# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Parameter for cell."""
import numbers
from copy import copy
from mindspore import context
from . import dtype as mstype
from .initializer import initializer, Initializer
from .tensor import Tensor, MetaTensor
from .._checkparam import _check_str_by_regular
from ..parallel._utils import _set_clone_info, _CloneInfo
from ..parallel._tensor import _get_slice_index
__all__ = ['Parameter', 'ParameterTuple']
PARAMETER_NAME_DEFAULT = "Parameter"
PARAMETER_NAME_PREFIX_MAX_LEN = 1024
def _check_type(x):
"""Check input data type"""
if not isinstance(x, Parameter):
raise ValueError("Should be `Parameter` collection.")
return True
[docs]class Parameter:
"""
Parameter types of cell models.
Note:
Each parameter of Cell is represented by Parameter class.
Args:
default_input (Union[Tensor, Initializer]): Parameter data, when `default_input` is` Initializer`,
the data stored by Parameter is `MetaTensor`, otherwise it is `Tensor`.
name (str): Name of the child parameter.
requires_grad (bool): True if the parameter requires gradient. Default: True.
layerwise_parallel (bool): A kind of model parallel mode. When layerwise_parallel is true in paralle mode,
broadcast and gradients communication would not be applied on parameters. Default: False.
sparse_grad (str): Set if the parameter's gradient is sparse. Default: empty.
"""
def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False, sparse_grad=""):
self.set_parameter_data(default_input)
self.name = name
self.requires_grad = requires_grad
self.layerwise_parallel = layerwise_parallel
self.sparse_grad = sparse_grad
self._is_init = False
self._sliced = False
self.clone_info = _CloneInfo()
if context.get_context("mode") == context.PYNATIVE_MODE:
self.init_data()
def __repr__(self):
format_str = 'Parameter (name={name})'
return format_str.format(name=self._name)
def __parameter__(self):
"""For parse check."""
@property
def name(self):
"""Get the name of the parameter."""
return self._name
@name.setter
def name(self, name_):
"""
Define a name for the parameter.
Args:
name_ (`str` or `None`): The name of the parameter. When the parameter is None or an empty string,
the default value `PARAMETER_NAME_DEFAULT` is used.
"""
if name_ is None:
name_ = PARAMETER_NAME_DEFAULT
elif isinstance(name_, str):
name_ = name_.strip()
if name_ == '':
name_ = PARAMETER_NAME_DEFAULT
if len(name_) > PARAMETER_NAME_PREFIX_MAX_LEN:
raise ValueError("The length of the '{}' name should be less than {}.".
format(name_, PARAMETER_NAME_PREFIX_MAX_LEN))
else:
raise ValueError("The type of the name should be `str` or `None`.")
self._name = name_
@property
def sliced(self):
"""Get slice status of the parameter."""
return self._sliced
@sliced.setter
def sliced(self, sliced_):
self._sliced = sliced_
@property
def is_init(self):
"""Get init status of the parameter."""
return self._is_init
@is_init.setter
def is_init(self, is_init_):
"""
Set init status of the parameter.
Args:
is_init_ (bool): The init status of the parameter.
"""
self._is_init = is_init_
[docs] def clone(self, prefix, init='same'):
"""
Clone the parameter.
Args:
prefix (str): Namespace of parameter.
init (Union[Tensor, str, Initializer, numbers.Number]): Initialize the shape of the parameter.
Default: 'same'.
Returns:
Parameter, a new parameter.
"""
_check_str_by_regular(prefix)
x = copy(self)
x.name = prefix + '.' + x.name
x.is_init = False
if init != 'same':
shape = self.default_input.shape
dtype = self.default_input.dtype
if isinstance(init, (str, Initializer, numbers.Number)):
x.init_mode = initializer(init, shape=shape, dtype=dtype)
x.default_input = MetaTensor(dtype, shape)
if context.get_context("mode") == context.PYNATIVE_MODE:
x.init_data()
else:
x.default_input = initializer(init, shape=shape, dtype=dtype)
x.clone_info = copy(self.clone_info)
_set_clone_info(self.clone_info, x.clone_info)
return x
@property
def layerwise_parallel(self):
return self._layerwise_parallel
@layerwise_parallel.setter
def layerwise_parallel(self, value=True):
if not isinstance(value, bool):
raise TypeError("`layerwise_parallel` parameter must be bool type")
self._layerwise_parallel = value
@property
def requires_grad(self):
"""Return whether the parameter requires gradient."""
return self._requires_grad
@requires_grad.setter
def requires_grad(self, value=True):
if not isinstance(value, bool):
raise TypeError("`requires_grad` parameter must be bool type")
self._requires_grad = value
@property
def sparse_grad(self):
"""Return whether the parameter's gradient is sparse."""
return self._sparse_grad
@sparse_grad.setter
def sparse_grad(self, value=""):
if not isinstance(value, str):
raise TypeError("`sparse_grad` parameter must be str type")
self._sparse_grad = value
@property
def data(self):
return self.default_input
def __add__(self, other):
return self.default_input + other
def __sub__(self, other):
return self.default_input - other
def __mul__(self, other):
return self.default_input * other
def __truediv__(self, other):
return self.default_input / other
def __setitem__(self, index, value):
default_input = self.default_input
default_input[index] = value
return self
[docs] def set_parameter_data(self, data):
"""Set `default_input` of current `Parameter`."""
if isinstance(data, bool):
raise ValueError('Parameter data can not be `bool`')
if isinstance(data, Tensor):
# make a copy of Tensor to init the parameter
data = Tensor(data.asnumpy().copy())
data.init_flag = False
elif isinstance(data, Initializer):
self.init_mode = data
data = MetaTensor(self.init_mode.dtype, self.init_mode.shape)
elif isinstance(data, int):
data = Tensor(data, dtype=mstype.int32)
elif isinstance(data, float):
data = Tensor(data, dtype=mstype.float32)
else:
data = Tensor(data)
data.init_flag = False
self.default_input = data
[docs] def init_data(self, layout=None, set_sliced=False):
"""
Init data of the parameter.
Args:
layout (list[list[int]]): Parameter slice layout [dev_mat, tensor_map, slice_shape].
- dev_mat (list[int]): Device matrix.
- tensor_map (list[int]): Tensor map.
- slice_shape (list[int]): Shape of slice.
set_sliced (bool): True if should set parameter sliced after init the data of initializer.
Default: False.
"""
if not isinstance(self.default_input, MetaTensor):
return
if layout is not None:
if not isinstance(layout, list):
raise TypeError("The layout should be list! layout is {}."
.format(layout))
if len(layout) != 3:
raise ValueError("The length of layout must be 3! layout is {}."
.format(layout))
slice_index = int(_get_slice_index(layout[0], layout[1]))
self.default_input = self.init_mode.to_tensor(slice_index, layout[2])
else:
self.default_input = self.init_mode.to_tensor()
self.init_mode = None
if set_sliced:
self.sliced = True
[docs]class ParameterTuple(tuple):
"""
Class for storing tuple of parameters.
Note:
Used to store the parameters of the network into the parameter tuple collection.
"""
def __new__(cls, iterable):
"""Create instance object of ParameterTuple."""
g = (x for x in iterable if _check_type(x))
return tuple.__new__(ParameterTuple, g)
[docs] def clone(self, prefix, init='same'):
"""
Clone the parameter.
Args:
prefix (str): Namespace of parameter.
init (str): Initialize the shape of the parameter. Default: 'same'.
Returns:
Tuple, the new Parameter tuple.
"""
_check_str_by_regular(prefix)
new = []
for x in self:
x1 = x.clone(prefix, init)
new.append(x1)
return ParameterTuple(new)
def __parameter_tuple__(self):
"""For parse check."""