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hapi
未验证 提交 e93f9d5d 编写于 作者: P pkpk 提交者： GitHub
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Merge pull request #29 from guoshengCS/fix-transformer 

Update transformer
上级 187b3537 92b48a8a
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Showing with 493 addition and 253 deletion
text.py
浏览文件 @ e93f9d5d
......@@ -521,7 +521,15 @@ class DynamicDecode(Layer):
(step_outputs, next_states, next_inputs,
next_finished) = self.decoder.step(step_idx_tensor, inputs,
states, **kwargs)
if not self.decoder.tracks_own_finished:
# BeamSearchDecoder would track it own finished, since
# beams would be reordered and the finished status of each
# entry might change. Otherwise, perform logical OR which
# would not change the already finished.
next_finished = layers.logical_or(next_finished, finished)
# To confirm states.finished/finished be consistent with
# next_finished.
layers.assign(next_finished, finished)
next_sequence_lengths = layers.elementwise_add(
sequence_lengths,
layers.cast(
......
transformer/README.md
浏览文件 @ e93f9d5d
......@@ -34,8 +34,8 @@
克隆代码库到本地
```shell
git clone https://github.com/PaddlePaddle/models.git
cd models/dygraph/transformer
git clone https://github.com/PaddlePaddle/hapi
cd hapi/transformer
```
3. 环境依赖
......@@ -62,7 +62,7 @@
### 单机训练
### 单机单卡
#### 单机单卡
以提供的英德翻译数据为例，可以执行以下命令进行模型训练：
......@@ -100,28 +100,24 @@ python -u train.py \
--prepostprocess_dropout 0.3
```
另外，如果在执行训练时若提供了 `save_model`（默认为 trained_models），则每隔一定 iteration 后（通过参数 `save_step` 设置，默认为10000）将保存当前训练的到相应目录（会保存分别记录了模型参数和优化器状态的 `transformer.pdparams` 和 `transformer.pdopt` 两个文件），每隔一定数目的 iteration (通过参数 `print_step` 设置，默认为100)将打印如下的日志到标准输出：
另外，如果在执行训练时若提供了 `save_model`（默认为 trained_models），则每个 epoch 将保存当前训练的到相应目录（会保存分别记录了模型参数和优化器状态的 `epoch_id.pdparams` 和 `epoch_id.pdopt` 两个文件），每隔一定数目的 iteration (通过参数 `print_step` 设置，默认为100)将打印如下的日志到标准输出：
```txt
[2019-08-02 15:30:51,656 INFO train.py:262] step_idx: 150100, epoch: 32, batch: 1364, avg loss: 2.880427, normalized loss: 1.504687, ppl: 17.821888, speed: 3.34 step/s
[2019-08-02 15:31:19,824 INFO train.py:262] step_idx: 150200, epoch: 32, batch: 1464, avg loss: 2.955965, normalized loss: 1.580225, ppl: 19.220257, speed: 3.55 step/s
[2019-08-02 15:31:48,151 INFO train.py:262] step_idx: 150300, epoch: 32, batch: 1564, avg loss: 2.951180, normalized loss: 1.575439, ppl: 19.128502, speed: 3.53 step/s
[2019-08-02 15:32:16,401 INFO train.py:262] step_idx: 150400, epoch: 32, batch: 1664, avg loss: 3.027281, normalized loss: 1.651540, ppl: 20.641024, speed: 3.54 step/s
[2019-08-02 15:32:44,764 INFO train.py:262] step_idx: 150500, epoch: 32, batch: 1764, avg loss: 3.069125, normalized loss: 1.693385, ppl: 21.523066, speed: 3.53 step/s
[2019-08-02 15:33:13,199 INFO train.py:262] step_idx: 150600, epoch: 32, batch: 1864, avg loss: 2.869379, normalized loss: 1.493639, ppl: 17.626074, speed: 3.52 step/s
[2019-08-02 15:33:41,601 INFO train.py:262] step_idx: 150700, epoch: 32, batch: 1964, avg loss: 2.980905, normalized loss: 1.605164, ppl: 19.705633, speed: 3.52 step/s
[2019-08-02 15:34:10,079 INFO train.py:262] step_idx: 150800, epoch: 32, batch: 2064, avg loss: 3.047716, normalized loss: 1.671976, ppl: 21.067181, speed: 3.51 step/s
[2019-08-02 15:34:38,598 INFO train.py:262] step_idx: 150900, epoch: 32, batch: 2164, avg loss: 2.956475, normalized loss: 1.580735, ppl: 19.230072, speed: 3.51 step/s
step 100/1 - loss: 9.165776 - normalized loss: 7.790036 - ppl: 9564.142578 - 247ms/step
step 200/1 - loss: 8.037900 - normalized loss: 6.662160 - ppl: 3096.104492 - 227ms/step
step 300/1 - loss: 7.668307 - normalized loss: 6.292567 - ppl: 2139.457031 - 221ms/step
step 400/1 - loss: 7.598633 - normalized loss: 6.222893 - ppl: 1995.466797 - 218ms/step
```
也可以使用 CPU 训练(通过参数 `--use_cuda False` 设置)，训练速度较慢。
#### 单机多卡
Paddle动态图支持多进程多卡进行模型训练，启动训练的方式如下：
支持多进程多卡进行模型训练，启动训练的方式如下：
```sh
python -m paddle.distributed.launch --started_port 8999 --selected_gpus=0,1,2,3,4,5,6,7 --log_dir ./mylog train.py \
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch --started_port 8999 --selected_gpus=0,1,2,3,4,5,6,7 train.py \
--epoch 30 \
--src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
......@@ -129,25 +125,27 @@ python -m paddle.distributed.launch --started_port 8999 --selected_gpus=0,1,2,3,
--training_file gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
--validation_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 4096 \
--print_step 100 \
--use_cuda True \
--save_step 10000
--print_step 100
```
此时，程序会将每个进程的输出log导入到`./mylog`路径下，只有第一个工作进程会保存模型。
#### 静态图训练
默认使用动态图模式进行训练，可以通过设置 `eager_run` 参数为False来以静态图模式进行训练，如下：
```sh
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch --started_port 8999 --selected_gpus=0,1,2,3,4,5,6,7 train.py \
--epoch 30 \
--src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--special_token '<s>' '<e>' '<unk>' \
--training_file gen_data/wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de \
--validation_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 4096 \
--print_step 100 \
--eager_run False
```
.
├── mylog
│   ├── workerlog.0
│   ├── workerlog.1
│   ├── workerlog.2
│   ├── workerlog.3
│   ├── workerlog.4
│   ├── workerlog.5
│   ├── workerlog.6
│   └── workerlog.7
```
### 模型推断
......@@ -163,13 +161,13 @@ python -u predict.py \
--special_token '<s>' '<e>' '<unk>' \
--predict_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 32 \
--init_from_params trained_params/step_100000 \
--init_from_params base_model_dygraph/step_100000/transformer \
--beam_size 5 \
--max_out_len 255 \
--output_file predict.txt
```
由 `predict_file` 指定的文件中文本的翻译结果会输出到 `output_file` 指定的文件。执行预测时需要设置 `init_from_params` 来给出模型所在目录，更多参数的使用可以在 `transformer.yaml` 文件中查阅注释说明并进行更改设置。注意若在执行预测时设置了模型超参数，应与模型训练时的设置一致，如若训练时使用 big model 的参数设置，则预测时对应类似如下命令：
由 `predict_file` 指定的文件中文本的翻译结果会输出到 `output_file` 指定的文件。执行预测时需要设置 `init_from_params` 来给出模型文件路径（不包含扩展名），更多参数的使用可以在 `transformer.yaml` 文件中查阅注释说明并进行更改设置。注意若在执行预测时设置了模型超参数，应与模型训练时的设置一致，如若训练时使用 big model 的参数设置，则预测时对应类似如下命令：
```sh
# setting visible devices for prediction
......@@ -181,7 +179,7 @@ python -u predict.py \
--special_token '<s>' '<e>' '<unk>' \
--predict_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 32 \
--init_from_params trained_params/step_100000 \
--init_from_params base_model_dygraph/step_100000/transformer \
--beam_size 5 \
--max_out_len 255 \
--output_file predict.txt \
......@@ -191,6 +189,24 @@ python -u predict.py \
--prepostprocess_dropout 0.3
```
和训练类似，预测时同样可以以静态图模式进行，如下：
```sh
# setting visible devices for prediction
export CUDA_VISIBLE_DEVICES=0
python -u predict.py \
--src_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--trg_vocab_fpath gen_data/wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--special_token '<s>' '<e>' '<unk>' \
--predict_file gen_data/wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 32 \
--init_from_params base_model_dygraph/step_100000/transformer \
--beam_size 5 \
--max_out_len 255 \
--output_file predict.txt \
--eager_run False
```
### 模型评估
......
transformer/gen_data.sh 0 → 100644
浏览文件 @ e93f9d5d
#! /usr/bin/env bash
set -e
OUTPUT_DIR=$PWD/gen_data
###############################################################################
# change these variables for other WMT data
###############################################################################
OUTPUT_DIR_DATA="${OUTPUT_DIR}/wmt16_ende_data"
OUTPUT_DIR_BPE_DATA="${OUTPUT_DIR}/wmt16_ende_data_bpe"
LANG1="en"
LANG2="de"
# each of TRAIN_DATA: data_url data_file_lang1 data_file_lang2
TRAIN_DATA=(
'http://www.statmt.org/europarl/v7/de-en.tgz'
'europarl-v7.de-en.en' 'europarl-v7.de-en.de'
'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz'
'commoncrawl.de-en.en' 'commoncrawl.de-en.de'
'http://data.statmt.org/wmt16/translation-task/training-parallel-nc-v11.tgz'
'news-commentary-v11.de-en.en' 'news-commentary-v11.de-en.de'
)
# each of DEV_TEST_DATA: data_url data_file_lang1 data_file_lang2
DEV_TEST_DATA=(
'http://data.statmt.org/wmt16/translation-task/dev.tgz'
'newstest201[45]-deen-ref.en.sgm' 'newstest201[45]-deen-src.de.sgm'
'http://data.statmt.org/wmt16/translation-task/test.tgz'
'newstest2016-deen-ref.en.sgm' 'newstest2016-deen-src.de.sgm'
)
###############################################################################
###############################################################################
# change these variables for other WMT data
###############################################################################
# OUTPUT_DIR_DATA="${OUTPUT_DIR}/wmt14_enfr_data"
# OUTPUT_DIR_BPE_DATA="${OUTPUT_DIR}/wmt14_enfr_data_bpe"
# LANG1="en"
# LANG2="fr"
# # each of TRAIN_DATA: ata_url data_tgz data_file
# TRAIN_DATA=(
# 'http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz'
# 'commoncrawl.fr-en.en' 'commoncrawl.fr-en.fr'
# 'http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz'
# 'training/europarl-v7.fr-en.en' 'training/europarl-v7.fr-en.fr'
# 'http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz'
# 'training/news-commentary-v9.fr-en.en' 'training/news-commentary-v9.fr-en.fr'
# 'http://www.statmt.org/wmt10/training-giga-fren.tar'
# 'giga-fren.release2.fixed.en.*' 'giga-fren.release2.fixed.fr.*'
# 'http://www.statmt.org/wmt13/training-parallel-un.tgz'
# 'un/undoc.2000.fr-en.en' 'un/undoc.2000.fr-en.fr'
# )
# # each of DEV_TEST_DATA: data_url data_tgz data_file_lang1 data_file_lang2
# DEV_TEST_DATA=(
# 'http://data.statmt.org/wmt16/translation-task/dev.tgz'
# '.*/newstest201[45]-fren-ref.en.sgm' '.*/newstest201[45]-fren-src.fr.sgm'
# 'http://data.statmt.org/wmt16/translation-task/test.tgz'
# '.*/newstest2016-fren-ref.en.sgm' '.*/newstest2016-fren-src.fr.sgm'
# )
###############################################################################
mkdir -p $OUTPUT_DIR_DATA $OUTPUT_DIR_BPE_DATA
# Extract training data
for ((i=0;i<${#TRAIN_DATA[@]};i+=3)); do
data_url=${TRAIN_DATA[i]}
data_tgz=${data_url##*/} # training-parallel-commoncrawl.tgz
data=${data_tgz%.*} # training-parallel-commoncrawl
data_lang1=${TRAIN_DATA[i+1]}
data_lang2=${TRAIN_DATA[i+2]}
if [ ! -e ${OUTPUT_DIR_DATA}/${data_tgz} ]; then
echo "Download "${data_url}
wget -O ${OUTPUT_DIR_DATA}/${data_tgz} ${data_url}
fi
if [ ! -d ${OUTPUT_DIR_DATA}/${data} ]; then
echo "Extract "${data_tgz}
mkdir -p ${OUTPUT_DIR_DATA}/${data}
tar_type=${data_tgz:0-3}
if [ ${tar_type} == "tar" ]; then
tar -xvf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
else
tar -xvzf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
fi
fi
# concatenate all training data
for data_lang in $data_lang1 $data_lang2; do
for f in `find ${OUTPUT_DIR_DATA}/${data} -regex ".*/${data_lang}"`; do
data_dir=`dirname $f`
data_file=`basename $f`
f_base=${f%.*}
f_ext=${f##*.}
if [ $f_ext == "gz" ]; then
gunzip $f
l=${f_base##*.}
f_base=${f_base%.*}
else
l=${f_ext}
fi
if [ $i -eq 0 ]; then
cat ${f_base}.$l > ${OUTPUT_DIR_DATA}/train.$l
else
cat ${f_base}.$l >> ${OUTPUT_DIR_DATA}/train.$l
fi
done
done
done
# Clone mosesdecoder
if [ ! -d ${OUTPUT_DIR}/mosesdecoder ]; then
echo "Cloning moses for data processing"
git clone https://github.com/moses-smt/mosesdecoder.git ${OUTPUT_DIR}/mosesdecoder
fi
# Extract develop and test data
dev_test_data=""
for ((i=0;i<${#DEV_TEST_DATA[@]};i+=3)); do
data_url=${DEV_TEST_DATA[i]}
data_tgz=${data_url##*/} # training-parallel-commoncrawl.tgz
data=${data_tgz%.*} # training-parallel-commoncrawl
data_lang1=${DEV_TEST_DATA[i+1]}
data_lang2=${DEV_TEST_DATA[i+2]}
if [ ! -e ${OUTPUT_DIR_DATA}/${data_tgz} ]; then
echo "Download "${data_url}
wget -O ${OUTPUT_DIR_DATA}/${data_tgz} ${data_url}
fi
if [ ! -d ${OUTPUT_DIR_DATA}/${data} ]; then
echo "Extract "${data_tgz}
mkdir -p ${OUTPUT_DIR_DATA}/${data}
tar_type=${data_tgz:0-3}
if [ ${tar_type} == "tar" ]; then
tar -xvf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
else
tar -xvzf ${OUTPUT_DIR_DATA}/${data_tgz} -C ${OUTPUT_DIR_DATA}/${data}
fi
fi
for data_lang in $data_lang1 $data_lang2; do
for f in `find ${OUTPUT_DIR_DATA}/${data} -regex ".*/${data_lang}"`; do
data_dir=`dirname $f`
data_file=`basename $f`
data_out=`echo ${data_file} | cut -d '-' -f 1` # newstest2016
l=`echo ${data_file} | cut -d '.' -f 2` # en
dev_test_data="${dev_test_data}\|${data_out}" # to make regexp
if [ ! -e ${OUTPUT_DIR_DATA}/${data_out}.$l ]; then
${OUTPUT_DIR}/mosesdecoder/scripts/ems/support/input-from-sgm.perl \
< $f > ${OUTPUT_DIR_DATA}/${data_out}.$l
fi
done
done
done
# Tokenize data
for l in ${LANG1} ${LANG2}; do
for f in `ls ${OUTPUT_DIR_DATA}/*.$l | grep "\(train${dev_test_data}\)\.$l$"`; do
f_base=${f%.*} # dir/train dir/newstest2016
f_out=$f_base.tok.$l
if [ ! -e $f_out ]; then
echo "Tokenize "$f
${OUTPUT_DIR}/mosesdecoder/scripts/tokenizer/tokenizer.perl -q -l $l -threads 8 < $f > $f_out
fi
done
done
# Clean data
for f in ${OUTPUT_DIR_DATA}/train.${LANG1} ${OUTPUT_DIR_DATA}/train.tok.${LANG1}; do
f_base=${f%.*} # dir/train dir/train.tok
f_out=${f_base}.clean
if [ ! -e $f_out.${LANG1} ] && [ ! -e $f_out.${LANG2} ]; then
echo "Clean "${f_base}
${OUTPUT_DIR}/mosesdecoder/scripts/training/clean-corpus-n.perl $f_base ${LANG1} ${LANG2} ${f_out} 1 80
fi
done
# Clone subword-nmt and generate BPE data
if [ ! -d ${OUTPUT_DIR}/subword-nmt ]; then
git clone https://github.com/rsennrich/subword-nmt.git ${OUTPUT_DIR}/subword-nmt
fi
# Generate BPE data and vocabulary
for num_operations in 32000; do
if [ ! -e ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations} ]; then
echo "Learn BPE with ${num_operations} merge operations"
cat ${OUTPUT_DIR_DATA}/train.tok.clean.${LANG1} ${OUTPUT_DIR_DATA}/train.tok.clean.${LANG2} | \
${OUTPUT_DIR}/subword-nmt/learn_bpe.py -s $num_operations > ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations}
fi
for l in ${LANG1} ${LANG2}; do
for f in `ls ${OUTPUT_DIR_DATA}/*.$l | grep "\(train${dev_test_data}\)\.tok\(\.clean\)\?\.$l$"`; do
f_base=${f%.*} # dir/train.tok dir/train.tok.clean dir/newstest2016.tok
f_base=${f_base##*/} # train.tok train.tok.clean newstest2016.tok
f_out=${OUTPUT_DIR_BPE_DATA}/${f_base}.bpe.${num_operations}.$l
if [ ! -e $f_out ]; then
echo "Apply BPE to "$f
${OUTPUT_DIR}/subword-nmt/apply_bpe.py -c ${OUTPUT_DIR_BPE_DATA}/bpe.${num_operations} < $f > $f_out
fi
done
done
if [ ! -e ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations} ]; then
echo "Create vocabulary for BPE data"
cat ${OUTPUT_DIR_BPE_DATA}/train.tok.clean.bpe.${num_operations}.${LANG1} ${OUTPUT_DIR_BPE_DATA}/train.tok.clean.bpe.${num_operations}.${LANG2} | \
${OUTPUT_DIR}/subword-nmt/get_vocab.py | cut -f1 -d ' ' > ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations}
fi
done
# Adapt to the reader
for f in ${OUTPUT_DIR_BPE_DATA}/*.bpe.${num_operations}.${LANG1}; do
f_base=${f%.*} # dir/train.tok.clean.bpe.32000 dir/newstest2016.tok.bpe.32000
f_out=${f_base}.${LANG1}-${LANG2}
if [ ! -e $f_out ]; then
paste -d '\t' $f_base.${LANG1} $f_base.${LANG2} > $f_out
fi
done
if [ ! -e ${OUTPUT_DIR_BPE_DATA}/vocab_all.bpe.${num_operations} ]; then
sed '1i\<s>\n<e>\n<unk>' ${OUTPUT_DIR_BPE_DATA}/vocab.bpe.${num_operations} > ${OUTPUT_DIR_BPE_DATA}/vocab_all.bpe.${num_operations}
fi
echo "All done."
transformer/predict.py
浏览文件 @ e93f9d5d
......@@ -77,11 +77,12 @@ def do_predict(args):
token_delimiter=args.token_delimiter,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2])
unk_mark=args.special_token[2],
byte_data=True)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = dataset.get_vocab_summary()
trg_idx2word = Seq2SeqDataset.load_dict(
dict_path=args.trg_vocab_fpath, reverse=True)
dict_path=args.trg_vocab_fpath, reverse=True, byte_data=True)
batch_sampler = Seq2SeqBatchSampler(
dataset=dataset,
use_token_batch=False,
......@@ -91,10 +92,12 @@ def do_predict(args):
dataset=dataset,
batch_sampler=batch_sampler,
places=device,
feed_list=None
if fluid.in_dygraph_mode() else [x.forward() for x in inputs],
collate_fn=partial(
prepare_infer_input, src_pad_idx=args.eos_idx, n_head=args.n_head),
prepare_infer_input,
bos_idx=args.bos_idx,
eos_idx=args.eos_idx,
src_pad_idx=args.eos_idx,
n_head=args.n_head),
num_workers=0,
return_list=True)
......@@ -124,7 +127,7 @@ def do_predict(args):
# load the trained model
assert args.init_from_params, (
"Please set init_from_params to load the infer model.")
transformer.load(os.path.join(args.init_from_params, "transformer"))
transformer.load(args.init_from_params)
# TODO: use model.predict when support variant length
f = open(args.output_file, "wb")
......
transformer/reader.py
浏览文件 @ e93f9d5d
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -15,8 +15,9 @@
import glob
import six
import os
import tarfile
import io
import itertools
from functools import partial
import numpy as np
import paddle.fluid as fluid
......@@ -24,16 +25,67 @@ from paddle.fluid.dygraph.parallel import ParallelEnv
from paddle.fluid.io import BatchSampler, DataLoader, Dataset
def prepare_train_input(insts, src_pad_idx, trg_pad_idx, n_head):
def create_data_loader(args, device):
data_loaders = [None, None]
data_files = [args.training_file, args.validation_file
] if args.validation_file else [args.training_file]
for i, data_file in enumerate(data_files):
dataset = Seq2SeqDataset(
fpattern=data_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2],
byte_data=True)
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = dataset.get_vocab_summary()
batch_sampler = Seq2SeqBatchSampler(
dataset=dataset,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size,
pool_size=args.pool_size,
sort_type=args.sort_type,
shuffle=args.shuffle,
shuffle_batch=args.shuffle_batch,
max_length=args.max_length,
distribute_mode=True
if i == 0 else False) # every device eval all data
data_loader = DataLoader(
dataset=dataset,
batch_sampler=batch_sampler,
places=device,
collate_fn=partial(
prepare_train_input,
bos_idx=args.bos_idx,
eos_idx=args.eos_idx,
src_pad_idx=args.eos_idx,
trg_pad_idx=args.eos_idx,
n_head=args.n_head),
num_workers=0, # TODO: use multi-process
return_list=True)
data_loaders[i] = data_loader
return data_loaders
def prepare_train_input(insts, bos_idx, eos_idx, src_pad_idx, trg_pad_idx,
n_head):
"""
Put all padded data needed by training into a list.
"""
src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
[inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
[inst[0] + [eos_idx] for inst in insts],
src_pad_idx,
n_head,
is_target=False)
src_word = src_word.reshape(-1, src_max_len)
src_pos = src_pos.reshape(-1, src_max_len)
trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
[inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
[[bos_idx] + inst[1] for inst in insts],
trg_pad_idx,
n_head,
is_target=True)
trg_word = trg_word.reshape(-1, trg_max_len)
trg_pos = trg_pos.reshape(-1, trg_max_len)
......@@ -41,7 +93,7 @@ def prepare_train_input(insts, src_pad_idx, trg_pad_idx, n_head):
[1, 1, trg_max_len, 1]).astype("float32")
lbl_word, lbl_weight, num_token = pad_batch_data(
[inst[2] for inst in insts],
[inst[1] + [eos_idx] for inst in insts],
trg_pad_idx,
n_head,
is_target=False,
......@@ -60,20 +112,21 @@ def prepare_train_input(insts, src_pad_idx, trg_pad_idx, n_head):
return data_inputs
def prepare_infer_input(insts, src_pad_idx, n_head):
def prepare_infer_input(insts, bos_idx, eos_idx, src_pad_idx, n_head):
"""
Put all padded data needed by beam search decoder into a list.
"""
src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
[inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
[inst[0] + [eos_idx] for inst in insts],
src_pad_idx,
n_head,
is_target=False)
trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
[1, 1, 1, 1]).astype("float32")
src_word = src_word.reshape(-1, src_max_len)
src_pos = src_pos.reshape(-1, src_max_len)
data_inputs = [
src_word, src_pos, src_slf_attn_bias, trg_src_attn_bias
]
data_inputs = [src_word, src_pos, src_slf_attn_bias, trg_src_attn_bias]
return data_inputs
......@@ -142,29 +195,30 @@ class SortType(object):
class Converter(object):
def __init__(self, vocab, beg, end, unk, delimiter, add_beg):
def __init__(self, vocab, beg, end, unk, delimiter, add_beg, add_end):
self._vocab = vocab
self._beg = beg
self._end = end
self._unk = unk
self._delimiter = delimiter
self._add_beg = add_beg
self._add_end = add_end
def __call__(self, sentence):
return ([self._beg] if self._add_beg else []) + [
self._vocab.get(w, self._unk)
for w in sentence.split(self._delimiter)
] + [self._end]
] + ([self._end] if self._add_end else [])
class ComposedConverter(object):
def __init__(self, converters):
self._converters = converters
def __call__(self, parallel_sentence):
def __call__(self, fields):
return [
self._converters[i](parallel_sentence[i])
for i in range(len(self._converters))
converter(field)
for field, converter in zip(fields, self._converters)
]
......@@ -201,10 +255,11 @@ class TokenBatchCreator(object):
class SampleInfo(object):
def __init__(self, i, max_len, min_len):
def __init__(self, i, lens):
self.i = i
self.min_len = min_len
self.max_len = max_len
# take bos and eos into account
self.min_len = min(lens[0] + 1, lens[1] + 2)
self.max_len = max(lens[0] + 1, lens[1] + 2)
class MinMaxFilter(object):
......@@ -229,98 +284,109 @@ class Seq2SeqDataset(Dataset):
src_vocab_fpath,
trg_vocab_fpath,
fpattern,
tar_fname=None,
field_delimiter="\t",
token_delimiter=" ",
start_mark="<s>",
end_mark="<e>",
unk_mark="<unk>",
only_src=False):
# convert str to bytes, and use byte data
only_src=False,
trg_fpattern=None,
byte_data=False):
if byte_data:
# The WMT16 bpe data used here seems including bytes can not be
# decoded by utf8. Thus convert str to bytes, and use byte data
field_delimiter = field_delimiter.encode("utf8")
token_delimiter = token_delimiter.encode("utf8")
start_mark = start_mark.encode("utf8")
end_mark = end_mark.encode("utf8")
unk_mark = unk_mark.encode("utf8")
self._src_vocab = self.load_dict(src_vocab_fpath)
self._trg_vocab = self.load_dict(trg_vocab_fpath)
self._byte_data = byte_data
self._src_vocab = self.load_dict(src_vocab_fpath, byte_data=byte_data)
self._trg_vocab = self.load_dict(trg_vocab_fpath, byte_data=byte_data)
self._bos_idx = self._src_vocab[start_mark]
self._eos_idx = self._src_vocab[end_mark]
self._unk_idx = self._src_vocab[unk_mark]
self._only_src = only_src
self._field_delimiter = field_delimiter
self._token_delimiter = token_delimiter
self.load_src_trg_ids(fpattern, tar_fname)
self.load_src_trg_ids(fpattern, trg_fpattern)
def load_src_trg_ids(self, fpattern, tar_fname):
converters = [
Converter(vocab=self._src_vocab,
def load_src_trg_ids(self, fpattern, trg_fpattern=None):
src_converter = Converter(
vocab=self._src_vocab,
beg=self._bos_idx,
end=self._eos_idx,
unk=self._unk_idx,
delimiter=self._token_delimiter,
add_beg=False)
]
if not self._only_src:
converters.append(
Converter(vocab=self._trg_vocab,
add_beg=False,
add_end=False)
trg_converter = Converter(
vocab=self._trg_vocab,
beg=self._bos_idx,
end=self._eos_idx,
unk=self._unk_idx,
delimiter=self._token_delimiter,
add_beg=True))
add_beg=False,
add_end=False)
converters = ComposedConverter(converters)
converters = ComposedConverter([src_converter, trg_converter])
self._src_seq_ids = []
self._trg_seq_ids = None if self._only_src else []
self._trg_seq_ids = []
self._sample_infos = []
for i, line in enumerate(self._load_lines(fpattern, tar_fname)):
src_trg_ids = converters(line)
self._src_seq_ids.append(src_trg_ids[0])
lens = [len(src_trg_ids[0])]
if not self._only_src:
self._trg_seq_ids.append(src_trg_ids[1])
lens.append(len(src_trg_ids[1]))
self._sample_infos.append(SampleInfo(i, max(lens), min(lens)))
slots = [self._src_seq_ids, self._trg_seq_ids]
for i, line in enumerate(self._load_lines(fpattern, trg_fpattern)):
lens = []
for field, slot in zip(converters(line), slots):
slot.append(field)
lens.append(len(field))
self._sample_infos.append(SampleInfo(i, lens))
def _load_lines(self, fpattern, tar_fname):
def _load_lines(self, fpattern, trg_fpattern=None):
fpaths = glob.glob(fpattern)
fpaths = sorted(fpaths) # TODO: Add custum sort
assert len(fpaths) > 0, "no matching file to the provided data path"
if len(fpaths) == 1 and tarfile.is_tarfile(fpaths[0]):
if tar_fname is None:
raise Exception("If tar file provided, please set tar_fname.")
f = tarfile.open(fpaths[0], "rb")
for line in f.extractfile(tar_fname):
fields = line.strip(b"\n").split(self._field_delimiter)
if (not self._only_src
and len(fields) == 2) or (self._only_src
and len(fields) == 1):
yield fields
else:
(f_mode, f_encoding,
endl) = ("rb", None, b"\n") if self._byte_data else ("r", "utf8",
"\n")
if trg_fpattern is None:
for fpath in fpaths:
if not os.path.isfile(fpath):
raise IOError("Invalid file: %s" % fpath)
with open(fpath, "rb") as f:
with io.open(fpath, f_mode, encoding=f_encoding) as f:
for line in f:
fields = line.strip(b"\n").split(self._field_delimiter)
if (not self._only_src and len(fields) == 2) or (
self._only_src and len(fields) == 1):
fields = line.strip(endl).split(self._field_delimiter)
yield fields
else:
# separated source and target language data files
# assume we can get aligned data by sort the two language files
# TODO: Need more rigorous check
trg_fpaths = glob.glob(trg_fpattern)
trg_fpaths = sorted(trg_fpaths)
assert len(fpaths) == len(
trg_fpaths
), "the number of source language data files must equal \
with that of source language"
for fpath, trg_fpath in zip(fpaths, trg_fpaths):
with io.open(fpath, f_mode, encoding=f_encoding) as f:
with io.open(
trg_fpath, f_mode, encoding=f_encoding) as trg_f:
for line in zip(f, trg_f):
fields = [field.strip(endl) for field in line]
yield fields
@staticmethod
def load_dict(dict_path, reverse=False):
def load_dict(dict_path, reverse=False, byte_data=False):
word_dict = {}
with open(dict_path, "rb") as fdict:
(f_mode, f_encoding,
endl) = ("rb", None, b"\n") if byte_data else ("r", "utf8", "\n")
with io.open(dict_path, f_mode, encoding=f_encoding) as fdict:
for idx, line in enumerate(fdict):
if reverse:
word_dict[idx] = line.strip(b"\n")
word_dict[idx] = line.strip(endl)
else:
word_dict[line.strip(b"\n")] = idx
word_dict[line.strip(endl)] = idx
return word_dict
def get_vocab_summary(self):
......@@ -328,9 +394,8 @@ class Seq2SeqDataset(Dataset):
self._trg_vocab), self._bos_idx, self._eos_idx, self._unk_idx
def __getitem__(self, idx):
return (self._src_seq_ids[idx], self._trg_seq_ids[idx][:-1],
self._trg_seq_ids[idx][1:]
) if not self._only_src else self._src_seq_ids[idx]
return (self._src_seq_ids[idx], self._trg_seq_ids[idx]
) if self._trg_seq_ids else self._src_seq_ids[idx]
def __len__(self):
return len(self._sample_infos)
......@@ -348,6 +413,7 @@ class Seq2SeqBatchSampler(BatchSampler):
shuffle_batch=False,
use_token_batch=False,
clip_last_batch=False,
distribute_mode=True,
seed=0):
for arg, value in locals().items():
if arg != "self":
......@@ -355,6 +421,7 @@ class Seq2SeqBatchSampler(BatchSampler):
self._random = np.random
self._random.seed(seed)
# for multi-devices
self._distribute_mode = distribute_mode
self._nranks = ParallelEnv().nranks
self._local_rank = ParallelEnv().local_rank
self._device_id = ParallelEnv().dev_id
......@@ -362,8 +429,8 @@ class Seq2SeqBatchSampler(BatchSampler):
def __iter__(self):
# global sort or global shuffle
if self._sort_type == SortType.GLOBAL:
infos = sorted(self._dataset._sample_infos,
key=lambda x: x.max_len)
infos = sorted(
self._dataset._sample_infos, key=lambda x: x.max_len)
else:
if self._shuffle:
infos = self._dataset._sample_infos
......@@ -383,9 +450,9 @@ class Seq2SeqBatchSampler(BatchSampler):
batches = []
batch_creator = TokenBatchCreator(
self._batch_size
) if self._use_token_batch else SentenceBatchCreator(self._batch_size *
self._nranks)
self.
_batch_size) if self._use_token_batch else SentenceBatchCreator(
self._batch_size * self._nranks)
batch_creator = MinMaxFilter(self._max_length, self._min_length,
batch_creator)
......@@ -413,11 +480,21 @@ class Seq2SeqBatchSampler(BatchSampler):
# for multi-device
for batch_id, batch in enumerate(batches):
if batch_id % self._nranks == self._local_rank:
if not self._distribute_mode or (
batch_id % self._nranks == self._local_rank):
batch_indices = [info.i for info in batch]
yield batch_indices
if self._local_rank > len(batches) % self._nranks:
if self._distribute_mode and len(batches) % self._nranks != 0:
if self._local_rank >= len(batches) % self._nranks:
# use previous data to pad
yield batch_indices
def __len__(self):
return 100
if not self._use_token_batch:
batch_number = (
len(self._dataset) + self._batch_size * self._nranks - 1) // (
self._batch_size * self._nranks)
else:
# TODO(guosheng): fix the uncertain length
batch_number = 1
return batch_number
transformer/run.sh 已删除 100644 → 0
浏览文件 @ 187b3537
python -u train.py \
--epoch 30 \
--src_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--trg_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--special_token '<s>' '<e>' '<unk>' \
--training_file wmt16_ende_data_bpe/train.tok.clean.bpe.32000.en-de.tiny \
--validation_file wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 4096 \
--print_step 1 \
--use_cuda True \
--random_seed 1000 \
--save_step 10 \
--eager_run True
#--init_from_pretrain_model base_model_dygraph/step_100000/ \
#--init_from_checkpoint trained_models/step_200/transformer
#--n_head 16 \
#--d_model 1024 \
#--d_inner_hid 4096 \
#--prepostprocess_dropout 0.3
exit
echo `date`
python -u predict.py \
--src_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--trg_vocab_fpath wmt16_ende_data_bpe/vocab_all.bpe.32000 \
--special_token '<s>' '<e>' '<unk>' \
--predict_file wmt16_ende_data_bpe/newstest2014.tok.bpe.32000.en-de \
--batch_size 64 \
--init_from_params base_model_dygraph/step_100000/ \
--beam_size 5 \
--max_out_len 255 \
--output_file predict.txt \
--eager_run True
#--max_length 500 \
#--n_head 16 \
#--d_model 1024 \
#--d_inner_hid 4096 \
#--prepostprocess_dropout 0.3
echo `date`
\ No newline at end of file
transformer/train.py
浏览文件 @ e93f9d5d
......@@ -17,12 +17,10 @@ import os
import six
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from functools import partial
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph import to_variable
from paddle.fluid.io import DataLoader
from utils.configure import PDConfig
......@@ -30,33 +28,39 @@ from utils.check import check_gpu, check_version
from model import Input, set_device
from callbacks import ProgBarLogger
from reader import prepare_train_input, Seq2SeqDataset, Seq2SeqBatchSampler
from transformer import Transformer, CrossEntropyCriterion, NoamDecay
from reader import create_data_loader
from transformer import Transformer, CrossEntropyCriterion
class LoggerCallback(ProgBarLogger):
def __init__(self, log_freq=1, verbose=2, loss_normalizer=0.):
super(LoggerCallback, self).__init__(log_freq, verbose)
# TODO: wrap these override function to simplify
class TrainCallback(ProgBarLogger):
def __init__(self, args, verbose=2):
# TODO(guosheng): save according to step
super(TrainCallback, self).__init__(args.print_step, verbose)
# the best cross-entropy value with label smoothing
loss_normalizer = -(
(1. - args.label_smooth_eps) * np.log(
(1. - args.label_smooth_eps)) + args.label_smooth_eps *
np.log(args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20))
self.loss_normalizer = loss_normalizer
def on_train_begin(self, logs=None):
super(LoggerCallback, self).on_train_begin(logs)
super(TrainCallback, self).on_train_begin(logs)
self.train_metrics += ["normalized loss", "ppl"]
def on_train_batch_end(self, step, logs=None):
logs["normalized loss"] = logs["loss"][0] - self.loss_normalizer
logs["ppl"] = np.exp(min(logs["loss"][0], 100))
super(LoggerCallback, self).on_train_batch_end(step, logs)
super(TrainCallback, self).on_train_batch_end(step, logs)
def on_eval_begin(self, logs=None):
super(LoggerCallback, self).on_eval_begin(logs)
self.eval_metrics += ["normalized loss", "ppl"]
super(TrainCallback, self).on_eval_begin(logs)
self.eval_metrics = list(
self.eval_metrics) + ["normalized loss", "ppl"]
def on_eval_batch_end(self, step, logs=None):
logs["normalized loss"] = logs["loss"][0] - self.loss_normalizer
logs["ppl"] = np.exp(min(logs["loss"][0], 100))
super(LoggerCallback, self).on_eval_batch_end(step, logs)
super(TrainCallback, self).on_eval_batch_end(step, logs)
def do_train(args):
......@@ -100,44 +104,7 @@ def do_train(args):
]
# def dataloader
data_loaders = [None, None]
data_files = [args.training_file, args.validation_file
] if args.validation_file else [args.training_file]
for i, data_file in enumerate(data_files):
dataset = Seq2SeqDataset(
fpattern=data_file,
src_vocab_fpath=args.src_vocab_fpath,
trg_vocab_fpath=args.trg_vocab_fpath,
token_delimiter=args.token_delimiter,
start_mark=args.special_token[0],
end_mark=args.special_token[1],
unk_mark=args.special_token[2])
args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
args.unk_idx = dataset.get_vocab_summary()
batch_sampler = Seq2SeqBatchSampler(
dataset=dataset,
use_token_batch=args.use_token_batch,
batch_size=args.batch_size,
pool_size=args.pool_size,
sort_type=args.sort_type,
shuffle=args.shuffle,
shuffle_batch=args.shuffle_batch,
max_length=args.max_length)
data_loader = DataLoader(
dataset=dataset,
batch_sampler=batch_sampler,
places=device,
feed_list=None if fluid.in_dygraph_mode() else
[x.forward() for x in inputs + labels],
collate_fn=partial(
prepare_train_input,
src_pad_idx=args.eos_idx,
trg_pad_idx=args.eos_idx,
n_head=args.n_head),
num_workers=0, # TODO: use multi-process
return_list=True)
data_loaders[i] = data_loader
train_loader, eval_loader = data_loaders
train_loader, eval_loader = create_data_loader(args, device)
# define model
transformer = Transformer(
......@@ -149,8 +116,10 @@ def do_train(args):
transformer.prepare(
fluid.optimizer.Adam(
learning_rate=fluid.layers.noam_decay(args.d_model,
args.warmup_steps),
learning_rate=fluid.layers.noam_decay(
args.d_model,
args.warmup_steps,
learning_rate=args.learning_rate),
beta1=args.beta1,
beta2=args.beta2,
epsilon=float(args.eps),
......@@ -161,32 +130,19 @@ def do_train(args):
## init from some checkpoint, to resume the previous training
if args.init_from_checkpoint:
transformer.load(
os.path.join(args.init_from_checkpoint, "transformer"))
transformer.load(args.init_from_checkpoint)
## init from some pretrain models, to better solve the current task
if args.init_from_pretrain_model:
transformer.load(
os.path.join(args.init_from_pretrain_model, "transformer"),
reset_optimizer=True)
# the best cross-entropy value with label smoothing
loss_normalizer = -(
(1. - args.label_smooth_eps) * np.log(
(1. - args.label_smooth_eps)) + args.label_smooth_eps *
np.log(args.label_smooth_eps / (args.trg_vocab_size - 1) + 1e-20))
transformer.load(args.init_from_pretrain_model, reset_optimizer=True)
# model train
transformer.fit(train_data=train_loader,
eval_data=eval_loader,
epochs=1,
epochs=args.epoch,
eval_freq=1,
save_freq=1,
verbose=2,
callbacks=[
LoggerCallback(
log_freq=args.print_step,
loss_normalizer=loss_normalizer)
])
save_dir=args.save_model,
callbacks=[TrainCallback(args)])
if __name__ == "__main__":
......
transformer/transformer.py
浏览文件 @ e93f9d5d
......@@ -79,7 +79,8 @@ 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
self.functors.append(
self.add_sublayer(
......@@ -169,7 +170,7 @@ 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:
......
transformer/transformer.yaml
浏览文件 @ e93f9d5d
# used for continuous evaluation
enable_ce: False
eager_run: False
eager_run: True
# The frequency to save trained models when training.
save_step: 10000
......
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