* devops : do not install recommended packages
Also:
- Replace apt-get with apt
- Explicitly install ca-certificates
* devops : remove development packages from runtime
* devops : copy only CLI executables and shared libraries
Won't copy compiled tests, source code and other build artifacts.
* devops : remove bash as entrypoint
With this change, arguments passed to `docker run` are forwarded
directly to the selected command.
Having `bash -c` in the entrypoint will cause additional arguments to be
interpreted according to shell command-string semantics, which makes
commands such as model downloaders and CLI tools behave unexpectedly
unless `--entrypoint` is specified explicitly or the whole command is
quoted.
Removing it restores the conventional Docker behavior of: `docker run
IMAGE COMMAND ARG...` passing `ARG` to `COMMAND` (not to `bash`).
* docs : fix + enhance Docker examples
macOS ships BSD realpath, which rejects -e and is missing on older
releases. Use plain realpath when present, otherwise cd and pwd -P.
Fixes#530
Co-authored-by: Aurora-NICExq <Aurora-NICExq@users.noreply.github.com>
* docs(stream): clarify VAD mode timestamps and CWD model path errors
AI use: I found this while building against the library and used an AI
assistant to help verify the relevant source (file/line refs
above). The report and the documentation wording are mine, and I've checked every claim against the source myself.
* Update examples/stream/README.md
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* readme : document the ANEForge encoder backend
* Improve clarity in ANEForge support section of README
Reworded sentences for clarity and improved readability in the ANEForge section.
* Improve formatting of ANEForge support section
Reformat ANEForge section for better readability.
* whisper : optional ANEForge encoder backend (Apple Neural Engine)
Runs the Whisper encoder directly on the Apple Neural Engine via ANEForge, an
alternative to the CoreML encoder backend. About 2x faster than the CoreML encoder
and faster than the Metal GPU encoder on M-series, at ~5x lower energy, with the
same transcripts (cosine 0.999 vs the reference encoder).
Mirrors the existing CoreML/OpenVINO seam: fills embd_enc from the external aneforge
package via the same whisper_encode_external path, gated at runtime by the
ANEFORGE_ENCODER env var. With the variable unset the build behaves exactly like
stock whisper.cpp (the backend dlopens its dispatch dylib only when enabled, no new
link-time dependency). Encoder only; the decoder is untouched.
Adds src/aneforge/whisper-aneforge.{h,cpp}, one line to src/CMakeLists.txt, and ~30
lines to src/whisper.cpp (include, state field, encode branch, init, free).
* aneforge: one statement per line, align dlsym block (review)
* aneforge: guard the backend to Apple Silicon, stub elsewhere (fix non-Apple CI)
whisper-aneforge.cpp uses <dlfcn.h> and __fp16, which are unavailable on Windows
(MSVC) and non-ARM Linux, so the unconditional build broke those CI jobs. Wrap the
real implementation in #if defined(__APPLE__) && defined(__aarch64__) and provide
no-op stubs elsewhere; the encoder is Apple-Neural-Engine-only anyway, and the
library now links on every platform.
* examples : include whisper-aneforge.cpp in android examples
Refs: https://github.com/ggml-org/whisper.cpp/actions/runs/32758607709/job/97689705921?pr=3905#step:5:200
* ci : specify explicit packages (platform-tools)
This commit updates the android jobs to specify platform-tools as an
explicit package.
The motivation for this to try to fix the Setup Android SDK step which
is also trying to install an obolete tools package.
Refs: https://github.com/ggml-org/whisper.cpp/actions/runs/35215042978/job/105181619536?pr=3905
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* sycl: GPU-resident TOP_K for large k, parallelised over the device
The SYCL backend refused GGML_OP_TOP_K above k = 32 and let it fall back to
the CPU, a backend round-trip per call. The limit was not conservatism: the
scan-merge kernels keep (split_block + 1) * k candidate (value, index) pairs
in SLM, so at k = 128 a work-group already needs 132 KB and cannot launch.
qwen4exp's sparse-attention indexer asks for k = 2048 in 12 layers on every
token, so this fired at every context length.
Add a radix select for large k. The k-th largest is found by four
most-significant-first passes over an order-preserving unsigned key: histogram
the digit over the candidate set, walk the buckets from the top, and recurse
into the one where the running count reaches what is still needed. SLM holds
the histogram rather than candidates, so the footprint is independent of k.
A final pass emits every column beating the pivot plus exactly as many
pivot-equal columns as are still missing, so duplicate keys still yield
exactly k distinct indices. Output order is not required and is not paid for:
ggml-cpu/ops.cpp swaps its first two outputs to say so.
The key folds -0.0 onto +0.0 so its equivalence classes match the reference
comparator, under which the two tie. NaN has no defined order in the reference
(its comparator is not a strict weak order there); here +NaN keys above +inf
and -NaN below -inf, which at least makes the result deterministic.
One work-group per row leaves the device idle whenever a graph has fewer rows
than it has cores, which at batch size 1 means one work-group full stop:
qwen4exp tops-k a tensor of shape [n_kv, n_tokens/n_stream, n_stream], so
token generation gives nrows == 1, and the backend sampler reshapes logits to
a single row as well. Measured, ne=[200000,1] and ne=[200000,16] cost 358.0 us
and 363.4 us -- sixteen rows for 1.5% more wall-clock.
So also spread a row over several groups when there are too few rows to cover
the device. Per-pass state moves to global memory and each digit pass becomes
its own launch, since a work-group barrier can no longer span the row. Groups
accumulate in SLM and contribute 256 global atomics each, keeping global
traffic per-group rather than per-element, and the last group of a row -- the
one whose fetch_add returns G-1 -- performs that pass's scan, holding the
launch count at one per digit plus one emit. The group count comes from the
device and is floor-divided by nrows, so a row count that already covers the
device is left whole and pays nothing. Below 64K columns the single-group
kernel finishes inside the cost of the extra launches and stays in charge.
Reading the row's prefix/mask/need through a device-scope atomic_ref costs
more than the sweep it guards: those loads are uncached, so passes 2-4 ran at
49 us against 12 us for pass 1. One lane reads them into SLM and the group
takes them from there -- 208 us -> 44.6 us at ne=[131072,1], k=2048.
The block size now takes the device's max_work_group_size instead of a cap of
512. The cap was never a floor, so a device reporting 512 is unaffected; one
allowing 1024 was being given half its width.
Finally, put the scan-merge gate where the two paths actually cross. That
kernel's cost climbs with k while the radix select's does not; measured over
widths from 2 to 200K columns and row counts from 1 to 8192, radix is ahead
everywhere from k = 8 up and behind at k <= 2, where scan-merge's smaller
fixed cost wins. The short-row corner (ncols=2, nrows=65536, as in bailingmoe2
group selection) is exactly where radix loses at low k, and the gate keeps it
on scan-merge.
Op-level against the CPU-fallback path this replaces, and against the
single-group radix select for the split: 4.98x at ne=[131072,1] k=2048,
6.65x at ne=[151936,1] k=40, 13.35x at k=20, 118x at ne=[65000,16] k=32.
No measured shape regressed. End to end on 3x Arc Pro B60 with
Qwen3.8-Flash-Next UD-IQ4_XS, llama-bench tg64, the parallelisation is worth
5.91 -> 6.05 t/s at d=131072 and a wash at shallower depths. Perplexity over
wikitext-2 is unchanged within noise at both 512 and 81920 context.
test-backend-ops: 525/525 TOP_K (previously every k > 32 case was refused),
880/880 MUL_MAT_ID. Perf coverage added for k > 32 at large widths and for the
short-row corner, neither of which was exercised before.
* move topk-select to topk-radix.{cpp|hpp}
---------
Co-authored-by: cwriter <cwriter@localhost>
Disable the ggml-cpu precompiled header and remove the
std::hardware_destructive_interference_size branch from CACHE_LINE_SIZE.
The PCH force-includes ggml-impl.h before ops.h, which pulls in <new>
via <array>/<vector> and defines __cpp_lib_hardware_interference_size.
This makes the C++ kernels use CACHE_LINE_SIZE = 256 (hardware
destructive interference size) while the C work-buffer sizing code in
ggml-cpu.c always uses the fallback 64. The mismatch undersizes the
rope work buffer by (CACHE_LINE_SIZE/4 - 16) * n_threads * 4 bytes,
causing a heap-buffer-overflow that corrupts the heap and later crashes
in ggml_compute_forward_rope_flt.
Disabling the ggml-cpu PCH restores the natural include order so
ops.h is processed before <new>, keeping CACHE_LINE_SIZE consistent.
Removing the std::hardware_destructive_interference_size branch makes
the value deterministic and include-order independent.
ref: https://github.com/ggml-org/llama.cpp/issues/28858
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* ggml-cuda: fallback to F32 on device without BF16 hardware acceleration: (Nvidia >= AMPERE, AMD >= RDNA3 or = CDNA)
* apply logic to NVIDIA as well
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* fix for unsupport zes API
* optimize the code
* adjust the log level
* rm unused head files
* Update docs/backend/SYCL.md
Co-authored-by: Titaniumtown <titaniumtown@proton.me>
* fix the error to detect level zero SDK/dev package, stop build after detect the error
* update the message
* fix the build error when missed to install level zero dev package
* rm GGML_SYCL_DEV_DEBUG, mv read env vars in all entry functions
---------
Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>
Co-authored-by: Titaniumtown <titaniumtown@proton.me>
Co-authored-by: Neo Zhang <NA>
There is a driver bug where two queues on the same VkDevice simultaneously
submitting can break some internal synchronization. Until it's fixed, add a
mutex around queuesubmit.
Walk the binding offset back until the distance to the tensor is a
whole number of blocks, so block quantized views get a valid element
offset in the shader.
* hex-row-split: add support for multi-device row spliting
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* hex-mdev: add work splitting to fused kernels
* hex-mdev: use mdev_ prefix for all multi-device state
* hex-mdev: make device configuration more expressive to support device groups
* hex-mdev: fix mdev session init
* hex-mdev: fused nx (2x,3x) matmuls must update row counts for each w/o
* hex-mdev: fix MUL_MAT work partitioning bugs introduced by mdev
* hex-cont: fix crashes with new tests due to wrong striding
* hex-mdev: move fences after l2flushes
* hex-cont: fix work splitting for mnpu -- align chunks to cachelines
* hex-mdev: fix CPY tests with multi-dev
* hex-mmid: fix work partitioning with mnpu
* hex-mm: fix test failures with mdev
* hex-binary: fix work partitioning for mdev
* hex-argsort: fix mdev partitioning
* hex-mdev: fix work partitioning and general updates for all simple ops
* hex-fa: fix mdev work splitting issues
* hex-mdev: fixing more failing ops test
* hex-mdev: update the rest of the ops
* hex-mdev: refactor all mdev splitting logic to be contained within if (mdev_count > 1) {...}
* hex-mdev: fix macros
* hex-mdev: simplify session flush logic
* hex-sync: fix recursion in session flush
* hex-mdev: factor out fence buffer and allocator
* hex-fence: make fence allocation more robust with reserved slots for mdev
* hex-mdev: keep all mdev state in htp_mdev_group
* hex-mdev: further cleanup mdev group handling at the host
* hex-mdev: update group idx in the opbatch before serializing
* hex-batch: remove separate op_pending and use batch_req/rsp_seq
* hex-async: workaround another missing tensor_init in ggml-meta
* hex-fence: cleanup and robustify fences and error handling in multi-device scenarios
* hex-ar: improve ALLREDUCE error handling
* hex-async: robust error handling for op_cpy_fence
* hex-async: use seq0 from allreduce context to allocate fence_seq
* hex-mdev: fix remaining issues with fence and barrier clearing in CPY_FENCE
* hex-misc: realign macros and fix misplaces trace events
* hex-misc: align macros
* hex-mdev: fix unclone buffer re-entrancy
* hex-glu: fix mdev partitioning logic
* hex-mdev: make buffer uncloning/cleanup work with tensor-split scenarios
* hex-mdev: tighten up the can_split check in act-ops
* hex-mdev: factor out common bits of the partitioning logic
* hex-mm: minor realignment of the macros
* hex-bufs: fix incorrectly placed assert for MAX_BUFS
* hex-pad: tighten up gating checks for PAD
* hex-kparams: make sure all kernels properly use kparams->n_threads
* hex-docs: update user and developer docs with new features and detailed guide for ops development
* hex-scripts: update run script to properly parse dev groups
* hex-misc: formatting
* hex-sess: minor cleanup for session init
* hex-ar: fix vtcm size calc in allreduce kparams
* hex-scripts: fix flake8 warnings
* hex-rope: update ROPE to support mdev work split
* hex-ops: remove redunant checks and minor reformat
* hex-dev-guide: update dev-guide to avoid redundant null checks
* hex-async: improve event_wait, event_sync and fence implementations
* hex-async: remove synchronous flush from event_sync
* hex-async: symplify fence recovery protocol and make sync more robust
* hex-async: futher simplify error recovery for fences
* hex-err: return status instead of just -1
* hex-async: print all seq nums in hex
* hex-async: make sure fences flush dirty ranges
* hex-async: add dirty ranges merging to reduce fence flushes
* hex-async: properly sync before freeing the event
* hex-async: make sure fence owner session is not overriden
* hex-async: more fence write order more robust
* hex-async: make sure not to fuse ALLREDUCE+ADD if their dsts overlap
* hex-fusion: cleanup redundant checks
---------
Co-authored-by: Alexander Lu <alexlu@qti.qualcomm.com>
* ggml-webgpu: Update to a recent version of Dawn
* No module scanning
* Accept review suggestion to update comment
Co-authored-by: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
---------
Co-authored-by: Masashi Yoshimura <yoshimura.masashi.frbs@gmail.com>
kernel_mul_mm_id splits its NR1 = 32 token tile into two 16-row halves and skips
the upper half when the expert did not fill it, on both the tensor and simdgroup
paths. The tB extents are corrected to (NK, NR1H) for the [NR1][NK] row-major tile.
The B tile is staged unconditionally, as on master: rows past nr1 restage a clamped
duplicate of a valid row, lie in the output-row dimension so they never contribute
to a valid row, and are dropped by the final store loop.
test-backend-ops: re-draw the expert ids between perf iterations of test_mul_mat_id
so MoE perf numbers are not warm-cache, and add token-tile boundary coverage using
n_used == n_mats, which routes every token to every expert so each expert receives
exactly n rows; n = 32, 33, 47, 48, 49 reach mul_mm_id and leave a last tile of 32,
1, 15, 16 and 17 rows.
* scripts : add initial profiling script (wip)
* src : add precompile headers (PCH) for models.h
* common : add common.h as PCH
* ggml : add PCH for ggml-impl.h
* mtmd : use PCH for models.h
* scripts : add script to build with Server/Tools/Tests
* server : add PCH for common.h
* docs: add profiling progress notes (wip)
* ggml : add exclude for GCC + SVE on ARM
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33393906061/job/99493756214?pr=28091
* ggml : attempt to fix use of std::hardware_destructive_inference_size
Refs: https://github.com/ggml-org/llama.cpp/actions/runs/33396221677/job/99501265689?pr=28091
* squash! ggml : attempt to fix use of std::hardware_destructive_inference_size
Add a version check for GCC 12 to conditionally apply the `-Winterference-size`
pragma.
* editorconfig : exclude profiling reports dir
This directory will not be included in the merge later and this commit
can be ignore at that point. Just fixing to keep CI happy.
* ggml : skip PCH for gcc on non-x86 architectures
* tests : add PCH for peg-parser/tests.h
There are 7 peg-parser tests that can share one PCH instead of then each
parsing the full tests.h.
* common : add PCH for chat.h
* docs : update linux build profiling full results
Just updating after a number of PCH additions. These are not exact
figures and will vary a bit from run to run, but they give a general idea
of the performance impact of PCH.
* cmake : introduce unity build for models
This commit introduces a unity build for the models to improve
compilation time.
The improvements were roughly the following:
```console
+------------------------+-----+------------+------------+------------+
| Build | TUs | Frontend | Backend | Total |
+------------------------+-----+------------+------------+------------+
| Full, master | 396 | 811.0 s | 692.2 s | 1,503.2 s |
| Full, with PCH | 405 | 380.0 s | 664.7 s | 1,044.7 s |
| Full, with PCH + UB | 264 | 357.7 s | 635.7 s | 993.4 s |
+------------------------+-----+------------+------------+------------+
TU = Translation Unit.
Full = includes Server, Tools, and Tests.
PCH = precompiled headers.
UB = unity build for models.
```
* docs : update linux profiling table with unitiy build results
* docs : update mac profiling results to include unity build [no ci]
* docs: remove profiling reports
* scripts : merge build profile scripts into one script
I was lazy before and just copied the first script to enable Tests,
Server, and Tools. This now merges them into a single script.
* Revert "editorconfig : exclude profiling reports dir" [no ci]
This reverts commit 2922a12118a0730d2f7632bcba265b44a0856c59.
* src : rename ggml_view_2d_slice to gemma3n_view_2d_slice
This is to be consistent with the rename in gemma4.cpp which was
required to avoid a name clash.
* cmake : add build profile script for windows [no ci]
This commit adds a port of the scripts/build-profile.sh script to
windows powershell.
This was developed on Windows on ARM but should work on X64 as well but
needs to be tested there as well.
* metal : rework fusion patterns into a single table
All fusable op patterns for the Metal backend are now declared once in a
fusion table (ggml-metal-fuse.cpp) and consumed by both the graph optimizer
(ggml_metal_fuse_max, packing) and the op encoders (ggml_metal_fuse_next,
compute). The two phases share the same pattern table plus ggml_can_fuse_subgraph_ext
for the structural checks, and differ only in the mode used for the pattern
check (STRUCTURAL at optimize time, since tensors are not allocated yet, and
FULL at compute time, including Metal buffer placement). This also protects the
snake activation (MUL + SIN + SQR + MUL + ADD) from being reordered during graph
optimization, which was previously unprotected.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : fix absolute output indices in fusion patterns
ggml_can_fuse_subgraph_ext expects the outputs array to contain absolute graph
node indices (it indexes cgraph->nodes[outputs[i]]), but the fusion table query
was passing a relative index (n_ops - 1). As a result the last node of every
pattern was not recognized as an output and was subjected to the elidable
use-count check, which failed for essentially all fusions. This silently
disabled the norm/MUL fusion and caused a ~5% token-generation regression.
Pass the absolute graph index of the last node instead.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : fuse gated_delta_net with cache cpy
Add GGML_METAL_FUSE_GDN_CACHE to the fusion table: when the gated_delta_net
kernel is followed by a cpy that scatters its recurrent state snapshots into
the KV cache, the kernel writes the snapshots straight into the cache buffer
and the trailing cpy is elided.
The gdn output has other consumers (the attn scores view), so unlike the
elision-chain patterns this is not a simple chain: a 'raw' flag on the fusion
pattern skips the generic chain/shape and ggml_can_fuse_subgraph_ext checks,
making the pattern-specific check callback the sole validator. Packing
(ggml_metal_fuse_max) now matches on the same view-transparent node sequence
that the compute phase uses, so the gdn + cache cpy group is packed along with
any intermediate views and stays adjacent through the reorder.
The fused cpy is a view consumer of the gdn (it writes the cache directly),
so its mem-range is skipped in the encoder; the skip is restricted to CPY
nodes consuming the previous fused node through a view so other fusions are
unaffected.
Add test_gated_delta_net_cache_fusion and register 5 cases.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : drop is_view_consumer mem-range skip
The is_view_consumer skip was carried over from the upstream gated_delta_net
cache-fusion draft, but it is not needed: keeping the elided cpy's mem-range in
the concurrency tracker only ever adds a (conservative) memory barrier at the
fusion point. It can never remove a barrier, so it cannot introduce a race. The
worst case is one spurious barrier per gdn+cache-cpy fusion, which is within
run-to-run noise on Qwen3.5-0.8B Q8_0.
Dropping the check keeps the mem-range loop uniform for all fused groups.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : rename gated_delta_net fused state output args
Rename the fused cache-write kernel argument to match the rest of the kargs:
state_out_stride -> nb_out (and widen it to uint64_t), and the local buffer id
bid_state_out -> bid_out.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : rename raw fusion flag to unsafe
raw did not convey that the flag opts a fusion pattern out of the generic
elision-chain safety net (ggml_can_fuse_subgraph_ext + chain/shape checks).
rename it to 'unsafe' to make explicit that the pattern's check callback is the
sole validator and must re-establish the safety guarantees itself.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : tidy fusion pattern checks and table
- const-correct ggml_metal_fuse_outputs buffer
- annotate unused check-callback parameters
- drop a redundant size_t cast
- align the ops/table initializers and add blank-line separation
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* metal : add generic fusion stats via ad-hoc proc-address API
Add a device-owned fusion context that lets a test tool count how many
times each fusion pattern fires and toggle fusion. It is exposed through
the ad-hoc ggml_backend_reg_get_proc_address mechanism with generic names
so the testing tool is backend-agnostic:
- ggml_backend_fusion_stats_init: start collecting fusion stats; when a
context is created afterwards it registers the labels/counters and
encodes single-threaded (n_cb == 0) so the counters are race-free
- ggml_backend_fusion_stats_reset / _get_stats / _set_enabled
The context lives on the metal device (not on the last backend context),
so counters accumulate across contexts and reads are always consistent.
The enable/disable toggle is initialized from GGML_METAL_FUSION_DISABLE
and can be overridden by the test through set_enabled. Labels are
synthesized from the fuse table via ggml_metal_fuse_label (e.g.
"GATED_DELTA_NET+CPY").
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : add fusion count regression test with per-backend baseline
test-fusion runs every dummy model generated by test-llama-archs on a
single backend (single-threaded encoding, n_cb == 0) with fusion enabled
and disabled, and for each mode (prefill / decode) reports the per-fusion
counters and the NMSE between the fused and unfused logits, plus the NMSE
against a CPU reference.
A fusion pattern that silently stops matching (or fires when it should
not) is caught as a regression by comparing the counters against a
committed per-backend TSV baseline:
- --record writes the golden baseline, --check (default) validates it
- the unfused run doubles as a control: its counters must be all-zero
- NMSE is skipped when it is NaN or the arch is already broken on the
device (e.g. plamo2 on Metal), so the count check is the hard gate
- baseline counts depend only on graph structure, not weights (verified
stable across weight seeds)
- the fusion stats API is resolved through the ad-hoc get_proc_address
mechanism with generic names; a backend that does not export it makes
the test fail with an error
The committed MTL0.tsv baseline covers 110 dummy archs (298 rows).
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : rename fusion api helpers to match stats_init signature
Align the test with the ad-hoc fusion stats API: fusion_stats_init no
longer takes an enable bool (stats are turned on by calling it), so the
proc-address wrappers and typedefs are renamed to the api_* convention.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : rename backend to device in fusion test CLI
The fusion test operates on a compute device (e.g. MTL0), not a backend,
so rename the --backend argument to --device and the backend_name
variable to device_name. Keep "backend" where it refers to the ggml
backend interface (the ad-hoc proc-address mechanism).
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : add --model and --help to fusion test
--model FILE runs the fusion regression test over a single model file
instead of enumerating a --models DIR. --models and --model are mutually
exclusive. Also add a --help/-h option that prints the usage.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : use backend base name for fusion baseline output
The fusion test is invoked with a specific device name (e.g. MTL0), but
its output - the recorded baseline and the header it writes - should be
named after the backend base name (e.g. MTL, via ggml_backend_reg_name),
since the counters depend on the backend, not on the specific device
index. Rename the committed baseline to MTL.tsv.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : run fusion test from ci instead of ctest
The fusion test needs Metal and generates a lot of dummy models, so it
does not belong in the generic ctest suite. Move it to ci/run.sh as
gg_run_test_fusion, gated on GG_BUILD_METAL like
gg_run_test_llama_archs_tensor_split: it generates the dummy models with
test-llama-archs -o and then validates the fusion counts against the
committed baseline. test-fusion.cpp is still built (llama_build) but no
longer registered as a ctest.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : align fusion baseline TSV columns
Pad the TSV fields to fixed widths so the columns line up regardless of
the variable arch and fusion-label lengths, and trim each field on parse
so the padded file is still accepted. Regenerate the committed MTL.tsv
baseline in the padded format (data unchanged, verified identical modulo
padding).
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : widen label column and align fusion TSV header
Give the label column more room (28 chars) and fix the column header
widths so they match the data rows (moe/mode/label), keeping the header
aligned with the values. Regenerate the MTL.tsv baseline in the new
format (data unchanged).
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : switch fusion baseline from TSV to CSV
Use comma-separated values like the rest of the project, keeping the
padded, aligned columns. Split on ',' and trim on parse. Rename the
committed baseline to MTL.csv (data unchanged, verified identical modulo
padding/separator). Update the ci/run.sh check path accordingly.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* cont : rebase + update MTL stats
* tests : avoid graph reallocations for some archs
* metal : tidy fusion debugging context and op init
- simplify the shared fusion debugging context comments
- shorten the ggml_metal_fusion struct comment
- align the ggml_metal_fuse struct fields and comments
- move the fusion parameter of ggml_metal_op_init right after dev
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : dedup fusion baseline into any mode
prefill and decode always produce the same per-graph fusion count, so
store a single row per label with mode = "any" and the per-graph count
instead of two rows. this halves the baseline size and keeps the check
stable.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* ci : move fusion model generation to a separate step
the dummy models generated by test-llama-archs are reused by other tests,
so generate them once in their own step instead of inside test_fusion.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : bump nmse thold
* models : fix plamo2 graph
* tests : remove "skip" logic from test-fusion
* tests : set qwen3tts dummy vocab to codec head size
the dummy qwen3tts model used a vocab of 4096 while the codec head is
3072, so the graph padded the output with -inf which made the NMSE in
test-fusion produce NaN. use the exact codec head size instead so the
padding is not generated at all.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* tests : regen fusion baseline
reflect the plamo2 graph fix, which changed its fusion pattern split
(RMS_NORM+MUL 11->10, RMS_NORM+MUL+ADD 3->4; same total).
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* ci : skip dummy model generation on OpenVINO
test-llama-archs does not build on the OpenVINO platform, so do not try
to generate the dummy models there.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* cont : minor
* tests : enable test-llama-archs on windows
* cont : disable on windows + workaround
* metal : naming nits
* test-fusion : add instructions to update baseline
* context : fix Kimi-K3 graph reserve
* fusion : update MTL
* cont : fix naming
* metal : rework fusion info storage
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : align fusion info API
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* metal : use opaque fusion handle in ad-hoc API
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* ci : move fusion test to dedicated workflow
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-Vision-Exp
* cont : run only on ggml changes
* cont : simplify
* fusion : remove multi-output stuff for now
* ci : fix typo
* metal : fix idle threads in the remaining iq mul_mv kernels for ne00 < 1024
Generalize the row split from #28086 to the six other kernels that use the
same lane-to-block mapping: iq1_s, iq1_m, iq2_xxs, iq2_xs, iq2_s and iq3_s.
Each of them assigns one 32-element chunk per thread, so when a row has
fewer than 32 chunks the rest of the simdgroup is idle. When nb32 < 32 and
nb32 divides 32, 32/nb32 threads now share each chunk and each takes a
slice of the rows, reusing the FC_mul_mv_split function constant and the
dispatch wrapper introduced for iq3_xxs.
The plain path is untouched: wide matrices keep one thread per chunk and
N_R0_<TYPE> = 4. Only the split path uses N_R0_<TYPE>_SPLIT = 8. The
K-quants have the same idle-thread issue but a different lane mapping, so
they are left for a separate change.
* metal : offset the src0 row pointer once in the iq mul_mv kernels
q2, dh, sc, qh and signs are all derived from xr, so the row slice
offset only has to be applied to xr.
* metal : fold iq mul_mv row split into offset0
Compute row0 and row1 before initializing the source pointers and apply
the row slice directly to offset0.
This keeps x and its derived pointers on the existing path while applying
the split row offset once.
* HIP: enable mma FA for head size 256 on RDNA4, tune configs
Assisted-by: Claude
Assisted-by: Codex
* HIP: prefer whole-tile FA grids over stream-k on AMD WMMA
Assisted-by: Claude
Assisted-by: Codex
* revise stream_k logic
* revise kernel selection logic
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
argsort had a data race in the inner loop, which VVL caught. But I don't think
this was causing failures in practice.
argsort_large has OOB accesses which might explain the failures in CI, but I
couldn't reproduce it locally and I don't think it's a convincing explanation
of the failures.
* vulkan: optimize m=1 mul_mat by swapping A/B
* vulkan: Improve small M perf
Allow split_k with small M.
Make small vs med tile selection (for coopmat2) depend on M, not just N.
The Imagination proprietary Vulkan compiler returns VK_ERROR_UNKNOWN from
vkCreateComputePipelines for every dequant mul_mat_vec shader built with the
subgroup-only reduction that requires a subgroup size >= 16. That covers the
k-quants, the i-quants, TQ2_0, MXFP4 and NVFP4. ggml rethrows, so the first
generated token of any such model kills the process.
Reproduced on a Pixel 11 Pro (PowerVR C-Series CXTP-48-1536 MC1, driver
1.662.3024, subgroup size 128, min 32, max 128). The failure is independent of
subgroup size: 32, 64 and 128 all fail, as does dropping the full-subgroups
flag and the required-subgroup-size pNext. The legacy quants, which use the
plain subgroup reduction, compile and run fine.
The shared-memory reduction variant compiles and matches the CPU reference for
q2_K, q3_K, q4_K, q5_K and q6_K. The hybrid variant also compiles but costs
27% of token throughput (3.78 vs 5.20 t/s on Qwen3.5-2B-Q4_K_M).
* vulkan: use spec constant for mul mat type_a
vulkan: use map for mul_mm shapes
cleanup
fix indentation
fix cm2 and shmem init
fix cm2 spec constants
fix cm2 bindings
consolidate shmem tables and reduce size by type spec constant
fix compiler warning
fix missing Q2_0 type
fix unused warning when integer dot glslc support is missing
use minimal shmem size 8 instead of 1 to workaround cm2 compiler bug
fix missing Q2_0 type in cm2 matmul
fix types
* remove LUT quants from unified shader
* clean up
* restore coopmat2 q4_k/q5_k optimization
* split out q4_k/q5_k cm2 shader to fix Ampere regression
* revert iq shmem table renames
* simplify cm2 code with single uint8_t buffer
* fix fp4 extension use switch being overwritten by generic shader
* clean up
* adapt TQ1_0 changes
* adapt #27471 f16 Intel tuning changes
* divide workload to 2D
This is to workaround FILL exceeding maxComputeWorkGroupCount for Intel GPUs on Qwen 3.8 flash next
* minor change
* Fixed comment
* vulkan: add dedicated iq4_xs mat-vec shader
Dedicated mul_mat_vec_iq4_xs for the dmmv path, replacing the generic fallback. ~+6-17% token generation on RDNA4 depending on model.
Assisted-by: Pi agent with Qwen3.8 27B
* vulkan iq4_xs: remove dead n_it unroll branch
Remove the n_it <= 8 experimental branch that attempted to fully unroll
the block loop. Since n_it is a runtime value, [[unroll]] is ignored by
the compiler, making both branches equivalent. Kept the simple loop
matching mul_mat_vec_iq3_s.comp.
Álvaro Justen