update bert and text.py

examples/bert/bert_classifier.py

@@ -18,10 +18,10 @@ from hapi.metrics import Accuracy
 from hapi.configure import Config
 from hapi.text.bert import BertEncoder
 from paddle.fluid.dygraph import Linear, Layer
-from hapi.model import set_device, Model, Input
 from hapi.loss import SoftmaxWithCrossEntropy
+from hapi.model import set_device, Model, Input
 import hapi.text.tokenizer.tokenization as tokenization
-from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
+from hapi.text.bert import BertConfig, BertDataLoader, BertInputExample, make_optimizer
 
 
 class ClsModelLayer(Model):
@@ -128,7 +128,7 @@ def main():
             [None, None], 'int64', name='src_ids'), Input(
                 [None, None], 'int64', name='pos_ids'), Input(
                     [None, None], 'int64', name='sent_ids'), Input(
-                        [None, None], 'float32', name='input_mask')
+                        [None, None, 1], 'float32', name='input_mask')
     ]
 
     labels = [Input([None, 1], 'int64', name='label')]
@@ -139,13 +139,13 @@ def main():
         len(["contradiction", "entailment", "neutral"]),
         return_pooled_out=True)
 
-    optimizer = Optimizer(
+    optimizer = make_optimizer(
         warmup_steps=warmup_steps,
         num_train_steps=max_train_steps,
         learning_rate=config.learning_rate,
-        model_cls=cls_model,
         weight_decay=config.weight_decay,
         scheduler=config.lr_scheduler,
+        model=cls_model,
         loss_scaling=config.loss_scaling,
         parameter_list=cls_model.parameters())
 
@@ -157,8 +157,7 @@ def main():
         labels,
         device=device)
 
-    cls_model.bert_layer.init_parameters(
-        config.init_pretraining_params, verbose=config.verbose)
+    cls_model.bert_layer.load("./bert_small", reset_optimizer=True)
 
     # do train
     cls_model.fit(train_data=train_dataloader.dataloader,

examples/bert/run_classifier_single_gpu.sh

@@ -4,7 +4,7 @@ TASK_NAME='MNLI'
 DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
-export CUDA_VISIBLE_DEVICES=0
+export CUDA_VISIBLE_DEVICES=1
 
 # start fine-tuning
 python3.7 bert_classifier.py\

examples/bert_leveldb/bert_classifier.py

@@ -18,10 +18,10 @@ from hapi.metrics import Accuracy
 from hapi.configure import Config
 from hapi.text.bert import BertEncoder
 from paddle.fluid.dygraph import Linear, Layer
-from hapi.model import set_device, Model, Input
 from hapi.loss import SoftmaxWithCrossEntropy
+from hapi.model import set_device, Model, Input
 import hapi.text.tokenizer.tokenization as tokenization
-from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
+from hapi.text.bert import BertConfig, BertDataLoader, BertInputExample, make_optimizer
 
 
 class ClsModelLayer(Model):
@@ -99,12 +99,12 @@ def main():
 
     train_dataloader = BertDataLoader(
         "./data/glue_data/MNLI/train.tsv",
-        tokenizer, ["contradiction", "entailment", "neutral"],
+        tokenizer,
+        ["contradiction", "entailment", "neutral"],
         max_seq_length=config.max_seq_len,
         batch_size=config.batch_size,
         line_processor=mnli_line_processor,
-        mode="leveldb",
-        phase="train")
+        mode="leveldb", )
 
     test_dataloader = BertDataLoader(
         "./data/glue_data/MNLI/dev_matched.tsv",
@@ -130,7 +130,7 @@ def main():
             [None, None], 'int64', name='src_ids'), Input(
                 [None, None], 'int64', name='pos_ids'), Input(
                     [None, None], 'int64', name='sent_ids'), Input(
-                        [None, None], 'float32', name='input_mask')
+                        [None, None, 1], 'float32', name='input_mask')
     ]
 
     labels = [Input([None, 1], 'int64', name='label')]
@@ -141,13 +141,13 @@ def main():
         len(["contradiction", "entailment", "neutral"]),
         return_pooled_out=True)
 
-    optimizer = Optimizer(
+    optimizer = make_optimizer(
         warmup_steps=warmup_steps,
         num_train_steps=max_train_steps,
         learning_rate=config.learning_rate,
-        model_cls=cls_model,
         weight_decay=config.weight_decay,
         scheduler=config.lr_scheduler,
+        model=cls_model,
         loss_scaling=config.loss_scaling,
         parameter_list=cls_model.parameters())
 
@@ -159,8 +159,7 @@ def main():
         labels,
         device=device)
 
-    cls_model.bert_layer.init_parameters(
-        config.init_pretraining_params, verbose=config.verbose)
+    cls_model.bert_layer.load("./bert_small", reset_optimizer=True)
 
     # do train
     cls_model.fit(train_data=train_dataloader.dataloader,

examples/bert_leveldb/run_classifier_multi_gpu.sh

@@ -5,7 +5,7 @@ DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
 # start fine-tuning
-python3.7 -m paddle.distributed.launch --started_port 8899 --selected_gpus=0,1,2,3 bert_classifier.py\
+python3.7 -m paddle.distributed.launch --started_port 8899 --selected_gpus=1,2,3 bert_classifier.py\
     --use_cuda true \
     --do_train true \
     --do_test true \

examples/bert_leveldb/run_classifier_single_gpu.sh

@@ -4,7 +4,7 @@ TASK_NAME='MNLI'
 DATA_PATH="./data/glue_data/MNLI/"
 CKPT_PATH="./data/saved_model/mnli_models"
 
-export CUDA_VISIBLE_DEVICES=0
+export CUDA_VISIBLE_DEVICES=1
 
 # start fine-tuning
 python3.7 bert_classifier.py\

hapi/text/bert/__init__.py

@@ -13,7 +13,9 @@
 # limitations under the License.
 
 from hapi.text.bert.bert import BertConfig as BertConfig
-from hapi.text.bert.optimization import Optimizer as Optimizer
+from hapi.text.bert.dygraph_optimization import DyOptimizer as DyOptimizer
+from hapi.text.bert.static_optimization import StOptimizer as StOptimizer
+from hapi.text.bert.optimization import make_optimizer as make_optimizer
 from hapi.text.bert.dataloader import BertDataLoader as BertDataLoader
 from hapi.text.bert.dataloader import BertInputExample as BertInputExample
 from hapi.text.tokenizer import tokenization as tokenization

hapi/text/bert/bert.py

@@ -23,8 +23,8 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
+from hapi.model import Model
 from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, to_variable, Layer, guard
-
 from hapi.text.text import PrePostProcessLayer, TransformerEncoder
 from hapi.text.bert.utils.init import init_from_static_model
 
@@ -52,7 +52,7 @@ class BertConfig(object):
         print('------------------------------------------------')
 
 
-class BertEncoder(Layer):
+class BertEncoder(Model):
     """
     bert
     """

hapi/text/bert/dygraph_optimization.py

+#   Copyright (c) 2020 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.
+"""Optimization and learning rate scheduling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
+
+
+class ConstantLR(LearningRateDecay):
+    def __init__(self, learning_rate, begin=0, step=1, dtype='float32'):
+        super(ConstantLR, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+
+    def step(self):
+        return self.learning_rate
+
+
+class LinearDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 warmup_steps,
+                 decay_steps,
+                 end_learning_rate=0.0001,
+                 power=1.0,
+                 cycle=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(LinearDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.warmup_steps = warmup_steps
+        self.decay_steps = decay_steps
+        self.end_learning_rate = end_learning_rate
+        self.power = power
+        self.cycle = cycle
+
+    def step(self):
+        if self.step_num < self.warmup_steps:
+            decayed_lr = self.learning_rate * (self.step_num /
+                                               self.warmup_steps)
+            decayed_lr = self.create_lr_var(decayed_lr)
+        else:
+            tmp_step_num = self.step_num
+            tmp_decay_steps = self.decay_steps
+            if self.cycle:
+                div_res = fluid.layers.ceil(
+                    self.create_lr_var(tmp_step_num / float(self.decay_steps)))
+                if tmp_step_num == 0:
+                    div_res = self.create_lr_var(1.0)
+                tmp_decay_steps = self.decay_steps * div_res
+            else:
+                tmp_step_num = self.create_lr_var(
+                    tmp_step_num
+                    if tmp_step_num < self.decay_steps else self.decay_steps)
+                decayed_lr = (self.learning_rate - self.end_learning_rate) * \
+                    ((1 - tmp_step_num / tmp_decay_steps) ** self.power) + self.end_learning_rate
+
+        return decayed_lr
+
+
+class DyOptimizer(object):
+    def __init__(self,
+                 warmup_steps,
+                 num_train_steps,
+                 learning_rate,
+                 model_cls,
+                 weight_decay,
+                 scheduler='linear_warmup_decay',
+                 loss_scaling=1.0,
+                 parameter_list=None):
+        self.warmup_steps = warmup_steps
+        self.num_train_steps = num_train_steps
+        self.learning_rate = learning_rate
+        self.model_cls = model_cls
+        self.weight_decay = weight_decay
+        self.scheduler = scheduler
+        self.loss_scaling = loss_scaling
+        self.parameter_list = parameter_list
+
+        self.scheduled_lr = 0.0
+        self.optimizer = self.lr_schedule()
+
+    def lr_schedule(self):
+        if self.warmup_steps > 0:
+            if self.scheduler == 'noam_decay':
+                self.scheduled_lr = fluid.dygraph.NoamDecay(1 / (
+                    self.warmup_steps * (self.learning_rate**2)),
+                                                            self.warmup_steps)
+            elif self.scheduler == 'linear_warmup_decay':
+                self.scheduled_lr = LinearDecay(self.learning_rate,
+                                                self.warmup_steps,
+                                                self.num_train_steps, 0.0)
+            else:
+                raise ValueError("Unkown learning rate scheduler, should be "
+                                 "'noam_decay' or 'linear_warmup_decay'")
+            optimizer = fluid.optimizer.Adam(
+                learning_rate=self.scheduled_lr,
+                parameter_list=self.parameter_list)
+        else:
+            self.scheduled_lr = ConstantLR(self.learning_rate)
+            optimizer = fluid.optimizer.Adam(
+                learning_rate=self.scheduled_lr,
+                parameter_list=self.parameter_list)
+
+        return optimizer
+
+    def exclude_from_weight_decay(self, name):
+        if name.find("layer_norm") > -1:
+            return True
+        bias_suffix = ["_bias", "_b", ".b_0"]
+        for suffix in bias_suffix:
+            if name.endswith(suffix):
+                return True
+        return False
+
+    def state_dict(self):
+        return self.optimizer.state_dict()
+
+    def set_dict(self, state_dict):
+        return self.optimizer.set_dict(state_dict)
+
+    def get_opti_var_name_list(self):
+        return self.optimizer.get_opti_var_name_list()
+
+    def current_step_lr(self):
+        return self.optimizer.current_step_lr()
+
+    def minimize(self, loss, use_data_parallel=False, model=None):
+        param_list = dict()
+
+        clip_norm_thres = 1.0
+        #grad_clip = fluid.clip.GradientClipByGlobalNorm(clip_norm_thres)
+
+        if use_data_parallel:
+            loss = model.scale_loss(loss)
+
+        loss.backward()
+
+        if self.weight_decay > 0:
+            for param in self.model_cls.parameters():
+                param_list[param.name] = param * 1.0
+                param_list[param.name].stop_gradient = True
+
+        if use_data_parallel:
+            assert model is not None
+            model.apply_collective_grads()
+
+        #_, param_grads = self.optimizer.minimize(loss, grad_clip=grad_clip)
+        _, param_grads = self.optimizer.minimize(loss)
+
+        if self.weight_decay > 0:
+            for param, grad in param_grads:
+                if self.exclude_from_weight_decay(param.name):
+                    continue
+                if isinstance(self.scheduled_lr.step(), float):
+                    updated_param = param.numpy() - param_list[
+                        param.name].numpy(
+                        ) * self.weight_decay * self.scheduled_lr.step()
+                else:
+                    updated_param = param.numpy(
+                    ) - param_list[param.name].numpy(
+                    ) * self.weight_decay * self.scheduled_lr.step().numpy()
+                updated_param_var = fluid.dygraph.to_variable(updated_param)
+                param = updated_param_var
+                #param = fluid.layers.reshape(x=updated_param_var, shape=list(updated_param_var.shape))

hapi/text/bert/optimization.py

@@ -11,172 +11,35 @@
 # 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.
-"""Optimization and learning rate scheduling."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import paddle.fluid as fluid
-
-from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
-
-
-class ConstantLR(LearningRateDecay):
-    def __init__(self, learning_rate, begin=0, step=1, dtype='float32'):
-        super(ConstantLR, self).__init__(begin, step, dtype)
-        self.learning_rate = learning_rate
-
-    def step(self):
-        return self.learning_rate
-
-
-class LinearDecay(LearningRateDecay):
-    def __init__(self,
-                 learning_rate,
-                 warmup_steps,
-                 decay_steps,
-                 end_learning_rate=0.0001,
-                 power=1.0,
-                 cycle=False,
-                 begin=0,
-                 step=1,
-                 dtype='float32'):
-        super(LinearDecay, self).__init__(begin, step, dtype)
-        self.learning_rate = learning_rate
-        self.warmup_steps = warmup_steps
-        self.decay_steps = decay_steps
-        self.end_learning_rate = end_learning_rate
-        self.power = power
-        self.cycle = cycle
-
-    def step(self):
-        if self.step_num < self.warmup_steps:
-            decayed_lr = self.learning_rate * (self.step_num /
-                                               self.warmup_steps)
-            decayed_lr = self.create_lr_var(decayed_lr)
-        else:
-            tmp_step_num = self.step_num
-            tmp_decay_steps = self.decay_steps
-            if self.cycle:
-                div_res = fluid.layers.ceil(
-                    self.create_lr_var(tmp_step_num / float(self.decay_steps)))
-                if tmp_step_num == 0:
-                    div_res = self.create_lr_var(1.0)
-                tmp_decay_steps = self.decay_steps * div_res
-            else:
-                tmp_step_num = self.create_lr_var(
-                    tmp_step_num
-                    if tmp_step_num < self.decay_steps else self.decay_steps)
-                decayed_lr = (self.learning_rate - self.end_learning_rate) * \
-                    ((1 - tmp_step_num / tmp_decay_steps) ** self.power) + self.end_learning_rate
-
-        return decayed_lr
-
-
-class Optimizer(object):
-    def __init__(self,
-                 warmup_steps,
-                 num_train_steps,
-                 learning_rate,
-                 model_cls,
-                 weight_decay,
-                 scheduler='linear_warmup_decay',
-                 loss_scaling=1.0,
-                 parameter_list=None):
-        self.warmup_steps = warmup_steps
-        self.num_train_steps = num_train_steps
-        self.learning_rate = learning_rate
-        self.model_cls = model_cls
-        self.weight_decay = weight_decay
-        self.scheduler = scheduler
-        self.loss_scaling = loss_scaling
-        self.parameter_list = parameter_list
-
-        self.scheduled_lr = 0.0
-        self.optimizer = self.lr_schedule()
-
-    def lr_schedule(self):
-        if self.warmup_steps > 0:
-            if self.scheduler == 'noam_decay':
-                self.scheduled_lr = fluid.dygraph.NoamDecay(1 / (
-                    self.warmup_steps * (self.learning_rate**2)),
-                                                            self.warmup_steps)
-            elif self.scheduler == 'linear_warmup_decay':
-                self.scheduled_lr = LinearDecay(self.learning_rate,
-                                                self.warmup_steps,
-                                                self.num_train_steps, 0.0)
-            else:
-                raise ValueError("Unkown learning rate scheduler, should be "
-                                 "'noam_decay' or 'linear_warmup_decay'")
-            optimizer = fluid.optimizer.Adam(
-                learning_rate=self.scheduled_lr,
-                parameter_list=self.parameter_list)
-        else:
-            self.scheduled_lr = ConstantLR(self.learning_rate)
-            optimizer = fluid.optimizer.Adam(
-                learning_rate=self.scheduled_lr,
-                parameter_list=self.parameter_list)
-
-        return optimizer
-
-    def exclude_from_weight_decay(self, name):
-        if name.find("layer_norm") > -1:
-            return True
-        bias_suffix = ["_bias", "_b", ".b_0"]
-        for suffix in bias_suffix:
-            if name.endswith(suffix):
-                return True
-        return False
-
-    def state_dict(self):
-        return self.optimizer.state_dict()
-
-    def set_dict(self, state_dict):
-        return self.optimizer.set_dict(state_dict)
-
-    def get_opti_var_name_list(self):
-        return self.optimizer.get_opti_var_name_list()
-
-    def current_step_lr(self):
-        return self.optimizer.current_step_lr()
-
-    def minimize(self, loss, use_data_parallel=False, model=None):
-        param_list = dict()
-
-        clip_norm_thres = 1.0
-        #grad_clip = fluid.clip.GradientClipByGlobalNorm(clip_norm_thres)
-
-        if use_data_parallel:
-            loss = model.scale_loss(loss)
-
-        loss.backward()
-
-        if self.weight_decay > 0:
-            for param in self.model_cls.parameters():
-                param_list[param.name] = param * 1.0
-                param_list[param.name].stop_gradient = True
-
-        if use_data_parallel:
-            assert model is not None
-            model.apply_collective_grads()
-
-        #_, param_grads = self.optimizer.minimize(loss, grad_clip=grad_clip)
-        _, param_grads = self.optimizer.minimize(loss)
-
-        if self.weight_decay > 0:
-            for param, grad in param_grads:
-                if self.exclude_from_weight_decay(param.name):
-                    continue
-                if isinstance(self.scheduled_lr.step(), float):
-                    updated_param = param.numpy() - param_list[
-                        param.name].numpy(
-                        ) * self.weight_decay * self.scheduled_lr.step()
-                else:
-                    updated_param = param.numpy(
-                    ) - param_list[param.name].numpy(
-                    ) * self.weight_decay * self.scheduled_lr.step().numpy()
-                updated_param_var = fluid.dygraph.to_variable(updated_param)
-                param = updated_param_var
-                #param = fluid.layers.reshape(x=updated_param_var, shape=list(updated_param_var.shape))
+from paddle.fluid.framework import in_dygraph_mode
+from hapi.text.bert.dygraph_optimization import DyOptimizer as DyOptimizer
+from hapi.text.bert.static_optimization import StOptimizer as StOptimizer
+
+
+def make_optimizer(warmup_steps,
+                   num_train_steps,
+                   learning_rate,
+                   weight_decay,
+                   model,
+                   scheduler='linear_warmup_decay',
+                   loss_scaling=1.0,
+                   parameter_list=None):
+
+    if in_dygraph_mode():
+        return DyOptimizer(
+            warmup_steps=warmup_steps,
+            num_train_steps=num_train_steps,
+            learning_rate=learning_rate,
+            model_cls=model,
+            weight_decay=weight_decay,
+            scheduler=scheduler,
+            loss_scaling=loss_scaling,
+            parameter_list=parameter_list)
+    else:
+        return StOptimizer(
+            warmup_steps=warmup_steps,
+            num_train_steps=num_train_steps,
+            learning_rate=learning_rate,
+            weight_decay=weight_decay,
+            scheduler=scheduler)

hapi/text/bert/static_optimization.py

@@ -19,7 +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, apply_dynamic_loss_scaling
 
 
 def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps):
@@ -51,128 +50,95 @@ def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps):
         return lr
 
 
-def optimization(loss,
+class StOptimizer(fluid.optimizer.Optimizer):
+    def __init__(self,
                  warmup_steps,
                  num_train_steps,
                  learning_rate,
-                 train_program,
-                 startup_prog,
                  weight_decay,
-                 scheduler='linear_warmup_decay',
-                 use_fp16=False,
-                 use_dynamic_loss_scaling=False,
-                 init_loss_scaling=1.0,
-                 incr_every_n_steps=1000,
-                 decr_every_n_nan_or_inf=2,
-                 incr_ratio=2.0,
-                 decr_ratio=0.8):
-
-    scheduled_lr, loss_scaling = None, None
-    if scheduler == 'noam_decay':
-        if warmup_steps > 0:
-            scheduled_lr = fluid.layers.learning_rate_scheduler\
-             .noam_decay(1/(warmup_steps *(learning_rate ** 2)),
-           warmup_steps)
+                 scheduler='linear_warmup_decay'):
+        super(StOptimizer, self).__init__(
+            learning_rate=learning_rate,
+            parameter_list=None,
+            regularization=None,
+            grad_clip=None,
+            name=None)
+        self.warmup_steps = warmup_steps
+        self.num_train_steps = num_train_steps
+        self.learning_rate = learning_rate
+        self.weight_decay = weight_decay
+        self.scheduler = scheduler
+
+    def minimize(self, loss):
+
+        train_program = fluid.default_main_program()
+        startup_program = fluid.default_startup_program()
+
+        if self.scheduler == 'noam_decay':
+            if self.warmup_steps > 0:
+                scheduled_lr = fluid.layers.learning_rate_scheduler\
+                 .noam_decay(1/(self.warmup_steps *(self.learning_rate ** 2)),
+                self.warmup_steps)
+            else:
+                print(
+                    "WARNING: noam decay of learning rate should have postive warmup "
+                    "steps but given {}, using constant learning rate instead!"
+                    .format(self.warmup_steps))
+                scheduled_lr = fluid.layers.create_global_var(
+                    name=fluid.unique_name.generate("learning_rate"),
+                    shape=[1],
+                    value=self.learning_rate,
+                    dtype='float32',
+                    persistable=True)
+        elif self.scheduler == 'linear_warmup_decay':
+            if self.warmup_steps > 0:
+                scheduled_lr = linear_warmup_decay(self.learning_rate,
+                                                   self.warmup_steps,
+                                                   self.num_train_steps)
+            else:
+                print(
+                    "WARNING: linear warmup decay of learning rate should have "
+                    "postive warmup steps but given {}, use constant learning rate "
+                    "instead!".format(self.warmup_steps))
+                scheduled_lr = fluid.layers.create_global_var(
+                    name=fluid.unique_name.generate("learning_rate"),
+                    shape=[1],
+                    value=self.learning_rate,
+                    dtype='float32',
+                    persistable=True)
         else:
-            print(
-                "WARNING: noam decay of learning rate should have postive warmup "
-                "steps but given {}, using constant learning rate instead!"
-                .format(warmup_steps))
-            scheduled_lr = fluid.layers.create_global_var(
-                name=fluid.unique_name.generate("learning_rate"),
-                shape=[1],
-                value=learning_rate,
-                dtype='float32',
-                persistable=True)
-    elif scheduler == 'linear_warmup_decay':
-        if warmup_steps > 0:
-            scheduled_lr = linear_warmup_decay(learning_rate, warmup_steps,
-                                               num_train_steps)
-        else:
-            print(
-                "WARNING: linear warmup decay of learning rate should have "
-                "postive warmup steps but given {}, use constant learning rate "
-                "instead!".format(warmup_steps))
-            scheduled_lr = fluid.layers.create_global_var(
-                name=fluid.unique_name.generate("learning_rate"),
-                shape=[1],
-                value=learning_rate,
-                dtype='float32',
-                persistable=True)
-    else:
-        raise ValueError("Unkown learning rate scheduler, should be "
-                         "'noam_decay' or 'linear_warmup_decay'")
-
-    optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
-    fluid.clip.set_gradient_clip(
-        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0))
-
-    def exclude_from_weight_decay(param):
-        name = param.name.rstrip(".master")
-        if name.find("layer_norm") > -1:
-            return True
-        bias_suffix = ["_bias", "_b", ".b_0"]
-        for suffix in bias_suffix:
-            if name.endswith(suffix):
-                return True
-        return False
-
-    param_list = dict()
-
-    if use_fp16:
-        loss_scaling = fluid.layers.create_global_var(
-            name=fluid.unique_name.generate("loss_scaling"),
-            shape=[1],
-            value=init_loss_scaling,
-            dtype='float32',
-            persistable=True)
-        loss *= loss_scaling
-
-        param_grads = optimizer.backward(loss)
-        master_param_grads = create_master_params_grads(
-            param_grads, train_program, startup_prog, loss_scaling)
-
-        if weight_decay > 0:
-            for param, _ in master_param_grads:
-                param_list[param.name] = param * 1.0
-                param_list[param.name].stop_gradient = True
+            raise ValueError("Unkown learning rate scheduler, should be "
+                             "'noam_decay' or 'linear_warmup_decay'")
 
-        if use_dynamic_loss_scaling:
-            apply_dynamic_loss_scaling(
-                loss_scaling, master_param_grads, incr_every_n_steps,
-                decr_every_n_nan_or_inf, incr_ratio, decr_ratio)
+        optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
+        fluid.clip.set_gradient_clip(
+            clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=1.0))
 
-        optimizer.apply_gradients(master_param_grads)
-
-        if weight_decay > 0:
-            for param, grad in master_param_grads:
-                if exclude_from_weight_decay(param):
-                    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)
+        def exclude_from_weight_decay(param):
+            name = param.name.rstrip(".master")
+            if name.find("layer_norm") > -1:
+                return True
+            bias_suffix = ["_bias", "_b", ".b_0"]
+            for suffix in bias_suffix:
+                if name.endswith(suffix):
+                    return True
+            return False
 
-        master_param_to_train_param(master_param_grads, param_grads,
-                                    train_program)
+        param_list = dict()
 
-    else:
-        if weight_decay > 0:
+        if self.weight_decay > 0:
             for param in train_program.all_parameters():
                 param_list[param.name] = param * 1.0
                 param_list[param.name].stop_gradient = True
 
         _, param_grads = optimizer.minimize(loss)
 
-        if weight_decay > 0:
+        if self.weight_decay > 0:
             for param, grad in param_grads:
                 if exclude_from_weight_decay(param):
                     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
+                        param.name] * self.weight_decay * scheduled_lr
                     fluid.layers.assign(output=param, input=updated_param)
-
-    return scheduled_lr, loss_scaling

hapi/text/text.py

@@ -1096,7 +1096,8 @@ class PrePostProcessLayer(Layer):
         self.functors = []
         for cmd in self.process_cmd:
             if cmd == "a":  # add residual connection
-                self.functors.append(lambda x, y: x + y if y else x)
+                self.functors.append(
+                    lambda x, y: x + y if y is not None else x)
             elif cmd == "n":  # add layer normalization
                 if reused_layer_norm is not None:
                     layer_norm = reused_layer_norm
@@ -1218,7 +1219,7 @@ class MultiHeadAttention(Layer):
         # scale dot product attention
         product = layers.matmul(
             x=q, y=k, transpose_y=True, alpha=self.d_model**-0.5)
-        if attn_bias:
+        if attn_bias is not None:
             product += attn_bias
         weights = layers.softmax(product)
         if self.dropout_rate: