Support training without amp

ppcls/engine/train/train_metabin.py

@@ -98,7 +98,6 @@ def train_epoch_metabin(engine, epoch_id, print_batch_step):
              for key, value in mtest_loss_dict.items()}
         }
         # step lr (by iter)
-        # the last lr_sch is cyclic_lr
         for i in range(len(engine.lr_sch)):
             if not getattr(engine.lr_sch[i], "by_epoch", False):
                 engine.lr_sch[i].step()
@@ -117,7 +116,6 @@ def train_epoch_metabin(engine, epoch_id, print_batch_step):
         tic = time.time()
 
     # step lr(by epoch)
-    # the last lr_sch is cyclic_lr
     for i in range(len(engine.lr_sch)):
         if getattr(engine.lr_sch[i], "by_epoch", False) and \
                 type_name(engine.lr_sch[i]) != "ReduceOnPlateau":
@@ -191,10 +189,16 @@ def get_meta_data(meta_dataloader_iter, num_domain):
 def forward(engine, batch, loss_func):
     batch_info = defaultdict()
     batch_info = {"label": batch[1], "domain": batch[2]}
-    amp_level = engine.config["AMP"].get("level", "O1").upper()
-    with paddle.amp.auto_cast(
-            custom_black_list={"flatten_contiguous_range", "greater_than"},
-            level=amp_level):
+    if engine.amp:
+        amp_level = engine.config["AMP"].get("level", "O1").upper()
+        with paddle.amp.auto_cast(
+                custom_black_list={
+                    "flatten_contiguous_range", "greater_than"
+                },
+                level=amp_level):
+            out = engine.model(batch[0], batch[1])
+            loss_dict = loss_func(out, batch_info)
+    else:
         out = engine.model(batch[0], batch[1])
         loss_dict = loss_func(out, batch_info)
     return out, loss_dict
@@ -202,9 +206,13 @@ def forward(engine, batch, loss_func):
 
 def backward(engine, loss, optimizer):
     optimizer.clear_grad()
-    scaled = engine.scaler.scale(loss)
-    scaled.backward()
-    engine.scaler.minimize(optimizer, scaled)
+    if engine.amp:
+        scaled = engine.scaler.scale(loss)
+        scaled.backward()
+        engine.scaler.minimize(optimizer, scaled)
+    else:
+        loss.backward()
+        optimizer.step()
     for name, layer in engine.model.backbone.named_sublayers():
         if "gate" == name.split('.')[-1]:
             layer.clip_gate()