Skip to content

[WIP]ggml-hexagon: improve leftover element calc at vec_dot_f16_f32 #18336

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
Draft
chraac wants to merge 9 commits into
base: master
Choose a base branch
from
Draft

[WIP]ggml-hexagon: improve leftover element calc at vec_dot_f16_f32 #18336

chraac wants to merge 9 commits into from
+80 −76

Conversation

@chraac
Copy link
Contributor

@chraac chraac commented Dec 24, 2025 •
edited
Loading

Performance

Device: 8Gen2

Baseline: ed7597771
Optimization: 2058f28b3

Operation Params Baseline (GFLOPS) Optimization (GFLOPS) Speedup
MUL_MAT (f16, f32) k=128, n=1 3.68 5.74 1.56x
MUL_MAT (f16, f32) k=14336, n=1 3.43 7.26 2.12x
MUL_MAT (f16, f32) k=14336, n=2 3.46 7.29 2.11x
MUL_MAT (f16, f32) k=14336, n=3 3.46 7.29 2.11x
MUL_MAT (f16, f32) k=14336, n=4 3.46 7.34 2.12x
MUL_MAT (f16, f32) k=14336, n=5 3.46 7.31 2.11x
MUL_MAT (f16, f32) k=14336, n=8 3.48 7.35 2.11x

@chraac chraac marked this pull request as draft December 24, 2025 03:01
@github-actions github-actions bot added the ggml changes relating to the ggml tensor library for machine learning label Dec 24, 2025
@loci-dev loci-dev mentioned this pull request Dec 24, 2025
Open
chraac
chraac commented Dec 24, 2025
View reviewed changes
ggml/src/ggml-hexagon/htp/matmul-ops.c
volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
volatile HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
Copy link
Contributor Author

@chraac chraac Dec 24, 2025 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Based on my observations, using volatile here seems to have several drawbacks:

  • Prevents inlining: With volatile, the binary retains a separate vec_dot_f16_f32 function instead of inlining it into matmul_f16_f32. image
  • Generates extra store instructions: noticed that the compiler generates extra vmem instructions to write the result registers to the stack, as seen in the highlight below. This will increase the mem bandwidth pressure, which impacts the processing speed.
    image

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

@max-krasnyansky max-krasnyansky Awaiting requested review from max-krasnyansky max-krasnyansky is a code owner

@lhez lhez Awaiting requested review from lhez lhez is a code owner

At least 1 approving review is required to merge this pull request.

Assignees

No one assigned

Labels

ggml changes relating to the ggml tensor library for machine learning

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

1 participant

@chraac