v2.0.2

版本发布时间: 2021-04-16 14:47:11

PaddlePaddle/Paddle最新发布版本:v3.0.0-beta0(2024-06-27 18:00:34)

2.0.2 Release Note

本版本主要是对2.0.1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：

paddle.nn.functional.cross_entropy 新增了 use_softmax 参数，控制是否在计算交叉熵前先进行softmax运算；并给paddle.nn.functional.softmax_with_cross_entropy 添加了 deprecated 标志，该API将在未来的版本中废弃。
修复了分布式训练中参数服务器模式下的多个问题。
升级Paddle的oneDNN版本至2.2版本，提升了多个模型的预测性能。

修复 paddle.nn.MaxPool3D和 paddle.nn.AvgPool3D 的stride 和 padding 没有默认值的问题。(#32014)
修复支持cudnn的 RNN 创建参数时报告重复创建的问题。(#31916)
修复 paddle.nn.functional.cross_entropy 的 soft_label 为 True，并指定 weight 参数时报错的问题；新增参数 use_softmax，用于控制是否在计算交叉熵前先进行softmax运算；同时，给 paddle.nn.functional.softmax_with_cross_entropy 添加 deprecated 说明，该API将会在未来的版本中废弃。(#31953、#32105、#32035)
修复paddle.nn.ClipByNorm在梯度全部为零时产生NaN数值的问题，该问题会导致使用混合精度训练时不收敛。(#32038)
修复 paddle.stack 内存越界访问的问题。(#32005)

升级Paddle的oneDNN版本到oneDNN 2.2,多个模型预测性能有提升。(#31270)
- Upgrade onednn to onednn 2.2 which improved many models inference performance. (#31270)
添加hard_swish oneDNN算子支持，增加 conv + hard_swish 算子融合, 使得ocr_det模型性能在SkyLake上提升18%。(#31870)
- Add hard_swish oneDNN support and conv + hard_swish fusion, which improved ocr_det model inference performance by 18%. (#31870)

修复rnn模型动态图转静态图导出保存后，运行时崩溃问题。(#31846)
修复了开启oneDNN预测连续多个图像时报错的问题。(#31837)
- Fix continuous images inference failure when oneDNN is ON. (#31837)
修复了部署在CPU上的部分oneDNN int8 模型与原量化模型存在精度差的问题。(#31810)
- Fix the accuracy difference between fake quantized models and deployed oneDNN int8 models. (#31810)
去除了SkipLayerNorm融合的多余限制条件。 (#32082、#32119)

This version fixed some function and performance issues of PaddlePaddle 2.0.1, and optimized some function. The important updates are as following:

Add the use_softmax parameter to paddle.nn.functional.cross_entropy, which controls whether to perform softmax operation before calculating the cross entropy; add the deprecated mark to paddle.nn.functional.softmax_with_cross_entropy, for this API will be deprecated in the future version.
Fix multiple issues of distributed training in parameter server mode。
Upgrade Paddle's oneDNN version to 2.2, which improves the inference performance of multiple models.

Fix the issue that the stride and padding of paddle.nn.MaxPool3D and paddle.nn.AvgPool3D do not have default values. (#32014)
Fix the issue that when RNN supporting cudnn creates parameters, repeated creations are reported. (#31916)
Fix the issue that when the soft_label of paddle.nn.functional.cross_entropy is True, and the weight parameter is specified, an error will be reported; add the use_softmax parameter to paddle.nn.functional.cross_entropy, which controls whether to perform softmax operation before calculating the cross entropy; add the deprecated mark to paddle.nn.functional.softmax_with_cross_entropy, for this API will be deprecated in the future version. (#31953, #32105, #32035)
Fix the issue of paddle.nn.ClipByNorm generating NaN values as the gradients are all zero, which will lead to non-convergence when using mixed precision training. (#32038)
Fix the issue of accessing array out of bounds in paddle.stack. (#32005)

Fix the issue that in parameter server mode the calculation graph segmentation supports GradClip strategy.(#31945)
Fix the initialization of truncated gaussian distribution in parameter server mode.(#31945)
Fix the issue of incorrectly printing the Profiler's multi-threaded information in parameter server mode.(#31945)
Fix the Python3 incompatibility issue when data are read by Dataset and output by zip.(#31945)
Clean up redundant log information and optimize the output format of exe.train_from_dataset.(#32009)

Paddle-TRT adapts to the ERNIE/BERT model trained and saved by PaddlePaddle 2.0.(#31959)

Upgrade onednn to version 2.2, which has improved many models inference performance. (#31270)
Add hard_swish oneDNN support and conv + hard_swish fusion, which has improved ocr_det model inference performance by 18% on SkyLake. (#31870)

Fix the issue that a run of the rnn model, which is saved after the dynamic graph to static graph, will crash.(#31846)
Fix the error of inferring continuous images when oneDNN is ON. (#31837)
Fix the accuracy difference between fake quantized models and deployed oneDNN int8 models. (#31810)
Remove the redundant constraints of SkipLayerNorm fusion. (#32082、#32119)