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rewamp optimization

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This commit is contained in:
thomwolf
2019-07-11 14:48:22 +02:00
parent 4fef5919a5
commit ec07cf5a66
7 changed files with 138 additions and 389 deletions
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75
examples/run_glue.py
View File

@@ -25,19 +25,21 @@ import random
import numpy as np
import torch
from tensorboardX import SummaryWriter
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from pytorch_transformers import WEIGHTS_NAME
from pytorch_transformers import (BertConfig, BertForSequenceClassification,
BertTokenizer, XLMConfig,
XLMForSequenceClassification, XLMTokenizer,
XLNetConfig, XLNetForSequenceClassification,
from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
BertForSequenceClassification, BertTokenizer,
XLMConfig, XLMForSequenceClassification,
XLMTokenizer, XLNetConfig,
XLNetForSequenceClassification,
XLNetTokenizer)
from pytorch_transformers.optimization import BertAdam
from pytorch_transformers import AdamW, WarmupLinearSchedule
from utils_glue import (compute_metrics, convert_examples_to_features,
output_modes, processors)
@@ -56,24 +58,24 @@ def train(args, train_dataset, model, tokenizer):
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * args.n_gpu
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if args.max_steps > 0:
num_train_optimization_steps = args.max_steps
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = BertAdam(optimizer_grouped_parameters, lr=args.learning_rate,
t_total=num_train_optimization_steps, warmup=args.warmup_proportion)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
schedule = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
if args.fp16:
try:
from apex import amp
@@ -89,11 +91,11 @@ def train(args, train_dataset, model, tokenizer):
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", num_train_optimization_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
optimizer.zero_grad()
model.zero_grad()
for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
model.train()
@@ -103,7 +105,7 @@ def train(args, train_dataset, model, tokenizer):
'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None, # XLM don't use segment_ids
'labels': batch[3]}
ouputs = model(**inputs)
loss = ouputs[0]
loss = ouputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
@@ -113,22 +115,25 @@ def train(args, train_dataset, model, tokenizer):
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
scheduler.step() # Update learning rate schedule
optimizer.step()
optimizer.zero_grad()
model.zero_grad()
global_step += 1
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1: # Only evaluate on single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer, prefix=global_step)
if args.local_rank == -1: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
logging_loss = tr_loss
@@ -140,6 +145,7 @@ def train(args, train_dataset, model, tokenizer):
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
break
@@ -162,20 +168,21 @@ def evaluate(args, model, tokenizer, prefix=""):
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * args.n_gpu
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation *****")
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = None
out_label_ids = None
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
@@ -213,7 +220,7 @@ def evaluate(args, model, tokenizer, prefix=""):
return results
def load_and_cache_examples(args, task, tokenizer, evaluate=False, overwrite_cache=False):
def load_and_cache_examples(args, task, tokenizer, evaluate=False):
processor = processors[task]()
output_mode = output_modes[task]
# Load data features from cache or dataset file
@@ -285,20 +292,22 @@ def main():
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU for training.")
parser.add_argument("--eval_batch_size", default=8, type=int,
help="Total batch size for eval.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU for evaluation.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Proportion of training with linear learning rate warmup (0.1 = 10%% of training).")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
@@ -409,6 +418,7 @@ def main():
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
@@ -427,15 +437,18 @@ def main():
if args.do_eval and args.local_rank in [-1, 0]:
checkpoints = [args.output_dir + './' + WEIGHTS_NAME]
if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
results = {}
for checkpoint in checkpoints:
global_step = int(checkpoints.split('-')[-1])
model = model_class.from_pretrained(checkpoints)
global_step = int(checkpoint.split('-')[-1])
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=global_step)
result = dict(n + '_{}'.format())
result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
return results

4
pytorch_transformers/__init__.py
View File

@@ -36,7 +36,7 @@ from .modeling_xlm import (XLMConfig, XLMModel,
from .modeling_utils import (WEIGHTS_NAME, CONFIG_NAME, TF_WEIGHTS_NAME,
PretrainedConfig, PreTrainedModel, prune_layer, Conv1D)
from .optimization import BertAdam
from .optimization_openai import OpenAIAdam
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
from .file_utils import (PYTORCH_PRETRAINED_BERT_CACHE, cached_path)

2
pytorch_transformers/modeling_utils.py
View File

@@ -104,7 +104,7 @@ class PretrainedConfig(object):
for key in to_remove:
kwargs.pop(key, None)
logger.info("Model config {}".format(config))
logger.info("Model config %s", config)
return config
@classmethod

13
pytorch_transformers/modeling_xlnet.py
View File

@@ -211,10 +211,6 @@ class XLNetConfig(PretrainedConfig):
layer_norm_eps=1e-12,
dropout=0.1,
dropatt=0.1,
init="normal",
init_range=0.1,
init_std=0.02,
mem_len=None,
reuse_len=None,
bi_data=False,
@@ -258,11 +254,6 @@ class XLNetConfig(PretrainedConfig):
dropout: float, dropout rate.
dropatt: float, dropout rate on attention probabilities.
init: str, the initialization scheme, either "normal" or "uniform".
init_range: float, initialize the parameters with a uniform distribution
in [-init_range, init_range]. Only effective when init="uniform".
init_std: float, initialize the parameters with a normal distribution
with mean 0 and stddev init_std. Only effective when init="normal".
mem_len: int, the number of tokens to cache.
reuse_len: int, the number of tokens in the currect batch to be cached
and reused in the future.
@@ -297,11 +288,7 @@ class XLNetConfig(PretrainedConfig):
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.init = init
self.init_range = init_range
self.init_std = init_std
self.dropout = dropout
self.dropatt = dropatt
self.mem_len = mem_len
self.reuse_len = reuse_len
self.bi_data = bi_data

259
pytorch_transformers/optimization.py
View File

@@ -14,174 +14,92 @@
# limitations under the License.
"""PyTorch optimization for BERT model."""
import logging
import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
import logging
import abc
import sys
from torch.optim.lr_scheduler import LambdaLR
logger = logging.getLogger(__name__)
class ConstantLRSchedule(LambdaLR):
def __init__(self, optimizer, last_epoch=-1):
super(ConstantLR, self).__init__(optimizer, lambda x: x, last_epoch=last_epoch)
if sys.version_info >= (3, 4):
ABC = abc.ABC
else:
ABC = abc.ABCMeta('ABC', (), {})
class _LRSchedule(ABC):
""" Parent of all LRSchedules here. """
warn_t_total = False # is set to True for schedules where progressing beyond t_total steps doesn't make sense
def __init__(self, warmup=0.002, t_total=-1, **kw):
class WarmupCosineSchedule(LambdaLR):
"""
:param warmup: what fraction of t_total steps will be used for linear warmup
:param t_total: how many training steps (updates) are planned
:param kw:
"""
super(_LRSchedule, self).__init__(**kw)
if t_total < 0:
logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
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))
warmup = max(warmup, 0.)
self.warmup, self.t_total = float(warmup), float(t_total)
self.warned_for_t_total_at_progress = -1
def get_lr(self, step, nowarn=False):
"""
:param step: which of t_total steps we're on
:param nowarn: set to True to suppress warning regarding training beyond specified 't_total' steps
:return: learning rate multiplier for current update
"""
if self.t_total < 0:
return 1.
progress = float(step) / self.t_total
ret = self.get_lr_(progress)
# warning for exceeding t_total (only active with warmup_linear
if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
logger.warning(
"Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
.format(ret, self.__class__.__name__))
self.warned_for_t_total_at_progress = progress
# end warning
return ret
@abc.abstractmethod
def get_lr_(self, progress):
"""
:param progress: value between 0 and 1 (unless going beyond t_total steps) specifying training progress
:return: learning rate multiplier for current update
"""
return 1.
class ConstantLR(_LRSchedule):
def get_lr_(self, progress):
return 1.
class WarmupCosineSchedule(_LRSchedule):
"""
Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
Linearly increases learning rate from 0 to 1 over `warmup` training steps.
Decreases learning rate from 1. to 0. over remaining `t_total - warmup` steps following a cosine curve.
If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
"""
warn_t_total = True
def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
"""
:param warmup: see LRSchedule
:param t_total: see LRSchedule
:param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
:param kw:
"""
super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
self.cycles = cycles
warn_t_total = True
def __init__(self, optimizer, warmup_steps, t_total, cycles=.5, last_epoch=-1):
def get_lr_(self, progress):
if progress < self.warmup:
return progress / self.warmup
def lr_lambda(step):
if step < warmup_steps:
return step / max(1, warmup_steps)
else:
progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
progress = (step - warmup_steps) / max(1, t_total - warmup_steps) # progress after warmup
return 0.5 * (1. + math.cos(math.pi * cycles * 2 * progress))
super(WarmupCosineSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch)
class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
class WarmupCosineWithHardRestartsSchedule(LambdaLR):
"""
Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
learning rate (with hard restarts).
"""
def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
assert(cycles >= 1.)
def __init__(self, optimizer, warmup_steps, t_total, cycles=1., last_epoch=-1):
def get_lr_(self, progress):
if progress < self.warmup:
return progress / self.warmup
def lr_lambda(step):
if step < warmup_steps:
return step / max(1, warmup_steps)
else:
progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
progress = (step - warmup_steps) / max(1, t_total - warmup_steps) # progress after warmup
ret = 0.5 * (1. + math.cos(math.pi * ((cycles * progress) % 1)))
return ret
class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
"""
All training progress is divided in `cycles` (default=1.) parts of equal length.
Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
followed by a learning rate decreasing from 1. to 0. following a cosine curve.
"""
def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
assert(warmup * cycles < 1.)
warmup = warmup * cycles if warmup >= 0 else warmup
super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
def get_lr_(self, progress):
progress = progress * self.cycles % 1.
if progress < self.warmup:
return progress / self.warmup
else:
progress = (progress - self.warmup) / (1 - self.warmup) # progress after warmup
ret = 0.5 * (1. + math.cos(math.pi * progress))
return ret
super(WarmupCosineWithHardRestartsSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch)
class WarmupConstantSchedule(_LRSchedule):
class WarmupConstantSchedule(LambdaLR):
"""
Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Keeps learning rate equal to 1. after warmup.
"""
def get_lr_(self, progress):
if progress < self.warmup:
return progress / self.warmup
def __init__(self, optimizer, warmup_steps, last_epoch=-1):
def lr_lambda(step):
if step < warmup_steps:
return step / warmup_steps
return 1.
super(WarmupConstantSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch)
class WarmupLinearSchedule(_LRSchedule):
class WarmupLinearSchedule(LambdaLR):
"""
Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
"""
warn_t_total = True
def get_lr_(self, progress):
if progress < self.warmup:
return progress / self.warmup
return max((progress - 1.) / (self.warmup - 1.), 0.)
def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1):
def lr_lambda(step):
if step < warmup_steps:
return step / max(1, warmup_steps)
return (t_total - step) / max(1, t_total - warmup_steps)
super(WarmupLinearSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch)
SCHEDULES = {
None: ConstantLR,
"none": ConstantLR,
"warmup_cosine": WarmupCosineSchedule,
"warmup_constant": WarmupConstantSchedule,
"warmup_linear": WarmupLinearSchedule
}
class BertAdam(Optimizer):
"""Implements BERT version of Adam algorithm with weight decay fix.
class AdamW(Optimizer):
""" Implements Adam algorithm with weight decay fix.
Parameters:
lr: learning rate
@@ -197,46 +115,21 @@ class BertAdam(Optimizer):
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
correct_bias: can be set to False to avoid correcting bias in Adam (e.g. like in Bert repository)
"""
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, **kwargs):
if lr is not required and lr < 0.0:
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01, correct_bias=True):
if lr < 0.0:
raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
raise ValueError("Invalid schedule parameter: {}".format(schedule))
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:
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1] ))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
# initialize schedule object
if not isinstance(schedule, _LRSchedule):
schedule_type = SCHEDULES[schedule]
schedule = schedule_type(warmup=warmup, t_total=t_total)
else:
if warmup != -1 or t_total != -1:
logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
"Please specify custom warmup and t_total in _LRSchedule object.")
defaults = dict(lr=lr, schedule=schedule,
b1=b1, b2=b2, e=e, weight_decay=weight_decay,
max_grad_norm=max_grad_norm)
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
correct_bias=correct_bias)
super(BertAdam, self).__init__(params, defaults)
def get_lr(self):
lr = []
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
state = self.state[p]
if len(state) == 0:
return [0]
lr_scheduled = group['lr']
lr_scheduled *= group['schedule'].get_lr(state['step'])
lr.append(lr_scheduled)
return lr
def step(self, closure=None):
"""Performs a single optimization step.
@@ -262,22 +155,28 @@ class BertAdam(Optimizer):
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['next_m'] = torch.zeros_like(p.data)
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['next_v'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros_like(p.data)
next_m, next_v = state['next_m'], state['next_v']
beta1, beta2 = group['b1'], group['b2']
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
# Add grad clipping
if group['max_grad_norm'] > 0:
clip_grad_norm_(p, group['max_grad_norm'])
state['step'] += 1
# 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'])
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['eps'])
step_size = group['lr']
if group['correct_bias']: # No bias correction for Bert
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = step_size * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
@@ -286,20 +185,8 @@ class BertAdam(Optimizer):
# 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
lr_scheduled = group['lr']
lr_scheduled *= group['schedule'].get_lr(state['step'])
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']
# Add weight decay at the end (fixed version)
if group['weight_decay'] > 0:
p.data.add_(-group['lr'] * group['weight_decay'], p.data)
return loss

127
pytorch_transformers/optimization_openai.py
View File

@@ -1,127 +0,0 @@
# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace 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 OpenAI GPT model."""
import math
import torch
from torch.optim import Optimizer
from torch.optim.optimizer import required
from torch.nn.utils import clip_grad_norm_
import logging
from .optimization import SCHEDULES, _LRSchedule, WarmupCosineWithWarmupRestartsSchedule, \
WarmupCosineWithHardRestartsSchedule, WarmupCosineSchedule, WarmupLinearSchedule, WarmupConstantSchedule
logger = logging.getLogger(__name__)
class OpenAIAdam(Optimizer):
"""Implements Open AI version of Adam algorithm with weight decay fix.
"""
def __init__(self, params, lr=required, schedule='warmup_linear', warmup=-1, t_total=-1,
b1=0.9, b2=0.999, e=1e-8, weight_decay=0,
vector_l2=False, max_grad_norm=-1, **kwargs):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
raise ValueError("Invalid schedule parameter: {}".format(schedule))
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))
# initialize schedule object
if not isinstance(schedule, _LRSchedule):
schedule_type = SCHEDULES[schedule]
schedule = schedule_type(warmup=warmup, t_total=t_total)
else:
if warmup != -1 or t_total != -1:
logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
"Please specify custom warmup and t_total in _LRSchedule object.")
defaults = dict(lr=lr, schedule=schedule,
b1=b1, b2=b2, e=e, weight_decay=weight_decay, vector_l2=vector_l2,
max_grad_norm=max_grad_norm)
super(OpenAIAdam, 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]
lr_scheduled = group['lr']
lr_scheduled *= group['schedule'].get_lr(state['step'])
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['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['b1'], group['b2']
state['step'] += 1
# 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
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['e'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
lr_scheduled = group['lr']
lr_scheduled *= group['schedule'].get_lr(state['step'])
step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
# Add weight decay at the end (fixed version)
if (len(p.size()) > 1 or group['vector_l2']) and group['weight_decay'] > 0:
p.data.add_(-lr_scheduled * group['weight_decay'], p.data)
return loss

29
pytorch_transformers/tests/optimization_test.py
View File

@@ -20,10 +20,9 @@ import unittest
import torch
from pytorch_transformers import BertAdam
from pytorch_transformers import OpenAIAdam
from pytorch_transformers.optimization import ConstantLR, WarmupLinearSchedule, WarmupConstantSchedule, \
WarmupCosineWithWarmupRestartsSchedule, WarmupCosineWithHardRestartsSchedule, WarmupCosineSchedule
from pytorch_transformers import (AdamW, ConstantLRSchedule, WarmupConstantSchedule,
WarmupCosineSchedule, WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
import numpy as np
@@ -34,12 +33,12 @@ class OptimizationTest(unittest.TestCase):
for a, b in zip(list1, list2):
self.assertAlmostEqual(a, b, delta=tol)
def test_adam(self):
def test_adam_w(self):
w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
target = torch.tensor([0.4, 0.2, -0.5])
criterion = torch.nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
optimizer = BertAdam(params=[w], lr=2e-1,
optimizer = AdamW(params=[w], lr=2e-1,
weight_decay=0.0,
max_grad_norm=-1)
for _ in range(100):
@@ -52,23 +51,13 @@ class OptimizationTest(unittest.TestCase):
class ScheduleInitTest(unittest.TestCase):
def test_bert_sched_init(self):
def test_sched_init(self):
m = torch.nn.Linear(50, 50)
optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
optim = AdamW(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = BertAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
optim = AdamW(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = BertAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
# shouldn't fail
def test_openai_sched_init(self):
m = torch.nn.Linear(50, 50)
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule=None)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.1, t_total=1000, schedule="none")
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], ConstantLR))
optim = OpenAIAdam(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
optim = AdamW(m.parameters(), lr=0.001, warmup=.01, t_total=1000)
self.assertTrue(isinstance(optim.param_groups[0]["schedule"], WarmupLinearSchedule))
# shouldn't fail