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| # coding=utf-8 | |
| # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. | |
| # | |
| # 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. | |
| """PyTorch optimization for BERT model.""" | |
| import math | |
| import torch | |
| from torch.optim import Optimizer | |
| from torch.optim.optimizer import required | |
| from torch.nn.utils import clip_grad_norm_ | |
| from collections import defaultdict | |
| from torch._six import container_abcs | |
| from copy import deepcopy | |
| from itertools import chain | |
| def warmup_cosine(x, warmup=0.002): | |
| if x < warmup: | |
| return x/warmup | |
| return 0.5 * (1.0 + torch.cos(math.pi * x)) | |
| def warmup_constant(x, warmup=0.002): | |
| if x < warmup: | |
| return x/warmup | |
| return 1.0 | |
| def warmup_linear(x, warmup=0.002): | |
| if x < warmup: | |
| return x/warmup | |
| return max((x-1.)/(warmup-1.), 0) | |
| SCHEDULES = { | |
| 'warmup_cosine': warmup_cosine, | |
| 'warmup_constant': warmup_constant, | |
| 'warmup_linear': warmup_linear, | |
| } | |
| class BertAdam(Optimizer): | |
| """Implements BERT version of Adam algorithm with weight decay fix. | |
| Params: | |
| lr: learning rate | |
| warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1 | |
| t_total: total number of training steps for the learning | |
| rate schedule, -1 means constant learning rate. Default: -1 | |
| schedule: schedule to use for the warmup (see above). Default: 'warmup_linear' | |
| b1: Adams b1. Default: 0.9 | |
| b2: Adams b2. Default: 0.999 | |
| e: Adams epsilon. Default: 1e-6 | |
| weight_decay: Weight decay. Default: 0.01 | |
| max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 | |
| """ | |
| def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0): | |
| if lr is not required and lr < 0.0: | |
| raise ValueError( | |
| "Invalid learning rate: {} - should be >= 0.0".format(lr)) | |
| if schedule not in SCHEDULES: | |
| raise ValueError("Invalid schedule parameter: {}".format(schedule)) | |
| if not 0.0 <= warmup < 1.0 and not warmup == -1: | |
| raise ValueError( | |
| "Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup)) | |
| if not 0.0 <= b1 < 1.0: | |
| raise ValueError( | |
| "Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1)) | |
| if not 0.0 <= b2 < 1.0: | |
| raise ValueError( | |
| "Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2)) | |
| if not e >= 0.0: | |
| raise ValueError( | |
| "Invalid epsilon value: {} - should be >= 0.0".format(e)) | |
| defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total, | |
| b1=b1, b2=b2, e=e, weight_decay=weight_decay, | |
| max_grad_norm=max_grad_norm) | |
| super(BertAdam, self).__init__(params, defaults) | |
| def get_lr(self): | |
| lr = [] | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| state = self.state[p] | |
| if len(state) == 0: | |
| return [0] | |
| if group['t_total'] != -1: | |
| schedule_fct = SCHEDULES[group['schedule']] | |
| lr_scheduled = group['lr'] * schedule_fct( | |
| state['step']/group['t_total'], group['warmup']) | |
| else: | |
| lr_scheduled = group['lr'] | |
| lr.append(lr_scheduled) | |
| return lr | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model | |
| and returns the loss. | |
| """ | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError( | |
| 'Adam does not support sparse gradients, please consider SparseAdam instead') | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| # Exponential moving average of gradient values | |
| state['next_m'] = torch.zeros_like(p.data) | |
| # Exponential moving average of squared gradient values | |
| state['next_v'] = torch.zeros_like(p.data) | |
| next_m, next_v = state['next_m'], state['next_v'] | |
| beta1, beta2 = group['b1'], group['b2'] | |
| # Add grad clipping | |
| if group['max_grad_norm'] > 0: | |
| clip_grad_norm_(p, group['max_grad_norm']) | |
| # Decay the first and second moment running average coefficient | |
| # In-place operations to update the averages at the same time | |
| next_m.mul_(beta1).add_(1 - beta1, grad) | |
| next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad) | |
| update = next_m / (next_v.sqrt() + group['e']) | |
| # Just adding the square of the weights to the loss function is *not* | |
| # the correct way of using L2 regularization/weight decay with Adam, | |
| # since that will interact with the m and v parameters in strange ways. | |
| # | |
| # Instead we want to decay the weights in a manner that doesn't interact | |
| # with the m/v parameters. This is equivalent to adding the square | |
| # of the weights to the loss with plain (non-momentum) SGD. | |
| if group['weight_decay'] > 0.0: | |
| update += group['weight_decay'] * p.data | |
| if group['t_total'] != -1: | |
| schedule_fct = SCHEDULES[group['schedule']] | |
| lr_scheduled = group['lr'] * schedule_fct( | |
| state['step']/group['t_total'], group['warmup']) | |
| else: | |
| lr_scheduled = group['lr'] | |
| update_with_lr = lr_scheduled * update | |
| p.data.add_(-update_with_lr) | |
| state['step'] += 1 | |
| # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1 | |
| # No bias correction | |
| # bias_correction1 = 1 - beta1 ** state['step'] | |
| # bias_correction2 = 1 - beta2 ** state['step'] | |
| return loss | |
| class BertAdamFineTune(BertAdam): | |
| def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear', b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0): | |
| self.init_param_group = [] | |
| super(BertAdamFineTune, self).__init__(params, lr, warmup, | |
| t_total, schedule, b1, b2, e, weight_decay, max_grad_norm) | |
| def save_init_param_group(self, param_groups, name_groups, missing_keys): | |
| self.init_param_group = [] | |
| for group, name in zip(param_groups, name_groups): | |
| if group['weight_decay'] > 0.0: | |
| init_p_list = [] | |
| for p, n in zip(group['params'], name): | |
| init_p = p.data.clone().detach() | |
| if any(mk in n for mk in missing_keys): | |
| print("[no finetuning weight decay]", n) | |
| # should use the original weight decay | |
| init_p.zero_() | |
| init_p_list.append(init_p) | |
| self.init_param_group.append(init_p_list) | |
| else: | |
| # placeholder | |
| self.init_param_group.append([]) | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model | |
| and returns the loss. | |
| """ | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for i_group, group in enumerate(self.param_groups): | |
| for i_p, p in enumerate(group['params']): | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError( | |
| 'Adam does not support sparse gradients, please consider SparseAdam instead') | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| # Exponential moving average of gradient values | |
| state['next_m'] = torch.zeros_like(p.data) | |
| # Exponential moving average of squared gradient values | |
| state['next_v'] = torch.zeros_like(p.data) | |
| next_m, next_v = state['next_m'], state['next_v'] | |
| beta1, beta2 = group['b1'], group['b2'] | |
| # Add grad clipping | |
| if group['max_grad_norm'] > 0: | |
| clip_grad_norm_(p, group['max_grad_norm']) | |
| # Decay the first and second moment running average coefficient | |
| # In-place operations to update the averages at the same time | |
| next_m.mul_(beta1).add_(1 - beta1, grad) | |
| next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad) | |
| update = next_m / (next_v.sqrt() + group['e']) | |
| # Just adding the square of the weights to the loss function is *not* | |
| # the correct way of using L2 regularization/weight decay with Adam, | |
| # since that will interact with the m and v parameters in strange ways. | |
| # | |
| # Instead we want to decay the weights in a manner that doesn't interact | |
| # with the m/v parameters. This is equivalent to adding the square | |
| # of the weights to the loss with plain (non-momentum) SGD. | |
| if group['weight_decay'] > 0.0: | |
| if self.init_param_group: | |
| update += group['weight_decay'] * \ | |
| (2.0 * p.data - | |
| self.init_param_group[i_group][i_p]) | |
| else: | |
| update += group['weight_decay'] * p.data | |
| if group['t_total'] != -1: | |
| schedule_fct = SCHEDULES[group['schedule']] | |
| lr_scheduled = group['lr'] * schedule_fct( | |
| state['step']/group['t_total'], group['warmup']) | |
| else: | |
| lr_scheduled = group['lr'] | |
| update_with_lr = lr_scheduled * update | |
| p.data.add_(-update_with_lr) | |
| state['step'] += 1 | |
| # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1 | |
| # No bias correction | |
| # bias_correction1 = 1 - beta1 ** state['step'] | |
| # bias_correction2 = 1 - beta2 ** state['step'] | |
| return loss | |
| def load_state_dict_subset_finetune(self, state_dict, num_load_group): | |
| r"""Loads the optimizer state. | |
| Arguments: | |
| state_dict (dict): optimizer state. Should be an object returned | |
| from a call to :meth:`state_dict`. | |
| """ | |
| # deepcopy, to be consistent with module API | |
| state_dict = deepcopy(state_dict) | |
| # Validate the state_dict | |
| groups = self.param_groups | |
| saved_groups = state_dict['param_groups'] | |
| if len(groups) < num_load_group or len(saved_groups) < num_load_group: | |
| raise ValueError("loaded state dict has a different number of " | |
| "parameter groups") | |
| param_lens = (len(g['params']) for g in groups[:num_load_group]) | |
| saved_lens = (len(g['params']) for g in saved_groups[:num_load_group]) | |
| if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)): | |
| raise ValueError("loaded state dict contains a parameter group " | |
| "that doesn't match the size of optimizer's group") | |
| # Update the state | |
| id_map = {old_id: p for old_id, p in | |
| zip(chain(*(g['params'] for g in saved_groups[:num_load_group])), | |
| chain(*(g['params'] for g in groups[:num_load_group])))} | |
| def cast(param, value): | |
| r"""Make a deep copy of value, casting all tensors to device of param.""" | |
| if isinstance(value, torch.Tensor): | |
| # Floating-point types are a bit special here. They are the only ones | |
| # that are assumed to always match the type of params. | |
| if param.is_floating_point(): | |
| value = value.to(param.dtype) | |
| value = value.to(param.device) | |
| return value | |
| elif isinstance(value, dict): | |
| return {k: cast(param, v) for k, v in value.items()} | |
| elif isinstance(value, container_abcs.Iterable): | |
| return type(value)(cast(param, v) for v in value) | |
| else: | |
| return value | |
| # Copy state assigned to params (and cast tensors to appropriate types). | |
| # State that is not assigned to params is copied as is (needed for | |
| # backward compatibility). | |
| state = defaultdict(dict) | |
| for k, v in state_dict['state'].items(): | |
| if k in id_map: | |
| param = id_map[k] | |
| state[param] = cast(param, v) | |
| else: | |
| state[k] = v | |
| # handle additional params | |
| for k, v in self.state: | |
| if k not in state: | |
| state[k] = v | |
| # do not change groups: {'weight_decay': 0.01, 'lr': 9.995e-06, 'schedule': 'warmup_linear', 'warmup': 0.1, 't_total': 400000, 'b1': 0.9, 'b2': 0.999, 'e': 1e-06, 'max_grad_norm': 1.0, 'params': [...]} | |
| # # Update parameter groups, setting their 'params' value | |
| # def update_group(group, new_group): | |
| # new_group['params'] = group['params'] | |
| # return new_group | |
| # param_groups = [ | |
| # update_group(g, ng) for g, ng in zip(groups[:num_load_group], saved_groups[:num_load_group])] | |
| # # handle additional params | |
| # param_groups.extend(groups[num_load_group:]) | |
| self.__setstate__({'state': state, 'param_groups': groups}) | |
| def find_state_dict_subset_finetune(org_state_dict, org_name_list, no_decay, param_optimizer): | |
| # only use the bert encoder and embeddings | |
| want_name_set = set() | |
| for n in org_name_list: | |
| if ('bert.encoder' in n) or ('bert.embeddings' in n): | |
| want_name_set.add(n) | |
| # original: name to pid, pid to name | |
| org_grouped_names = [[n for n in org_name_list if not any(nd in n for nd in no_decay)], | |
| [n for n in org_name_list if any(nd in n for nd in no_decay)]] | |
| org_n2id, org_id2n = {}, {} | |
| for ng, pg in zip(org_grouped_names, org_state_dict['param_groups']): | |
| for n, pid in zip(ng, pg['params']): | |
| org_n2id[n] = pid | |
| org_id2n[pid] = n | |
| # group by: whether pretrained; whether weight decay | |
| g_np_list = [ | |
| [(n, p) for n, p in param_optimizer if n in want_name_set and not any( | |
| nd in n for nd in no_decay)], | |
| [(n, p) for n, p in param_optimizer if n in want_name_set and any( | |
| nd in n for nd in no_decay)], | |
| [(n, p) for n, p in param_optimizer if n not in want_name_set and not any( | |
| nd in n for nd in no_decay)], | |
| [(n, p) for n, p in param_optimizer if n not in want_name_set and any( | |
| nd in n for nd in no_decay)], | |
| ] | |
| optimizer_grouped_parameters = [ | |
| {'params': [p for n, p in g_np_list[0]], 'weight_decay': 0.01}, | |
| {'params': [p for n, p in g_np_list[1]], 'weight_decay': 0.0}, | |
| {'params': [p for n, p in g_np_list[2]], 'weight_decay': 0.01}, | |
| {'params': [p for n, p in g_np_list[3]], 'weight_decay': 0.0} | |
| ] | |
| new_state_dict = {} | |
| # regroup the original state_dict | |
| new_state_dict['state'] = {pid: v for pid, v in org_state_dict['state'].items( | |
| ) if pid not in org_id2n or org_id2n[pid] in want_name_set} | |
| # reset step count to 0 | |
| for pid, st in new_state_dict['state'].items(): | |
| st['step'] = 0 | |
| def _filter_group(group, g_np_list, i, org_n2id): | |
| packed = {k: v for k, v in group.items() if k != 'params'} | |
| packed['params'] = [pid for pid in group['params'] | |
| if pid in org_id2n and org_id2n[pid] in want_name_set] | |
| assert len(g_np_list[i]) == len(packed['params']) | |
| # keep them the same order | |
| packed['params'] = [org_n2id[n] for n, p in g_np_list[i]] | |
| return packed | |
| new_state_dict['param_groups'] = [_filter_group( | |
| g, g_np_list, i, org_n2id) for i, g in enumerate(org_state_dict['param_groups'])] | |
| return new_state_dict, optimizer_grouped_parameters | |