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perf(moss): reduce CUDA Q8 inference overhead while preserving output pixels - #2201
Each replacement reports its own application-only results with pinned ggml and its incremental result with the companion backend, where applicable. SiTU acceleration explicitly depends on leejet/ggml#16; old headers compile the original fallback. The ggml submodule revision is unchanged in all three PRs.
Positional caching is deferred: 17.93 s versus the 17.925 s pinned baseline mean, and 15.98 s versus the 16.025 s companion baseline mean. These samples do not establish a generation speedup for caching. It is excluded from the replacement submissions.
The fresh independent evaluation used one warmup and one timed image per process, with two fresh processes only for the baselines. All 24 images matched the original decoded RGB pixels exactly. Individual PRs contain the settings, limits and measurements.
The original combined submission below is preserved as historical context, including its comparison picture. Its combined speedup is not attributed to any individual replacement.
Original submission (historical)
Summary
Reduce intermediate tensors, memory traffic and kernel launches in MOSS-Image
inference while retaining the original arithmetic order on the eligible CUDA
paths. Saved RTX 3090 measurements for MOSS-Image-SFT Q8_0 show 17.88 s →
12.18 s generation, or 31.9% less time (1.47× throughput), with identical
decoded RGB pixels for the tested configuration. SageAttention is disabled.
Reuse shape/circular-mode positional embeddings and shared modulation slices.
Use Q/K projection views, supported fused SiTU, and direct V layout conversion
to FP16 instead of redundant intermediate copies.
Document the settings, limitations and original-versus-current picture.
Related Issue / Discussion
The complete measured improvement requires the matching ggml backend changes
in the companion backend PR, based on leejet/ggml:sd.cpp at a5915eb0e47adef21c3f25b889a008925ea838c2.
The ggml submodule revision is unchanged, in accordance with the project's
dependency-update policy. A checkout with the current pin now compiles and uses
the original composite SiTU activation. Fused SiTU is compiled only when the
selected header advertises GGML_HAS_SITU, and the existing backend capability
checks still select the fallback when a device does not implement the operation.
The maintainer can validate/integrate the backend changes and update the
submodule separately. The benchmark below measures both sets of performance
changes, not the model-only diff or the old-backend fallback.
Additional Information
Recorded benchmark
Implementation
Generation
Denoising total
Denoising per step
Peak VRAM
Unmodified
17.88 s
17.31 s
1.731 s
17.04 GiB
Optimized
12.18 s
11.61 s
1.161 s
17.04 GiB
RTX 3090 (SM86), Windows, MOSS-Image-SFT-Q8_0.gguf,
Qwen3.5-9B-Q8_0.gguf, FLUX.2 VAE and external Qwen3.5 tokenizer;
1024×1024, 10 Euler steps, CFG 3, flow shift 5, seed 42, empty negative prompt,
batch 1. --flash-attn auto --conv-direct auto --auto-fit on --max-vram -1 --conditioning-cache-size 0; SageAttention and CUDA Graphs off.
Each measured configuration used one warmup and one timed image. These are
saved results from separate sessions, not a new paired statistical benchmark.
Generation is the native server time, excluding startup and HTTP/PNG transfer;
per-step time divides native denoising time by ten. Device-wide VRAM was sampled
every 100 ms. Timing uncertainty was not quantified.
Prompt:
A Victorian portrait of a closeup Scottish 60 years old man in a kilt, mountain nature and blue sky, sharp focus, DSLR, Fujifilm XT3, full body visible.
The image is the unchanged requested two-panel comparison. The underlying
1024×1024 RGB arrays were compared directly: zero changed pixels, zero maximum
channel error, MAE 0 and RMSE 0. RGB SHA-256: 64421a17073d604411015d6052870811e38a513e1430361ed410a498a427e09e.
Verification and limits
Compatibility follow-up for the missing-enum review finding:
reproduced the original compilation failure against the pinned ggml, then
completed clean CPU CLI builds against both that pin and the companion
backend. The actual MOSS translation unit also compiles with upstream mode
enabled. With the companion backend, preprocessed accelerated code matches
the pre-fix version apart from blank lines. No arithmetic or kernel changes
were made for this fix; installed CUDA/Vulkan binaries remain unchanged and
no additional image generations were needed.
Original and optimized source derive from stable-diffusion.cpp 95ff76230889898e1bf40058ba272a3699b50dc6 and ggml a5915eb0e47adef21c3f25b889a008925ea838c2.
Installed binaries match the archived measured executables by SHA-256.
CUDA and Vulkan server/CLI builds pass. The latest targeted CUDA pass has 62
numerical cases matching preserved reference hashes, including direct FP16 K,
attention helpers, partial tiles, strided layouts and fallback paths. CUDA
Compute Sanitizer reports zero errors for its 25 head/cast cases. These are
recorded checks; no additional images were generated to prepare this PR.
Changed source ranges were formatted; token comparison confirms formatting
did not change the measured source's code or comments. Both patches apply
cleanly to their recorded bases and pass git diff --check.
Fresh Q8 transfer tests (8 steps, one warmup + one timed image/build) measured
Z-Image Turbo 7.70 → 7.65 s, Qwen Image 2.1 Turbo 9.60 → 9.43 s, and Krea2
Turbo 13.61 → 13.63 s, all pixel-identical. Those small differences do not
establish a general speedup for other model families.
Pixel equality is established for the recorded model/settings, not all GPUs,
prompts, seeds, resolutions or numerical inputs. No new CPU/Vulkan image
inference, multi-GPU, offload or other-architecture validation is claimed.
No public stable-diffusion API, model weights, sampling settings, GPU clocks
or power settings changed. Test scripts, models and binaries are excluded.
Improved MOSS-Image processing on supported CUDA hardware, including optimized attention and feed-forward operations.
Reuses position embeddings for matching image dimensions, text-token counts, and settings.
Documentation
Added CUDA performance measurements for a specified RTX 3090 configuration, including runtime and output comparison. Notes clarify that timing uncertainty was not quantified and results may not apply to untested configurations.
No actionable comments were generated in the recent review.
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Files selected for processing: 2
docs/moss_image.md
src/model/diffusion/moss_image.hpp
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docs/moss_image.md
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Walkthrough
MOSS-Image adds position-embedding caching and backend-specific execution paths for modulation, activation, QKV, and attention tensors. The documentation describes these paths and reports benchmark results and decoded-image equality for one tested configuration.
MOSSImageRunner caches embeddings by image dimensions, text-token count, and circular-axis settings. compute retrieves embeddings through the cache.
Block operations and graph inputs src/model/diffusion/moss_image.hpp
MOSS-Image passes six modulation tensors to blocks. Supported backends can use fused SiLU operations and CUDA MMA QKV views. Graph context creation now receives the graph.
Attention preparation and benchmark documentation src/core/ggml_extend.cpp, docs/moss_image.md
An eligible flash-attention path casts the permuted F32 value tensor to F16 before reshaping. The documentation describes optimization paths and reports benchmark results and decoded-image equality for the tested configuration.
The change now builds against the currently pinned ggml because the optional fused activation is guarded. The author states the PR is a dependent draft and should not merge until the backend is integrated. When the backend is integrated, the fused activation's gate ordering should be validated against the fallback.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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Status
Explanation
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Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 11 functions across 2 files. (1 skipped: 1…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title clearly identifies the primary change: reducing CUDA Q8 MOSS inference overhead while preserving output pixels.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Description check
The description follows the repository template's core sections (Summary, Related Issue / Discussion, Additional Information, Checklist), and it is detailed and specific. It states what changed (share…
Full details: Description check
Explanation
The description follows the repository template's core sections (Summary, Related Issue / Discussion, Additional Information, Checklist), and it is detailed and specific. It states what changed (shared modulation slices, CUDA Q/K/V layouts, fused SiTU, direct V-to-FP16 conversion), why, and how it was verified (benchmark table, pixel comparison, CUDA and Vulkan builds, Compute Sanitizer results). It also states its dependency on leejet/ggml#16 and the fallback behavior when the pinned ggml lacks GGML_HAS_SITU. The description has gaps. The top-level Summary is missing for the current PR, because the text describes a superseded combined submission and the three replacement PRs. The Related Issue section links the companion backend and superseded PRs but does not clearly separate them. The checklist is left unchecked in the new top section, and the original submission's checked box appears only in the historical part. The Additional Information section mixes historical and current results, so a reviewer must read carefully to know which numbers apply to this PR. The description also says the PR is superseded, but the PR under review still contains the combined changes, so its scope is unclear.
Full details: Docstring Coverage
Explanation
Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 11 functions across 2 files. (1 skipped: 1 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/model/diffusion/moss_image.hpp:
- Line 60: Update the pinned ggml dependency and integrate the companion backend
change so GGML_GLU_OP_SITU is defined and supported on the default non-upstream
GGML path used by ggml_glu_split; apply the same dependency fix to the other use
of this enum.
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src/core/ggml_extend.cpp
src/model/diffusion/moss_image.hpp
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This PR combines positional embedding caching, shared modulation slices, Q/K/V layout changes, and fused SiTU integration. These changes have different dependencies and should be evaluated separately.
Please split them into smaller, focused PRs and provide performance and correctness results for each. Application-side optimizations that work with the pinned ggml should be measured independently; changes requiring the companion backend PR should clearly identify that dependency and their incremental benefit.
The reported combined speedup does not establish which application changes are worth merging. I would prefer reviewing and integrating these optimizations individually, with broadly applicable backend work submitted to upstream llama.cpp wherever practical.
This PR combines positional embedding caching, shared modulation slices, Q/K/V layout changes, and fused SiTU integration. These changes have different dependencies and should be evaluated separately.
Please split them into smaller, focused PRs and provide performance and correctness results for each. Application-side optimizations that work with the pinned ggml should be measured independently; changes requiring the companion backend PR should clearly identify that dependency and their incremental benefit.
The reported combined speedup does not establish which application changes are worth merging. I would prefer reviewing and integrating these optimizations individually, with broadly applicable backend work submitted to upstream llama.cpp wherever practical.
Sure
No problem if you want
But that is literally a small change under ggml and moos implementation itself ... 60 lines of code.
Do you you really want to divide that to separate PRs?
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Superseded by focused PRs
Split following the request for independent evaluation:
Each replacement reports its own application-only results with pinned ggml and its incremental result with the companion backend, where applicable. SiTU acceleration explicitly depends on leejet/ggml#16; old headers compile the original fallback. The ggml submodule revision is unchanged in all three PRs.
Positional caching is deferred: 17.93 s versus the 17.925 s pinned baseline mean, and 15.98 s versus the 16.025 s companion baseline mean. These samples do not establish a generation speedup for caching. It is excluded from the replacement submissions.
The fresh independent evaluation used one warmup and one timed image per process, with two fresh processes only for the baselines. All 24 images matched the original decoded RGB pixels exactly. Individual PRs contain the settings, limits and measurements.
The original combined submission below is preserved as historical context, including its comparison picture. Its combined speedup is not attributed to any individual replacement.
Original submission (historical)
Summary
Reduce intermediate tensors, memory traffic and kernel launches in MOSS-Image
inference while retaining the original arithmetic order on the eligible CUDA
paths. Saved RTX 3090 measurements for MOSS-Image-SFT Q8_0 show 17.88 s →
12.18 s generation, or 31.9% less time (1.47× throughput), with identical
decoded RGB pixels for the tested configuration. SageAttention is disabled.
to FP16 instead of redundant intermediate copies.
Related Issue / Discussion
The complete measured improvement requires the matching ggml backend changes
in the companion backend PR, based on
leejet/ggml:sd.cppata5915eb0e47adef21c3f25b889a008925ea838c2.The ggml submodule revision is unchanged, in accordance with the project's
dependency-update policy. A checkout with the current pin now compiles and uses
the original composite SiTU activation. Fused SiTU is compiled only when the
selected header advertises
GGML_HAS_SITU, and the existing backend capabilitychecks still select the fallback when a device does not implement the operation.
The maintainer can validate/integrate the backend changes and update the
submodule separately. The benchmark below measures both sets of performance
changes, not the model-only diff or the old-backend fallback.
Additional Information
Recorded benchmark
RTX 3090 (SM86), Windows, MOSS-Image-SFT-Q8_0.gguf,
Qwen3.5-9B-Q8_0.gguf, FLUX.2 VAE and external Qwen3.5 tokenizer;
1024×1024, 10 Euler steps, CFG 3, flow shift 5, seed 42, empty negative prompt,
batch 1.
--flash-attn auto --conv-direct auto --auto-fit on --max-vram -1 --conditioning-cache-size 0; SageAttention and CUDA Graphs off.Each measured configuration used one warmup and one timed image. These are
saved results from separate sessions, not a new paired statistical benchmark.
Generation is the native server time, excluding startup and HTTP/PNG transfer;
per-step time divides native denoising time by ten. Device-wide VRAM was sampled
every 100 ms. Timing uncertainty was not quantified.
Prompt:
The image is the unchanged requested two-panel comparison. The underlying
1024×1024 RGB arrays were compared directly: zero changed pixels, zero maximum
channel error, MAE 0 and RMSE 0. RGB SHA-256:
64421a17073d604411015d6052870811e38a513e1430361ed410a498a427e09e.Verification and limits
reproduced the original compilation failure against the pinned ggml, then
completed clean CPU CLI builds against both that pin and the companion
backend. The actual MOSS translation unit also compiles with upstream mode
enabled. With the companion backend, preprocessed accelerated code matches
the pre-fix version apart from blank lines. No arithmetic or kernel changes
were made for this fix; installed CUDA/Vulkan binaries remain unchanged and
no additional image generations were needed.
95ff76230889898e1bf40058ba272a3699b50dc6and ggmla5915eb0e47adef21c3f25b889a008925ea838c2.Installed binaries match the archived measured executables by SHA-256.
numerical cases matching preserved reference hashes, including direct FP16 K,
attention helpers, partial tiles, strided layouts and fallback paths. CUDA
Compute Sanitizer reports zero errors for its 25 head/cast cases. These are
recorded checks; no additional images were generated to prepare this PR.
did not change the measured source's code or comments. Both patches apply
cleanly to their recorded bases and pass
git diff --check.Z-Image Turbo 7.70 → 7.65 s, Qwen Image 2.1 Turbo 9.60 → 9.43 s, and Krea2
Turbo 13.61 → 13.63 s, all pixel-identical. Those small differences do not
establish a general speedup for other model families.
prompts, seeds, resolutions or numerical inputs. No new CPU/Vulkan image
inference, multi-GPU, offload or other-architecture validation is claimed.
No public stable-diffusion API, model weights, sampling settings, GPU clocks
or power settings changed. Test scripts, models and binaries are excluded.
Checklist
Summary by CodeRabbit