From 9b4e9cc8a0311e5243d69b73ed073e7ea441982e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 三月 2024 16:05:29 +0800
Subject: [PATCH] train update
---
funasr/train_utils/trainer.py | 762 +++++++++++++++++++++++++++++++++++++++++-----------------
1 files changed, 541 insertions(+), 221 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 59aeaf0..116c9e3 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -1,233 +1,553 @@
-import torch
+import math
import os
-from funasr.train_utils.device_funcs import to_device
-import logging
import time
+import torch
+import logging
from tqdm import tqdm
-from contextlib import nullcontext
+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
+
+@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.
+ """
+ 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.
+ 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.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:
+ 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", 5000)
+ 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", 5)
+ 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_eoch = {}
+ self.val_loss_step_or_eoch = {}
+
+ def save_checkpoint(self, epoch,
+ step=None,
+ model=None,
+ optim=None,
+ scheduler=None,
+ scaler=None,
+ ):
+ """
+ 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:
+ epoch (int): The epoch number at which the checkpoint is being saved.
+ """
+
+ if self.rank == 0:
+ logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
+ # self.step_or_epoch += 1
+ state = {
+ 'epoch': epoch,
+ 'state_dict': model.state_dict(),
+ 'optimizer': optim.state_dict(),
+ 'scheduler': scheduler.state_dict(),
+ "saved_ckpts": self.saved_ckpts,
+ "val_acc_step_or_eoch": self.val_acc_step_or_eoch,
+ "val_loss_step_or_eoch": self.val_loss_step_or_eoch,
+ "best_step_or_epoch": self.best_step_or_epoch,
+ "avg_keep_nbest_models_type": slef.avg_keep_nbest_models_type,
+ }
+ 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'\nCheckpoint saved to {filename}\n')
+ 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_eoch[ckpt_name] >= self.val_acc_step_or_eoch[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_eoch[self.best_step_or_epoch]}, {best_ckpt}")
+ else:
+ logging.info(f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]}")
+ elif self.avg_keep_nbest_models_type == "loss":
+ if self.val_loss_step_or_eoch[ckpt_name] <= self.val_loss_step_or_eoch[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_eoch[self.best_step_or_epoch]}, {best_ckpt}")
+ else:
+ logging.info(f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]} > {self.val_loss_step_or_eoch[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_eoch")[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 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()
-
- 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()
+ 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'] + 1
+ # 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_eoch = checkpoint["val_acc_step_or_eoch"] if "val_acc_step_or_eoch" in checkpoint else {}
+ self.val_loss_step_or_eoch = checkpoint["val_loss_step_or_eoch"] if "val_loss_step_or_eoch" in checkpoint else {}
+ self.val_loss_step_or_eoch = checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
+ 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,
+ ):
+ """
+ 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.local_rank}\n")
+ model.train()
- 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
+ # Set the number of steps for gradient accumulation
+ accum_grad = self.accum_grad
+ # Initialize the gradient accumulation
+ optim.zero_grad()
+ speed_stats = {}
+ time5 = time.perf_counter()
+ iterator_stop = torch.tensor(0).to(self.device)
+
+ dataloader_train.batch_sampler.set_epoch(epoch)
+ 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
+ time1 = time.perf_counter()
+ speed_stats["data_load"] = f"{time1-time5:0.3f}"
+
+ batch = to_device(batch, self.device)
+
+ my_context = model.no_sync if batch_idx % accum_grad != 0 else nullcontext
+ with my_context():
+ time2 = time.perf_counter()
+ with maybe_autocast(self.use_fp16):
+ 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 / accum_grad
+ if self.use_fp16:
+ scaler.scale(loss).backward()
+ else:
+ loss.backward()
+ time4 = time.perf_counter()
+ speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
+
+ self.train_loss_avg = (self.train_loss_avg*batch_idx + loss.detach().cpu().item())/(batch_idx+1)
+ if "acc" in stats:
+ self.train_acc_avg = (self.train_acc_avg * batch_idx + stats["acc"].detach().cpu().item()) / (batch_idx + 1)
+ 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
+
+
+ # 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)
+ 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
+ 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,
+ batch_num_epoch=batch_num_epoch,
+ lr=lr,
+ loss=loss.detach().cpu().item(),
+ speed_stats=speed_stats,
+ stats=stats,
+ writer=writer,
+ tag="train",
+ )
+
+ if (batch_idx + 1) % self.validate_interval == 0:
+ self.validate_epoch(
+ model=model,
+ dataloader_val=dataloader_val,
+ epoch=epoch,
+ writer=writer
+ )
+
+ if (batch_idx+1) % self.save_checkpoint_interval == 0:
+ self.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler, step=batch_idx+1)
+
+ 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.local_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()
+
+ 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", None) is None:
+ ckpt_name = f'model.pt.ep{epoch}'
+ else:
+ ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step")}'
+ self.val_acc_step_or_eoch[ckpt_name] = self.val_acc_avg
+ self.val_loss_step_or_eoch[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,
+ batch_num_epoch=-1,
+ lr=0.0,
+ loss=0.0,
+ speed_stats=None,
+ stats=None,
+ writer=None,
+ tag="train",
+ ):
+
+ if (batch_idx + 1) % self.log_interval == 0:
+
+ 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.local_rank}, "
+ f"epoch: {epoch}/{self.max_epoch}, "
+ f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
+ f"(loss_avg_rank: {loss:.3f}), "
+ f"(loss_avg_epoch: {loss_avg_epoch:.3f}), "
+ f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3f}), "
+ f"(acc_avg_epoch: {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)
+
+ if writer is not None:
+ writer.add_scalar(f'rank{self.local_rank}_loss/{tag}', loss, self.batch_total)
+ writer.add_scalar(f'rank{self.local_rank}_lr/{tag}', lr, self.batch_total)
+ writer.add_scalar(f'rank{self.local_rank}_lr/{tag}', lr, self.batch_total)
+ for key, var in stats.items():
+ writer.add_scalar(f'stats_rank{self.local_rank}_{key}/{tag}', var.item(), self.batch_total)
+ for key, var in speed_stats.items():
+ writer.add_scalar(f'stats_rank{self.local_rank}_{key}/{tag}', eval(var), 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()
\ No newline at end of file
--
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