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@zhengshengning zhengshengning commented Sep 17, 2025

PR Category

Operator Mechanism

PR Types

New features

Description

主要改动点(相对老代码):

数学公式切换

  1. 实数:由 dx = dout / cos(x)^2 改为 dx = dout * (1 + tan(x)^2)。
  2. 复数:由 dx = dout / conj(cos(x) * cos(x)) 改为 dx = dout * conj(tan(x) * tan(x) + 1)。
    目的:避免在 cos(x)≈0 时的数值不稳定与放大误差,更贴近 PyTorch 的实现与精度表现。

精度与舍入控制

  1. 拆分 double / float 两条路径,分别用 ::tan 和 ::tanf 计算。
  2. 使用 __dmul_rn/__dadd_rn 与 __fmul_rn/__fadd_rn 显式“先乘后加”,禁止 FMA,保证与 PyTorch 更一致的舍入行为。

复测结果:
paddle.tan 对齐case(16 个)
未对齐case(1个float16类型的case)

遗留问题:

  1. flaot16还是存在精度误差。

pcard-67164

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paddle-bot bot commented Sep 17, 2025

你的PR提交成功,感谢你对开源项目的贡献!
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LGTM

@zhengshengning zhengshengning merged commit 9778e11 into PaddlePaddle:develop Sep 22, 2025
55 of 60 checks passed
zhengshengning added a commit to zhengshengning/Paddle that referenced this pull request Oct 24, 2025
… *(1 + tan(x)^2) (PaddlePaddle#75335)

* Tan reverse calculation: dx = dout *(1 + tan(x)^2)
zhengshengning added a commit to zhengshengning/Paddle that referenced this pull request Oct 24, 2025
… *(1 + tan(x)^2) (PaddlePaddle#75335)

* Tan reverse calculation: dx = dout *(1 + tan(x)^2)
zhengshengning added a commit that referenced this pull request Oct 27, 2025
* CallScalarFunction uses the dtype of 'self' as the type of 'other' when opotype is 'div'(#75237)

* LinspaceKernel uses the dtype of 'self' as the type of 'step' when tensor is floating (#75238)

* align LinspaceKernel

* update meta

* update gpu kernel

* fix LinspaceKernelInner

* improve kernel

* fix CudaSigmoidGradFunctor and CudaSiluGradFunctor (#75341)

* Softplus accuracy and torch alignment 1 (#75363)

* [Precision Depth Alignment] paddle.tan reverse calculation: dx = dout *(1 + tan(x)^2) (#75335)

* Tan reverse calculation: dx = dout *(1 + tan(x)^2)

* [Precision Depth Alignment] Add support for CUDNN to paddle.nn.functional.grid_sample to align with torch accuracy.  (#75355)

* accuracy_stable_grid_sample

* fix

* correlation supports big tensor (#75383)

* fix

* fix test

* fix

* paddle.tanh Grad and torch alignment (float16) (#75454)

* [Precision Depth Alignment] paddle.sin and paddle.cos aligns with torch precision. (#75503)

* accuracy_stable_sin

* accuracy_stable_cos

* [深度对齐]Divide (#75379)

* fix

* fix

* fix

* fix

* fix

* [Precision Depth Alignment] fix precision for float16 of paddle.tan backward (#75525)

* fix precision for float16 of paddle.tan backward

* fix else branch of CudaTanGradFunctor

* [Precision Depth Alignment] fix precision for  paddle.expm1 (#75549)

* accuracy_stable_expm1

* fix

* Bigtensor排查修复[Paddle/paddle/phi/kernels/funcs] (#75523)

* fix

* fix

* [Precision Depth Alignment]  fix beta and threshold of paddle.nn.functional.softplus  to double (#75426)

* fix beta and threshold of Softplus to double

* fix test_softplus_activation_fuse_pass v1

* fix test_activation_zero

* fix flaot of SoftplusDoubleGradKernel to double

* add op_patches for softplus

* add yaml for ops/yaml/legacy

* fix infershape/operator for FLOAT64

* fix

* add SoftPlusOpTranscriber

* fix

* fix

* fix1

* fix2

* fix coverage

* fix coverage2

* fix (#75605)

* [深度对齐] dot (#75717)

* fix

* fix

* fix dcu

* [Precision Depth Alignment]  paddle.log aligns with torch precision (#75799)

* accuracy_stable_log

* accuracy_stable_log

* fix

* fix

* fix

* fix

* fix5

* [Precision Depth Alignment] fix eps of paddle.logit from float to double (#75816)

* accuracy_stable_logit

* add LogitOpTranscriber

* fix coverage

* fix 0yaml

* [Precision Depth Alignment] paddle.log_sigmoid (#75898)

* accuracy_stable_log_sigmoid

* fix test_activation_stride_op.py

* [Precision Depth Alignment] Modify the negative_slope parameter of the paddle.nn.functional.leaky_relu API to double (#75547)

* [big tensor] Paddle/paddle/phi/kernels/funcs gpuBigtensor (#75856)

* fix funcs

* gpu

* fix

* fix

* 修改PADDLE_ENFORCE信息

* fix cpu error

* fix dcu

* fix dcu

* fix

* [Fix] log sigmoid complex (#75953)

* feature: Add specialized LogSigmoidFunctor and CudaLogSigmoidFunctor for complex numbers

This commit introduces specialized implementations of LogSigmoidFunctor and CudaLogSigmoidFunctor to handle complex number inputs. The new implementations utilize direct formulas for improved accuracy and stability in calculations involving complex types.

* refactor: Optimize LogSigmoidFunctor and CudaLogSigmoidFunctor for complex types by caching exp(-x) to reduce redundant computations. This change enhances performance while maintaining accuracy in calculations.

* refactor: modified the formula in LogSigmoidFunctor to make it numerical stable

---------

Co-authored-by: Zhan Rongrui <46243324+zrr1999@users.noreply.github.com>
Co-authored-by: 正在学习 <62892980+cszdrg@users.noreply.github.com>
Co-authored-by: Bvicii <98971614+scyyh11@users.noreply.github.com>
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2 participants