# 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. import collections import contextlib import sys import numpy as np import collections import six from . import parallel_helper from .. import unique_name from paddle.fluid import core from .layer_object_helper import LayerObjectHelper from .base import program_desc_tracing_guard from paddle.fluid import framework from ..param_attr import ParamAttr import copy import warnings __all__ = ['Layer'] class Layer(core.Layer): """Dynamic graph Layer based on OOD, includes the parameters of the layer, the structure of the forward graph and so on. Parameters: name_scope (str, optional): prefix name used by the layer to name parameters. If prefix is "my_layer", parameter name in MyLayer can be "mylayer_0.w_n", where w is the parameter base name and n is an unique suffix auto-generated. If None, prefix name will be lower cased class name. Default: None. dtype(str or core.VarDesc.VarType, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: ``core.VarDesc.VarType.FP32`` Returns: None """ def __init__(self, name_scope=None, dtype=core.VarDesc.VarType.FP32): if name_scope is None: name_scope = self.__class__.__name__.lower() self._full_name = unique_name.generate(name_scope) else: # TODO: remove name_scope parameter and all hard-coded usages self._full_name = unique_name.generate(name_scope + "/" + self.__class__.__name__) self._helper = LayerObjectHelper(self._full_name) self._built = False self._dtype = dtype self._parameters = collections.OrderedDict() self._sub_layers = collections.OrderedDict() self._loaddict_holder = collections.OrderedDict() def train(self): framework._dygraph_tracer().train_mode() def eval(self): framework._dygraph_tracer().eval_mode() def full_name(self): """Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__ Returns: str: full name of this layer. """ return self._full_name def create_parameter(self, shape, attr=None, dtype='float32', is_bias=False, default_initializer=None): """Create parameters for this layer. Parameters: shape(list): Shape of the parameter. attr(ParamAttr, optional): Parameter attribute of weight. Please refer to :ref:`api_fluid_ParamAttr`. Default: None. dtype(str or core.VarDesc.VarType or str, optional): Data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32". is_bias(bool, optional): if this is a bias parameter. Default: False. default_initializer(Initializer, optional): the default initializer for this parameter. If set None, default initializer will be set to :ref:`api_fluid_initializer_XavierInitializer` and :ref:`api_fluid_initializer_ConstantInitializer` for non-bias and bias parameter, respectively. Default: None. Returns: :ref:`api_guide_Variable_en` : created parameter. """ temp_attr = copy.deepcopy(attr) if isinstance(temp_attr, six.string_types) and temp_attr == "": temp_attr = None return self._helper.create_parameter(temp_attr, shape, dtype, is_bias, default_initializer) # TODO: Add more parameter list when we need them def create_variable(self, name=None, persistable=None, dtype=None, type=core.VarDesc.VarType.LOD_TENSOR): """Create Variable for this layer. Parameters: name(str, optional): name of the variable. Please refer to :ref:`api_guide_Name` . Default: None persistable(bool, optional): if set this variable persistable. Default: False dtype(str or core.VarDesc.VarType, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be ``core.VarDesc.VarType.FP32``. Default: None type(core.VarDesc.VarType, optional): type of the variable. No need to set this parameter. Default: ``core.VarDesc.VarType.LOD_TENSOR`` Returns: :ref:`api_guide_Variable_en` : created Variable. """ if name is not None: var_name = ".".join([self._full_name, name]) else: var_name = unique_name.generate(".".join( [self._full_name, "_generated_var"])) return self._helper.main_program.current_block().create_var( name=var_name, persistable=persistable, dtype=dtype, type=type) def parameters(self, include_sublayers=True): """Returns a list of all Parameters from current layer and its sub-layers. Parameters: include_sublayers(bool, optional): Whether include the parameters of sublayers. If True, also include the parameters from sublayers. Default: True Returns: list of :ref:`api_guide_Variable_en` : a list of Parameters. """ ret = [p for p in self._parameters.values()] parameters_set = set(ret) if include_sublayers: for l in self._sub_layers.values(): for p in l.parameters(include_sublayers): if p in parameters_set: continue parameters_set.add(p) ret.append(p) return ret def sublayers(self, include_sublayers=True): """Returns a list of sub layers. Parameters: include_sublayers(bool, optional): Whether return the sublayers of sublayers. If True, also include the sublayers of sublayers. Default: True Returns: list of Layer : a list of sub layers. """ ret = [l for l in self._sub_layers.values()] if include_sublayers: for l in self._sub_layers.values(): for sub_l in l.sublayers(include_sublayers): ret.append(sub_l) return ret def clear_gradients(self): for p in self.parameters(): if p.trainable: p.clear_gradient() def _build_once(self, *args, **kwargs): pass def __call__(self, *inputs, **kwargs): if not self._built: with program_desc_tracing_guard(False): self._build_once(*inputs, **kwargs) if parallel_helper._is_data_parallel_mode(): parallel_helper._broadcast_parameters( self._parameters.values()) self._built = True outputs = self.forward(*inputs, **kwargs) return outputs def forward(self, *inputs, **kwargs): """ Defines the computation performed at every call. Should be overridden by all subclasses. Parameters: *inputs(tuple): unpacked tuple arguments **kwargs(dict): unpacked dict arguments """ raise NotImplementedError def backward(self, *inputs): raise ValueError("Layer shouldn't implement backward") def add_sublayer(self, name, sublayer): """Adds a sub Layer instance. Added sublayer can be accessed by self.name Parameters: name(str): name of this sublayer. sublayer(Layer): an instance of Layer. Returns: Layer: the sublayer passed in. """ assert isinstance(sublayer, core.Layer) self._sub_layers[name] = sublayer return sublayer def add_parameter(self, name, parameter): """Adds a Parameter instance. Added parameter can be accessed by self.name Parameters: name(str): name of this sublayer. parameter(Parameter): an instance of Parameter. Returns: Parameter: the parameter passed in. """ assert isinstance(parameter, framework.Parameter) if len(self._loaddict_holder) > 0: assert parameter.name in self._loaddict_holder, "Parameter not found, Can't not find [ {} ] in stat_dict".format( parameter.name) parameter.set_value(self._loaddict_holder[parameter.name]) self._parameters[name] = parameter return parameter def __getattr__(self, name): if name in self._parameters: return self._parameters[name] elif name in self._sub_layers: return self._sub_layers[name] else: return object.__getattribute__(self, name) def __setattr__(self, name, value): if isinstance(getattr(type(self), name, None), property): object.__setattr__(self, name, value) if isinstance(value, framework.Parameter): params = self.__dict__.get('_parameters', None) if params is None: raise ValueError( "super(YourLayer, self).__init__() should be called first") if len(self._loaddict_holder) > 0: assert value.name in self._loaddict_holder, "Parameter not found, Can't not find [ {} ] in stat_dict".format( value.name) value.set_value(self._loaddict_holder[value.name]) params[name] = value elif isinstance(value, core.Layer): layers = self.__dict__.get('_sub_layers', None) if layers is None: raise ValueError( "super(YourLayer, self).__init__() should be called first") layers[name] = value else: object.__setattr__(self, name, value) def __delattr__(self, name): if name in self._parameters: del self._parameters[name] elif name in self._sub_layers: del self._sub_layers[name] else: object.__delattr__(self, name) def state_dict(self, destination=None, include_sublayers=True, structured_name_prefix=""): ''' Get all parameters of current layer and its sub-layers. And set all the parameters into a dict Parameters: destination(dict, optional) : If provide, all the parameters will set to this dict . Default: None include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True Retruns: dict: a dict contains all the parameters Examples: .. code-block:: python import paddle.fluid as fluid with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) state_dict = emb.state_dict() fluid.save_dygraph( state_dict, "paddle_dy") ''' if destination is None: destination = collections.OrderedDict() for name, data in self._parameters.items(): if data is not None: destination[structured_name_prefix + name] = data if include_sublayers: for layer_name, layer_item in self._sub_layers.items(): if layer_item is not None: destination_temp = destination.copy() destination_temp.update( layer_item.state_dict( destination_temp, include_sublayers, structured_name_prefix + layer_name + ".")) destination = destination_temp return destination def set_dict(self, stat_dict, include_sublayers=True, use_structured_name=True): ''' Set parameters from stat_dict. All the parameters will be reset by the tensor in the stat_dict Parameters: state_dict(dict) : Dict contains all the parameters include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter name as key. Default: True Returns: None Examples: .. code-block:: python import paddle.fluid as fluid with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) state_dict = emb.state_dict() fluid.save_dygraph( state_dict, "paddle_dy") para_state_dict, _ = fluid.load_dygraph( "paddle_dy") emb.set_dict( para_state_dict ) ''' self.load_dict( stat_dict, include_sublayers=include_sublayers, use_structured_name=use_structured_name) def load_dict(self, stat_dict, include_sublayers=True, use_structured_name=True): ''' Set parameters from stat_dict. All the parameters will be reset by the tensor in the stat_dict This api will be Deprecated. Please use set_dict Parameters: state_dict(dict) : Dict contains all the parameters include_sublayers(bool, optional) : If true, also include the parameters from sublayers. Default: True use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter name as key. Default: True Returns: None Examples: .. code-block:: python import paddle.fluid as fluid with fluid.dygraph.guard(): emb = fluid.dygraph.Embedding([10, 10]) state_dict = emb.state_dict() fluid.save_dygraph( state_dict, "paddle_dy") para_state_dict, _ = fluid.load_dygraph( "paddle_dy") emb.load_dict( para_state_dict ) ''' inner_state_dict = self.state_dict() for name, para in inner_state_dict.items(): key_name = name if use_structured_name else para.name if key_name in stat_dict: para.set_value(stat_dict[key_name]) else: raise RuntimeError( "Parameter not found, Can't not find [ {} ] in stat_dict" "use_structured_name is set to [{}]".format( key_name, use_structured_name)) unused_para_list = [] for k, v in stat_dict.items(): if k not in inner_state_dict: unused_para_list.append(k) if len(unused_para_list) > 0: warnings.warn( "Varibale [ {} ] are not used, because not included in layers state_dict". format(" ".join(unused_para_list)))