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feat: support for cross-device row split#1735

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leejet merged 4 commits into
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pwilkin:row-split
Jul 4, 2026
Merged

feat: support for cross-device row split#1735
leejet merged 4 commits into
leejet:masterfrom
pwilkin:row-split

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@pwilkin

@pwilkin pwilkin commented Jul 3, 2026

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Summary

Part 2 of the changes split in #1470 containing the row-split allocation (slower, but better at conserving memory), requires split buffer support.

Requires #1734

Related Issue / Discussion

#1470

Additional Information

pwilkin and others added 2 commits July 3, 2026 16:21
A --backend module assignment can now list several devices separated by
'&'. The module's transformer blocks are partitioned into contiguous
ranges sized proportionally to each device's free memory (minus a fixed
compute headroom) and registered with the ModelManager with per-tensor
compute backends; the existing allocation/staging/LoRA/residency
machinery handles the weights unchanged. The module's graphs execute on
a ggml_backend_sched spanning the devices, pinning each node to the
device of the most recently consumed weight (view ops are never pinned)
and splitting each graph exactly once. Supported for the diffusion and
te modules; for te the dominant encoder (t5xxl or the LLM) splits while
small sub-runners stay on the main device. Graph-cut segmentation and
--stream-layers are disabled for split modules. Adds --list-devices to
print the ggml device names accepted by the backend specs.

Manual placement only; row/tensor split and auto-fit are follow-ups.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…ode row)

--split-mode selects how a module assigned multiple runtime devices
distributes its weights: layer (default) or row. In row mode the module
keeps executing on its main device while its transformer-block matmul
weights are allocated in the backend's row-split buffer type
(resolved through the "ggml_backend_split_buffer_type" proc, CUDA only
for now), which slices each weight's rows across the devices in
proportion to free memory and runs the matmuls multi-GPU internally.

The ModelManager owns the split buffer types (set_split_buffer_type):
params_buffer_type_for returns the split type for eligible tensors when
params live on the compute backend, and the staging path groups by
(backend, buffer type) and allocates with
ggml_backend_alloc_ctx_tensors_from_buft so cpu/disk params residency
stages straight into split buffers. Eligibility is limited to
contiguous 2D weights of at least 256x256 inside transformer blocks:
anything else is consumed by non-matmul ops or sliced into views,
which split buffers do not support. Direct LoRA application skips
row-split tensors; the automatic LoRA mode selects at_runtime when row
split is active. Falls back to a layer split when the backend has no
split buffer type or the devices span registries.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
@pwilkin pwilkin changed the title Row split feat: support for cross-device row split Jul 3, 2026
@leejet leejet merged commit 68f3d6d into leejet:master Jul 4, 2026
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@leejet

leejet commented Jul 4, 2026

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Thank you for your contribution.

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2 participants