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.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""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."""