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提交 1373e294 编写于 作者: X xyzhou-puck
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add leveldb reader for bert

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examples/bert_leveldb/bert.yaml 0 → 100644
浏览文件 @ 1373e294
bert_config_path: "./config/bert_config.json"
init_checkpoint: None
init_pretraining_params: None
checkpoints: "./saved_model"
epoch: 3
learning_rate: 0.0001
lr_scheduler: "linear_warmup_decay"
weight_decay: 0.01
warmup_proportion: 0.1
save_steps: 100000
validation_steps: 100000
loss_scaling: 1.0
skip_steps: 100
data_dir: None
vocab_path: None
max_seq_len: 512
batch_size: 32
in_tokens: False
do_lower_case: True
random_seed: 5512
use_cuda: False
shuffle: True
do_train: True
do_test: True
use_data_parallel: False
verbose: False
examples/bert_leveldb/bert_classifier.py 0 → 100755
浏览文件 @ 1373e294
# 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.
"""BERT fine-tuning in Paddle Dygraph Mode."""
import paddle.fluid as fluid
from hapi.metrics import Accuracy
from hapi.configure import Config
from hapi.model import set_device, Model, SoftmaxWithCrossEntropy, Input
from cls import ClsModelLayer
import hapi.text.tokenizer.tokenization as tokenization
from hapi.text.bert import Optimizer, BertConfig, BertDataLoader, BertInputExample
def train():
config = Config(yaml_file="./bert.yaml")
config.build()
config.Print()
device = set_device("gpu" if config.use_cuda else "cpu")
fluid.enable_dygraph(device)
bert_config = BertConfig(config.bert_config_path)
bert_config.print_config()
trainer_count = fluid.dygraph.parallel.Env().nranks
tokenizer = tokenization.FullTokenizer(
vocab_file=config.vocab_path, do_lower_case=config.do_lower_case)
def mnli_line_processor(line_id, line):
if line_id == "0":
return None
uid = tokenization.convert_to_unicode(line[0])
text_a = tokenization.convert_to_unicode(line[8])
text_b = tokenization.convert_to_unicode(line[9])
label = tokenization.convert_to_unicode(line[-1])
if label not in ["contradiction", "entailment", "neutral"]:
label = "contradiction"
return BertInputExample(
uid=uid, text_a=text_a, text_b=text_b, label=label)
bert_dataloader = BertDataLoader(
"./data/glue_data/MNLI/train.tsv",
tokenizer, ["contradiction", "entailment", "neutral"],
max_seq_length=64,
batch_size=32,
line_processor=mnli_line_processor,
mode="leveldb")
num_train_examples = len(bert_dataloader.dataset)
max_train_steps = config.epoch * num_train_examples // config.batch_size // trainer_count
warmup_steps = int(max_train_steps * config.warmup_proportion)
print("Trainer count: %d" % trainer_count)
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
inputs = [
Input(
[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')
]
labels = [Input([None, 1], 'int64', name='label')]
cls_model = ClsModelLayer(
config,
bert_config,
len(["contradiction", "entailment", "neutral"]),
is_training=True,
return_pooled_out=True)
optimizer = 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,
loss_scaling=config.loss_scaling,
parameter_list=cls_model.parameters())
cls_model.prepare(
optimizer,
SoftmaxWithCrossEntropy(),
Accuracy(topk=(1, 2)),
inputs,
labels,
device=device)
cls_model.bert_layer.init_parameters(
config.init_pretraining_params, verbose=config.verbose)
cls_model.fit(train_data=bert_dataloader.dataloader, epochs=config.epoch)
return cls_model
if __name__ == '__main__':
cls_model = train()
examples/bert_leveldb/cls.py 0 → 100644
浏览文件 @ 1373e294
# 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.
"dygraph transformer layers"
import six
import json
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph import Linear, Layer
from hapi.text.bert import BertEncoder
from hapi.model import Model
class ClsModelLayer(Model):
"""
classify model
"""
def __init__(self,
args,
config,
num_labels,
is_training=True,
return_pooled_out=True,
use_fp16=False):
super(ClsModelLayer, self).__init__()
self.config = config
self.is_training = is_training
self.use_fp16 = use_fp16
self.loss_scaling = args.loss_scaling
self.bert_layer = BertEncoder(
config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
self.cls_fc = Linear(
input_dim=self.config["hidden_size"],
output_dim=num_labels,
param_attr=fluid.ParamAttr(
name="cls_out_w",
initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
bias_attr=fluid.ParamAttr(
name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
def forward(self, src_ids, position_ids, sentence_ids, input_mask):
"""
forward
"""
enc_output, next_sent_feat = self.bert_layer(src_ids, position_ids,
sentence_ids, input_mask)
cls_feats = fluid.layers.dropout(
x=next_sent_feat,
dropout_prob=0.1,
dropout_implementation="upscale_in_train")
logits = self.cls_fc(cls_feats)
return logits
examples/bert_leveldb/nohup.out 0 → 100644
浏览文件 @ 1373e294
grep: warning: GREP_OPTIONS is deprecated; please use an alias or script
2020-04-13 13:08:30,568-WARNING: use_shared_memory can only be used in multi-process mode(num_workers > 0), set use_shared_memory as False
W0413 13:08:31.584532 119379 device_context.cc:237] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.1, Runtime API Version: 9.0
W0413 13:08:31.589192 119379 device_context.cc:245] device: 0, cuDNN Version: 7.5.
----------------------------------------------------------------------
bert_config_path: ./data/pretrained_models/uncased_L-12_H-768_A-12//bert_config.json
init_checkpoint: None
init_pretraining_params: ./data/pretrained_models/uncased_L-12_H-768_A-12//dygraph_params/
checkpoints: ./data/saved_model/mnli_models
epoch: 3
learning_rate: 5e-05
lr_scheduler: linear_warmup_decay
weight_decay: 0.01
warmup_proportion: 0.1
save_steps: 1000
validation_steps: 100
loss_scaling: 1.0
skip_steps: 10
data_dir: ./data/glue_data/MNLI/
vocab_path: ./data/pretrained_models/uncased_L-12_H-768_A-12//vocab.txt
max_seq_len: 128
batch_size: 64
in_tokens: False
do_lower_case: True
random_seed: 5512
use_cuda: True
shuffle: True
do_train: True
do_test: True
use_data_parallel: False
verbose: False
----------------------------------------------------------------------
attention_probs_dropout_prob: 0.1
hidden_act: gelu
hidden_dropout_prob: 0.1
hidden_size: 768
initializer_range: 0.02
intermediate_size: 3072
max_position_embeddings: 512
num_attention_heads: 12
num_hidden_layers: 12
type_vocab_size: 2
vocab_size: 30522
------------------------------------------------
Trainer count: 1
Num train examples: 392703
Max train steps: 18407
Num warmup steps: 1840
Epoch 1/3
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step 50/12272 - loss: 1.1202 - acc_top1: 0.3756 - acc_top2: 0.7006 - 1s/step
step 60/12272 - loss: 1.1291 - acc_top1: 0.3703 - acc_top2: 0.6990 - 1s/step
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step 100/12272 - loss: 1.0949 - acc_top1: 0.3638 - acc_top2: 0.6878 - 1s/step
step 110/12272 - loss: 1.1120 - acc_top1: 0.3608 - acc_top2: 0.6895 - 1s/step
step 120/12272 - loss: 1.1105 - acc_top1: 0.3622 - acc_top2: 0.6922 - 1s/step
step 130/12272 - loss: 1.0958 - acc_top1: 0.3623 - acc_top2: 0.6940 - 1s/step
step 140/12272 - loss: 1.0995 - acc_top1: 0.3636 - acc_top2: 0.6926 - 1s/step
step 150/12272 - loss: 1.1272 - acc_top1: 0.3671 - acc_top2: 0.6950 - 1s/step
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step 260/12272 - loss: 1.0770 - acc_top1: 0.3751 - acc_top2: 0.7085 - 1s/step
step 270/12272 - loss: 1.1008 - acc_top1: 0.3730 - acc_top2: 0.7088 - 1s/step
step 280/12272 - loss: 1.0850 - acc_top1: 0.3737 - acc_top2: 0.7098 - 1s/step
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step 300/12272 - loss: 1.0352 - acc_top1: 0.3758 - acc_top2: 0.7108 - 1s/step
step 310/12272 - loss: 1.0224 - acc_top1: 0.3786 - acc_top2: 0.7127 - 1s/step
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step 1250/12272 - loss: 0.8668 - acc_top1: 0.5517 - acc_top2: 0.8237 - 1s/step
step 1260/12272 - loss: 0.6057 - acc_top1: 0.5527 - acc_top2: 0.8243 - 1s/step
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examples/bert_leveldb/run_classifier_single_gpu.sh 0 → 100755
浏览文件 @ 1373e294
#!/bin/bash
BERT_BASE_PATH="./data/pretrained_models/uncased_L-12_H-768_A-12/"
TASK_NAME='MNLI'
DATA_PATH="./data/glue_data/MNLI/"
CKPT_PATH="./data/saved_model/mnli_models"
export CUDA_VISIBLE_DEVICES=7
# start fine-tuning
python3.7 bert_classifier.py\
--use_cuda true \
--do_train true \
--do_test true \
--batch_size 64 \
--init_pretraining_params ${BERT_BASE_PATH}/dygraph_params/ \
--data_dir ${DATA_PATH} \
--vocab_path ${BERT_BASE_PATH}/vocab.txt \
--checkpoints ${CKPT_PATH} \
--save_steps 1000 \
--weight_decay 0.01 \
--warmup_proportion 0.1 \
--validation_steps 100 \
--epoch 3 \
--max_seq_len 128 \
--bert_config_path ${BERT_BASE_PATH}/bert_config.json \
--learning_rate 5e-5 \
--skip_steps 10 \
--shuffle true
hapi/text/bert/dataloader.py
浏览文件 @ 1373e294
...@@ -19,6 +19,7 @@ import csv ...@@ -19,6 +19,7 @@ import csv
import glob import glob
import tarfile import tarfile
import itertools import itertools
import leveldb
from functools import partial from functools import partial
import numpy as np import numpy as np
...@@ -167,10 +168,14 @@ class SingleSentenceDataset(Dataset): ...@@ -167,10 +168,14 @@ class SingleSentenceDataset(Dataset):
assert isinstance(mode, assert isinstance(mode,
str), "mode of SingleSentenceDataset should be str" str), "mode of SingleSentenceDataset should be str"
assert mode in [ assert mode in [
"all_in_memory", "leveldb" "all_in_memory", "leveldb", "streaming"
], "mode of SingleSentenceDataset should be in [all_in_memory, leveldb], but get" % mode ], "mode of SingleSentenceDataset should be in [all_in_memory, leveldb, streaming], but get" % mode
self.delimiter = None
self.mode = mode
self.examples = [] self.examples = []
self._db = None
self._line_processor = None
def load_all_data_in_memory(self, def load_all_data_in_memory(self,
input_file, input_file,
...@@ -202,13 +207,87 @@ class SingleSentenceDataset(Dataset): ...@@ -202,13 +207,87 @@ class SingleSentenceDataset(Dataset):
tokenizer) tokenizer)
self.examples.append(input_feature) self.examples.append(input_feature)
def prepare_leveldb(self,
input_file,
leveldb_file,
label_list,
max_seq_length,
tokenizer,
line_processor=None,
delimiter="\t",
quotechar=None):
def default_line_processor(line_id, line):
assert len(line) == 2
text_a = line[0]
label = line[1]
return BertInputExample(
str(line_id), text_a=text_a, text_b=None, label=label)
if line_processor is None:
line_processor = default_line_processor
if not os.path.exists(leveldb_file):
print("putting data %s into leveldb %s" %
(input_file, leveldb_file))
_example_num = 0
_db = leveldb.LevelDB(leveldb_file, create_if_missing=True)
with io.open(input_file, "r", encoding="utf8") as f:
reader = csv.reader(
f, delimiter=delimiter, quotechar=quotechar)
line_id = 0
for (_line_id, line) in enumerate(reader):
if line_processor(str(_line_id), line) is None:
continue
line_str = delimiter.join(line)
_db.Put(
str(line_id).encode("utf8"), line_str.encode("utf8"))
line_id += 1
_example_num += 1
_db.Put("_example_num_".encode("utf8"),
str(_example_num).encode("utf8"))
else:
_db = leveldb.LevelDB(leveldb_file, create_if_missing=False)
self.label_list = label_list
self.max_seq_length = max_seq_length
self.tokenizer = tokenizer
self.delimiter = delimiter
self._db = _db
self._line_processor = line_processor
def __getitem__(self, idx): def __getitem__(self, idx):
return self.examples[idx].input_ids, self.examples[
idx].pos_ids, self.examples[idx].segment_ids, self.examples[ if self.mode == "all_in_memory":
idx].label_id return self.examples[idx].input_ids, self.examples[
idx].pos_ids, self.examples[idx].segment_ids, self.examples[
idx].label_id
if self.mode == "leveldb":
assert self._db is not None, "you shold call prepare_leveldb before you run dataloader"
line_str = self._db.Get(str(idx).encode("utf8"))
line_str = line_str.decode("utf8")
line = line_str.split(self.delimiter)
input_example = self._line_processor(str(idx + 1), line)
input_example = convert_single_example(
str(idx + 1), input_example, self.label_list,
self.max_seq_length, self.tokenizer)
return input_example.input_ids, input_example.pos_ids, input_example.segment_ids, input_example.label_id
def __len__(self): def __len__(self):
return len(self.examples) if self.mode == "all_in_memory":
return len(self.examples)
if self.mode == "leveldb":
assert self._db is not None, "you shold call prepare_leveldb before you run dataloader"
exmaple_num = self._db.Get("_example_num_".encode("utf8"))
exmaple_num = exmaple_num.decode("utf8")
return int(exmaple_num)
class SentencePairDataset(Dataset): class SentencePairDataset(Dataset):
...@@ -299,6 +378,7 @@ class BertDataLoader(object): ...@@ -299,6 +378,7 @@ class BertDataLoader(object):
shuffle=False, shuffle=False,
drop_last=False, drop_last=False,
mode="all_in_memory", mode="all_in_memory",
leveldb_file="./leveldb",
line_processor=None, line_processor=None,
delimiter="\t", delimiter="\t",
quotechar=None, quotechar=None,
...@@ -314,8 +394,10 @@ class BertDataLoader(object): ...@@ -314,8 +394,10 @@ class BertDataLoader(object):
input_file, label_list, max_seq_length, tokenizer, input_file, label_list, max_seq_length, tokenizer,
line_processor, delimiter, quotechar) line_processor, delimiter, quotechar)
elif mode == "leveldb": elif mode == "leveldb":
#TODO add leveldb reader #prepare_leveldb(self, input_file, leveldb_file, label_list, max_seq_length, tokenizer, line_processor=None, delimiter="\t", quotechar=None):
pass self.dataset.prepare_leveldb(input_file, leveldb_file, label_list,
max_seq_length, tokenizer,
line_processor, delimiter, quotechar)
else: else:
raise ValueError("mode should be in [all_in_memory, leveldb]") raise ValueError("mode should be in [all_in_memory, leveldb]")
......
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