Spaces:
Sleeping
Sleeping
File size: 17,562 Bytes
6fc683c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 |
# 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
|