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Bump paddlepaddle from 2.2.0 to 2.6.1 in /examples/tutorials/parl2_dygraph #1142

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@dependabot dependabot bot commented on behalf of github May 7, 2024

Bumps paddlepaddle from 2.2.0 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 2.2.0 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@v2.2.0...v2.6.1)

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

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