From 7eec63e93b277cf210bbd3d3fc715da6da6176af Mon Sep 17 00:00:00 2001 From: TCChenlong <1300851984@qq.com> Date: Mon, 6 Dec 2021 20:34:05 +0800 Subject: [PATCH] update to 2.2.1 --- .../cv/convnet_image_classification.ipynb | 6 +- docs/practices/cv/image_classification.ipynb | 6 +- docs/practices/cv/image_ocr.ipynb | 6 +- docs/practices/cv/image_search.ipynb | 6 +- docs/practices/cv/image_segmentation.ipynb | 4 +- docs/practices/cv/landmark_detection.ipynb | 4 +- docs/practices/cv/pointnet.ipynb | 8 +- .../cv/super_resolution_sub_pixel.ipynb | 4 +- .../practices/jit/image_search_with_jit.ipynb | 4 +- docs/practices/nlp/addition_rnn.ipynb | 6 +- .../nlp/imdb_bow_classification.ipynb | 6 +- docs/practices/nlp/n_gram_model.ipynb | 6 +- .../nlp/pretrained_word_embeddings.ipynb | 6 +- .../nlp/seq2seq_with_attention.ipynb | 6 +- .../practices/quick_start/dynamic_graph.ipynb | 6 +- docs/practices/quick_start/hello_paddle.ipynb | 2 +- .../quick_start/high_level_api.ipynb | 6 +- .../quick_start/linear_regression.ipynb | 6 +- docs/practices/quick_start/save_model.ipynb | 8 +- .../collaborative_filtering.ipynb | 6 +- .../actor_critic_method.ipynb | 6 +- .../advantage_actor_critic.ipynb | 4 +- .../deep_deterministic_policy_gradient.ipynb | 4 +- docs/practices/time_series/autoencoder.ipynb | 8 +- docs/release_note_cn.md | 99 ++++++++++++++++- docs/release_note_en.md | 104 +++++++++++++++++- 26 files changed, 267 insertions(+), 70 deletions(-) diff --git a/docs/practices/cv/convnet_image_classification.ipynb b/docs/practices/cv/convnet_image_classification.ipynb index 96a1ae88778..0efd4f52bc9 100644 --- a/docs/practices/cv/convnet_image_classification.ipynb +++ b/docs/practices/cv/convnet_image_classification.ipynb @@ -9,7 +9,7 @@ "# 使用卷积神经网络进行图像分类\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程将会演示如何使用飞桨的卷积神经网络来完成图像分类任务。这是一个较为简单的示例，将会使用一个由三个卷积层组成的网络完成[cifar10](https://www.cs.toronto.edu/~kriz/cifar.html)数据集的图像分类任务。" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0 。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2 。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/cv/image_classification.ipynb b/docs/practices/cv/image_classification.ipynb index 479ab7dfec0..ac57d813890 100644 --- a/docs/practices/cv/image_classification.ipynb +++ b/docs/practices/cv/image_classification.ipynb @@ -9,7 +9,7 @@ "# 使用LeNet在MNIST数据集实现图像分类\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0 。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/cv/image_ocr.ipynb b/docs/practices/cv/image_ocr.ipynb index d3b9c516c16..c0e2f6049cb 100644 --- a/docs/practices/cv/image_ocr.ipynb +++ b/docs/practices/cv/image_ocr.ipynb @@ -10,7 +10,7 @@ "\n", "**作者:** [GT_老张](https://github.com/GT-ZhangAcer) \n", "\n", - "**时间:** 2021.11\n", + "**时间:** 2021.12\n", "\n", "**摘要:** 本篇将介绍如何通过飞桨实现简单的CRNN+CTC自定义数据集OCR识别模型，数据集采用[CaptchaDataset](https://github.com/GT-ZhangAcer/CaptchaDataset)中OCR部分的9453张图像，其中前8453张图像在本案例中作为训练集，后1000张则作为测试集。 \n", "在更复杂的场景中推荐使用[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)产出工业级模型，模型轻量且精度大幅提升。 \n", @@ -25,7 +25,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.2 。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) PaddlePaddle 2.2 。" ] }, { @@ -39,7 +39,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/cv/image_search.ipynb b/docs/practices/cv/image_search.ipynb index 6ec764b9655..9118bc454dd 100644 --- a/docs/practices/cv/image_search.ipynb +++ b/docs/practices/cv/image_search.ipynb @@ -9,7 +9,7 @@ "# 基于图片相似度的图片搜索\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例简要介绍如何通过飞桨开源框架，实现图片搜索的功能。" ] }, @@ -34,7 +34,7 @@ "source": [ "## 二、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick)。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick)。" ] }, { @@ -48,7 +48,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/cv/image_segmentation.ipynb b/docs/practices/cv/image_segmentation.ipynb index e2fd7e890de..2cf09cacb23 100644 --- a/docs/practices/cv/image_segmentation.ipynb +++ b/docs/practices/cv/image_segmentation.ipynb @@ -9,7 +9,7 @@ "# 基于U-Net卷积神经网络实现宠物图像分割\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程使用U-Net实现图像分割。" ] }, @@ -47,7 +47,7 @@ { "data": { "text/plain": [ - "'2.2.0'" + "'2.2.1'" ] }, "execution_count": null, diff --git a/docs/practices/cv/landmark_detection.ipynb b/docs/practices/cv/landmark_detection.ipynb index f50ec98c003..82d8eea08cd 100644 --- a/docs/practices/cv/landmark_detection.ipynb +++ b/docs/practices/cv/landmark_detection.ipynb @@ -9,7 +9,7 @@ "# 人脸关键点检测\n", "\n", "**作者:** [ssz95](https://github.com/zzs95)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程将会演示如何使用飞桨实现人脸关键点检测。" ] }, @@ -49,7 +49,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/cv/pointnet.ipynb b/docs/practices/cv/pointnet.ipynb index ce033e52d9a..0b51f9d2878 100644 --- a/docs/practices/cv/pointnet.ipynb +++ b/docs/practices/cv/pointnet.ipynb @@ -8,8 +8,8 @@ "source": [ "# **点云处理：实现PointNet点云分类**\n", "**作者**：[Zhihao Cao](https://github.com/WhiteFireFox)
\n", - "**日期**：2021.11
\n", - "**摘要**：本示例在于演示如何基于Paddle2.2实现PointNet在ShapeNet数据集上进行点云分类处理。" + "**日期**：2021.12
\n", + "**摘要**：本示例在于演示如何基于 Paddle2.2 实现PointNet在ShapeNet数据集上进行点云分类处理。" ] }, { @@ -34,7 +34,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], @@ -689,4 +689,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/docs/practices/cv/super_resolution_sub_pixel.ipynb b/docs/practices/cv/super_resolution_sub_pixel.ipynb index 9265a2ee55f..e70f9162bae 100644 --- a/docs/practices/cv/super_resolution_sub_pixel.ipynb +++ b/docs/practices/cv/super_resolution_sub_pixel.ipynb @@ -8,7 +8,7 @@ "source": [ "# 通过Sub-Pixel实现图像超分辨率\n", "**作者:** [Ralph LU](https://github.com/ralph0813)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例通过Sub-Pixel实现图像超分辨率。" ] }, @@ -50,7 +50,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/jit/image_search_with_jit.ipynb b/docs/practices/jit/image_search_with_jit.ipynb index ffddbfec3ca..9ef267a6d04 100644 --- a/docs/practices/jit/image_search_with_jit.ipynb +++ b/docs/practices/jit/image_search_with_jit.ipynb @@ -60,7 +60,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], @@ -777,4 +777,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/docs/practices/nlp/addition_rnn.ipynb b/docs/practices/nlp/addition_rnn.ipynb index b9fe8b70052..0779a32046f 100644 --- a/docs/practices/nlp/addition_rnn.ipynb +++ b/docs/practices/nlp/addition_rnn.ipynb @@ -9,7 +9,7 @@ "# 使用序列到序列模型完成数字加法\n", "\n", "**作者:** [jm12138](https://github.com/jm12138)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例介绍如何使用飞桨完成一个数字加法任务，将会使用飞桨提供的`LSTM`，组建一个序列到序列模型，并在随机生成的数据集上完成数字加法任务的模型训练与预测。" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "paddle version: 2.2.0\n" + "paddle version: 2.2.1\n" ] } ], diff --git a/docs/practices/nlp/imdb_bow_classification.ipynb b/docs/practices/nlp/imdb_bow_classification.ipynb index a8ccfb2e43d..b22e779206e 100644 --- a/docs/practices/nlp/imdb_bow_classification.ipynb +++ b/docs/practices/nlp/imdb_bow_classification.ipynb @@ -9,7 +9,7 @@ "# IMDB 数据集使用BOW网络的文本分类\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程演示如何在IMDB数据集上用简单的BOW网络完成文本分类的任务。" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/nlp/n_gram_model.ipynb b/docs/practices/nlp/n_gram_model.ipynb index 22e4ec5c496..7c9030079cc 100644 --- a/docs/practices/nlp/n_gram_model.ipynb +++ b/docs/practices/nlp/n_gram_model.ipynb @@ -9,7 +9,7 @@ "# 用N-Gram模型在莎士比亚文集中训练word embedding\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** \n", "N-gram 是计算机语言学和概率论范畴内的概念，是指给定的一段文本中N个项目的序列。N=1 时 N-gram 又称为 unigram，N=2 称为 bigram，N=3 称为 trigram，以此类推。实际应用通常采用 bigram 和 trigram 进行计算。本示例在莎士比亚文集上实现了trigram。" ] @@ -22,7 +22,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ { "data": { "text/plain": [ - "'2.2.0'" + "'2.2.1'" ] }, "execution_count": null, diff --git a/docs/practices/nlp/pretrained_word_embeddings.ipynb b/docs/practices/nlp/pretrained_word_embeddings.ipynb index bacf3c90998..2ac3f7092a4 100644 --- a/docs/practices/nlp/pretrained_word_embeddings.ipynb +++ b/docs/practices/nlp/pretrained_word_embeddings.ipynb @@ -9,7 +9,7 @@ "# 使用预训练的词向量完成文本分类任务\n", "\n", "**作者**: [fiyen](https://github.com/fiyen)
\n", - "**日期**: 2021.11
\n", + "**日期**: 2021.12
\n", "**摘要**: 本示例教程将会演示如何使用飞桨内置的Imdb数据集，并使用预训练词向量进行文本分类。" ] }, @@ -20,7 +20,7 @@ }, "source": [ "## 一、环境设置\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -34,7 +34,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/nlp/seq2seq_with_attention.ipynb b/docs/practices/nlp/seq2seq_with_attention.ipynb index 26e91fd7b2a..a0f9b40191b 100644 --- a/docs/practices/nlp/seq2seq_with_attention.ipynb +++ b/docs/practices/nlp/seq2seq_with_attention.ipynb @@ -9,7 +9,7 @@ "# 使用注意力机制的LSTM的机器翻译\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程介绍如何使用飞桨完成一个机器翻译任务。通过使用飞桨提供的LSTM的API，组建一个`sequence to sequence with attention`的机器翻译的模型，并在示例的数据集上完成从英文翻译成中文的机器翻译。" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/quick_start/dynamic_graph.ipynb b/docs/practices/quick_start/dynamic_graph.ipynb index 80fabba0d8a..6f67085ca2d 100644 --- a/docs/practices/quick_start/dynamic_graph.ipynb +++ b/docs/practices/quick_start/dynamic_graph.ipynb @@ -9,7 +9,7 @@ "# 动态图\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 从飞桨框架2.0版本开始，飞桨默认为开启了动态图开发模式。在这种模式下，每次执行一个运算，可以立即得到结果（而不是事先定义好网络结构，然后再执行）。在动态图模式下，你可以更加方便的组织代码，更容易的调试程序，本示例教程将向你介绍飞桨的动态图的使用。\n" ] }, @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本教程基于 Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于 Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/quick_start/hello_paddle.ipynb b/docs/practices/quick_start/hello_paddle.ipynb index 9529bbc2cef..4a0fb20b941 100644 --- a/docs/practices/quick_start/hello_paddle.ipynb +++ b/docs/practices/quick_start/hello_paddle.ipynb @@ -9,7 +9,7 @@ "# hello paddle: 从普通程序走向机器学习程序\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 这篇示例向你介绍普通程序跟机器学习程序的区别，并带着你用飞桨框架，实现第一个机器学习程序。" ] }, diff --git a/docs/practices/quick_start/high_level_api.ipynb b/docs/practices/quick_start/high_level_api.ipynb index 4b2420272e0..e263e0ca1ba 100644 --- a/docs/practices/quick_start/high_level_api.ipynb +++ b/docs/practices/quick_start/high_level_api.ipynb @@ -9,7 +9,7 @@ "# 飞桨高层API使用指南\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程是对飞桨高层API的详细说明，会介绍如何使用高层API，快速完成深度学习任务。" ] }, @@ -46,7 +46,7 @@ "source": [ "## 二、安装并使用飞桨高层API\n", "\n", - "飞桨高层API无需独立安装，只需要安装好paddlepaddle即可。如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。\n", + "飞桨高层API无需独立安装，只需要安装好paddlepaddle即可。如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。\n", "\n", "安装完成后import paddle即可使用相关高层API，如：[paddle.Model](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/Model_cn.html#model)、视觉领域[paddle.vision](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/vision/Overview_cn.html#paddle-vision)、NLP领域[paddle.text](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/text/Overview_cn.html#paddle-text)。" ] @@ -61,7 +61,7 @@ { "data": { "text/plain": [ - "'2.2.0'" + "'2.2.1'" ] }, "execution_count": null, diff --git a/docs/practices/quick_start/linear_regression.ipynb b/docs/practices/quick_start/linear_regression.ipynb index 520554a3773..698d212f70b 100644 --- a/docs/practices/quick_start/linear_regression.ipynb +++ b/docs/practices/quick_start/linear_regression.ipynb @@ -9,7 +9,7 @@ "# 使用线性回归预测波士顿房价\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本示例教程将会演示如何使用线性回归完成波士顿房价预测。" ] }, @@ -32,7 +32,7 @@ "source": [ "## 二、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -46,7 +46,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/quick_start/save_model.ipynb b/docs/practices/quick_start/save_model.ipynb index 4689f04a046..8885091848a 100644 --- a/docs/practices/quick_start/save_model.ipynb +++ b/docs/practices/quick_start/save_model.ipynb @@ -9,7 +9,7 @@ "# 模型保存及加载\n", "\n", "**作者:** [PaddlePaddle](https://github.com/PaddlePaddle)
\n", - "**日期:** 2021.11
\n", + "**日期:** 2021.12
\n", "**摘要:** 本教程将基于Paddle高阶API对模型参数的保存和加载进行讲解。" ] }, @@ -32,7 +32,7 @@ "source": [ "## 二、环境配置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -46,7 +46,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], @@ -427,4 +427,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/docs/practices/recommendations/collaborative_filtering.ipynb b/docs/practices/recommendations/collaborative_filtering.ipynb index 237b5860196..5d394a8099e 100644 --- a/docs/practices/recommendations/collaborative_filtering.ipynb +++ b/docs/practices/recommendations/collaborative_filtering.ipynb @@ -9,7 +9,7 @@ "# 使用协同过滤实现电影推荐\n", "\n", "**作者：** [HUANGCHENGAI](https://github.com/HUANGCHENGAI)
\n", - "**日期：** 2021.11
\n", + "**日期：** 2021.12
\n", "**摘要：** 本案例使用飞桨框架实现推荐电影的协同过滤算法。" ] }, @@ -49,7 +49,7 @@ "source": [ "## 二、 环境设置\n", "\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -63,7 +63,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/reinforcement_learning/actor_critic_method.ipynb b/docs/practices/reinforcement_learning/actor_critic_method.ipynb index 138e4510bd6..71a4ebffd28 100644 --- a/docs/practices/reinforcement_learning/actor_critic_method.ipynb +++ b/docs/practices/reinforcement_learning/actor_critic_method.ipynb @@ -8,7 +8,7 @@ "source": [ "# **强化学习——Actor Critic Method**\n", "**作者：** [EastSmith](https://github.com/EastSmith)
\n", - "**日期：** 2021.11
\n", + "**日期：** 2021.12
\n", "**摘要：** 展示 `CartPole-V0` 环境中 `Actor-Critic` 方法的一个实现。" ] }, @@ -39,7 +39,7 @@ }, "source": [ "## **二、环境配置**\n", - "本教程基于Paddle 2.2.0-rc0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0-rc0 。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2 。" ] }, { @@ -53,7 +53,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/practices/reinforcement_learning/advantage_actor_critic.ipynb b/docs/practices/reinforcement_learning/advantage_actor_critic.ipynb index 6362b510720..e43ee3b7bab 100644 --- a/docs/practices/reinforcement_learning/advantage_actor_critic.ipynb +++ b/docs/practices/reinforcement_learning/advantage_actor_critic.ipynb @@ -9,7 +9,7 @@ "# **强化学习——Advantage Actor-Critic(A2C)**\n", "**作者：**:[EastSmith](https://github.com/EastSmith)\n", "\n", - "**日期：** 2021.11\n", + "**日期：** 2021.12\n", "\n", "**AI Studio项目**：[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/1766508)\n", "## **一、介绍**\n", @@ -57,7 +57,7 @@ }, "source": [ "## **二、环境配置**\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { diff --git a/docs/practices/reinforcement_learning/deep_deterministic_policy_gradient.ipynb b/docs/practices/reinforcement_learning/deep_deterministic_policy_gradient.ipynb index 12ac7b050f8..eb046e6085b 100644 --- a/docs/practices/reinforcement_learning/deep_deterministic_policy_gradient.ipynb +++ b/docs/practices/reinforcement_learning/deep_deterministic_policy_gradient.ipynb @@ -9,7 +9,7 @@ "# **强化学习——Deep Deterministic Policy Gradient (DDPG)**\n", "**作者：**:[EastSmith](https://github.com/EastSmith)\n", "\n", - "**日期：** 2021.11\n", + "**日期：** 2021.12\n", "\n", "**AI Studio项目**：[点击体验](https://aistudio.baidu.com/aistudio/projectdetail/1702021)\n", "\n", @@ -52,7 +52,7 @@ }, "source": [ "## **二、环境配置**\n", - "本教程基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本教程基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { diff --git a/docs/practices/time_series/autoencoder.ipynb b/docs/practices/time_series/autoencoder.ipynb index 8582aecaa78..a2f2b757a02 100644 --- a/docs/practices/time_series/autoencoder.ipynb +++ b/docs/practices/time_series/autoencoder.ipynb @@ -9,8 +9,8 @@ "# 通过AutoEncoder实现时序数据异常检测\n", "\n", "**作者:** [Reatris](https://github.com/Reatris) \n", - "**日期:** 2021.11
\n", - "**摘要:** 本示例将会演示如何使用飞桨2.2完成时序异常检测任务。这是一个较为简单的示例，将会构建一个AutoEncoder网络完成任务。" + "**日期:** 2021.12
\n", + "**摘要:** 本示例将会演示如何使用飞桨2.2 完成时序异常检测任务。这是一个较为简单的示例，将会构建一个AutoEncoder网络完成任务。" ] }, { @@ -21,7 +21,7 @@ "source": [ "## 一、环境配置\n", "\n", - "本示例基于Paddle 2.2.0 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2.0。" + "本示例基于Paddle 2.2 编写，如果你的环境不是本版本，请先参考官网[安装](https://www.paddlepaddle.org.cn/install/quick) Paddle 2.2。" ] }, { @@ -35,7 +35,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.2.0\n" + "2.2.1\n" ] } ], diff --git a/docs/release_note_cn.md b/docs/release_note_cn.md index 781b1b76957..a05d463b364 100644 --- a/docs/release_note_cn.md +++ b/docs/release_note_cn.md @@ -1,5 +1,102 @@  -# Release Note +# 2.2.1 Release Note + +## 1. 重要更新 + +我们很高兴的发布飞桨框架2.2.1版本，主要是对2.2.0中一些功能和性能问题的修复，并对部分功能点做了增强，重点如下： + +- 新增 ``paddle.linalg.triangular_solve``，用于计算带有三角系数矩阵的线性方程组。 +- 新增 `paddle.device.cuda.graphs.CUDAGraph` API，支持NVIDIA的[CUDA Graph](https://developer.nvidia.com/blog/cuda-graphs/)功能，注意目前该API还处于实验阶段，尚未稳定。 +- 修复了基础API、Tensor 索引中的已知问题。 + + +## 2. 训练框架（含分布式） + +### （1）新功能 + +#### API + +- 新增``paddle.linalg.triangular_solve`` API，用于计算带有三角系数矩阵的线性方程组。([#36714](https://github.com/PaddlePaddle/Paddle/pull/36714)) +- 新增`paddle.device.cuda.graphs.CUDAGraph` API，支持NVIDIA的[CUDA Graph](https://developer.nvidia.com/blog/cuda-graphs/)功能，可以将GPU计算全部捕捉到一张CUDA Graph中，往后多次调用，可以去除框架的额外开销，提升运行性能。注意目前该API还处于实验阶段，尚未稳定。([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) +- 新增``paddle.incubate.graph_send_recv`` API，主要应用于图学习领域，目的是为了减少在消息传递过程中带来的中间变量显存或内存的损耗，包含 SUM、MEAN、MIN、MAX 共四种更新模式。([#37205](https://github.com/PaddlePaddle/Paddle/pull/37205)) +- 新增`paddle.incubate.operators.ResNetUnit` API，用于 ResNet 网络里的卷积、批归一化、shortcut/bottleneck操作融合。([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) + + +### （2）功能优化 + +#### API + +- `paddle.incubate.FusedTransformerEncoderLayer`，添加 `src_mask=None` 的支持，添加pure fp16的支持。 ([#37229](https://github.com/PaddlePaddle/Paddle/pull/37229)) + +#### IR(Intermediate Representation) + +- 动态图转静态图 + - 使用`@paddle.jit.to_static`装饰单独的 function 时，提供 `train()、eval()` 函数支持切换到 `train、eval` 模式。([#37383](https://github.com/PaddlePaddle/Paddle/pull/37383)) + + +#### 分布式训练 +- 异构参数服务器完善任意次切图能力，增加流水线训练功能，提升训练吞吐。([#37446](https://github.com/PaddlePaddle/Paddle/pull/37446)) + + +#### 其他 + +- 针对 `paddle.scatter` 的 ``index`` 越界导致 core dump 的问题，加强了越界检查，并完善对应的报错信息。([#37431](https://github.com/PaddlePaddle/Paddle/pull/37431)) + + +### （3）性能优化 + +- 优化 `paddle.top_k`，根据 ``k`` 的大小和 ``input_width`` 大小进行选择不同的实现方案，当 k>=75% input_width 时选择 cub 实现，否则选择手写 kernel 实现。([#37325](https://github.com/PaddlePaddle/Paddle/pull/37325)) +- 优化`paddle.fluid.optimizer.LarsMomentumOptimizer`，通过 optimizer 算子融合 + [CUDA Cooperative Groups](https://developer.nvidia.com/blog/cooperative-groups/)的方式提高OP性能。([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) + + + +### （4）问题修复 + +#### API +- 修复`paddle.nn.ELU` 与 `paddle.nn.functional.elu` 的计算公式，解决 alpha<0 时结果错误的问题；`paddle.nn.functional.elu_`不支持 alpha<0 的场景，在 alpha<0 时会报错。([#37437](https://github.com/PaddlePaddle/Paddle/pull/37437)) +- 修复`paddle.slice`反向执行时出现 `out_of_range` 的问题。([#37584](https://github.com/PaddlePaddle/Paddle/pull/37584)) +- `paddle.shape` 没有反向，显式设置 ``stop_gradient`` 为 ``True``。([#37412](https://github.com/PaddlePaddle/Paddle/pull/37412)) +- `paddle.arange` 没有反向，显式设置 ``stop_gradient`` 为 ``True``。([#37486](https://github.com/PaddlePaddle/Paddle/pull/37486)) +- `paddle.shard_index` 在输入数据的最后一维不为1时进行报错提示。([#37421](https://github.com/PaddlePaddle/Paddle/pull/37421)) +- 修复 ``paddle.matmul`` 使用int8量化，反量化时维度错误的问题。([#36982](https://github.com/PaddlePaddle/Paddle/pull/36982)) +- 修复 `paddle.nn.Dropout` 在 `eval` 模式下不计算梯度的问题。([#37305](https://github.com/PaddlePaddle/Paddle/pull/37305)) +- 修复 `paddle.nn.functional.dropout` 在静态图下输入 `Tenor` 形状中有 -1 并指定 drop 该维时报错的问题。([#37223](https://github.com/PaddlePaddle/Paddle/pull/37223)) +- 修复RNN类API `paddle.nn.LSTM`,`paddle.nn.GRU`, `paddle.nn.SimpleRNN`在CPU训练时多层RNN（dropout设置为0）反向计算出错的问题。([#37086](https://github.com/PaddlePaddle/Paddle/pull/37086)) +- 修复 `paddle.incubate.FusedTransformerEncoderLayer` 反向计算梯度错误、pre_layer_norm 处理不正确、参数处理不正确，漏传参数、 add_bias 计算错误等问题。 ([#37229](https://github.com/PaddlePaddle/Paddle/pull/37229)) +- 修复 `paddle.incubate.fused_multi_head_attention` 不支持 ``bias`` 为`None` 的问题。([#37411](https://github.com/PaddlePaddle/Paddle/pull/37411), [#37566](https://github.com/PaddlePaddle/Paddle/pull/37566)) +- 修复`paddle.vision.datasets.Cifar10`, `paddle.vision.datasets.Cifar100`加载数据没有顺序的问题。 ([#37528](https://github.com/PaddlePaddle/Paddle/pull/37528)) +- 修复一维`Tensor`在使用省略号(...)索引时维度检测异常报错的问题。([#37192](https://github.com/PaddlePaddle/Paddle/pull/37192)) +- 修复`Tensor`索引赋值(`setitem`)梯度属性无法传播的问题，详见[issue](https://github.com/PaddlePaddle/Paddle/issues/36902)。([#37028](https://github.com/PaddlePaddle/Paddle/pull/37028)) + + +#### IR(Intermediate Representation) + +- 动态图转静态图 + - 动转静后的模型调用 `paddle.flops` 能够正确统计模型参数。([#36852](https://github.com/PaddlePaddle/Paddle/pull/36852)) + - 动转静模块能够正确转换`for i in [1, 2, 3]`循环语句。([#37259](https://github.com/PaddlePaddle/Paddle/pull/37259)) + +#### 分布式训练 + + - `fleet.load_model`: 修复参数服务器模式下模型加载API不可用问题。([#37461](https://github.com/PaddlePaddle/Paddle/pull/37461)) + - `fleet.save_inference_model`: 修复参数服务器模式下模型保存 dense 参数前，未从 server 端拉取参数的问题。([#37461](https://github.com/PaddlePaddle/Paddle/pull/37461)) + + +#### 其他 + +- 修复动态图 inplace 操作的问题：对一个非叶子节点进行 inplace 操作后，立即执行 backward，该节点及更前的节点的梯度计算错误。([#37420](https://github.com/PaddlePaddle/Paddle/pull/37420)) + + +## 4. 部署方向（Paddle Inference） + +### （1）问题修复 + +- 在明确关闭日志的情况下，进一步去除冗余的调试日志。([#37212](https://github.com/PaddlePaddle/Paddle/pull/37212)) +- 修复内存/显存优化策略，避免因不当的内存/显存优化导致预测结果有误或崩溃。([#37324](https://github.com/PaddlePaddle/Paddle/pull/37324), [#37123](https://github.com/PaddlePaddle/Paddle/pull/37123)) +- 修复 Transformer 模型的 MultiHead 结构中融合后 QkvToContextPluginDynamicscale 的 scale 计算错误问题，这是由于 cuda 函数的 block 和 thread 设置错误引起的。([#37096](https://github.com/PaddlePaddle/Paddle/pull/37096)) +- 将所有的推理OP在in8量化的功能中注册：解决因历史原因有些推理OP没有在int8量化中注册的问题。([#37266](https://github.com/PaddlePaddle/Paddle/pull/37266)) + + +# 2.2.0 Release Note ## 1. 重要更新 diff --git a/docs/release_note_en.md b/docs/release_note_en.md index 349796fdebb..81fbdc09a32 100644 --- a/docs/release_note_en.md +++ b/docs/release_note_en.md @@ -1,5 +1,105 @@  -# Release Note +# 2.2.1 Release Note + +## 1. Important Updates + +This version fixed some function and performance issues of PaddlePaddle 2.2.0, and optimized some functions. The highlights are as follows： + +- Add ``paddle.linalg.triangular_solve`` to calculate linear equations with triangular coefficient matrices. +- Add `paddle.device.cuda.graphs.CUDAGraph` API that supports the [CUDA Graph](https://developer.nvidia.com/blog/cuda-graphs/) function of NVIDIA. Note that the API is still experimental and not yet stable. +- Fix known issues of basic API and Tensor index. + + +## 2. Training Framework（Distributed Included） + +### （1）New Functions + +#### API + +- Add ``paddle.linalg.triangular_solve`` API to calculate linear equations with triangular coefficient matrices. ([#36714](https://github.com/PaddlePaddle/Paddle/pull/36714)) +- Add `paddle.device.cuda.graphs.CUDAGraph` API that supports the [CUDA Graph](https://developer.nvidia.com/blog/cuda-graphs/) function of NVIDIA by capturing all GPU calculations into a single CUDA Graph and calling them for later use, which not only cuts the extra overhead but also improves the runtime performance. Note that the API is still experimental and not yet stable. ([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) +- Add``paddle.incubate.graph_send_recv`` API for image learning to reduce the loss of intermediate variables in memory or video memory during message passing. It contains four update modes, namely, SUM, MEAN, MIN, and MAX. ([#37205](https://github.com/PaddlePaddle/Paddle/pull/37205)) +- Add `paddle.incubate.operators.ResNetUnit` API to integrate the convolution, batch normalization, and shortcut/bottleneck operation in the ResNet network. ([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) + + +### （2）Function Optimization + +#### API + +- `paddle.incubate.FusedTransformerEncoderLayer` adds `src_mask=None` and supports pure fp16.([#37229](https://github.com/PaddlePaddle/Paddle/pull/37229)) + +#### IR(Intermediate Representation) + +- Dynamic Graph to Static Graph + - When adopting`@paddle.jit.to_static` to decorate single function, `train()、eval()` functions are provided to support the swtich to `train、eval` mode. ([#37383](https://github.com/PaddlePaddle/Paddle/pull/37383)) + + +#### Distributed Training + +- Optimize the ability of arbitrary cutting and add pipeline training in the heterogeneous parameter server, which enhance training throughput.([#37446](https://github.com/PaddlePaddle/Paddle/pull/37446)) + + +#### Others + +- Enhance the out-of-bounds check for the ``index`` of ``paddle.scatter` that causes core dump, and improve the corresponding error message. ([#37431](https://github.com/PaddlePaddle/Paddle/pull/37431)) + + +### （3）Performance Optimization + +- Optimize `paddle.top_k` by enabling it to choose different implementations according to the size of ``k`` and ``input_width``: cub implementation when k>=75% input_width, otherwise the handwritten kernel implementation.([#37325](https://github.com/PaddlePaddle/Paddle/pull/37325)) +- Optimize `paddle.fluid.optimizer.LarsMomentumOptimizer` to improve OP performance by integrating optimizer operator and [CUDA Cooperative Groups](https://developer.nvidia.com/blog/cooperative-groups/). ([#37109](https://github.com/PaddlePaddle/Paddle/pull/37109)) + + + +### （4）Bug Fixes + +#### API + +- Fix the calculation error of `paddle.nn.ELU` and `paddle.nn.functional.elu` when alpha<0；the error report of`paddle.nn.functional.elu_`when alpha<0 due to its objection to such scenario. ([#37437](https://github.com/PaddlePaddle/Paddle/pull/37437)) +- Fix the problem of `out_of_range` when the `paddle.slice` is revesely excuted. ([#37584](https://github.com/PaddlePaddle/Paddle/pull/37584)) +- `paddle.shape` doesn't support backward, explicitly set ``stop_gradient`` to ``True``. ([#37412](https://github.com/PaddlePaddle/Paddle/pull/37412)) +- `paddle.arange` doesn't support backward, explicitly set ``stop_gradient`` to ``True``.([#37486](https://github.com/PaddlePaddle/Paddle/pull/37486)) +- `paddle.shard_index` reports an error if the last dimension of the input data is not 1. ([#37421](https://github.com/PaddlePaddle/Paddle/pull/37421)) +- Fix the wrong dimension of inverse quantization when ``paddle.matmul`` adopts int8 quantization. ([#36982](https://github.com/PaddlePaddle/Paddle/pull/36982)) +- Fix the issue that `paddle.nn.Dropout`, under `eval`, does not calculate the gradient. ([#37305](https://github.com/PaddlePaddle/Paddle/pull/37305)) +- Fix the issue that `paddle.nn.functional.dropout`, in static graph mode, reports an error when -1 is included in the input shape of `Tensor` and it is specified to drop this dimension. ([#37223](https://github.com/PaddlePaddle/Paddle/pull/37223)) +- Fix the backward calculation errors of multi-layer RNN (dropout set 0) in CPU training by RNN API `paddle.nn.LSTM`,`paddle.nn.GRU`, `paddle.nn.SimpleRNN`. ([#37086](https://github.com/PaddlePaddle/Paddle/pull/37086)) +- Fix issues such as the gradient error of`paddle.incubate.FusedTransformerEncoderLayer` backward calculation, incorrect processing of pre_layer_norm, incorrect parameter processing, missing parameters, calculation errors of add_bias, etc. ([#37229](https://github.com/PaddlePaddle/Paddle/pull/37229)) +- Fix the issue that `paddle.incubate.fused_multi_head_attention` does not support ``bias`` as `None`.([#37411](https://github.com/PaddlePaddle/Paddle/pull/37411), [#37566](https://github.com/PaddlePaddle/Paddle/pull/37566)) +- Fix the disordered data loaded by `paddle.vision.datasets.Cifar10`, `paddle.vision.datasets.Cifar100`. ([#37528](https://github.com/PaddlePaddle/Paddle/pull/37528)) +- Fix the issue that one-dimensional `Tensor` reports an exception error of dimension detection when using ellipsis(...) indexing. ([#37192](https://github.com/PaddlePaddle/Paddle/pull/37192)) +- Fix the issue that the gradient attribute of`Tensor` cannot be spread during indexing and assignment (`setitem`), see [issue](https://github.com/PaddlePaddle/Paddle/issues/36902) for details. ([#37028](https://github.com/PaddlePaddle/Paddle/pull/37028)) + + +#### IR(Intermediate Representation) + +- Dynamic Graph to Static Graph + - The model can call `paddle.flops` to count the model parameters correctly. ([#36852](https://github.com/PaddlePaddle/Paddle/pull/36852)) + - The model can correctly convert the loop statements `for i in [1, 2, 3]`.([#37259](https://github.com/PaddlePaddle/Paddle/pull/37259)) + +#### Distributed Training + + - `fleet.load_model`: Fix the unavailable API loaded by the model in parameter server mode.([#37461](https://github.com/PaddlePaddle/Paddle/pull/37461)) + - `fleet.save_inference_model`: Fix the issue that the model does not pull parameters from the server side before saving dense parameters in parameter server mode. ([#37461](https://github.com/PaddlePaddle/Paddle/pull/37461)) + + +#### Others + +- Fix the problem of inplace operation of dynamic graph: after performing inplace operation on a non-leaf node, followed by immediate execution of backward, the gradient of this node and the nodes before is calculated incorrectly. ([#37420](https://github.com/PaddlePaddle/Paddle/pull/37420)) + + + + +## 4. Paddle Inference + +### （1）Bug Fixes + +- Further removal of redundant debug logs in the case of clear log closure.([#37212](https://github.com/PaddlePaddle/Paddle/pull/37212)) +- Fix memory/video memory optimization policies to avoid incorrect prediction results or crashes due to improper memory/video memory optimization. ([#37324](https://github.com/PaddlePaddle/Paddle/pull/37324), [#37123](https://github.com/PaddlePaddle/Paddle/pull/37123)) +- Fix the scale calculation error in the MultiHead structure of Transformer model after integrating QkvToContextPluginDynamicscale, which is caused by wrong block and thread settings of cuda function. ([#37096](https://github.com/PaddlePaddle/Paddle/pull/37096)) +- Register all inference OPs in the function of in8 quantization: Solve the issues that some inference OPs are not registered in int8 quantization due to historical reasons. ([#37266](https://github.com/PaddlePaddle/Paddle/pull/37266 + +# 2.2.0 Release Note ## **1. Highlights** @@ -996,4 +1096,4 @@ paddle.int64 This release contains contributions from: -0x45f, 123malin, Adam Osewski, Aganlengzi, Aurelius84, Baibaifan, Bo Liu, CheQiXiao, Chen Long, Chen Weihang, CtfGo, Double\_V, Ethanzjp, Fan Zhang, Feiyu Chan, Feng Xing, From00, GT-Zhang, Guanghua Yu, Guoxia Wang, Haipeng Wang, Hao Lin, Haohongxiang, Hui Zhang, Huihuang Zheng, HydrogenSulfate, IMMORTAL, JYChen, JZ-LIANG, Jacek Czaja, Jack Zhou, Jackwaterveg, Jeng Bai-Cheng, Jiangxinz, Jiaqi Liu, Jiawei Wang, JingZhuangzhuang, June Weng, Kaipeng Deng, Kqnonrime, LJQ❤️, Leo Chen, Li Min, LielinJiang, Lijunhui, Linjie Chen, Liu-xiandong, LiuWei, Ming-Xu Huang, MissPenguin, PaddlePM, Pei Yang, Peihan, Qi Li, QingshuChen, Ren Wei (任卫), Roc, Shang Zhizhou, ShenLiang, Shibo Tao, Siming Dai, Sing\_chan, TCChenLong, TTerror, TeslaZhao, Thomas Young, Thunderbrook, Tongxin Bai, WJJ1995, WangXi, Wangzheee, Wei Shengyu, WeiXin, Weilong Wu, Wenyu, Wilber, XGZhang, XYZ, XYZ916829, XiangGao, Xiaoxu Chen, YUNSHEN XIE, Yanxing Shi, Yiqun Liu, YuanRisheng, Yuang Liu, Yulong Ao, Zeng Jinle, Zhang Ting, Zhang Zheng, Zhanlue Yang, Zhen Wang, Zhong Hui, Zhou Wei, andreazanetti, andyjpaddle, arlesniak, baoachun, cc, ceci3, chajchaj, chenenquan, chenjian, chentianyu03, crystal, cuicheng01, danleifeng, denglin-github, duanboqiang, dyning, feng626, feng_shuai, furnace, gongweibao, heliqi, hlygit66666, hong, hong19860320, houj04, huangjun12, huangxu96, huzhiqiang, iducn, jakpiase, jiangcheng, joanna.wozna.intel, jzhang533, kuizhiqing, levi131, lidanqing, lilong12, limingshu, littletomatodonkey, liu zhengxi, liutiexing, liuyuhui, liym27, lyuwenyu, lzzyzlbb, niuliling123, pangyoki, parap1uie-s, ronnywang, root, seemingwang, shangliang Xu, shiyutang, smallv0221, sunli, sunzhongkai588, taixiurong, tangwei12, tianshuo78520a, veyron95, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangxinxin08, wangzhen38, wangzhuang01, wawltor, wenbin, whs, will-jl944, wuhuachaocoding, wuhuanzhou, xiaoting, xiaoxiaohehe001, xiayanming, xiegegege, xiemoyuan, xiongkun, yaoxuefeng, yeliang2258, yingyibiao, zhangbo9674, zhangchunle, zhangkaihuo, zhaoyingli, zhiboniu, zhoujun, zhouzj, zhulei, zhupengyang, zlsh80826, zmx, zyfncg, 李季, 津, 王明冬, 石晓伟 \ No newline at end of file +0x45f, 123malin, Adam Osewski, Aganlengzi, Aurelius84, Baibaifan, Bo Liu, CheQiXiao, Chen Long, Chen Weihang, CtfGo, Double\_V, Ethanzjp, Fan Zhang, Feiyu Chan, Feng Xing, From00, GT-Zhang, Guanghua Yu, Guoxia Wang, Haipeng Wang, Hao Lin, Haohongxiang, Hui Zhang, Huihuang Zheng, HydrogenSulfate, IMMORTAL, JYChen, JZ-LIANG, Jacek Czaja, Jack Zhou, Jackwaterveg, Jeng Bai-Cheng, Jiangxinz, Jiaqi Liu, Jiawei Wang, JingZhuangzhuang, June Weng, Kaipeng Deng, Kqnonrime, LJQ❤️, Leo Chen, Li Min, LielinJiang, Lijunhui, Linjie Chen, Liu-xiandong, LiuWei, Ming-Xu Huang, MissPenguin, PaddlePM, Pei Yang, Peihan, Qi Li, QingshuChen, Ren Wei (任卫), Roc, Shang Zhizhou, ShenLiang, Shibo Tao, Siming Dai, Sing\_chan, TCChenLong, TTerror, TeslaZhao, Thomas Young, Thunderbrook, Tongxin Bai, WJJ1995, WangXi, Wangzheee, Wei Shengyu, WeiXin, Weilong Wu, Wenyu, Wilber, XGZhang, XYZ, XYZ916829, XiangGao, Xiaoxu Chen, YUNSHEN XIE, Yanxing Shi, Yiqun Liu, YuanRisheng, Yuang Liu, Yulong Ao, Zeng Jinle, Zhang Ting, Zhang Zheng, Zhanlue Yang, Zhen Wang, Zhong Hui, Zhou Wei, andreazanetti, andyjpaddle, arlesniak, baoachun, cc, ceci3, chajchaj, chenenquan, chenjian, chentianyu03, crystal, cuicheng01, danleifeng, denglin-github, duanboqiang, dyning, feng626, feng_shuai, furnace, gongweibao, heliqi, hlygit66666, hong, hong19860320, houj04, huangjun12, huangxu96, huzhiqiang, iducn, jakpiase, jiangcheng, joanna.wozna.intel, jzhang533, kuizhiqing, levi131, lidanqing, lilong12, limingshu, littletomatodonkey, liu zhengxi, liutiexing, liuyuhui, liym27, lyuwenyu, lzzyzlbb, niuliling123, pangyoki, parap1uie-s, ronnywang, root, seemingwang, shangliang Xu, shiyutang, smallv0221, sunli, sunzhongkai588, taixiurong, tangwei12, tianshuo78520a, veyron95, wangguanqun, wangguanzhong, wanghuancoder, wangna11BD, wangxinxin08, wangzhen38, wangzhuang01, wawltor, wenbin, whs, will-jl944, wuhuachaocoding, wuhuanzhou, xiaoting, xiaoxiaohehe001, xiayanming, xiegegege, xiemoyuan, xiongkun, yaoxuefeng, yeliang2258, yingyibiao, zhangbo9674, zhangchunle, zhangkaihuo, zhaoyingli, zhiboniu, zhoujun, zhouzj, zhulei, zhupengyang, zlsh80826, zmx, zyfncg, 李季, 津, 王明冬, 石晓伟