# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # 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 functools import layers import framework from . import core __all__ = [ 'GradientClipByValue', 'ErrorClipByValue', 'append_gradient_clip_ops', 'error_clip_callback', ] class BaseErrorClipAttr(object): def append_clip_op(self, block, grad_name): raise NotImplementedError() class ErrorClipByValue(BaseErrorClipAttr): def __init__(self, max, min=None): max = float(max) if min is None: min = -max else: min = float(min) self.max = max self.min = min def append_clip_op(self, block, grad_name): clip_op_desc = block.desc.append_op() clip_op_desc.set_type("clip") clip_op_desc.set_input("X", [grad_name]) clip_op_desc.set_output("Out", [grad_name]) clip_op_desc.set_attr("min", self.min) clip_op_desc.set_attr("max", self.max) def error_clip_callback(block, context): # the context is a grad_to_var map grad_to_var = context op_desc = block.desc.op(block.desc.op_size() - 1) for grad_n in filter(lambda n: grad_to_var.has_key(n), op_desc.output_arg_names()): fwd_var = block.var_recursive(grad_to_var[grad_n]) error_clip = getattr(fwd_var, "error_clip", None) if not (error_clip is None or isinstance(error_clip, BaseErrorClipAttr)): raise TypeError( "Variable's error_clip should be an instance of BaseErrorClipAttr or None." ) if error_clip is not None: error_clip.append_clip_op(block, grad_n) class BaseGradientClipAttr(object): def process_context(self, context, param, grad): raise NotImplementedError() def create_operators(self, param, grad): raise NotImplementedError() class NullGradientClipAttr(BaseGradientClipAttr): def process_context(self, context, param, grad): pass def create_operators(self, param, grad): return param, grad class GradientClipByValue(BaseGradientClipAttr): def __init__(self, max, min=None): max = float(max) if min is None: min = -max else: min = float(min) self.max = max self.min = min def process_context(self, context, param, grad): pass def create_operators(self, param, grad): new_grad = layers.clip(x=grad, min=self.min, max=self.max) return param, new_grad class GradientClipByNorm(BaseGradientClipAttr): def __init__(self, clip_norm): self.clip_norm = clip_norm def process_context(self, context, param, grad): pass def create_operators(self, param, grad): new_grad = layers.clip_by_norm(x=grad, max_norm=self.clip_norm) return param, new_grad class GradientClipByGlobalNorm(BaseGradientClipAttr): global_norm_var = None local_norm_var = None clip_norm_var = None scale_var = None @classmethod def init(cls, clip_norm): if not (isinstance(clip_norm, int) or isinstance(clip_norm, float)): raise TypeError("The 'clip_norm' must be a value of int or float") cls.global_norm_var = layers.fill_constant( shape=[1], dtype="float32", value=0.0) cls.local_norm_var = framework.default_main_program().current_block( ).create_var( name=framework.unique_name("local_norm"), dtype="float32", persistable=False) cls.clip_norm_var = layers.fill_constant( shape=[1], dtype="float32", value=clip_norm) @classmethod def check_init(cls): if not (isinstance(cls.global_norm_var, framework.Variable) and isinstance(cls.local_norm_var, framework.Variable) and isinstance(cls.clip_norm_var, framework.Variable)): raise ValueError( "Class 'GradientClipByGlobalNorm' has not been properly initialized. \ Please call GradientClipByGlobalNorm.init() first.") def process_context(self, context, param, grad): cls = self.__class__ cls.check_init() cls.local_norm_var = layers.reduce_sum( input=layers.pow(x=grad, factor=2.0)) layers.sums( input=[cls.local_norm_var, cls.global_norm_var], out=[cls.global_norm_var]) def create_operators(self, param, grad): cls = self.__class__ cls.check_init() if cls.scale_var is None: layers.sqrt(x=cls.global_norm_var, out=cls.global_norm_var) cls.scale_var = layers.elementwise_div( x=cls.clip_norm_var, y=layers.elementwise_max( x=cls.clip_norm_var, y=cls.global_norm_var)) assert cls.scale_var.shape == (1L, ) new_grad = layers.elementwise_mul(x=grad, y=cls.scale_var) return param, new_grad def gradient_clip_by_global_norm(clip_norm, param_list=None, program=None): if program is None: program = framework.default_main_program() if param_list is None: param_list = program.block(0).all_parameters() if all(isinstance(elem, basestring) for elem in param_list): param_list = [program.block(0).var(elem) for elem in param_list] if not all(isinstance(elem, framework.Parameter) for elem in param_list): raise TypeError( "'param_list' should be a list of Parameter or basestring(parameter's name)." ) GradientClipByGlobalNorm.init(clip_norm) for param in param_list: param.gradient_clip_attr = GradientClipByGlobalNorm() def append_gradient_clip_ops(param_grad): context = dict() create_op_callbacks = [] for p, g in param_grad: clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr()) if clip_attr is None: clip_attr = NullGradientClipAttr() if not isinstance(clip_attr, BaseGradientClipAttr): raise TypeError( "clip attribute should be an instance of BaseGradientClipAttr") clip_attr.process_context(context=context, param=p, grad=g) create_op_callbacks.append( functools.partial( clip_attr.create_operators, param=p, grad=g)) return [each_callback() for each_callback in create_op_callbacks] ClipByValue = GradientClipByValue