add gpt2 model for the paddlenlp

add gpt2 model for the paddlenlp

PaddleNLP/examples/language_model/gpt2/README.md

+# GPT2
+
+## 模型介绍
+[GPT2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)(Language Models are Unsupervised Multitask Learners) 以[Transformer](https://arxiv.org/abs/1706.03762) 解码器为网络基本组件，使用自回归的方式在大规模无标注文本语料上进行预训练（pre-train），得到的语言生成模型。
+
+本项目是语言模型 GPT2 的 PaddlePaddle 实现， 包含模型训练，预测等内容。下是本例的简要目录结构及说明：
+
+```text
+.
+├── data.py                 # 数据处理
+├── decompress.sh           # 数据集解压脚本
+├── generate_sample.py      # inference demo
+├── lr.py                   # 学习率控制
+├── process_data.py         # 数据预处理脚本
+├── README.md               # 文档
+├── run_pretrain.py         # 预训练入口
+└── scripts                 # 训练脚本
+```
+
+## 快速开始
+
+### 安装说明
+
+1. paddle安装
+
+    本项目依赖于 PaddlePaddle 2.0rc1及以上版本或适当的develop版本，请参考 [安装指南](https://www.paddlepaddle.org.cn/install/quick) 进行安装
+
+2. 下载代码
+
+    克隆代码库到本地
+
+3. 环境依赖
+
+    该模型使用PaddlePaddle，关于环境依赖部分，请先参考PaddlePaddle[安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/index_cn.html)关于环境依赖部分的内容。
+
+
+### 数据准备
+
+#### 原始数据获取
+
+[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。
+
+下载以后通过以下命令解压：
+
+```shell
+xz -d openwebtext.tar.xz
+tar xf openwebtext.tar
+mkdir raw_data
+bash decompress.sh  
+```
+
+解压以后得到的raw_data目录大小约为54GB。
+
+#### 数据预处理
+
+为了提升训练速度，我们在训练前将文本数据转成相应的id，并保存为npz格式：
+
+```shell
+python process_data.py --input_path raw_data \
+ --model_name gpt2-medium-en \
+ --append_eod \
+ --workers 8
+```
+
+运行命令后，产出`raw_data_ids.npz`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://paddlenlp.bj.bcebos.com/models/transformers/gpt2/train.data.json_ids.npz
+```
+
+将所有预处理得到的npz文件统一放入一个文件夹中，以备训练使用：
+
+```
+mkdir data
+mv train.data.json_ids.npz data
+```
+
+
+#### 单卡训练
+
+```shell
+CUDA_VISIBLE_DEVICES=0 python run_pretrain.py --model_name_or_path gpt2-small-en \
+    --input_dir "./data"\
+    --output_dir "output"\
+    --weight_decay 0.01\
+    --grad_clip 1.0\
+    --max_steps 500000\
+    --save_steps 100000\
+    --warmup_rate 0.01\
+    --batch_size 8\
+    --device gpu
+```
+
+其中参数释义如下：
+- `model_name_or_path` 要训练的模型或者之前训练的checkpoint。
+- `input_dir` 指定输入文件，可以使用目录，指定目录时将包括目录中的所有文件。
+- `output_dir` 指定输出文件。
+- `weight_decay` 权重衰减参数。
+- `grad_clip` 梯度裁剪范围。
+- `max_steps` 最大训练步数
+- `save_steps` 保存模型间隔
+- `batch_size` 训练的batch大小
+- `device` 训练设备
+
+用户也可以使用提供的shell脚本直接训练`sh scripts/run.sh`.
+
+### 单机多卡
+
+同样，可以执行如下命令实现八卡训练：
+
+```shell
+unset CUDA_VISIBLE_DEVICES
+python -m paddle.distributed.launch --gpus "0,1,2,3,4,5,6,7" run_pretrain.py --model_name_or_path gpt2-small-en \
+    --input_dir "./data"\
+    --output_dir "output"\
+    --weight_decay 0.01\
+    --grad_clip 1.0\
+    --max_steps 500000\
+    --save_steps 100000\
+    --warmup_rate 0.01\
+    --batch_size 8\
+    --device gpu
+
+```
+
+用户也可以使用提供的shell脚本直接训练`sh scripts/run_multi.sh`.
+
+#### 文本生成
+
+本项目提供了简单的文本生成的demo，供用户测试文本生成效果。
+
+```shell
+python generate_sample.py
+```
+
+生成效果展示:
+```text
+问题：中国的首都是哪里？答案：北京。
+问题：百度的厂长是谁? 答案：
+
+李彦宏。
+
+默写古诗: 大漠孤烟直，长河落日圆。
+举杯邀明月，
+
+对影成三人。
+```
+
+## 参考文献
+- [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+- [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413)

PaddleNLP/examples/language_model/gpt2/data.py

+# 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 time
+import os
+
+import numpy as np
+import paddle
+
+
+def construct_samples_and_shuffle_data(name, data_prefix, documents, sizes,
+                                       num_samples, seq_length, seed,
+                                       worker_index):
+    # Number of tokens in each epoch and number of required epochs.
+    tokens_per_epoch = _num_tokens(sizes)
+    num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples)
+    # rng state
+    np_rng = np.random.RandomState(seed=seed)
+
+    # Filename of the index mappings.
+    _filename = data_prefix
+    _filename += '_{}_indexmap'.format(name)
+    _filename += '_{}ns'.format(num_samples)
+    _filename += '_{}sl'.format(seq_length)
+    doc_idx_filename = _filename + '_doc_idx.npy'
+    sample_idx_filename = _filename + '_sample_idx.npy'
+    shuffle_idx_filename = _filename + '_shuffle_idx.npy'
+    # Build the indexed mapping if not exist.
+    if worker_index == 0:
+        if (not os.path.isfile(doc_idx_filename)) or \
+           (not os.path.isfile(sample_idx_filename)) or \
+           (not os.path.isfile(shuffle_idx_filename)):
+            if num_epochs == 1:
+                separate_last_epoch = False
+            else:
+                num_samples_from_epochs_minus_one = (
+                    (num_epochs - 1) * tokens_per_epoch - 1) // seq_length
+                last_epoch_num_samples = num_samples - \
+                                         num_samples_from_epochs_minus_one
+                assert last_epoch_num_samples >= 0, \
+                    'last epoch number of samples should be non-negative.'
+                num_samples_per_epoch = (tokens_per_epoch - 1) // seq_length
+                assert last_epoch_num_samples < (num_samples_per_epoch + 1), \
+                    'last epoch number of samples exceeded max value.'
+                separate_last_epoch = (
+                    last_epoch_num_samples < int(0.80 * num_samples_per_epoch))
+            doc_idx = _build_doc_idx(documents, num_epochs, np_rng,
+                                     separate_last_epoch)
+            np.save(doc_idx_filename, doc_idx, allow_pickle=True)
+            # sample-idx.
+            assert doc_idx.dtype == np.int32
+            sample_idx = _build_sample_idx(sizes, doc_idx, seq_length,
+                                           num_epochs, tokens_per_epoch)
+            np.save(sample_idx_filename, sample_idx, allow_pickle=True)
+            if separate_last_epoch:
+                num_samples_ = num_samples_from_epochs_minus_one
+            else:
+                num_samples_ = sample_idx.shape[0] - 1
+            shuffle_idx = _build_shuffle_idx(num_samples_,
+                                             sample_idx.shape[0] - 1, np_rng)
+            np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True)
+    else:
+        while True:
+            if (not os.path.isfile(doc_idx_filename)) or \
+               (not os.path.isfile(sample_idx_filename)) or \
+               (not os.path.isfile(shuffle_idx_filename)):
+                time.sleep(3)
+            else:
+                break
+
+    # Load mappings.
+    doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode='r')
+    sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode='r')
+    shuffle_idx = np.load(
+        shuffle_idx_filename, allow_pickle=True, mmap_mode='r')
+    return doc_idx, sample_idx, shuffle_idx
+
+
+def _num_tokens(lens):
+    """Total number of tokens in the dataset."""
+    return np.sum(lens)
+
+
+def _num_epochs(tokens_per_epoch, seq_length, num_samples):
+    """Based on number of samples and sequence lenght, calculate how many
+    epochs will be needed."""
+    num_epochs = 0
+    total_tokens = 0
+    while True:
+        num_epochs += 1
+        total_tokens += tokens_per_epoch
+        if ((total_tokens - 1) // seq_length) >= num_samples:
+            return num_epochs
+
+
+def _build_doc_idx(documents, num_epochs, np_rng, separate_last_epoch):
+    """Build an array with length = number-of-epochs * number-of-dcuments.
+    Each index is mapped to a corresponding document."""
+    if not separate_last_epoch or num_epochs == 1:
+        doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1]
+        doc_idx[:] = documents
+        doc_idx = doc_idx.reshape(-1)
+        doc_idx = doc_idx.astype(np.int32)
+        # np_rng.shuffle(doc_idx)
+        return doc_idx
+
+    doc_idx_first = _build_doc_idx(documents, num_epochs - 1, np_rng, False)
+    doc_idx_last = _build_doc_idx(documents, 1, np_rng, False)
+    return np.concatenate((doc_idx_first, doc_idx_last))
+
+
+def _build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch):
+    num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length
+    sample_idx = np.zeros([int(num_samples) + 1, 2], dtype=np.int32)
+
+    sample_index = 0
+    doc_idx_index = 0
+    doc_offset = 0
+    sample_idx[sample_index][0] = doc_idx_index
+    sample_idx[sample_index][1] = doc_offset
+    sample_index += 1
+    while sample_index <= num_samples:
+        remaining_seq_length = seq_length + 1
+        while remaining_seq_length != 0:
+            doc_id = doc_idx[doc_idx_index]
+            doc_length = sizes[doc_id] - doc_offset
+            remaining_seq_length -= doc_length
+            if remaining_seq_length <= 0:
+                doc_offset += (remaining_seq_length + doc_length - 1)
+                remaining_seq_length = 0
+            else:
+                doc_idx_index += 1
+                doc_offset = 0
+        sample_idx[sample_index][0] = doc_idx_index
+        sample_idx[sample_index][1] = doc_offset
+        sample_index += 1
+
+    return sample_idx
+
+
+def _build_shuffle_idx(num_samples, total_size, np_rng):
+    dtype_ = np.uint32
+    if total_size >= (np.iinfo(np.uint32).max - 1):
+        dtype_ = np.int64
+
+    shuffle_idx_first = np.arange(
+        start=0, stop=num_samples, step=1, dtype=dtype_)
+    np_rng.shuffle(shuffle_idx_first)
+    if num_samples == total_size:
+        return shuffle_idx_first
+
+    shuffle_idx_last = np.arange(
+        start=num_samples, stop=total_size, step=1, dtype=dtype_)
+    np_rng.shuffle(shuffle_idx_last)
+
+    return np.concatenate((shuffle_idx_first, shuffle_idx_last))
+
+
+class GPT2Dataset(paddle.io.Dataset):
+    def __init__(self,
+                 file_path,
+                 worker_index,
+                 num_samples,
+                 eod_id,
+                 name="gpt2",
+                 max_seq_len=1024,
+                 mode="train",
+                 seed=1234):
+        self.file_path = file_path
+        self.max_seq_len = max_seq_len
+        self.name = name
+        process_datas = np.load(
+            self.file_path, mmap_mode="r+", allow_pickle=True)
+        self.sample_ids = process_datas["ids"]
+        self.sample_lens = process_datas["lens"]
+        document_ids = np.arange(0, self.sample_lens.shape[0])
+        self.eod_id = eod_id
+        self.doc_idx, self.sample_idx, self.shuffle_idx = \
+            construct_samples_and_shuffle_data(self.name, self.file_path, document_ids,\
+                self.sample_lens, num_samples, max_seq_len, seed, worker_index)
+        self.start_pos = [0] + np.cumsum(self.sample_lens).tolist()
+
+    def _construct_sample(self, tokens):
+        tokens = np.array(tokens).astype("int64").tolist()
+        labels = tokens[1:]
+        tokens = tokens[:-1]
+        seq_length = len(tokens)
+        # attention mask for the attention calulate
+        attention_mask = np.tri(seq_length, seq_length).reshape(
+            (1, seq_length, seq_length))
+
+        # the pad and eod tokens do not contribute the loss
+        loss_mask = np.ones(seq_length, dtype="float32")
+        loss_mask[np.where(np.array(tokens) == self.eod_id)] = 0.0
+        position_ids = np.arange(0, seq_length, dtype="int64")
+
+        # -INF mask value as default
+        attention_mask = (attention_mask - 1.0) * 1e9
+        # Bool mask of attention
+        # attention_mask = attention_mask.astype("float32")
+        return [tokens, loss_mask, attention_mask, position_ids, labels]
+
+    def _get_single_sample_from_idx(self, doc_index_f, doc_index_l, offset_f,
+                                    offset_l):
+        if doc_index_f == doc_index_l:
+            current_start_pos = self.start_pos[doc_index_f]
+            return self.sample_ids[current_start_pos+offset_f:\
+                       current_start_pos+offset_l+1].tolist()
+        elif doc_index_f < doc_index_l:
+            current_start_pos = self.start_pos[doc_index_f]
+            next_start_pos = self.start_pos[doc_index_f + 1]
+            tokens = self.sample_ids[current_start_pos + offset_f:
+                                     next_start_pos].tolist()
+            for i in range(doc_index_f + 1, doc_index_l):
+                current_start_pos = self.start_pos[i]
+                next_start_pos = self.start_pos[i + 1]
+                tokens.extend(self.sample_ids[current_start_pos:next_start_pos]
+                              .tolist())
+            last_start_pos = self.start_pos[doc_index_l]
+            tokens.extend(self.sample_ids[last_start_pos:last_start_pos +
+                                          offset_l + 1].tolist())
+        else:
+            current_start_pos = self.start_pos[doc_index_f]
+            next_start_pos = self.start_pos[-1]
+            tokens = self.sample_ids[current_start_pos + offset_f:
+                                     next_start_pos].tolist()
+            for i in range(0, doc_index_l):
+                current_start_pos = self.start_pos[i]
+                next_start_pos = self.start_pos[i + 1]
+                tokens.extend(self.sample_ids[current_start_pos:next_start_pos]
+                              .tolist())
+            last_start_pos = self.start_pos[doc_index_l]
+            tokens.extend(self.sample_ids[last_start_pos:last_start_pos +
+                                          offset_l + 1].tolist())
+        return tokens
+
+    def __getitem__(self, index):
+        idx = self.shuffle_idx[index]
+        # Start and end documents and offsets.
+        doc_index_f_raw = self.sample_idx[idx][0]
+        doc_index_l_raw = self.sample_idx[idx + 1][0]
+        doc_index_f = self.doc_idx[self.sample_idx[idx][0]]
+        doc_index_l = self.doc_idx[self.sample_idx[idx + 1][0]]
+        offset_f = self.sample_idx[idx][1]
+        offset_l = self.sample_idx[idx + 1][1]
+        tokens = self._get_single_sample_from_idx(doc_index_f, doc_index_l,
+                                                  offset_f, offset_l)
+        token_arr = np.array(tokens, dtype="int64")
+        return self._construct_sample(tokens)
+
+    def __len__(self):
+        return self.sample_idx.shape[0] - 1

PaddleNLP/examples/language_model/gpt2/decompress.sh

+#!/bin/bash
+
+n=0
+maxjobs=2  # 最大进程数
+m=0
+maxfiles=12800  # 每个目录中的最大文件数
+
+for i in $(ls openwebtext); do 
+    echo $i; 
+    if  ((n % $maxfiles == 0)); then
+        ((m=n))
+        mkdir -p raw_data/data_$m
+    fi
+    if  ((++n % $maxjobs == 0)) ; then
+        wait 
+    fi
+    tar xJf openwebtext/$i --warning=no-timestamp -C raw_data/data_$m/ &
+done

PaddleNLP/examples/language_model/gpt2/generate_sample.py

+# 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.
+
+# Many thanks for following projects.
+# https://github.com/TsinghuaAI/CPM-Generate
+# https://github.com/jm12138/CPM-Generate-Paddle
+
+import argparse
+import numpy as np
+
+import paddle
+from paddlenlp.utils.tools import loadz
+from paddlenlp.transformers import GPT2Model, GPT2ForPretraining
+from paddlenlp.transformers import GPT2ChineseTokenizer, GPT2Tokenizer
+from paddlenlp.utils.log import logger
+
+MODEL_CLASSES = {
+    "gpt2-base-cn": (GPT2ForPretraining, GPT2ChineseTokenizer),
+    "gpt2-medium-en": (GPT2ForPretraining, GPT2Tokenizer),
+}
+
+
+class Demo:
+    def __init__(self, model_name_or_path="gpt2-base-cn"):
+        model_class, tokenizer_class = MODEL_CLASSES[model_name_or_path]
+        self.tokenizer = tokenizer_class.from_pretrained(model_name_or_path)
+        logger.info('Loading the model parameters, please wait...')
+        self.model = model_class.from_pretrained(model_name_or_path)
+        self.model.eval()
+        logger.info('Model loaded.')
+
+    # prediction function
+    def predict(self, text, max_len=10):
+        ids = self.tokenizer.encode(text)
+        input_id = paddle.to_tensor(
+            np.array(ids).reshape(1, -1).astype('int64'))
+        output, cached_kvs = self.model(input_id, use_cache=True, cache=None)
+        nid = int(np.argmax(output[0, -1].numpy()))
+        ids.append(nid)
+        out = [nid]
+        for i in range(max_len):
+            input_id = paddle.to_tensor(
+                np.array([nid]).reshape(1, -1).astype('int64'))
+            output, cached_kvs = self.model(
+                input_id, use_cache=True, cache=cached_kvs)
+            nid = int(np.argmax(output[0, -1].numpy()))
+            ids.append(nid)
+            # if nid is '\n', the predicion is over.
+            if nid == 3:
+                break
+            out.append(nid)
+        logger.info(text)
+        logger.info(self.tokenizer.decode(out))
+
+    # One shot example
+    def ask_question(self, question, max_len=10):
+        self.predict("问题：中国的首都是哪里？答案：北京。\n问题：%s 答案：" % question, max_len)
+
+    # dictation poetry
+    def dictation_poetry(self, front, max_len=10):
+        self.predict('''默写古诗: 大漠孤烟直，长河落日圆。\n%s''' % front, max_len)
+
+
+if __name__ == "__main__":
+    demo = Demo("gpt2-base-cn")
+    demo.ask_question("百度的厂长是谁?")
+    demo.dictation_poetry("举杯邀明月，")
+    del demo
+    # demo = Demo("gpt2-medium-en")
+    # demo.predict("Question: Where is the capital of China? Answer: Beijing. \nQuestion: Who is the CEO of Apple? Answer:", 20)

PaddleNLP/examples/language_model/gpt2/lr.py

+# 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=-1,
+                 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)

PaddleNLP/examples/language_model/gpt2/process_data.py

+# 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 os
+import re
+import argparse
+import json
+import multiprocessing
+
+import numpy as np
+from paddlenlp.transformers import GPT2Tokenizer
+from tqdm import tqdm
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input_path', type=str, required=True, help='Path to input JSON')
+    parser.add_argument(
+        '--model_name', type=str, required=True, help='What model to use.')
+    parser.add_argument(
+        '--append_eod',
+        action='store_true',
+        help='Append an <eod> token to the end of a document.')
+    parser.add_argument(
+        '--workers',
+        type=int,
+        default=1,
+        help='Number of worker processes to launch')
+    args = parser.parse_args()
+    return args
+
+
+class Converter(object):
+    def __init__(self, model_name, append_eod):
+        self.append_eod = append_eod
+        tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+        Converter.tokenizer = tokenizer
+        self.eod_id = tokenizer.command_name_map["eod"].Id
+        self.vocab_size = len(tokenizer)
+
+    def encode(self, text):
+        tokens = self.tokenizer.encode(text)
+        if self.append_eod:
+            tokens.append(self.eod_id)
+        return tokens, len(tokens)
+
+
+def main():
+    args = get_args()
+    file_paths = []
+    if os.path.isfile(args.input_path):
+        file_paths.append(args.input_path)
+    else:
+        for root, _, fs in os.walk(args.input_path):
+            for f in fs:
+                file_paths.append(os.path.join(root, f))
+    all_doc_ids = []
+    lens = []
+    convert = Converter(args.model_name, args.append_eod)
+    pool = multiprocessing.Pool(args.workers)
+    if convert.vocab_size < 65500:
+        save_dtype = np.uint16
+    else:
+        save_dtype = np.int32
+
+    for file_path in tqdm(file_paths):
+        text = open(file_path, 'r', encoding='utf-8').read()
+        text = re.sub('[\n]+', '\n', text)
+        text = re.sub('[ ]+', ' ', text)
+        encoded_docs = pool.imap(convert.encode, [text], 25)
+        for tokens, sizes in encoded_docs:
+            all_doc_ids.extend(tokens)
+            lens.append(sizes)
+    all_doc_ids = np.array(all_doc_ids, dtype=save_dtype)
+    lens = np.array(lens, dtype=save_dtype)
+    np.savez(args.input_path + "_ids.npz", ids=all_doc_ids, lens=lens)
+
+
+if __name__ == "__main__":
+    main()

PaddleNLP/examples/language_model/gpt2/run_pretrain.py

+# 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 argparse
+import math
+import os
+import random
+import time
+
+import numpy as np
+
+import paddle
+from paddle.io import DataLoader, Dataset
+
+from paddlenlp.data import Stack, Tuple, Pad
+from paddlenlp.transformers import GPT2Model, GPT2ForPretraining, GPT2PretrainingCriterion
+from paddlenlp.transformers import GPT2Tokenizer
+from paddlenlp.utils.log import logger
+from data import GPT2Dataset
+import lr
+
+MODEL_CLASSES = {
+    "gpt2-small-en": (GPT2ForPretraining, GPT2Tokenizer),
+    "gpt2-medium-en": (GPT2ForPretraining, GPT2Tokenizer),
+    "gpt2-large-en": (GPT2ForPretraining, GPT2Tokenizer),
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name_or_path",
+        default=None,
+        type=str,
+        required=True,
+        help="Path to pre-trained model or shortcut name selected in the list: "
+        + ", ".join(
+            sum([
+                list(classes[-1].pretrained_init_configuration.keys())
+                for classes in MODEL_CLASSES.values()
+            ], [])), )
+    parser.add_argument(
+        "--input_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The input directory where the data will be read from.", )
+    parser.add_argument(
+        "--output_dir",
+        default=None,
+        type=str,
+        required=True,
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--batch_size",
+        default=8,
+        type=int,
+        help="Batch size per GPU/CPU for training.", )
+    parser.add_argument(
+        "--weight_decay",
+        default=0.0,
+        type=float,
+        help="Weight decay if we apply some.")
+    parser.add_argument(
+        "--grad_clip",
+        default=0.0,
+        type=float,
+        help="Grad clip for the parameter.")
+    parser.add_argument(
+        "--adam_epsilon",
+        default=1e-8,
+        type=float,
+        help="Epsilon for Adam optimizer.")
+    parser.add_argument(
+        "--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument(
+        "--num_train_epochs",
+        default=1,
+        type=int,
+        help="Total number of training epochs to perform.", )
+    parser.add_argument(
+        "--max_steps",
+        default=520000,
+        type=int,
+        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
+    )
+    parser.add_argument(
+        "--decay_steps",
+        default=360000,
+        type=int,
+        help="The steps use to control the learing rate. If the step > decay_steps, will use the min_lr.",
+    )
+    parser.add_argument(
+        "--max_lr",
+        default=1e-5,
+        type=float,
+        help="The initial max learning rate for Adam.")
+    parser.add_argument(
+        "--min_lr",
+        default=5e-5,
+        type=float,
+        help="The initial min learning rate for Adam.")
+    parser.add_argument(
+        "--warmup_rate",
+        default=0.01,
+        type=float,
+        help="Linear warmup over warmup_steps.")
+
+    parser.add_argument(
+        "--logging_steps",
+        type=int,
+        default=1,
+        help="Log every X updates steps.")
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save checkpoint every X updates steps.")
+    parser.add_argument(
+        "--seed", type=int, default=42, help="random seed for initialization")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="gpu",
+        help="select cpu, gpu, xpu devices.")
+    args = parser.parse_args()
+    return args
+
+
+class WorkerInitObj(object):
+    def __init__(self, seed):
+        self.seed = seed
+
+    def __call__(self, id):
+        np.random.seed(seed=self.seed + id)
+        random.seed(self.seed + id)
+
+
+def create_pretrained_dataset(args, input_path, worker_init, worker_index,
+                              eod_id):
+    train_data = GPT2Dataset(
+        file_path=input_path,
+        worker_index=worker_index,
+        num_samples=args.batch_size * args.max_steps,
+        eod_id=eod_id,
+        seed=args.seed + worker_index)
+    train_batch_sampler = paddle.io.DistributedBatchSampler(
+        train_data, batch_size=args.batch_size, shuffle=True, drop_last=True)
+
+    train_data_loader = DataLoader(
+        dataset=train_data,
+        batch_sampler=train_batch_sampler,
+        num_workers=0,
+        worker_init_fn=worker_init,
+        collate_fn=Tuple(Stack(), Stack(), Stack(), Stack(), Stack()))
+    return train_data_loader
+
+
+def set_seed(args):
+    if args.device == "cpu":
+        idx = 0
+    else:
+        idx = paddle.distributed.get_rank()
+    random.seed(args.seed + idx)
+    np.random.seed(args.seed + idx)
+    paddle.seed(args.seed + idx)
+
+
+def do_train(args):
+    assert args.device in [
+        "cpu", "gpu", "xpu"
+    ], "Invalid device! Available device should be cpu, gpu, or xpu."
+    paddle.set_device(args.device)
+    if paddle.distributed.get_world_size() > 1:
+        paddle.distributed.init_parallel_env()
+
+    worker_index = paddle.distributed.get_rank()
+    worker_num = paddle.distributed.get_world_size()
+    set_seed(args)
+    worker_init = WorkerInitObj(args.seed + paddle.distributed.get_rank())
+    model_class, tokenizer_class = MODEL_CLASSES[args.model_name_or_path]
+    tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
+    eod_id = tokenizer.command_name_map["eod"].Id
+
+    model = GPT2ForPretraining(
+        GPT2Model(**model_class.pretrained_init_configuration[
+            args.model_name_or_path]))
+    # creat the critrion for the gpt model
+    criterion = GPT2PretrainingCriterion()
+
+    if args.decay_steps is None:
+        args.decay_steps = args.max_steps
+    warmup_step = args.warmup_rate * args.decay_steps
+    lr_scheduler = lr.CosineAnnealingWithWarmupDecay(
+        max_lr=args.max_lr,
+        min_lr=args.min_lr,
+        warmup_step=warmup_step,
+        decay_step=args.decay_steps)
+
+    clip = None
+    if args.grad_clip > 0:
+        clip = paddle.nn.ClipGradByNorm(clip_norm=args.grad_clip)
+
+    optimizer = paddle.optimizer.AdamW(
+        learning_rate=lr_scheduler,
+        epsilon=args.adam_epsilon,
+        parameters=model.parameters(),
+        weight_decay=args.weight_decay,
+        grad_clip=clip,
+        apply_decay_param_fun=lambda x: x in [
+            p.name for n, p in model.named_parameters()
+            if not any(nd in n for nd in ["bias", "norm"])
+        ])
+
+    global_step = 0
+    tic_train = time.time()
+    for epoch in range(args.num_train_epochs):
+        files = [
+            os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
+            if (os.path.isfile(os.path.join(args.input_dir, f)) and "npz_"
+                not in str(f))
+        ]
+        files.sort()
+        num_files = len(files)
+        for f_id in range(num_files):
+            data_file = files[f_id]
+            train_data_loader = create_pretrained_dataset(
+                args, data_file, worker_init, worker_index, eod_id=eod_id)
+            for step, batch in enumerate(train_data_loader):
+                global_step += 1
+                tokens, loss_mask, attention_mask, position_ids, labels = batch
+
+                loss_mask.stop_gradient = True
+                attention_mask.stop_gradient = True
+
+                preds = model(tokens, position_ids, attention_mask)
+                loss = criterion(preds, labels, loss_mask)
+
+                if global_step % args.logging_steps == 0:
+                    if worker_index == 0:
+                        logger.info(
+                            "global step %d, epoch: %d, lr: %.10f, batch: %d, loss: %f, speed: %.2f step/s"
+                            % (global_step, epoch, optimizer.get_lr(), step,
+                               loss,
+                               args.logging_steps / (time.time() - tic_train)))
+                    tic_train = time.time()
+                loss.backward()
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.clear_gradients()
+                if global_step % args.save_steps == 0:
+                    if worker_index == 0:
+                        output_dir = os.path.join(args.output_dir,
+                                                  "model_%d" % global_step)
+                        if not os.path.exists(output_dir):
+                            os.makedirs(output_dir)
+                        # need better way to get inner model of DataParallel
+                        model_to_save = model._layers if isinstance(
+                            model, paddle.DataParallel) else model
+                        model_to_save.save_pretrained(output_dir)
+                if global_step >= args.max_steps:
+                    del train_data_loader
+                    return
+            del train_data_loader
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    do_train(args)

PaddleNLP/examples/language_model/gpt2/scripts/run.sh

+export CUDA_VISIBLE_DEVICES=0
+python run_pretrain.py --model_name_or_path gpt2-small-en --input_dir "./data"\
+    --output_dir "output"\
+    --max_lr 0.00015\
+    --min_lr 0.00001\
+    --weight_decay 0.01\
+    --grad_clip 1.0\
+    --max_steps 500000\
+    --save_steps 100000\
+    --decay_steps 320000\
+    --warmup_rate 0.01\
+    --batch_size 8\
+    --device gpu

PaddleNLP/examples/language_model/gpt2/scripts/run_multi.sh

+unset CUDA_VISIBLE_DEVICES
+python -m paddle.distributed.launch --gpus "0,1" run_pretrain.py --model_name_or_path gpt2-small-en --input_dir "./data"\
+    --output_dir "output"\
+    --max_lr 0.00015\
+    --min_lr 0.00001\
+    --weight_decay 0.01\
+    --grad_clip 1.0\
+    --max_steps 500000\
+    --save_steps 100000\
+    --decay_steps 320000\
+    --warmup_rate 0.01\
+    --batch_size 8\
+    --device gpu

PaddleNLP/paddlenlp/transformers/__init__.py

@@ -19,6 +19,8 @@ from .bert.modeling import *
 from .bert.tokenizer import *
 from .ernie.modeling import *
 from .ernie.tokenizer import *
+from .gpt2.modeling import *
+from .gpt2.tokenizer import *
 from .roberta.modeling import *
 from .roberta.tokenizer import *
 from .electra.modeling import *

PaddleNLP/paddlenlp/transformers/gpt2/__init__.py

+from .modeling import *
+from .tokenizer import GPT2ChineseTokenizer

PaddleNLP/paddlenlp/transformers/gpt2/modeling.py

+# 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 collections
+import numpy as np
+import paddle
+import paddle.nn as nn
+import paddle.tensor as tensor
+import paddle.nn.functional as F
+import math
+from paddle.fluid import layers
+
+from .. import PretrainedModel, register_base_model
+from paddle.nn.layer.transformer import _convert_param_attr_to_list
+
+__all__ = [
+    'GPT2Model',
+    "GPT2PretrainedModel",
+    'GPT2ForPretraining',
+    'GPT2PretrainingCriterion',
+]
+
+
+class MultiHeadAttention(nn.Layer):
+    """
+    Attention mapps queries and a set of key-value pairs to outputs, and
+    Multi-Head Attention performs multiple parallel attention to jointly attending
+    to information from different representation subspaces.
+
+    """
+
+    Cache = collections.namedtuple("Cache", ["k", "v"])
+    StaticCache = collections.namedtuple("StaticCache", ["k", "v"])
+
+    def __init__(self,
+                 embed_dim,
+                 num_heads,
+                 dropout=0.,
+                 kdim=None,
+                 vdim=None,
+                 need_weights=False,
+                 weight_attr=None,
+                 bias_attr=None):
+        super(MultiHeadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.need_weights = need_weights
+
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.q_proj = nn.Linear(
+            embed_dim, embed_dim, weight_attr, bias_attr=bias_attr)
+        self.k_proj = nn.Linear(
+            self.kdim, embed_dim, weight_attr, bias_attr=bias_attr)
+        self.v_proj = nn.Linear(
+            self.vdim, embed_dim, weight_attr, bias_attr=bias_attr)
+        self.out_proj = nn.Linear(
+            embed_dim, embed_dim, weight_attr, bias_attr=bias_attr)
+
+    def _prepare_qkv(self, query, key, value, use_cache=False, cache=None):
+        r"""
+        Prapares linear projected queries, keys and values for usage of subsequnt
+        multiple parallel attention. If `cache` is not None, using cached results
+        to reduce redundant calculations.
+
+        """
+
+        q = self.q_proj(query)
+        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
+        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])
+
+        if isinstance(cache, self.StaticCache):
+            # for encoder-decoder attention in inference and has cached
+            k, v = cache.k, cache.v
+        else:
+            k, v = self.compute_kv(key, value)
+
+        if isinstance(cache, self.Cache):
+            # for decoder self-attention in inference
+            k = tensor.concat([cache.k, k], axis=2)
+            v = tensor.concat([cache.v, v], axis=2)
+        if use_cache is True:
+            cache = self.Cache(k, v)
+
+        return (q, k, v) if use_cache is False else (q, k, v, cache)
+
+    def compute_kv(self, key, value):
+        r"""
+        Applies linear projection on input keys and values, then splits heads
+        (reshape and transpose) to get keys and values from different representation
+        subspaces. The results are used as key-values pairs for subsequent multiple
+        parallel attention.
+
+        It is part of calculations in multi-head attention, and is provided as
+        a method to pre-compute and prefetch these results, thus we can use them
+        to construct cache for inference.
+
+        """
+        k = self.k_proj(key)
+        v = self.v_proj(value)
+        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
+        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
+        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
+        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
+        return k, v
+
+    def gen_cache(self, key, value=None, type=Cache):
+        """
+        Generates cache for `forward` usage in inference accroding to arguments.
+        The generated cache is an instance of `MultiHeadAttention.Cache` or an
+        instance of `MultiHeadAttention.StaticCache`.
+        """
+        if type == MultiHeadAttention.StaticCache:  # static_kv
+            k, v = self.compute_kv(key, value)
+            return self.StaticCache(k, v)
+        elif value is None:  # incremental_state
+            k = layers.fill_constant_batch_size_like(
+                input=key,
+                shape=[-1, self.num_heads, 0, self.head_dim],
+                dtype=key.dtype,
+                value=0)
+            v = layers.fill_constant_batch_size_like(
+                input=key,
+                shape=[-1, self.num_heads, 0, self.head_dim],
+                dtype=key.dtype,
+                value=0)
+            return self.Cache(k, v)
+        else:
+            # incremental_state with initial value, mainly for usage like UniLM
+            return self.Cache(key, value)
+
+    def forward(self,
+                query,
+                key,
+                value,
+                attn_mask=None,
+                use_cache=False,
+                cache=None):
+        r"""
+        Applies multi-head attention to map queries and a set of key-value pairs
+        to outputs.
+        """
+        key = query if key is None else key
+        value = query if value is None else value
+        # compute q ,k ,v
+        if use_cache is False:
+            q, k, v = self._prepare_qkv(query, key, value, use_cache, cache)
+        else:
+            q, k, v, cache = self._prepare_qkv(query, key, value, use_cache,
+                                               cache)
+        # scale dot product attention
+        product = layers.matmul(
+            x=q, y=k, transpose_y=True, alpha=self.head_dim**-0.5)
+        if attn_mask is not None:
+            product = product + attn_mask
+        weights = F.softmax(product)
+        if self.dropout:
+            weights = F.dropout(
+                weights,
+                self.dropout,
+                training=self.training,
+                mode="upscale_in_train")
+
+        out = tensor.matmul(weights, v)
+
+        # combine heads
+        out = tensor.transpose(out, perm=[0, 2, 1, 3])
+        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
+
+        # project to output
+        out = self.out_proj(out)
+
+        outs = [out]
+        if self.need_weights:
+            outs.append(weights)
+        if use_cache:
+            outs.append(cache)
+        return out if len(outs) == 1 else tuple(outs)
+
+
+class TransformerDecoder(nn.Layer):
+    """
+    TransformerDecoder is a stack of N decoder layers.
+    """
+
+    def __init__(self, decoder_layer, num_layers, norm=None):
+        super(TransformerDecoder, self).__init__()
+        self.layers = nn.LayerList([(
+            decoder_layer
+            if i == 0 else type(decoder_layer)(**decoder_layer._config))
+                                    for i in range(num_layers)])
+        self.num_layers = num_layers
+        self.norm = norm
+        self.checkpoints = []
+
+    def forward(self,
+                tgt,
+                memory,
+                tgt_mask=None,
+                memory_mask=None,
+                use_cache=False,
+                cache=None):
+        r"""
+        Applies a stack of N Transformer decoder layers on inputs. If `norm` is
+        provided, also applies layer normalization on the output of last decoder
+        layer.
+        """
+        output = tgt
+        new_caches = []
+        self.checkpoints = []
+        for i, mod in enumerate(self.layers):
+            if cache is None:
+                if use_cache:
+                    output, new_cache = mod(output,
+                                            memory,
+                                            tgt_mask=tgt_mask,
+                                            use_cache=use_cache,
+                                            cache=cache)
+                    new_caches.append(new_cache)
+                else:
+                    output = mod(output,
+                                 memory,
+                                 tgt_mask=tgt_mask,
+                                 use_cache=use_cache,
+                                 cache=cache)
+
+            else:
+                output, new_cache = mod(output,
+                                        memory,
+                                        tgt_mask=tgt_mask,
+                                        use_cache=use_cache,
+                                        cache=cache[i])
+                new_caches.append(new_cache)
+            self.checkpoints.append(output.name)
+
+        if self.norm is not None:
+            output = self.norm(output)
+        return output if use_cache is False else (output, new_caches)
+
+    def gen_cache(self, memory, do_zip=False):
+        r"""
+        Generates cache for `forward` usage. The generated cache is a list, and
+        each element in it is a tuple( :code:`(incremental_cache, static_cache)` )
+        produced by `TransformerDecoderLayer.gen_cache`. See `TransformerDecoderLayer.gen_cache`
+        for more details. If `do_zip` is True, apply `zip` on these tuples to get
+        a list with two elements.
+       """
+        cache = [layer.gen_cache(memory) for layer in self.layers]
+        if do_zip:
+            cache = list(zip(*cache))
+        return cache
+
+
+class TransformerDecoderLayer(nn.Layer):
+    """
+    The transformer decoder layer.
+
+    It contains multiheadattention and some linear layers.
+    """
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 dim_feedforward,
+                 dropout=0.1,
+                 activation="gelu",
+                 attn_dropout=None,
+                 act_dropout=None,
+                 normalize_before=True,
+                 weight_attr=None,
+                 bias_attr=None):
+        self._config = locals()
+        self._config.pop("self")
+        self._config.pop("__class__", None)  # py3
+
+        super(TransformerDecoderLayer, self).__init__()
+        attn_dropout = dropout if attn_dropout is None else attn_dropout
+        act_dropout = dropout if act_dropout is None else act_dropout
+        self.normalize_before = normalize_before
+
+        weight_attrs = _convert_param_attr_to_list(weight_attr, 3)
+        bias_attrs = _convert_param_attr_to_list(bias_attr, 3)
+
+        self.self_attn = MultiHeadAttention(
+            d_model,
+            nhead,
+            dropout=attn_dropout,
+            weight_attr=weight_attrs[0],
+            bias_attr=bias_attrs[0])
+        self.linear1 = nn.Linear(
+            d_model, dim_feedforward, weight_attrs[2], bias_attr=bias_attrs[2])
+        #self.dropout1 = nn.Dropout(act_dropout, mode="upscale_in_train")
+        self.linear2 = nn.Linear(
+            dim_feedforward, d_model, weight_attrs[2], bias_attr=bias_attrs[2])
+        self.norm1 = nn.LayerNorm(d_model, epsilon=1e-5)
+        self.norm2 = nn.LayerNorm(d_model, epsilon=1e-5)
+        self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
+        self.dropout2 = nn.Dropout(act_dropout, mode="upscale_in_train")
+        self.activation = getattr(F, activation)
+
+    def forward(self, tgt, memory, tgt_mask=None, use_cache=False, cache=None):
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm1(tgt)
+
+        if use_cache is False:
+            tgt = self.self_attn(tgt, tgt, tgt, tgt_mask, use_cache, cache)
+        else:
+            tgt, incremental_cache = self.self_attn(tgt, tgt, tgt, tgt_mask,
+                                                    use_cache, cache)
+        tgt = residual + self.dropout1(tgt)
+        if not self.normalize_before:
+            tgt = self.norm1(tgt)
+
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm2(tgt)
+        tgt = self.dropout2(
+            self.linear2(F.gelu(
+                self.linear1(tgt), approximate=True)))
+        tgt = residual + tgt
+
+        if not self.normalize_before:
+            tgt = self.norm2(tgt)
+
+        return tgt if use_cache is False else (tgt, incremental_cache)
+
+    def gen_cache(self, memory):
+        incremental_cache = self.self_attn.gen_cache(
+            memory, type=self.self_attn.Cache)
+        return incremental_cache
+
+
+class GPT2Embeddings(nn.Layer):
+    """
+    Include embeddings from word, position and token_type embeddings
+    """
+
+    def __init__(self,
+                 vocab_size,
+                 hidden_size=768,
+                 hidden_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=16,
+                 initializer_range=0.02):
+        super(GPT2Embeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(
+            vocab_size,
+            hidden_size,
+            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
+                mean=0.0, std=initializer_range)))
+        self.position_embeddings = nn.Embedding(
+            max_position_embeddings,
+            hidden_size,
+            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
+                mean=0.0, std=initializer_range)))
+        self.dropout = nn.Dropout(hidden_dropout_prob)
+
+    def forward(self, input_ids, position_ids=None):
+        if position_ids is None:
+            ones = paddle.ones_like(input_ids, dtype="int64")
+            seq_length = paddle.cumsum(ones, axis=1)
+            position_ids = seq_length - ones
+        input_embedings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+
+        embeddings = input_embedings + position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class GPT2PretrainedModel(PretrainedModel):
+    """
+    An abstract class for pretrained GPT2 models. It provides GPT2 related
+    `model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
+    `pretrained_init_configuration`, `base_model_prefix` for downloading and
+    loading pretrained models. See `PretrainedModel` for more details.
+    """
+
+    model_config_file = "model_config.json"
+    pretrained_init_configuration = {
+        "gpt2-base-cn": {
+            "vocab_size": 30000,
+            "hidden_size": 2560,
+            "num_hidden_layers": 32,
+            "num_attention_heads": 32,
+            "intermediate_size": 10240,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 1024,
+            "type_vocab_size": 1,  # no use
+            "initializer_range": 0.02,
+            "pad_token_id": 0,
+        },
+        "gpt2-large-en": {
+            "vocab_size": 50304,
+            "hidden_size": 4096,
+            "num_hidden_layers": 50,
+            "num_attention_heads": 32,
+            "intermediate_size": 16384,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 1024,
+            "type_vocab_size": 1,  # no use
+            "initializer_range": 0.02,
+        },
+        "gpt2-medium-en": {
+            "vocab_size": 50304,
+            "hidden_size": 1024,
+            "num_hidden_layers": 24,
+            "num_attention_heads": 16,
+            "intermediate_size": 4096,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 1024,
+            "type_vocab_size": 1,  # no use
+            "initializer_range": 0.02,
+        },
+        "gpt2-small-en": {
+            "vocab_size": 50304,
+            "hidden_size": 1024,
+            "num_hidden_layers": 4,
+            "num_attention_heads": 4,
+            "intermediate_size": 4096,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 1024,
+            "type_vocab_size": 1,  # no use
+            "initializer_range": 0.02,
+        },
+    }
+    resource_files_names = {"model_state": "model_state.pdparams"}
+    pretrained_resource_files_map = {
+        "model_state": {
+            "gpt2-base-cn":
+            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn.pdparams",
+            "gpt2-medium-en":
+            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en.pdparams",
+        }
+    }
+    base_model_prefix = "gpt2"
+
+    def init_weights(self, layer):
+        """ Initialization hook """
+        if isinstance(layer, (nn.Linear, nn.Embedding)):
+            # In the dygraph mode, use the `set_value` to reset the parameter directly,
+            # and reset the `state_dict` to update parameter in static mode.
+            if isinstance(layer.weight, paddle.Tensor):
+                layer.weight.set_value(
+                    paddle.tensor.normal(
+                        mean=0.0,
+                        std=self.initializer_range
+                        if hasattr(self, "initializer_range") else
+                        self.gpt2.config["initializer_range"],
+                        shape=layer.weight.shape))
+
+
+@register_base_model
+class GPT2Model(GPT2PretrainedModel):
+    """
+    The base model of gpt2.
+    """
+
+    def __init__(self,
+                 vocab_size,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=16,
+                 initializer_range=0.02,
+                 pad_token_id=0):
+        super(GPT2Model, self).__init__()
+        self.pad_token_id = pad_token_id
+        self.initializer_range = initializer_range
+        self.embeddings = GPT2Embeddings(
+            vocab_size, hidden_size, hidden_dropout_prob,
+            max_position_embeddings, type_vocab_size, self.initializer_range)
+        decoder_layer = TransformerDecoderLayer(
+            d_model=hidden_size,
+            nhead=num_attention_heads,
+            dim_feedforward=intermediate_size,
+            dropout=hidden_dropout_prob,
+            activation=hidden_act,
+            attn_dropout=attention_probs_dropout_prob,
+            act_dropout=0,
+            weight_attr=paddle.ParamAttr(initializer=nn.initializer.Normal(
+                mean=0.0, std=self.initializer_range)),
+            bias_attr=None)
+        self.decoder = TransformerDecoder(
+            decoder_layer, num_hidden_layers, norm=nn.LayerNorm(hidden_size))
+        self.apply(self.init_weights)
+        self.checkpoints = []
+
+    def forward(self,
+                input_ids,
+                position_ids=None,
+                attention_mask=None,
+                use_cache=False,
+                cache=None):
+        self.checkpoints = []
+        if attention_mask is None:
+            length = input_ids.shape[1]
+            attention_mask = paddle.tensor.triu(
+                (paddle.ones(
+                    (length, length),
+                    dtype=self.embeddings.word_embeddings.weight.dtype) * -1e9),
+                1)
+        if position_ids is None:
+            past_length = 0
+            if cache is not None:
+                past_length = cache[0].k.shape[-2]
+            position_ids = paddle.arange(
+                past_length, input_ids.shape[-1] + past_length, dtype='int64')
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids, position_ids=position_ids)
+        encoder_outputs = self.decoder(
+            embedding_output,
+            memory=None,
+            tgt_mask=attention_mask,
+            use_cache=use_cache,
+            cache=cache)
+        self.checkpoints.extend(self.decoder.checkpoints)
+        return encoder_outputs
+
+
+class GPT2ForPretraining(GPT2PretrainedModel):
+    """
+    The pretraining model of GPT2.
+
+    It returns some logits and cached_kvs.
+    """
+
+    def __init__(self, gpt2):
+        super(GPT2ForPretraining, self).__init__()
+        self.gpt2 = gpt2
+        self.apply(self.init_weights)
+
+    def forward(self,
+                input_ids,
+                position_ids=None,
+                attention_mask=None,
+                masked_positions=None,
+                use_cache=False,
+                cache=None):
+        outputs = self.gpt2(
+            input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            use_cache=use_cache,
+            cache=cache)
+        if use_cache:
+            encoder_outputs, cached_kvs = outputs[:2]
+        else:
+            encoder_outputs = outputs
+        logits = paddle.matmul(
+            encoder_outputs,
+            self.gpt2.embeddings.word_embeddings.weight,
+            transpose_y=True)
+
+        if use_cache:
+            return logits, cached_kvs
+        else:
+            return logits
+
+
+class GPT2PretrainingCriterion(paddle.nn.Layer):
+    """
+    Criterion for GPT2.
+
+    It calculates the final loss.
+    """
+
+    def __init__(self):
+        super(GPT2PretrainingCriterion, self).__init__()
+        self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none")
+
+    def forward(self, prediction_scores, masked_lm_labels, loss_mask):
+        masked_lm_loss = self.loss_func(prediction_scores,
+                                        masked_lm_labels.unsqueeze(2))
+        loss_mask = loss_mask.reshape([-1])
+        masked_lm_loss = paddle.sum(masked_lm_loss.reshape([-1]) * loss_mask)
+        loss = masked_lm_loss / loss_mask.sum()
+        return loss

PaddleNLP/paddlenlp/transformers/gpt2/tokenizer.py

+# 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 os
+import regex as re
+import unicodedata
+import json
+import sentencepiece
+import jieba
+
+from functools import lru_cache
+from collections import namedtuple
+from .. import PretrainedTokenizer
+from ..tokenizer_utils import convert_to_unicode, whitespace_tokenize,\
+    _is_whitespace, _is_control, _is_punctuation
+
+__all__ = [
+    'GPT2Tokenizer',
+    'GPT2ChineseTokenizer',
+]
+
+COMMAND_TUPLE = namedtuple('CommandToken', ('name', 'token', 'Id'))
+TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id'))
+
+
+class CommandToken(object):
+    def __init__(self, name, token, Id):
+        self.name = name
+        self.token = token
+        self.Id = Id
+
+    def __str__(self):
+        return str(COMMAND_TUPLE(self.name, self.token, self.Id))
+
+
+@lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    _chr = chr
+    bs = list(range(ord("!"), ord("~") + 1)) + list(
+        range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [_chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+class GPT2ChineseTokenizer(PretrainedTokenizer):
+    """
+    Constructs a GPT2 Chinese tokenizer. It uses a basic tokenizer to do punctuation
+    splitting, lower casing and so on, and follows a WordPiece tokenizer to
+    tokenize as subwords.
+    """
+    resource_files_names = {
+        "vocab_file": "vocab.json",
+        "model_file": "sentencepiece.model"
+    }  # for save_pretrained
+    pretrained_resource_files_map = {
+        "vocab_file": {
+            "gpt2-base-cn":
+            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn-vocab.json",
+        },
+        "model_file": {
+            "gpt2-base-cn":
+            "https://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-base-cn-sentencepiece.model"
+        }
+    }
+    pretrained_init_configuration = {"gpt2-base-cn": {"do_lower_case": True}, }
+
+    def __init__(self,
+                 vocab_file,
+                 model_file,
+                 do_lower_case=True,
+                 max_len=512,
+                 bod_id="<bod>",
+                 eod_id="<eod>",
+                 max_length=None):
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                "Can't find a vocabulary file at path '{}'. To load the "
+                "vocabulary from a pretrained model please use "
+                "`tokenizer = GPT2Tokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+                .format(vocab_file))
+        self.max_len = max_len if max_len is not None else int(1e12)
+        self.encoder = json.load(open(vocab_file))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.sp = sentencepiece.SentencePieceProcessor(model_file=model_file)
+        self.translator = str.maketrans(" \n", "\u2582\u2583")
+
+    def tokenize(self, text):
+        """ Tokenize a string. """
+        seg_list = [
+            x.translate(self.translator) for x in jieba.cut(text, cut_all=False)
+        ]
+        new_seg = " ".join(seg_list)
+        return self.sp.encode(new_seg)
+
+    def encode(self, text):
+        return self.convert_tokens_to_ids(text)
+
+    def decode(self, tokens):
+        return self.convert_ids_to_tokens(tokens)
+
+    def convert_tokens_to_ids(self, text):
+        res = self.tokenize(text)
+        return res
+
+    def convert_ids_to_tokens(self, tokens):
+        text = self.sp.decode(tokens)
+        text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583',
+                                                                    '\n')
+        return text
+
+
+class GPT2Tokenizer(PretrainedTokenizer):
+    resource_files_names = {
+        "vocab_file": "vocab.json",
+        "merges_file": "merges.txt"
+    }  # for save_pretrained
+    pretrained_resource_files_map = {
+        "vocab_file": {
+            "gpt2-large-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-large-en-vocab.json",
+            "gpt2-medium-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en-vocab.json",
+            "gpt2-small-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-small-en-vocab.json",
+        },
+        "merges_file": {
+            "gpt2-large-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-large-en-merges.txt",
+            "gpt2-medium-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-medium-en-merges.txt",
+            "gpt2-small-en":
+            "http://paddlenlp.bj.bcebos.com/models/transformers/gpt2/gpt2-small-en-merges.txt",
+        }
+    }
+    pretrained_init_configuration = {
+        "gpt2-large-en": {
+            "do_lower_case": True
+        },
+        "gpt2-medium-en": {
+            "do_lower_case": True
+        },
+        "gpt2-small-en": {
+            "do_lower_case": True
+        },
+    }
+
+    def __init__(self,
+                 vocab_file,
+                 merges_file,
+                 errors='replace',
+                 special_tokens=None,
+                 max_len=None,
+                 do_lower_case=True):
+        self.max_len = int(1e12)
+        self.num_command_tokens = 2
+        self.num_type_tokens = 2
+
+        self.encoder = json.load(open(vocab_file))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+
+        # construct the command tokens
+        self._command_tokens = [
+            CommandToken('pad', '<|endoftext|>', self.encoder['<|endoftext|>']),
+            CommandToken('eod', '<|endoftext|>', self.encoder['<|endoftext|>']),
+        ]
+        self.command_name_map = {tok.name: tok for tok in self._command_tokens}
+        self.command_token_map = {
+            tok.token: tok
+            for tok in self._command_tokens
+        }
+        self.command_id_map = {tok.Id: tok for tok in self._command_tokens}
+
+        self.num_tokens = len(self.encoder)
+        self.num_text_tokens = self.num_tokens - 1
+        self.errors = errors  # how to handle errors in decoding
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
+        bpe_merges = [tuple(merge.split()) for merge in bpe_data]
+        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
+        self.cache = {}
+
+        self.pat = re.compile(
+            r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
+        )
+
+        self.special_tokens = {}
+        self.special_tokens_decoder = {}
+        self.set_special_tokens(special_tokens)
+
+    def __len__(self):
+        return len(self.encoder) + len(self.special_tokens)
+
+    def set_special_tokens(self, special_tokens):
+        """ Add a list of additional tokens to the encoder.
+            The additional tokens are indexed starting from the last index of the
+            current vocabulary in the order of the `special_tokens` list.
+        """
+        if not special_tokens:
+            self.special_tokens = {}
+            self.special_tokens_decoder = {}
+            return
+        self.special_tokens = dict((tok, len(self.encoder) + i)
+                                   for i, tok in enumerate(special_tokens))
+        self.special_tokens_decoder = {
+            v: k
+            for k, v in self.special_tokens.items()
+        }
+        logger.info("Special tokens {}".format(self.special_tokens))
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token
+
+        while True:
+            bigram = min(
+                pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i +
+                                                                   1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = ' '.join(word)
+        self.cache[token] = word
+        return word
+
+    def tokenize(self, text):
+        """ Tokenize a string. """
+        bpe_tokens = []
+        for token in re.findall(self.pat, text):
+            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+            bpe_tokens.extend(
+                bpe_token for bpe_token in self.bpe(token).split(' '))
+        return bpe_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """ Converts a sequence of tokens into ids using the vocab. """
+        ids = []
+        if isinstance(tokens, str):
+            if tokens in self.special_tokens:
+                return self.special_tokens[tokens]
+            else:
+                return self.encoder.get(tokens, 0)
+        for token in tokens:
+            if token in self.special_tokens:
+                ids.append(self.special_tokens[token])
+            else:
+                ids.append(self.encoder.get(token, 0))
+        if len(ids) > self.max_len:
+            logger.warning(
+                "Token indices sequence length is longer than the specified maximum "
+                " sequence length for this OpenAI GPT model ({} > {}). Running this"
+                " sequence through the model will result in indexing errors".
+                format(len(ids), self.max_len))
+        return ids
+
+    def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
+        tokens = []
+        for i in ids:
+            if i in self.special_tokens_decoder:
+                if not skip_special_tokens:
+                    tokens.append(self.special_tokens_decoder[i])
+            else:
+                tokens.append(self.decoder[i])
+        return tokens
+
+    def encode(self, text, fn=None):
+        processed_text = text
+        if fn is not None:
+            processed_text = fn(text)
+        ids = self.convert_tokens_to_ids(self.tokenize(processed_text))
+        return ids
+
+    def decode(self, tokens):
+        # TODO
+        text = ''.join([self.decoder[token] for token in tokens])
+        text = bytearray([self.byte_decoder[c] for c in text]).decode(
+            'utf-8', errors=self.errors)
+        return text