# Copyright (c) 2020 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. """ This module provide MpcLayerHelper, which are similar to LayerHelper in PaddlePaddle. """ # system module import copy import six # paddle module from paddle.fluid.layer_helper import LayerHelper from paddle.fluid import core from paddle.fluid import unique_name from paddle.fluid.param_attr import ParamAttr, WeightNormParamAttr from paddle.fluid.initializer import ConstantInitializer # mpc_paddle module from .framework import MpcVariable, MpcParameter, create_mpc_parameter, create_mpc_var from .initializer import XavierInitializer class MpcLayerHelper(LayerHelper): """ Refer to paddle.fluid.LayerHelper. Add new methods for MpcVariable and MpcOps. """ def create_global_mpc_variable(self, persistable=False, *args, **kwargs): """ Refer to paddle.fluid.layer_helper_base.create_global_variable(). Create global mpc variable. :param persistable: :param args: :param kwargs: :return: """ mpc_block = self.main_program.global_block() mpc_var = MpcVariable( block=mpc_block, *args, persistable=persistable, **kwargs) if 'initializer' in kwargs: kwargs['initializer'](mpc_var, self) return mpc_var # TODO(Paddle1.7): hide the func after we move the layers to Layers def create_mpc_parameter(self, attr, shape, dtype, is_bias=False, default_initializer=None, stop_gradient=False, type=core.VarDesc.VarType.LOD_TENSOR): """ Create mpc parameters for this layers. Refer to LayerHelper.create_parameter in Paddle 1.7. :param attr: :param shape: :param dtype: :param is_bias: :param default_initializer: :param stop_gradient: :param type: :return: """ # Deepcopy the attr so that parameters can be shared in program attr = copy.deepcopy(attr) attr = ParamAttr._to_attr(attr) if not attr: return None assert isinstance(attr, ParamAttr) suffix = 'b' if is_bias else 'w' if attr.name is None: attr.name = unique_name.generate(".".join([self.name, suffix])) if default_initializer is None and attr.initializer is None: if isinstance(dtype, core.VarDesc.VarType): if dtype != core.VarDesc.VarType.INT64: raise TypeError( "Can not create mpc parameter with default initializer " "when dtype is not int64 type. Set default_initializer " "to fit the parameter dtype!") else: if not dtype == "int64": raise TypeError( "Can not create mpc parameter with default initializer when " "dtype is not int64 type. Set default_initializer to " "fit the parameter dtype!") if is_bias: attr._set_default_bias_initializer() else: attr._set_default_initializer(XavierInitializer(seed=65536)) else: attr._set_default_initializer(default_initializer) # TODO(xukun07): not support WeightNormParamAttr in this first version # Paddle1.7: If weight normalization is set, insert extra parameters and ops. # Refer to https://arxiv.org/pdf/1602.07868.pdf if isinstance(attr, WeightNormParamAttr): # param = self._create_weight_normalize(attr, shape, dtype) # WeightNormParamAttr.params_with_weight_norm.append(param) # return param raise NotImplementedError( "The WeightNormParamAttr for attr is not " "supported in this version") startup_program_global_block = self.startup_program.global_block() create_mpc_parameter( block=startup_program_global_block, dtype=dtype, shape=shape, type=type, **attr._to_kwargs(with_initializer=True)) main_program_global_block = self.main_program.global_block() return create_mpc_parameter( block=main_program_global_block, dtype=dtype, shape=shape, type=type, **attr._to_kwargs()) # Note : not sure if this rewrite is needed def get_mpc_parameter(self, name): """ Refer to LayerHelper.get_parameter in Paddle. :param name: :return: """ param = self.main_program.global_block().var(name) if not isinstance(param, MpcParameter): raise ValueError("no MpcParameter name %s found" % name) return param def create_mpc_variable_for_type_inference(self, dtype, stop_gradient=False): """ Create a temporary mpc variable that should be type inferred layer. Refer to LayerHelperBase.create_variable_for_type_inference in Paddle 1.7. :param dtype: :param stop_gradient: :return: """ main_program_current_block = self.main_program.current_block() return create_mpc_var( block=main_program_current_block, name=unique_name.generate_with_ignorable_key(".".join( [self.name, 'tmp'])), dtype=dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=stop_gradient) def append_mpc_bias_op(self, input_var, dim_start=1, dim_end=None): """ Append bias operator and return its mpc output. If the user does not set bias_attr, append_bias_op will return mpc input_var Refer to LayerHelper.append_bias_op in Paddle 1.7. Return mpc var. :param input_var: :param dim_start: :param dim_end: :return: """ size = list(input_var.shape[ dim_start + 1:dim_end]) # dims[0]: share_num; dims[1]: batch_size bias_attr = self.bias_attr if not bias_attr: return input_var b = self.create_mpc_parameter( attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True) tmp = self.create_mpc_variable_for_type_inference( dtype=input_var.dtype) # Note: the type of mpc op = "mpc_" + paddle op self.append_op( type='mpc_elementwise_add', inputs={'X': [input_var], 'Y': [b]}, outputs={'Out': [tmp]}, attrs={'axis': dim_start}) return tmp def append_mpc_activation(self, input_var): """ Append mpc activation for this layer. Refer to LayerHelper.append_activation in Paddle 1.7. Return mpc ver. :param input_var: :return: """ act = self.kwargs.get('act', None) if act is None: return input_var if isinstance(act, six.string_types): act = {'type': act} else: raise TypeError(str(act) + " should be unicode or str") if 'use_cudnn' in self.kwargs and self.kwargs.get('use_cudnn'): act['use_cudnn'] = self.kwargs.get('use_cudnn') if 'use_mkldnn' in self.kwargs: act['use_mkldnn'] = self.kwargs.get('use_mkldnn') act_type = act.pop('type') tmp = self.create_mpc_variable_for_type_inference( dtype=input_var.dtype) derivative = self.create_mpc_variable_for_type_inference( dtype=input_var.dtype) # add "mpc_" as prefix of mpc activation self.append_op( type="mpc_" + act_type, inputs={"X": [input_var]}, <<<<<<< HEAD outputs={"Out": [tmp], ======= outputs={"Y": [tmp], >>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259 "Derivative": [derivative]}, attrs=act) return tmp def create_mpc_variable(self, *args, **kwargs): """ Create MpcVariable for this layers. Refer to LayerHelperBase.create_variable in Paddle 1.7. Return created MpcVariable. :param args: :param kwargs: :return: """ main_program_current_block = self.main_program.current_block() return create_mpc_var( block=main_program_current_block, *args, **kwargs)