update lr scheduler

examples/vctk/ernie_sat/conf/default.yaml

@@ -71,9 +71,10 @@ model:
 ###########################################################
 #                     OPTIMIZER SETTING                   #
 ###########################################################
-optimizer:
-    optim: adam               # optimizer type
-    learning_rate: 0.001      # learning rate
+scheduler_params:
+    d_model: 384
+    warmup_steps: 4000
+grad_clip: 1.0
 
 ###########################################################
 #                     TRAINING SETTING                    #
@@ -84,7 +85,7 @@ num_snapshots: 5
 ###########################################################
 #                       OTHER SETTING                     #
 ###########################################################
-seed: 10086
+seed: 0
 
 token_list:
 - <blank>

paddlespeech/t2s/exps/ernie_sat/train.py

@@ -23,8 +23,10 @@ import paddle
 import yaml
 from paddle import DataParallel
 from paddle import distributed as dist
+from paddle import nn
 from paddle.io import DataLoader
 from paddle.io import DistributedBatchSampler
+from paddle.optimizer import Adam
 from yacs.config import CfgNode
 
 from paddlespeech.t2s.datasets.am_batch_fn import build_erniesat_collate_fn
@@ -34,7 +36,6 @@ from paddlespeech.t2s.models.ernie_sat import ErnieSATEvaluator
 from paddlespeech.t2s.models.ernie_sat import ErnieSATUpdater
 from paddlespeech.t2s.training.extensions.snapshot import Snapshot
 from paddlespeech.t2s.training.extensions.visualizer import VisualDL
-from paddlespeech.t2s.training.optimizer import build_optimizers
 from paddlespeech.t2s.training.seeding import seed_everything
 from paddlespeech.t2s.training.trainer import Trainer
 
@@ -118,12 +119,27 @@ def train_sp(args, config):
 
     odim = config.n_mels
     model = ErnieSAT(idim=vocab_size, odim=odim, **config["model"])
+    # model_path = "/home/yuantian01/PaddleSpeech_ERNIE_SAT/PaddleSpeech/examples/ernie_sat/pretrained_model/paddle_checkpoint_en/model.pdparams"
+    # state_dict = paddle.load(model_path)
+    # new_state_dict = {}
+    # for key, value in state_dict.items():
+    #     new_key = "model." + key
+    #     new_state_dict[new_key] = value
+    # model.set_state_dict(new_state_dict)
 
     if world_size > 1:
         model = DataParallel(model)
     print("model done!")
 
-    optimizer = build_optimizers(model, **config["optimizer"])
+    scheduler = paddle.optimizer.lr.NoamDecay(
+        d_model=config["scheduler_params"]["d_model"],
+        warmup_steps=config["scheduler_params"]["warmup_steps"])
+    grad_clip = nn.ClipGradByGlobalNorm(config["grad_clip"])
+    optimizer = Adam(
+        learning_rate=scheduler,
+        grad_clip=grad_clip,
+        parameters=model.parameters())
+
     print("optimizer done!")
 
     output_dir = Path(args.output_dir)
@@ -136,6 +152,7 @@ def train_sp(args, config):
     updater = ErnieSATUpdater(
         model=model,
         optimizer=optimizer,
+        scheduler=scheduler,
         dataloader=train_dataloader,
         text_masking=config["model"]["text_masking"],
         odim=odim,

paddlespeech/t2s/models/ernie_sat/ernie_sat_updater.py

@@ -18,6 +18,7 @@ from paddle import distributed as dist
 from paddle.io import DataLoader
 from paddle.nn import Layer
 from paddle.optimizer import Optimizer
+from paddle.optimizer.lr import LRScheduler
 
 from paddlespeech.t2s.modules.losses import MLMLoss
 from paddlespeech.t2s.training.extensions.evaluator import StandardEvaluator
@@ -34,12 +35,14 @@ class ErnieSATUpdater(StandardUpdater):
     def __init__(self,
                  model: Layer,
                  optimizer: Optimizer,
+                 scheduler: LRScheduler,
                  dataloader: DataLoader,
                  init_state=None,
                  text_masking: bool=False,
                  odim: int=80,
                  output_dir: Path=None):
         super().__init__(model, optimizer, dataloader, init_state=None)
+        self.scheduler = scheduler
 
         self.criterion = MLMLoss(text_masking=text_masking, odim=odim)
 
@@ -75,10 +78,12 @@ class ErnieSATUpdater(StandardUpdater):
 
         loss = mlm_loss + text_mlm_loss if text_mlm_loss is not None else mlm_loss
 
-        optimizer = self.optimizer
-        optimizer.clear_grad()
+        self.optimizer.clear_grad()
+
         loss.backward()
-        optimizer.step()
+        self.optimizer.step()
+        self.scheduler.step()
+        scheduler_msg = 'lr: {}'.format(self.scheduler.last_lr)
 
         report("train/loss", float(loss))
         report("train/mlm_loss", float(mlm_loss))
@@ -90,6 +95,7 @@ class ErnieSATUpdater(StandardUpdater):
         losses_dict["loss"] = float(loss)
         self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
                               for k, v in losses_dict.items())
+        self.msg += ', ' + scheduler_msg
 
 
 class ErnieSATEvaluator(StandardEvaluator):