9938 Commits

Author SHA1 Message Date
RapidMark ccb0c34223 ggml-hip: enable -funsafe-math-optimizations (#24668)
CUDA is compiled with fast math and AMD/HIP is not — this flag lets AMD use fast math too.

We can't use -ffast-math: it implies -ffinite-math-only, which won't compile (ggml uses INFINITY for masking) and produces NaNs. -funsafe-math-optimizations gives the speedup without the NaN problems.

Co-authored-by: Mark Caldwell <mark@cloudhands.ai>
b9938
2026-07-09 11:02:26 +03:00
Pascal 2021515a1a cuda: align snake fusion matcher with the other backends (#25460)
* cuda: fix snake fusion type predicate, a and inv_b are F32

The matcher required a->type == x->type while launch_snake reads both
as const float *, matching the CPU and Metal contract where a and inv_b
stay F32. F16/BF16 chains never fused and fell back to the naive path,
and a hypothetical all F16 chain would have read F16 bits as float.
Aligns the predicate and the comment with ggml-cpu.c

* cuda: reject snake fusion on non-contiguous operands

The kernel reads x[idx] and a[c] / inv_b[c] linearly, so a
non-contiguous view passing the matcher would silently read wrong data.
Mirror the contiguity guard already present in the CPU, Vulkan and
Metal matchers.
b9937
2026-07-09 11:00:06 +03:00
Aldehir Rojas 64c8b7db72 server : respect min-step when splitting prompt batches (#25420) b9936 2026-07-09 01:23:30 -05:00
Aparna M P f2d1c2f398 hexagon: add VISION RoPE support (#25216)
* hexagon: add VISION RoPE support

* hexagon: support RoPE on strided half-dim views for all modes

* hex-rope: decouple src0 DMA copy size from row stride

* hex-rope: support non-contiguous dst for RoPE

* hex-rope: fix dst spad pitch for non-contiguous dst
b9935
2026-07-08 21:55:00 -07:00
Masashi Yoshimura 32e41fa5b4 ggml-webgpu: tune subgroup split (d_split) in flash_attn_vec (#25418) b9934 2026-07-09 08:34:19 +09:00
Hongqiang Wang 92366df30d opencl: Q6_K GEMM/GEMV fix for ne01 of weights that are not multiples of 128. (#25464)
* opencl: fix garbled output for Q6_K weights with ne01 % 128 != 0 on Adreno

Observed with granite-3.1-3b-a800m-instruct, whose vocab is an odd number.

Route Q6_K dense mul_mat with ne01 % 128 != 0 off the noshuffle path:
decode (ne1==1) uses the correct flat GEMV and the matching GEMM (ne1>1)
falls back to CPU (the flat convert has no verified small-batch GEMM kernel
for these shapes). All standard hidden/FFN/vocab dims are multiples of 128
and keep the noshuffle path.

* opencl: reserve alignment slack for the SOA subbuffer carve in alloc size

set_tensor carves quantized weights into per-component subbuffers (d/q,
ql/qh/s/d, ...) whose origins are each rounded up to the device base
address alignment. When a component's size is not a multiple of the
alignment, the carve extends past ggml_nbytes(tensor) and the last
subbuffer overlaps the next tensor in the pool -- e.g. q6_K [1536, 49155]:
size_s = 49155*96 ends 32 bytes past a 128-byte boundary, so the d
subbuffer ends 96 bytes past the tensor's allocation, and whichever of the
two neighboring tensors is uploaded last silently corrupts the other (here:
the last vocab rows' block scales). This affects any quant type whose
component sizes can be misaligned, on any shape with ne01 not a multiple of
the alignment granularity; standard power-of-two dims are unaffected.

Implement get_alloc_size for the OpenCL buffer type and reserve the
worst-case carve slack (4 aligned gaps; 5 components max, q5_K) for
quantized tensors. Costs at most 512 bytes per quantized tensor at the
observed 128-byte alignment.

* opencl: use lm based q6_k mm when ne1 is not multiple of 128

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>
b9933
2026-07-08 15:52:21 -07:00
Ruben Ortlam a646006f09 vulkan: disable FA mask_opt on GCN to improve performance (#24362)
* vulkan: disable FA mask_opt on GCN to improve performance

* reenable mask opt over attention head size 256
b9932
2026-07-08 19:01:25 +02:00
Hongqiang Wang 167d057604 opencl: ragged-tile MoE prefill FP16 GEMM optimization (skip padded expert tiles) (#25433)
* opencl: ragged-tile MoE prefill GEMM (skip padded expert tiles)

The MoE prefill GEMM groups tokens into TILESIZE_N=32 per-expert tiles; at low
tokens-per-expert most tiles are mostly padding. When a tile's upper 16 slots
are all padding (router index 0xFFFFFFFF), skip the second dotx16_reduce8 half.
Numerically identical (skipped lanes are padding). Applied to all eight *_f32_ns
MoE GEMMs; default on, opt out with GGML_OPENCL_MOE_RAGGED_FP16=0.

* opencl: quarter-granularity ragged MoE tile-skip (8-col skip-groups)

Replace the two half-tile dotx16_reduce8 calls in the 8 *_f32_ns MoE GEMMs with
four dotx8_reduce4 (8-column) calls, skipping each empty trailing skip-group
independently. Padding is always trailing, so the kernel rounds the valid count
up to the skip granularity and skips fully-padding groups. Byte-identical to the
non-skipped path. New env GGML_OPENCL_MOE_RAGGED_GRAN={8,16,32} (quarter/half/
off); default quarter.

* opencl: move ragged moe env var in cl_init

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>
b9931
2026-07-08 09:44:55 -07:00
Aman Gupta 1ee093937f llama-batch: fix allowed decreasing pos in a seq (#25449) b9930 2026-07-08 19:24:34 +03:00
Ruben Ortlam 0bbc87b163 vulkan: for small AMD GPUs, reduce submission threshold based on CU count (#25240) b9929 2026-07-08 18:15:18 +02:00
Max Krasnyansky 81ff7abe50 hexagon: new vtcm layouts and improved pipelines for MUL_MAT, MUL_MAT_ID and FLASH_ATTN_EXT (#25425)
* hex-fa: refactor kernel param compute to use common layout builder

* hmx: add explicit compiler barriers to make hmx funcs more robust

* hex-vtcm: more generic vtcm layout builder for mm and flash-attn kernels

* hex-hmx: unroll inner kernels

* hex-hmx: use inline asm instead of intrinsics to avoid compiler issues

* hex-hmx: define inline asm macros and simplify code

* hex-hmx: replace leftover intrinsics

* hmx-fa: minor cleanup for hmx asm

* hmx-mm: move per-task stucts out of the kernels header

* hmx-mm: simplify core_dot_chunk

* hmx-mm: simplify inner loops that call hmx instructions

* hmx-mm: proper instrumentation for activation prep work for dma pipelined version

* hmx-mm: update a-prep loop for better prefetch

* hex-vtcm: improved vtcm layout alloc for mm to support overlapping areas

* hmx-mm: reduce the number of act fetch tows to 4 for now, going larger doesnt help here

* hex-hmx: always use hmx-queue in all modes

* hmx-mm: update comments and minor formatting

* hmx-mm: further improve synchro fallback path to prefetch the weights earlier

* hex-fa: further pipeline improvements (earlier prefetch)

* hmx-mm: cleanup dma pipelines to use dst cached in the queue

* hmx-fa: minor cleanup and opts for fa dma pipelines

* hmx-fa: optimize q-prep stage with dma and unrolling

* hmx-fa: use o_tile size from layout instead of computing it

* hmx-mm: cleanup types and size handling

* hmx-mm: replace divs with fastdiv in qprep loops

* hmx-fa: minor update/formatting to q_tile handling

* hmx-fa: cleanup the layout to avoid overpadding

* hmx-fa: simplified and improved cost mode for hmx fa solver that uses vtcm layout funcs

* hmx-queue: add support queue wakeup and make suspend async to avoid hmx-lock latency

* hex-hmx: move queue wakeup / suspend to the op-batch level

* hex-threads: add hybrid polling to workpool

* hex-mm: fix trailing spaces
b9928
2026-07-08 07:38:27 -07:00
Xuan-Son Nguyen c264f65ff9 cli : move to HTTP-based implementation (#24948)
* cli: move to HTTP-based implementation

* wip

* working

* remote server ok

* cli support router mode

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>

* case: router with only one model

* Apply suggestions from code review

Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>

* remove outdated comment

* use destructor instead

* add ftype

* cli-view --> cli-ui

* pimpl

* no more json in header

* nits fixes

* also show model aliases

---------

Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
b9927
2026-07-08 14:52:43 +02:00
Oliver Simons 07e012afdc Make hip quality check run on all changes (#25403)
Improvement of the CI to run on all hip-related changes as a follow-up to
https://github.com/ggml-org/llama.cpp/pull/25373
so breakage is more likely to be caught in future
2026-07-08 14:38:51 +02:00
fairydreaming ed8c26150e cuda : add support for f16->f16 GGML_OP_SET_ROWS (#25367) b9925 2026-07-08 19:24:20 +08:00
Aman Gupta 90e0f5cfcb llama: refactor fused ops (#24646) b9924 2026-07-08 18:18:09 +08:00
Pascal bbebeec4a8 server-stream: follow-up on SSE Replay Buffer (#23226) (#25047)
* server-stream : pimpl

* server-stream: prefix free functions with server_stream_

address review from ggerganov: scope the public stream functions under the
server_stream_ prefix, matching server_stream_session_manager_start/stop.

* server-stream: guard session and manager state with the mutex

address review from ggerganov: make done, completed_ts and the GC running flag plain members under their
mutex and set the condvar predicates under the lock. keep cancelled atomic for
the lock-free should_stop poll.

* server-stream: trim comments to the non-obvious

address review from ggerganov: drop comments that restate the code, keep the
concurrency, lifetime and ordering rationale. de-stale a few comments left by the
pimpl: g_stream_sessions is now internal and the /v1/streams listing is gone.

* server-stream: update dev docs for the pimpl and prefix

reflect server_stream_session_manager_start/stop and the server_stream_ prefix,
note the manager is now a file-static singleton hidden in the .cpp

* server-stream: move stream traces to debug level

keep the bring-up traces for diagnostics but off the default log: skip
drain, draining, drain ended, DELETE evict, attach_pipe, and the router
stream resume proxy.

* server-stream: align router stream resume proxy trace with upstream

the child-side bring-up traces are already SRV_TRC on master, move the
router stream resume proxy trace to the same level.

* server-stream: move stream_read_status enum to the cpp

it is only used by the hidden session and consumer types, so it belongs
with them behind the pimpl boundary, not on the public header surface.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
b9923
2026-07-08 12:02:50 +03:00
Aman Gupta 230ea9d214 llama-batch: add n_keep_tail in split_equal for recurrent models (#25278) b9922 2026-07-08 15:55:19 +08:00
rankaiyx f296fdfbed common: auto-create prompts-log-dir at argument parsing, so all tools using the flag benefit (#25322) 2026-07-08 09:45:28 +02:00
Aleksander Grygier f1161b15f2 ui: Context usage gauge and panel (#25340)
* feat: WIP

* feat: Retire ChatScreenProcessingInfo component, context, and keepStatsVisible settings

* feat: Always-on gauge with active-model /props, conversation stats and live-reactive reading/output/avg

* feat: Add /tokenize endpoint, TokenizeService, FNV-1a and JSON Schema utilities

* feat: Surface enabled-tools token count in context hover card

* refactor(tools): make toolsStore the sole owner of the OpenAI wire format

Previously mcpStore.getToolDefinitionsForLLM() owned the MCP->OpenAI
shape conversion (plus normalizeSchemaProperties). That created two
sources of truth for what gets sent to the LLM, with the
duplication-prone risk of the deduplicated enabled list (which feeds
the token-count cache) drifting from the bytes actually shipped on
chat.

Now:
- mcpStore: pure protocol state + routing. Drop getToolDefinitionsForLLM
  and the inline OpenAIToolDefinition conversion + normalizeSchemaProperties.
  Doc comment adjusted to declare wire-format ownership as belonging
  to toolsStore. Connection lifecycle, health checks, executeTool,
  and the connections/toolsIndex remain.
- toolsStore: owns the wire shape (added earlier this series). mcpEntries()
  inlines the MCP tool conversion; uses normalizeJsonSchema (the JSON
  Schema util extracted in the prior commit) so missing 'type' fields
  are inferred from defaults. mcpTools getter iterates mcpEntries() so
  the Settings UI and the deduplicated enabled list see the same
  definitions. getEnabledToolsForLLM iterates mcpEntries() instead of
  calling mcpStore, so the JSON sent to the LLM is identical to what
  toolsStore.refreshEnabledToolsTokenCount tokenizes.
- agentic: the chat-completion tools field's type was annotated as
  ReturnType<typeof mcpStore.getToolDefinitionsForLLM>, claiming the
  shape was owned by mcpStore. Switch to ReturnType<typeof
  toolsStore.getEnabledToolsForLLM>, the actual source.

Assisted-by: Claude

* feat: UI WIP

* feat: UI WIP

* feat: UI WIP

* feat: Adjust reasoning submenu layout and spacing

* feat: Adjust context usage gauge thresholds and styling

* feat: Split context usage gauge stats into current and cumulative breakdowns

* chore: Format

* refactor: Cleanup

* refactor: Cleanup

* feat: improve token gauge accuracy and display

* refactor: remove MCP recommendation gating and simplify server visibility

* feat: add token audit logging to ChatStore for debugging

* refactor: Simplify context token reading to use server promptTokens directly

* feat: Replace last-known token tracking with live server-derived stats for accurate streaming gauges

* feat: UI Improvements

* feat: Move prompt processing stats to the preceding user message

* feat: Fix context token double-counting and refine gauge layout

* refactor: remove always-show-agentic-turns setting and simplify agentic turn display

* feat: track and display cache tokens in context gauge

* feat: add diagnostic logging for chat completion requests

* refactor: improve token audit console output with fresh/cached breakdown

* fix: invalidate enabled tools token count cache on tool changes

* test: add unit tests for tools store token count invalidation

* refactor: Remove tools token counting infrastructure

* refactor: Update ChatFormContextGauge to use simplified token tracking

* refactor: Update ChatStore to remove tools token counting

* chore: Formatting

* feat: Improve UI text

* feat: simplify context usage derivation and refine gauge labels

* refactor: cleanup logs

* cleaning

* fix: UI

* refactor: Enums

* refactor: Extract context gauge logic into hook and split UI into sub-components

* refactor: Cleanup comments

---------

Co-authored-by: Pascal <admin@serveurperso.com>
2026-07-08 09:22:35 +02:00
Georgi Gerganov da46e59cbf llama-eval : fix crash when answer is None in HTML dump (#25435)
dict.get("key", default) returns None (not default) when the key
exists but its value is explicitly None. This caused an AttributeError
in _escape_html() when a task errored before grading and answer was
set to None.

Assisted-by: pi:llama.cpp/Qwen3.6-27B
2026-07-08 10:00:03 +03:00
fairydreaming 0512ef1e5a metal : add set_rows with src0 f16 (#25434)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
b9918
2026-07-08 09:49:07 +03:00
hourhl 4a7ee3126d fix: OOB reads in UGM tokenizer (precompiled_charsmap handling) (#18750)
* fix: OOB reads in UGM tokenizer (precompiled_charsmap handling)

- Validate minimum size (4 bytes) before reading xcda_blob_size
- Use strnlen with bounds check instead of unsafe strlen

Both issues allow heap-buffer-overflow from malicious T5/UGM GGUF files.

* Replace unsafe strnlen() with a bounds-checked loop that scans for \0 within the remaining array size.

* move bounds checks to load

* typo merge fix

---------

Co-authored-by: hourhl <hourhl8200@gmail.com>
Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com>
b9917
2026-07-08 08:02:09 +03:00
tyronecai 57b50e1f6b ggml : fix A indexing in simd_gemm scalar tail-column path (#25390)
`simd_gemm()` has an incorrect A-matrix index in the scalar tail-column path for full row blocks.
b9916
2026-07-08 08:00:05 +03:00
fairydreaming 68a521b591 ggml : add support for CPU f16->f16 GGML_OP_SET_ROWS (#25344)
* ggml : add support for CPU f16->f16 GGML_OP_SET_ROWS

* ggml : add missing type checks in f16 GGML_OP_SET_ROWS

* ggml : merge ggml_compute_forward_set_rows_f32() and ggml_compute_forward_set_rows_f16() into ggml_compute_forward_set_rows_impl()

* chore : replace assert() with GGML_ASSERT()

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
b9915
2026-07-08 11:46:28 +08:00
lhez 931ca30bef opencl: fix potential crash in aos reconstruct (#25383) b9914 2026-07-07 20:34:29 -07:00
Pasha Khosravi bec4772f6a Add Q2_0 quantization: type definition and CPU backend (#24448) b9913 2026-07-07 12:05:47 -07:00
Georgi Gerganov c198af4dc2 spec : fix naming, spacing (#25410) b9912 2026-07-07 18:52:30 +03:00
Oliver Simons 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
Alex 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 LECONTE 5eca4e3cab server : add timings and progress to /responses API stream (#25348) b9909 2026-07-07 16:13:03 +02:00
Thiago Padilha 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
zhangrunda 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
asf0 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 Gupta 024c46ae4e llama: fix quantized kv-cache for dsv4 (#25202) b9905 2026-07-07 17:46:57 +08:00
Neo Zhang 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 Zhang 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 Zhang 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 Malsbary 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 Zhang 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 Zhang 9e5ef0dbb1 sycl : enhance argsort to support all UT cases (#25125) b9899 2026-07-07 10:39:29 +03:00
Neo Zhang 3d4cbdf18a sycl : use sycl func to fix AOT double type issue (#25081) b9898 2026-07-07 10:38:33 +03:00
Neo Zhang 26145b3db7 sycl : rename the env vars from "disable" to "enable" (#25042) b9897 2026-07-07 10:33:51 +03:00
An Long 1a7c25bfdb ggml : make ggml_time_init idempotent (#24422) 2026-07-07 10:29:17 +03:00
o7si 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
fairydreaming 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
Hongqiang Wang 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-ibm ee445f93d8 common: Set optimal default thread count for ppc ( linux as well as AIX) (#25237) b9892 2026-07-07 05:35:20 +08:00
Pascal 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 Gäßler 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
Pascal 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