import torch
|
import os
|
from funasr.train_utils.device_funcs import to_device
|
import logging
|
import time
|
from tqdm import tqdm
|
from contextlib import nullcontext
|
import torch.distributed as dist
|
from funasr.train_utils.recursive_op import recursive_average
|
|
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, model,
|
optim,
|
scheduler,
|
dataloader_train,
|
dataloader_val,
|
local_rank,
|
use_ddp=False,
|
use_fsdp=False,
|
**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.model = model
|
self.optim = optim
|
self.scheduler = scheduler
|
self.dataloader_train = dataloader_train
|
self.dataloader_val = dataloader_val
|
self.output_dir = kwargs.get('output_dir', './')
|
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 = next(model.parameters()).device
|
self.kwargs = kwargs
|
|
if self.resume:
|
self._resume_checkpoint(self.resume)
|
|
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
|
|
def _save_checkpoint(self, epoch):
|
"""
|
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.
|
"""
|
state = {
|
'epoch': epoch,
|
'state_dict': self.model.state_dict(),
|
'optimizer': self.optim.state_dict(),
|
'scheduler': self.scheduler.state_dict(),
|
}
|
# Create output directory if it does not exist
|
os.makedirs(self.output_dir, exist_ok=True)
|
filename = os.path.join(self.output_dir, f'model.e{epoch}.pb')
|
torch.save(state, filename)
|
print(f'Checkpoint saved to {filename}')
|
|
def _resume_checkpoint(self, resume_path):
|
"""
|
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 os.path.isfile(resume_path):
|
checkpoint = torch.load(resume_path)
|
self.start_epoch = checkpoint['epoch'] + 1
|
self.model.load_state_dict(checkpoint['state_dict'])
|
self.optim.load_state_dict(checkpoint['optimizer'])
|
self.scheduler.load_state_dict(checkpoint['scheduler'])
|
print(f"Checkpoint loaded successfully from '{resume_path}' at (epoch {checkpoint['epoch']})")
|
else:
|
print(f"No checkpoint found at '{resume_path}', starting from scratch")
|
|
def run(self):
|
"""
|
Starts the training process, iterating over epochs, training the model,
|
and saving checkpoints at the end of each epoch.
|
"""
|
for epoch in range(self.start_epoch, self.max_epoch + 1):
|
self._train_epoch(epoch)
|
# self._validate_epoch(epoch)
|
if self.rank == 0:
|
self._save_checkpoint(epoch)
|
self.scheduler.step()
|
break
|
|
def _train_epoch(self, epoch):
|
"""
|
Defines the training process for a single epoch with gradient accumulation.
|
Args:
|
epoch (int): The current epoch number.
|
"""
|
self.model.train()
|
pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
|
dynamic_ncols=True)
|
|
# Set the number of steps for gradient accumulation
|
accum_grad = self.kwargs.get("accum_grad", 1)
|
# Initialize the gradient accumulation
|
self.optim.zero_grad()
|
speed_stats = {}
|
time5 = time.perf_counter()
|
for batch_idx, batch in enumerate(self.dataloader_train):
|
time1 = time.perf_counter()
|
speed_stats["data_load"] = f"{time1-time5:0.3f}"
|
# import pdb;
|
# pdb.set_trace()
|
batch = to_device(batch, self.device)
|
|
my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
|
with my_context():
|
time2 = time.perf_counter()
|
retval = self.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)
|
# Now weight is summation over all workers
|
loss /= weight
|
# 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 / accum_grad
|
loss.backward()
|
time4 = time.perf_counter()
|
speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
|
|
# Perform an optimizer step only after accumulating enough gradients
|
if (batch_idx + 1) % accum_grad == 0 or (batch_idx + 1) == len(self.dataloader_train):
|
# Perform gradient clipping if it is set
|
if self.kwargs.get("grad_clip", None) is not None:
|
grad_norm = torch.nn.utils.clip_grad_norm_(
|
self.model.parameters(),
|
max_norm=self.kwargs.get("grad_clip", 10.0),
|
norm_type=self.kwargs.get("grad_clip_type", 2.0),
|
)
|
if not torch.isfinite(grad_norm):
|
logging.warning(
|
f"The grad norm is {grad_norm}. Skipping updating the model."
|
)
|
self.optim.zero_grad() # Reset gradients
|
continue
|
|
# Execute an optimization step (update model parameters)
|
self.optim.step()
|
self.scheduler.step()
|
# Clear gradients for the next accumulation stage
|
self.optim.zero_grad()
|
total_time = f"{time.perf_counter() - time5:0.3f}"
|
time5 = time.perf_counter()
|
speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
|
|
speed_stats["total_time"] = total_time
|
|
# import pdb;
|
# pdb.set_trace()
|
pbar.update(1)
|
if self.local_rank == 0:
|
description = (
|
f"Epoch: {epoch + 1}/{self.max_epoch}, "
|
f"step {batch_idx}/{len(self.dataloader_train)}, "
|
f"{speed_stats}, "
|
f"(loss: {loss.detach().cpu().item():.3f}), "
|
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
|
)
|
pbar.set_description(description)
|
|
# if batch_idx == 2:
|
# break
|
pbar.close()
|
|
def _validate_epoch(self, epoch):
|
"""
|
Defines the validation process for a single epoch.
|
Should be implemented with the actual model validation steps.
|
|
Args:
|
epoch (int): The current epoch number.
|
"""
|
self.model.eval()
|
with torch.no_grad():
|
for data, target in self.dataloader_val:
|
# Implement the model validation steps here
|
pass
|
