2.1.2 Release Note

重要更新

本版本主要是对2.1.1中一些功能和性能问题的修复，重点如下：

• 修复了基础API中的已知问题。
• 修复了动转静语法转写已知的若干问题。
• 自定义OP编译时C++版本检查由C++11升级为C++14。

训练框架

功能优化（含分布式）

基础API

• 修复 paddle.vision 路径下部分API无法访问的问题。(#34489)
• 修复paddle.concat在应用到多个大shape 的Tensor时溢出的问题。(#34396)
• paddle.flip支持输入axis为整型，并提升了动态图模式下的性能。(#34477)
• 修复paddle.slice 输入输出地址相同时越界访问问题。(#34265)
• 修复 paddle.nn.Unfold 的输入参数顺序错误的问题。(#34251)
• 新增了静态图下 Tensor 的若干接口，如 size()、detach()等。 (#33330)
• Tensor.grad 的 Warning内容中增加了不兼容升级的说明。(#34262)
• 下线 paddle.save 保存 Layer 的功能。(#34039)
• 修复 paddle.jit.save在Mac系统上保存的模型，在Linux平台上无法对模型进行重训练的问题。(#34154)
• 修复 layer_norm 在大 size 输入时 cuda kernel 参数错误的问题。(#33893)
• 修复paddle.io.DataLoader误报不兼容升级warning问题。(#34001)
• 修复paddle.io.DataLoader内存泄漏问题。(#34301)

动态图转静态图

• 新增对 Sequential 容器类嵌套使用时的语法支持。(#34246)
• 新增对 Python3 type hint 语法的兼容支持。(#33745)
• @to_static 中 input_spec 参数新增支持非 Tensor 类型，如 int、float、string、bool等。(#33464)
• 修复了动转静语法转写已知的若干问题。(#33963)

自定义OP

• 自定义OP编译时C++版本检查由C++11升级为C++14。 (#30415)

推理部署

Paddle Inference

问题修复

• 修复batch_size > 1时ERNIE模型计算结果错误的问题。(#33784)
• 修复windows下TensortRT推理路径用右斜杠分割导致的崩溃。(#33885)
• 修复MKLDNN elementwise系列OP的X不支持广播的问题。（#33845）

环境适配

编译安装

• 限定了依赖的 Gast 库的版本范围。( gast>=0.3.3, <=0.4.0)(#33850)
• 优化了Avx/No-Avx相关的安装报错信息，减少了冗余的Warning信息。(#33885)

新硬件适配

昆仑硬件训练支持

• 修改昆仑的cmake文件，统一更新昆仑的算子库。（#34000）

Important Updates

This release mainly fixes some features and performance issues in 2.1.1. See the following highlights:

• Fix several known issues in frontend APIs.
• Fix several known issues with dynamic to static syntax transcriptions.
• C++ version check upgrade from C++11 to C++14 during Custom OP compile.

Training framework

Functional optimization (including distributed)

Basic API

• Fix some APIs located in paddle.vision are not accessible issues. (#34489)
• Fix paddle.concat overflow when applied to multiple Tensor with large shape. (#34396)
• paddle.flip supports input axis as integer, and improves performance in dynamic graph mode. (#34477)
• Fix paddle.slice out-of-bounds access problem when input and output addresses are the same. (#34265)
• Fix the problem of wrong order of input parameters of paddle.nn.Unfold. (#34251)
• Add several interfaces for Tensor under static graphs such as size(), detach(), etc. (#33330)
• Add incompatible upgrade note to the Warning content of Tensor.grad.(#34262)
• Downlink paddle.save to save the function of Layer. (#34039)
• Fix paddle.jit.save for saving models on Mac systems that cannot be retrained on Linux platforms. (#34154)
• Fix layer_norm with wrong cuda kernel parameters for large size input. (#33893)
• Fix paddle.io.DataLoader error reporting incompatible upgrade warning issue. (#34001)
• Fix paddle.io.DataLoader memory leak problem. (#34301)

Dynamic to static map

• Add syntax support for nested use of Sequential container classes. (#34246)
• Add compatibility support for Python3 type hint syntax. (#33745)
• Add support for non-Tensor types including int, float, string, bool in the input_spec argument of @to_static. (#33464)
• Fix a number of known problems with the transcription of dynamic to static syntax. (#33963)

Custom OP

• C++ version check upgrade from C++11 to C++14 during Custom OP compile. (#30415)

Inference Deployment

Paddle Inference

bugfix

• Fix wrong calculation result of ERNIE model when batch_size > 1. (#33784)
• Fix the crash caused by splitting TensortRT inference path with right slash under windows.(#33885)
• Fix MKLDNN elementwise series OP's X does not support broadcast .（#33845）

Environment adaptation

Compile and install

• Restrict the version range of dependent Gast libraries ( gast>=0.3.3, <=0.4.0). (#33850)
• Optimize Avx/No-Avx related installation error messages, reduce redundant Warning messages. (#33885)

New Hardware Adaptation

Kunlun hardware training support

• Modify the cmake file of Kunlun to unify and update its operator library.（#34000）

Thanks to our Contributors

This release contains contributions from:

0x45f、Aurelius84、Chen Weihang、chentianyu03、HexToString、iducn、Jacek Czaja、Kaipeng Deng、Leo Chen、lzzyzlbb、Peihan、taixiurong、tianshuo78520a、WeiXin、wenbin、Wilber、wuhuachaocoding、xiongkun、Zhou Wei、 winter-wang .