import math
|
import os
|
import time
|
import torch
|
import logging
|
from tqdm import tqdm
|
from datetime import datetime
|
import torch.distributed as dist
|
from torch.cuda.amp import autocast, GradScaler
|
from contextlib import nullcontext, contextmanager
|
from pathlib import Path
|
|
from funasr.train_utils.device_funcs import to_device
|
from funasr.train_utils.recursive_op import recursive_average
|
from funasr.train_utils.average_nbest_models import average_checkpoints
|
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
|
|
try:
|
import wandb
|
except:
|
wandb = None
|
|
|
@contextmanager
|
def maybe_autocast(enabled):
|
if enabled:
|
with autocast():
|
yield
|
else:
|
yield
|
|
|
class Trainer:
|
"""
|
A simple trainer class for training a PyTorch model, saving checkpoints at the end of each epoch,
|
and optionally resuming from a saved checkpoint.
|
|
Attributes:
|
max_epoch (int): Maximum number of epochs for training.
|
model (torch.nn.Module): The model to be trained.
|
optim (torch.optim.Optimizer): The optimizer to use for training.
|
scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler.
|
dataloader_train (torch.utils.data.DataLoader): DataLoader for the training dataset.
|
dataloader_val (torch.utils.data.DataLoader): DataLoader for the validation dataset.
|
output_dir (str): Directory where model checkpoints will be saved.
|
resume (str, optional): Path to a checkpoint to resume training from.
|
"""
|
|
def __init__(
|
self,
|
local_rank,
|
use_ddp: bool = False,
|
use_fsdp: bool = False,
|
use_fp16: bool = False,
|
output_dir: str = "./",
|
**kwargs,
|
):
|
"""
|
Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings.
|
|
Args:
|
model (torch.nn.Module): The model to be trained.
|
optim (torch.optim.Optimizer): The optimizer to use for training.
|
scheduler (torch.optim.lr_scheduler._LRScheduler): The learning rate scheduler.
|
dataloader_train (torch.utils.data.DataLoader): The DataLoader for the training dataset.
|
dataloader_val (torch.utils.data.DataLoader): The DataLoader for the validation dataset.
|
**kwargs: Additional keyword arguments:
|
max_epoch (int): The maximum number of epochs for training.
|
output_dir (str): The directory where model checkpoints will be saved. Default is './'.
|
resume (str, optional): The file path to a checkpoint to resume training from.
|
"""
|
|
self.output_dir = output_dir
|
if not os.path.exists(self.output_dir):
|
os.makedirs(self.output_dir, exist_ok=True)
|
self.resume = kwargs.get("resume", True)
|
self.start_epoch = 0
|
self.max_epoch = kwargs.get("max_epoch", 100)
|
self.local_rank = local_rank
|
self.use_ddp = use_ddp
|
self.use_fsdp = use_fsdp
|
self.device = kwargs.get("device", "cuda")
|
# self.kwargs = kwargs
|
self.log_interval = kwargs.get("log_interval", 50)
|
self.batch_total = 0
|
self.use_fp16 = use_fp16
|
self.save_checkpoint_interval = kwargs.get("save_checkpoint_interval", 5000)
|
self.validate_interval = kwargs.get("validate_interval", -1)
|
if self.validate_interval < 0:
|
self.validate_interval = self.save_checkpoint_interval
|
assert (
|
self.save_checkpoint_interval == self.validate_interval
|
), f"save_checkpoint_interval must equal to validate_interval"
|
self.keep_nbest_models = kwargs.get("keep_nbest_models", 500)
|
self.avg_keep_nbest_models_type = kwargs.get("avg_keep_nbest_models_type", "acc")
|
self.avg_nbest_model = kwargs.get("avg_nbest_model", 10)
|
self.accum_grad = kwargs.get("accum_grad", 1)
|
self.grad_clip = kwargs.get("grad_clip", 10.0)
|
self.grad_clip_type = kwargs.get("grad_clip_type", 2.0)
|
|
try:
|
rank = dist.get_rank()
|
world_size = dist.get_world_size()
|
except:
|
rank = 0
|
world_size = 1
|
logging.warning("distributed is not initialized, only single shard")
|
self.rank = rank
|
self.world_size = world_size
|
self.train_acc_avg = 0.0
|
self.train_loss_avg = 0.0
|
self.val_acc_avg = 0.0
|
self.val_loss_avg = 0.0
|
self.best_acc_idx = 0
|
self.saved_ckpts = {}
|
self.step_or_epoch = -1
|
self.best_step_or_epoch = ""
|
self.val_acc_step_or_epoch = {}
|
self.val_loss_step_or_epoch = {}
|
|
self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
|
self.start_data_split_i = 0
|
self.start_step = 0
|
self.step_in_epoch = 0
|
self.use_wandb = kwargs.get("use_wandb", False)
|
if self.use_wandb:
|
wandb.login(key=kwargs.get("wandb_token"))
|
wandb.init(
|
config=kwargs,
|
project=kwargs.get("wandb_project", "my_project"),
|
entity=kwargs.get("wandb_team", "my_team"),
|
name=kwargs.get("wandb_exp_name", "my_exp"),
|
dir=output_dir,
|
job_type="training",
|
reinit=True,
|
)
|
|
def save_checkpoint(
|
self,
|
epoch,
|
step=None,
|
model=None,
|
optim=None,
|
scheduler=None,
|
scaler=None,
|
step_in_epoch=None,
|
**kwargs,
|
):
|
"""
|
Saves a checkpoint containing the model's state, the optimizer's state,
|
and the scheduler's state at the end of the given epoch. This method is
|
intended to be called at the end of each epoch to save the training progress.
|
|
Args:
|
epoch (int): The epoch number at which the checkpoint is being saved.
|
"""
|
|
step_in_epoch = None if step is None else step_in_epoch
|
if self.rank == 0:
|
logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
|
# self.step_or_epoch += 1
|
state = {
|
"epoch": epoch,
|
"step": step,
|
"total_step": self.batch_total,
|
"state_dict": model.state_dict(),
|
"optimizer": optim.state_dict(),
|
"scheduler": scheduler.state_dict(),
|
"saved_ckpts": self.saved_ckpts,
|
"val_acc_step_or_epoch": self.val_acc_step_or_epoch,
|
"val_loss_step_or_epoch": self.val_loss_step_or_epoch,
|
"best_step_or_epoch": self.best_step_or_epoch,
|
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
|
"step_in_epoch": step_in_epoch,
|
"data_split_i": kwargs.get("data_split_i", 0),
|
"data_split_num": kwargs.get("data_split_num", 1),
|
"batch_total": self.batch_total,
|
"train_loss_avg": kwargs.get("train_loss_avg", 0),
|
"train_acc_avg": kwargs.get("train_acc_avg", 0),
|
}
|
step = step_in_epoch
|
if hasattr(model, "module"):
|
state["state_dict"] = model.module.state_dict()
|
|
if scaler:
|
state["scaler_state"] = scaler.state_dict()
|
|
# Create output directory if it does not exist
|
os.makedirs(self.output_dir, exist_ok=True)
|
if step is None:
|
ckpt_name = f"model.pt.ep{epoch}"
|
else:
|
ckpt_name = f"model.pt.ep{epoch}.{step}"
|
filename = os.path.join(self.output_dir, ckpt_name)
|
torch.save(state, filename)
|
logging.info(f"Checkpoint saved to {filename}")
|
|
latest = Path(os.path.join(self.output_dir, f"model.pt"))
|
torch.save(state, latest)
|
|
if self.best_step_or_epoch == "":
|
self.best_step_or_epoch = ckpt_name
|
|
if self.avg_keep_nbest_models_type == "acc":
|
if (
|
self.val_acc_step_or_epoch[ckpt_name]
|
>= self.val_acc_step_or_epoch[self.best_step_or_epoch]
|
):
|
self.best_step_or_epoch = ckpt_name
|
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
|
torch.save(state, best_ckpt)
|
logging.info(
|
f"Update best acc: {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
|
)
|
else:
|
logging.info(
|
f"No improvement in acc: {self.val_acc_step_or_epoch[ckpt_name]:.4f} < {self.val_acc_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
|
)
|
elif self.avg_keep_nbest_models_type == "loss":
|
if (
|
self.val_loss_step_or_epoch[ckpt_name]
|
<= self.val_loss_step_or_epoch[self.best_step_or_epoch]
|
):
|
self.best_step_or_epoch = ckpt_name
|
best_ckpt = Path(os.path.join(self.output_dir, f"model.pt.best"))
|
torch.save(state, best_ckpt)
|
logging.info(
|
f"Update best loss: {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {best_ckpt}"
|
)
|
else:
|
logging.info(
|
f"No improvement in loss: {self.val_loss_step_or_epoch[ckpt_name]:.4f} > {self.val_loss_step_or_epoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
|
)
|
else:
|
print("Undo")
|
self.saved_ckpts[ckpt_name] = getattr(
|
self, f"val_{self.avg_keep_nbest_models_type}_step_or_epoch"
|
)[ckpt_name]
|
if self.keep_nbest_models > 0:
|
if len(self.saved_ckpts) > self.keep_nbest_models:
|
if self.avg_keep_nbest_models_type == "acc":
|
key = min(self.saved_ckpts, key=self.saved_ckpts.get)
|
else:
|
key = max(self.saved_ckpts, key=self.saved_ckpts.get)
|
if key in self.saved_ckpts:
|
del self.saved_ckpts[key]
|
filename = os.path.join(self.output_dir, key)
|
logging.info(f"Delete: {filename}")
|
if os.path.exists(filename):
|
os.remove(filename)
|
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
|
def resume_checkpoint(
|
self,
|
model=None,
|
optim=None,
|
scheduler=None,
|
scaler=None,
|
):
|
"""
|
Resumes training from a checkpoint at the given file path.
|
Loads the model's state, the optimizer's state, and the scheduler's state.
|
|
Args:
|
resume_path (str): The file path to the checkpoint to resume from.
|
"""
|
if self.resume:
|
ckpt = os.path.join(self.output_dir, "model.pt")
|
if os.path.isfile(ckpt):
|
checkpoint = torch.load(ckpt, map_location="cpu")
|
self.start_epoch = checkpoint["epoch"]
|
# self.model.load_state_dict(checkpoint['state_dict'])
|
src_state = checkpoint["state_dict"]
|
dst_state = model.state_dict()
|
for k in dst_state.keys():
|
if not k.startswith("module.") and "module." + k in src_state.keys():
|
k_ddp = "module." + k
|
elif k.startswith("module.") and "module." + k not in src_state.keys():
|
k_ddp = k.replace("module.", "", 1)
|
else:
|
k_ddp = k
|
|
if k_ddp in src_state.keys():
|
dst_state[k] = src_state[k_ddp]
|
else:
|
print(f"Miss key in ckpt: model: {k}, ckpt: {k_ddp}")
|
|
model.load_state_dict(dst_state)
|
optim.load_state_dict(checkpoint["optimizer"])
|
scheduler.load_state_dict(checkpoint["scheduler"])
|
if scaler is not None and "scaler_state" in checkpoint:
|
scaler.load_state_dict(checkpoint["scaler_state"])
|
|
self.saved_ckpts = checkpoint["saved_ckpts"]
|
self.val_acc_step_or_epoch = (
|
checkpoint["val_acc_step_or_epoch"]
|
if "val_acc_step_or_epoch" in checkpoint
|
else {}
|
)
|
self.val_loss_step_or_epoch = (
|
checkpoint["val_loss_step_or_epoch"]
|
if "val_loss_step_or_epoch" in checkpoint
|
else {}
|
)
|
self.best_step_or_epoch = (
|
checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
|
)
|
self.start_data_split_i = (
|
checkpoint["data_split_i"] if "data_split_i" in checkpoint else 0
|
)
|
self.batch_total = checkpoint["batch_total"] if "batch_total" in checkpoint else 0
|
self.start_step = checkpoint["step"] if "step" in checkpoint else 0
|
self.start_step = 0 if self.start_step is None else self.start_step
|
self.step_in_epoch = (
|
checkpoint["step_in_epoch"] if "step_in_epoch" in checkpoint else 0
|
)
|
self.step_in_epoch = 0 if self.step_in_epoch is None else self.step_in_epoch
|
print(checkpoint["train_acc_avg"])
|
self.train_acc_avg = (
|
checkpoint["train_acc_avg"] if "train_acc_avg" in checkpoint else 0
|
)
|
self.train_loss_avg = (
|
checkpoint["train_loss_avg"] if "train_loss_avg" in checkpoint else 0
|
)
|
model.to(self.device)
|
print(f"Checkpoint loaded successfully from '{ckpt}'")
|
else:
|
print(f"No checkpoint found at '{ckpt}', does not resume status!")
|
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
|
def train_epoch(
|
self,
|
model=None,
|
optim=None,
|
scheduler=None,
|
scaler=None,
|
dataloader_train=None,
|
dataloader_val=None,
|
epoch=None,
|
writer=None,
|
**kwargs,
|
):
|
"""
|
Defines the training process for a single epoch with gradient accumulation.
|
Args:
|
epoch (int): The current epoch number.
|
"""
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
logging.info(f"Train epoch: {epoch}, rank: {self.rank}\n")
|
model.train()
|
|
# Set the number of steps for gradient accumulation
|
accum_grad = self.accum_grad
|
# Initialize the gradient accumulation
|
optim.zero_grad()
|
speed_stats = {}
|
|
iterator_stop = torch.tensor(0).to(self.device)
|
|
dataloader_train.batch_sampler.set_epoch(epoch)
|
time_beg = time.perf_counter()
|
time5 = time_beg
|
for batch_idx, batch in enumerate(dataloader_train):
|
# if self.use_ddp or self.use_fsdp:
|
# dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
|
# if iterator_stop > 0:
|
# break
|
self.batch_total += 1
|
self.step_in_epoch += 1
|
time1 = time.perf_counter()
|
speed_stats["data_load"] = f"{time1-time_beg:0.3f}"
|
|
batch = to_device(batch, self.device)
|
|
my_context = nullcontext
|
if self.use_ddp or self.use_fsdp:
|
my_context = model.no_sync if batch_idx % accum_grad != 0 else my_context
|
with my_context():
|
time2 = time.perf_counter()
|
with maybe_autocast(self.use_fp16):
|
retval = model(**batch)
|
|
# if (
|
# self.reset_gpu_cache
|
# and (torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024) > 70
|
# ):
|
# torch.cuda.empty_cache()
|
|
loss, stats, weight = retval
|
stats = {k: v for k, v in stats.items() if v is not None}
|
if self.use_ddp or self.use_fsdp:
|
# Apply weighted averaging for loss and stats
|
loss = (loss * weight.type(loss.dtype)).sum()
|
# if distributed, this method can also apply all_reduce()
|
# stats, weight = recursive_average(stats, weight, distributed=True)
|
if self.use_ddp or self.use_fsdp:
|
dist.all_reduce(weight, op=dist.ReduceOp.SUM)
|
# Now weight is summation over all workers
|
loss /= weight.sum() # shape:[1] -> shape:[]
|
# Multiply world_size because DistributedDataParallel
|
# automatically normalizes the gradient by world_size.
|
loss *= self.world_size
|
# loss *= self.world_size
|
# Scale the loss since we're not updating for every mini-batch
|
loss = loss / accum_grad
|
|
time3 = time.perf_counter()
|
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
|
if self.use_fp16:
|
scaler.scale(loss).backward()
|
else:
|
loss.backward()
|
time4 = time.perf_counter()
|
speed_stats["backward_and_AllReaduce_time"] = f"{time4 - time3:0.3f}"
|
|
self.train_loss_avg = (
|
self.train_loss_avg * (batch_idx + kwargs.get("start_step", 0))
|
+ loss.detach().cpu().item()
|
) / (batch_idx + kwargs.get("start_step", 0) + 1)
|
if "acc" in stats:
|
self.train_acc_avg = (
|
self.train_acc_avg * (batch_idx + kwargs.get("start_step", 0))
|
+ stats["acc"].detach().cpu().item()
|
) / (batch_idx + kwargs.get("start_step", 0) + 1)
|
|
# Perform an optimizer step only after accumulating enough gradients
|
if (batch_idx + 1) % accum_grad == 0:
|
# Perform gradient clipping if it is set
|
if self.grad_clip > 0:
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
model.parameters(),
|
max_norm=self.grad_clip,
|
norm_type=self.grad_clip_type,
|
)
|
if not torch.isfinite(grad_norm):
|
logging.warning(
|
f"The grad norm is {grad_norm}. Skipping updating the model."
|
)
|
optim.zero_grad() # Reset gradients
|
continue
|
|
# Execute an optimization step (update model parameters)
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
if self.use_fp16:
|
scaler.step(optim)
|
scaler.update()
|
else:
|
optim.step()
|
scheduler.step()
|
# Clear gradients for the next accumulation stage
|
optim.zero_grad(set_to_none=True)
|
|
if self.use_ddp or self.use_fsdp:
|
train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
|
self.device
|
)
|
train_acc_avg = torch.tensor(self.train_acc_avg, dtype=torch.float32).to(
|
self.device
|
)
|
dist.all_reduce(train_loss_avg, op=dist.ReduceOp.SUM)
|
dist.all_reduce(train_acc_avg, op=dist.ReduceOp.SUM)
|
self.train_loss_avg = train_loss_avg.detach().cpu().item() / self.world_size
|
self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size
|
|
total_time = f"{(time.perf_counter() - time5)/accum_grad:0.3f}"
|
time5 = time.perf_counter()
|
|
speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
|
|
speed_stats["total_time"] = total_time
|
lr = scheduler.get_last_lr()[0]
|
batch_num_epoch = 1
|
if hasattr(dataloader_train, "__len__"):
|
batch_num_epoch = len(dataloader_train)
|
self.log(
|
epoch,
|
batch_idx,
|
log_step=batch_idx + kwargs.get("start_step", 0),
|
step_in_epoch=self.step_in_epoch,
|
batch_num_epoch=batch_num_epoch,
|
lr=lr,
|
loss=accum_grad * loss.detach().cpu().item(),
|
speed_stats=speed_stats,
|
stats=stats,
|
writer=writer,
|
tag="train",
|
data_split_i=kwargs.get("data_split_i", 0),
|
data_split_num=kwargs.get("data_split_num", 1),
|
)
|
|
if self.step_in_epoch % self.validate_interval == 0:
|
self.validate_epoch(
|
model=model,
|
dataloader_val=dataloader_val,
|
epoch=epoch,
|
writer=writer,
|
step=batch_idx + 1,
|
step_in_epoch=self.step_in_epoch,
|
)
|
|
if self.step_in_epoch % self.save_checkpoint_interval == 0:
|
self.save_checkpoint(
|
epoch,
|
model=model,
|
optim=optim,
|
scheduler=scheduler,
|
scaler=scaler,
|
step=batch_idx + 1,
|
step_in_epoch=self.step_in_epoch,
|
data_split_i=kwargs.get("data_split_i", 0),
|
data_split_num=kwargs.get("data_split_num", 1),
|
train_loss_avg=self.train_loss_avg,
|
train_acc_avg=self.train_acc_avg,
|
)
|
|
time_beg = time.perf_counter()
|
# else:
|
# if self.use_ddp or self.use_fsdp:
|
# iterator_stop.fill_(1)
|
# dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
|
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
# iterator_stop = torch.tensor(0).to(self.device)
|
|
def validate_epoch(
|
self,
|
model=None,
|
dataloader_val=None,
|
epoch=None,
|
writer=None,
|
**kwargs,
|
):
|
"""
|
Defines the validation process for a single epoch.
|
Should be implemented with the actual model validation steps.
|
|
Args:
|
epoch (int): The current epoch number.
|
"""
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
|
model.eval()
|
|
with torch.no_grad():
|
|
speed_stats = {}
|
time5 = time.perf_counter()
|
iterator_stop = torch.tensor(0).to(self.device)
|
dataloader_val.batch_sampler.set_epoch(epoch)
|
for batch_idx, batch in enumerate(dataloader_val):
|
if self.use_ddp or self.use_fsdp:
|
dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
|
if iterator_stop > 0:
|
break
|
time1 = time.perf_counter()
|
speed_stats["data_load"] = f"{time1 - time5:0.3f}"
|
batch = to_device(batch, self.device)
|
|
time2 = time.perf_counter()
|
retval = model(**batch)
|
time3 = time.perf_counter()
|
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
|
loss, stats, weight = retval
|
stats = {k: v for k, v in stats.items() if v is not None}
|
|
if self.use_ddp or self.use_fsdp:
|
# Apply weighted averaging for loss and stats
|
loss = (loss * weight.type(loss.dtype)).sum()
|
# if distributed, this method can also apply all_reduce()
|
# stats, weight = recursive_average(stats, weight, distributed=True)
|
if self.use_ddp or self.use_fsdp:
|
dist.all_reduce(weight, op=dist.ReduceOp.SUM)
|
# Now weight is summation over all workers
|
loss /= weight.sum() # shape:[1] -> shape:[]
|
# Multiply world_size because DistributedDataParallel
|
# automatically normalizes the gradient by world_size.
|
loss *= self.world_size
|
|
# Scale the loss since we're not updating for every mini-batch
|
loss = loss
|
time4 = time.perf_counter()
|
|
if torch.isfinite(loss):
|
self.val_loss_avg = (
|
self.val_loss_avg * batch_idx + loss.detach().cpu().item()
|
) / (batch_idx + 1)
|
|
if "acc" in stats:
|
self.val_acc_avg = (
|
self.val_acc_avg * batch_idx + stats["acc"].detach().cpu().item()
|
) / (batch_idx + 1)
|
|
if self.use_ddp or self.use_fsdp:
|
val_loss_avg = torch.tensor(self.val_loss_avg, dtype=torch.float32).to(
|
self.device
|
)
|
val_acc_avg = torch.tensor(self.val_acc_avg, dtype=torch.float32).to(
|
self.device
|
)
|
dist.all_reduce(val_loss_avg, op=dist.ReduceOp.SUM)
|
dist.all_reduce(val_acc_avg, op=dist.ReduceOp.SUM)
|
self.val_loss_avg = val_loss_avg.detach().cpu().item() / self.world_size
|
self.val_acc_avg = val_acc_avg.detach().cpu().item() / self.world_size
|
|
time5 = time.perf_counter()
|
batch_num_epoch = 1
|
if hasattr(dataloader_val, "__len__"):
|
batch_num_epoch = len(dataloader_val)
|
self.log(
|
epoch,
|
batch_idx,
|
batch_num_epoch=batch_num_epoch,
|
lr=0.0,
|
loss=loss.detach().cpu().item(),
|
speed_stats=speed_stats,
|
stats=stats,
|
writer=writer,
|
tag="val",
|
)
|
|
else:
|
if self.use_ddp or self.use_fsdp:
|
iterator_stop.fill_(1)
|
dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
|
|
if kwargs.get("step_in_epoch", None) is None:
|
ckpt_name = f"model.pt.ep{epoch}"
|
else:
|
ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_in_epoch")}'
|
self.val_acc_step_or_epoch[ckpt_name] = self.val_acc_avg
|
self.val_loss_step_or_epoch[ckpt_name] = self.val_loss_avg
|
model.train()
|
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
iterator_stop = torch.tensor(0).to(self.device)
|
|
def log(
|
self,
|
epoch=0,
|
batch_idx=0,
|
step_in_epoch=0,
|
batch_num_epoch=-1,
|
lr=0.0,
|
loss=0.0,
|
speed_stats=None,
|
stats=None,
|
writer=None,
|
tag="train",
|
data_split_i=0,
|
data_split_num=1,
|
log_step=None,
|
**kwargs,
|
):
|
|
if (batch_idx + 1) % self.log_interval == 0:
|
batch_idx = log_step if log_step is not None else batch_idx
|
gpu_info = (
|
"GPU, memory: usage: {:.3f} GB, "
|
"peak: {:.3f} GB, "
|
"cache: {:.3f} GB, "
|
"cache_peak: {:.3f} GB".format(
|
torch.cuda.memory_allocated() / 1024 / 1024 / 1024,
|
torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024,
|
torch.cuda.memory_reserved() / 1024 / 1024 / 1024,
|
torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024,
|
)
|
)
|
|
loss_avg_epoch = getattr(self, f"{tag}_loss_avg")
|
acc_avg_epoch = getattr(self, f"{tag}_acc_avg")
|
description = (
|
f"{tag}, "
|
f"rank: {self.rank}, "
|
f"epoch: {epoch}/{self.max_epoch}, "
|
f"data_slice: {data_split_i}/{data_split_num}, "
|
f"step_in_slice: {batch_idx + 1}/{batch_num_epoch}, step_in_epoch: {step_in_epoch}, total step: {self.batch_total}, "
|
f"(loss_avg_rank: {loss:.3f}), "
|
f"(loss_avg_slice: {loss_avg_epoch:.3f}), "
|
f"(ppl_avg_slice: {math.exp(loss_avg_epoch):.3e}), "
|
f"(acc_avg_slice: {acc_avg_epoch:.3f}), "
|
f"(lr: {lr:.3e}), "
|
f"{[(k, round(v.detach().cpu().item(), 3)) for k, v in stats.items()]}, "
|
f"{speed_stats}, "
|
f"{gpu_info}"
|
)
|
logging.info(description)
|
|
description_dict = {
|
f"rank{self.rank}_loss/{tag}": loss,
|
f"rank{self.rank}_lr/{tag}": lr,
|
}
|
|
if writer is not None:
|
writer.add_scalar(f"rank{self.rank}_loss/{tag}", loss, self.batch_total)
|
writer.add_scalar(f"rank{self.rank}_lr/{tag}", lr, self.batch_total)
|
for key, var in stats.items():
|
writer.add_scalar(
|
f"stats_rank{self.rank}_{key}/{tag}", var.item(), self.batch_total
|
)
|
description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = var.item()
|
for key, var in speed_stats.items():
|
writer.add_scalar(
|
f"stats_rank{self.rank}_{key}/{tag}", eval(var), self.batch_total
|
)
|
description_dict[f"stats_rank{self.rank}_{key}/{tag}"] = eval(var)
|
if self.use_wandb and wandb is not None:
|
wandb.log(
|
description_dict,
|
setp=self.batch_total,
|
)
|
|
def close(self, writer=None):
|
|
if self.use_ddp or self.use_fsdp:
|
dist.barrier()
|
|
if writer is not None:
|
writer.close()
|
|
if self.use_ddp or self.use_fsdp:
|
torch.distributed.destroy_process_group()
|
