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Bump paddlepaddle from 2.0.0 to 2.6.1 in /examples/AlphaZero #1144

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Bumps paddlepaddle from 2.0.0 to 2.6.1.

Release notes

Sourced from paddlepaddle's releases.

PaddlePaddle 2.6.1 Release Note

发版说明

此版本在新功能方面,引入了对Fake GroupWise Quant的支持,有助于用户更好地进行模型量化。同时新增了图神经网络训练引擎PGLBox,支持超大规模图模型GPU多机多卡高效训练。此外,增加了对自定义设备的支持,进一步扩展PaddlePaddle的功能范围。在bug方面,解决了一些核心功能、数据加载以及网络通信等方面的问题。修复了多个安全问题,包括一些潜在的安全漏洞,进一步提高框架代码安全性,并更新了安全公告。

新特性

  • 支持Fake GroupWise量化(#61900:新增对一种量化方法的支持,可以提高模型的性能效率。
  • 支持图神经网络GPU训练(#60495#62111:新增图神经网络训练引擎PGLBox,支持超大规模图模型GPU多机多卡高效训练。
  • 其他改进:支持tile op int8模式推理,并添加vlog语句 (#60261),repeat_interleave支持bfloat16数据类型的Tensor输入 (#61854),自定义设备支持动态图模式的c_embedding算子 (#60774),在CINN(自定义中间网络)框架中,将IntrinsicOps添加到ir_codes_collector中。

Bug修复

  • 修复权重量化内核错误(#60184:解决了在权重量化内核中当n不能被64整除时的问题。
  • 修复量化感知测试问题(#61211:修复了量化感知训练(QAT)测试中的问题,以确保其正常运行。
  • 修复Paddle-TRT集成问题(#61806, #61605, #61966:对Paddle-TRT集成进行了多项修复,包括缓存键值(KV)量化和单元测试失败问题。
  • 禁用LLM_INT8 UT(#62282:禁用了大型语言模型(LLM)INT8精度的单元测试,以避免不必要的运行时。
  • 修复test_benchmark单测编译失败问题(#61427:修复了test_benchmark单测编译失败的问题#60092
  • 修复工具包的数据加载器(#61867:对工具包的数据加载器进行了必要的更正。
  • 修复put_along_axis系列问题(#62065:对reduce参数新增支持min/max/mean三种可选值,修复了reduce=add/mul下的反向梯度计算Bug, 修复了reduce=mul下的GPU前向计算Bug,修复了size过大时的前向计算Bug。
  • 修复Windows平台下的编译Bug(#60308:修复windows平台下的编译找不到common库的bug。
  • 修复OpenSSL-CPU编译错误(#62079:修复cpu-openblas编译场景中未正确链接 Python 库导致的编译bug。

安全修复

文档

  • 文档风格改进(#61688:改进了文档的样式和格式。
  • 安全公告更新(#60532, #60649:更新了2023年的安全公告,以通知用户潜在的安全问题。

PaddlePaddle 2.6.0 Release Note

1. 重要更新

  • 新一代中间表示 PIR:为了进一步提升飞桨框架的可扩展性,研制了新一代中间表示 PIR(Paddle Intermediate Representation)。实现系统性的抽象飞桨框架底层核心概念,如:Operation、Attribute 和 Type 等,为开发者提供了灵活、高效的基础组件。通过引入 Dialect 机制,可以全面、分层次地满足各模块对中间表示的需求,从而极大地提升了框架的扩展性。PIR 严格遵循 SSA(Static Single Assignment)原则,在实现了顶层结构的统一的同时,还确保了“算子顺序性”与“计算图语义”的和谐共存。此外,PIR 提供了更为简洁、低成本的 Pass 开发流程,内置了一系列丰富且功能完备的 Pass 优化策略,为大型模型的极致性能优化提供了技术支撑。
  • 动转静编译优化架构:为了进一步提升框架的模型开发性能,飞桨动转静训练能力全面升级,支持自适应的图构建能力,在 700 多个飞桨产业级模型上验证,一键动转静训练成功率达到 100%。同时,飞桨框架的神经网络编译器 CINN 整合入飞桨主 Repo,使得编译器与飞桨更加融为一体。CINN 完成了架构的梳理和扩展能力的完善,提升系统稳定性。基于 PIR 完成动转静、组合算子、执行器和编译器的紧密联动,为飞桨框架整体性能的提升提供了更大的空间。
  • 增强动态图分布式能力:大模型对框架的分布式训练性能提出了更高的需求。飞桨在通信库、图分析、分布式策略和任务启停等维度进行了全面优化,增强了飞桨动态图的分布式计算能力,为大型模型高效训练提供了支持。在性能方面,通过减少流水线 GPU 显存占用、采用 TensorFusion 技术、实现通信计算 overlap 以及减少非必要的数据同步拷贝等方式,进一步提升了训练性能。同时,通过环境变量控制 Optimizer 等方式提高了混合并行调试的灵活性。此外,通过相关 Bug 的修复,显著提升了系统的稳定性。

... (truncated)

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Bumps [paddlepaddle](https://github.com/paddlepaddle/paddle) from 2.0.0 to 2.6.1.
- [Release notes](https://github.com/paddlepaddle/paddle/releases)
- [Changelog](https://github.com/PaddlePaddle/Paddle/blob/develop/RELEASE.md)
- [Commits](PaddlePaddle/Paddle@v2.0.0...v2.6.1)

---
updated-dependencies:
- dependency-name: paddlepaddle
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label May 7, 2024
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