Support amp/fp16 training (#1192)

* support amp
Co-authored-by: yukang <yukangchen@cse.cuhk.edu.hk>

tools/train.py

@@ -48,6 +48,7 @@ def parse_config():
     parser.add_argument('--logger_iter_interval', type=int, default=50, help='')
     parser.add_argument('--ckpt_save_time_interval', type=int, default=300, help='in terms of seconds')
     parser.add_argument('--wo_gpu_stat', action='store_true', help='')
+    parser.add_argument('--use_amp', action='store_true', help='use mix precision training')
     
 
     args = parser.parse_args()
@@ -56,6 +57,8 @@ def parse_config():
     cfg.TAG = Path(args.cfg_file).stem
     cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1])  # remove 'cfgs' and 'xxxx.yaml'
     
+    args.use_amp = args.use_amp or cfg.OPTIMIZATION.get('USE_AMP', False)
+
     if args.set_cfgs is not None:
         cfg_from_list(args.set_cfgs, cfg)
 
@@ -187,7 +190,8 @@ def main():
         logger_iter_interval=args.logger_iter_interval,
         ckpt_save_time_interval=args.ckpt_save_time_interval,
         use_logger_to_record=not args.use_tqdm_to_record, 
-        show_gpu_stat=not args.wo_gpu_stat
+        show_gpu_stat=not args.wo_gpu_stat,
+        use_amp=args.use_amp
     )
 
     if hasattr(train_set, 'use_shared_memory') and train_set.use_shared_memory:

tools/train_utils/train_utils.py

-import glob
 import os
 
 import torch
 import tqdm
 import time
+import glob
 from torch.nn.utils import clip_grad_norm_
 from pcdet.utils import common_utils, commu_utils
 
@@ -11,13 +11,15 @@ from pcdet.utils import common_utils, commu_utils
 def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg,
                     rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False, 
                     use_logger_to_record=False, logger=None, logger_iter_interval=50, cur_epoch=None, 
-                    total_epochs=None, ckpt_save_dir=None, ckpt_save_time_interval=300, show_gpu_stat=False):
+                    total_epochs=None, ckpt_save_dir=None, ckpt_save_time_interval=300, show_gpu_stat=False, use_amp=False):
     if total_it_each_epoch == len(train_loader):
         dataloader_iter = iter(train_loader)
 
     ckpt_save_cnt = 1
     start_it = accumulated_iter % total_it_each_epoch
 
+    scaler = torch.cuda.amp.GradScaler(enabled=use_amp, init_scale=optim_cfg.get('LOSS_SCALE_FP16', 2.0**16))
+    
     if rank == 0:
         pbar = tqdm.tqdm(total=total_it_each_epoch, leave=leave_pbar, desc='train', dynamic_ncols=True)
         data_time = common_utils.AverageMeter()
@@ -49,11 +51,14 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
         model.train()
         optimizer.zero_grad()
 
+        with torch.cuda.amp.autocast(enabled=use_amp):
             loss, tb_dict, disp_dict = model_func(model, batch)
 
-        loss.backward()
+        scaler.scale(loss).backward()
+        scaler.unscale_(optimizer)
         clip_grad_norm_(model.parameters(), optim_cfg.GRAD_NORM_CLIP)
-        optimizer.step()
+        scaler.step(optimizer)
+        scaler.update()
 
         accumulated_iter += 1
  
@@ -127,7 +132,7 @@ def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, ac
 def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_cfg,
                 start_epoch, total_epochs, start_iter, rank, tb_log, ckpt_save_dir, train_sampler=None,
                 lr_warmup_scheduler=None, ckpt_save_interval=1, max_ckpt_save_num=50,
-                merge_all_iters_to_one_epoch=False, 
+                merge_all_iters_to_one_epoch=False, use_amp=False,
                 use_logger_to_record=False, logger=None, logger_iter_interval=None, ckpt_save_time_interval=None, show_gpu_stat=False):
     accumulated_iter = start_iter
     with tqdm.trange(start_epoch, total_epochs, desc='epochs', dynamic_ncols=True, leave=(rank == 0)) as tbar:
@@ -160,7 +165,8 @@ def train_model(model, optimizer, train_loader, model_func, lr_scheduler, optim_
                 use_logger_to_record=use_logger_to_record, 
                 logger=logger, logger_iter_interval=logger_iter_interval,
                 ckpt_save_dir=ckpt_save_dir, ckpt_save_time_interval=ckpt_save_time_interval, 
-                show_gpu_stat=show_gpu_stat
+                show_gpu_stat=show_gpu_stat,
+                use_amp=use_amp
             )
 
             # save trained model