Source code for mindspore.common.parameter

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

"""Parameter for cell."""
from copy import copy
from .._c_expression import ParamValue
from . import dtype as mstype
from .initializer import initializer, Initializer
from .tensor import Tensor, MetaTensor
from .._checkparam import _check_str_by_regular
from ..parallel._tensor import _get_slice_index
from ..parallel._auto_parallel_context import auto_parallel_context

__all__ = ['Parameter', 'ParameterTuple']


def _is_in_parallel_mode():
    """Get parallel mode."""
    return auto_parallel_context().get_parallel_mode() in ["semi_auto_parallel", "auto_parallel"]

[docs]class Parameter(MetaTensor): """ Parameter types of cell models. After initialized `Parameter` is a subtype of `Tensor`. In auto_parallel mode of "semi_auto_parallel" and "auto_parallel", if init `Parameter` by a `Initializer`, the type of Parameter will be a `MetaTensor` not a `Tensor`. `MetaTensor` only save the shape type info of a tensor with no memory usage. The shape can be change while compile for auto-parallel. Call `init_data` will return a Tensor Parameter with initialized data. 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 to parameters. Default: False. """ __base_type__ = {} def __new__(cls, default_input, name, *args, **kwargs): input_class, *class_init_args = Parameter._get_parameter_new_args(default_input) new_type = Parameter._get_base_class(input_class) obj = input_class.__new__(new_type) input_class.__init__(obj, *class_init_args) # it's better to make the Initializer a kind of metatensor. obj.init_mode = None if not isinstance(obj, Tensor): obj.init_mode = default_input return obj def __reduce_ex__(self, _): data = self if self.init_mode is not None: data = self.init_mode else: # cast to break deep infinit loop while deepcopy data = Tensor(self) return ( Parameter, (data,, self.requires_grad, self.layerwise_parallel)) def __init__(self, default_input, name, requires_grad=True, layerwise_parallel=False): self._value = ParamValue() = name self.requires_grad = requires_grad self.layerwise_parallel = layerwise_parallel # this flag for tensor copy data. self.init_flag = False # this flag is for ge variable copy data. self._is_init = False self._inited_param = None self._sliced = False self.is_param_ps = False self._cast_type = None self.init_in_server = False @staticmethod def _get_base_class(input_class): input_class_name = f'Parameter{input_class.__name__}' if input_class_name in Parameter.__base_type__: new_type = Parameter.__base_type__[input_class_name] else: new_type = type(input_class_name, (Parameter, input_class), {}) Parameter.__base_type__[input_class_name] = new_type return new_type @staticmethod def _get_parameter_new_args(data): """Set `default_input` of current `Parameter`.""" if isinstance(data, bool): raise ValueError('Parameter data can not be `bool`') if isinstance(data, Initializer): if _is_in_parallel_mode(): # do not init data while in auto parallel. return (MetaTensor, data.dtype, data.shape) data = data.to_tensor() if isinstance(data, Tensor): # make a copy of Tensor to init the parameter return (Tensor, data.asnumpy(),) if isinstance(data, int): return (Tensor, data, mstype.int32) if isinstance(data, float): return (Tensor, data, mstype.float32) return (Tensor, data) def __str__(self): value_str = MetaTensor.__str__(self) if isinstance(self, Tensor): value_str = Tensor.__str__(self) return f'Parameter (name={}, value={value_str})' def __repr__(self): value_str = MetaTensor.__repr__(self) if isinstance(self, Tensor): value_str = Tensor.__repr__(self) return f'Parameter (name={}, value={value_str})' def __parameter__(self): """For parse check.""" def set_param_ps(self, init_in_server=False): self.is_param_ps = True self.init_in_server = init_in_server @property def inited_param(self): """Get the new parameter after call the init_data.""" return self._inited_param @property def name(self): """Get the name of the parameter.""" return @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`.") = name_ @property def cast_type(self): return self._cast_type @cast_type.setter def cast_type(self, dst_type): if dst_type not in (mstype.float16, mstype.float32, None): raise ValueError("The type of the name should be type of [float32, float16] or `None`.") self._cast_type = dst_type @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 the initialization 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) # pylint: disable=protected-access x._value = self._value.clone() = prefix + '.' + x.is_init = False if init != 'same': shape = self.shape dtype = self.dtype x.default_input = initializer(init, shape=shape, dtype=dtype) return x
@property def layerwise_parallel(self): return self._value.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._value.layerwise_parallel = value @property def requires_grad(self): """Return whether the parameter requires gradient.""" return self._value.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._value.requires_grad = value @property def data(self): return self.default_input @property def default_input(self): return self @default_input.setter def default_input(self, data): self.set_parameter_data(data) def _update_tensor_data(self, data): "Update the parameter by a Tensor." if isinstance(self, Tensor): # for Tensor same shape: self.init_flag = False return self.assign_value(data) # create a new tensor return Parameter(data,, self.requires_grad)
[docs] def set_parameter_data(self, data, slice_shape=False): """ Set `default_input` of current `Parameter`. Args: data (Union[Tensor, Initializer]): new data. slice_shape (bool): If slice the Parameter. Default: False. Retruns: Parameter, the parameter after set data. """ if not isinstance(data, (MetaTensor, Initializer)): raise ValueError(f"Parameter data must be `Initializer` or a kind of `MetaTensor` " f"(like `Tensor` or `MetaTensor`). But with type {type(data)}.") # both not init. is_incoming_tensor = isinstance(data, Tensor) is_current_tensor = isinstance(self, Tensor) if is_incoming_tensor and not is_current_tensor: raise TypeError("Parameter is a `MetaTensor` and not initializered, `data` for `set_parameter_data`" "should be a Initializer. If you want to update it by Tensor, call method" "`init_parameters_data` of `Cell` to init and replace all the Parameter of" "network, then call this method.") if tuple(self.shape) != tuple(data.shape): # If Slice create Parameter shape can be change. if slice_shape: self._update_tensor_data(data) self.sliced = True else: raise ValueError(f"Can not change the shape of Parameter which has been initialized." f" Current shape is {self.shape}, and incoming is {data.shape}.") if self.dtype != data.dtype: raise ValueError(f"Can not change the Parameter dtype. Current dtype is {self.set_dtype}" f", and incoming is {data.dtype}. Use .set_dtype(xxx) to change the dtype.") if isinstance(data, Initializer): # The parameter has been initializered, directly update by the data if is_current_tensor: self._update_tensor_data(data.to_tensor()) else: self.init_mode = data elif is_incoming_tensor or is_current_tensor: self._update_tensor_data(data) else: raise ValueError(f"Not support to update the Parameter by {data}") return self
[docs] def init_data(self, layout=None, set_sliced=False): """ Initialize the parameter data. 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 the parameter is set sliced after initializing the data. Default: False. Returns: Parameter, the `Parameter` after initializing data. If current `Parameter` was already initialized before, returns the same initialized `Parameter`. """ if self.init_mode is None: return self if self.inited_param is not None: return self.inited_param 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 larger than 3! layout is {}.".format(layout)) slice_index = int(_get_slice_index(layout[0], layout[1])) if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)): data = self.init_mode.to_tensor(0, [1]) else: data = self.init_mode.to_tensor(slice_index, layout[2]) else: if (self.init_in_server and self.is_param_ps and isinstance(self.init_mode, Initializer)): data = self.init_mode.to_tensor(0, [1]) else: data = self.init_mode.to_tensor() obj = self._update_tensor_data(data) if id(obj) != id(self): self._inited_param = obj obj.init_mode = None if set_sliced: obj.sliced = True return obj
[docs]class ParameterTuple(tuple): """ Class for storing tuple of parameters. Note: It is used to store the parameters of the network into the parameter tuple collection. """ def __new__(cls, iterable): """Create instance object of ParameterTuple.""" data = tuple(iterable) for x in data: if not isinstance(x, Parameter): raise TypeError(f"ParameterTuple input should be `Parameter` collection." f"But got a {type(iterable)}, {iterable}") return tuple.__new__(ParameterTuple, tuple(data))
[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."""