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未验证 提交 defa38e7 编写于 作者: M Meiyim 提交者： GitHub
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Dygraph (#436) 

* fix pretrain reader  encoding

* dygraph

* update bce

* fix readme

* update readme

* fix readme

* remove debug log

* remove chn example

* fix pretrain

* tokenizer can escape special token
rename mlm_bias

* update readme

* 1. MRC tasks
2. optimize truncate strategy in tokenzier
3. rename `ernie.cls_fc` to `ernie.classifyer`
4. reorg pretrain dir

* + doc string

* update readme

* Update README.md

* Update README.md

* Update README.md

* update pretrain

* Update README.md

* show gif

* update readme

* fix gif; disable save_dygrpah

* bugfix

* + save inf model
+ inference
+ distill
+ readme

* Update README.md

* + propeller server

* Update README.md

* Update README.md

* Update README.md

* + sementic analysis(another text classification)

* transformer + cache
fix tokenization bug

* update reamde; fix tokenization

* Update README.md

* Update README.md

* Add files via upload

* Update README.md

* Update README.md

* Update README.md

* infer senti analysis

* + readme header

* transformer cache has gradients

* + seq2seq

* +experimental

* reorg

* update readme

* Update README.md

* seq2seq

* + cnndm evluation scripts

* update README.md

* +zh readme

* + publish ernie gen model

* update README.md

* Update README.zh.md

* Update README.md

* Update README.zh.md

* Update README.md

* Update README.zh.md

* Update README.md

* Update README.zh.md

* Update README.md

* Update README.zh.md

* Update README.zh.md

* Add files via upload

* Update README.md

* Update README.zh.md

* Add files via upload

* release ernie-gen (#444)

* release ernie-gen

* add .meta

* del tag

* fix tag

* Add files via upload

* Update README.zh.md

* Update README.md

* Update and rename README.zh.md to README.eng.md

* Update README.eng.md

* Update README.md

* Update README.eng.md

* Update README.md

* Update README.eng.md

* Update README.md

* Update README.md

* Update README.md

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Co-authored-by: Nkirayummy <shi.k.feng@gmail.com>
Co-authored-by: Nzhanghan <zhanghan17@baidu.com>
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*.pyc
*.un~
*~
*.pyc
*.swp
*.egg-info/
*.swn
*.swo
dist/
paddle_ernie.egg-info/
.pre-commit-config.yaml 已删除 100644 → 0
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- repo: https://github.com/PaddlePaddle/mirrors-yapf.git
sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37
hooks:
- id: yapf
files: \.py$
- repo: https://github.com/pre-commit/pre-commit-hooks
sha: a11d9314b22d8f8c7556443875b731ef05965464
hooks:
- id: check-merge-conflict
- id: check-symlinks
- id: detect-private-key
files: (?!.*paddle)^.*$
- id: end-of-file-fixer
files: \.md$
- id: trailing-whitespace
files: \.md$
- repo: https://github.com/Lucas-C/pre-commit-hooks
sha: v1.0.1
hooks:
- id: forbid-crlf
files: \.md$
- id: remove-crlf
files: \.md$
- id: forbid-tabs
files: \.md$
- id: remove-tabs
files: \.md$
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set -eux
export FLAGS_sync_nccl_allreduce=1
MODEL_PATH=ERNIE_1.0.1
TASK_DATA_PATH=task_data
train() {
python -u run_classifier.py \
--use_cuda true \
--do_train true \
--do_val true \
--do_test true \
--verbose true \
--batch_size 8192 \
--in_tokens true \
--init_pretraining_params ${MODEL_PATH}/params \
--train_set ${TASK_DATA_PATH}/xnli/train.tsv \
--dev_set ${TASK_DATA_PATH}/xnli/dev.tsv \
--test_set ${TASK_DATA_PATH}/xnli/test.tsv \
--test_save ./test_save_xnli \
--vocab_path config/vocab.txt \
--label_map ${TASK_DATA_PATH}/xnli/label_map.json \
--ernie_config_path config/ernie_config.json \
--checkpoints ./checkpoints \
--save_steps 2000 \
--weight_decay 0.01 \
--warmup_proportion 0.0 \
--validation_steps 25 \
--epoch 1 \
--max_seq_len 512 \
--learning_rate 1e-4 \
--skip_steps 10 \
--num_iteration_per_drop_scope 1 \
--num_labels 3 \
--random_seed 100 \
--enable_ce \
--shuffle false
}
export CUDA_VISIBLE_DEVICES=0
train | python _ce.py
export CUDA_VISIBLE_DEVICES=0,1,2,3
train | python _ce.py
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[style]
based_on_style = pep8
column_limit = 80
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language: python
compiler: gcc
cache: ccache
addons:
apt:
packages:
- git
# ssh_known_hosts: 13.229.163.131
env:
- JOB=PRE_COMMIT
matrix:
include:
- python: 2.7
- python: 3.7
dist: xenial # required for Python >= 3.7 (travis-ci/travis-ci#9069)
before_install:
- pip install --upgrade pip
- pip install pre-commit
script:
- exit_code=0
- .travis/precommit.sh || exit_code=$(( exit_code | $? ))
notifications:
email:
on_success: change
on_failure: always
.travis/precommit.sh 已删除 100755 → 0
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#!/bin/bash
function abort(){
echo "Your commit does not fit PaddlePaddle code style" 1>&2
echo "Please use pre-commit scripts to auto-format your code" 1>&2
exit 1
}
trap 'abort' 0
set -e
cd `dirname $0`
cd ..
export PATH=/usr/bin:$PATH
pre-commit install
if ! pre-commit run -a ; then
ls -lh
git diff --exit-code
exit 1
fi
trap : 0
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English|[简体中文](./README.md)
![./.metas/ERNIE_milestone.png](./.metas/ERNIE_milestone.png)
**Remind： This repo has been refactored, for paper re-production or backward compatibility; plase checkout to [repro branch](https://github.com/PaddlePaddle/ERNIE/tree/repro)**
ERNIE 2.0 is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning.
ERNIE 2.0 builds a strong basic for nearly every NLP tasks: Text Classification, Ranking, NER, machine reading comprehension, text genration and so on.
# News
- May.20.2020: Try ERNIE in "`dygraph`", with:
- Pretrain and finetune ERNIE with [PaddlePaddle v1.8](https://github.com/PaddlePaddle/Paddle/tree/release/1.8).
- Eager execution with `paddle.fluid.dygraph`.
- Distributed training.
- Easy deployment.
- Learn NLP in Aistudio tutorials.
- Backward compatibility for old-styled checkpoint
- Release **[ERNIE-GEN](https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-gen)**, a state-of-the-art pre-trained model for generation tasks: base/large/large-160G
- Apr.30.2020: Release [ERNIESage](https://github.com/PaddlePaddle/PGL/tree/master/examples/erniesage), a novel Graph Neural Network Model using ERNIE as its aggregator. It is implemented through [PGL](https://github.com/PaddlePaddle/PGL)
- Mar.27.2020: [Champion on 5 SemEval2020 sub tasks](https://www.jiqizhixin.com/articles/2020-03-27-8)
- Dec.26.2019: [1st place on GLUE leaderboard](https://www.technologyreview.com/2019/12/26/131372/ai-baidu-ernie-google-bert-natural-language-glue/)
- Nov.6.2019: [Introducing ERNIE-tiny](https://www.jiqizhixin.com/articles/2019-11-06-9)
- Jul.7.2019: [Introducing ERNIE2.0](https://www.jiqizhixin.com/articles/2019-07-31-10)
- Mar.16.2019: [Introducing ERNIE1.0](https://www.jiqizhixin.com/articles/2019-03-16-3)
# Table of contents
* [Tutorials](#tutorials)
* [Setup](#setup)
* [Fine-tuning](#fine-tuning)
* [Pre-training with ERNIE 1.0](#pre-training-with-ernie-10)
* [Online inference](#online-inference)
* [Distillation](#distillation)
# Quick Tour
```python
import numpy as np
import paddle.fluid.dygraph as D
from ernie.tokenizing_ernie import ErnieTokenizer
from ernie.modeling_ernie import ErnieModel
D.guard().__enter__() # activate paddle `dygrpah` mode
model = ErnieModel.from_pretrained('ernie-1.0') # Try to get pretrained model from server, make sure you have network connection
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
ids, _ = tokenizer.encode('hello world')
ids = D.to_variable(np.expand_dims(ids, 0)) # insert extra `batch` dimension
pooled, encoded = model(ids) # eager execution
print(pooled.numpy()) # convert results to numpy
```
# Tutorials
Don't have GPU? try ERNIE in [AIStudio](https://aistudio.baidu.com/aistudio/index)!
(please choose the latest version and apply for a GPU environment)
1. [ERNIE for beginners](https://aistudio.baidu.com/aistudio/projectdetail/266623)
1. [Sementic Analysis](https://aistudio.baidu.com/aistudio/projectdetail/427482)
2. [Cloze Test](https://aistudio.baidu.com/aistudio/projectdetail/433491)
3. [Knowledge Distillation](https://aistudio.baidu.com/aistudio/projectdetail/439460)
4. [Ask Ernie](https://aistudio.baidu.com/aistudio/projectdetail/456443)
# Setup
##### 1. install ernie
```script
pip install paddle-ernie==0.0.1.dev1
```
or
```shell
git clone -b dygraph https://github.com/PaddlePaddle/ERNIE.git --single-branch
cd ERNIE
pip install -r requirement.txt
pip setup.py -e .
```
##### 3. download pretrained models (optional)
| Model | Description |
| :------------------------------------------------- | :----------------------------------------------------------- |
| [ERNIE 1.0 Base for Chinese](https://ernie-github.cdn.bcebos.com/model-ernie1.0.1.tar.gz) | L12H768A12 |
| [ERNIE Tiny](https://ernie-github.cdn.bcebos.com/model-ernie_tiny.1.tar.gz) | L3H1024A16 |
| [ERNIE 2.0 Base for English](https://ernie-github.cdn.bcebos.com/model-ernie2.0-en.1.tar.gz) | L12H768A12 |
| [ERNIE 2.0 Large for English](https://ernie-github.cdn.bcebos.com/model-ernie2.0-large-en.1.tar.gz) | L24H1024A16 |
| [ERNIE Gen base for English](https://ernie-github.cdn.bcebos.com/model-ernie-gen-base-en.1.tar.gz) | L12H768A12 |
| [ERNIE Gen Large for English](https://ernie-github.cdn.bcebos.com/model-ernie-gen-large-en.1.tar.gz)| L24H1024A16 |
##### 4. download datasets
**English Datasets**
Download the [GLUE datasets](https://gluebenchmark.com/tasks) by running [this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
the `--data_dir` option in the following section assumes a directory tree like this:
```shell
data/xnli
├── dev
│   └── 1
├── test
│   └── 1
└── train
└── 1
```
see [demo](https://ernie-github.cdn.bcebos.com/data-mnli-m.tar.gz) data for MNLI task.
**Chinese Datasets**
| Datasets|Description|
|:--------|:----------|
| [XNLI](https://ernie-github.cdn.bcebos.com/data-xnli.tar.gz) |XNLI is a natural language inference dataset in 15 languages. It was jointly built by Facebook and New York University. We use Chinese data of XNLI to evaluate language understanding ability of our model. [url](https://github.com/facebookresearch/XNLI)|
| [ChnSentiCorp](https://ernie-github.cdn.bcebos.com/data-chnsenticorp.tar.gz) |ChnSentiCorp is a sentiment analysis dataset consisting of reviews on online shopping of hotels, notebooks and books.|
| [MSRA-NER](https://ernie-github.cdn.bcebos.com/data-msra_ner.tar.gz) |MSRA-NER (SIGHAN2006) dataset is released by MSRA for recognizing the names of people, locations and organizations in text.|
| [NLPCC2016-DBQA](https://ernie-github.cdn.bcebos.com/data-dbqa.tar.gz) |NLPCC2016-DBQA is a sub-task of NLPCC-ICCPOL 2016 Shared Task which is hosted by NLPCC(Natural Language Processing and Chinese Computing), this task targets on selecting documents from the candidates to answer the questions. [url: http://tcci.ccf.org.cn/conference/2016/dldoc/evagline2.pdf]|
|[CMRC2018](https://ernie-github.cdn.bcebos.com/data-cmrc2018.tar.gz)|CMRC2018 is a evaluation of Chinese extractive reading comprehension hosted by Chinese Information Processing Society of China (CIPS-CL). [url](https://github.com/ymcui/cmrc2018)|
# Fine-tuning
- try eager execution with `dygraph model` :
```script
python3 ./demo/finetune_classifier_dygraph.py \
--from_pretrained ernie_1.0 \
--data_dir ./data/xnli
```
- Distributed finetune
`paddle.distributed.launch` is a process manager, we use it to launch python processes on each avalible GPU devices:
when in distributed training, `max_steps` is used as stopping criteria rather than `epoch` to prevent dead block.
also notice than we shard the train data according to device id to prevent over fitting.
demo:
```script
python3 -m paddle.distributed.launch \
./demo/finetune_classifier_dygraph_distributed.py \
--data_dir data/mnli \
--max_steps 10000 \
--from_pretrained ernie2.0-en
```
many other demo python scripts:
1. [Sentiment Analysis](./demo/finetune_sentiment_analysis_dygraph.py)
1. [Semantic Similarity](./demo/finetune_classifier_dygraph.py)
1. [Name Entity Recognition(NER)](./demo/finetune_ner_dygraph.py)
1. [Machine Reading Comprehension](./demo/finetune_mrc_dygraph.py)
1. [Text generation](./experimental/seq2seq/README.md)
**recomended hyper parameters:**
|tasks|batch size|learning rate|
|--|--|--|
| CoLA | 32 / 64 (base) | 3e-5 |
| SST-2 | 64 / 256 (base) | 2e-5 |
| STS-B | 128 | 5e-5 |
| QQP | 256 | 3e-5(base)/5e-5(large) |
| MNLI | 256 / 512 (base)| 3e-5 |
| QNLI | 256 | 2e-5 |
| RTE | 16 / 4 (base) | 2e-5(base)/3e-5(large) |
| MRPC | 16 / 32 (base) | 3e-5 |
| WNLI | 8 | 2e-5 |
| XNLI | 512 | 1e-4(base)/4e-5(large) |
| CMRC2018 | 64 | 3e-5 |
| DRCD | 64 | 5e-5(base)/3e-5(large) |
| MSRA-NER(SIGHAN2006) | 16 | 5e-5(base)/1e-5(large) |
| ChnSentiCorp | 24 | 5e-5(base)/1e-5(large) |
| LCQMC | 32 | 2e-5(base)/5e-6(large) |
| NLPCC2016-DBQA| 64 | 2e-5(base)/1e-5(large) |
# Pretraining with ERNIE 1.0
see [here](./demo/pretrain/README.md)
# Online inference
If `--inference_model_dir` is passed to `finetune_classifier_dygraph.py`,
a deployable model will be generated at the end of finetuning and your model is ready to serve.
For details about online inferece, see [C++ inference API](./inference/README.md),
or you can start a multi-gpu inference server with a few lines of codes:
```shell
python -m propeller.tools.start_server -m /path/to/saved/inference_model -p 8881
```
and call the server just like calling local function (python3 only):
```python
from propeller.service.client import InferenceClient
from ernie.tokenizing_ernie import ErnieTokenizer
client = InferenceClient('tcp://localhost:8881')
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
ids, sids = tokenizer.encode('hello world')
ids = np.expand_dims(ids, 0)
sids = np.expand_dims(sids, 0)
result = client(ids, sids)
```
A pre-made `inference model` for ernie-1.0 can be downloaded at [here](https://ernie.bj.bcebos.com/ernie1.0_zh_inference_model.tar.gz).
It can be used for feature-based finetuning or feature extraction.
# Distillation
Knowledge distillation is good way to compress and accelerate ERNIE.
For details about distillation, see [here](./distill/README.md)
### Citation
please cite [ERNIE 2.0](https://arxiv.org/abs/1907.12412):
```
@article{SunERNIE,
title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
}
```
and [ERNIE Gen](https://arxiv.org/abs/2001.11314)
```
@article{Xiao2020ERNIE,
title={ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation},
author={Xiao, Dongling and Zhang, Han and Li, Yukun and Sun, Yu and Tian, Hao and Wu, Hua and Wang, Haifeng},
year={2020},
}
```
For full reproduction of paper results, please checkout to `repro` branch of this repo.
### Communication
- [Github Issues](https://github.com/PaddlePaddle/ERNIE/issues): bug reports, feature requests, install issues, usage issues, etc.
- QQ discussion group: 760439550 (ERNIE discussion group).
- [Forums](http://ai.baidu.com/forum/topic/list/168?pageNo=1): discuss implementations, research, etc.
README.md
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{
"attention_probs_dropout_prob": 0.1,
"hidden_act": "relu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"max_position_embeddings": 513,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 18000
}
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demo/finetune_classifier.py 0 → 100644
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# Copyright (c) 2018 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.
from __future__ import absolute_import
import os
import re
import time
import logging
from random import random
import json
from functools import reduce, partial
import numpy as np
import multiprocessing
import tempfile
import jieba
import re
import paddle
import paddle.fluid as F
import paddle.fluid.layers as L
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
from ernie.optimization import optimization
#import utils.data
from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
def model_fn(features, mode, params, run_config):
ernie = ErnieModelForSequenceClassification(params, name='')
if not params is propeller.RunMode.TRAIN:
ernie.eval()
metrics, loss = None, None
if mode is propeller.RunMode.PREDICT:
src_ids, sent_ids = features
_, logits = ernie(src_ids, sent_ids)
predictions = [logits,]
else:
src_ids, sent_ids, labels = features
if mode is propeller.RunMode.EVAL:
loss, logits = ernie(src_ids, sent_ids, labels=labels)
pred = L.argmax(logits, axis=1)
acc = propeller.metrics.Acc(labels, pred)
metrics = {'acc': acc}
predictions = [pred]
else:
loss, logits = ernie(src_ids, sent_ids, labels=labels)
scheduled_lr, _ = optimization(
loss=loss,
warmup_steps=int(run_config.max_steps * params['warmup_proportion']),
num_train_steps=run_config.max_steps,
learning_rate=params['learning_rate'],
train_program=F.default_main_program(),
startup_prog=F.default_startup_program(),
use_fp16=params.use_fp16,
weight_decay=params['weight_decay'],
scheduler="linear_warmup_decay",
)
propeller.summary.scalar('lr', scheduled_lr)
predictions = [logits,]
return propeller.ModelSpec(loss=loss, mode=mode, metrics=metrics, predictions=predictions)
if __name__ == '__main__':
parser = propeller.ArgumentParser('DAN model with Paddle')
parser.add_argument('--do_predict', action='store_true')
parser.add_argument('--max_seqlen', type=int, default=128)
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--from_pretrained', type=str, required=True)
parser.add_argument('--warm_start_from', type=str)
parser.add_argument('--epoch', type=int, default=3)
parser.add_argument('--use_fp16', action='store_true')
args = parser.parse_args()
if not os.path.exists(args.from_pretrained):
raise ValueError('--from_pretrained not found: %s' % args.from_pretrained)
cfg_file_path = os.path.join(args.from_pretrained, 'ernie_config.json')
param_path = os.path.join(args.from_pretrained, 'params')
vocab_path = os.path.join(args.from_pretrained, 'vocab.txt')
assert os.path.exists(cfg_file_path) and os.path.exists(param_path) and os.path.exists(vocab_path)
hparams_cli = propeller.parse_hparam(args)
hparams_config_file = json.loads(open(cfg_file_path).read())
default_hparams = propeller.HParams(
batch_size=32,
num_labels=3,
warmup_proportion=0.1,
learning_rate=5e-5,
weight_decay=0.01,
use_task_id=False,
use_fp16=args.use_fp16,
)
hparams = default_hparams.join(propeller.HParams(**hparams_config_file)).join(hparams_cli)
default_run_config=dict(
max_steps=args.epoch * 390000 / hparams.batch_size,
save_steps=1000,
log_steps=10,
max_ckpt=1,
skip_steps=0,
model_dir=tempfile.mkdtemp(),
eval_steps=100)
run_config = dict(default_run_config, **json.loads(args.run_config))
run_config = propeller.RunConfig(**run_config)
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
#tokenizer = ErnieTinyTokenizer.from_pretrained(args.from_pretrained)
unk_id = tokenizer.vocab['[UNK]']
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1])
types = ('int64', 'int64', 'int64')
if not args.do_predict:
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('title', unk_id=unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.TextColumn('comment', unk_id=unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.LabelColumn('label', vocab_dict={
b"contradictory": 0,
b"contradiction": 0,
b"entailment": 1,
b"neutral": 2,
}),
])
def map_fn(seg_a, seg_b, label):
seg_a, seg_b = tokenizer.truncate(seg_a, seg_b, seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, seg_b)
#label = np.expand_dims(label, -1) #
return sentence, segments, label
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=True, use_gz=False) \
.map(map_fn) \
.padded_batch(hparams.batch_size)
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(hparams.batch_size)
test_ds = feature_column.build_dataset('test', data_dir=os.path.join(args.data_dir, 'test'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(hparams.batch_size) \
train_ds.data_shapes = shapes
train_ds.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
test_ds.data_shapes = shapes
test_ds.data_types = types
varname_to_warmstart = re.compile(r'^encoder.*[wb]_0$|^.*embedding$|^.*bias$|^.*scale$|^pooled_fc.[wb]_0$')
ws = propeller.WarmStartSetting(
predicate_fn=lambda v: varname_to_warmstart.match(v.name) and os.path.exists(os.path.join(param_path, v.name)),
from_dir=param_path,
)
best_exporter = propeller.train.exporter.BestExporter(os.path.join(run_config.model_dir, 'best'), cmp_fn=lambda old, new: new['dev']['acc'] > old['dev']['acc'])
propeller.train.train_and_eval(
model_class_or_model_fn=model_fn,
params=hparams,
run_config=run_config,
train_dataset=train_ds,
eval_dataset={'dev': dev_ds, 'test': test_ds},
warm_start_setting=ws,
exporters=[best_exporter])
print('dev_acc3\t%.5f\ntest_acc3\t%.5f' % (best_exporter._best['dev']['acc'], best_exporter._best['test']['acc']))
else:
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('title', unk_id=unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.TextColumn('comment', unk_id=unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
])
def map_fn(seg_a, seg_b):
seg_a, seg_b = tokenizer.truncate(seg_a, seg_b, seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, seg_b)
return sentence, segments
predict_ds = feature_column.build_dataset_from_stdin('predict') \
.map(map_fn) \
.padded_batch(hparams.batch_size) \
predict_ds.data_shapes = shapes[: -1]
predict_ds.data_types = types[: -1]
est = propeller.Learner(model_fn, run_config, hparams)
for res, in est.predict(predict_ds, ckpt=-1):
print('%d\t%.5f\t%.5f\t%.5f' % (np.argmax(res), res[0], res[1], res[2]))
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# Copyright (c) 2018 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.
import os
import re
import time
import logging
import json
from random import random
from tqdm import tqdm
from functools import reduce, partial
import numpy as np
import logging
import argparse
import paddle
import paddle.fluid as F
import paddle.fluid.dygraph as FD
import paddle.fluid.layers as L
from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
#from model.bert import BertConfig, BertModelLayer
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
from ernie.optimization import AdamW, LinearDecay
if __name__ == '__main__':
parser = argparse.ArgumentParser('classify model with ERNIE')
parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag')
parser.add_argument('--max_seqlen', type=int, default=128, help='max sentence length, should not greater than 512')
parser.add_argument('--bsz', type=int, default=32, help='batchsize')
parser.add_argument('--epoch', type=int, default=3, help='epoch')
parser.add_argument('--data_dir', type=str, required=True, help='data directory includes train / develop data')
parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE')
parser.add_argument('--warmup_proportion', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=5e-5, help='learning rate')
parser.add_argument('--inference_model_dir', type=str, default=None, help='inference model output directory')
parser.add_argument('--save_dir', type=str, default=None, help='model output directory')
parser.add_argument('--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer')
args = parser.parse_args()
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
#tokenizer = ErnieTinyTokenizer.from_pretrained(args.from_pretrained)
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.TextColumn('seg_b', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.LabelColumn('label', vocab_dict={
b"contradictory": 0,
b"contradiction": 0,
b"entailment": 1,
b"neutral": 2,
}),
])
def map_fn(seg_a, seg_b, label):
seg_a, seg_b = tokenizer.truncate(seg_a, seg_b, seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, seg_b)
return sentence, segments, label
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz, (0, 0, 0))
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz, (0, 0, 0))
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1])
types = ('int64', 'int64', 'int64')
train_ds.data_shapes = shapes
train_ds.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
place = F.CUDAPlace(0)
with FD.guard(place):
model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='')
opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd)
g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental
for epoch in range(args.epoch):
for step, d in enumerate(tqdm(train_ds.start(place), desc='training')):
ids, sids, label = d
loss, _ = model(ids, sids, labels=label)
loss.backward()
if step % 10 == 0:
log.debug('train loss %.5f lr %.3e' % (loss.numpy(), opt.current_step_lr()))
opt.minimize(loss, grad_clip=g_clip)
model.clear_gradients()
if step % 100 == 0:
acc = []
with FD.base._switch_tracer_mode_guard_(is_train=False):
model.eval()
for step, d in enumerate(tqdm(dev_ds.start(), desc='evaluating %d' % epoch)):
ids, sids, label = d
loss, logits = model(ids, sids, labels=label)
#print('\n'.join(map(str, logits.numpy().tolist())))
a = L.argmax(logits, -1) == label
acc.append(a.numpy())
model.train()
log.debug('acc %.5f' % np.concatenate(acc).mean())
if args.save_dir is not None:
F.save_dygraph(model.state_dict(), args.save_dir)
if args.inference_model_dir is not None:
log.debug('saving inference model')
class InferemceModel(ErnieModelForSequenceClassification):
def forward(self, *args, **kwargs):
_, logits = super(InferemceModel, self).forward(*args, **kwargs)
return logits
model.__class__ = InferemceModel #dynamic change model type, to make sure forward output doesn't contain `None`
src_placeholder = FD.to_variable(np.ones([1, 1], dtype=np.int64))
sent_placehodler = FD.to_variable(np.zeros([1, 1], dtype=np.int64))
model(src_placeholder, sent_placehodler)
_, static_model = FD.TracedLayer.trace(model, inputs=[src_placeholder, sent_placehodler])
static_model.save_inference_model(args.inference_model_dir)
log.debug('done')
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# Copyright (c) 2018 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.
import os
import re
import time
import logging
import json
from random import random
from tqdm import tqdm
from functools import reduce, partial
import numpy as np
import logging
import paddle
import paddle.fluid as F
import paddle.fluid.dygraph as FD
import paddle.fluid.layers as L
from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
#from model.bert import BertConfig, BertModelLayer
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
from ernie.optimization import AdamW, LinearDecay
if __name__ == '__main__':
parser = propeller.ArgumentParser('classify model with ERNIE')
parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag')
parser.add_argument('--max_seqlen', type=int, default=128, help='max sentence length, should not greater than 512')
parser.add_argument('--bsz', type=int, default=32, help='batchsize')
parser.add_argument('--data_dir', type=str, required=True, help='data directory includes train / develop data')
parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE')
parser.add_argument('--warmup_proportion', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=5e-5, help='learning rate')
parser.add_argument('--save_dir', type=str, default=None, help='model output directory')
parser.add_argument('--wd', type=int, default=0.01, help='weight decay, aka L2 regularizer')
args = parser.parse_args()
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
#tokenizer = ErnieTinyTokenizer.from_pretrained(args.from_pretrained)
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.TextColumn('seg_b', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.LabelColumn('label', vocab_dict={
b"0": 0,
b"1": 1,
b"2": 2,
}),
])
def map_fn(seg_a, seg_b, label):
seg_a, seg_b = tokenizer.truncate(seg_a, seg_b, seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, seg_b)
return sentence, segments, label
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=False, repeat=True, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz, (0, 0, 0))
train_ds = train_ds.shard(propeller.train.distribution.status.num_replica, propeller.train.distribution.status.replica_id)
log.debug('shard %d/%d'%(propeller.train.distribution.status.num_replica, propeller.train.distribution.status.replica_id))
train_ds = train_ds.shuffle(10000)
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz, (0, 0, 0))
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1])
types = ('int64', 'int64', 'int64')
train_ds.data_shapes = shapes
train_ds.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
place = F.CUDAPlace(FD.parallel.Env().dev_id)
with FD.guard(place):
ctx = FD.parallel.prepare_context()
model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='')
model = FD.parallel.DataParallel(model, ctx)
opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd)
g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental
for step, d in enumerate(tqdm(train_ds.start(place), desc='training')):
ids, sids, label = d
loss, _ = model(ids, sids, labels=label)
scaled_loss = model.scale_loss(loss)
scaled_loss.backward()
model.apply_collective_grads()
opt.minimize(scaled_loss, grad_clip=g_clip)
model.clear_gradients()
if step % 10 == 0:
log.debug('train loss %.5f, lr %.e3' % (loss.numpy(), opt.current_step_lr()))
if step % 100 == 0 and FD.parallel.Env().dev_id == 0:
acc = []
with FD.base._switch_tracer_mode_guard_(is_train=False):
model.eval()
for step, d in enumerate(tqdm(dev_ds.start(place), desc='evaluating')):
ids, sids, label = d
loss, logits = model(ids, sids, labels=label)
#print('\n'.join(map(str, logits.numpy().tolist())))
a = L.argmax(logits, -1) == label
acc.append(a.numpy())
model.train()
log.debug('acc %.5f' % np.concatenate(acc).mean())
if step > args.max_steps:
break
if args.save_dir is not None:
F.save_dygraph(model.state_dict(), args.save_dir)
demo/finetune_mrc_dygraph.py 0 → 100644
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# Copyright (c) 2018 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.
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import os
import re
import time
import logging
import json
from random import random
from tqdm import tqdm
from functools import reduce, partial
import pickle
import argparse
import numpy as np
import logging
import paddle
import paddle.fluid as F
import paddle.fluid.dygraph as D
import paddle.fluid.layers as L
from propeller import log
import propeller.paddle as propeller
from ernie.modeling_ernie import ErnieModel, ErnieModelForQuestionAnswering
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
from ernie.optimization import AdamW, LinearDecay
from ernie.mrc import mrc_reader
from ernie.mrc import mrc_metrics
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
def evaluate(model, ds, all_examples, all_features, tokenizer, args):
dev_file = json.loads(open(args.dev_file).read())
with D.base._switch_tracer_mode_guard_(is_train=False):
log.debug('start eval')
model.eval()
all_res = []
for step, (uids, token_ids, token_type_ids, _, __) in enumerate(ds.start(place)):
_ , start_logits, end_logits = model(token_ids, token_type_ids)
res = [mrc_metrics.RawResult(unique_id=u, start_logits=s, end_logits=e)
for u, s, e in zip(uids.numpy(), start_logits.numpy(), end_logits.numpy())]
all_res += res
open('all_res', 'wb').write(pickle.dumps(all_res))
all_pred, all_nbests = mrc_metrics.make_results(
tokenizer,
all_examples,
all_features,
all_res,
n_best_size=args.n_best_size,
max_answer_length=args.max_answer_length,
do_lower_case=tokenizer.lower)
f1, em, _, __ = mrc_metrics.evaluate(dev_file, all_pred)
model.train()
log.debug('done eval')
return f1, em
def train(model, train_dataset, dev_dataset, dev_examples, dev_features, tokenizer, args):
ctx = D.parallel.prepare_context()
model = D.parallel.DataParallel(model, ctx)
max_steps = len(train_features) * args.epoch // args.bsz
opt = AdamW(learning_rate=args.lr, parameter_list=model.parameters(), weight_decay=args.wd)
g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental
train_dataset = train_dataset \
.repeat() \
.shard(D.parallel.Env().nranks, D.parallel.Env().dev_id) \
.shuffle(1000) \
.padded_batch(args.bsz)
log.debug('init training with args: %s' % repr(args))
for step, (_, token_ids, token_type_ids, start_pos, end_pos) in enumerate(train_dataset.start(place)):
loss, _, __ = model(token_ids, token_type_ids, start_pos=start_pos, end_pos=end_pos)
scaled_loss = model.scale_loss(loss)
scaled_loss.backward()
model.apply_collective_grads()
opt.minimize(scaled_loss, grad_clip=g_clip)
model.clear_gradients()
if D.parallel.Env().dev_id == 0 and step % 10 == 0:
log.debug('[step %d] train loss %.5f lr %.3e' % (step, loss.numpy(), opt.current_step_lr()))
if D.parallel.Env().dev_id == 0 and step % 100 == 0:
f1, em = evaluate(model, dev_dataset, dev_examples, dev_features, tokenizer, args)
log.debug('[step %d] eval result: f1 %.5f em %.5f' % (step, f1, em))
if step > max_steps:
break
if __name__ == "__main__":
parser = argparse.ArgumentParser('MRC model with ERNIE')
parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag')
parser.add_argument('--max_seqlen', type=int, default=512, help='max sentence length, should not greater than 512')
parser.add_argument('--bsz', type=int, default=8, help='batchsize')
parser.add_argument('--epoch', type=int, default=2, help='epoch')
parser.add_argument('--train_file', type=str, required=True, help='data directory includes train / develop data')
parser.add_argument('--dev_file', type=str, required=True, help='data directory includes train / develop data')
parser.add_argument('--warmup_proportion', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=3e-5, help='learning rate')
parser.add_argument('--save_dir', type=str, default=None, help='model output directory')
parser.add_argument('--n_best_size', type=int, default=20, help='nbest prediction to keep')
parser.add_argument('--max_answer_length', type=int, default=100, help='max answer span')
parser.add_argument('--wd', type=float, default=0.00, help='weight decay, aka L2 regularizer')
args = parser.parse_args()
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
if not os.path.exists(args.train_file):
raise RuntimeError('input data not found at %s' % args.train_file)
if not os.path.exists(args.dev_file):
raise RuntimeError('input data not found at %s' % args.dev_file)
log.info('making train/dev data...')
train_examples = mrc_reader.read_files(args.train_file, is_training=True)
train_features = mrc_reader.convert_example_to_features(train_examples, args.max_seqlen, tokenizer, is_training=True)
dev_examples = mrc_reader.read_files(args.dev_file, is_training=False)
dev_features = mrc_reader.convert_example_to_features(dev_examples, args.max_seqlen, tokenizer, is_training=False)
log.info('train examples: %d, features: %d' % (len(train_examples), len(train_features)))
def map_fn(unique_id, example_index, doc_span_index, tokens, token_to_orig_map, token_is_max_context, token_ids, position_ids, text_type_ids, start_position, end_position):
if start_position is None:
start_position = 0
if end_position is None:
end_position = 0
return np.array(unique_id), np.array(token_ids), np.array(text_type_ids), np.array(start_position), np.array(end_position)
train_dataset = propeller.data.Dataset.from_list(train_features).map(map_fn)
dev_dataset = propeller.data.Dataset.from_list(dev_features).map(map_fn).padded_batch(args.bsz)
shapes = ([-1], [-1, args.max_seqlen], [-1, args.max_seqlen], [-1], [-1])
types = ('int64', 'int64', 'int64', 'int64', 'int64')
train_dataset.name = 'train'
dev_dataset.name = 'dev'
train_dataset.data_shapes = shapes
train_dataset.data_types = types
dev_dataset.data_shapes = shapes
dev_dataset.data_types = types
place = F.CUDAPlace(D.parallel.Env().dev_id)
D.guard(place).__enter__()
model = ErnieModelForQuestionAnswering.from_pretrained(args.from_pretrained, name='')
train(model, train_dataset, dev_dataset, dev_examples, dev_features, tokenizer, args)
if D.parallel.Env().dev_id == 0:
f1, em = evaluate(model, dev_dataset, dev_examples, dev_features, tokenizer, args)
log.debug('final eval result: f1 %.5f em %.5f' % (f1, em))
if D.parallel.Env().dev_id == 0 and args.save_dir is not None:
F.save_dygraph(model.state_dict(), args.save_dir)
demo/finetune_ner_dygraph.py 0 → 100644
浏览文件 @ defa38e7
# Copyright (c) 2018 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.
import os
import re
import time
import logging
import six
import json
from random import random
from tqdm import tqdm
from collections import OrderedDict
from functools import reduce, partial
import numpy as np
import multiprocessing
import pickle
import jieba
import logging
from sklearn.metrics import f1_score
import paddle
import paddle.fluid as F
import paddle.fluid.dygraph as FD
import paddle.fluid.layers as L
from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification, ErnieModelForTokenClassification
from ernie.tokenizing_ernie import ErnieTokenizer
from ernie.optimization import AdamW, LinearDecay
if __name__ == '__main__':
parser = propeller.ArgumentParser('NER model with ERNIE')
parser.add_argument('--max_seqlen', type=int, default=256)
parser.add_argument('--bsz', type=int, default=32)
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--epoch', type=int, default=3)
parser.add_argument('--warmup_steps', type=int, default=1000)
parser.add_argument('--max_steps', type=int, default=30000)
parser.add_argument('--from_pretrained', type=str, required=True)
parser.add_argument('--lr', type=float, default=5e-5)
args = parser.parse_args()
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
def tokenizer_func(inputs):
ret = inputs.split(b'\2')
tokens, orig_pos = [], []
for i, r in enumerate(ret):
t = tokenizer.tokenize(r)
for tt in t:
tokens.append(tt)
orig_pos.append(i)
assert len(tokens) == len(orig_pos)
return tokens + orig_pos
def tokenizer_func_for_label(inputs):
return inputs.split(b'\2')
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('text_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer_func),
propeller.data.TextColumn('label', unk_id=6, vocab_dict={
b"B-PER": 0,
b"I-PER": 1,
b"B-ORG": 2,
b"I-ORG": 3,
b"B-LOC": 4,
b"I-LOC": 5,
},
tokenizer=tokenizer_func_for_label,)
])
def before(seg, label):
seg, orig_pos = np.split(seg, 2)
aligned_label = label[orig_pos]
seg, _ = tokenizer.truncate(seg, [], args.max_seqlen)
aligned_label, _ = tokenizer.truncate(aligned_label, [], args.max_seqlen)
orig_pos, _ = tokenizer.truncate(orig_pos, [], args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg) #utils.data.build_1_pair(seg, max_seqlen=args.max_seqlen, cls_id=cls_id, sep_id=sep_id)
aligned_label = np.concatenate([[-100], aligned_label, [-100]], 0)
orig_pos = np.concatenate([[-100], orig_pos, [-100]])
assert len(aligned_label) == len(sentence) == len(orig_pos), (len(aligned_label), len(sentence), len(orig_pos)) # alinged
return sentence, segments, aligned_label, label, orig_pos
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \
.map(before) \
.padded_batch(args.bsz, (0, 0, -100, -100, -100)) \
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
.map(before) \
.padded_batch(args.bsz, (0, 0, -100, -100, -100)) \
test_ds = feature_column.build_dataset('test', data_dir=os.path.join(args.data_dir, 'test'), shuffle=False, repeat=False, use_gz=False) \
.map(before) \
.padded_batch(args.bsz, (0, 0, -100, -100, -100)) \
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1, args.max_seqlen])
types = ('int64', 'int64', 'int64')
train_ds.data_shapes = shapes
train_ds.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
test_ds.data_shapes = shapes
test_ds.data_types = types
with FD.guard():
model = ErnieModelForTokenClassification.from_pretrained(args.from_pretrained, num_labels=7, name='')
opt = AdamW(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters(), weight_decay=0.01)
#opt = F.optimizer.AdamOptimizer(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters())
for epoch in range(args.epoch):
for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(train_ds.start())):
loss, _ = model(ids, sids, labels=aligned_label)
loss.backward()
if step % 10 == 0 :
log.debug('train loss %.5f' % loss.numpy())
opt.minimize(loss)
model.clear_gradients()
if step % 100 == 0 :
all_pred, all_label = [], []
with FD.base._switch_tracer_mode_guard_(is_train=False):
model.eval()
for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(dev_ds.start())):
loss, logits = model(ids, sids, labels=aligned_label)
#print('\n'.join(map(str, logits.numpy().tolist())))
for pos, lo, la in zip(orig_pos.numpy(), logits.numpy(), label.numpy()):
_dic = OrderedDict()
for p, l in zip(pos, lo):
_dic.setdefault(p, []).append(l)
del _dic[-100] # delete cls/sep/pad position
merged_lo = np.array([np.array(l).mean(0) for _, l in six.iteritems(_dic)])
merged_preds = np.argmax(merged_lo, -1)
la = la[np.where(la!=-100)] #remove pad
if len(la) > len(merged_preds):
log.warn('accuracy loss due to truncation: label len:%d, truncate to %d' % (len(la), len(merged_preds)))
merged_preds = np.pad(merged_preds, [0, len(la) - len(merged_preds)], mode='constant', constant_values=-100)
all_label.append(la)
all_pred.append(merged_preds)
model.train()
f1 = f1_score(np.concatenate(all_label), np.concatenate(all_pred), average='macro')
log.debug('eval f1: %.5f' % f1)
F.save_dygraph(model.state_dict(), './saved')
demo/finetune_sentiment_analysis_dygraph.py 0 → 100644
浏览文件 @ defa38e7
# Copyright (c) 2018 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.
import os
import re
import time
import logging
import json
from random import random
from tqdm import tqdm
from functools import reduce, partial
import numpy as np
import logging
import argparse
import paddle
import paddle.fluid as F
import paddle.fluid.dygraph as FD
import paddle.fluid.layers as L
from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)
logging.getLogger().addHandler(log.handlers[0])
logging.getLogger().setLevel(logging.DEBUG)
log = logging.getLogger()
#from model.bert import BertConfig, BertModelLayer
from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification
from ernie.tokenizing_ernie import ErnieTokenizer, ErnieTinyTokenizer
from ernie.optimization import AdamW, LinearDecay
if __name__ == '__main__':
parser = argparse.ArgumentParser('classify model with ERNIE')
parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag')
parser.add_argument('--max_seqlen', type=int, default=128, help='max sentence length, should not greater than 512')
parser.add_argument('--bsz', type=int, default=32, help='batchsize')
parser.add_argument('--epoch', type=int, default=3, help='epoch')
parser.add_argument('--data_dir', type=str, required=True, help='data directory includes train / develop data')
parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE')
parser.add_argument('--warmup_proportion', type=float, default=0.1)
parser.add_argument('--lr', type=float, default=5e-5, help='learning rate')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--save_dir', type=str, default=None, help='model output directory')
parser.add_argument('--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer')
args = parser.parse_args()
tokenizer = ErnieTokenizer.from_pretrained(args.from_pretrained)
#tokenizer = ErnieTinyTokenizer.from_pretrained(args.from_pretrained)
place = F.CUDAPlace(0)
with FD.guard(place):
model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='')
if not args.eval:
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
propeller.data.LabelColumn('label'),
])
def map_fn(seg_a, label):
seg_a, _ = tokenizer.truncate(seg_a, [], seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, [])
return sentence, segments, label
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz)
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(args.bsz)
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen], [-1])
types = ('int64', 'int64', 'int64')
train_ds.data_shapes = shapes
train_ds.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
opt = AdamW(learning_rate=LinearDecay(
args.lr,
int(args.warmup_proportion * args.max_steps), args.max_steps),
parameter_list=model.parameters(),
weight_decay=args.wd)
g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental
for epoch in range(args.epoch):
for step, d in enumerate(tqdm(train_ds.start(place), desc='training')):
ids, sids, label = d
loss, _ = model(ids, sids, labels=label)
loss.backward()
if step % 10 == 0:
log.debug('train loss %.5f lr %.3e' % (loss.numpy(), opt.current_step_lr()))
opt.minimize(loss, grad_clip=g_clip)
model.clear_gradients()
if step % 100 == 0:
acc = []
with FD.base._switch_tracer_mode_guard_(is_train=False):
model.eval()
for step, d in enumerate(tqdm(dev_ds.start(), desc='evaluating %d' % epoch)):
ids, sids, label = d
loss, logits = model(ids, sids, labels=label)
#print('\n'.join(map(str, logits.numpy().tolist())))
a = L.argmax(logits, -1) == label
acc.append(a.numpy())
model.train()
log.debug('acc %.5f' % np.concatenate(acc).mean())
if args.save_dir is not None:
F.save_dygraph(model.state_dict(), args.save_dir)
else:
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize),
])
assert args.save_dir is not None
sd, _ = FD.load_dygraph(args.save_dir)
model.set_dict(sd)
model.eval()
def map_fn(seg_a):
seg_a, _ = tokenizer.truncate(seg_a, [], seqlen=args.max_seqlen)
sentence, segments = tokenizer.build_for_ernie(seg_a, [])
return sentence, segments
predict_ds = feature_column.build_dataset_from_stdin('predict') \
.map(map_fn) \
.padded_batch(args.bsz)
shapes = ([-1, args.max_seqlen], [-1, args.max_seqlen])
types = ('int64', 'int64')
predict_ds.data_shapes = shapes
predict_ds.data_types = types
for step, (ids, sids) in enumerate(predict_ds.start(place)):
_, logits = model(ids, sids)
pred = logits.numpy().argmax(-1)
print('\n'.join(map(str, pred.tolist())))
demo/mrc/mrc_reader.py 0 → 100644
浏览文件 @ defa38e7
此差异已折叠。
demo/pretrain/README.md 0 → 100644
浏览文件 @ defa38e7
# Distributed Pretrain
only **mask word** strategy from [Ernie1.0](https://arxiv.org/pdf/1904.09223.pdf) is illustrated in this section.
1. make pretrain data
we use documents from multiple datasource (e.g. Wikipedia) to pretrain.
input text should be segmented with space (even in chinese, this segmentation is used for *mask word*).
each line corresonds to a *sentence*.
empty line indicates end of document.
example:
> 数学 是 利用 符号语言 研究 数量 、 结构 、 变化 以及 空间 等 概念 的 一门 学科 ， 从 某种 角度看 属于 形式 科学 的 一种 。
> 数学 透过 抽象化 和 逻辑推理 的 使用 ， 由 计数 、 计算 、 量度 和 对 物体 形状 及 运动 的 观察 而 产生 。
> 数学家 们 拓展 这些 概念 ， 为了 公式化 新 的 猜想 以及 从 选定 的 公理 及 定义 中 建立 起 严谨 推导 出 的 定理 。
> 基础 数学 的 知识 与 运用 总是 个人 与 团体 生活 中 不可或缺 的 一环 。
> 对 数学 基本概念 的 完善 ， 早 在 古埃及 、 美索不达米亚 及 古印度 内 的 古代 数学 文本 便 可观 见 ， 而 在 古希腊 那里 有 更为 严谨 的 处理 。
> 从 那时 开始 ， 数学 的 发展 便 持续 不断 地 小幅 进展 ， 至 16 世纪 的 文艺复兴 时期 ， 因为 新 的 科学 发现 和 数学 革新 两者 的 交互 ， 致使 数学 的 加速 发展 ， 直至 今日 。
>
> 云外镜 （ ） 是 一种 能 反映 遥远 地方 影像 的 镜子 ， 就 好比 现在 的 电视 ， 或是 吉卜赛人 占卜 用 的 水晶球 一样 。
> 它 属于 付丧神 的 一种 ， 是 镜子 历经 百年 后 幻化 而成 的 妖怪 ， 又名 镜 妖 。
> 也 有人 说云 外镜 是 狸 妖 幻化 而成 的 ， 当狸 妖 的 肚子 胀大 ， 像 电视 的 映像管 一样 发光 时 ， 就 可以 自由 地 显现出 远方 的 情景 。
> 著名 的 妖怪 绘师 鸟 山石 燕曾 记载 云外镜 经常 容易 跟 照妖镜 搞混 ， 因为 照妖镜 可以 映照 出 肉眼 看不见 的 妖怪 ， 这点 与 云外 镜会 映照 出 怪异 的 脸孔 是 有些 相似 。
> 据说 在 阴历 八月 十五日 的 夜晚 ， 在 水晶 盆内 注满 水 ， 将 镜子 平 放在 水面 ， 若 是 映照 出 妖怪 的 模样 ， 就 表示 这 面 镜子 里 住 著 妖怪 。
make pretrain data with:
```script
python3 ernie/pretrain/make_pretrain_data.py input_file output_file.gz --vocab ./pretrained/vocab.txt
```
2. run distributed pretrain
```sript
python3 -m paddle.distributed.launch \
./ernie/pretrain/pretrain_dygraph.py \
--data_dir data/* \
--from_pretrained ./ernie_1.0_pretrain_dir/
```
demo/pretrain/make_pretrain_data.py 0 → 100644
浏览文件 @ defa38e7
import sys
import argparse
import struct
#import jieba
import random as r
import re
import gzip
import logging
from itertools import accumulate
from functools import reduce, partial, wraps
from propeller import log
from propeller.paddle.data import feature_pb2, example_pb2
#jfrom data_util import RawtextColumn
import io
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')
def gen_segs(segment_piece):
if len(segment_piece) == 0:
return []
else:
return [min(segment_piece)] * len(segment_piece)
whit_space_pat = re.compile(r'\S+')
def segment(inputs, inputs_segment):
ret = [r.span() for r in whit_space_pat.finditer(inputs)]
ret = [(inputs[s: e], gen_segs(inputs_segment[s: e])) for i, (s, e) in enumerate(ret)]
return ret
def tokenize(sen, seg_info):
"""
char tokenizer (wordpiece english)
normed txt(space seperated or not) => list of word-piece
"""
sen = sen.lower()
res_word, res_segments = [], []
for match in pat.finditer(sen):
words, pos = _wordpiece(match.group(0), vocab=vocab_set, unk_token='[UNK]')
start_of_word = match.span()[0]
for w, p in zip(words, pos):
res_word.append(w)
res_segments.append(gen_segs(seg_info[p[0] + start_of_word: p[1] + start_of_word]))
return res_word, res_segments
def parse_txt(line):
if len(line) == 0:
return []
line = line.decode('utf8')
ret_line, ret_seginfo = [], []
for l, i in segment(line, list(range(len(line)))):
for ll, ii in zip(*tokenize(l, i)):
ret_line.append(ll)
ret_seginfo.append(ii)
if args.check and r.random() < 0.005:
print('****', file=sys.stderr)
print(line, file=sys.stderr)
print('|'.join(ret_line), file=sys.stderr)
print(ret_seginfo, file=sys.stderr)
print('****', file=sys.stderr)
ret_line = [vocab.get(r, vocab['[UNK]']) for r in ret_line]
ret_seginfo = [[-1] if i == [] else i for i in ret_seginfo] #for sentence piece only
ret_seginfo = [min(i) for i in ret_seginfo]
return ret_line, ret_seginfo
def build_example(slots):
txt, seginfo = slots
txt_fe_list = feature_pb2.FeatureList(feature=[feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=t)) for t in txt])
segsinfo_fe_list = feature_pb2.FeatureList(feature=[feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=s)) for s in seginfo])
assert len(txt_fe_list.feature) == len(segsinfo_fe_list.feature), 'txt[%d] and seginfo[%d] size not match' % (len(txt_fe_list.feature), len(segsinfo_fe_list.feature))
features = {
'txt': txt_fe_list,
'segs': segsinfo_fe_list,
}
ex = example_pb2.SequenceExample(feature_lists=feature_pb2.FeatureLists(feature_list=features))
return ex
def write_gz(serialized, to_file):
l = len(serialized)
packed_data = struct.pack('i%ds' % l, l, serialized)
to_file.write(packed_data)
def build_bb(from_file, to_file):
slots = []
for i, line in enumerate(from_file):
line = line.strip()
if args.verbose and i % 10000 == 0:
log.debug(i)
if len(line) == 0:
if len(slots) != 0:
transposed_slots = list(zip(*slots))
ex = build_example(transposed_slots)
write_gz(ex.SerializeToString(), to_file)
slots = []
continue
parsed_line = parse_txt(line)
slots.append(parsed_line)
if len(slots) != 0:
transposed_slots = list(zip(*slots))
ex = build_example(transposed_slots)
write_gz(ex.SerializeToString(), to_file)
slots = []
if __name__ == '__main__':
parser = argparse.ArgumentParser('Pretrain Data Maker')
parser.add_argument('src', type=str)
parser.add_argument('tgt', type=str)
parser.add_argument('--vocab', type=str, required=True)
parser.add_argument('-v', '--verbose', action='store_true')
parser.add_argument('-c', '--check', action='store_true')
args = parser.parse_args()
log.setLevel(logging.DEBUG)
from tokenizing_ernie import _wordpiece
pat = re.compile(r'([a-zA-Z0-9]+|\S)')
vocab = {j.strip().split(b'\t')[0].decode('utf8'): i for i, j in enumerate(open(args.vocab, 'rb'))}
vocab_set = set(vocab.keys())
with open(args.src, 'rb') as from_file, gzip.open(args.tgt, 'wb') as to_file:
log.info('making gz from bb %s ==> %s' % (from_file, to_file))
build_bb(from_file, to_file)
log.info('done: %s' % to_file)
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......@@ -2,8 +2,6 @@
* [ERNIE数据蒸馏三步](#ernie数据蒸馏三步)
* [数据增强](#数据增强)
* [使用教程](#使用教程)
* [离线蒸馏](#离线蒸馏)
* [在线蒸馏](#在线蒸馏)
* [效果验证](#效果验证)
* [Case#1 用户提供“无标注数据”](#case1)
* [Case#2 用户未提供“无标注数据”](#case2)
......@@ -39,73 +37,11 @@
# 使用教程
我们采用上述3种增强策略制作了chnsenticorp的增强数据：增强后的数据为原训练数据的10倍(96000行)，可以从[这里](https://ernie.bj.bcebos.com/distill_data.tar.gz)下载。将下载的 `distill` 文件夹放入 `${TASK_DATA_PATH}` 后即可执行下面的脚本开始蒸馏。
我们采用上述3种增强策略制作了chnsenticorp的增强数据：增强后的数据为原训练数据的10倍(96000行)，可以从[这里](https://ernie.bj.bcebos.com/distill_data.tar.gz)下载。即可执行下面的脚本开始蒸馏。
### 离线蒸馏
离线蒸馏指的是先通过训练好的ERNIE模型预测出无监督数据的label，然后student模型去学习这些label。只需执行
```script
sh ./distill/script/distill_chnsenticorp.sh
```shell
python ./distill/distill.py
```
即可开始离线蒸馏。
该脚本会进行前述的三步：1. 在任务数据上Fine-tune。 2. 加载Fine-tune好的模型对增强数据进行打分。 3.使用Student模型进行训练。脚本采用hard-label蒸馏，在第二步中将会直接预测出ERNIE标注的label。
该脚本涉及两个python文件:`./example/finetune_classifier.py` 负责finetune以及预测teacher模型， `distill/distill_chnsentocorp.py` 负责student模型的训练。事先构造好的增强数据放在`${TASK_DATA_PATH}/distill/chnsenticorp/student/unsup_train_aug`
在脚本的第二步中，使用 `--do_predict` 参数进入预测模式:
```script
cat ${TASK_DATA_PATH}/distill/chnsenticorp/student/unsup_train_aug/part.0 |python3 -u ./example/finetune_classifier.py \
--do_predict \
--data_dir ${TASK_DATA_PATH}/distill/chnsenticorp/teacher \
--warm_start_from ${MODEL_PATH}/params \
--vocab_file ${MODEL_PATH}/vocab.txt \
...
```
脚本从标准输入获取明文输入，并将打分输出到标准输出。用这种方式对数据增强后的无监督训练预料进行标注。最终的标注结果放在 `prediction_output/part.0` 文件中。标注结果包含两列, 第一列为明文，第二列为标注label。
在第三步开始student模型的训练：
```script
python3 ./distill/distill_chnsentocorp.py \
--data_dir ${TASK_DATA_PATH}/distill/chnsenticorp/student \
--vocab_file ${TASK_DATA_PATH}/distill/chnsenticorp/student/vocab.txt \
--unsupervise_data_dir ./prediction_output/ \
--max_seqlen 128 \
...
```
训练流程与第一步相同，`--data_dir` 指定的监督数据，`--unsupervise_data_dir` 指定ERNIE标注数据。Student模型是一个简单的BOW模型，其定义位于`distill/distill_chnsentocorp.py`。用户只需改写其中的model部分即可实现定制蒸馏模型。
如果用户已经拥有了无监督数据，则可以将无监督数据放入 `${TASK_DATA_PATH}/distill/chnsenticorp/student/unsup_train_aug` 即可。
### 在线蒸馏
考虑到在某些场景下，无监督数据过大导致预测过程十分耗时，或者ERNIE预测出的分布过大而无法预先存放在磁盘中。针对这种场景我们提出一种 **在线蒸馏** 方案。采用`propeller` 进行fine-tune并使用 `BestInferenceModelExporter` 后，`propeller` 会自动将指标最好的模型保存为paddle inference model格式，随后启动一个预测服务。Student模型在训练的同时，实时地访问这个服务来获得ERNIE的预测打分。只需执行
```
sh ./distill/script/distill_chnsenticorp_with_propeller_server.sh
```
即可完成上述流程。
流程包含3步：1. finetune ERNIE模型。2. 取指标最好的ERNIE模型启动`propeller`服务。 3.在student模型的训练过程中访问服务获取teacher模型的标注。
此流程涉及两个python文件: `example/finetune_classifier.py` 与 `distill/distill_chnsentocorp_with_propeller_server.py` 。其中第一步与离线蒸馏中的用法完全一样。
第二步中使用
```script
python3 -m propeller.tools.start_server -p 8113 -m ${teacher_dir}/best/inference/ &
```
启动一个ernie预测服务
第三步开始student模型的同步训练：
```script
python3 ./distill/distill_chnsentocorp_with_propeller_server.py \
--data_dir ${TASK_DATA_PATH}/distill/chnsenticorp/student \
--vocab_file ${TASK_DATA_PATH}/distill/chnsenticorp/student/vocab.txt \
--teacher_vocab_file ${MODEL_PATH}/vocab.txt \
--max_seqlen 128 \
--teacher_max_seqlen 128 \
--server_batch_size 64 \
--teacher_host tcp://localhost:8113 \
--num_coroutine 10
```
该脚本将`${TASK_DATA_PATH}/distill/chnsenticorp/student/unsup_train_aug` 目录下的增强数据进行切字并请求`propeller` 服务。`--num_coroutine` 指定了请求的并发数，`--teacher_host` 指定了服务的端口和IP，`--server_batch_size` 指定了请求的batch_size，在实际的请求中每个batch的数据会拆分成若干个 `--server_batch_size` 大小的数据去请求服务。
# 效果验证
我们将实际应用场景分类为两种：
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