From 806a03609df033d61f824f1ab8527eb88fe837ad Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 十二月 2023 19:43:13 +0800
Subject: [PATCH] funasr2 paraformer biciparaformer contextuaparaformer
---
funasr/cli/trainer.py | 129 +++++++++++++++++++++----------------------
1 files changed, 63 insertions(+), 66 deletions(-)
diff --git a/funasr/cli/trainer.py b/funasr/cli/trainer.py
index 30e0419..0178767 100644
--- a/funasr/cli/trainer.py
+++ b/funasr/cli/trainer.py
@@ -2,8 +2,11 @@
import os
from funasr.torch_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.torch_utils.recursive_op import recursive_average
class Trainer:
"""
@@ -51,17 +54,27 @@
self.dataloader_train = dataloader_train
self.dataloader_val = dataloader_val
self.output_dir = kwargs.get('output_dir', './')
- self.resume = kwargs.get('resume', None)
+ self.resume = kwargs.get('resume', True)
self.start_epoch = 1
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 = torch.device("cuda", local_rank)
+ 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):
"""
@@ -80,7 +93,7 @@
}
# 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.{epoch}.pb')
+ filename = os.path.join(self.output_dir, f'model.e{epoch}.pb')
torch.save(state, filename)
print(f'Checkpoint saved to {filename}')
@@ -110,8 +123,10 @@
for epoch in range(self.start_epoch, self.max_epoch + 1):
self._train_epoch(epoch)
# self._validate_epoch(epoch)
- self._save_checkpoint(epoch)
+ if dist.get_rank() == 0:
+ self._save_checkpoint(epoch)
self.scheduler.step()
+ break
def _train_epoch(self, epoch):
"""
@@ -124,24 +139,44 @@
dynamic_ncols=True)
# Set the number of steps for gradient accumulation
- accumulation_steps = self.kwargs.get("accumulation_steps", 1)
+ 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 = model.no_sync if batch_idx % accumulation_steps != 0 else nullcontext
+ 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 / accumulation_steps
+ 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) % accumulation_steps == 0 or (batch_idx + 1) == len(self.dataloader_train):
+ 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_(
@@ -161,49 +196,27 @@
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
+
pbar.update(1)
- pbar.set_description(
- f"Training Epoch: {epoch + 1}/{self.max_epoch}, step {batch_idx}/{len(self.dataloader_train)} (loss: {loss.detach().float()})")
-
+ 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().float():.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 _train_epoch(self, epoch):
- # """
- # Defines the training process for a single epoch.
- # Should be implemented with the actual model training steps.
- #
- # 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)
- # for batch_idx, batch in enumerate(self.dataloader_train):
- # batch = to_device(batch, "cpu")
- # retval = self.model(**batch)
- # loss, stats, weight = retval
- # self.optim.zero_grad()
- # loss.backward()
- #
- # # compute the gradient norm to check if it is normal or not
- # 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."
- # )
- # continue
- # self.optim.step()
- # self.scheduler.step()
- # pbar.update(1)
- # pbar.set_description(
- # f"Training Epoch: {epoch + 1}/{self.max_epoch}, step {batch_idx}/{len(self.dataloader_train)} (loss: {loss.detach().float()})")
- #
- # pbar.close()
- #
def _validate_epoch(self, epoch):
"""
@@ -218,19 +231,3 @@
for data, target in self.dataloader_val:
# Implement the model validation steps here
pass
-
-# # Example usage
-# if __name__ == "__main__":
-# # Assuming the following objects have already been correctly created and initialized:
-# # model, optim, scheduler, dataloader_train, and dataloader_val.
-# trainer = Trainer(
-# max_epoch=10,
-# model=model,
-# optim=optim,
-# scheduler=scheduler,
-# dataloader_train=dataloader_train,
-# dataloader_val=dataloader_val,
-# output_dir='path_to_save_model',
-# resume='path_to_checkpoint_if_any'
-# )
-# trainer.run()
\ No newline at end of file
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