Fleet support pure_fp16 in bert benchmark (#5255)

* bert benchmark fleet support pure_fp16

* fix use_pure_fp16

PaddleNLP/benchmark/bert/run_pretrain.py

@@ -231,9 +231,13 @@ def dist_optimizer(args, optimizer):
     dist_strategy.fuse_grad_size_in_MB = 16
     if args.use_amp:
         dist_strategy.amp = True
+
+        custom_black_list = ['lookup_table', 'lookup_table_v2'] if args.use_pure_fp16 else None
         dist_strategy.amp_configs = {
             'custom_white_list': ['softmax', 'layer_norm', 'gelu'],
             'init_loss_scaling': args.scale_loss,
+            'custom_black_list': custom_black_list,
+            'use_pure_fp16': args.use_pure_fp16
         }
     if args.gradient_merge_steps > 1:
         dist_strategy.gradient_merge = True
@@ -320,15 +324,20 @@ def do_train(args):
         apply_decay_param_fun=lambda x: x in [
             p.name for n, p in model.named_parameters()
             if not any(nd in n for nd in ["bias", "norm"])
-        ])
+        ],
+        multi_precision=args.use_pure_fp16)
     if worker_num == 1 and args.use_amp:
-        amp_list = paddle.fluid.contrib.mixed_precision.AutoMixedPrecisionLists(
-            custom_white_list=['softmax', 'layer_norm', 'gelu'])
-        optimizer = paddle.fluid.contrib.mixed_precision.decorate(
+        custom_black_list=(['lookup_table', 'lookup_table_v2']
+            if args.use_pure_fp16 else None)
+        amp_list = paddle.static.amp.AutoMixedPrecisionLists(
+            custom_white_list=['softmax', 'layer_norm', 'gelu'],
+            custom_black_list=custom_black_list)
+        optimizer = paddle.static.amp.decorate(
             optimizer,
             amp_list,
             init_loss_scaling=args.scale_loss,
-            use_dynamic_loss_scaling=True)
+            use_dynamic_loss_scaling=True,
+            use_pure_fp16=args.use_pure_fp16)
 
     if worker_num > 1:
         # Use the fleet api to compile the distributed optimizer
@@ -343,6 +352,8 @@ def do_train(args):
     # Use the state dict to update the parameter
     reset_state_dict = reset_program_state_dict(model, state_dict)
     paddle.static.set_program_state(main_program, reset_state_dict)
+    if args.use_amp:
+        optimizer.amp_init(place)
 
     if worker_num == 1:
         # Construct the compiled program