Commit Graph
9930 Commits
Author SHA1 Message Date
Xuan Son Nguyen 99b9a6c08a also show model aliases 2026-07-08 00:35:05 +02:00
Xuan Son Nguyen 1729662ea3 nits fixes 2026-07-07 22:25:21 +02:00
Xuan Son Nguyen 50ed8076fb no more json in header 2026-07-07 22:10:31 +02:00
Xuan Son Nguyen a87b2d77cf pimpl 2026-07-07 21:56:44 +02:00
Xuan Son Nguyen b9617e860a cli-view --> cli-ui 2026-07-07 21:39:11 +02:00
Xuan Son Nguyen 28b71c022a add ftype 2026-07-07 21:36:55 +02:00
Xuan Son Nguyen 7cd7832297 Merge branch 'master' into xsn/cli_http_based 2026-07-07 21:11:05 +02:00
Pasha KhosraviandGitHub bec4772f6a Add Q2_0 quantization: type definition and CPU backend (#24448) b9913 2026-07-07 12:05:47 -07:00
Georgi GerganovandGitHub c198af4dc2 spec : fix naming, spacing (#25410) b9912 2026-07-07 18:52:30 +03:00
3899b39ce2 CUDA: Fuse MMVQ post-scale for NVFP4 (#24481)
* CUDA: Fuse MMVQ for NVFP4 and BS 1

TODO:
1. Add tests to test-backend-ops (did verify correctness manually for
   one model)
2. Reorder bias/scale once PRs for NVFP4 are merged/landed

* Add dense MMVQ fusion as well

Perf numbers on B4500. Note qwen35 is FP8->Q8
+ ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
| Model                    | Test         |   t/s master |   t/s osimons/nvfp4_fuse_mmvq |   Speedup |
|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
| qwen35moe 35B.A3B NVFP4  | tg128@d32768 |       150.15 |                        156.29 |      1.04 |
| qwen35moe 35B.A3B Q4_K_M | tg128@d32768 |       157.91 |                        157.64 |      1.00 |

Perf numbers on DGX Spark
+ ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
| Model                    | Test         |   t/s master |   t/s osimons/nvfp4_fuse_mmvq |   Speedup |
|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
| qwen35moe 35B.A3B NVFP4  | tg128@d32768 |        58.31 |                         59.69 |      1.02 |
| qwen35moe 35B.A3B Q4_K_M | tg128@d32768 |        54.94 |                         54.79 |      1.00 |

* Add tests for the added fusion ops

* Cleanup test-backend-ops

* Cleanup ggml-cuda/mmvq

1. Unrestrict post-scale fusion
2. Rename names accordingly
3. Remove env variable to disable fusion

* Merge old mul_mat patterns into the lane-based approach

* Enable fusion for MoE in shared MMVQ

* Restrict scale_view_nodes, enroll MM + ADD into lane-matcher

* Refactor mmvq loads, still does not help non-nvfp4 kernels

* Restrict scale-fusion to NVFP4

This is necessary, as the prolog is quite heavy in GEMV for some
quants/model configs, leading to net perf regression.
We should really be looking to refactor this such that ratio of
prologue/hot-loop/epilogue is better on the hot-loop
front:

+ ./scripts/compare-llama-bench.py -b master -c c1b9381d32 --tool llama-bench -i llama-bench.sqlite
| CPU                         | Model                    | Test         |   t/s master |   t/s c1b9381d3 |   Speedup |
|:----------------------------|:-------------------------|:-------------|-------------:|----------------:|----------:|
| INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B NVFP4     | tg128@d32768 |       151.70 |          154.32 |      1.02 |
| INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B Q4_K_M    | tg128@d32768 |       187.95 |          185.73 |      0.99 |
| INTEL(R) XEON(R) GOLD 6542Y | gpt-oss 20B MXFP4 MoE    | tg128@d32768 |       304.62 |          300.69 |      0.99 |
| INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B NVFP4  | tg128@d32768 |       193.72 |          211.99 |      1.09 |
| INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 |       217.76 |          218.15 |      1.00

* Reorder scale & bias-add to adhere to #24331

* Restrict lane scale to NVFP4

Don't need to test unfused combinations

* Cleanup

* Merge single-lane mm-fusion helpers

* Refactor and clean-up host-side fusion logic

* Move gate_bias and scale into the same active-thread guard

Latest perf numbers:
B6000

build: 5b7d9f272 (9578)
+ ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
| CPU                         | Model                    | Test         |   t/s master |   t/s osimons/nvfp4_fuse_mmvq |   Speedup |
|:----------------------------|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
| INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B NVFP4     | tg128@d32768 |       151.79 |                        154.10 |      1.02 |
| INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B Q4_K_M    | tg128@d32768 |       187.90 |                        187.27 |      1.00 |
| INTEL(R) XEON(R) GOLD 6542Y | gpt-oss 20B MXFP4 MoE    | tg128@d32768 |       303.77 |                        306.56 |      1.01 |
| INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B NVFP4  | tg128@d32768 |       193.41 |                        207.99 |      1.08 |
| INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 |       217.60 |                        218.58 |      1.00 |

DGX Spark

build: 5b7d9f272 (9578)
+ ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
| CPU   | Model                    | Test         |   t/s master |   t/s osimons/nvfp4_fuse_mmvq |   Speedup |
|:------|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
| CPU   | gemma4 26B.A4B NVFP4     | tg128@d32768 |        34.61 |                         34.84 |      1.01 |
| CPU   | gemma4 26B.A4B Q4_K_M    | tg128@d32768 |        46.95 |                         46.90 |      1.00 |
| CPU   | gpt-oss 20B MXFP4 MoE    | tg128@d32768 |        64.84 |                         64.62 |      1.00 |
| CPU   | qwen35moe 35B.A3B NVFP4  | tg128@d32768 |        59.63 |                         60.72 |      1.02 |
| CPU   | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 |        56.53 |                         56.55 |      1.00 |

PPL values for 5 chunks:
this PR

model                                                                                                       mode             ppl         uncertainty  log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf                                 fusion_enabled   5.2892      0.35389      ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_enabled.log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf                                 fusion_disabled  5.2742      0.35215      ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_disabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf  fusion_enabled   5.4487      0.36866      ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf  fusion_disabled  5.4403      0.36782      ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_disabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf                              fusion_enabled   17342.4348  3703.13932   ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf                              fusion_disabled  18627.0624  3998.42475   ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_disabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf                                            fusion_enabled   363.8913    33.14007     ppl-value-checks/gpt-oss-20b-mxfp4.fusion_enabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf                                            fusion_disabled  363.8913    33.14007     ppl-value-checks/gpt-oss-20b-mxfp4.fusion_disabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf                          fusion_enabled   17330.3926  3716.70472   ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_enabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf                          fusion_disabled  17933.9524  3883.17066   ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_disabled.log

master:
summary: ppl-value-checks/summary.tsv
model                                                                                                       mode             ppl         uncertainty  log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf                                 fusion_enabled   5.2892      0.35389      ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_enabled.log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf                                 fusion_disabled  5.2742      0.35215      ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_disabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf  fusion_enabled   5.4487      0.36866      ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf  fusion_disabled  5.4403      0.36782      ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_disabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf                              fusion_enabled   17342.4348  3703.13932   ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf                              fusion_disabled  18627.0624  3998.42475   ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_disabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf                                            fusion_enabled   363.8913    33.14007     ppl-value-checks/gpt-oss-20b-mxfp4.fusion_enabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf                                            fusion_disabled  363.8913    33.14007     ppl-value-checks/gpt-oss-20b-mxfp4.fusion_disabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf                          fusion_enabled   17330.3926  3716.70472   ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_enabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf                          fusion_disabled  17933.9524  3883.17066   ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_disabled.log

* Allow views to weights in ggml_can_fuse_subgraph

* Remove gate_first from test_mul_mat_vec_fusion

* Ditch lane-parsing approach in favor of hard-coded patterns

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Rename ggml_is_constant_view_src to ggml_is_constant

* Finish renaming of 0905129e9d

* Readd descriptive prints for fusion debugging

* Add weight-buffer pre-allocation to `test_case`

This is required so we correctly test fusion of NVFP4.

* Update ggml/src/ggml.c

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Add 2nd context for weights as suggested by @JohannesGaessler

This reflects more natural use of ggml compared to artifically
pre-allocating weights into the same context

* Exclude fused tests from gradient mode

I'm unsure of the current state, but naively every fusion pattern
should require its own backpropagation implementation. I don't see these
implemented for the CUDA backend, so we can disable tests to avoid
triggering GGML_ASSERT for

    ggml_tensor * build_graph(ggml_context * ctx) override {
        GGML_ASSERT(!use_weight_context());
        return build_graph(ctx, nullptr);
    }

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
b9911
2026-07-07 17:12:19 +02:00
AlexandGitHub f5525f7e7a server : fix draft model fit vs load inconsistency (#25056)
* fix: draft model fit vs load inconsistency

* refactor(server): unify draft/mtp parameter initialization, model, and context load
- moves speculative init to speculative.cpp
- changes server_context_impl model_dft and ctx_dft to use raw pointers

- fix: don't throttle progress callback when loading draft model
- refactor: rename draft model/ctx load method

* fix: valign
b9910
2026-07-07 17:20:42 +03:00
Thomas LECONTEandGitHub 5eca4e3cab server : add timings and progress to /responses API stream (#25348) b9909 2026-07-07 16:13:03 +02:00
Thiago PadilhaandGitHub 6c487e2f79 server: enforce prompt cache RAM limit (#25070)
Before this commit, --cache-ram was not a hard limit:

- The cache always kept at least one entry, even if that entry exceeded the
  RAM/token limits.
- Old entries were only evicted for the RAM/token limits after saving the new
  one, which could cause the cache to temporarily exceed the RAM/token limits
  even if individual entries were below the limit.

Now, ensure that the RAM limit is strict with these changes:

- Skip saving state to cache if by itself it exceeds the RAM limit.
- Evict old entries as necessary to make the new entry fit.

Additionally, token-limit cleanup may now evict the last remaining cache entry
instead of always preserving one.
b9908
2026-07-07 15:24:35 +02:00
zhangrundaandGitHub c1a411fb1b common : add missing <fstream> include in common.h (#25220)
Signed-off-by: zhangrunda <zhangrunda1234@outlook.com>
b9907
2026-07-07 15:23:53 +02:00
asf0andGitHub 33ca0dcb9d ggml-hip : add -fno-finite-math-only alongside -ffast-math (#25373)
-ffast-math implies -ffinite-math-only under ROCm/clang 22, which
disables INFINITY/NaN and triggers -Wnan-infinity-disabled (errors
under -Werror in CI). Re-enable infinity handling without dropping
the rest of fast-math.

Fixes #25361
b9906
2026-07-07 13:27:50 +02:00
Aman GuptaandGitHub 024c46ae4e llama: fix quantized kv-cache for dsv4 (#25202) b9905 2026-07-07 17:46:57 +08:00
Neo ZhangandGitHub 108f186d17 [SYCL] fix unsupported UT cases of CONT & CPY (#25231)
* fix unsupported UT cases of CONT & CPY

* update ops.md

* rm unused head file
b9904
2026-07-07 12:20:52 +03:00
Neo ZhangandGitHub 47e1de77aa [SYCL] support op col2im_1d (#25264)
* 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
2026-07-07 11:07:46 +03:00
Neo ZhangandGitHub 55edb2de44 [SYCL] support OP cross_entropy_loss, cross_entropy_loss_back (#25236)
* support OP cross_entropy_loss, cross_entropy_loss_back

* correct format issue
b9902
2026-07-07 10:48:50 +03:00
Todd MalsbaryandGitHub d209086157 sycl : set K_QUANTS_PER_ITERATION to 1 on DMMV path (#25063)
* sycl: add supported types to ggml_sycl_supports_reorder_dmmv

The reordered feature is implemented in ggml_sycl_op_dequantize_mul_mat_vec,
but gated by ggml_sycl_supports_reorder_dmmv. This commit fixes the gate.

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* sycl: set K_QUANTS_PER_ITERATION=1 to improve utilization

When combined with opening the reorder gate, this improves GPU
utilization on B70, giving a significant boost to tg t/s.

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* sycl: replace QK_WARP_SIZE with WARP_SIZE for QK_5

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

* sycl: add missing types to ggml_backend_sycl_buffer_init_tensor

Without this, the extra field is not allocated and the reorder path
will not take effect.

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>

---------

Signed-off-by: Todd Malsbary <todd.malsbary@intel.com>
b9901
2026-07-07 10:43:41 +03:00
Neo ZhangandGitHub 95e5254c0a [SYCL] fix unsupport ACC UT cases for noncontiguous (#25124)
* fix unsupport ACC UT cases for noncontiguous

* update ops.md
2026-07-07 10:40:38 +03:00
Neo ZhangandGitHub 9e5ef0dbb1 sycl : enhance argsort to support all UT cases (#25125) b9899 2026-07-07 10:39:29 +03:00
Neo ZhangandGitHub 3d4cbdf18a sycl : use sycl func to fix AOT double type issue (#25081) b9898 2026-07-07 10:38:33 +03:00
Neo ZhangandGitHub 26145b3db7 sycl : rename the env vars from "disable" to "enable" (#25042) b9897 2026-07-07 10:33:51 +03:00
An LongandGitHub 1a7c25bfdb ggml : make ggml_time_init idempotent (#24422) 2026-07-07 10:29:17 +03:00
o7siandGitHub defa95c306 speculative : fix out-of-bounds read in ngram-map on prompt shrink (#23936)
* speculative : fix out-of-bounds read in ngram-map on prompt shrink

* speculative : fix ngram-map cleanup cutoff after prompt shrink
b9895
2026-07-07 10:25:04 +03:00
a8cfdbb9e4 vulkan : check src0 type in GGML_OP_SET_ROWS to avoid failures due to unimplemented f16 support (#25351)
* vulkan : check src0 type in GGML_OP_SET_ROWS to avoid failures due to unimplemented f16 support

* chore : get rid of else

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
b9894
2026-07-07 12:56:02 +08:00
6f8895feec opencl: general flash attention decode performance optimizations (#25366)
* opencl: vec flash-attention decode kernels for f16/q8_0/q4_0 KV

* opencl: improve non FA KQ mv kernels

* opencl: tweaks for multiquery FA

* opencl: some tweaks for FA q1 kernels

* opencl: FA with DK=DV=512 for gemma-4

* opencl: various fixes

* opencl: cleanup

* opencl: fix FA decode crash for DK=512 (gemma-4)

The DK=512 decode-only program does not create the f32_f16 prefill
kernel, so the compiled check in ensure_fa_variant never hit and
supports_op gave inconsistent answers for the same op. block_n is also
unset for DK=512 decode; guard it to avoid an out-of-range read at
dispatch.

* opencl: run DK=512 FA decode on CPU

DK=512 decode is bandwidth-bound and faster on the CPU than the GPU,
increasingly so with depth. Decline it in supports_op; prefill stays on the GPU.

* opencl: compile MQ_GQA=8 FA kernels in a minimal program

The full program compiled with -D MQ_GQA=8 runs the Adreno compiler out
of memory at DK>=256. Only the vec_mq kernels are used from this
program, so compile it with FA_MQ_ONLY, which excludes everything else.
Also include the program name in the compile error log.

* opencl: remove stray token in flash_attn_f32_f16.cl

A stray "." broke the f32_f16 program build.

* opencl: split f16-KV FA decode finer (FD_KV_PER_SPLIT_F16)

The 2048 default under-fills the GPU on single-query f16-KV decode;
use 512 for f16 KV to get more splits. Quantized KV keeps 2048.

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>
b9893
2026-07-06 19:57:52 -07:00
shalinib-ibmandGitHub ee445f93d8 common: Set optimal default thread count for ppc ( linux as well as AIX) (#25237) b9892 2026-07-07 05:35:20 +08:00
f36e5c348b metal: add col2im_1d op (f32/f16/bf16) (#25176)
* metal: add col2im_1d op (f32/f16/bf16)

Gather kernel mirroring the CPU/CUDA path: each output (t_out, oc)
reads its ceil(K/s0) source columns with an F32 accumulator, a single
write and no atomics. One thread per output element, 256 per
threadgroup.

* metal: check dst contiguity and type match in supports_op for COL2IM_1D

Align the GGML_OP_COL2IM_1D predicate with the CPU, CUDA, and Vulkan
backends: the kernel writes dst with linear indexing and assumes the
same type as src0, so supports_op must also require a contiguous dst
and op->type == op->src[0]->type.

* Update ggml/src/ggml-metal/ggml-metal.metal

Co-authored-by: YiChen Lv <63285796+forforever73@users.noreply.github.com>

---------

Co-authored-by: YiChen Lv <63285796+forforever73@users.noreply.github.com>
b9891
2026-07-06 20:47:36 +02:00
Johannes GäßlerandGitHub 74976e1aef CUDA: remove -sm row, refactor cuBLAS (#24216)
* CUDA: remove -sm row, refactor cuBLAS

* fix CDNA + BF16 logic

* fix bad return

* fix src0 strides, contiguous requirements

* fix GGML_CUDA_FORCE_CUBLAS

* fix casts to BF16
b9890
2026-07-06 20:04:53 +02:00
PascalandGitHub 9abce7473a server: fix deadlock in load_models() when erasing a finished download (#25358)
* server: fix deadlock in load_models() when erasing a finished download

The download monitoring thread acquires the models mutex on its way out,
but load_models() joined it from the erase loop while holding that mutex.
Join it outside the lock via threads_to_join like the other monitoring
threads.

* server: add default timeout to test requests

A hung server now fails the test after 10 minutes instead of stalling
the CI job for hours. Explicit timeouts are unchanged.
2026-07-06 19:26:06 +02:00
Alexey KopytkoandGitHub cb295bf596 CUDA: extend K-type validation to V-types for flash attention (#24403)
* CUDA: extend K-type validation to V-types for flash attention

* reorder
b9888
2026-07-06 16:26:50 +02:00
Xuan-Son NguyenandGitHub bfdf581b8b server: temporary skip model downloading API test (#25355) 2026-07-06 16:10:04 +02:00
ragz4125andGitHub 20a04b2206 ggml-cpu: use UE4M3 LUT in ARM NVFP4 dot product (#25331) b9886 2026-07-06 19:06:40 +08:00
shalinib-ibmandGitHub 3b4fca11ac ggml-cpu: Enable tiled matmul on AIX (#25199)
The matmul_tiled path uses large local stack buffers for A_pack and B_pack. On AIX this can trigger a segmentation fault, so reduce the buffer footprint there to keep the tiled path usable.

 Performance Impact:
    ~ 2x gains in PP_Speed for FP32, Q4_0 and Q8_0 models tested with llama-bench, llama-batched-bench and llama-cli.
    Models used: Llama3.2 3b Instruct F32, qwen 2.5 3b Q4_0 and Q8_0
b9885
2026-07-06 18:18:17 +08:00
hokanosekaiandGitHub 86961efd56 vulkan: fix 32-bit integer overflow in CEIL_DIV (#25245) b9884 2026-07-06 10:35:57 +02:00
PascalandGitHub d80e878501 ui: restore Ctrl+B sidebar toggle shortcut (#25307) 2026-07-06 10:30:07 +02:00
Adrien GallouëtandGitHub 48719618e8 scripts : use HF_TOKEN when downloading UI assets (#25280)
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
b9882
2026-07-06 09:53:35 +02:00
a-hukandGitHub d06ddd3589 ggml-hip: enable -ffast-math for HIP builds (#23862) b9881 2026-07-06 15:02:26 +08:00
Xuan-Son NguyenandGitHub 898b08854d ui: fake 200 for proxy DELETE req (#25298) 2026-07-06 08:41:39 +02:00
adavyasandGitHub 72874f559c ggml-cuda: optimize conv_transpose_1d indexing (#25310) b9879 2026-07-06 11:49:06 +08:00
Al GandGitHub 2da6686176 Fix stale tensor-split params for draft models (#24814)
* meta: fix tensor split metadata for GQA attention

* Tidied the code a bit to match existing style

* Revert "Tidied the code a bit to match existing style"

This reverts commit b90c6c6300.

* Reverted the ggml-backend-meta asset hack.
b9878
2026-07-05 20:39:36 +02:00
EveandGitHub 3e5036fbfb abort if we see a multi buffer (#25276) b9877 2026-07-05 20:38:47 +02:00
liminfei-amdandGitHub 4b2a0cdee1 ggml : fix tensor-parallel + -ncmoe crash on MoE models (#25028)
Tensor parallelism (-sm tensor) combined with -ncmoe (CPU-offloaded MoE
experts) aborts during warm-up on MoE models with
GGML_ASSERT(ggml_is_contiguous(tensor)) in ggml-backend-meta.cpp.

The failing tensor is the MoE router output (ffn_moe_topk): it is mirrored
(GGML_BACKEND_SPLIT_AXIS_MIRRORED, replicated across backends since routing
must be identical) and happens to be a non-contiguous view.
ggml_backend_meta_buffer_{get,set}_tensor asserted contiguity before
consulting the split state, so a mirrored non-contiguous tensor tripped the
assert even though the GGML_BACKEND_SPLIT_AXIS_MIRRORED case right below
already handles it.

Move the split-state lookup above the assert and allow the mirrored case in
both get_tensor and set_tensor.

Diagnosis credit to the reporter (@nathanmp).

Fixes #24886

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
b9876
2026-07-05 19:56:11 +02:00
7a63fdede1 ggml: Update VMM Pool allocation ggml-cuda.cu - Turing P2P access fix (fixes #24489) (#24491)
* Update ggml-cuda.cu - Turing P2P access fix.

* Add original code as fallback behaviour when NCCL or P2P is not set/true.

* Update ggml/src/ggml-cuda/ggml-cuda.cu to add comment as per suggestion

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-07-05 19:10:09 +02:00
78d2f52468 cuda : concat implementation for quantized types (#25303)
* cuda : concat implementation for quantized types

* chore : apply am17an clever suggestion to shorten the code

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
b9874
2026-07-05 23:26:24 +08:00
liminfei-amdandGitHub a4107133a6 llama : add guard for K/V rotation input when buffer is unallocated (#25215)
llm_graph_input_attn_kv::set_input and llm_graph_input_attn_kv_iswa::set_input
call set_input_k_rot / set_input_v_rot whenever the rotation tensor pointer is
non-null, but the tensor's buffer can be unallocated (NULL) when a graph only
stores K/V without attending -- e.g. DFlash speculative decoding's KV-injection
pass. set_input_k_rot then calls ggml_backend_buffer_is_host() on a NULL buffer
and aborts with GGML_ASSERT(buffer).

Guard the four k_rot/v_rot inputs with the same "&& ->buffer" check that the
adjacent kq_mask inputs already use in these two functions. When the buffer is
unallocated there is no data to upload, so skipping is correct.

Fixes #25191

Signed-off-by: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
b9873
2026-07-04 22:37:38 +02:00
PascalandGitHub 665892536d ui: add sync blocks so display/behavior settings can be set via --ui-config-file (#25132)
* ui: add sync blocks so display/behavior settings can be set via --ui-config-file

* ui: remove enable thinking setting
2026-07-04 16:12:27 +02:00
ef2d770117 ggml : fix broken CPU concat implementation for quantized types (#25247)
* ggml : fix broken CPU concat implementation for quantized types

* tests : concat tests for quantized types

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
b9871
2026-07-04 13:37:37 +02:00