Skip to content

CANN: Add broadcast for softmax and FA #15208

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Merged
merged 6 commits into from
Aug 11, 2025
Merged

CANN: Add broadcast for softmax and FA #15208

merged 6 commits into from
Aug 11, 2025
+213 −340

Conversation

hipudding
Copy link
Collaborator

@hipudding hipudding commented Aug 10, 2025 •
edited
Loading

Make sure to read the contributing guidelines before submitting a PR

@sa #14435

@hipudding hipudding changed the title Softmax CANN: Add broadcast for softmax and FA Aug 10, 2025
@github-actions github-actions bot added testing Everything test related ggml changes relating to the ggml tensor library for machine learning Ascend NPU issues specific to Ascend NPUs labels Aug 10, 2025
@hipudding
Copy link
Collaborator Author

./bin/test-backend-ops test -b CANN0 -o SOFT_MAX | grep -v "not supported"
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f32,nr23=[3,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[2,3],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f16,nr23=[3,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[2,3],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f32,nr23=[3,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[2,3],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f16,nr23=[3,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[2,3],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f32,nr23=[3,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[2,3],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f16,nr23=[3,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[2,3],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f32,nr23=[3,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[2,3],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,16,1,3],mask=1,sinks=0,m_prec=f16,nr23=[3,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[2,3],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[15,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,15,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1024,1024,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[1023,1023,1,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000): OK
SOFT_MAX(type=f32,ne=[16,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
SOFT_MAX(type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000): OK
10829/10829 tests passed

@hipudding hipudding marked this pull request as ready for review August 11, 2025 06:49
@hipudding hipudding requested review from ggerganov and ngxson and removed request for ggerganov August 11, 2025 12:48
@ngxson ngxson removed their request for review August 11, 2025 14:12
@hipudding hipudding merged commit be48528 into ggml-org:master Aug 11, 2025
48 checks passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers

@ggerganov ggerganov ggerganov approved these changes

Assignees
No one assigned
Labels
Ascend NPU issues specific to Ascend NPUs ggml changes relating to the ggml tensor library for machine learning testing Everything test related
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
2 participants
@hipudding @ggerganov