Merge pull request #1585 from zhangbo9674/dev/resnet50_optimize

Accelerate dynamic graph amp training

ppcls/engine/engine.py

@@ -250,6 +250,8 @@ class Engine(object):
             self.scaler = paddle.amp.GradScaler(
                 init_loss_scaling=self.scale_loss,
                 use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
+            if self.config['AMP']['use_pure_fp16'] is True:
+                self.model = paddle.amp.decorate(models=self.model, level='O2')
 
         self.max_iter = len(self.train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(self.train_dataloader)

ppcls/engine/train/train.py

@@ -21,6 +21,7 @@ from ppcls.utils import profiler
 
 def train_epoch(engine, epoch_id, print_batch_step):
     tic = time.time()
+    v_current = [int(i) for i in paddle.__version__.split(".")]
     for iter_id, batch in enumerate(engine.train_dataloader):
         if iter_id >= engine.max_iter:
             break
@@ -41,13 +42,14 @@ def train_epoch(engine, epoch_id, print_batch_step):
 
         # image input
         if engine.amp:
-            with paddle.amp.auto_cast(custom_black_list={
-                    "flatten_contiguous_range", "greater_than"
-            }):
+            amp_level = 'O1'
+            if engine.config['AMP']['use_pure_fp16'] is True:
+                amp_level = 'O2'
+            with paddle.amp.auto_cast(custom_black_list={"flatten_contiguous_range", "greater_than"}, level=amp_level):
                 out = forward(engine, batch)
+                loss_dict = engine.train_loss_func(out, batch[1])
         else:
             out = forward(engine, batch)
-
             loss_dict = engine.train_loss_func(out, batch[1])
 
         # step opt and lr

ppcls/optimizer/optimizer.py

@@ -17,6 +17,7 @@ from __future__ import division
 from __future__ import print_function
 
 from paddle import optimizer as optim
+import paddle
 
 from ppcls.utils import logger
 
@@ -36,7 +37,7 @@ class Momentum(object):
                  momentum,
                  weight_decay=None,
                  grad_clip=None,
-                 multi_precision=False):
+                 multi_precision=True):
         super().__init__()
         self.learning_rate = learning_rate
         self.momentum = momentum
@@ -55,6 +56,15 @@ class Momentum(object):
             grad_clip=self.grad_clip,
             multi_precision=self.multi_precision,
             parameters=parameters)
+        if hasattr(opt, '_use_multi_tensor'):
+            opt = optim.Momentum(
+                learning_rate=self.learning_rate,
+                momentum=self.momentum,
+                weight_decay=self.weight_decay,
+                grad_clip=self.grad_clip,
+                multi_precision=self.multi_precision,
+                parameters=parameters,
+                use_multi_tensor=True)
         return opt