# 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 import logging from functools import reduce from .meta_optimizer_base import MetaOptimizerBase __all__ = [] import paddle from paddle import framework from paddle.common_ops_import import LayerHelper from paddle.fluid.clip import GradientClipByNorm, append_gradient_clip_ops from paddle.fluid.dygraph import base as imperative_base from paddle.fluid.layers import tensor from paddle.fluid.optimizer import Momentum, Optimizer from paddle.framework import core class DGCMomentumOptimizer(Optimizer): _u_velocity_acc_str = "_dgc_u_" _v_velocity_acc_str = "_dgc_v_" def __init__( self, learning_rate, momentum, rampup_begin_step, rampup_step=1, sparsity=[0.999], parameter_list=None, use_nesterov=False, num_trainers=None, regularization=None, grad_clip=None, name=None, ): if framework._non_static_mode(): raise Exception("In dygraph, don't support DGCMomentumOptimizer.") assert ( core.is_compiled_with_cuda() ), "Paddle is not compiled with CUDA. DGC is only support GPU for now." assert learning_rate is not None assert momentum is not None super().__init__( learning_rate=learning_rate, parameter_list=parameter_list, regularization=regularization, grad_clip=grad_clip, name=name, ) self.type = "dgc_momentum" self._momentum = momentum self._use_nesterov = bool(use_nesterov) assert rampup_begin_step >= 0, "rampup_begin_step must >= 0" self._rampup_begin_step = rampup_begin_step self._rampup_step = rampup_step self._sparsity = sparsity self._rampup_begin_step_var = None self._global_step_var = None self._dgc_clip_norm = None if grad_clip is not None: if not isinstance(grad_clip, GradientClipByNorm): raise TypeError( "The type of grad_clip should be 'GradientClipByNorm', because DGCMomentumOptimizer only support GradientClipByNorm" ) assert isinstance(num_trainers, int), ( "The type of num_trainers should be 'int', but received %s" % type(num_trainers) ) assert ( num_trainers > 0 ), "The value of num_trainers should be greater than 0!" self._num_trainers = num_trainers self._dgc_clip_norm = grad_clip.clip_norm * (num_trainers**-0.5) self.regular_type, self.regular_coeff = self._get_regularization_param( self.regularization ) def _get_regularization_param(self, regularization): regular_type = 0 regular_coeff = 0.0 if regularization is not None: regular_coeff = regularization._regularization_coeff from paddle.fluid.regularizer import L1Decay, L2Decay if isinstance(regularization, L1Decay): regular_type = 1 elif isinstance(regularization, L2Decay): regular_type = 2 else: assert False, 'regularization must be None|L1Decay|L2Deacy' return regular_type, regular_coeff def _is_use_dgc(self, param_var, grad_var): var_numel = abs(reduce(lambda x, y: x * y, param_var.shape)) if ( var_numel < 16384 or param_var.type == core.VarDesc.VarType.SELECTED_ROWS or grad_var.type == core.VarDesc.VarType.SELECTED_ROWS or param_var.dtype != core.VarDesc.VarType.FP32 ): return False return True def _append_optimize_op(self, block, param_and_grad): assert isinstance(block, paddle.fluid.framework.Block) velocity_acc = self._get_accumulator( self._u_velocity_acc_str, param_and_grad[0] ) assert velocity_acc is not None inputs = { "Param": param_and_grad[0], "Grad": param_and_grad[1], "Velocity": velocity_acc, "LearningRate": self._create_param_lr(param_and_grad), } outputs = { "ParamOut": param_and_grad[0], "VelocityOut": velocity_acc, } attrs = {"mu": self._momentum, "use_nesterov": self._use_nesterov} if not self._is_use_dgc(param_and_grad[0], param_and_grad[1]): type = "momentum" else: type = "dgc_momentum" inputs.update( { "current_step": self._global_step_var, "nranks": self._nranks_var, } ) outputs.update({'Grad_out': param_and_grad[1]}) attrs.update({"rampup_begin_step": float(self._rampup_begin_step)}) # create the dgc momentum optimize op dgc_momentum_op = block.append_op( type=type, inputs=inputs, outputs=outputs, attrs=attrs, stop_gradient=True, ) return dgc_momentum_op def _add_auto_increment_var(self, counter_name, begin, step=1): helper = LayerHelper('global_step_counter') counter, is_new_var = helper.create_or_get_global_variable( name=counter_name, dtype='float32', shape=[1], persistable=True ) if is_new_var: helper.set_variable_initializer( counter, initializer=paddle.fluid.initializer.Constant( value=float(begin - 1), force_cpu=True ), ) helper.main_program.global_block()._prepend_op( type='increment', inputs={'X': [counter]}, outputs={'Out': [counter]}, attrs={'step': float(step)}, stop_gradient=True, ) counter.stop_gradient = True return counter def _add_nranks_var(self, name, value=-1): helper = LayerHelper('global_step_counter') counter, is_new_var = helper.create_or_get_global_variable( name=name, dtype='float32', shape=[1], persistable=True ) if is_new_var: helper.set_variable_initializer( counter, initializer=paddle.fluid.initializer.Constant( value=float(value), force_cpu=True ), ) counter.stop_gradient = True return counter def _append_dgc_ops(self, param_and_grads): main_program = paddle.static.default_main_program() main_program._enable_dgc = True # step counter self._global_step_var = self._add_auto_increment_var( counter_name=core.dgc.kDGCCounterName(), begin=0 ) self._nranks_var = self._add_nranks_var( name=core.dgc.kDGCNRanksName(), value=-1 ) # rampup begin step var for all_reduce_op_handle self._rampup_begin_step_var = tensor.create_global_var( shape=[1], dtype=core.VarDesc.VarType.FP32, persistable=True, name=core.dgc.kDGCRampUpBeginStepName(), value=self._rampup_begin_step * 1.0, force_cpu=True, ) self.helper = LayerHelper(self.__class__.__name__) for param_var, grad_var in param_and_grads: # reuse velocity in dgc_op and dgc_momentum_op u_var = self._add_accumulator(self._u_velocity_acc_str, param_var) if not self._is_use_dgc(param_var, grad_var): continue v_var = self._add_accumulator(self._v_velocity_acc_str, param_var) k_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCKName(), value=0.0, force_cpu=True, ) encoded_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCEncodedName(), value=0.0, force_cpu=False, ) gather_var = tensor.create_global_var( shape=[1], dtype=param_var.dtype, persistable=True, name=param_var.name + core.dgc.kDGCGatherName(), value=0.0, force_cpu=False, ) # del back oprolevarname op_maker = core.op_proto_and_checker_maker backward = core.op_proto_and_checker_maker.OpRole.Backward for op in main_program.global_block().ops: if not self._is_the_backward_op(op): continue var_attr = op.all_attrs()[op_maker.kOpRoleVarAttrName()] if param_var.name not in var_attr: continue var_attr.remove(param_var.name) var_attr.remove(grad_var.name) if len(var_attr) > 1: op._set_attr(op_maker.kOpRoleVarAttrName(), var_attr) else: op._remove_attr(op_maker.kOpRoleVarAttrName()) clip_var = grad_var if self._dgc_clip_norm is not None: clip_var = self._append_clip_norm(grad_var, self._dgc_clip_norm) self._dgc_op( param_var, clip_var, grad_var, u_var, v_var, k_var, encoded_var, gather_var, ) def _is_the_backward_op(self, op): op_maker = core.op_proto_and_checker_maker backward = core.op_proto_and_checker_maker.OpRole.Backward if op_maker.kOpRoleVarAttrName() in op.attr_names and int( op.all_attrs()[op_maker.kOpRoleAttrName()] ) == int(backward): return True return False def _clip_by_norm(self, x, max_norm, name=None): args = {'x': x, 'max_norm': max_norm, 'name': name} helper = LayerHelper("dgc_clip_by_norm_op", **args) if name is None: name = paddle.fluid.unique_name.generate_with_ignorable_key( ".".join([helper.name, 'tmp']) ) out = helper.create_variable( type=x.type, name=name, dtype=x.dtype, persistable=False ) helper.append_op( type="dgc_clip_by_norm", inputs={"X": x, "current_step": self._global_step_var}, attrs={ "max_norm": max_norm, "rampup_begin_step": float(self._rampup_begin_step), }, outputs={"Out": out}, ) return out def _append_clip_norm(self, grad_var, clip_norm): with grad_var.block.program._backward_role_guard(): return self._clip_by_norm( x=grad_var, max_norm=clip_norm, name=grad_var.name ) def _dgc_op( self, param_var, clip_var, grad_var, u_var, v_var, k_var, encoded_var, gather_var, ): block = paddle.static.default_main_program().global_block() op_maker = core.op_proto_and_checker_maker regular_type = self.regular_type regular_coeff = self.regular_coeff # The regularizer of the Parameters have higher priority if param_var.regularizer is not None: regular_type, regular_coeff = self._get_regularization_param( param_var.regularizer ) dgc_op = block.append_op( type="dgc", inputs={ "U": u_var, "V": v_var, "Grad": clip_var, "Param": param_var, "current_step": self._global_step_var, "nranks": self._nranks_var, }, outputs={ "U_out": u_var, "V_out": v_var, "EncodeGrad": encoded_var, "k": k_var, "Grad_out": grad_var, "GatherBuff": gather_var, }, attrs={ "m": self._momentum, "sparsity": self._sparsity, "use_nesterov": self._use_nesterov, "rampup_begin_step": float(self._rampup_begin_step), "rampup_step": float(self._rampup_step), "regular_coeff": float(regular_coeff), "regular_type": int(regular_type), }, stop_gradient=True, ) backward = op_maker.OpRole.Backward dgc_op._set_attr(op_maker.kOpRoleAttrName(), backward) dgc_op._set_attr( op_maker.kOpRoleVarAttrName(), [param_var.name, grad_var.name] ) @imperative_base.no_grad() def apply_gradients(self, params_grads): # Note: since we can't use all_reduce_op now, # dgc_op should be the last op of one grad. # Maybe need a grad allreduce pass. self._append_dgc_ops(params_grads) params_grads = sorted(params_grads, key=lambda x: x[0].name) ( params_grads, table_param_and_grad, table_optimize_op, ) = self._process_distribute_lookuptable(params_grads) not_dgc_params_grads = [] dgc_params_grads = [] # DGC clip and regularization in optimizer.backward for param, grad in params_grads: if not self._is_use_dgc(param, grad): not_dgc_params_grads.append((param, grad)) else: dgc_params_grads.append((param, grad)) # 'optimizer(grad_clip)' or 'set_gradient_clip' if self._grad_clip is not None: not_dgc_params_grads = self._grad_clip(not_dgc_params_grads) else: not_dgc_params_grads = append_gradient_clip_ops( not_dgc_params_grads ) not_dgc_params_grads = self.append_regularization_ops( not_dgc_params_grads, self.regularization ) params_grads = not_dgc_params_grads + dgc_params_grads params_grads = sorted(params_grads, key=lambda x: x[0].name) optimize_ops = self._create_optimization_pass(params_grads) if table_optimize_op is not None: optimize_ops.append(table_optimize_op) params_grads.append(table_param_and_grad) return optimize_ops class DGCOptimizer(MetaOptimizerBase): def __init__(self, optimizer): super().__init__(optimizer) self.inner_opt = optimizer self.dgc_opt = None # we do not allow meta optimizer to be inner optimizer currently self.meta_optimizers_white_list = [] self.meta_optimizers_black_list = [] def _set_basic_info( self, loss, role_maker, user_defined_optimizer, user_defined_strategy ): super()._set_basic_info( loss, role_maker, user_defined_optimizer, user_defined_strategy ) def _init_dgc_opt(self): if self.dgc_opt is not None: return opt = self.inner_opt if not self.role_maker._is_collective: return if not isinstance(opt, Momentum): return configs = self.user_defined_strategy.dgc_configs if len(configs['sparsity']) == 0: # default is [0.999] configs['sparsity'] = [0.999] self.dgc_opt = DGCMomentumOptimizer( learning_rate=opt._learning_rate, momentum=opt._momentum, rampup_begin_step=configs['rampup_begin_step'], rampup_step=configs['rampup_step'], sparsity=configs['sparsity'], parameter_list=opt._parameter_list, use_nesterov=opt._use_nesterov, num_trainers=self.role_maker._worker_num(), regularization=opt.regularization, grad_clip=opt._grad_clip, name=opt._name, ) def _can_apply(self): if not self.role_maker._is_collective: return False if self.user_defined_strategy.dgc: if not isinstance(self.inner_opt, Momentum): logging.warn("dgc only works on Momentum optimizer") return False if self.role_maker._worker_num() <= 1: logging.warn("dgc only works on multi cards") return False return True return False def _disable_strategy(self, dist_strategy): dist_strategy.dgc = False dist_strategy.dgc_configs = {} def _enable_strategy(self, dist_strategy, context): dist_strategy.dgc = True dist_strategy.dgc_configs = {"rampup_begin_step": 0, "rampup_step": 1} def backward( self, loss, startup_program=None, parameter_list=None, no_grad_set=None, callbacks=None, ): self._init_dgc_opt() return self.dgc_opt.backward( loss, startup_program, parameter_list, no_grad_set, callbacks ) def apply_gradients(self, params_grads): self._init_dgc_opt() return self.dgc_opt.apply_gradients(params_grads=params_grads) def apply_optimize(self, loss, startup_program, params_grads): self._init_dgc_opt() return self.dgc_opt.apply_optimize( loss, startup_program=startup_program, params_grads=params_grads ) def minimize_impl( self, loss, startup_program=None, parameter_list=None, no_grad_set=None ): self._init_dgc_opt() optimize_ops, params_grads = self.dgc_opt.minimize( loss, startup_program, parameter_list, no_grad_set ) return optimize_ops, params_grads