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tinybert hub

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pull/6656/head
yoonlee666 5 years ago
parent fe219b5680
commit 25b2c1944f
8 changed files with 82 additions and 92 deletions
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4
model_zoo/official/nlp/bert/mindspore_hub_conf.py
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@ -59,11 +59,11 @@ def create_network(name, *args, **kwargs):
if name == 'bert_base':
if "seq_length" in kwargs:
bert_net_cfg_base.seq_length = kwargs["seq_length"]
is_training = kwargs.get("is_training", default=False)
is_training = kwargs.get("is_training", False)
return BertModel(bert_net_cfg_base, is_training, *args)
if name == 'bert_nezha':
if "seq_length" in kwargs:
bert_net_cfg_nezha.seq_length = kwargs["seq_length"]
is_training = kwargs.get("is_training", default=False)
is_training = kwargs.get("is_training", False)
return BertModel(bert_net_cfg_nezha, is_training, *args)
raise NotImplementedError(f"{name} is not implemented in the repo")

4
model_zoo/official/nlp/tinybert/README.md
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@ -207,6 +207,7 @@ options:
`gd_config.py` and `td_config.py` contain parameters of BERT model and options for optimizer and lossscale.
### Options:
```
batch_size batch size of input dataset: N, default is 16
Parameters for lossscale:
loss_scale_value initial value of loss scale: N, default is 2^8
scale_factor factor used to update loss scale: N, default is 2
@ -223,7 +224,6 @@ Parameters for optimizer:
### Parameters:
```
Parameters for bert network:
batch_size batch size of input dataset: N, default is 16
seq_length length of input sequence: N, default is 128
vocab_size size of each embedding vector: N, must be consistant with the dataset you use. Default is 30522
hidden_size size of bert encoder layers: N
@ -239,8 +239,6 @@ Parameters for bert network:
type_vocab_size size of token type vocab: N, default is 2
initializer_range initialization value of TruncatedNormal: Q, default is 0.02
use_relative_positions use relative positions or not: True | False, default is False
input_mask_from_dataset use the input mask loaded form dataset or not: True | False, default is True
token_type_ids_from_dataset use the token type ids loaded from dataset or not: True | False, default is True
dtype data type of input: mstype.float16 | mstype.float32, default is mstype.float32
compute_type compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16
```

49
model_zoo/official/nlp/tinybert/mindspore_hub_conf.py
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@ -0,0 +1,49 @@
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
'''
Bert hub interface for bert base and bert nezha
'''
from src.tinybert_model import TinyBertModel
from src.tinybert_model import BertConfig
import mindspore.common.dtype as mstype
tinybert_student_net_cfg = BertConfig(
seq_length=128,
vocab_size=30522,
hidden_size=384,
num_hidden_layers=4,
num_attention_heads=12,
intermediate_size=1536,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
dtype=mstype.float32,
compute_type=mstype.float16
)
def create_network(name, *args, **kwargs):
'''
Create tinybert network.
'''
if name == "tinybert":
if "seq_length" in kwargs:
tinybert_student_net_cfg.seq_length = kwargs["seq_length"]
is_training = kwargs.get("is_training", False)
return TinyBertModel(tinybert_student_net_cfg, is_training, *args)
raise NotImplementedError(f"{name} is not implemented in the repo")

2
model_zoo/official/nlp/tinybert/run_general_distill.py
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@ -110,7 +110,7 @@ def run_general_distill():
dataset_type = DataType.MINDRECORD
else:
raise Exception("dataset format is not supported yet")
dataset = create_tinybert_dataset('gd', bert_teacher_net_cfg.batch_size, device_num, rank,
dataset = create_tinybert_dataset('gd', common_cfg.batch_size, device_num, rank,
args_opt.do_shuffle, args_opt.data_dir, args_opt.schema_dir,
data_type=dataset_type)
dataset_size = dataset.get_dataset_size()

10
model_zoo/official/nlp/tinybert/run_task_distill.py
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@ -29,7 +29,7 @@ from mindspore import log as logger
from src.dataset import create_tinybert_dataset, DataType
from src.utils import LossCallBack, ModelSaveCkpt, EvalCallBack, BertLearningRate
from src.assessment_method import Accuracy
from src.td_config import phase1_cfg, phase2_cfg, td_teacher_net_cfg, td_student_net_cfg
from src.td_config import phase1_cfg, phase2_cfg, eval_cfg, td_teacher_net_cfg, td_student_net_cfg
from src.tinybert_for_gd_td import BertEvaluationWithLossScaleCell, BertNetworkWithLoss_td, BertEvaluationCell
from src.tinybert_model import BertModelCLS
@ -130,7 +130,7 @@ def run_predistill():
dataset_type = DataType.MINDRECORD
else:
raise Exception("dataset format is not supported yet")
dataset = create_tinybert_dataset('td', td_teacher_net_cfg.batch_size,
dataset = create_tinybert_dataset('td', cfg.batch_size,
device_num, rank, args_opt.do_shuffle,
args_opt.train_data_dir, args_opt.schema_dir,
data_type=dataset_type)
@ -194,7 +194,7 @@ def run_task_distill(ckpt_file):
rank = 0
device_num = 1
train_dataset = create_tinybert_dataset('td', td_teacher_net_cfg.batch_size,
train_dataset = create_tinybert_dataset('td', cfg.batch_size,
device_num, rank, args_opt.do_shuffle,
args_opt.train_data_dir, args_opt.schema_dir)
@ -224,7 +224,7 @@ def run_task_distill(ckpt_file):
optimizer = AdamWeightDecay(group_params, learning_rate=lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)
eval_dataset = create_tinybert_dataset('td', td_teacher_net_cfg.batch_size,
eval_dataset = create_tinybert_dataset('td', eval_cfg.batch_size,
device_num, rank, args_opt.do_shuffle,
args_opt.eval_data_dir, args_opt.schema_dir)
print('td2 eval dataset size: ', eval_dataset.get_dataset_size())
@ -269,7 +269,7 @@ def do_eval_standalone():
load_param_into_net(eval_model, new_param_dict)
eval_model.set_train(False)
eval_dataset = create_tinybert_dataset('td', batch_size=td_student_net_cfg.batch_size,
eval_dataset = create_tinybert_dataset('td', batch_size=eval_cfg.batch_size,
device_num=1, rank=0, do_shuffle="false",
data_dir=args_opt.eval_data_dir,
schema_dir=args_opt.schema_dir)

7
model_zoo/official/nlp/tinybert/src/gd_config.py
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@ -20,6 +20,7 @@ from easydict import EasyDict as edict
from .tinybert_model import BertConfig
common_cfg = edict({
'batch_size': 32,
'loss_scale_value': 2 ** 16,
'scale_factor': 2,
'scale_window': 1000,
@ -38,7 +39,6 @@ teacher network: The BERT-base network.
student network: The network which is inherited from teacher network.
'''
bert_teacher_net_cfg = BertConfig(
batch_size=32,
seq_length=128,
vocab_size=30522,
hidden_size=768,
@ -52,13 +52,10 @@ bert_teacher_net_cfg = BertConfig(
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16
)
bert_student_net_cfg = BertConfig(
batch_size=32,
seq_length=128,
vocab_size=30522,
hidden_size=384,
@ -72,8 +69,6 @@ bert_student_net_cfg = BertConfig(
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16
)

12
model_zoo/official/nlp/tinybert/src/td_config.py
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@ -20,6 +20,7 @@ from easydict import EasyDict as edict
from .tinybert_model import BertConfig
phase1_cfg = edict({
'batch_size': 32,
'loss_scale_value': 2 ** 8,
'scale_factor': 2,
'scale_window': 50,
@ -36,6 +37,7 @@ phase1_cfg = edict({
})
phase2_cfg = edict({
'batch_size': 32,
'loss_scale_value': 2 ** 16,
'scale_factor': 2,
'scale_window': 50,
@ -51,13 +53,16 @@ phase2_cfg = edict({
}),
})
eval_cfg = edict({
'batch_size': 32,
})
'''
Including two kinds of network: \
teacher network: The BERT-base network with finetune.
student network: The model which is producted by GD phase.
'''
td_teacher_net_cfg = BertConfig(
batch_size=32,
seq_length=128,
vocab_size=30522,
hidden_size=768,
@ -71,13 +76,10 @@ td_teacher_net_cfg = BertConfig(
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16
)
td_student_net_cfg = BertConfig(
batch_size=32,
seq_length=128,
vocab_size=30522,
hidden_size=384,
@ -91,8 +93,6 @@ td_student_net_cfg = BertConfig(
type_vocab_size=2,
initializer_range=0.02,
use_relative_positions=False,
input_mask_from_dataset=True,
token_type_ids_from_dataset=True,
dtype=mstype.float32,
compute_type=mstype.float16
)

86
model_zoo/official/nlp/tinybert/src/tinybert_model.py
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