optimizer must be decorated when training with AMPO2

ppcls/engine/engine.py

@@ -274,33 +274,17 @@ class Engine(object):
                 self.config["AMP"]["use_fp16_test"] = True
                 self.amp_eval = True
 
-            # TODO(gaotingquan): to compatible with different versions of Paddle
-            paddle_version = paddle.__version__[:3]
-            # paddle version < 2.3.0 and not develop
-            if paddle_version not in ["2.3", "0.0"]:
-                if self.mode == "train":
-                    self.model, self.optimizer = paddle.amp.decorate(
-                        models=self.model,
-                        optimizers=self.optimizer,
-                        level=self.amp_level,
-                        save_dtype='float32')
-                elif self.amp_eval:
-                    if self.amp_level == "O2":
-                        msg = "The PaddlePaddle that installed not support FP16 evaluation in AMP O2. Please use PaddlePaddle version >= 2.3.0. Use FP32 evaluation instead and please notice the Eval Dataset output_fp16 should be 'False'."
-                        logger.warning(msg)
-                        self.amp_eval = False
-                    else:
-                        self.model, self.optimizer = paddle.amp.decorate(
-                            models=self.model,
-                            level=self.amp_level,
-                            save_dtype='float32')
-            # paddle version >= 2.3.0 or develop
-            else:
-                if self.mode == "train" or self.amp_eval:
-                    self.model = paddle.amp.decorate(
-                        models=self.model,
-                        level=self.amp_level,
-                        save_dtype='float32')
+            if self.mode == "train":
+                self.model, self.optimizer = paddle.amp.decorate(
+                    models=self.model,
+                    optimizers=self.optimizer,
+                    level=self.amp_level,
+                    save_dtype='float32')
+            elif self.amp_eval:
+                self.model = paddle.amp.decorate(
+                    models=self.model,
+                    level=self.amp_level,
+                    save_dtype='float32')
 
             if self.mode == "train" and len(self.train_loss_func.parameters(
             )) > 0: