File size: 13,466 Bytes
a43ef32 |
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 |
"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import datetime
import logging
import os
import time
import torch
import torch.distributed as dist
import webdataset as wds
from lavis.common.dist_utils import download_cached_file, is_main_process, main_process
from lavis.common.registry import registry
from lavis.common.utils import is_url
from lavis.datasets.data_utils import concat_datasets, reorg_datasets_by_split
from lavis.runners.runner_base import RunnerBase
from torch.utils.data.dataset import ChainDataset
@registry.register_runner("runner_iter")
class RunnerIter(RunnerBase):
"""
Run training based on the number of iterations. This is common when
the training dataset size is large. Underhood logic is similar to
epoch-based training by considering every #iters_per_inner_epoch as an
inner epoch.
In iter-based runner, after every #iters_per_inner_epoch steps, we
1) do a validation epoch;
2) schedule the learning rate;
3) save the checkpoint.
We refer every #iters_per_inner_epoch steps as an inner epoch.
"""
def __init__(self, cfg, task, model, datasets, job_id):
super().__init__(cfg, task, model, datasets, job_id)
self.start_iters = 0
self.max_iters = int(self.config.run_cfg.get("max_iters", -1))
assert self.max_iters > 0, "max_iters must be greater than 0."
self.iters_per_inner_epoch = int(
self.config.run_cfg.get("iters_per_inner_epoch", -1)
)
assert (
self.iters_per_inner_epoch > 0
), "iters_per_inner_epoch must be greater than 0."
@property
def max_epoch(self):
return int(self.max_iters / self.iters_per_inner_epoch)
@property
def cur_epoch(self):
try:
return self.train_loader.epoch
except AttributeError:
# pipeline data (e.g. LAION) is streaming, have no concept of epoch
return 0
def _progress(self, cur_iters):
return "{}_iters={}".format(self.cur_epoch, cur_iters)
def train(self):
start_time = time.time()
best_agg_metric = 0
best_iters = 0
self.log_config()
# resume from checkpoint if specified
if not self.evaluate_only and self.resume_ckpt_path is not None:
self._load_checkpoint(self.resume_ckpt_path)
for start_iters in range(
self.start_iters, self.max_iters, self.iters_per_inner_epoch
):
end_iters = start_iters + self.iters_per_inner_epoch
# training phase
if not self.evaluate_only:
logging.info(
"Start training, max_iters={}, in total {} inner epochs.".format(
self.max_iters, int(self.max_iters / self.iters_per_inner_epoch)
)
)
if start_iters == self.start_iters:
self.task.before_training(
model=self.unwrap_dist_model(self.model),
dataset=self.datasets,
)
train_stats = self.train_iters(self.cur_epoch, start_iters)
self.log_stats(split_name="train", stats=train_stats)
# evaluation phase
if len(self.valid_splits) > 0:
for split_name in self.valid_splits:
logging.info("Evaluating on {}.".format(split_name))
val_log = self.eval_epoch(
split_name=split_name, cur_epoch=self._progress(end_iters)
)
if val_log is not None:
if is_main_process():
assert (
"agg_metrics" in val_log
), "No agg_metrics found in validation log."
agg_metrics = val_log["agg_metrics"]
if agg_metrics > best_agg_metric and split_name == "val":
best_iters, best_agg_metric = end_iters, agg_metrics
self._save_checkpoint(end_iters, is_best=True)
val_log.update({"best_iters": best_iters})
self.log_stats(val_log, split_name)
else:
# if no validation split is provided, we just save the checkpoint at the end of each inner epoch.
if not self.evaluate_only:
self._save_checkpoint(end_iters, is_best=False)
if self.evaluate_only:
break
dist.barrier()
# testing phase
self.evaluate(cur_epoch=self.cur_epoch)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logging.info("Training time {}".format(total_time_str))
def train_iters(self, epoch, start_iters):
# train by iterations
self.model.train()
return self.task.train_iters(
epoch=epoch,
start_iters=start_iters,
iters_per_inner_epoch=self.iters_per_inner_epoch,
model=self.model,
data_loader=self.train_loader,
optimizer=self.optimizer,
scaler=self.scaler,
lr_scheduler=self.lr_scheduler,
cuda_enabled=self.cuda_enabled,
log_freq=self.log_freq,
accum_grad_iters=self.accum_grad_iters,
)
@main_process
def _save_checkpoint(self, cur_iters, is_best=False):
model_no_ddp = self.unwrap_dist_model(self.model)
param_grad_dic = {
k: v.requires_grad for (k, v) in model_no_ddp.named_parameters()
}
state_dict = model_no_ddp.state_dict()
for k in list(state_dict.keys()):
if k in param_grad_dic.keys() and not param_grad_dic[k]:
# delete parameters that do not require gradient
del state_dict[k]
save_obj = {
"model": state_dict,
"optimizer": self.optimizer.state_dict(),
"config": self.config.to_dict(),
"scaler": self.scaler.state_dict() if self.scaler else None,
"iters": cur_iters,
}
save_to = os.path.join(
self.output_dir,
"checkpoint_{}.pth".format("best" if is_best else cur_iters),
)
logging.info("Saving checkpoint at iters {} to {}.".format(cur_iters, save_to))
torch.save(save_obj, save_to)
def _load_checkpoint(self, url_or_filename):
"""
Resume from a checkpoint.
"""
if is_url(url_or_filename):
cached_file = download_cached_file(
url_or_filename, check_hash=False, progress=True
)
checkpoint = torch.load(cached_file, map_location=self.device)
elif os.path.isfile(url_or_filename):
checkpoint = torch.load(url_or_filename, map_location=self.device)
else:
raise RuntimeError("checkpoint url or path is invalid")
state_dict = checkpoint["model"]
self.unwrap_dist_model(self.model).load_state_dict(state_dict)
self.optimizer.load_state_dict(checkpoint["optimizer"])
if self.scaler and "scaler" in checkpoint:
self.scaler.load_state_dict(checkpoint["scaler"])
self.start_iters = checkpoint["iters"] + 1
logging.info("Resume checkpoint from {}".format(url_or_filename))
@property
def dataloaders(self) -> dict:
"""
A property to get and create dataloaders by split just in need.
If no train_dataset_ratio is provided, concatenate map-style datasets and
chain wds.DataPipe datasets separately. Training set becomes a tuple
(ConcatDataset, ChainDataset), both are optional but at least one of them is
required. The resultant ConcatDataset and ChainDataset will be sampled evenly.
If train_dataset_ratio is provided, create a MultiIterLoader to sample
each dataset by ratios during training.
Currently do not support multiple datasets for validation and test.
Returns:
dict: {split_name: (tuples of) dataloader}
"""
if self._dataloaders is None:
# reoganize datasets by split and concatenate/chain if necessary
dataset_ratios = self.config.run_cfg.get("train_dataset_ratios", None)
if dataset_ratios is None:
# concatenate map-style datasets and chain wds.DataPipe datasets separately
# training set becomes a tuple (ConcatDataset, ChainDataset), both are
# optional but at least one of them is required. The resultant ConcatDataset
# and ChainDataset will be sampled evenly.
logging.info(
"dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline)."
)
datasets = reorg_datasets_by_split(self.datasets)
self.datasets = concat_datasets(datasets)
else:
# create multi-loader with the provided ratios, without concatenating or chaining
missing_keys = [k for k in dataset_ratios if k not in self.datasets]
if len(missing_keys) > 0:
raise ValueError(
"Datasets with the following split names are not found: {}".format(
missing_keys
)
)
unexpected_keys = [k for k in self.datasets if k not in dataset_ratios]
if len(unexpected_keys) > 0:
raise ValueError(
"Datasets with the following split names are not expected: {}".format(
unexpected_keys
)
)
dataset_ratios = [float(dataset_ratios[k]) for k in self.datasets]
self.datasets = reorg_datasets_by_split(self.datasets)
# to keep the same structure as return value of concat_datasets
self.datasets = {
k: v[0] if len(v) == 1 else v for k, v in datasets.items()
}
# print dataset statistics after concatenation/chaining
for split_name in self.datasets:
if isinstance(self.datasets[split_name], tuple) or isinstance(
self.datasets[split_name], list
):
# mixed wds.DataPipeline and torch.utils.data.Dataset
num_records = sum(
[
len(d)
if not type(d) in [wds.DataPipeline, ChainDataset]
else 0
for d in self.datasets[split_name]
]
)
else:
try:
# a single map-style dataset
num_records = len(self.datasets[split_name])
except TypeError:
# a single wds.DataPipeline or ChainDataset
num_records = -1
logging.info(
"Only a single wds.DataPipeline dataset, no __len__ attribute."
)
if num_records >= 0:
logging.info(
"Loaded {} records for {} split from the dataset.".format(
num_records, split_name
)
)
# create dataloaders
split_names = sorted(self.datasets.keys())
datasets = [self.datasets[split] for split in split_names]
is_trains = [split in self.train_splits for split in split_names]
batch_sizes = [
self.config.run_cfg.batch_size_train
if split == "train"
else self.config.run_cfg.batch_size_eval
for split in split_names
]
collate_fns = []
for dataset in datasets:
if isinstance(dataset, tuple) or isinstance(dataset, list):
collate_fns.append([getattr(d, "collater", None) for d in dataset])
else:
collate_fns.append(getattr(dataset, "collater", None))
dataloaders = self.create_loaders(
datasets=datasets,
num_workers=self.config.run_cfg.num_workers,
batch_sizes=batch_sizes,
is_trains=is_trains,
collate_fns=collate_fns,
dataset_ratios=dataset_ratios,
)
self._dataloaders = {k: v for k, v in zip(split_names, dataloaders)}
return self._dataloaders
|