From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/train_utils/trainer.py | 788 +++++++++++++++++++++++++++++++++++++++----------------
1 files changed, 552 insertions(+), 236 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 14abd6c..3e69985 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -1,3 +1,4 @@
+import math
import os
import time
import torch
@@ -7,14 +8,18 @@
import torch.distributed as dist
from torch.cuda.amp import autocast, GradScaler
from contextlib import nullcontext, contextmanager
-# from torch.utils.tensorboard import SummaryWriter
-from tensorboardX import SummaryWriter
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):
@@ -23,6 +28,7 @@
yield
else:
yield
+
class Trainer:
"""
@@ -39,18 +45,16 @@
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: bool = False,
- use_fsdp: bool = False,
- use_fp16: bool = False,
- output_dir: str="./",
- **kwargs):
+
+ 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.
@@ -65,32 +69,35 @@
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 = output_dir
- self.resume = kwargs.get('resume', True)
+ 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.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.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
- self.kwargs = kwargs
+ 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.disable_gpu_cache = kwargs.get("disable_gpu_cache", True)
- scaler = GradScaler(enabled=use_fp16) if use_fp16 else None
- scaler = ShardedGradScaler(enabled=use_fp16) if use_ddp else scaler
- self.scaler = scaler
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()
@@ -100,12 +107,45 @@
logging.warning("distributed is not initialized, only single shard")
self.rank = rank
self.world_size = world_size
-
- os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True)
- self.writer = SummaryWriter(os.path.join(self.output_dir, "tensorboard")) if rank == 0 else None
-
-
- def _save_checkpoint(self, epoch, step=None):
+ 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
@@ -114,29 +154,111 @@
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(),
- }
- if self.scaler:
- state["scaler_state"] = self.scaler.state_dict()
- # Create output directory if it does not exist
- os.makedirs(self.output_dir, exist_ok=True)
- if step is None:
- filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
- else:
- filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}.{step}')
-
- torch.save(state, filename)
-
- print(f'\nCheckpoint saved to {filename}\n')
- latest = Path(os.path.join(self.output_dir, f'model.pt'))
- torch.save(state, latest)
-
- def _resume_checkpoint(self, resume_path):
+ 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.
@@ -144,271 +266,465 @@
Args:
resume_path (str): The file path to the checkpoint to resume from.
"""
- ckpt = os.path.join(resume_path, "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 = self.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
- 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}")
-
- self.model.load_state_dict(dst_state)
- self.optim.load_state_dict(checkpoint['optimizer'])
- self.scheduler.load_state_dict(checkpoint['scheduler'])
- if self.scaler and 'scaler_state' in checkpoint:
- self.scaler.load_state_dict(checkpoint['scaler_state'])
- print(f"Checkpoint loaded successfully from '{ckpt}'")
- else:
- print(f"No checkpoint found at '{ckpt}', does not resume status!")
-
- self.model.to(self.device)
- if self.use_ddp or self.use_fsdp:
- dist.barrier()
-
- def run(self):
- """
- Starts the training process, iterating over epochs, training the model,
- and saving checkpoints at the end of each epoch.
- """
if self.resume:
- self._resume_checkpoint(self.output_dir)
-
- for epoch in range(self.start_epoch, self.max_epoch + 1):
- time1 = time.perf_counter()
- self._train_epoch(epoch)
+ 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}")
-
- if self.use_ddp or self.use_fsdp:
- dist.barrier()
-
- self._validate_epoch(epoch)
+ 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"])
- if self.use_ddp or self.use_fsdp:
- dist.barrier()
-
-
- if self.rank == 0:
- self._save_checkpoint(epoch)
-
- if self.use_ddp or self.use_fsdp:
- dist.barrier()
-
- self.scheduler.step()
+ 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!")
- time2 = time.perf_counter()
- time_escaped = (time2 - time1)/3600.0
- print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f} hours\n")
-
- if self.rank == 0:
- average_checkpoints(self.output_dir, self.avg_nbest_model)
-
if self.use_ddp or self.use_fsdp:
dist.barrier()
-
- if self.writer:
- self.writer.close()
-
-
- def _train_epoch(self, epoch):
+ 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.
"""
- self.model.train()
- pbar = tqdm(colour="blue", desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
- dynamic_ncols=True)
-
+ 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.kwargs.get("accum_grad", 1)
+ accum_grad = self.accum_grad
# Initialize the gradient accumulation
- self.optim.zero_grad()
+ optim.zero_grad()
speed_stats = {}
- time5 = time.perf_counter()
-
- for batch_idx, batch in enumerate(self.dataloader_train):
+
+ 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-time5:0.3f}"
+ speed_stats["data_load"] = f"{time1-time_beg:0.3f}"
batch = to_device(batch, self.device)
-
- my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
+
+ 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 = self.model(**batch)
-
- if self.disable_gpu_cache: torch.cuda.empty_cache()
+ retval = model(**batch)
- time3 = time.perf_counter()
- speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
+ # 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)
+ # 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
+ 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:
- self.scaler.scale(loss).backward()
+ scaler.scale(loss).backward()
else:
loss.backward()
time4 = time.perf_counter()
- speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
-
+ 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 or (batch_idx + 1) == len(self.dataloader_train):
+ if (batch_idx + 1) % accum_grad == 0:
# Perform gradient clipping if it is set
- if self.kwargs.get("grad_clip", None) is not None:
+ if self.grad_clip > 0:
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),
+ 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."
)
- self.optim.zero_grad() # Reset gradients
+ 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:
- self.scaler.step(self.optim)
- self.scaler.update()
+ scaler.step(optim)
+ scaler.update()
else:
- self.optim.step()
- self.scheduler.step()
+ optim.step()
+ scheduler.step()
# Clear gradients for the next accumulation stage
- self.optim.zero_grad(set_to_none=True)
- total_time = f"{time.perf_counter() - time5:0.3f}"
+ 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
-
-
-
- if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_train):
- pbar.update(self.log_interval)
- gpu_info = "GPU, memory: {:.3f} GB, " \
- "{:.3f} GB, "\
- "{:.3f} GB, "\
- "{:.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,
- )
- lr = self.scheduler.get_last_lr()[0]
- time_now = datetime.now()
- time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
- description = (
- f"{time_now}, "
- f"rank: {self.local_rank}, "
- f"epoch: {epoch}/{self.max_epoch}, "
- f"step: {batch_idx+1}/{len(self.dataloader_train)}, total step: {self.batch_total}, "
- f"(loss: {loss.detach().cpu().item():.3f}), "
- f"(lr: {lr:.3e}), "
- f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
- f"{speed_stats}, "
- f"{gpu_info}"
+ 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),
)
- pbar.set_description(description)
- if self.writer:
- self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(), self.batch_total)
- self.writer.add_scalar(f'rank{self.local_rank}_lr/train', lr, self.batch_total)
- for key, var in stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(), self.batch_total)
- for key, var in speed_stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), self.batch_total)
- if (batch_idx+1) % self.save_checkpoint_interval == 0 and self.rank == 0:
- self._save_checkpoint(epoch, step=batch_idx+1)
- pbar.close()
-
+ 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,
+ )
- def _validate_epoch(self, 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.
"""
- self.model.eval()
+ 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():
- pbar = tqdm(colour="red", desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}", total=len(self.dataloader_val),
- dynamic_ncols=True)
+
speed_stats = {}
time5 = time.perf_counter()
- for batch_idx, batch in enumerate(self.dataloader_val):
+ 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 = self.model(**batch)
+ 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)
+ # 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
+ 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 (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
- pbar.update(self.log_interval)
- time_now = datetime.now()
- time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
- description = (
- f"{time_now}, "
- f"rank: {self.local_rank}, "
- f"validation epoch: {epoch}/{self.max_epoch}, "
- f"step: {batch_idx+1}/{len(self.dataloader_val)}, "
- f"(loss: {loss.detach().cpu().item():.3f}), "
- f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
- f"{speed_stats}, "
- )
- pbar.set_description(description)
- if self.writer:
- self.writer.add_scalar(f"rank{self.local_rank}_Loss/val", loss.item(),
- epoch*len(self.dataloader_val) + batch_idx)
- for key, var in stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', var.item(),
- epoch * len(self.dataloader_val) + batch_idx)
- for key, var in speed_stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/val', eval(var),
- epoch * len(self.dataloader_val) + batch_idx)
+ if torch.isfinite(loss):
+ self.val_loss_avg = (
+ self.val_loss_avg * batch_idx + loss.detach().cpu().item()
+ ) / (batch_idx + 1)
- self.model.train()
\ No newline at end of file
+ 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,
+ step=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()
--
Gitblit v1.9.1