bugfix bert script

model_zoo/official/nlp/bert/run_classifier.py

@@ -50,7 +50,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+        other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
 
@@ -70,7 +70,9 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)
-    ckpoint_cb = ModelCheckpoint(prefix="classifier", directory=save_checkpoint_path, config=ckpt_config)
+    ckpoint_cb = ModelCheckpoint(prefix="classifier",
+                                 directory=None if save_checkpoint_path == "" else save_checkpoint_path,
+                                 config=ckpt_config)
     param_dict = load_checkpoint(load_checkpoint_path)
     load_param_into_net(network, param_dict)
 

model_zoo/official/nlp/bert/run_ner.py

@@ -52,7 +52,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = network.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+        other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
         optimizer = AdamWeightDecay(group_params, lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
@@ -71,7 +71,9 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)
-    ckpoint_cb = ModelCheckpoint(prefix="ner", directory=save_checkpoint_path, config=ckpt_config)
+    ckpoint_cb = ModelCheckpoint(prefix="ner",
+                                 directory=None if save_checkpoint_path == "" else save_checkpoint_path,
+                                 config=ckpt_config)
     param_dict = load_checkpoint(load_checkpoint_path)
     load_param_into_net(network, param_dict)
 

model_zoo/official/nlp/bert/run_pretrain.py

@@ -51,7 +51,7 @@ def run_pretrain():
     parser.add_argument("--do_shuffle", type=str, default="true", help="Enable shuffle for dataset, default is true.")
     parser.add_argument("--enable_data_sink", type=str, default="true", help="Enable data sink, default is true.")
     parser.add_argument("--data_sink_steps", type=int, default="1", help="Sink steps for each epoch, default is 1.")
-    parser.add_argument("--save_checkpoint_path", type=str, default="", help="Save checkpoint path")
+    parser.add_argument("--save_checkpoint_path", type=str, default=None, help="Save checkpoint path")
     parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint steps, "
                                                                                 "default is 1000.")
@@ -142,7 +142,7 @@ def run_pretrain():
         raise ValueError("Don't support optimizer {}, only support [Lamb, Momentum, AdamWeightDecay]".
                          format(cfg.optimizer))
     callback = [TimeMonitor(args_opt.data_sink_steps), LossCallBack()]
-    if args_opt.enable_save_ckpt == "true":
+    if args_opt.enable_save_ckpt == "true" and args_opt.device_id % min(8, device_num) == 0:
         config_ck = CheckpointConfig(save_checkpoint_steps=args_opt.save_checkpoint_steps,
                                      keep_checkpoint_max=args_opt.save_checkpoint_num)
         ckpoint_cb = ModelCheckpoint(prefix='checkpoint_bert', directory=ckpt_save_dir, config=config_ck)

model_zoo/official/nlp/bert/run_squad.py

@@ -52,7 +52,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = network.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+        other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
 
@@ -72,7 +72,9 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
 
     # load checkpoint into network
     ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=1)
-    ckpoint_cb = ModelCheckpoint(prefix="squad", directory=save_checkpoint_path, config=ckpt_config)
+    ckpoint_cb = ModelCheckpoint(prefix="squad",
+                                 directory=None if save_checkpoint_path == "" else save_checkpoint_path,
+                                 config=ckpt_config)
     param_dict = load_checkpoint(load_checkpoint_path)
     load_param_into_net(network, param_dict)
 

model_zoo/official/nlp/tinybert/run_general_distill.py

@@ -99,7 +99,7 @@ def run_general_distill():
                                    power=common_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(common_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+    other_params = list(filter(lambda x: not common_cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': common_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]

model_zoo/official/nlp/tinybert/run_task_distill.py

@@ -107,7 +107,7 @@ def run_predistill():
                                    power=optimizer_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+    other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]
@@ -165,7 +165,7 @@ def run_task_distill(ckpt_file):
                                    power=optimizer_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
+    other_params = list(filter(lambda x: not optimizer_cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]