[BugFix] Fix model zoo relative import (PaddlePaddle#2130)

* fix as relative import

* add soft link

* refine

* copy data tools to model

* fix gpt-3

* refine gpt readme.

* delete unused dataset utils code

* delete some code.

* softlink

* move data_tools to ernie.

* fix

examples/language_model/gpt-3/README.md

@@ -22,11 +22,53 @@ GPT-[3](https://arxiv.org/pdf/2005.14165.pdf) 是以[Transformer](https://arxiv.
 注：需要PaddlePaddle版本大于等于2.2rc，或者使用最新develop版本，安装方法请参见Paddle[官网](https://www.paddlepaddle.org.cn)。
 
 
+### 数据获取与制作
+
+[OpenWebTextCorpus](https://skylion007.github.io/OpenWebTextCorpus/)是一个开源的英文网页文本数据集，数据来源于Reddit，经过去重、清洗、提取，最终包含800多万个文档。
+本示例采用EleutherAI清洗好的[OpenWebText2数据](https://openwebtext2.readthedocs.io/en/latest/index.html#download-plug-and-play-version)
+
+下载以后通过以下命令解压：
+
+```shell
+wget https://mystic.the-eye.eu/public/AI/pile_preliminary_components/openwebtext2.jsonl.zst.tar
+tar -xvf openwebtext2.json.zst.tar -C  /path/to/openwebtext
+```
+
+然后使用[data_tools](./data_tools)工具下的`create_pretraining_data.py`脚本进行数据集制作：
+```
+python -u  create_pretraining_data.py \
+    --model_name gpt2-en \
+    --tokenizer_name GPTTokenizer \
+    --data_format JSON \
+    --input_path /path/to/openwebtext/ \
+    --append_eos \
+    --output_prefix gpt_openwebtext  \
+    --workers 40 \
+    --log_interval 10000
+```
+处理时间约一个小时左右，就可以得到我们需要的`gpt_openwebtext_ids.npy`, `gpt_openwebtext_idx.npz`数据集文件。
+
+为了方便用户运行测试本模型，本项目提供了处理好的300M的训练样本：
+```shell
+wget https://bj.bcebos.com/paddlenlp/models/transformers/gpt/data/gpt_en_dataset_300m_ids.npy
+wget https://bj.bcebos.com/paddlenlp/models/transformers/gpt/data/gpt_en_dataset_300m_idx.npz
+```
+
+将所有预处理得到的文件统一放入一个文件夹中，以备训练使用：
+
+```
+mkdir data
+mv gpt_en_dataset_300m_ids.npy ./data
+mv gpt_en_dataset_300m_idx.npz ./data
+```
+
+
 ```shell
 cd static # 或者 cd dygraph
 # 下载样例数据
 mkdir data && cd data
-wget https://bj.bcebos.com/paddlenlp/models/transformers/gpt/train.data.json_ids.npz
+wget https://bj.bcebos.com/paddlenlp/models/transformers/gpt/data/gpt_en_dataset_300m_ids.npy
+wget https://bj.bcebos.com/paddlenlp/models/transformers/gpt/data/gpt_en_dataset_300m_idx.npz
 cd ..
 # 运行pretrian 脚本
 sh run.sh

examples/language_model/gpt-3/data_tools

@@ -0,0 +1 @@
+../../../model_zoo/ernie-1.0/data_tools

examples/language_model/gpt-3/dygraph/lr.py

@@ -0,0 +1,49 @@
+# Copyright (c) 2021 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 math
+import numpy
+import warnings
+from paddle import Tensor
+from paddle.optimizer.lr import LRScheduler
+
+
+class CosineAnnealingWithWarmupDecay(LRScheduler):
+    def __init__(self,
+                 max_lr,
+                 min_lr,
+                 warmup_step,
+                 decay_step,
+                 last_epoch=0,
+                 verbose=False):
+
+        self.decay_step = decay_step
+        self.warmup_step = warmup_step
+        self.max_lr = max_lr
+        self.min_lr = min_lr
+        super(CosineAnnealingWithWarmupDecay, self).__init__(max_lr, last_epoch,
+                                                             verbose)
+
+    def get_lr(self):
+        if self.warmup_step > 0 and self.last_epoch <= self.warmup_step:
+            return float(self.max_lr) * (self.last_epoch) / self.warmup_step
+
+        if self.last_epoch > self.decay_step:
+            return self.min_lr
+
+        num_step_ = self.last_epoch - self.warmup_step
+        decay_step_ = self.decay_step - self.warmup_step
+        decay_ratio = float(num_step_) / float(decay_step_)
+        coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
+        return self.min_lr + coeff * (self.max_lr - self.min_lr)

examples/language_model/gpt-3/dygraph/run_pretrain.py

@@ -29,7 +29,7 @@
 
 # to import data_tools
 filepath = os.path.abspath(os.path.dirname(__file__))
-sys.path.insert(0, os.path.join(filepath, "../../"))
+sys.path.insert(0, os.path.join(filepath, "../"))
 
 from dataset import create_pretrained_dataset
 from args import parse_args