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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import random
import shutil
import time
from abc import abstractmethod
from pathlib import Path
import accelerate
import json5
import numpy as np
import torch
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration
from torch.utils.data import ConcatDataset, DataLoader
from tqdm import tqdm
from models.base.base_sampler import build_samplers
from optimizer.optimizers import NoamLR
class BaseTrainer(object):
r"""The base trainer for all tasks. Any trainer should inherit from this class."""
def __init__(self, args=None, cfg=None):
super().__init__()
self.args = args
self.cfg = cfg
cfg.exp_name = args.exp_name
# init with accelerate
self._init_accelerator()
self.accelerator.wait_for_everyone()
# Use accelerate logger for distributed training
with self.accelerator.main_process_first():
self.logger = get_logger(args.exp_name, log_level=args.log_level)
# Log some info
self.logger.info("=" * 56)
self.logger.info("||\t\t" + "New training process started." + "\t\t||")
self.logger.info("=" * 56)
self.logger.info("\n")
self.logger.debug(f"Using {args.log_level.upper()} logging level.")
self.logger.info(f"Experiment name: {args.exp_name}")
self.logger.info(f"Experiment directory: {self.exp_dir}")
self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint")
if self.accelerator.is_main_process:
os.makedirs(self.checkpoint_dir, exist_ok=True)
self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}")
# init counts
self.batch_count: int = 0
self.step: int = 0
self.epoch: int = 0
self.max_epoch = (
self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")
)
self.logger.info(
"Max epoch: {}".format(
self.max_epoch if self.max_epoch < float("inf") else "Unlimited"
)
)
# Check values
if self.accelerator.is_main_process:
self.__check_basic_configs()
# Set runtime configs
self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride
self.checkpoints_path = [
[] for _ in range(len(self.save_checkpoint_stride))
]
self.keep_last = [
i if i > 0 else float("inf") for i in self.cfg.train.keep_last
]
self.run_eval = self.cfg.train.run_eval
# set random seed
with self.accelerator.main_process_first():
start = time.monotonic_ns()
self._set_random_seed(self.cfg.train.random_seed)
end = time.monotonic_ns()
self.logger.debug(
f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
)
self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
# setup data_loader
with self.accelerator.main_process_first():
self.logger.info("Building dataset...")
start = time.monotonic_ns()
self.train_dataloader, self.valid_dataloader = self._build_dataloader()
end = time.monotonic_ns()
self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms")
# setup model
with self.accelerator.main_process_first():
self.logger.info("Building model...")
start = time.monotonic_ns()
self.model = self._build_model()
end = time.monotonic_ns()
self.logger.debug(self.model)
self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms")
self.logger.info(
f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M"
)
# optimizer & scheduler
with self.accelerator.main_process_first():
self.logger.info("Building optimizer and scheduler...")
start = time.monotonic_ns()
self.optimizer = self._build_optimizer()
self.scheduler = self._build_scheduler()
end = time.monotonic_ns()
self.logger.info(
f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms"
)
# accelerate prepare
self.logger.info("Initializing accelerate...")
start = time.monotonic_ns()
self._accelerator_prepare()
end = time.monotonic_ns()
self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms")
# create criterion
with self.accelerator.main_process_first():
self.logger.info("Building criterion...")
start = time.monotonic_ns()
self.criterion = self._build_criterion()
end = time.monotonic_ns()
self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms")
# Resume or Finetune
with self.accelerator.main_process_first():
if args.resume:
if args.resume_from_ckpt_path == "":
## Automatically resume according to the current exprimental name
self.logger.info(
"Automatically resuming from latest checkpoint in {}...".format(
self.checkpoint_dir
)
)
start = time.monotonic_ns()
ckpt_path = self._load_model(
checkpoint_dir=self.checkpoint_dir, resume_type=args.resume_type
)
end = time.monotonic_ns()
self.logger.info(
f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms"
)
self.checkpoints_path = json.load(
open(os.path.join(ckpt_path, "ckpts.json"), "r")
)
else:
## Resume from the given checkpoint path
if not os.path.exists(args.resume_from_ckpt_path):
raise ValueError(
"[Error] The resumed checkpoint path {} don't exist.".format(
args.resume_from_ckpt_path
)
)
self.logger.info(
"Resuming from {}...".format(args.resume_from_ckpt_path)
)
start = time.monotonic_ns()
ckpt_path = self._load_model(
checkpoint_path=args.resume_from_ckpt_path,
resume_type=args.resume_type,
)
end = time.monotonic_ns()
self.logger.info(
f"Resuming from checkpoint done in {(end - start) / 1e6:.2f}ms"
)
# save config file path
self.config_save_path = os.path.join(self.exp_dir, "args.json")
def _accelerator_prepare(self):
(
self.train_dataloader,
self.valid_dataloader,
self.model,
self.optimizer,
self.scheduler,
) = self.accelerator.prepare(
self.train_dataloader,
self.valid_dataloader,
self.model,
self.optimizer,
self.scheduler,
)
### Following are abstract methods that should be implemented in child classes ###
@abstractmethod
def _build_dataset(self):
r"""Build dataset for model training/validating/evaluating."""
pass
@staticmethod
@abstractmethod
def _build_criterion():
r"""Build criterion function for model loss calculation."""
pass
@abstractmethod
def _build_model(self):
r"""Build model for training/validating/evaluating."""
pass
@abstractmethod
def _forward_step(self, batch):
r"""One forward step of the neural network. This abstract method is trying to
unify ``_train_step`` and ``_valid_step`` and avoid redundant implementation.
However, for special case that using different forward step pattern for
training and validating, you could just override this method with ``pass`` and
implement ``_train_step`` and ``_valid_step`` separately.
"""
pass
@abstractmethod
def _save_auxiliary_states(self):
r"""To save some auxiliary states when saving model's ckpt"""
pass
### Abstract methods end ###
### THIS IS MAIN ENTRY ###
def train_loop(self):
r"""Training loop. The public entry of training process."""
# Wait everyone to prepare before we move on
self.accelerator.wait_for_everyone()
# dump config file
if self.accelerator.is_main_process:
self.__dump_cfg(self.config_save_path)
self.model.train()
self.optimizer.zero_grad()
# Wait to ensure good to go
self.accelerator.wait_for_everyone()
while self.epoch < self.max_epoch:
self.logger.info("\n")
self.logger.info("-" * 32)
self.logger.info("Epoch {}: ".format(self.epoch))
### TODO: change the return values of _train_epoch() to a loss dict, or (total_loss, loss_dict)
### It's inconvenient for the model with multiple losses
# Do training & validating epoch
train_loss = self._train_epoch()
self.logger.info(" |- Train/Loss: {:.6f}".format(train_loss))
valid_loss = self._valid_epoch()
self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_loss))
self.accelerator.log(
{"Epoch/Train Loss": train_loss, "Epoch/Valid Loss": valid_loss},
step=self.epoch,
)
self.accelerator.wait_for_everyone()
# TODO: what is scheduler?
self.scheduler.step(valid_loss) # FIXME: use epoch track correct?
# Check if hit save_checkpoint_stride and run_eval
run_eval = False
if self.accelerator.is_main_process:
save_checkpoint = False
hit_dix = []
for i, num in enumerate(self.save_checkpoint_stride):
if self.epoch % num == 0:
save_checkpoint = True
hit_dix.append(i)
run_eval |= self.run_eval[i]
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process and save_checkpoint:
path = os.path.join(
self.checkpoint_dir,
"epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
self.epoch, self.step, train_loss
),
)
self.tmp_checkpoint_save_path = path
self.accelerator.save_state(path)
print(f"save checkpoint in {path}")
json.dump(
self.checkpoints_path,
open(os.path.join(path, "ckpts.json"), "w"),
ensure_ascii=False,
indent=4,
)
self._save_auxiliary_states()
# Remove old checkpoints
to_remove = []
for idx in hit_dix:
self.checkpoints_path[idx].append(path)
while len(self.checkpoints_path[idx]) > self.keep_last[idx]:
to_remove.append((idx, self.checkpoints_path[idx].pop(0)))
# Search conflicts
total = set()
for i in self.checkpoints_path:
total |= set(i)
do_remove = set()
for idx, path in to_remove[::-1]:
if path in total:
self.checkpoints_path[idx].insert(0, path)
else:
do_remove.add(path)
# Remove old checkpoints
for path in do_remove:
shutil.rmtree(path, ignore_errors=True)
self.logger.debug(f"Remove old checkpoint: {path}")
self.accelerator.wait_for_everyone()
if run_eval:
# TODO: run evaluation
pass
# Update info for each epoch
self.epoch += 1
# Finish training and save final checkpoint
self.accelerator.wait_for_everyone()
if self.accelerator.is_main_process:
self.accelerator.save_state(
os.path.join(
self.checkpoint_dir,
"final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format(
self.epoch, self.step, valid_loss
),
)
)
self._save_auxiliary_states()
self.accelerator.end_training()
### Following are methods that can be used directly in child classes ###
def _train_epoch(self):
r"""Training epoch. Should return average loss of a batch (sample) over
one epoch. See ``train_loop`` for usage.
"""
self.model.train()
epoch_sum_loss: float = 0.0
epoch_step: int = 0
for batch in tqdm(
self.train_dataloader,
desc=f"Training Epoch {self.epoch}",
unit="batch",
colour="GREEN",
leave=False,
dynamic_ncols=True,
smoothing=0.04,
disable=not self.accelerator.is_main_process,
):
# Do training step and BP
with self.accelerator.accumulate(self.model):
loss = self._train_step(batch)
self.accelerator.backward(loss)
self.optimizer.step()
self.optimizer.zero_grad()
self.batch_count += 1
# Update info for each step
# TODO: step means BP counts or batch counts?
if self.batch_count % self.cfg.train.gradient_accumulation_step == 0:
epoch_sum_loss += loss
self.accelerator.log(
{
"Step/Train Loss": loss,
"Step/Learning Rate": self.optimizer.param_groups[0]["lr"],
},
step=self.step,
)
self.step += 1
epoch_step += 1
self.accelerator.wait_for_everyone()
return (
epoch_sum_loss
/ len(self.train_dataloader)
* self.cfg.train.gradient_accumulation_step
)
@torch.inference_mode()
def _valid_epoch(self):
r"""Testing epoch. Should return average loss of a batch (sample) over
one epoch. See ``train_loop`` for usage.
"""
self.model.eval()
epoch_sum_loss = 0.0
for batch in tqdm(
self.valid_dataloader,
desc=f"Validating Epoch {self.epoch}",
unit="batch",
colour="GREEN",
leave=False,
dynamic_ncols=True,
smoothing=0.04,
disable=not self.accelerator.is_main_process,
):
batch_loss = self._valid_step(batch)
epoch_sum_loss += batch_loss.item()
self.accelerator.wait_for_everyone()
return epoch_sum_loss / len(self.valid_dataloader)
def _train_step(self, batch):
r"""Training forward step. Should return average loss of a sample over
one batch. Provoke ``_forward_step`` is recommended except for special case.
See ``_train_epoch`` for usage.
"""
return self._forward_step(batch)
@torch.inference_mode()
def _valid_step(self, batch):
r"""Testing forward step. Should return average loss of a sample over
one batch. Provoke ``_forward_step`` is recommended except for special case.
See ``_test_epoch`` for usage.
"""
return self._forward_step(batch)
def _load_model(
self,
checkpoint_dir: str = None,
checkpoint_path: str = None,
resume_type: str = "",
):
r"""Load model from checkpoint. If checkpoint_path is None, it will
load the latest checkpoint in checkpoint_dir. If checkpoint_path is not
None, it will load the checkpoint specified by checkpoint_path. **Only use this
method after** ``accelerator.prepare()``.
"""
if checkpoint_path is None:
ls = [str(i) for i in Path(checkpoint_dir).glob("*")]
ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
checkpoint_path = ls[0]
self.logger.info("Resume from {}...".format(checkpoint_path))
if resume_type in ["resume", ""]:
# Load all the things, including model weights, optimizer, scheduler, and random states.
self.accelerator.load_state(input_dir=checkpoint_path)
# set epoch and step
self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + 1
self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1
elif resume_type == "finetune":
# Load only the model weights
accelerate.load_checkpoint_and_dispatch(
self.accelerator.unwrap_model(self.model),
os.path.join(checkpoint_path, "pytorch_model.bin"),
)
self.logger.info("Load model weights for finetune...")
else:
raise ValueError("Resume_type must be `resume` or `finetune`.")
return checkpoint_path
def _build_dataloader(self):
Dataset, Collator = self._build_dataset()
# build dataset instance for each dataset and combine them by ConcatDataset
datasets_list = []
for dataset in self.cfg.dataset:
subdataset = Dataset(self.cfg, dataset, is_valid=False)
datasets_list.append(subdataset)
train_dataset = ConcatDataset(datasets_list)
train_collate = Collator(self.cfg)
_, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train")
self.logger.debug(f"train batch_sampler: {list(batch_sampler)}")
self.logger.debug(f"length: {train_dataset.cumulative_sizes}")
# TODO: use config instead of (sampler, shuffle, drop_last, batch_size)
train_loader = DataLoader(
train_dataset,
# shuffle=True,
collate_fn=train_collate,
batch_sampler=batch_sampler,
num_workers=self.cfg.train.dataloader.num_worker,
pin_memory=self.cfg.train.dataloader.pin_memory,
)
# Build valid dataloader
datasets_list = []
for dataset in self.cfg.dataset:
subdataset = Dataset(self.cfg, dataset, is_valid=True)
datasets_list.append(subdataset)
valid_dataset = ConcatDataset(datasets_list)
valid_collate = Collator(self.cfg)
_, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid")
self.logger.debug(f"valid batch_sampler: {list(batch_sampler)}")
self.logger.debug(f"length: {valid_dataset.cumulative_sizes}")
valid_loader = DataLoader(
valid_dataset,
collate_fn=valid_collate,
batch_sampler=batch_sampler,
num_workers=self.cfg.train.dataloader.num_worker,
pin_memory=self.cfg.train.dataloader.pin_memory,
)
return train_loader, valid_loader
@staticmethod
def _set_random_seed(seed):
r"""Set random seed for all possible random modules."""
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed)
def _check_nan(self, loss, y_pred, y_gt):
if torch.any(torch.isnan(loss)):
self.logger.error("Fatal Error: Training is down since loss has Nan!")
self.logger.error("loss = {:.6f}".format(loss.item()), in_order=True)
### y_pred ###
if torch.any(torch.isnan(y_pred)):
self.logger.error(
f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True
)
self.logger.error(f"y_pred: {y_pred}", in_order=True)
else:
self.logger.debug(
f"y_pred has Nan: {torch.any(torch.isnan(y_pred))}", in_order=True
)
self.logger.debug(f"y_pred: {y_pred}", in_order=True)
### y_gt ###
if torch.any(torch.isnan(y_gt)):
self.logger.error(
f"y_gt has Nan: {torch.any(torch.isnan(y_gt))}", in_order=True
)
self.logger.error(f"y_gt: {y_gt}", in_order=True)
else:
self.logger.debug(
f"y_gt has nan: {torch.any(torch.isnan(y_gt))}", in_order=True
)
self.logger.debug(f"y_gt: {y_gt}", in_order=True)
self.accelerator.end_training()
raise RuntimeError("Loss has Nan! See log for more info.")
### Protected methods end ###
## Following are private methods ##
def _build_optimizer(self):
r"""Build optimizer for model."""
# Make case-insensitive matching
if self.cfg.train.optimizer.lower() == "adadelta":
optimizer = torch.optim.Adadelta(
self.model.parameters(), **self.cfg.train.adadelta
)
self.logger.info("Using Adadelta optimizer.")
elif self.cfg.train.optimizer.lower() == "adagrad":
optimizer = torch.optim.Adagrad(
self.model.parameters(), **self.cfg.train.adagrad
)
self.logger.info("Using Adagrad optimizer.")
elif self.cfg.train.optimizer.lower() == "adam":
optimizer = torch.optim.Adam(self.model.parameters(), **self.cfg.train.adam)
self.logger.info("Using Adam optimizer.")
elif self.cfg.train.optimizer.lower() == "adamw":
optimizer = torch.optim.AdamW(
self.model.parameters(), **self.cfg.train.adamw
)
elif self.cfg.train.optimizer.lower() == "sparseadam":
optimizer = torch.optim.SparseAdam(
self.model.parameters(), **self.cfg.train.sparseadam
)
elif self.cfg.train.optimizer.lower() == "adamax":
optimizer = torch.optim.Adamax(
self.model.parameters(), **self.cfg.train.adamax
)
elif self.cfg.train.optimizer.lower() == "asgd":
optimizer = torch.optim.ASGD(self.model.parameters(), **self.cfg.train.asgd)
elif self.cfg.train.optimizer.lower() == "lbfgs":
optimizer = torch.optim.LBFGS(
self.model.parameters(), **self.cfg.train.lbfgs
)
elif self.cfg.train.optimizer.lower() == "nadam":
optimizer = torch.optim.NAdam(
self.model.parameters(), **self.cfg.train.nadam
)
elif self.cfg.train.optimizer.lower() == "radam":
optimizer = torch.optim.RAdam(
self.model.parameters(), **self.cfg.train.radam
)
elif self.cfg.train.optimizer.lower() == "rmsprop":
optimizer = torch.optim.RMSprop(
self.model.parameters(), **self.cfg.train.rmsprop
)
elif self.cfg.train.optimizer.lower() == "rprop":
optimizer = torch.optim.Rprop(
self.model.parameters(), **self.cfg.train.rprop
)
elif self.cfg.train.optimizer.lower() == "sgd":
optimizer = torch.optim.SGD(self.model.parameters(), **self.cfg.train.sgd)
else:
raise NotImplementedError(
f"Optimizer {self.cfg.train.optimizer} not supported yet!"
)
return optimizer
def _build_scheduler(self):
r"""Build scheduler for optimizer."""
# Make case-insensitive matching
if self.cfg.train.scheduler.lower() == "lambdalr":
scheduler = torch.optim.lr_scheduler.LambdaLR(
self.optimizer, **self.cfg.train.lambdalr
)
elif self.cfg.train.scheduler.lower() == "multiplicativelr":
scheduler = torch.optim.lr_scheduler.MultiplicativeLR(
self.optimizer, **self.cfg.train.multiplicativelr
)
elif self.cfg.train.scheduler.lower() == "steplr":
scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer, **self.cfg.train.steplr
)
elif self.cfg.train.scheduler.lower() == "multisteplr":
scheduler = torch.optim.lr_scheduler.MultiStepLR(
self.optimizer, **self.cfg.train.multisteplr
)
elif self.cfg.train.scheduler.lower() == "constantlr":
scheduler = torch.optim.lr_scheduler.ConstantLR(
self.optimizer, **self.cfg.train.constantlr
)
elif self.cfg.train.scheduler.lower() == "linearlr":
scheduler = torch.optim.lr_scheduler.LinearLR(
self.optimizer, **self.cfg.train.linearlr
)
elif self.cfg.train.scheduler.lower() == "exponentiallr":
scheduler = torch.optim.lr_scheduler.ExponentialLR(
self.optimizer, **self.cfg.train.exponentiallr
)
elif self.cfg.train.scheduler.lower() == "polynomiallr":
scheduler = torch.optim.lr_scheduler.PolynomialLR(
self.optimizer, **self.cfg.train.polynomiallr
)
elif self.cfg.train.scheduler.lower() == "cosineannealinglr":
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
self.optimizer, **self.cfg.train.cosineannealinglr
)
elif self.cfg.train.scheduler.lower() == "sequentiallr":
scheduler = torch.optim.lr_scheduler.SequentialLR(
self.optimizer, **self.cfg.train.sequentiallr
)
elif self.cfg.train.scheduler.lower() == "reducelronplateau":
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer, **self.cfg.train.reducelronplateau
)
elif self.cfg.train.scheduler.lower() == "cycliclr":
scheduler = torch.optim.lr_scheduler.CyclicLR(
self.optimizer, **self.cfg.train.cycliclr
)
elif self.cfg.train.scheduler.lower() == "onecyclelr":
scheduler = torch.optim.lr_scheduler.OneCycleLR(
self.optimizer, **self.cfg.train.onecyclelr
)
elif self.cfg.train.scheduler.lower() == "cosineannearingwarmrestarts":
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
self.optimizer, **self.cfg.train.cosineannearingwarmrestarts
)
elif self.cfg.train.scheduler.lower() == "noamlr":
scheduler = NoamLR(self.optimizer, **self.cfg.train.lr_scheduler)
else:
raise NotImplementedError(
f"Scheduler {self.cfg.train.scheduler} not supported yet!"
)
return scheduler
def _init_accelerator(self):
self.exp_dir = os.path.join(
os.path.abspath(self.cfg.log_dir), self.args.exp_name
)
project_config = ProjectConfiguration(
project_dir=self.exp_dir,
logging_dir=os.path.join(self.exp_dir, "log"),
)
self.accelerator = accelerate.Accelerator(
gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step,
log_with=self.cfg.train.tracker,
project_config=project_config,
)
if self.accelerator.is_main_process:
os.makedirs(project_config.project_dir, exist_ok=True)
os.makedirs(project_config.logging_dir, exist_ok=True)
with self.accelerator.main_process_first():
self.accelerator.init_trackers(self.args.exp_name)
def __check_basic_configs(self):
if self.cfg.train.gradient_accumulation_step <= 0:
self.logger.fatal("Invalid gradient_accumulation_step value!")
self.logger.error(
f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
)
self.accelerator.end_training()
raise ValueError(
f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive."
)
# TODO: check other values
@staticmethod
def __count_parameters(model):
model_param = 0.0
if isinstance(model, dict):
for key, value in model.items():
model_param += sum(p.numel() for p in model[key].parameters())
else:
model_param = sum(p.numel() for p in model.parameters())
return model_param
def __dump_cfg(self, path):
os.makedirs(os.path.dirname(path), exist_ok=True)
json5.dump(
self.cfg,
open(path, "w"),
indent=4,
sort_keys=True,
ensure_ascii=False,
quote_keys=True,
)
### Private methods end ###
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