
if "CPU_NUM" not in os.environ.keys():
if multiprocessing.cpu_count() > 1:
sys.stderr.write(
'!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list.\n'
'CPU_NUM indicates that how many CPUPlace are used in the current task.\n'
'And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster.\n\n'
'export CPU_NUM={} # for example, set CPU_NUM as number of physical CPU core which is {}.\n\n'
'!!! The default number of CPU_NUM=1.\n'.format(
multiprocessing.cpu_count(), multiprocessing.cpu_count()))
os.environ['CPU_NUM'] = str(1)
cpu_num = os.environ.get('CPU_NUM')
return int(cpu_num)
def _cuda_ids():
gpus_env = os.getenv("FLAGS_selected_gpus")
if gpus_env:
device_ids = [int(s) for s in gpus_env.split(",")]
else:
device_ids = six.moves.range(core.get_cuda_device_count())
return device_ids
def is_compiled_with_cuda():
"""
Whether this whl package can be used to run the model on GPU.
Returns (bool): support gpu or not.
Examples:
.. code-block:: python
import paddle.fluid as fluid
support_gpu = fluid.is_compiled_with_cuda()
"""
return core.is_compiled_with_cuda()
def cuda_places(device_ids=None):
"""
**Note**:
For multi-card tasks, please use `FLAGS_selected_gpus` environment variable to set the visible GPU device.
The next version will fix the problem with `CUDA_VISIBLE_DEVICES` environment variable.
This function creates a list of :code:`fluid.CUDAPlace` objects.
If :code:`device_ids` is None, environment variable of
:code:`FLAGS_selected_gpus` would be checked first. For example, if
:code:`FLAGS_selected_gpus=0,1,2`, the returned list would
be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
If :code:`FLAGS_selected_gpus` is not set, all visible
gpu places would be returned according to the :code:`CUDA_VISIBLE_DEVICES` environment variable.
If :code:`device_ids` is not None, it should be the device
ids of GPUs. For example, if :code:`device_ids=[0,1,2]`,
the returned list would be
[fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)].
Parameters:
device_ids (list or tuple of int, optional): list of GPU device ids.
Returns:
list of fluid.CUDAPlace: Created GPU place list.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cuda_places = fluid.cuda_places()
"""
assert core.is_compiled_with_cuda(), \
"Not compiled with CUDA"
if device_ids is None:
device_ids = _cuda_ids()
elif not isinstance(device_ids, (list, tuple)):
device_ids = [device_ids]
return [core.CUDAPlace(dev_id) for dev_id in device_ids]
def cpu_places(device_count=None):
"""
This function creates a list of :code:`fluid.CPUPlace` objects, and returns the created list.
If :code:`device_count` is None, the device count would
be determined by environment variable :code:`CPU_NUM`.
If :code:`CPU_NUM` is not set, the default value is 1,
i.e. CPU_NUM=1.
:code:`CPU_NUM` indicates the number of devices used in the current task.
The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
Parameters:
device_count (int, optional): device number. Default: None.
Returns:
list of fluid.CPUPlace: Created list of CPU places.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cpu_places = fluid.cpu_places()
"""
if device_count is None:
device_count = _cpu_num()
return [core.CPUPlace()] * device_count
def cuda_pinned_places(device_count=None):
"""
This function creates a list of :code:`fluid.CUDAPinnedPlace` objects.
If :code:`device_count` is None, the device count would
be determined by environment variable :code:`CPU_NUM`.
If :code:`CPU_NUM` is not set, the default value is 1,
i.e. CPU_NUM=1.
:code:`CPU_NUM` indicates the number of devices used in the current task.
The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
Parameters:
device_count (int, optional): device number. Default: None.
Returns:
list of fluid.CUDAPinnedPlace: Created list of CUDA pinned places.
Examples:
.. code-block:: python
import paddle.fluid as fluid
cuda_pinned_places_cpu_num = fluid.cuda_pinned_places()
# or
cuda_pinned_places = fluid.cuda_pinned_places(1)
"""
assert core.is_compiled_with_cuda(), \
"Not compiled with CUDA"
if device_count is None:
device_count = len(_cuda_ids())
return [core.CUDAPinnedPlace()] * device_count
class NameScope(object):
def __init__(self, name="", parent=None):
self._children = dict()
self._name = name
self._parent = parent
def child(self, prefix):
if prefix not in self._children:
new_child = NameScope(prefix, self)
self._children[prefix] = [new_child]
else:
new_child = NameScope(prefix + "_%d" % len(self._children[prefix]),
self)
self._children[prefix].append(new_child)
return new_child
def parent(self):
return self._parent
def name(self):
return self._name
_name_scope = NameScope()
@signature_safe_contextmanager
def name_scope(prefix=None):
"""
Generate hierarchical name prefix for the operators.
Note:
This should only used for debugging and visualization purpose.
Don't use it for serious analysis such as graph/program transformations.
Args:
prefix(str, optional): prefix. Default is none.
Examples:
.. code-block:: python
import paddle.fluid as fluid
with fluid.name_scope("s1"):
a = fluid.data(name='data', shape=[None, 1], dtype='int32')
b = a + 1
with fluid.name_scope("s2"):
c = b * 1
with fluid.name_scope("s3"):
d = c / 1
with fluid.name_scope("s1"):
f = fluid.layers.pow(d, 2.0)
with fluid.name_scope("s4"):
g = f - 1
# Op are created in the default main program.
for op in fluid.default_main_program().block(0).ops:
# elementwise_add is created in /s1/
if op.type == 'elementwise_add':
assert op.desc.attr("op_namescope") == '/s1/'
# elementwise_mul is created in '/s1/s2'
elif op.type == 'elementwise_mul':
assert op.desc.attr("op_namescope") == '/s1/s2/'
# elementwise_div is created in '/s1/s3'
elif op.type == 'elementwise_div':
assert op.desc.attr("op_namescope") == '/s1/s3/'
# elementwise_sum is created in '/s4'
elif op.type == 'elementwise_sub':
assert op.desc.attr("op_namescope") == '/s4/'
# pow is created in /s1_1/
elif op.type == 'pow':
assert op.desc.attr("op_namescope") == '/s1_1/'
"""
# TODO(panyx0718): Only [0-9a-z].
# in dygraph we don't need namescope since it will cause mem leak
if in_dygraph_mode():
yield
else:
assert prefix, "namescope prefix can not be empty."
global _name_scope
_name_scope = _name_scope.child(prefix)
yield
_name_scope = _name_scope.parent()
def _full_name_scope():
global _name_scope
scope = _name_scope
name = ""
while scope:
name = scope.name() + "/" + name
scope = scope.parent()
return name
def generate_control_dev_var_name():
import random
return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
def grad_var_name(var_name):
"""
Returns:
str: gradient name for a certain var name
"""
return var_name + GRAD_VAR_SUFFIX
def convert_np_dtype_to_dtype_(np_dtype):
"""
Convert the data type in numpy to the data type in Paddle
Args:
np_dtype(np.dtype): the data type in numpy.
Returns:
core.VarDesc.VarType: the data type in Paddle.
"""
dtype = np.dtype(np_dtype)
if dtype == np.float32:
return core.VarDesc.VarType.FP32
elif dtype == np.float64:
return core.VarDesc.VarType.FP64
elif dtype == np.float16:
return core.VarDesc.VarType.FP16
elif dtype == np.int32:
return core.VarDesc.VarType.INT32
elif dtype == np.int16:
return core.VarDesc.VarType.INT16
elif dtype == np.int64:
return core.VarDesc.VarType.INT64
elif dtype == np.bool:
return core.VarDesc.VarType.BOOL
elif dtype == np.uint16:
return core.VarDesc.VarType.INT16
elif dtype == np.uint8:
return core.VarDesc.VarType.UINT8
elif dtype == np.int8:
return core.VarDesc.VarType.INT8
else:
raise ValueError("Not supported numpy dtype %s" % dtype)
def dtype_is_floating(dtype):
"""
Check the data type is floating or not.
Args:
dtype(np.dtype|core.VarDesc.VarType): data type.
Could be numpy format or Paddle format
Returns(bool): True if data type is a float value
"""
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return dtype in [
core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
core.VarDesc.VarType.FP64
]
def _debug_string_(proto, throw_on_error=True):
"""
Get the debug string of a protobuf message. The message could be not
initialized.
Args:
proto(google.protobuf.message.Message): The protobuf message
throw_on_error(bool): True if raise an error when the protobuf message
is not initialized.
Returns(str): The debug string of the protobuf message
"""
error_fields = list()
if not proto.IsInitialized(error_fields) and throw_on_error:
raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
format(error_fields, proto))
return proto.__str__()
def _varbase_creator(type=core.VarDesc.VarType.LOD_TENSOR,
name=None,
shape=None,
dtype=None,
persistable=None,
**kwargs):
if dtype is not None:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
return core.VarBase(dtype if dtype else core.VarDesc.VarType.FP32,
list(shape) if shape else [], name, type
if type else core.VarDesc.VarType.LOD_TENSOR, True
if persistable else False)
class VariableMetaClass(type):
@classmethod
def __instancecheck__(cls, instance):
t = type(instance)
if in_dygraph_mode():
return issubclass(t, core.VarBase)
else:
return issubclass(t, Variable)
class ParameterMetaClass(VariableMetaClass):
@classmethod
def __instancecheck__(cls, instance):
t = type(instance)
if in_dygraph_mode():
return issubclass(t, ParamBase)
else:
return issubclass(t, Parameter)
def _getitem_impl_(var, item):
"""
Slice the variable.
Args:
item(int/slice/tuple) : the index.
Returns:
Sliced variable
"""
if not isinstance(item, tuple):
item = [item]
decrease_axis = []
slice_axis = []
slice_start = []
slice_end = []
slice_step = []
use_strided_slice = False
reverse_axis = []
def fill_constant(shape, value, force_cpu=False, out=None):
var.block.append_op(
type='fill_constant',
inputs={},
outputs={'Out': [out]},
attrs={
'shape': shape,
'dtype': out.dtype,
'value': float(value),
'force_cpu': force_cpu
},
stop_gradient=True)
out.stop_gradient = True
return out
for dim, slice_item in enumerate(item):
if isinstance(slice_item, slice):
start = slice_item.start
end = slice_item.stop
step = slice_item.step
if start is None and end is None and step is None:
continue
if step is None:
step = 1
if start is None and end is None:
assert (step == -1)
reverse_axis.append(dim)
continue
if start is None:
start = 0
if end is None:
end = 10000000
if step != 1:
use_strided_slice = True
slice_axis.append(dim)
slice_start.append(start)
slice_end.append(end)
slice_step.append(step)
else:
decrease_axis.append(dim)
slice_axis.append(dim)
slice_start.append(slice_item)
slice_step.append(1)
if isinstance(slice_item, Variable):
temp_1 = var.block.create_var(dtype='int32')
fill_constant([1], 1, force_cpu=True, out=temp_1)
temp_end = var.block.create_var(dtype='int32')
var.block.append_op(
type='elementwise_add',
inputs={'X': slice_item,
'Y': temp_1},
outputs={'Out': temp_end},
attrs={'axis': -1})
slice_end.append(temp_end)
else:
slice_end.append(slice_item + 1
if slice_item != -1 else 10000000)
def contain_var(one_list):
for ele in one_list:
if isinstance(ele, Variable):
return True
return False
def get_new_list_tensor(old_list):
new_list_tensor = []
for dim in old_list:
if isinstance(dim, Variable):
dim.stop_gradient = True
new_list_tensor.append(dim)
else:
assert (isinstance(dim, int))
temp_out = var.block.create_var(dtype='int32')
fill_constant([1], dim, force_cpu=True, out=temp_out)
new_list_tensor.append(temp_out)
return new_list_tensor
inputs = {'Input': [var]}
attrs = {
'axes': slice_axis,
'starts': [],
'ends': [],
'decrease_axis': decrease_axis
}
if (use_strided_slice == True):
attrs['strides'] = []
infer_flags = list(1 for i in range(len(slice_axis)))
# starts
if not contain_var(slice_start):
attrs['starts'] = slice_start
else:
inputs['StartsTensorList'] = get_new_list_tensor(slice_start)
for i, dim in enumerate(slice_start):
if isinstance(dim, Variable):
attrs['starts'].append(-1)
infer_flags[i] = -1
else:
attrs['starts'].append(dim)
# ends
if not contain_var(slice_end):
attrs['ends'] = slice_end
else:
inputs['EndsTensorList'] = get_new_list_tensor(slice_end)
for i, dim in enumerate(slice_end):
if isinstance(dim, Variable):
attrs['ends'].append(-1)
infer_flags[i] = -1
else:
attrs['ends'].append(dim)
# strides
if use_strided_slice == True:
if not contain_var(slice_step):
attrs['strides'] = slice_step
else:
inputs['StridesTensorList'] = get_new_list_tensor(slice_step)
for i, dim in enumerate(slice_step):
if isinstance(dim, Variable):
attrs['strides'].append(-1)
infer_flags[i] = -1
else:
attrs['strides'].append(dim)
# infer_flags
attrs['infer_flags'] = infer_flags
out = var
if use_strided_slice == False and len(slice_axis) > 0:
# append slice_op here
slice_out_var = var.block.create_var(
name=unique_name.generate_with_ignorable_key(var.name + "_slice"),
dtype=var.dtype)
var.block.append_op(
type="slice",
inputs=inputs,
outputs={'Out': [slice_out_var]},
attrs=attrs)
out = slice_out_var
elif use_strided_slice == True and len(slice_axis) > 0:
strided_slice_out_var = var.block.create_var(
name=unique_name.generate_with_ignorable_key(var.name +
"_strided_slice"),
dtype=var.dtype)
var.block.append_op(
type="strided_slice",
inputs=inputs,
outputs={'Out': [strided_slice_out_var]},
attrs=attrs)
out = strided_slice_out_var
if len(reverse_axis) > 0:
reverse_out_var = var.block.create_var(
name=unique_name.generate_with_ignorable_key(var.name +
"_slice_reverse"),
dtype=var.dtype)
var.block.append_op(
type="reverse",
inputs={'X': out},
outputs={'Out': [reverse_out_var]},
attrs={'axis': reverse_axis})
out = reverse_out_var
return out
@six.add_metaclass(VariableMetaClass)
class Variable(object):
"""
**Notes**:
**The constructor of Variable should not be invoked directly.**
**In Static Graph Mode: Please use** `Block.create_var` **to create a Static variable which has no data until being feed.**
**In Dygraph Mode: Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph variable with real data**
In Fluid, every input and output of an OP is a variable. In most
cases, variables are used for holding different kinds of data or training
labels. A variable belongs to a :ref:`api_guide_Block_en` . All variable has its own name and
two variables in different :ref:`api_guide_Block_en` could have the same name.
There are many kinds of variables. Each kind of them has its own attributes
and usages. Please refer to the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details.
Most of a Variable's member variables can be set to be None. It mean
it is not available or will be specified later.
Examples:
In Static Graph Mode:
.. code-block:: python
import paddle.fluid as fluid
cur_program = fluid.Program()
cur_block = cur_program.current_block()
new_variable = cur_block.create_var(name="X",
shape=[-1, 23, 48],
dtype='float32')
In `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ Mode:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
with fluid.dygraph.guard():
new_variable = fluid.dygraph.to_variable(np.arange(10))
"""
def __init__(self,
block,
type=core.VarDesc.VarType.LOD_TENSOR,
name=None,
shape=None,
dtype=None,
lod_level=None,
capacity=None,
persistable=None,
error_clip=None,
stop_gradient=False,
is_data=False,
need_check_feed=False,
belong_to_optimizer=False,
**kwargs):
self.block = block
if name is None:
name = unique_name.generate('_generated_var')
if dtype is not None:
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
self.belong_to_optimizer = belong_to_optimizer
self.error_clip = error_clip
is_new_var = False
name = cpt.to_text(name)
self.desc = self.block.desc.find_var(cpt.to_bytes(name))
if self.desc is None:
self.desc = self.block.desc.var(cpt.to_bytes(name))
is_new_var = True
if is_new_var:
self.desc.set_type(type)
elif self.desc.type() != type:
raise ValueError("Variable {0} has been created before. The "
"previous type is {1}; the new type is {2}. They"
" are not matched".format(self.name,
self.desc.type(), type))
if shape is not None:
if is_new_var:
self.desc.set_shape(shape)
else:
old_shape = self.shape
shape = tuple(shape)
if shape != old_shape:
raise ValueError(
"Variable {0} has been created before. the previous "
"shape is {1}; the new shape is {2}. They are not "
"matched.".format(self.name, old_shape, shape))
if dtype is not None:
if is_new_var:
self.desc.set_dtype(dtype)
else:
old_dtype = self.dtype
if dtype != old_dtype:
raise ValueError("Variable {0} has been created before. "
"The previous data type is {1}; the new "
"data type is {2}. They are not "
"matched.".format(self.name, old_dtype,
dtype))
if lod_level is not None:
if is_new_var:
self.desc.set_lod_level(lod_level)
else:
if lod_level != self.lod_level:
raise ValueError("Variable {0} has been created before. "
"The previous lod_level is {1}; the new "
"lod_level is {2}. They are not "
"matched".format(self.name, self.lod_level,
lod_level))
if persistable is not None:
if is_new_var:
self.desc.set_persistable(persistable)
else:
if persistable != self.persistable:
raise ValueError(
"Variable {0} has been created before."
"The previous persistable is {1}; the new "
"persistable is {2}. They are not matched".format(
self.name, self.persistable, persistable))
if need_check_feed and is_new_var:
self.desc.set_need_check_feed(need_check_feed)
if capacity is not None:
if is_new_var:
self.desc.set_capacity(capacity)
else:
# TODO(abhinavarora) : Compare with set capacity once,
# get_capacity is implemented
pass
self.block.vars[name] = self
self.op = None
self._stop_gradient = stop_gradient
self.is_data = is_data
@dygraph_only
def detach(self):
"""
**Notes**:
**This API is ONLY available in Dygraph mode**
Returns a new Variable, detached from the current graph.
Returns:
( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
Examples:
.. code-block:: python
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph import Linear
import numpy as np
data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
with fluid.dygraph.guard():
linear = Linear(32, 64)
data = to_variable(data)
x = linear(data)
y = x.detach()
"""
pass
@dygraph_only
def numpy(self):
"""
**Notes**:
**This API is ONLY available in Dygraph mode**
Returns a numpy array shows the value of current :ref:`api_guide_Variable_en`
Returns:
ndarray: The numpy value of current Variable.
Returns type:
ndarray: dtype is same as current Variable
Examples:
.. code-block:: python
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph import Linear
import numpy as np
data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
with fluid.dygraph.guard():
linear = Linear(32, 64)
data = to_variable(data)