rm op black list in amp

the op flatten_contiguous_range and greater_than has supported amp mode since paddle 2.4

ppcls/engine/engine.py

@@ -504,11 +504,7 @@ class Engine(object):
                 batch_tensor = paddle.to_tensor(batch_data)
 
                 if self.amp and self.amp_eval:
-                    with paddle.amp.auto_cast(
-                            custom_black_list={
-                                "flatten_contiguous_range", "greater_than"
-                            },
-                            level=self.amp_level):
+                    with paddle.amp.auto_cast(level=self.amp_level):
                         out = self.model(batch_tensor)
                 else:
                     out = self.model(batch_tensor)

ppcls/engine/evaluation/classification.py

@@ -56,11 +56,7 @@ def classification_eval(engine, epoch_id=0):
 
         # image input
         if engine.amp and engine.amp_eval:
-            with paddle.amp.auto_cast(
-                    custom_black_list={
-                        "flatten_contiguous_range", "greater_than"
-                    },
-                    level=engine.amp_level):
+            with paddle.amp.auto_cast(level=engine.amp_level):
                 out = engine.model(batch[0])
         else:
             out = engine.model(batch[0])
@@ -114,11 +110,7 @@ def classification_eval(engine, epoch_id=0):
         # calc loss
         if engine.eval_loss_func is not None:
             if engine.amp and engine.amp_eval:
-                with paddle.amp.auto_cast(
-                        custom_black_list={
-                            "flatten_contiguous_range", "greater_than"
-                        },
-                        level=engine.amp_level):
+                with paddle.amp.auto_cast(level=engine.amp_level):
                     loss_dict = engine.eval_loss_func(preds, labels)
             else:
                 loss_dict = engine.eval_loss_func(preds, labels)

ppcls/engine/evaluation/retrieval.py

@@ -137,11 +137,7 @@ def compute_feature(engine, name="gallery"):
             has_camera = True
             batch[2] = batch[2].reshape([-1, 1]).astype("int64")
         if engine.amp and engine.amp_eval:
-            with paddle.amp.auto_cast(
-                    custom_black_list={
-                        "flatten_contiguous_range", "greater_than"
-                    },
-                    level=engine.amp_level):
+            with paddle.amp.auto_cast(level=engine.amp_level):
                 out = engine.model(batch[0])
         else:
             out = engine.model(batch[0])

ppcls/engine/train/train.py

@@ -50,11 +50,7 @@ def train_epoch(engine, epoch_id, print_batch_step):
         # image input
         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):
+            with paddle.amp.auto_cast(level=amp_level):
                 out = forward(engine, batch)
                 loss_dict = engine.train_loss_func(out, batch[1])
         else:

ppcls/engine/train/train_fixmatch.py

@@ -64,11 +64,7 @@ def train_epoch_fixmatch(engine, epoch_id, print_batch_step):
         # image input
         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):
+            with paddle.amp.auto_cast(level=amp_level):
                 loss_dict, logits_label = get_loss(
                     engine, inputs, batch_size_label, temperture, threshold,
                     targets_x)

ppcls/engine/train/train_metabin.py

@@ -191,11 +191,7 @@ def forward(engine, batch, loss_func):
     batch_info = {"label": batch[1], "domain": batch[2]}
     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):
+        with paddle.amp.auto_cast(level=amp_level):
             out = engine.model(batch[0], batch[1])
             loss_dict = loss_func(out, batch_info)
     else: