[MLU] support SQuAD_Bert with mlu device

examples/machine_reading_comprehension/SQuAD/args.py

@@ -1,3 +1,17 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 import argparse
 
 
@@ -78,7 +92,7 @@ def parse_args():
                         help="random seed for initialization")
     parser.add_argument(
         '--device',
-        choices=['cpu', 'gpu'],
+        choices=['cpu', 'gpu', 'mlu'],
         default="gpu",
         help="Select which device to train model, defaults to gpu.")
     parser.add_argument(
@@ -131,5 +145,12 @@ def parse_args():
     parser.add_argument("--do_predict",
                         action='store_true',
                         help="Whether to predict.")
+    parser.add_argument("--use_amp",
+                        action='store_true',
+                        help="Whether to use AMP.")
+    parser.add_argument("--scale_loss",
+                        type=float,
+                        default=2**15,
+                        help="The value of scale_loss for fp16.")
     args = parser.parse_args()
     return args

examples/machine_reading_comprehension/SQuAD/run_squad.py

@@ -288,27 +288,46 @@ def run(args):
             apply_decay_param_fun=lambda x: x in decay_params)
         criterion = CrossEntropyLossForSQuAD()
 
+        if args.use_amp:
+            scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
+
         global_step = 0
         tic_train = time.time()
 
         for epoch in range(num_train_epochs):
             for step, batch in enumerate(train_data_loader):
                 global_step += 1
-                logits = model(input_ids=batch['input_ids'],
-                               token_type_ids=batch['token_type_ids'],
-                               attention_mask=batch['attention_mask'])
-                loss = criterion(
-                    logits, (batch['start_positions'], batch['end_positions']))
+                if args.use_amp:
+                    with paddle.amp.auto_cast(
+                            args.use_amp,
+                            custom_white_list=["layer_norm", "softmax",
+                                               "gelu"]):
+                        logits = model(input_ids=batch['input_ids'],
+                                       token_type_ids=batch['token_type_ids'],
+                                       attention_mask=batch['attention_mask'])
+                        loss = criterion(
+                            logits,
+                            (batch['start_positions'], batch['end_positions']))
+                    scaler.scale(loss).backward()
+                    scaler.minimize(optimizer, loss)
+                else:
+                    logits = model(input_ids=batch['input_ids'],
+                                   token_type_ids=batch['token_type_ids'],
+                                   attention_mask=batch['attention_mask'])
+                    loss = criterion(
+                        logits,
+                        (batch['start_positions'], batch['end_positions']))
+                    loss.backward()
+                    optimizer.step()
+                lr_scheduler.step()
+                optimizer.clear_grad()
+
                 if global_step % args.logging_steps == 0:
                     print(
                         "global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
                         % (global_step, epoch + 1, step + 1, loss,
                            args.logging_steps / (time.time() - tic_train)))
                     tic_train = time.time()
-                loss.backward()
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.clear_grad()
 
                 if global_step % args.save_steps == 0 or global_step == num_training_steps:
                     if rank == 0: