On device release with KV eviction, also call ggml_backend_free_scratch()
on each backend so a cold model drops its Vulkan compute preallocations
(reallocated lazily on the next compute via restore).
Also guard llama_context::memory_breakdown() against a freed scheduler:
evict_kv release resets sched to null, so a cold model that is then torn
down (e.g. terminated by the process manager) hit GGML_ASSERT(sched) in
ggml_backend_sched_get_buffer_type. Skip the compute-buffer accounting
when sched is null.
Assisted-by: Claude
Add an optional backend interface method free_scratch (with a public
ggml_backend_free_scratch wrapper) that frees transient/scratch device
memory a backend holds outside of any allocated buffer, keeping the
backend usable - the scratch is reallocated lazily on the next compute.
Implement it for the Vulkan backend (ggml_backend_vk_free_scratch): free
the prealloc_x/y/split_k/add_rms_partials and sync_staging device buffers
and reset their sizes, so an idle/cold model does not hold the vision or
matmul compute preallocations in VRAM. All other backends leave the hook
null (no-op).
Assisted-by: Claude
The recurrent (SSM/conv) state of hybrid models (e.g. Qwen3.5) was left
resident when a model's device buffers were released for on-demand VRAM
sharing - llama_memory_recurrent::release_device_buffers() was a no-op
default. Implement it (and restore_device_buffers) with the same
capture-host-shadow / free / reallocate pattern as llama_kv_cache, so
llama_memory_hybrid now evicts both its attention KV and its recurrent
state. The state is read-write, so its shadow is recaptured on every
release.
Assisted-by: Claude
Under LLAMA_SLEEP_EVICT_KV a cold model still held the scheduler's worst-case
compute buffer (hundreds of MiB, e.g. ~700 MiB for gemma-26B) plus the resident
experts. release_device(evict_kv) now also frees the sched (sched.reset() +
sched_need_reserve), and restore_device() rebuilds it via sched_reserve(), so a
fully-evicted model holds essentially no VRAM (gemma cold: 7147 -> 51 MiB).
Trades a heavier re-warm (weights+KV H2D + sched reserve) for the freed VRAM;
weights-only mode is unchanged (fast switch, keeps KV+compute).
Assisted-by: Claude
With LLAMA_SLEEP_EVICT_KV the KV cache device buffers are freed when a model
goes cold. update_slots() touches the KV before the decode-time vram_ensure_warm
(e.g. SWA models create a checkpoint that reads the KV via ggml_backend_tensor_get),
so the KV must already be resident by then. Move the restore to the sleep-wake
handler (handle_sleeping_state(false)), which runs before update_slots, fixing a
GGML_ASSERT(buffer) crash on iSWA models (gemma) when KV eviction is enabled.
Assisted-by: Claude
release_device_weights()/release_device_buffers() leave a null buffer in
ctxs_bufs while the device memory is released for on-demand VRAM sharing.
The memory-breakdown / total_size / clear paths iterated these and called
ggml_backend_buffer_get_size()/get_type()/clear() on the null buffer, tripping
GGML_ASSERT(buffer) and aborting on shutdown of a cold model. Skip null buffers
in llama_model::memory_breakdown and llama_kv_cache::{memory_breakdown,
total_size,clear}.
Assisted-by: Claude
Before uploading a model's weights, acquire the shared VRAM token (ring the
doorbell so any resident model releases first). Previously load uploaded weights
to VRAM before the arbiter was active, so loading a large model while another
(e.g. the warm 4B task model) held VRAM could exceed the budget and OOM.
- add vram_arena_open() (idempotent flock/doorbell setup) and
vram_acquire_for_load(), called from load_model() before
common_init_from_params().
- a coordinated load now stays warm holding the token and serves its first
request without a re-warm (drop the init-time go_cold cold-start).
Assisted-by: Claude
The request handler only calls wait_until_no_sleep() (which wakes a server out
of its sleeping state) when sleep_idle_seconds >= 0. But the VRAM arbiter's
cross-process doorbell can put a server to sleep even when idle-sleep is
disabled, so without this a doorbell-slept server would never wake and requests
to it would hang until timeout.
Do not bypass the wake path when LLAMA_SLEEP_VRAM_ONLY is set, so the arbiter no
longer depends on --sleep-idle-seconds being configured.
Assisted-by: Claude
Extend on-demand device residency to the KV cache so that when a model's KV
plus another model would not fit in VRAM, the KV can also be evicted to a host
shadow (D2H on release, H2D on restore) instead of only the weights.
- llama_memory_i: add release_device_buffers()/restore_device_buffers()
(default no-op). Implemented in llama_kv_cache (D2H shadow of the live
ctxs_bufs, freed and reallocated like the weights); llama_memory_hybrid and
llama_kv_cache_iswa delegate to their child caches.
- llama_context::release_device(evict_kv): also evict the memory's device
buffers when requested; restore_device() rebuilds them. Public API
llama_context_release_device gains an evict_kv flag.
- server: LLAMA_SLEEP_EVICT_KV=1 enables it. Off by default (weights-only),
since the KV shadow adds a D2H/H2D copy of the live cache each cycle.
Validated on RX 580 (Vulkan), 4B @ 32k ctx: weights-only cold VRAM 1750 MB
(KV stays); weights+KV cold VRAM 726 MB (KV freed, ~1 GB reclaimed). KV
survives the round-trip: prompt cache reused after the cycle (prompt_n 4 vs
42), correct output.
Assisted-by: Claude
Add release/restore of a model's GPU weight buffers (keeping a host shadow
and the KV cache) so several always-loaded llama-server processes can
time-share a single GPU without reloading or losing the prompt cache.
- llama-model: release_device_weights()/restore_device_weights() capture a
compact host shadow (stable iteration order, view-skipping) and free then
realloc the device weight buffers; weights_resident() query.
- llama-context: release_device()/restore_device() wrappers; decode() auto-
restores; public C API llama_context_release_device/restore_device.
- server: LLAMA_SLEEP_VRAM_ONLY makes idle-sleep release only the VRAM weights
(not a full unload/reload). A cross-process flock token in LLAMA_VRAM_ARENA
enforces "resident iff holds token"; an inotify doorbell forces the holder
to release on contention. The warden thread only touches the task queue, so
releases run on the loop thread and never race a decode.
Validated on RX 580 (Vulkan): two models share 8GB, never both resident,
correct output under contention, KV cache preserved (no re-prefill).
Assisted-by: Claude
Extends --n-cpu-moe to accept a fractional layer count. The integer part
offloads whole layers as before; the fractional part offloads a subset of the
boundary layer expert tensors (gate, then up), keeping down_proj resident.
This realizes ATSInfer tensor-granularity static placement, giving sub-layer
control over expert VRAM residency. Placement-only, lossless.
Assisted-by: Claude
Add the flash-attn mask_opt change (auto-on for GCN large head sizes,
lossless, +12% pp @ 32k on the RX 580) and correct the earlier pinning
advice: pinning helps in isolated llama-bench but fails in a long-running
server on RADV, so --no-mmap is the serving path. Add a recommended
Polaris serving command.
Assisted-by: Claude
mask_opt was disabled on AMD GCN for head sizes <= 256, but it is
beneficial there in high-context prefill: on fully-visible causal
blocks it skips the per-block mask load+add, and it skips fully-masked
blocks entirely. The attention op on GCN is compute-bound on the
softmax path (no matrix cores), so this cuts real work.
Verified lossless (perplexity bit-identical with it on vs off) and a
measured prefill win on Qwen3.5-35B-A3B (head_dim 256) on an RX 580:
pp2048 unchanged at short context, +8% @ 16k, +12% @ 32k, growing with
depth. Enable for GCN when HSK/HSV >= 256; the existing large-mask
conditions keep it off for decode.
Assisted-by: Claude
Pinning helps on Vulkan too, but GGML_SCHED_PREFETCH_EXPERTS regresses
there (second backend shares one device queue, no overlap). Add an RX 580
Polaris benchmark and the context-dependent --n-cpu-moe guidance.
Assisted-by: Claude
Pinned async host uploads can record into compute_ctx before graph
compute, so the perf logger's opening timestamp must append after them
instead of asserting the context is expired. Without this, profiling
(GGML_VK_PERF_LOGGER) crashes whenever host-register pinning is on.
Assisted-by: Claude
VK_EXT_external_memory_host import fails on RADV for file-backed mmap pages,
so register_host_buffer no-op'd and every MoE-expert upload paid a slow
single-threaded pageable memcpy into the staging buffer each eval (~3 GB/s
effective on the 35B, ~25% of PCIe bandwidth).
When the import fails, fall back to a one-time copy of the region into a
host-visible Vulkan buffer registered in device->pinned_memory under the
mmap's address range. The existing pinned fast path then DMAs straight from
that buffer each eval at full PCIe bandwidth - no per-eval CPU memcpy and no
blocking sync. Costs one-time host-visible memory equal to the CPU-resident
weight region.
Assisted-by: opencode
The generic concat shader reads a transposed source (nb[1]==type_size) with an
uncoalesced stride, which is catastrophically slow on discrete GPUs (~5.6ms for
a 16MB concat on the RX 580 vs ~130us of memory bandwidth), a ~9% prefill
hotspot on Qwen3.5-4B (delta-net state concat).
Add a fast path for concat along dim 0 where one source is stored transposed and
the other source + dst are contiguous along dim 0: copy the contiguous source
with copy.comp and transpose the other source into the matching dst sub-region
with the existing tiled copy_transpose shader (shared-memory 32x32 transpose,
coalesced read+write). Reuses pipeline_cpy_* / pipeline_cpy_transpose_* with
custom push constants + doffset, no new shader. Falls back to the generic path
otherwise (gated on type/shape/contiguity + 16-bit doffset bound).
Assisted-by: opencode
Export register_host_buffer/unregister via the backend reg so the existing
GGML_CUDA_REGISTER_HOST path in llama-model-loader pins mmap'd expert
weights on Vulkan. Imports host pages through VK_EXT_external_memory_host
into device->pinned_memory, letting the existing pinned fast path in
ggml_vk_buffer_write_2d_async DMA straight from system RAM instead of
bouncing through the staging buffer + blocking host memcpy.
A single Vulkan buffer cannot cover a multi-GB mmap (capped at
device->max_buffer_size), so the region is imported in page-aligned
chunks; a bound check makes tensors straddling a chunk boundary fall back
to staging instead of reading out of bounds. Opt-in via
GGML_CUDA_REGISTER_HOST=1 or GGML_VK_REGISTER_HOST=1, no-op otherwise.
Assisted-by: opencode
Lumpiasty