improve amp training and fix nan error

configs/keypoint/tiny_pose/tinypose_128x96.yml

@@ -14,6 +14,9 @@ trainsize: &trainsize [*train_width, *train_height]
 hmsize: &hmsize [24, 32]
 flip_perm: &flip_perm [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16]]
 
+# AMP training
+init_loss_scaling: 32752
+master_grad: true
 
 #####model
 architecture: TopDownHRNet

configs/ppyolo/_base_/ppyolov2_r50vd_dcn.yml

@@ -4,6 +4,9 @@ norm_type: sync_bn
 use_ema: true
 ema_decay: 0.9998
 
+# AMP training
+master_grad: true
+
 YOLOv3:
   backbone: ResNet
   neck: PPYOLOPAN

configs/ppyolo/ppyolo_mbv3_large_coco.yml

@@ -9,6 +9,9 @@ _BASE_: [
 snapshot_epoch: 10
 weights: output/ppyolo_mbv3_large_coco/model_final
 
+# AMP training
+master_grad: true
+
 TrainReader:
   inputs_def:
     num_max_boxes: 90

ppdet/engine/trainer.py

@@ -72,6 +72,7 @@ def __init__(self, cfg, mode='train'):
         self.amp_level = self.cfg.get('amp_level', 'O1')
         self.custom_white_list = self.cfg.get('custom_white_list', None)
         self.custom_black_list = self.cfg.get('custom_black_list', None)
+        self.use_master_grad = self.cfg.get('master_grad', False)
         if 'slim' in cfg and cfg['slim_type'] == 'PTQ':
             self.cfg['TestDataset'] = create('TestDataset')()
 
@@ -179,10 +180,19 @@ def __init__(self, cfg, mode='train'):
                 self.pruner = create('UnstructuredPruner')(self.model,
                                                            steps_per_epoch)
         if self.use_amp and self.amp_level == 'O2':
-            self.model, self.optimizer = paddle.amp.decorate(
-                models=self.model,
-                optimizers=self.optimizer,
-                level=self.amp_level)
+            paddle_version = paddle.__version__[:3]
+            # paddle version >= 2.5.0 or develop
+            if paddle_version in ["2.5", "0.0"]:
+                self.model, self.optimizer = paddle.amp.decorate(
+                    models=self.model,
+                    optimizers=self.optimizer,
+                    level=self.amp_level,
+                    master_grad=self.use_master_grad)
+            else:
+                self.model, self.optimizer = paddle.amp.decorate(
+                    models=self.model,
+                    optimizers=self.optimizer,
+                    level=self.amp_level)
         self.use_ema = ('use_ema' in cfg and cfg['use_ema'])
         if self.use_ema:
             ema_decay = self.cfg.get('ema_decay', 0.9998)

ppdet/modeling/layers.py

@@ -362,6 +362,8 @@ def forward(self, x):
                 padding=self.block_size // 2,
                 data_format=self.data_format)
             mask = 1. - mask_inv
+            mask = mask.astype('float32')
+            x = x.astype('float32')
             y = x * mask * (mask.numel() / mask.sum())
             return y
 

ppdet/modeling/losses/yolo_loss.py

@@ -190,8 +190,9 @@ def forward(self, inputs, targets, anchors):
         self.distill_pairs.clear()
         for x, t, anchor, downsample in zip(inputs, gt_targets, anchors,
                                             self.downsample):
-            yolo_loss = self.yolov3_loss(x, t, gt_box, anchor, downsample,
-                                         self.scale_x_y)
+            yolo_loss = self.yolov3_loss(
+                x.astype('float32'), t, gt_box, anchor, downsample,
+                self.scale_x_y)
             for k, v in yolo_loss.items():
                 if k in yolo_losses:
                     yolo_losses[k] += v

ppdet/optimizer/ema.py

@@ -69,9 +69,9 @@ def __init__(self,
         self.state_dict = dict()
         for k, v in model.state_dict().items():
             if k in self.ema_black_list:
-                self.state_dict[k] = v
+                self.state_dict[k] = v.astype('float32')
             else:
-                self.state_dict[k] = paddle.zeros_like(v)
+                self.state_dict[k] = paddle.zeros_like(v, dtype='float32')
 
         self._model_state = {
             k: weakref.ref(p)
@@ -114,7 +114,7 @@ def update(self, model=None):
 
         for k, v in self.state_dict.items():
             if k not in self.ema_black_list:
-                v = decay * v + (1 - decay) * model_dict[k]
+                v = decay * v + (1 - decay) * model_dict[k].astype('float32')
                 v.stop_gradient = True
                 self.state_dict[k] = v
         self.step += 1
@@ -123,13 +123,15 @@ def apply(self):
         if self.step == 0:
             return self.state_dict
         state_dict = dict()
+        model_dict = {k: p() for k, p in self._model_state.items()}
         for k, v in self.state_dict.items():
             if k in self.ema_black_list:
                 v.stop_gradient = True
-                state_dict[k] = v
+                state_dict[k] = v.astype(model_dict[k].dtype)
             else:
                 if self.ema_decay_type != 'exponential':
                     v = v / (1 - self._decay**self.step)
+                    v = v.astype(model_dict[k].dtype)
                 v.stop_gradient = True
                 state_dict[k] = v
         self.epoch += 1