* spec: add DSpark speculative decoding
DSpark (DeepSpec, 2026) on top of the merged DFlash drafter. It reuses the
DFlash encoder/decoder graph, target feature extraction and KV-cache injection,
and the verify/accept path unchanged; the draft model is a new "dspark" arch
adding a low-rank Markov head (markov_w1/w2) and an optional (unused here)
confidence head. No new public APIs.
The proposal is the only change: the block is anchor-first (position 0 already
predicts the first draft) and the decoder graph applies a semi-autoregressive,
previous-token conditioned logit bias in-graph, chained per block position:
logits'(i) = logits(i) + markov_w2 . markov_w1[prev(i)]
prev(0) = the block's anchor token, prev(i>0) = argmax(logits'(i-1))
vectorized across all blocks in the batch; the anchors are fed through a
dedicated graph input (token 0 of every block). Greedy stays lossless
(verify unchanged, same as DFlash).
- new arch "dspark" (llama_model_dspark : llama_model_dflash, reuses the graph,
loads the markov/confidence tensors; shares the target's embed/lm_head).
- Qwen3DSparkModel converter.
- new spec type "draft-dspark" (common_speculative_impl_draft_dspark :
common_speculative_impl_draft_dflash, overrides draft() only: submits whole
anchor-first blocks and greedily reads back the biased logits).
* spec: read draft block size in the dflash impl
* docs: add DSpark section to speculative.md
* spec: keep dspark block size read in the dspark impl
* dspark : add TODOs for incomplete parts
- confidence head is loaded but not used yet
- confidence-scheduled prefix pruning is not implemented
- the in-graph Markov chain is greedy-only
- only Qwen3 backbones are supported for now (also noted in docs)
* spec: fold DSpark into the DFlash arch
Address review: drop LLM_ARCH_DSPARK and the dspark.block_size /
markov_rank GGUF keys. A DSpark draft now converts to a DFlash GGUF;
the Markov head tensors are detected by presence (like eagle3 d2t),
block_size is read from the existing dflash.block_size key, and the
block anchors are taken as a strided view of the decoder's token
input instead of a separate graph input.
* spec: add confidence-based draft pruning for DSpark
The DSpark confidence head predicts per-position acceptance of the
drafted block. --spec-draft-conf-min truncates the block at the first
position below the threshold (default 0 = disabled).
* fold the dspark impl into dflash, selected by spec type
* address review comments
* dspark: clean up and improve naming
* update readme
* remove trailing whitespace
* dflash: draft full n_max blocks, defer dp.n_max to the central truncation
The DSpark markov head views the draft batch as a uniform [n_seqs x block]
grid, but the per-seq dp.n_max clamp could produce blocks of different
sizes, silently corrupting the strided views and the resulting logits.
Drop the clamp and always draft the full n_max block for every sequence:
dp.n_max is already enforced by the central truncation in
common_speculative_draft(), the same way eagle3 handles it.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: assert the markov head block-uniformity invariant, require the conf head
With the draft batch always submitting equal-size n_max blocks, a
non-divisible token count can only mean the batch was split across
ubatches or a caller broke the layout - fail loudly instead of silently
dropping the markov bias. The block_drafts > block_size early return
stays: worst-case graph reserve passes legitimately build with
n_seq_tokens > block_size.
Also make conf_proj required when the markov head is present: the
confidence head is part of the DSpark checkpoint format, and a missing
head would otherwise leave --spec-draft-conf-min silently reading stale
embeddings instead of confidences.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dspark: fold conf_min into p_min
p_min and conf_min express the same thing - the minimum predicted
survival probability for a drafted position - differing only in how the
estimate is obtained: token probability for regular drafters, the
trained confidence head for DSpark. The DSpark readback never used
p_min, so reuse it for the confidence threshold and drop the separate
--spec-draft-conf-min flag. Both defaulted to 0 (disabled), so behavior
is unchanged.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: note the confidence broadcast workaround
Requested in review: the ggml_repeat only adapts the [1, n_tok]
confidences to the n_embd-wide embd_nextn transport so that
llama_get_embeddings_nextn can be reused - not a placeholder.
Co-authored-by: Zaire404 <3147879462@qq.com>
* cont : clarify
[no ci]
---------
Co-authored-by: Ruixiang Wang <wangruixiang07@outlook.com>
Co-authored-by: Zaire404 <3147879462@qq.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sycl: fix use-after-return of the SDPA scale in the oneDNN flash-attention path
The scale was uploaded with an async memcpy sourced from a stack local. On the
in-order queue that copy is ordered behind the K/V staging kernels; once n_kv is
large enough (>= ~26k observed on Arc Pro B70) the staging outlives the host
stack frame and the copy reads recycled memory, feeding the SDPA a garbage scale.
Output then collapses to a single repeated token and the KV cache is poisoned
for the rest of the session.
Short contexts win the race by accident, and test-backend-ops caps
FLASH_ATTN_EXT at kv=1024, which is why CI never caught it. The previous
device_count > 1 wait_and_throw() gate (and reverting it, PR #25741) fixes the
symptom only by keeping the frame alive across the copy at the cost of a host
sync on every FA call.
Fix: cache one device scalar per (device, value) -- the scale is constant per
model -- and upload it synchronously once. The single-device fast path (no
per-call host sync) is then safe: every device-side hazard already serializes
on the in-order queue. The multi-GPU conservative wait is kept unchanged.
Also:
- GGML_SYCL_FA_ONEDNN_MAX_KV env (0 = unlimited): optional n_kv ceiling that
routes very long sequences to the native FA kernel.
- test-backend-ops: FLASH_ATTN_EXT F16 cases up to kv=65536 (Qwen3.6-27B
geometry hsk=hsv=256 GQA 6, and hsk=128 GQA 4), closing the kv=1024 blind
spot. Note the race itself needs a live multi-op pipeline to reproduce;
single-op runs pass even on broken builds.
Verified on Arc Pro B70 (bmg_g31), Qwen3.6-27B Q4_K, -c 131072: output
byte-identical at temp 0 to the native FA path through 32k-deep prefill, with
prefill depth-flat at 820-840 t/s (vs 340-350 native at 32k depth).
Assisted-by: Claude Fable 5
* sycl: handle GGML_SYCL_FA_ONEDNN_MAX_KV like the other runtime env vars and document it
Review feedback on #25880:
- read the variable once at backend init into g_ggml_sycl_fa_onednn_max_kv via
ggml_sycl_get_env, and print it in the startup env listing (-lv 4 shows it)
- document GGML_SYCL_FA_ONEDNN and GGML_SYCL_FA_ONEDNN_MAX_KV in the SYCL.md
runtime table
Also trim the added FLASH_ATTN_EXT cases to kv={4096,16384}: the 32768/65536
shapes exceed the legacy NMSE threshold on both the oneDNN and native kernels
(long-sequence fp16 accumulation drift, present before this PR) and would fail
CI for an unrelated reason.
Assisted-by: Claude Fable 5
* sycl: clarify GGML_SYCL_FA_ONEDNN_MAX_KV default is disabled
Assisted-by: Claude Fable 5
* sycl: state default behavior of GGML_SYCL_FA_ONEDNN_MAX_KV explicitly
Assisted-by: Claude Fable 5
* Update ggml/src/ggml-sycl/fattn-onednn.cpp
Co-authored-by: Neo Zhang <zhang.jianyu@outlook.com>
* sycl: write the SDPA scale from a kernel instead of caching it
The per-(device, value) scale cache was a function-local static
unordered_map with no synchronization, so concurrent backend instances
could access and rehash it at the same time.
Write the scalar with a single_task instead. The value is captured into
the command, so no host memory has to outlive the call -- which is what
the use-after-return fix needed in the first place. That removes the
shared container, the leaked device allocation and the string key, and
it also closes the remaining async-memcpy-from-a-stack-local on the
first flash-attention call.
Ordering does not rely on timing: the queue is created with
sycl::property::queue::in_order and the dnnl stream wraps that same
queue, so the write completes before the SDPA reads the scalar. The
multi-GPU wait_and_throw() branch is unchanged.
Also drop the <cstdlib> include, which is unused.
Assisted-by: Claude Opus 5
---------
Co-authored-by: Neo Zhang <zhang.jianyu@outlook.com>
* webgpu : add CONV_2D_DW (depthwise conv2d) kernel
Implement GGML_OP_CONV_2D_DW for the WebGPU backend,
ported from the Vulkan backend's conv2d_dw.comp.
Assisted-by: Claude Opus-4.8
* Remove unnecessary comments in webgpu support
* update supported ops tables, triggered by adding webgpu CONV_2D_DW
* support op col2im_1d
* update ops.md
* rm unused words
* update for bf16
* optimize 1%-11% as the review comments
* fix the format issue
* update as the review comments
* Sycl tp stage1 (#1)
* SYCL: tensor parallelism (--split-mode tensor) for dual-GPU
Adds the comm_init/comm_free/comm_allreduce_tensor trio that the
meta-backend queries via get_proc_address to enable backend-specific
all-reduce, mirroring the pattern used by ggml-cuda.cu.
For N=2 (the common dual-GPU case) implements a degenerate ring
all-reduce with two size-branched paths:
* Small (nelem < 32768): FP32 direct memcpy + per-device ADD kernel
chained via depends_on(memcpy_event). 4 SYCL submissions/call.
* Large (nelem >= 32768): BF16-compressed. Each device compresses
FP32 -> BF16 in a local outbox, cross-device memcpys to the peer's
inbox (HALF the PCIe bytes), then decompresses + adds into the
local FP32 partial. 6 SYCL submissions/call but PCIe bytes halved
-- wins for any tensor where PCIe dominates kernel time.
Threshold and BF16 path pattern mirror the CUDA NCCL allreduce.
Storage: ONE persistent uint8_t buffer per device, 4 * nelem bytes
(matches both path layouts: FP32 nelem floats; BF16 outbox+inbox =
2 * nelem uint16_t each). Single alloc+free per device keeps the
SYCL pool's strict-LIFO invariant trivial.
Initial impl handles N=2 FP32 contiguous tensors. Other cases return
false, causing the meta-backend to use its generic butterfly fallback.
Per-call sync is intentionally omitted. SYCL in-order queue semantics
ensure that the meta-backend's next compute on the same per-device
queue waits for our final ADD, and the next allreduce's first op on
the same persistent buffer waits via the same queue. Only comm_free
does an explicit final wait.
OneCCL is NOT used: OneCCL 2021.17 hardcodes single-device-per-process
in communicator_impl.hpp:47 (condition devices.size() == 1), which is
incompatible with llama.cpp's single-process multi-GPU model.
Measured on dual Intel Arc Pro B70 (NEO 26.05.x, oneAPI 2025.3 +
DPC++ nightly):
Llama-3.3-70B Q4_K_M, -sm tensor -fa 1 -ctk f16 -ctv f16:
pp512 = 377.08 t/s (vs 313.65 layer mode = +20.2%)
tg128 = 17.40 t/s (vs 9.74 layer mode = +78.6%)
Qwen3-Coder-Next-80B-A3B Q3_K_M (MoE):
pp512 = 216.56 t/s (vs 156.58 meta-backend butterfly = +38.3%)
tg128 = 17.60 t/s (vs 14.31 meta-backend butterfly = +23.0%)
Qwen3-4B Q4_K_M:
pp64 = 984.51 t/s, tg16 = 49.29 t/s
Llama-3.3-70B in SYCL TP now comfortably beats production layer mode
on both prefill and decode. Coder-Next-80B-A3B (MoE) also wins on
both — the BF16 path is what unlocks the many-medium-allreduces
prefill pattern.
Build/CMake: no changes. No new dependencies. ~210 lines added across
ggml-sycl.h and ggml-sycl.cpp.
* Fix comments
* documentation update to address PR feedback
* Bring over my device-to-device memcpy chagnes
* move the dev2dev_memcpy calls to the upstream 7-parameter variety
* Fix a typo and remove a trailing whitespace
* hex-mm: new weight layout and fusion updates
* hvx-mm: unroll the new tiled vec_dots to optimize hvx register util
* hex-mm: optimize dyn.quant format for q8_0 and q8_1 to reduce overhead in vec_dots.
* hvx-mm: parallel quantizer per block for large rows
* hvx-mm: simplify and futher optimize dyn.quant and vec_dots
* hvx-mm: keep intermediate per tile accumulators in fp16
* hmx-mm: optimize weight dequant by aligning the repacked tiles with the DMA
* hmx-mm: remove qweight scratch and just use vtcm_weight
* hmx-mm: remove all unused and obsolete code
* hmx-mm: the new tiled repack format is here to stay -- rename all x4x2 to _tiled
* hmx-mm: improve activation processing with dma prefetch
* hex-mm: fix hmx/hvx fallback logic and MUL_MAT_ID allocation (unbreaks OLMoE)
* hex-mm: align the weight tiles with dma just like we did in hmx-mm
* hex-mm: factor out common mm bits into htp/matmul-ops.h
* hex-mm: start moving mm kernel selection to the host
* hex-mm: move all of the matmul param compute into the host
* hmx-mm: restore pipelined mode
* hmx-mm: unroll the dequant functions to optimize register usage
* hmx-mm: further improve activation process
* hex-mm: use vtcm_seq_alloc for all vtcm allocations and define more common functions
* hex-mm: improve mm optimizer to acount for number of activation threads
* hex-mm: fix matmul-id kernel params selection (unbreaks OLMoE and LFM)
* hexagon: remove support for arch < v73 since HMX is now required for most use-cases
* hex-mm: cleanup naming for consistency
* hex-mm: make sure matmul fusion accounts for vtcm allocation
* hex-mm: minor cleanup for kernel_params definition
* hex-mm: replace hardcoded limits with proper checks for vtcm requirements
* hex-mm: add support for non-tiled mm as a fallback option and factor out hvx kernels into separate header
* hex-mm: remove unused functions
* hex-mm: add shorthand for MM_SELECT in run-tool script
* hvx-mm: factor out hvx/hmx microkernels and unify matmul entry and dispatch
* hex-mm: further cleanup matmul fallback path
* hex-mm: refactor matmul entry point and dispatch a bit further
* hexagon: update cmake build to enable hmx for everything
* hex-ops: optimize kernel_param updates and include summary in the logs
* hex-mm: add support for GGML_HEXAGON_MM_SELECT
* hex-mm: add hex-common header
* hex-mm: pass correct number of tasks to workpool
* hex-mm: add proper checks for no-work in dyn.quant tasks
* hex-mm: convert all quantizers into a macro
* hex-mm: fix hvx-flat fallback to pass all MUL_MAT tests
* hex-mm: vectorize q8_1 quantizer
* hex-mm: improve fused ffn mm stride handling
* hex-mm: consistent use of n_threads and pipeline in kernel_params
* hexagon: minor formatting
* hex-mm: update MUL_MAT_ID kernel_param handling to make sure host/npu are in sync
* hvx-mm: go back to accumulating in fp32 in tiled hvx kernels, more accurate and same perf
* hvx-mm: unroll the loops and remove masking that is not needed for tiled accums
* hmx-mm: optimize activation processing (slit loops, some unrolling, etc)
* hmx-mm: minor optimization for output processing
* hex-mm: consistent use of uint32_t and size_t in mm kernels
* hex-mm: remove legacy restrictions for rows to be multiple of 256
* hexagon: replace sprintf with snprintf
* hex-mm: relax hardcoded nrows checks and rely on VTCM size requirements
* hexagon: minor alignment fix
* hexagon: fix trailing spaces
* hex-mm: relax padding from 256 to 128 (leftovers)
* hex-mm: remove redundant checks for weight align to 128
we always use 2D dma for the weights and align them properly
* hmx-mm: MUL_MAT_ID better work distribution between hvx threads and hmx tracing
* hex-mm: specialize per-token mmid activation handling
* hex-profile: update python scripts to handle kernel-params section in the logging output
* hex-mm: move n_prefetch (aka dma_depth) into kernel params and remove unused fields
* hex-trace: use easier to parse format, simply and fix post-proc scripts
* hmx-mm: relax 32 row limit for output processing which helps utilization
* hmx-mm: use start-chunk idx for tracing info
* hmx-mm: parameterize activation dma pipeline
* hexagon: add support for simple graph caching to avoid recomputing kernel-params
* hex-mm: remove left-over repack functions
* hex-mm: tighten n_prefetch asserts
* hex-mm: remove duplicate round/align_up helper
* hexagon: cleanup common header used in host/npu
* hexagon: update early wakeup threshold
* hmx-mm: define cost constants and update solver to assume that repacked ne[1] is padded to 32
* hmx-mm: make precompute_matmul a bit more readable (split into smaller functions, etc)
* hex-mm: remove n_threads constraint
* hex-mm: minor formatting updates
* hex-mm: remove obsolete profiling logs
* hex-mm: restore hardcode gate to refuse lm-head to avoid repacking that tensor
* rename GGML_SYCL_SUPPORT_LEVEL_ZERO to GGML_SYCL_SUPPORT_LEVEL_ZERO_API, and GGML_SYCL_ENABLE_LEVEL_ZERO to GGML_SYCL_USE_LEVEL_ZERO_API
* fix code format
* fix error when rebase
* add dev2dev memcpy by SYCL API
* mv GGML_SYCL_DEV2DEV_MEMCPY to runntime table
* update the detect method for p2p comm
* fix the erro created during fix confilct
---------
Co-authored-by: Neo Zhang <NA>
* Add interface is_model_splitted() to check the c-graph is splited or not
* Infer and propagate dynamic-dimension indices for all tensors in the GGML graph in api compute_model_outputs()
* Only do this for fallback sub graph
* Move dynamic dims compute in graph missmatch
* ggml-openvino: fix tensor data handling for PERMUTE/VIEW ops in split models
* ggml-openvino:add comments
* ggml-openvino: override VIEW op_case to 0 for split model inputs
* openvino backend: Handle unsupported VIEW shape-mismatch in OpenVINO backend
* Enable additional mul_mat tests and add tensor data saving function (#81)
* ggml-openvino: fix CONT/TRANSPOSE mapping and improve dynamic-dimension handling
* OpenVINO: add NORM/TANH support and rework SOFT_MAX translation
* ggml-openvino: extend VIEW handling
* Enable -fa off (#118)
* Enable --context-shift
* Fix llm param compute error for normal softmax not the softmax in attention
* OpenVINO backend: fix error for attention size compute in llm param
* use tensor->extra in infer_request i/o
* OpenVINO backend: refacter the compute_llm_params() func add get_attention_pattern_case to easy extand
* OpenVINO backend: clean unused code
* 1to1 match op update (#146)
* added translate_1to1_match_1_input function and updated gelu and tanh translations
* Remove unused translation function calls
---------
Co-authored-by: Mustafa Cavus <mustafacavus@intel.com>
* initial gemma4 support
* removed hardcoded names for kv cache slicing
* OpenVINO backend: Add new attention pattern for llm parameters compute
* flash attn Q shape static conversion
* Remove slice in permute translation when n_seq is 1
* return optional in extract_layer_from_name
* OpenVINO backend: refactor VIEW related operation (#148)
* OpenVINO backend: refactor VIEW related operation
* Enable VIEW handling in following ops
* OpenVINO backend does not support GGML_OP_NORM & GGML_OP_L2_NORM with VIEW input accuracy issue from OpenVINO
* OpenVINO backend: Add ops l2_norm & pad
* OpenVINO backend does not support CPY with non-contiguous data or mismatched types
* add op SSM_CONV GATED_DELTA_NET
* OpenVINO backend: fix error for bf16 in OV gpu plugin
* reverted static Q input shape for attention layer
* OpenVINO backend: remove hardcode name inp_tokens, which ignore some leaf case
* Disable remote tensor due to bug in ov gpu
* Disable n_token > 1 GATED_DELTA_NET on gpu
* OpenVINO backend: fix the view op dynamic handling issue in gemma4 & enable view + get_row
* OpenVINO backend: clean code
* OpenVINO backend: enable view + norm/rms_norm
* OpenVINO backend: concat op
* OpenVINO backend: argsort op
* OpenVINO backend: enable unary + view & GGML_UNARY_OP_SOFTPLUS
* Fix issue for test-backend-ops in TOPK_MOE, which compare VIEW ops result, VIEW node in OpenVINO no need compare, the whole graph result is correct
* OpenVINO backend: enable sum_rows
* OpenVINO backend: enable clamp
* OpenVINO backend: enable DIV
* OpenVINO backend: enable GGML_OP_MUL_MAT_ID
* OpenVINO backend: disable MUL_MAT_ID_FUSION case with large mem needed
* OpenVINO backend: Disable GGML_OP_ARGSORT, cause test_backend-ops failed
* OpenVINO backend: fix issue in mul_mat_id
* OpenVINO backend: Disable DIV with broadcast on GPU
* OpenVINO backend: update DIV
* use ov internal op GatedDeltaNet
* OpenVINO backend: enable llama erch test qwen3next
* OpenVINO backend: enable RMS_NORM + VIEW & remove op_case 2 for rope
* OpenVINO backend: fix error
* suggested changes, need review
* suggested changes, need review
* OpenVINO backend: clean unused code & fix build warning
* OpenVINO backend: enable minicpm3 for arch test
* Disable GDN op (#177)
* disable gated_delta_net
* update stateful_kv_size correctly in mismatch case
* OpenVINO backend: enable arch test for qwen3vl
* OpenVINO backend: enable cohere2 for arch test
* OpenVINO backend: enable t5 for arch test
* OpenVINO backend: enable jamba for arch test
* OpenVINO backend: remove warning for tmp
* OpenVINO backend: enable kimi-linear for arch test
* Remove unused
* Fix gpt-oss accuracy issue
* OpenVINO backend: enable arctic for arch test
* OpenVINO backend: enable grok for arch test
* Gemma4 initial npu support (#179)
* Initiall gemma4 npu support
* temp. fix for gemma4 accuracy bug on npu
* Remove hardcoded names for npu-fold handling
* revert static n tokens for cont translation as it is not needed
* removed unused variable
* ggml-openvino: add GGML_OPENVINO_ENABLE_CACHE env var to control decoder cache. Add environment variable GGML_OPENVINO_ENABLE_CACHE (default: YES). When set to NO, the decoder_cache is bypassed and models are rebuilt from the cgraph on every inference call in both dynamic and static compute paths. This is useful for debugging and verifying correctness without caching interference.
* Revert "Gemma4 initial npu support (#179)"
This reverts commit 0d29a9c4a52dc2c8aa52990f1a3854cfb01768ad.
* OpenVINO backend: disable debug log print
* Update TBB discovery. Delegated to OpenVINOs own config.
* OpenVINO backend: GGML_OPENVINO_ENABLE_CACHE YES -> 1
* OpenVINO backend: fallback FLASH_ATTN_EXT in gemma3n to CPU backend
* Add raw ov infer profiling metric
* Add OV raw infer time metric to static compute path
Co-authored-by: virajwad <84867530+virajwad@users.noreply.github.com>
* Modify precision of static profiling
* update to OV 2026.2, add OV windows CI
* fix editorconfig-checks
* Initiall gemma4 npu support
* temp. fix for gemma4 accuracy bug on npu
* Remove hardcoded names for npu-fold handling
* revert static n tokens for cont translation as it is not needed
* removed unused variable
* test-llama-archs fix
* Fix gemma4 flash_attn fallback
* support im2col
* fix code style
* disable add_rope_sin_cos optimization
* stateless boradcast and rope optimizations
* Enable manual gqa attn by default for stateless gpu
* manual gqa: fixed static batch
* gemma4 llama-bench ctx update fix
* Update OV win CI
* stateful rope fusion temp. fix
* OpenVINO backend: Conslolidate supported ops
* Exclude unsupported GGML_OP_SUB cases
* Exclude unsupported TOPK_MOE cases
* OpenVINO Backend: MUL_MAT enhancements
* Update OV CI
* support f16 mask input for npu
* Make GGML_OPENVINO_* env vars usage uniform
Standardize all GGML_OPENVINO_* env flags:
positive integers >0 to enable. Unset, empty, =0, or non-numeric values to disable.
This fixes cases where text values or empty strings enabled features.
* OpenVINO backend: Enhance envvar handling
* more cleanup
* move ggml_openvino_env_flag to appropriate place
* OpenVINO backend: add REPEAT translator, Q5_1 weights, and GLU view-input fix
* ggml-openvino: fix -Werror=cast-qual in extract_q5_1_data
* Update openvino.Dockerfile
Use BuildKit cache mounts for faster Docker rebuilds.
Use apt instead of dpkg, remove unused .ddeb downloads, add DLLAMA_BUILD_TESTS=OFF.
* ggml-openvino: centralize env var access via *getenv_str/getenv_int helpers
Replace getenv and legacy flags with _str and _int helpers.Minor cleanup, doc updates.
* OpenVINO backend: Enable GGML_OP_ADD_ID
* Uptade openvino backend clamg-format
* clang-format
* Update OPENVINO.md (#211)
* OpenVINO backend: fix accuracy issue for op CONCAT with i64 precision
* Remove strict concurrency for gpu-openvino-low-perf
* Update openvino CI keynames; add ccache-clear
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
* Fix formatting
---------
Co-authored-by: Xuejun Zhai <Xuejun.Zhai@intel.com>
Co-authored-by: Mustafa Cavus <mustafa.cavus@intel.com>
Co-authored-by: Mustafa Cavus <mustafacavus@intel.com>
Co-authored-by: Xuejun <XuejunZhai@intel.com>
Co-authored-by: Wang Yang <yang4.wang@intel.com>
Co-authored-by: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
Co-authored-by: virajwad <84867530+virajwad@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Mostafa Faheem <mostafaaafaheem@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
This introduces an optional feature to allocate large GPU buffers (≥ 1GB)
using USM system allocations if supported by the device. It allows using
buffers from the system allocator then letting the system manage memory
migrations between host and device as necessary.
This feature is disabled by default and requires the GGML_SYCL_USM_SYSTEM
environment variable to enable. If USM system allocations are not supported
by the device or the system, we fallback to regular allocations.
This feature can allow VRAM overcommit. For example, the test below fails
on B580 due to lack of memory for allocation, but it passes when enabling
USM system allocations:
./examples/sycl/test.sh -m Qwen3.5-27B-Q3_K_M.gguf -lv 4
Signed-off-by: Francois Dugast <francois.dugast@intel.com>
* vulkan: support non-contig unary/glu ops
Change unary/glu ops to pass in all strides and use fastdiv for the index
calculation. Put all unary ops in one file, similar to glu, to share the
code. codex went ahead and added expm1 without me asking, but I had to
make it do a real precision analysis rather than just making stuff up.
unary.comp initially couldn't use generic_unary_head because there wasn't
space for xielu's additional constants. Fixing this required packing the
fastdiv 'L' values.
* attempt to workaround compiler bug
* resolve conflict from #23991
* use expm1
* Tidy up SYCL doc a bit
- Add explicit links to referenced items
- Fix spelling errors
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
* Correct documented default for GGML_SYCL_GRAPH
The default is ON, not OFF:
$ cmake -LAH -B build | grep GGML_SYCL_GRAPH
...
GGML_SYCL_GRAPH:BOOL=ON
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
* Move docker instructions from SYCL.md to docker.md
This makes them directly accesible from the Quick Start section
of the top-level README.md.
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
* Refer to intel.Dockerfile for ARGs and their defaults
The defaults are always changing; this avoids accuracy errors
from duplicating the information.
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
* Remove mention of Nvidia in SYCL row of backend table
This support was removed in 2026.02 - refer to the SYCL.md News.
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
---------
Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
* add to support Q1_0, NVFP4, IQ2_XXS, IQ2_XS, IQ2_S, IQ3_XXS, IQ1_S, IQ1_M, IQ3_S, IQ4_NL, IQ4_XS, I32, MXFP4, Q2_K, Q3_K, Q5_K, and Q6_K in GET_ROWS OP
* correct the link
