Merge branch 'develop' into develop

.flake8

@@ -1,5 +1,5 @@
 [flake8]
-select = C,E,W
+select = E
 exclude =
     ./build,
     # Exclude third-party libraries
@@ -19,12 +19,10 @@ ignore =
     # Do not assign a lambda expression, use a def
     E731,
     # Do not use variables named ‘l’, ‘O’, or ‘I’
-    E741,
-    # Line break before binary operator, it is not compatible with black
-    W503
+    E741
 per-file-ignores =
     # These files need tabs for testing.
-    test/dygraph_to_static/test_legacy_error.py:E101,W191
+    test/dygraph_to_static/test_legacy_error.py:E101
 
     # Ignore compare with True in sot unittest
     test/sot/test_dup_top.py:E712

.github/ISSUE_TEMPLATE/3_build-installation-issue.yml

@@ -37,7 +37,7 @@ body:
       2. CPU（可选）：请提供CPU型号，MKL/OpenBlas/MKLDNN/等数学库的使用情况，是否支持AVX指令集。
       3. GPU：请提供GPU型号，CUDA（如cuda10.2）和CUDNN版本号（如cudnn7.6.5）。
       4. 系统环境：请说明系统类型、版本（如Mac OS 10.14）。
-      5. Python版本（如python 3.7）。
+      5. Python版本（如python 3.8）。
       6. （可选）若安装过程遇到问题，请提供安装方式（pip/conda/docker/源码编译）和相应的安装命令。
       7. （可选）若使用paddle过程中，遇到了无法使用gpu相关问题，请在命令行中键入`nvidia-smi`和`nvcc -V`，提供这两个命令输出的截图。
       8. （可选）若使用特殊硬件，请单独注明。

.gitignore

@@ -41,6 +41,7 @@ build_doc/
 *.user
 *.tmp
 *.pyc
+*.log
 
 .vscode
 .idea
@@ -82,7 +83,7 @@ paddle/fluid/operators/generated_op*.cc
 paddle/fluid/operators/generated_sparse_op.cc
 paddle/fluid/operators/generated_static_op.cc
 paddle/fluid/operators/generated_fused_op.cc
-paddle/phi/ops/compat/generated_*.cc
+paddle/fluid/operators/ops_signature/generated_*.cc
 paddle/phi/api/yaml/parsed_apis/
 paddle/fluid/operators/generator/parsed_ops/
 paddle/fluid/pybind/tmp_eager_op_function_impl.h
@@ -95,9 +96,12 @@ paddle/fluid/framework/__init__.py
 paddle/phi/api/profiler/__init__.py
 python/paddle/incubate/fleet/parameter_server/pslib/ps_pb2.py
 paddle/phi/kernels/fusion/cutlass/conv2d/generated/*
+paddle/phi/kernels/fusion/cutlass/conv2d/generated_tmp/*
+paddle/phi/ops/compat/generated_*
 python/paddle/base/incubate/fleet/parameter_server/pslib/ps_pb2.py
 paddle/fluid/ir_adaptor/translator/op_compat_info.cc
 paddle/phi/kernels/fusion/cutlass/cutlass_kernels/fpA_intB_gemm/autogen/*
+paddle/phi/kernels/fusion/cutlass/cutlass_kernels/fpA_intB_gemm/autogen_tmp/*
 paddle/fluid/pybind/static_op_function.*
 paddle/fluid/pybind/ops_api.cc
 paddle/fluid/pir/dialect/operator/ir/pd_api.*

CMakeLists.txt

@@ -61,7 +61,7 @@ option(WITH_IPU "Compile PaddlePaddle with Graphcore IPU" OFF)
 option(WITH_ONNXRUNTIME "Compile PaddlePaddle with ONNXRUNTIME" OFF)
 option(WITH_CUSPARSELT "Compile PaddlePaddle with CUSPARSELT" OFF)
 option(WITH_SETUP_INSTALL "Compile PaddlePaddle with setup.py" OFF)
-option(WITH_SHARED_PHI "Compile PaddlePaddle with SHARED LIB of PHI" OFF)
+option(WITH_SHARED_PHI "Compile PaddlePaddle with SHARED LIB of PHI" ON)
 option(CINN_ONLY "Compile CINN only in Paddle" OFF)
 option(CINN_WITH_CUDNN "Compile CINN with CUDNN support" ON)
 
@@ -128,6 +128,9 @@ if(WIN32)
   message("Build static library of PHI")
   set(CMAKE_SUPPRESS_REGENERATION ON)
   set(CMAKE_STATIC_LIBRARY_PREFIX lib)
+  set(WITH_SHARED_PHI
+      OFF
+      CACHE BOOL "Disable WITH_SHARED_PHI when compiling PADDLE ON WIN32" FORCE)
 
   set(CMAKE_C_FLAGS_DEBUG "${CMAKE_C_FLAGS_DEBUG} /bigobj")
   set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /bigobj")
@@ -339,7 +342,9 @@ unset(WITH_RECORD_BUILDTIME CACHE)
 
 # PY_VERSION
 if(NOT PY_VERSION)
-  set(PY_VERSION 3.7)
+  set(PY_VERSION 3.8)
+elseif(${PY_VERSION} VERSION_LESS 3.8)
+  message(FATAL_ERROR "Paddle only support Python version>=3.8 now")
 endif()
 set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
 
@@ -598,13 +603,6 @@ include(flags) # set paddle compile flags
 
 if(WITH_CINN)
   message(STATUS "Compile Paddle with CINN.")
-  message(
-    WARNING
-      "Enable WITH_SHARED_PHI when compiling with CINN. Force WITH_SHARED_PHI=ON."
-  )
-  set(WITH_SHARED_PHI
-      ON
-      CACHE BOOL "Enable WITH_SHARED_PHI when compiling with CINN" FORCE)
   # TODO(6clc): Use CINN_WITH_CUDNN to completely replace WITH_CUDNN in CINN.
   #             Use WITH_GPU to completely replace WITH_CUDA in CINN.
   set(WITH_MKL_CBLAS ${WITH_MKL})

README.md

@@ -17,21 +17,22 @@ Welcome to the PaddlePaddle GitHub.
 PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well as service platforms.
 PaddlePaddle is originated from industrial practices with dedication and commitments to industrialization. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service, and so on while serving more than 8 million developers, 220,000 companies and generating 800,000 models. With such advantages, PaddlePaddle has helped an increasing number of partners commercialize AI.
 
-
 ## Installation
 
 ### Latest PaddlePaddle Release: [v2.5](https://github.com/PaddlePaddle/Paddle/tree/release/2.5)
 
 Our vision is to enable deep learning for everyone via PaddlePaddle.
 Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest features of PaddlePaddle.
-### Install Latest Stable Release:
-```
+
+### Install Latest Stable Release
+
+``` sh
 # CPU
 pip install paddlepaddle
 # GPU
 pip install paddlepaddle-gpu
-
 ```
+
 For more information about installation, please view [Quick Install](https://www.paddlepaddle.org.cn/install/quick)
 
 Now our developers can acquire Tesla V100 online computing resources for free. If you create a program by AI Studio, you will obtain 8 hours to train models online per day. [Click here to start](https://aistudio.baidu.com/aistudio/index).
@@ -42,24 +43,20 @@ Now our developers can acquire Tesla V100 online computing resources for free. I
 
     The PaddlePaddle deep learning framework facilitates the development while lowering the technical burden, through leveraging a programmable scheme to architect the neural networks. It supports both declarative programming and imperative programming with both development flexibility and high runtime performance preserved.  The neural architectures could be automatically designed by algorithms with better performance than the ones designed by human experts.
 
-
--  **Support Ultra-Large-Scale Training of Deep Neural Networks**
+- **Support Ultra-Large-Scale Training of Deep Neural Networks**
 
     PaddlePaddle has made breakthroughs in ultra-large-scale deep neural networks training. It launched the world's first large-scale open-source training platform that supports the training of deep networks with 100 billion features and trillions of parameters using data sources distributed over hundreds of nodes. PaddlePaddle overcomes the online deep learning challenges for ultra-large-scale deep learning models, and further achieved real-time model updating with more than 1 trillion parameters.
      [Click here to learn more](https://github.com/PaddlePaddle/Fleet)
 
-
 - **High-Performance Inference Engines for Comprehensive Deployment Environments**
 
    PaddlePaddle is not only compatible with models trained in 3rd party open-source frameworks , but also offers complete inference products for various production scenarios. Our inference product line includes [Paddle Inference](https://paddle-inference.readthedocs.io/en/master/guides/introduction/index_intro.html): Native inference library for high-performance server and cloud inference; [Paddle Serving](https://github.com/PaddlePaddle/Serving): A service-oriented framework suitable for distributed and pipeline productions; [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite): Ultra-Lightweight inference engine for mobile and IoT environments; [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs): A frontend inference engine for browser and mini-apps. Furthermore, by great amounts of optimization with leading hardware in each scenario, Paddle inference engines outperform most of the other mainstream frameworks.
 
-
 - **Industry-Oriented Models and Libraries with Open Source Repositories**
 
      PaddlePaddle includes and maintains more than 100 mainstream models that have been practiced and polished for a long time in the industry. Some of these models have won major prizes from key international competitions. In the meanwhile, PaddlePaddle has further more than 200 pre-training models (some of them with source codes) to facilitate the rapid development of industrial applications.
      [Click here to learn more](https://github.com/PaddlePaddle/models)
 
-
 ## Documentation
 
 We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) and
@@ -81,16 +78,26 @@ We provide [English](https://www.paddlepaddle.org.cn/documentation/docs/en/guide
 
    We appreciate your contributions!
 
-## Communication
+## Open Source Community
 
 - [Github Issues](https://github.com/PaddlePaddle/Paddle/issues): bug reports, feature requests, install issues, usage issues, etc.
-- QQ discussion group: 441226485 (PaddlePaddle).
-- [Forums](https://aistudio.baidu.com/paddle/forum): discuss implementations, research, etc.
+
+- Open Source Contribution Activities:
+
+  - Beginner: Happy Open Source Activity（[Regular Season](https://github.com/PaddlePaddle/Paddle/issues/56689)、[Pre-Hackathon Camp](https://github.com/PaddlePaddle/Paddle/issues/58497)）
+  - Advanced: PaddlePaddle Hackathon（[Personal Challenge Competition](https://github.com/PaddlePaddle/Paddle/issues/57262)、[LLM Application Competition](https://github.com/PaddlePaddle/Paddle/issues/57585)、[Hackathon Code Camp](https://github.com/PaddlePaddle/Paddle/issues/57264)）
+
+- Community Organizations:
+  - Technical Organization: [Paddle Framework Contributor Club, PFCC](https://github.com/PaddlePaddle/community/tree/master/pfcc)
+  - Community Governance Organization: [PaddlePaddle OpenSource Development Working Group, PPOSDWG](https://github.com/PaddlePaddle/community/tree/master/pposdwg)
+
+- Community Blog: <https://pfcc.blog/>
 
 ## Courses
 
 - [Server Deployments](https://aistudio.baidu.com/aistudio/course/introduce/19084): Courses introducing high performance server deployments via local and remote services.
 - [Edge Deployments](https://aistudio.baidu.com/aistudio/course/introduce/22690): Courses introducing edge deployments from mobile, IoT to web and applets.
 
 ## Copyright and License
+
 PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).

README_cn.md

@@ -22,13 +22,15 @@
 
 跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
 
-### 安装最新稳定版本:
-```
+### 安装最新稳定版本
+
+``` sh
 # CPU
 pip install paddlepaddle
 # GPU
 pip install paddlepaddle-gpu
 ```
+
 更多安装信息详见官网 [安装说明](https://www.paddlepaddle.org.cn/install/quick)
 
 PaddlePaddle用户可领取**免费Tesla V100在线算力资源**，训练模型更高效。**每日登陆即送8小时**，[前往使用免费算力](https://aistudio.baidu.com/aistudio/index)。
@@ -39,48 +41,51 @@ PaddlePaddle用户可领取**免费Tesla V100在线算力资源**，训练模型
 
     飞桨深度学习框架采用基于编程逻辑的组网范式，对于普通开发者而言更容易上手，符合他们的开发习惯。同时支持声明式和命令式编程，兼具开发的灵活性和高性能。网络结构自动设计，模型效果超越人类专家。
 
-
 - **支持超大规模深度学习模型的训练**
 
     飞桨突破了超大规模深度学习模型训练技术，实现了支持千亿特征、万亿参数、数百节点的开源大规模训练平台，攻克了超大规模深度学习模型的在线学习难题，实现了万亿规模参数模型的实时更新。
     [查看详情](https://github.com/PaddlePaddle/Fleet)
 
-
 - **支持多端多平台的高性能推理部署工具**
 
     飞桨不仅广泛兼容第三方开源框架训练的模型部署，并且为不同的场景的生产环境提供了完备的推理引擎，包括适用于高性能服务器及云端推理的原生推理库 [Paddle Inference](https://www.paddlepaddle.org.cn/inference/product_introduction/inference_intro.html)，面向分布式、流水线生产环境下自动上云、A/B测试等高阶功能的服务化推理框架 [Paddle Serving](https://github.com/PaddlePaddle/Serving)，针对于移动端、物联网场景的轻量化推理引擎 [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite)，以及在浏览器、小程序等环境下使用的前端推理引擎 [Paddle.js](https://www.paddlepaddle.org.cn/paddle/paddlejs)。同时，透过与不同场景下的主流硬件高度适配优化及异构计算的支持, 飞桨的推理性能也领先绝大部分的主流实现。
 
-
 - **面向产业应用，开源开放覆盖多领域的工业级模型库。**
 
     飞桨官方支持100多个经过产业实践长期打磨的主流模型，其中包括在国际竞赛中夺得冠军的模型；同时开源开放200多个预训练模型，助力快速的产业应用。
     [查看详情](https://github.com/PaddlePaddle/models)
 
-
 ## 文档
 
-我们提供 [英文](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) 和
-[中文](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 文档
+我们提供 [英文](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html) 和 [中文](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html) 文档
 
-- [使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)：或许您想从深度学习基础开始学习飞桨
+- [使用指南](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/index_cn.html)：或许你想从深度学习基础开始学习飞桨
 
-- [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html)：使用飞桨搭建您的模型，更高效的完成深度学习任务
+- [应用实践](https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/index_cn.html)：使用飞桨搭建你的模型，更高效的完成深度学习任务
 
 - [API 文档](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/index_cn.html)：新的 API 支持代码更少更简洁的程序
 
+- [贡献方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_contribution/index_cn.html)：参与飞桨社区开源贡献的指南
+
+## 开源社区
 
-- [贡献方式](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/08_contribution/index_cn.html)：欢迎您的贡献!
+- [Github Issues](https://github.com/PaddlePaddle/Paddle/issues)：提交安装/使用问题、报告bug、建议新特性、沟通开发计划等
+- 社区活动：
 
-## 交流与反馈
+  - 入门：快乐开源活动（[热身打卡 + 常规赛](https://github.com/PaddlePaddle/Paddle/issues/56689)、[启航计划](https://github.com/PaddlePaddle/Paddle/issues/58497)）
+  - 进阶：飞桨黑客马拉松（[开源贡献个人挑战赛](https://github.com/PaddlePaddle/Paddle/issues/57262)、[大模型应用与创意赛](https://github.com/PaddlePaddle/Paddle/issues/57585)、[飞桨护航计划集训营](https://github.com/PaddlePaddle/Paddle/issues/57264)）
 
-- 欢迎您通过[Github Issues](https://github.com/PaddlePaddle/Paddle/issues)来提交问题、报告与建议
-- QQ群: 441226485 (PaddlePaddle)
-- [论坛](https://aistudio.baidu.com/paddle/forum): 欢迎大家在PaddlePaddle论坛分享在使用PaddlePaddle中遇到的问题和经验，营造良好的论坛氛围
+- 社区组织：
+  - 技术交流组织：[飞桨核心框架贡献者俱乐部 PFCC](https://github.com/PaddlePaddle/community/tree/master/pfcc)
+  - 社区治理组织：[飞桨社区开源发展工作组 PPOSDWG](https://github.com/PaddlePaddle/community/tree/master/pposdwg)
+
+- 社区博客：<https://pfcc.blog/>
 
 ## 课程
 
-- [服务器部署](https://aistudio.baidu.com/aistudio/course/introduce/19084): 详细介绍高性能服务器端部署实操，包含本地端及服务化Serving部署等
-- [端侧部署](https://aistudio.baidu.com/aistudio/course/introduce/22690): 详细介绍端侧多场景部署实操，从移动端设备、IoT、网页到小程序部署
+- [服务器部署](https://aistudio.baidu.com/aistudio/course/introduce/19084)：详细介绍高性能服务器端部署实操，包含本地端及服务化Serving部署等
+- [端侧部署](https://aistudio.baidu.com/aistudio/course/introduce/22690)：详细介绍端侧多场景部署实操，从移动端设备、IoT、网页到小程序部署
 
 ## 版权和许可证
+
 PaddlePaddle由[Apache-2.0 license](LICENSE)提供