delete fp16

mtl_config.yaml

@@ -31,6 +31,5 @@ max_seq_len: 512
 use_ema: True
 ema_decay: 0.9999
 random_seed: 0
-use_fp16: False
 loss_scaling: 1.0
 

mtl_run.py

@@ -275,14 +275,14 @@ def train(multitask_config):
                 exe,
                 args.pretrain_model_path,
                 main_program=startup_prog,
-                use_fp16=args.use_fp16)
+                use_fp16=False)
         if args.checkpoint_path:
             if os.path.exists(args.checkpoint_path):
                 init_checkpoint(
                     exe,
                     args.checkpoint_path,
                     main_program=startup_prog,
-                    use_fp16=args.use_fp16)
+                    use_fp16=False)
             else:
                 os.makedirs(args.checkpoint_path)
 
@@ -294,7 +294,7 @@ def train(multitask_config):
             exe,
             args.checkpoint_path,
             main_program=test_prog,
-            use_fp16=args.use_fp16)
+            use_fp16=False)
 
     if args.do_train:
         print('start training...')

optimizer/bert_optimizer.py

@@ -19,8 +19,6 @@ from __future__ import print_function
 
 import numpy as np
 import paddle.fluid as fluid
-from utils.fp16 import create_master_params_grads, master_param_to_train_param
-
 
 def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps):
     """ Applies linear warmup of learning rate from 0 and decay to 0."""
@@ -73,8 +71,6 @@ def optimization(loss, programs, args):
     clip_norm_thres = 1.0
     # When using mixed precision training, scale the gradient clip threshold
     # by loss_scaling
-    if args.use_fp16 and args.loss_scaling > 1.0:
-        clip_norm_thres *= args.loss_scaling
     fluid.clip.set_gradient_clip(
         clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres))
 
@@ -89,31 +85,6 @@ def optimization(loss, programs, args):
 
     param_list = dict()
 
-    if args.use_fp16:
-        param_grads = optimizer.backward(loss)
-        master_param_grads = create_master_params_grads(
-            param_grads, train_program, startup_prog, args.loss_scaling)
-
-        for param, _ in master_param_grads:
-            param_list[param.name] = param * 1.0
-            param_list[param.name].stop_gradient = True
-
-        optimizer.apply_gradients(master_param_grads)
-
-        if args.weight_decay > 0:
-            for param, grad in master_param_grads:
-                if exclude_from_weight_decay(param.name.rstrip(".master")):
-                    continue
-                with param.block.program._optimized_guard(
-                    [param, grad]), fluid.framework.name_scope("weight_decay"):
-                    updated_param = param - param_list[
-                        param.name] * weight_decay * scheduled_lr
-                    fluid.layers.assign(output=param, input=updated_param)
-
-        master_param_to_train_param(master_param_grads, param_grads,
-                                    train_program)
-
-    else:
     for param in train_program.global_block().all_parameters():
         param_list[param.name] = param * 1.0
         param_list[param.name].stop_gradient = True

paradigm/answer_matching.py

@@ -25,9 +25,6 @@ def compute_loss(output_tensors, args=None):
         logits=logits, label=labels, return_softmax=True)
     loss = fluid.layers.mean(x=ce_loss)
 
-    if args.use_fp16 and args.loss_scaling > 1.0:
-        loss *= args.loss_scaling
-
     return loss
 
 

paradigm/mask_language_model.py

@@ -42,7 +42,7 @@ def create_model(reader_input, base_model=None, is_training=True, args=None):
     _hidden_act = config['hidden_act']
 
     _word_emb_name = "word_embedding"
-    _dtype = "float16" if args.use_fp16 else "float32"
+    _dtype = "float32"
 
     _param_initializer = fluid.initializer.TruncatedNormal(
         scale=config['initializer_range'])

paradigm/reading_comprehension.py

@@ -35,8 +35,6 @@ def compute_loss(output_tensors, args=None):
     start_loss = _compute_single_loss(start_logits, start_positions)
     end_loss = _compute_single_loss(end_logits, end_positions)
     total_loss = (start_loss + end_loss) / 2.0
-    if args.use_fp16 and args.loss_scaling > 1.0:
-        total_loss = total_loss * args.loss_scaling
 
     return total_loss