* vulkan : copy strided f16 KV once so reads spread across memory channels
Assisted-by: Claude (Opus 4.8 / Opus 5)
* tests : cover the f16 KV copy to scratch
Assisted-by: Claude (Opus 4.8 / Opus 5)
* vulkan : gate the f16 KV scratch copy to AMD
Assisted-by: Claude (Opus 4.8 / Opus 5)
* vulkan : reuse the generic copy shader for the f16 KV scratch fill
The f16 clause needs only a strided copy, not a dequant, so drop the
dedicated shader and dispatch the existing copy pipeline instead. The
copy is described in source memory order so the strided access lands on
the writes, which stream, instead of the reads, which would concentrate
on a few memory channels.
Assisted-by: Claude (Opus 4.8 / Opus 5 / Fable 5)
* vulkan : extend the strided KV scratch copy to bf16
Assisted-by: Claude Opus 5
* vulkan : clean up comments
Assisted-by: Claude Opus 5
* consolidate copy shaders
* disable on RDNA4
---------
Co-authored-by: Ruben Ortlam <rortlam@redhat.com>
* cpu: add F16 SWIGLU_OAI reference implementation and F16 activation op test coverage
Adds ggml_compute_forward_swiglu_oai_f16() to the CPU backend, mirroring
the existing F32 path, and extends test-backend-ops with F16 test cases
for SWIGLU_OAI so downstream backends(e.g. Hexagon) can be CPU-diffed
for correctness.
* ggml-openvino: fix f16 SWIGLU_OAI type mismatch in alpha/one constants
translate_glu_swiglu_oai creates alpha_const and one as f32 constants
unconditionally, so f16 inputs hit a type mismatch in Swish/Add on GPU
("Swish inputs must have the same type but they are: f16 and f32").
translate_glu_swiglu_clamp already upcasts to f32 and converts back at
the end; apply the same pattern here.
---------
Co-authored-by: ravi9 <ravi.panchumarthy@intel.com>
* llama: honor PARTIAL_ONLY in the plain KV cache
A non-SWA llama_kv_cache rolls back with seq_rm, so it holds no
partial state, yet state_write ignored the flag and serialized every
cell. A draft context made of a plain KV cache (MTP head, dense -md
draft) therefore copied its whole KV into each server checkpoint,
growing it by the draft KV size per token.
The cache now writes and reads nothing for PARTIAL_ONLY unless it is
SWA, and the iSWA wrapper no longer gates its base cache since it
filters itself.
* Update src/llama-kv-cache.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama: delegate PARTIAL_ONLY to the caches of every memory wrapper
The hybrid, hybrid-idx and dsa-iswa wrappers skipped their attention
caches on PARTIAL_ONLY, which also dropped an SWA window that is
partial state. Each cache now applies the flag itself, so the
wrappers only delegate. The indexer is marked stale only when a
restore rewrites cells.
test-llama-context gains a case checking that the partial state is
not larger than the full state and, without SWA, does not grow with
the tokens.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cuda, hip: GDN chunked kernel
Ported-from: https://github.com/tetherto/qvac-fabric-llm.cpp/pull/257
Source-commit: 53f0027d8070715fe9ad2131f3201bb0001d4eed
Co-authored-by: Aman Gupta <aman.gupta@tether.io>
* fix older builds
* review fixes
* cuda: enable GDN MMA on tested Ampere and Ada GPUs
Enable SM86 and SM89 after RTX 3090 and RTX 4090 correctness and prefill benchmarks. Keep other untested architectures on the existing fallback.
Assisted-by: Codex
* cuda : use refactored swizzle API in GDN MMA
* ggml : GDN only guarantees the final state when n_tokens > K
* cuda : add short GDN MMA phase comments
---------
Co-authored-by: gianni-cor <gianfranco.cordella@tether.io>
Co-authored-by: Aman Gupta <aman.gupta@tether.io>
* ggml: fix conv_1d result layout for batched input
The GEMM behind conv_1d folds the batch into its rows, so the product
sits in memory as [OL, N, OC] while the reshape claims [OL, OC, N].
The shape is right and the contents are transposed, so nothing fires:
no assert, no abort, the graph just runs and the channels come out
interleaved with the batches. With N == 1 the two readings describe
the same buffer, which is why the three vocoders in the tree never
hit it, they all squeeze to 2d right after the call.
Reshape to the real layout and permute OC and N back. The N == 1 path
keeps its direct reshape, so the current callers build the same graph
and pay for no extra copy.
tests/test-conv-1d.cpp compares one batched convolution against N
single batch convolutions built from the same weight and the same
input slices. Five of the seven geometries fail before this change and
all seven pass after. N == 1 and OC == 1 pass either way, since
swapping two dimensions when one of them is 1 changes nothing.
The batched path matters to downstream audio projects where several
synthesis lanes share a single graph. Nothing in mtmd batches a
vocoder today, each one streams one flow and carries its left context
between calls, but the op is correct now if one of them grows a lane
dimension.
* ggml: build the same conv_1d graph for every batch size
Address review from ggerganov: drop the N == 1 branch of ggml_conv_1d.
The GEMM result is always reshaped to [OC, N, OL] and permuted back,
as ggml_conv_2d does, so every batch size builds the same graph. For
N == 1 this adds a cont on an already ordered tensor.
* common : add --spec-draft-n-ubatch
Allow configuring the physical batch size of the draft context. The
drafters feed it at most the drafted tokens per sequence and chunk larger
batches by its own n_ubatch, so it does not need the ubatch of the target.
A smaller value shrinks the draft compute buffer and leaves more VRAM for
the target KV cache (the draft is fitted together with the target).
The default 0 keeps the current behavior of following --n-ubatch.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : remove comment
[no ci]
tojson printed floats through a C++ stream (6 significant digits, no
".0"), so 48.858844 became 48.8588 and 1.0 became 1 in tool definitions,
tool-call arguments and other template output. Write the shortest digits
that read back as the same value, as json.dumps does. The string and safe
filters of a float use the same code.
* unicode : add opt-in NFC normalization for Unigram tokenizers
Note: used help from Codex, Sol 6.1 Medium
* Fixing initial bot comments + linter error (using existing out instead of print)
* removed test_nfc_tokenizer
The d1-omni and kev prompts escape "<|name|>" in the request text with
std::regex_replace. libstdc++ recurses for each char of the name, so a
state of "<|" and 30000 letters overflows the stack of the HTTP thread
and llama-server dies with SIGSEGV.
server_decision_escape_special_tokens() writes the same text with a loop.
test-server-component compares it with the regex on every string of up to
6 chars of "<|>a-".
* tests : consolidate the context state tests into test-llama-context
test-save-load-state, test-recurrent-state-rollback and
test-state-restore-fragmented check the same object from three places:
the KV cache and the recurrent state of a llama_context, saved, loaded,
copied, rolled back. Each had its own main, its own loop over the
models, its own copy of the token and the comparison helpers, and a run
of all three loaded the same models several times.
They are one binary now, and the case is the unit of the test:
tests/llama-context/test-case.h the interface a case implements
tests/llama-context/cases-kv.cpp the cases of the KV cache
tests/llama-context/cases-rs.cpp the cases of the recurrent state
tests/llama-context/test-util.cpp tokens, generation, comparison
tests/llama-context/test-runner.cpp the models of a case, on threads
tests/llama-context/test-log.cpp the log file of a model
A run is a table: a row per model, a column per case, a cell holds the
verdict of that pair. The models load on several threads (-j, --threads,
min(cores, 4) by default), the table is printed when it is complete and
the log of a model that failed is printed after it, in the order of the
rows. A case returns a verdict and the reason for it, and the runner is
the only one who reports - the test does not use tests/testing.h, a run
over 130 models is a matrix of verdicts and not a tree of subtests.
Staging no longer travels through the environment: llama_model_set_attn_rot
replaces the LLAMA_ATTN_ROT_DISABLE variable, which a model loaded in one
thread used to change for the models the other threads were loading, and
llama_memory_get_buffers is added for the cases that copy the buffers of
a memory. common_log_set_stdo keeps the console quiet while the table is
being built.
ggml_aligned_malloc allocates with posix_memalign when the thread
sanitizer is on. On macOS it allocates with the Mach VM, which the
sanitizer does not intercept, so it never sees a page range change hands
and reports the first write into recycled memory as a race - 30 of them
in this test, and every one of them false.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : run single-threaded by default
* cont : try to build the tests on all platforms
* cont : trim down some body fat
* cont : fix
* cont : fix the fix
* vulkan : split matmul dispatch when it would exceed maxComputeWorkGroupCount
* vulkan : fix narrowing in chunked matmul dispatch
* vulkan : zero fill the A side of mul_mmq for k steps past the end
The A side skipped the shared memory write for the k steps past end_k, while the B side was already told the bound and zero filled. The A registers are read for all BK_STEP steps, so the stale scale went into the dot product and produced NaN at k = 32.
Pass the bound to block_a_to_shmem and write zeros when it is false, for every DATA_A type, mirroring the B side.
Also assert that the B and D subbuffer sizes are not VK_WHOLE_SIZE before they are adjusted per chunk, and add two batched cases of the chunked shape.
Signed-off-by: losewayy <104015127+losewayy@users.noreply.github.com>
* vulkan : remove redundant matmul tile adjustment
---------
Signed-off-by: losewayy <104015127+losewayy@users.noreply.github.com>
* tests: cover weighted scalar broadcasts
Assisted-by: OpenCode
* ggml : support extended scalar DMA binary ops
Assisted-by: OpenCode
* hex-binary: remove all non-dma paths
* hex-binary: add support for non-contig strides and cleanup dma preable a bit
* hex-binary: cleanup the gating and simplify dma retry
* hex-geglu-erf: add a gate for F32 only, we dont support F16 for this op
* tests: cover weighted repeat broadcast
Assisted-by: OpenCode
* hex-binary: update REPEAT and COMPLEX kernels to use dma as well (missed them last time)
* hex-add-id: add dma support for src2 (idx)
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Give llama_build() and llama_build_and_test() a LINK keyword (default:
llama-common) so call sites can declare what a test actually needs:
- ggml-only tests now link just ggml: test-opt, test-metal-graph-optimize,
test-backend-ops, test-tiled-mulmat, test-barrier, test-quantize-fns,
test-quantize-perf, test-rope, test-col2im-1d, test-gguf, test-alloc
- llama-only tests drop llama-common: test-unicode, test-sampling,
test-grammar-parser, test-llama-grammar, test-batch-alloc,
test-gbnf-validator, test-quant-type-selection
- both mtmd tests link mtmd only, dropping the LLAMA_TEST_NAME /
target_link_libraries workarounds
Both helpers now set cxx_std_17 explicitly, since the tests no longer
inherit it from llama-common's PUBLIC interface.
tests/testing.h no longer includes common.h; the only symbol it used from
there (string_join) is inlined.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* vulkan: compute every row of an expert in mul_mm_id when ids repeat
* vulkan: handle mul_mat_id duplicates in the prepass rather than looping
Extend the "hoist row ids" optimization to always be enabled and to emit a
compact list of tile descriptions that need to run, and to emit multiple tiles
when needed. Then we can launch a tighter upper bound on the number of
workgroups and the tail can trivially early exit.
---------
Co-authored-by: Anjielon <[EMAIL_REDACTED]>
* CUDA: fuse copy of updated state snapshots into recurrent cache with ssm_scan
* CUDA: remove redundant cuda copies with K==1 (non spec-dec) scenario as well
* hexagon: fix IM2COL patch-embed DMA ring overflow
The exact-tiling (stride == kernel, no pad/dilation) IM2COL DMA kernel
issues IC*KH DDR->VTCM descriptors per output row without checking the
return value of dma_queue_push(), and then pops IC*KH times. The per-thread
DMA ring holds 256 entries and a push into a full ring returns false
and drops the transfer, so for IC*KH > 255 the remaining rows of the
VTCM staging buffer were never written and stale data (often NaN/inf)
leaked into the output.
Solution is to retire the oldest descriptor when the ring is full, just as the blocked
kernel in the same file already does, and wait with dma_queue_flush().
For testing, added exact-tiling test cases with IC*KH > 256 (2D 1x1, 2D 2x2 patch
embed and 1D, F16 and F32 dst), which fail on HTP without this fix.
* Apply suggestion from @max-krasnyansky
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* CUDA: radix top-k for large row counts
Replaces CUB's per-row DeviceTopKKernel with a grid-over-rows radix select,
gated on GGML_CUDA_TOPK_RADIX_MIN_ROWS. On qwen4exp at 34,816 tokens this cuts
top-k from 1,671,253 launches / 5,761.8 ms to 2,329 / 941.8 ms.
* CUDA: select the TOP_K implementation by shape
Replace the nrows/ncols special case with the decision boundary from #28547
(as implemented in #29278): bitonic for short rows, radix select for several
long rows, and DeviceTopK or CUB argsort for a single long row. The
thresholds stay overridable at build time.
Two refinements on top of that boundary:
- bitonic stays in use for rows up to a padded 1024 while the rows fit in one
wave of blocks (nrows <= number of SMs); radix select pays a fixed cost of
about a dozen launches that only amortizes over more rows
- with DeviceTopK available, it handles up to two rows
Radix select now processes rows in chunks so its scratch memory stays bounded,
and the bitonic path keeps its chunking. HIP and MUSA keep their previous
thresholds.
Add perf cases around the bitonic/radix crossover to test-backend-ops.
* CUDA: make top-k comments less verbose
* CUDA: remove the TOP_K width limit from supports_op
* CUDA: use DeviceTopK for single-row TOP_K if available
* CUDA: avoid ncols overflow in the TOP_K bitonic check
* CUDA: share the row chunking helper between argsort and top-k
* CUDA: do the TOP_K radix blocks_per_row math in int64_t
* CUDA: rename GGML_CUDA_TOP_K_NROWS_THRESHOLD_DEVICETOPK to GGML_CUDA_TOP_K_NROWS_THRESHOLD
* CUDA: share one sort helper between the bitonic and CUB TOP_K paths
* CUDA: update the TOP_K TODO, threshold and chunking comments
* tests: add TOP_K cases that span several row chunks
* CUDA: use int64_t col in the TOP_K radix loops, fix threshold comment
* CUDA: limit TOP_K and ARGSORT support to ne[0] <= INT_MAX
---------
Co-authored-by: praneshgo <227579474+praneshgo@users.noreply.github.com>
Co-authored-by: Pranesh Gonegandla <pgonegandla@nvidia.com>
The bucket search in topk_nary_search.comp started from the range
[0, 0xFF800000), which ends just below the ordered-uint mapping of +inf,
so +inf and NaN were never counted. A workgroup block with fewer than k
countable values left the ballot empty and the shader read uninitialized
shared state (hang/device lost on NVIDIA, wrong indices on AMD), and a few
+inf in a block were selected without being counted, dropping real top
values.
Map NaN to -inf on input, start from [0, 0xFFFFFFFF) so every value is
counted, and clamp the top bucket's end (2^32) instead of wrapping to 0.
The k = 1 path compared float bits as signed integers, which orders
negative values backwards; compare floats instead.
Add test_top_k_inf to test-backend-ops: negative values, fewer than k
+inf and many -inf, for k = 1, 10, 40.
Assisted-by: Claude Opus 5.5
* sycl: fuse the delta-net alpha gate (add + unary + mul)
* tests: cover the fused add + unary + mul chain
* sycl: give the fused alpha gate a flat path and pin the node skip
* bugfix: infinite recursion caused by a tool named 'call'(#29967)
* chat : use index for schema and argument rules
* tests : remove tests
* tests : add expect_rules to peg test parser
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
The generic few-row MMA kernel works for any type with a 16-weight
dequantizer, so it now also takes BF16, Q1_0, Q2_0, MXFP4, Q2_K, Q3_K,
TQ2_0 and the IQ types. Each type starts at the row count where it beats
the current kernels on an M3 Ultra: 5 rows for TQ2_0, 4 for BF16, 3
for MXFP4, Q2_0, Q2_K and IQ4_NL, and 2 for the others.
test-backend-ops perf -o MUL_MAT, m=4096, k=14336, M3 Ultra, time of this
change over master (mean of two interleaved runs each): 0.23 to 0.98 from
the threshold to 8 rows, 0.24 to 0.33 at 9 to 16 rows, and 0.99 to 1.01 at
1 and 512 rows.
* metal : fix MUL_MAT+ADD fusion when the residual is itself a MUL_MAT
ggml_metal_op_mul_mat_mma picks the residual of a fused MUL_MAT+ADD as
"the ADD operand whose op is not MUL_MAT". When both operands of the ADD
are mat-mul outputs (x = W1 @ u + W2 @ v), that test is true for both, so
the residual resolves to the fused mat-mul's own, never-written output and
the kernel adds whatever that buffer holds.
The fusion check (ggml_metal_mul_mat_add_operand) already selects the
operand by identity; make the encoder do the same.
Clef decision models hit this in their head (proj_option_context @ ctx +
proj_option_lexical @ lex, 9 option rows): on Metal, /v1/systemone
probabilities collapse toward uniform (billing 0.28 where the CPU backend
gives 0.977, Cloudflare_clef-flash Q8_0), deterministic per memory layout,
correct with GGML_METAL_FUSION_DISABLE=1. Not a quantization issue: the
same file is right on CPU.
Add a MUL_MAT_ADD mode to test-backend-ops where the residual is a second
mat-mul; on Metal it fails 27 of 28 cases before this change (the one pass
is f16 n=2, under the MMA row threshold, so nothing fuses).
* Update tests/test-backend-ops.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling : use greedy selection for eligible temperature-zero chains
Assisted-by: OpenAI Codex
* Apply suggestion from @ggerganov
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling: simplify zero-temperature greedy eligibility
Allow the same greedy selection on CPU and grammar/reasoning-budget paths.
Keep distribution sampling for dynamic temperature and requested probabilities.
Cover the common sampler selection and probability behavior in the existing sampler tests.
Assisted-by: OpenAI Codex
* sampling : use greedy selection after final top-k with k=1
Assisted-by: OpenAI Codex
* cont : clean-up
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
The XIELU CUDA kernel template is already generic over the element
type; only the F32/F16 type assertion and the else-if dispatch were
missing. Add the nv_bfloat16 branch to the launcher, and drop the
temporary supports_op gate in ggml-cuda.cu that rejected BF16+XIELU.
test-backend-ops gains two BF16 cases ([10,5,4,3] and [512,16,1,1]).
docs/ops/CUDA.csv and docs/ops.md are regenerated; the F32 xIELU row
flips from no to yes as well, i.e. the previous record was stale.
Tested:
- Mac CPU: xIELU F32/F16/BF16, 6/6
- Mac Metal: existing F32/F16, 4/4; BF16 still unsupported
- RTX 4090 CUDA: xIELU F32/F16/BF16, 6/6
- RTX 4090 CUDA BF16-only: 2/2
- git diff --check passes
* model: K2 Horizon gguf conversion code
* model: loading hparams and tensors in k2-horizon.cpp
* model: K2 Horizon compute graph
* model: K2 Horizon compute graph adjustment and registering tokenizers
* model: K2 Horizon chat template and accomodate safetensors naming
* unicode : add the K2-Horizon pre-tokenizer splitter
The K2-Horizon regex had no arm in unicode_regex_split_custom and fell through to the
general std::regex fallback, which fails two ways.
On MSVC std::regex rejects \p{...}, so no K2-Horizon GGUF loads on Windows at all:
llama-quantize, llama-imatrix and llama-perplexity all abort with
regex_error(error_escape) before a token is produced.
Where the fallback does compile it is still wrong. unicode_regex_split collapses each
codepoint to a single byte naming its Unicode category before matching, and U+200C/U+200D
are category Control, which has no entry in k_ucat_cpt, so both become the 0xD0 fallback
byte. The literal and alternatives in K2's regex can then never match and
every ZWNJ or ZWJ ends a letter run.
The splitter is the existing llama3 one with a single rule widened, since K2's regex
differs from llama3's only in that a letter run also takes marks, ZWNJ and ZWJ.
tests/test-unicode.cpp gains a case for this: it fails before the change with
[Amy] [ZWNJ khaham] and passes after with the run intact.
* tests: expand K2 Horizon unicode splitter coverage
* unicode: handle K2 Horizon case folding and empty input
Assisted-by: Codex
* jinja : support sequence indices in selectattr and rejectattr
Assisted-by: Codex
* model : add K2 Horizon dense and MoVA support
Includes the K2 Horizon implementation from ifm-ai/llama.cpp with converter, tensor-parallel and model save/reload fixes.
Assisted-by: Codex
* chat : support K2 Horizon reasoning and tool calls
Assisted-by: Codex
* conversion: remove obsolete K2 Aurora alias
Assisted-by: Codex
* k2-horizon: enforce response schemas and load YaRN betas
Constrain final JSON after reasoning, accept flexible JSON tool envelopes,
enforce XML dialects, and handle repeated or alternate thinking markers.
Load YaRN beta metadata instead of retaining the default values.
Add schema, streaming, continuation, and model reload regressions. Validate
CUDA and CPU builds and 0.9B, 4B, and MoVA conversation/tool round trips.
Assisted-by: Codex
* renaming template fixture
* adressing cisc follows ups
* desloppify the parser / adress aldehir comments
* clean test-chat
* remove fallback : model trained mostly on high anyway
* fix k2 attn_v_exp tn splitting and metal fusion baseline
* k2-horizon : forward expand views before sums
* k2-horizon: copy embds before group norm to fix TP
* disable tesnor parallelism
---------
Co-authored-by: Ryandito Diandaru <ryandito.diandaru@mbzuai.ac.ae>
Co-authored-by: WestWaters <mario.papaleo2013@gmail.com>
Co-authored-by: Natani L. Mayday <71436458+TaskPuppyNatani@users.noreply.github.com>
Co-authored-by: West <100190545+WestWaters@users.noreply.github.com>
Co-authored-by: aaryamonvikram <aaryamonvikram@gmail.com>
Co-authored-by: aaryamonvikram <96529820+aaryamonvikram@users.noreply.github.com>
* ggml: fix CLAMP on non-contiguous views (CPU, CUDA)
CUDA clamped ggml_nelements values flat and ignored the view strides.
CPU addressed row j as j*nb01 and ignored nb02/nb03. Both now follow the
strides of dims 1..3; CUDA supports_op requires contiguous rows, like
Metal. test_clamp gains a non-contiguous view case.
* cuda: clamp kernel uses fastdiv for the view strides
* ggml: refactor selective expert copying to user code
* tests: enroll two models into selective expert copy test
* tests: use deepseek2 as test model
* improve comment in ggml-backend.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cont: fix whitespace
* cont : better comments
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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