File size: 9,218 Bytes
59630ba | 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 | """
This repo is forked from [Boyuan Chen](https://boyuan.space/)'s research
template [repo](https://github.com/buoyancy99/research-template).
By its MIT license, you must keep the above sentence in `README.md`
and the `LICENSE` file to credit the author.
"""
from abc import ABC
from typing import Optional, Union, Dict
import pathlib
import hydra
import torch
from lightning.pytorch.strategies.ddp import DDPStrategy
import lightning.pytorch as pl
from lightning.pytorch.loggers.wandb import WandbLogger
from lightning.pytorch.callbacks import LearningRateMonitor, ModelCheckpoint
from omegaconf import DictConfig
from utils.print_utils import cyan
from utils.distributed_utils import rank_zero_print
from utils.lightning_utils import EMA
from .data_modules import BaseDataModule
torch.set_float32_matmul_precision("high")
class BaseExperiment(ABC):
"""
Abstract class for an experiment. This generalizes the pytorch lightning Trainer & lightning Module to more
flexible experiments that doesn't fit in the typical ml loop, e.g. multi-stage reinforcement learning benchmarks.
"""
# each key has to be a yaml file under '[project_root]/configurations/algorithm' without .yaml suffix
compatible_algorithms: Dict = NotImplementedError
def __init__(
self,
root_cfg: DictConfig,
logger: Optional[WandbLogger] = None,
ckpt_path: Optional[Union[str, pathlib.Path]] = None,
) -> None:
"""
Constructor
Args:
cfg: configuration file that contains everything about the experiment
logger: a pytorch-lightning WandbLogger instance
ckpt_path: an optional path to saved checkpoint
"""
super().__init__()
self.root_cfg = root_cfg
self.cfg = root_cfg.experiment
self.debug = root_cfg.debug
self.logger = logger if logger else False
self.ckpt_path = ckpt_path
self.algo = None
def _build_algo(self):
"""
Build the lightning module
:return: a pytorch-lightning module to be launched
"""
algo_name = self.root_cfg.algorithm._name
if algo_name not in self.compatible_algorithms:
raise ValueError(
f"Algorithm {algo_name} not found in compatible_algorithms for this Experiment class. "
"Make sure you define compatible_algorithms correctly and make sure that each key has "
"same name as yaml file under '[project_root]/configurations/algorithm' without .yaml suffix"
)
return self.compatible_algorithms[algo_name](self.root_cfg.algorithm)
def exec_task(self, task: str) -> None:
"""
Executing a certain task specified by string. Each task should be a stage of experiment.
In most computer vision / nlp applications, tasks should be just train and test.
In reinforcement learning, you might have more stages such as collecting dataset etc
Args:
task: a string specifying a task implemented for this experiment
"""
if hasattr(self, task) and callable(getattr(self, task)):
rank_zero_print(cyan("Executing task:"), f"{task} out of {self.cfg.tasks}")
getattr(self, task)()
else:
raise ValueError(
f"Specified task '{task}' not defined for class {self.__class__.__name__} or is not callable."
)
class BaseLightningExperiment(BaseExperiment):
"""
Abstract class for pytorch lightning experiments. Useful for computer vision & nlp where main components are
simply models, datasets and train loop.
"""
# each key has to be a yaml file under '[project_root]/configurations/algorithm' without .yaml suffix
compatible_algorithms: Dict = NotImplementedError
# each key has to be a yaml file under '[project_root]/configurations/dataset' without .yaml suffix
compatible_datasets: Dict = NotImplementedError
data_module_cls = BaseDataModule
def __init__(
self,
root_cfg: DictConfig,
logger: Optional[WandbLogger] = None,
ckpt_path: Optional[Union[str, pathlib.Path]] = None,
) -> None:
super().__init__(root_cfg, logger, ckpt_path)
self.data_module = self.data_module_cls(root_cfg, self.compatible_datasets)
def _build_common_callbacks(self):
return [EMA(**self.cfg.ema)]
def training(self) -> None:
"""
All training happens here
"""
if not self.algo:
self.algo = self._build_algo()
if self.cfg.training.compile:
self.algo = torch.compile(self.algo)
callbacks = []
if self.logger:
callbacks.append(LearningRateMonitor("step", True))
if "checkpointing" in self.cfg.training:
callbacks.append(
ModelCheckpoint(
pathlib.Path(
hydra.core.hydra_config.HydraConfig.get()["runtime"][
"output_dir"
]
)
/ "checkpoints",
**self.cfg.training.checkpointing,
)
)
callbacks += self._build_common_callbacks()
trainer = pl.Trainer(
accelerator="auto",
logger=self.logger,
devices="auto",
num_nodes=self.cfg.num_nodes,
strategy=(
DDPStrategy(find_unused_parameters=self.cfg.find_unused_parameters)
if torch.cuda.device_count() > 1
else "auto"
),
callbacks=callbacks,
gradient_clip_val=self.cfg.training.optim.gradient_clip_val,
val_check_interval=self.cfg.validation.val_every_n_step,
limit_val_batches=self.cfg.validation.limit_batch,
check_val_every_n_epoch=self.cfg.validation.val_every_n_epoch,
accumulate_grad_batches=self.cfg.training.optim.accumulate_grad_batches,
precision=self.cfg.training.precision,
detect_anomaly=False, # self.cfg.debug,
num_sanity_val_steps=(
int(self.cfg.debug)
if self.cfg.validation.num_sanity_val_steps is None
else self.cfg.validation.num_sanity_val_steps
),
max_epochs=self.cfg.training.max_epochs,
max_steps=self.cfg.training.max_steps,
max_time=self.cfg.training.max_time,
reload_dataloaders_every_n_epochs=self.cfg.reload_dataloaders_every_n_epochs,
)
# if self.debug:
# self.logger.watch(self.algo, log="all")
trainer.fit(
self.algo,
datamodule=self.data_module,
ckpt_path=self.ckpt_path,
)
def validation(self) -> None:
"""
All validation happens here
"""
if not self.algo:
self.algo = self._build_algo()
if self.cfg.validation.compile:
self.algo = torch.compile(self.algo)
callbacks = [] + self._build_common_callbacks()
# import pdb; pdb.set_trace()
trainer = pl.Trainer(
accelerator="auto",
logger=self.logger,
devices="auto",
num_nodes=self.cfg.num_nodes,
strategy=(
DDPStrategy(find_unused_parameters=self.cfg.find_unused_parameters)
if torch.cuda.device_count() > 1
else "auto"
),
callbacks=callbacks,
limit_val_batches=self.cfg.validation.limit_batch,
precision=self.cfg.validation.precision,
detect_anomaly=False, # self.cfg.debug,
inference_mode=self.cfg.validation.inference_mode,
)
# if self.debug:
# self.logger.watch(self.algo, log="all")
trainer.validate(
self.algo,
datamodule=self.data_module,
ckpt_path=self.ckpt_path,
)
def test(self) -> None:
"""
All testing happens here
"""
if not self.algo:
self.algo = self._build_algo()
if self.cfg.test.compile:
self.algo = torch.compile(self.algo)
callbacks = [] + self._build_common_callbacks()
trainer = pl.Trainer(
accelerator="auto",
logger=self.logger,
devices="auto",
num_nodes=self.cfg.num_nodes,
strategy=(
DDPStrategy(find_unused_parameters=self.cfg.find_unused_parameters)
if torch.cuda.device_count() > 1
else "auto"
),
callbacks=callbacks,
limit_test_batches=self.cfg.test.limit_batch,
precision=self.cfg.test.precision,
detect_anomaly=False, # self.cfg.debug,
inference_mode=self.cfg.test.inference_mode,
)
# Only load the checkpoint if only testing. Otherwise, it will have been loaded
# and further trained during train.
trainer.test(
self.algo,
datamodule=self.data_module,
ckpt_path=self.ckpt_path,
)
|