* vulkan : fuse UNARY(SIGMOID|SILU|SOFTPLUS) + MUL
* vulkan : fuse UNARY(SIGMOID|SILU|SOFTPLUS) + MUL
- implement fusion in unary.comp behind UNARY_MUL_FUSION ifdef,
specialized pipelines per op instead of runtime branching
- fuse adjacent nodes only, ordering handled by graph_optimize
- drop runtime consumer scan and pending_unary_mul deferral
* vulkan : fuse UNARY(GELU|SIGMOID|SILU|SOFTPLUS) + MUL
1. GELU: gelu_mul_f32/f16 pipelines registered, CREATE_UNARY_MUL(gelu), GELU in dispatch + fuse gate + perf fusion name
2. Renamed/moved: gate is now ggml_vk_can_fuse_unary_mul(cgraph, unary_idx, mul_idx), placed with the other can-fuse helpers
3. norepeat both variants: each op gets plain (spec {0}) + _norepeat (spec {1}) pipelines from the same SPIR-V, selected via ggml_are_same_shape(src0, src1); the shape gate now allows broadcast (other dims equal-or-1)
4. graph_optimize: lambda deleted; standard "// UNARY + MUL: pull the consuming MUL forward" block added alongside the SSM_CONV/ROPE/MUL_MAT reorderings, with the same "other src must be weights or already processed" readiness check
* vulkan : align unary_mul fusion with binary kernel layout, relax gelu test tolerance
- schedule the fused kernel like mul.comp (256 threads x 2 unrolled
iterations), recovering a 10-18% prompt-processing regression
- allow 5e-7 f32 error for gelu_mul: the shader evaluates gelu with an
exp-based tanh identity while the CPU reference uses tanhf (~1 ulp)
* vulkan : use ggml_can_repeat in UNARY+MUL fusion shape check
The fused kernel indexes src1 via per-dim fastmod (generic_binary_head.glsl),
which is exact whenever the other operand tiles into the unary result -- not
just when its dims are equal or 1. Replace the hand-rolled loop with
ggml_can_repeat(other, unary) so the check matches the kernel's actual
capability and reuses the standard helper. Argument order matters: reversed,
it would wrongly admit graphs where the unary result is mul->src[1] and the
other operand is larger, producing truncated output.
Also add a rep_ne0 layout to the fused unary+mul backend tests covering a
non-1 repeat factor along dim 0.
* vulkan : fuse UNARY+MUL pairs separated by zero-compute nodes
gemma4's per-layer embedding gating builds gelu -> view_2d_slice -> mul,
where the intervening view is a zero-compute node aliasing an input that
was computed much earlier. Strict adjacency requirements meant neither
CUDA nor the vulkan unary+mul fusion handled this pattern.
Extend ggml_vk_graph_optimize to detect a UNARY whose consuming MUL is
separated only by unscheduled zero-compute nodes (GGML_OP_NONE, VIEW,
RESHAPE, TRANSPOSE, PERMUTE) and schedule those nodes ahead of the pair,
making it adjacent so the existing fusion applies. The reorder is guarded
by ggml_vk_can_fuse_unary_mul, a source-availability check for every
interleaved node, and the protected fusion patterns (topk_moe*, snake);
if fusion is later rejected the reordered graph still executes correctly,
just unfused.
Add a view_mid layout to the fused unary+mul backend tests replicating
the gemma4 pattern.
* vulkan : support OP-on-B in UNARY+MUL fusion
Some models apply the unary activation to the smaller MUL operand, e.g.
qwen3next/qwen35moe shared-expert gating builds ffn_shexp * sigmoid(gate)
with a [1,n_tokens] gate tensor. This shape was correctly rejected before:
the fused kernel derives its iteration extent from the unary tensor and
would leave most of the destination unwritten, and the generic same-shape
requirement in ggml_can_fuse blocked the pair outright.
Add UNARY_MUL_B_FUSION shader variants computing dst = src0 * OP(src1):
the OP operand rides the existing per-dim fastmod indexing, while the
iteration extent now comes from mul. Route {UNARY, MUL} pairs through a
local can-fuse variant that drops the generic same-shape rule and instead
requires the unary result to tile into mul->src[0] (ggml_can_repeat);
pairs with the unary as src0 keep the previous direction check, and
equal-shape pairs keep using the original pipelines.
Add a "gate" layout to the fused unary+mul backend tests covering the
shared-expert gate shape for gelu/sigmoid/silu/softplus in f32 and f16.
* vulkan : fold unary+mul view-hoisting into graph_optimize dep checks
Replace the dedicated UNARY + EMPTY* + MUL scanning block with two small
extensions to the existing scheduling logic:
- a consuming MUL may now join its in-set UNARY across a gap of unused
zero-compute nodes (NONE/VIEW/RESHAPE/TRANSPOSE/PERMUTE), instead of
requiring strict adjacency
- while doing so, such zero-compute blockers are ignored for this pair
Fusion validity is still decided later by ggml_vk_can_fuse at dispatch
time, so a rejected pair simply executes adjacent-but-unfused. Note the
relaxation must stay scoped to this pattern: exempting zero-compute
blockers globally reproduces silent output corruption on gemma3n.
* vulkan : select unary_mul OP-on-B via specialization constant
Replace the UNARY_MUL_B_FUSION compile-time shader variants with an
op_on_b specialization constant on the existing unary_mul SPIR-V,
mirroring how the norepeat flag is handled. The four {op}_mul_b_{f32,f16}
shader artifacts are gone - the OP-on-B pipelines reuse the base SPIR-V
with two-entry {norepeat, op_on_b} spec lists - and the duplicated store
expression is collapsed into a single runtime branch that the driver
prunes per specialization.
The constant is declared only under UNARY_MUL_FUSION so every other
binary pipeline keeps its single-entry specialization list.
* vulkan : replace unary_mul pipeline switches with a lookup table
Collapse the four nested selection switches in ggml_vk_unary_mul into a
single indexed lookup against a pipeline_unary_mul[4][2][2][2] table
([unary op][f16][norepeat][op_on_b]), whose trailing dims mirror the
{norepeat, op_on_b} spec constant list. The op axis uses a small shared
index helper that also replaces the switch in ggml_vk_can_fuse_unary_mul,
making it the only place that maps ops to the table.
Pipeline names are unchanged. Adding another supported op now requires
one macro invocation line and one helper case instead of edits in four
separate switches.
* vulkan : use ggml_can_fuse_subgraph for unary_mul pairs
Replace the hand-rolled pair validation in ggml_vk_can_fuse_unary_mul_pair
(bounds, op match, compute flags, single-use elision) with the shared
ggml_can_fuse_subgraph helper; backend-specific shape/type rules remain in
ggml_vk_can_fuse_unary_mul. Unlike ggml_can_fuse, the subgraph helper has
no same-shape requirement, so it covers both operand slots including
OP-on-B gates, and additionally rejects intermediates flagged as graph
outputs and validates view-source confinement.
The outputs parameter takes absolute node indices into the cgraph.
* Fix Whitespace
* vulkan : drop redundant unary_mul gap check in graph_optimize
The zero-compute nodes separating a UNARY from its consuming MUL are
already scheduled ahead of the pair by pass 2 of an earlier
optimization window, so the scoped gap tolerance added for this pattern
is unreachable in practice - disabling it leaves gemma-3n dispatch
counts unchanged (841 GELU_MUL per pass). Remove the flag, the empty
blocker exemption, and the now-unused gap helper, restoring the strict
adjacency requirement of the UNARY -> MUL pull-forward.
Keep the relaxation scoped out entirely: generalizing "zero-compute
nodes never block" beyond this pattern previously reproduced silent
output corruption on gemma3n.
* vulkan: fix whitespace (tab in indent)
* vulkan: fix whitespace (extra blank line)
* vulkan : move op_on_b spec constant to unary.comp
op_on_b is only used by the fused unary*mul path. Keep
generic_binary_head.glsl generic by defining it in unary.comp
instead. Same constant_id=1 and guard, no functional change.
* vulkan : make RMS_NORM/UNARY fusion gap-tolerant for views
Strict j==c+1 blocked RMS_NORM->MUL and UNARY->MUL when a
VIEW sits between (e.g. rms_norm -> view -> mul). Allow
c==back() with an empty-or-scheduled gap, matching the
review suggestion to check src linkage instead of adjacency.
Scoped to the two blessed pairs; safe because gaps can only
contain zero-compute nodes.
* vulkan : trim comments in UNARY+MUL fusion
Assisted-by: Muse Spark
* tests: bind the L2_NORM batch count to a local
GCC cannot prove the loop fills norms up to the index read after it
while the bound is a class member, so it reports a maybe uninitialized
use. Reading the count once into a local restores the tracking.
* tests: initialize the L2_NORM batch array
The read after the fill loop is only provably defined once the array
carries an initializer, which GCC 12 requires on the aarch64 Release
build where warnings are fatal.
* vulkan: add DeepSeek-V4 hyper-connection fused ops (DSV4_HC_COMB/PRE/POST)
CUDA has these ops from the DeepSeek-V4 merge and Metal gained them in
PR 26459. Vulkan was the last major backend running the unfused primitive
chain. On DeepSeek-V4-Flash the unfused Sinkhorn comb chain alone takes
about 32% of decode op time on gfx1151 (Strix Halo), spread over roughly
16k dispatches per token.
dsv4_hc_comb runs the full 20-iteration Sinkhorn in registers. A token's
4x4 comb matrix lives in 16 consecutive subgroup lanes, with idst in bits
0-1 and isrc in bits 2-3 to match the CPU reference layout, so
subgroupShuffleXor by 1|2 reduces rows and by 4|8 reduces columns. One
dispatch replaces about 137 strictly ordered node executions per site.
The shuffle masks never cross a 16-lane boundary, so a subgroup of size
64 packs 4 independent tokens.
dsv4_hc_pre and dsv4_hc_post handle the elementwise stream collapse and
fan-out, with per-token coefficients staged in shared memory.
GGML_VK_DISABLE_DSV4_HC disables all three ops. The _COMB, _PRE and
_POST variants gate each op independently so a single kernel can be
bisected against the unfused graph.
Adds eval cases at the production n_iter=20 across batch sizes that
cross subgroup and workgroup boundaries.
* vulkan: dsv4 hc review fixes
Drop the per-op env-var disables and device flags, the stride divisibility
check (ggml guarantees it) and the workgroup-count fallback in supports_op.
Trim the comb shader comments to the lane layout.
---------
Co-authored-by: Kevin Hopper <no-reply@maestro.press>
* vulkan: fall back to CPU for GET_ROWS with misaligned offsets
The Vulkan GET_ROWS shader asserts when a tensor's backing-buffer offset
plus view_offs is misaligned w.r.t. minStorageBufferOffsetAlignment
(see init_pushconst_tensor_offsets). Previously this caused a hard crash
on models using ggml_view + ggml_get_rows (e.g. Qwen3-TTS, Qwen3-VL).
Return false from supports_op() in the misaligned case so the scheduler
falls back to CPU, matching the existing pattern for PAD_REFLECT_1D and
other unsupported op/shape combinations.
Repro: llama-tts -m Qwen3-TTS-*.gguf -mm mmproj-*.gguf -ngl 99
Crash: GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && ...)) failed
* vulkan: trim comment for GET_ROWS misalign fallback
* vulkan: fix file corruption in gated_linear_attn struct
* vulkan: properly handle misaligned offsets in GET_ROWS quantized path
- get_rows_quant.comp was missing get_aoffset()/get_boffset()/get_doffset()
calls that are already present in get_rows.comp, causing GGML_ASSERT crashes
when GET_ROWS operates on views with non-zero view_offs, as produced by
KV cache slices in Qwen3-TTS and Qwen3-VL.
- Remove the defensive misalignment GGML_ASSERT in init_pushconst_tensor_offsets
for the binary push-constants specialization, since both get_rows.comp and
get_rows_quant.comp now correctly apply per-tensor base offsets.
- Remove the workaround CPU fallback in supports_op() for GET_ROWS, since the
Vulkan backend now handles misaligned offsets natively (no more bailout).
- Add backend test coverage with view_src0=true (ggml_view_4d into a padded
tensor) for F32, F16, Q4_0, Q4_K, Q8_0, and I32 types, exercising both the
non-quantized (get_rows.comp) and quantized (get_rows_quant.comp) paths
with non-zero view_offs that reproduce the original Qwen3-TTS crash.
* tests: trim redundant comments in test_get_rows vs0 region
* tests: trim redundant comments in test_get_rows vs0 region (follow-up)
* vulkan: bind tensor base for binary ops, pass full view_offs via push constants
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, MUL, etc.),
bind the view_src base and pass the full view_offs divided by type_size via
push constant misalign_offsets. This avoids truncation when misalign_bytes is
not a multiple of quantized block size.
ggml_vk_tensor_subbuffer gains a use_view_offs parameter. When false, the
binding points to vk_tensor_offset (base) and size includes view_offs.
init_pushconst_tensor_offsets<binary> computes a/b/d_offset directly from
tensor->view_offs, which is always row-aligned and therefore exact.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA RTX 5060 Ti).
* vulkan: bind aligned offset for binary ops, pass adjusted misalign via push constants
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, etc.), bind
the buffer to an aligned position near the view offset (not the tensor base)
and pass the adjusted misalignment via push constants.
ggml_vk_get_adjusted_misalign finds the smallest misalign that is both a
multiple of minStorageBufferOffsetAlignment and type_size, ensuring
misalign/type_size is exact (no truncation for quantized block types).
ggml_vk_tensor_subbuffer gains use_view_offs parameter. When false, binds
to (target - adjusted_misalign) instead of the view_src base, keeping the
offset small enough for 16-bit/8-bit push constant fields.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA RTX 5060 Ti).
* vulkan: bind aligned offset for binary ops, fix UMA offset mismatch
For ops using vk_op_binary_push_constants (GET_ROWS, ADD, SUB, etc.), bind
the buffer to an aligned position near the view offset (not the tensor base)
and pass the adjusted misalignment via push constants.
Added ggml_vk_tensor_physical_offset to unify physical offset lookup across
UMA and non-UMA devices. On UMA, resolves via ggml_vk_host_get(tensor->data);
otherwise uses vk_tensor_offset(t) + t->view_offs. Both get_misalign_bytes and
the new ggml_vk_get_adjusted_misalign helper build on top of this function,
so buffer bindings and push constant offsets are always consistent regardless
of device memory model.
ggml_vk_get_adjusted_misalign finds the smallest misalign that is both a
multiple of minStorageBufferOffsetAlignment and type_size, ensuring
misalign/type_size is exact (no truncation for quantized block types) while
remaining small enough for 16-bit/8-bit push constant fields
(adjusted_misalign < lcm(align, type_size)).
ggml_vk_tensor_subbuffer gains use_view_offs parameter. When false, binds
to (physical_offset - adjusted_misalign) on both UMA and discrete GPUs,
fixing a bug where the UMA host_get path previously skipped the adjusted
misalign binding and returned the target offset directly.
Added non-zero view offset (offset_rows=3) backend tests for GET_ROWS across
all_types with be1={1,7}, v={false,true}, skipping gradient setup for view
tensors (GGML_OP_VIEW fails ggml_set_param).
All 223 GET_ROWS tests pass on Vulkan (NVIDIA GeForce RTX 5060 Ti).
* finish misalignment fix
* supports_op changes for openvino/webgpu
---------
Co-authored-by: AiChiTuDouPian <15327701848@qq.com>
Support RMS_NORM + MUL + ADD (+ MUL) and RMS_NORM + VIEW + SET_ROWS.
Extend ROPE + VIEW + SET_ROWS to support IMROPE.
Worth around 4% in gemma4 on my system.
* vulkan: add TQ1_0 support (mm, mat-vec, dequant, get_rows)
* vulkan: pack TQ1_0 powers of 3 into a 32-bit constant
Replaces the constant array with a packed 32-bit value (7 bits per entry,
max 81 < 128) extracted with shift/mask, as suggested in review — avoids a
constant array that may not be kept in registers.
test-backend-ops on gfx1151: tq1_0 MUL_MAT 11/11, MUL_MAT_ID 6/6,
GET_ROWS 4/4, unchanged.
* vulkan: address review - shared TQ1_0 decode helpers, fix standalone dequant shader
Review feedback from jeffbolznv, all points:
- Move the packed-pow3 decode into shared helpers in types.glsl
(tq1_0_byte_of / tq1_0_digit_of / tq1_0_trit) and use them from
dequant_funcs.glsl, mul_mm_funcs.glsl, dequant_funcs_cm2.glsl and
dequant_tq1_0.comp instead of repeating the logic. The cm2 path also
drops its constant array for the packed-constant extraction.
- Translate all remaining comments to English.
- dequant_tq1_0.comp: use dequant_head.glsl. The shader previously declared
its own single-field push constant while the pipeline is created with the
5-field layout, so p.ne read the wrong field - confirmed broken, as
suspected in review.
- Fix wg_denoms for the standalone dequant pipeline: one invocation decodes
4 elements with local_size 256, so a workgroup covers 256*4 elements, not
256*16. With the old value the dispatcher launched a quarter of the
required workgroups.
Verified by temporarily forcing the dequant + f16 matmul path for TQ1_0
(hack not committed): test-backend-ops MUL_MAT passes through the rewritten
standalone shader, and the standard MUL_MAT / MUL_MAT_ID / GET_ROWS
tq1_0 cases still pass on Vulkan (AMD gfx1151).
* vulkan: address review — English comments, shared tq1_0_trit, trim TQ1_0 test cases
- mul_mat_vec_tq1_0.comp: drop leftover non-English comment and the local
POW3_PACKED constant; all decode sites now call tq1_0_trit() from types.glsl
- types.glsl / dequant_funcs_cm2.glsl: ASCII-only, drop stale reviewer note
- test-backend-ops: remove the oversized MUL_MAT_ID case (432 MiB A tensor,
~172 GFLOP reference); move the two remaining ones next to the other
backend-specific mul_mat_id one-offs and document why they are needed
* metal: decline TQ1_0 for GET_ROWS and mat-mul in supports_op
The new TQ1_0 cases in test-backend-ops exposed that the Metal backend
claimed support for GET_ROWS/MUL_MAT/MUL_MAT_ID with TQ1_0 sources while
having no such kernels (ggml_metal_library_compile_pipeline aborted on the
missing kernel_get_rows_tq1_0). Decline the type so the ops fall back to
the CPU, matching the existing NVFP4 handling on the same lines.
Assisted-by: Claude Fable 5
* vulkan: trim the TQ1_0 comments
Addresses @0cc4m's review: keep only what the code does not already say.
Removed the block-format recaps (the layout is right there in the struct) and
the step-by-step decode walkthrough. Kept the two facts a reader cannot infer:
the 8-bit truncation is part of the format, not an optimisation, and the powers
of 3 are packed into one uint so they do not end up in a constant array that
may miss the registers.
No functional change.
* vulkan: address review — trim comments, fold Metal check, drop unused _v
Per @0cc4m's review:
- dequant_funcs.glsl, dequant_funcs_cm2.glsl: drop the "see types.glsl"
pointers — they apply to every quant and say nothing specific.
- dequant_tq1_0.comp: drop the wg_denoms note. It is a precondition, not
information.
- mul_mm_funcs.glsl: same pointer removed.
- types.glsl: the comment on tq1_0_trit is down to the one fact the code
cannot show — the 8-bit truncation is part of the format, matching the C
reference, not an optimisation.
- dequant_funcs_cm2.glsl: removed dequantFuncTQ1_0_v and its define. You were
right that it is optional: it wrapped four scalar decodes and vectorised
nothing, and mul_mm_cm2.comp already guards the path with
`#if defined(dequantFuncA_v)` (DATA_A_F32 omits it the same way).
- ggml-metal-device.m: folded TQ1_0 into the existing NVFP4 check instead of a
separate block, and dropped both comments.
- test-backend-ops.cpp: the two mul_mat_id cases stay — they cover the
block-stride loop and the per-expert base offset that k == 256 alone never
reaches — but the comment is now one line instead of five.
Kept: the one-line labels on the three block regions in mul_mat_vec_tq1_0.comp
and on tq1_0_byte_of(). Those state the 5-trits-per-byte packing, which the
loop bounds do not show. Happy to remove them too if you prefer.
Re-verified on AMD gfx1151 (Vulkan), test-backend-ops, 2/2 backends passed:
MUL_MAT 9 TQ1_0 cases, MUL_MAT_ID 5, GET_ROWS 4 — all OK, no failures.
The coopmat2 path is unchanged apart from the removed _v define.
* Reapply "sycl : add Kronecker product FWHT support for sizes 384, 640, 768, 12…" (#28184)
This reverts commit c845263f8b.
* tests : fix unused variable M in test-backend-ops
* tests: fix trailing space error and isolate kronecker tests for sycl backend only
Fuse RMS_NORM+MUL+ADD and ADD+ADD under GGML_SYCL_ENABLE_FUSION.
ADD+ADD uses the same binbcast indexing and type matrix as standalone
add() (f32, f16, f16/f32, i32, i16, bf16, including broadcast and
non-contiguous). Unsupported combinations fall back to two add() launches.
* metal : support n_kv_max sparse mask hint in flash attention vec kernel
- add kernel_flash_attn_ext_vec_idx: compacts finite mask entries into
a per-row index list (Hillis-Steele scan, one threadgroup per row)
- extend vec FA kernel with optional sparse index gathering (FC slot 5)
- add host-side gate: sparse path when n_kv_max > 0, mask present,
supported head sizes / KV types, n_kv_max <= 4096
- new buffer region extra_idx for the index list
- pipeline getter extended with has_sparse param
- add test cases: head sizes, quant types, nb>1, nr23 variants,
sinks, ALiBi, softcap, permute, v_view_of_k, no-mask fallback
Note: multi-row (nb*nr23[1] > 1) cases still failing - rid mapping
in the store phase needs revisiting for the sparse path.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : fix sparse flash attention row addressing
- kernel_flash_attn_ext_vec_idx: mask param is half* but nb31 is a byte
stride, so the per-row mask offset was scaled by 2x; cast to char*
before applying the byte strides
- kernel_flash_attn_ext_vec: sparse pidx param is char* so the per-row
element offset was under-scaled by sizeof(int); scale it by sizeof(int)
to get the correct byte offset
- fixes the multi-row (nb*nr23[1] > 1) sparse flash attention failures
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* cont : use sparse vec FA for prefill
* metal : single-pass flash attention sparse index compaction
The idx kernel previously read the mask row twice: once to count the finite
entries (for the prefix scan) and again to recover their positions. Since the
kernel is memory-bound, this doubled the mask traffic.
Keep the finite positions in a per-thread register array during the count
pass and write them out directly, avoiding the second mask read. A dense
mask with more than NLOCAL finite entries in a slice falls back to re-reading
the mask to write the remaining positions.
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* tests : add perf cases for sparse flash attention prefill
Measure the sparse vec FA kernel across KV sizes, n_kv_max hints and batch
sizes. Run with:
./build/bin/test-backend-ops -b MTL0 -o FLASH_ATTN_EXT -p "n_kv_max=[1-9]" perf
Assisted-by: pi:llama.cpp/DeepSeek-v4-0731
* qwen4 : enable sparse attention
* cont : adjust nsg
* cont : sync test-backend-ops
* cont : disable Qwen4 for now
* cont : clean-up + tests
* sycl: Q4_K Weight unpack optimization and reuse between destination Columns
* sycl: Q4_K small N (N=2..4) + two output rows by subgroup reuse of activation between two rows.
* sycl: gate Q4_K two-row reuse for small N=2
* sycl: Fix on magic number now uses Q4_K_MMVQ_ROW_PAIR_MIN_NROWS=6272 for it, added tests for coverage around Q4_K_MMVQ_ROW_PAIR_MIN_NROWS with perf support to test Q4_K MUL_MAT, applied the same reuse pattern to the activation as the weights.
Assisted-by: GPT-5.6 Sol
---------
Co-authored-by: RaulAbejonDelgado <raul.abejon.delgado@gmail.com>
* vulkan: handle larger batch sizes (>4) efficiently for IQ3_S mat-vec when NUM_COLS > 4. 5x perf at n=8
Assisted-by: Claude Opus 5
* adds 2 cases per quant type at `k=16*256` to the `all_types` mat-vec sweep
---------
Co-authored-by: Marshall <assistant@llama.cpp>
* cuda : fuse MoE weighted reduction (mul + view + add)
The MoE combine tail currently writes weighted expert outputs to
global memory before reducing them. That intermediate global-memory
traffic is the main cost. The production baseline generally runs two
physical fused kernels; this path runs one.
This change matches the full expert-weighting plus ordered-reduction
subgraph and replaces it with one weighted-reduction kernel.
Supported graphs:
- unscaled: experts * router_weights
- scaled: (experts * expert_scale) * router_weights
k = 2..15 is handled by one runtime-k kernel.
Matching is structural: op sequence, shapes, strides, expert views,
and the left-to-right ADD chain. The fused kernel keeps that same
reduction order. Results are not claimed bit-identical; CUDA FP32
contraction can change rounding slightly.
Allocator integration uses add_alloc_dep from the graph-optimizer
API so experts, router weights, and optional expert scales stay live
until the fused destination is written. Memory ranges are rechecked
before the fused kernel runs.
Unrecognized or unsafe graphs are left alone and keep the existing
per-op path. Set GGML_CUDA_MOE_WEIGHTED_REDUCTION=0 to disable the
fusion.
test-backend-ops covers scaled/unscaled, aligned/unaligned, and
representative values across k=2..15, plus a k=16 case that must
stay on the per-op path.
* Pruned the test matrix from 15 to 6
* Addressed the aman and olivers review comments
* Batched gemm for grid IQ quants
Style updates and a bit more performance
Clean up comments
Move code around
Vectorize IQ panel decode, lower threshold for speedup
IQ panel: single-source gather layout, gate bias, vectorize interleave
Add ggml_gemm_iqp_8x8_q8_K_p4 kernel, remove gather buffer
Move IQ panel code out of repack into iqp.cpp, clean up comments
Another comment sweep
* Add myself as iqp.* codeownder
* Remove ggml_cpu_iqp_scratch_offset and ggml_cpu_iqp_src1_conv_size
* Renaming and moving
* The other half of renaming and moving
* Move macros and ggml_cpu_iqp_mul_mat_id_min_batch definition
* Update ggml/src/ggml-cpu/iqp.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add iqp_rows work buffer
* Revert "Add iqp_rows work buffer"
This reverts commit 425542991eee1b01fa3844bf87fc4f205ddbfccb.
* Add NUMA fallback
* Add 10 row batch tests for IQP coverage on all grid IQ types
* Swap assert for return false in support check
* Move IQP mul_mat_id test
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
The N padding is needed for mul_mat, but not mul_mat_id. For mul_mat_id,
we indirect the row index through a shared memory lookup table which avoids
any OOB row coordinate. But that callback doesn't bounds check K, so we
actually need K padding instead.
* ggml : fix conv_transpose_2d for multiple batches
ggml_compute_forward_conv_transpose_2d_impl only computed the first
batch (ne[3] of the destination); every batch after the first was left
as zero. Both the src1 permutation and the main compute loop now iterate
over the batch dimension, and the work buffer size in ggml_graph_plan is
scaled by the src1 batch count so the extra permuted batches fit. A
multi-batch test case is added to test-backend-ops.
Fixesggml-org/ggml#1448
* metal : fix conv_transpose_2d for multiple batches
The kernel only computed batch 0 of the input (src1->ne[3]); every
output batch after the first was left as zero, so multi-batch
conv_transpose_2d results diverged from the CPU reference.
The grid now covers all batches (OW x OH x OC x N), the kernel decodes
the batch from the grid z coordinate and offsets both the input and
destination indices accordingly. nb3 is passed in the kernel args.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* spec : add DFlash2 support (local convolution + candidate selector) (#27342)
* support DFlash2
* Add p_min in DFlash2
Assisted-by: Claude Opus 5
* Revert unnecessary changes
Assisted-by: Claude Opus 5
* Revert draft sampling in rejection sampling
Assisted-by: Claude Opus 5
* Refactor code structure
Assisted-by: Claude Opus 5
* Delete embedding scaling
Assisted-by: Claude Opus 5
* Gate output transforms on DFlash2
Assisted-by: Claude Opus 5
* Optimize Dflash 2 cost
Assisted-by: Claude Opus 5
* Avoid using atoi
Assisted-by: Claude Opus 5
* Modify comments
Assisted-by: Claude Opus 5
* Move llama_model_dflash_selector_top_k to llama-ext.h
Assisted-by: Claude Opus 5
* Formatting
Assisted-by: Claude Opus 5
* Apply patch to fix the mrope bug
Assisted-by: Claude Opus 5
* fix ci
Assisted-by: Claude Opus 5
* Fix graph number calculation
Assisted-by: Claude Opus 5
* rename hid and unary
Assisted-by: Claude Opus 5
---------
Co-authored-by: Jian Chen <jianchen0311@gmail.com>
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* revert top-k.cu changes
---------
Co-authored-by: Zihan Zhang <tiancaizhangdaxian@sjtu.edu.cn>
Co-authored-by: Jian Chen <jianchen0311@gmail.com>
* hexagon: use non-host bufs by default and make the backend fully async
* hex-hb: remove optional hostbuf support and fix async copy
* hex-unary: relax supported unary check
* hex-bufs: use same get_alignment for host bufs
* snapdragon: bump android_platform to 34
* hex-rows: super hacky get/set rows for q8_0
* hex-get-rows: fix q8_0
* hex-get-rows: supprot for f16 and cleanup for q8_0
* hex-get-rows: generic macros and specialized thread funcs
* hex-get-rows: add DMA pipeline, vtcm_layout and kernel params
* hex-set-rows: fix q8_0 support, add dma and tracing
* hex-tests: override nmse threshold for HTP of Q8_0 quants
* hex-fa: add support for Q8_0 with inplace dequantizers
* hex-get-rows: simplify type dispatch
* hex-rows: simplify GET/SET_ROWS DMA pipeline
* hex-async: add events, set/get-tensor-async and rest of the async api support
* hex-repack: use slice instead of expert in repack functions
* hex-cpy: update event/async-cpy logging
* hex-set-rows: optimize smaller tensors
* hex-geglu: fix perf regression with larger tensors
* hex-get-rows: add missing header
* hex-set-rows: add missing header
* hex-bufs: ressurect GGML_HEXAGON_HOSTBUF but disable it by default
* hexagon: do not reject ops with non-heaxon buffers
* hex-get-rows: apply >=32 restriction only for q8_0
* hex-res: bump vtcm acquire timeout to 10 seconds
* hex-bufs: add support for cloning buffers between sessions to speed up tensor copies
* hex-async: rework event recording and batch flushing and integrate with meta backend
* hex-bufs: improved handling of repacked tensors
* hex-repack: handle get_tensor_2d offsets
* hex-dev: add support for devices with multiple NPUs
* hex-sync: add support for sync tokens to synchronize npu devices for async splits
* hex-mmap: cleanup mmap calls and add a retry for robustness
* hex-sync: add failsafe if sync wait gets stuck
* hex-sync: use sync_seq to check for completed events
* hex-sync: rotate tokens for extra robustness
* hex-devs: add supprot for legacy device names for now
* hex-bufs: add support for auto-cloning buffers from diff sessions
* hex-fusion: simplify and optimize htp-opnode fusion handling
* hex-sync: override opnode name so that it shows up in the profiles
* hex-trace: update scripts to handle multiple devices
* hex-sync: bump the size of the opbatch queue and number of sync tokens
* hex-cpy-sync: do not explicitly flush opbatches in cpy_tensor_async and add support for cpy-dma
* hex-sync: add graph-flush threshold to avoid single op batches
* hex-sync: add sync_peer so that we can flush peers we depend on during cross-device ops
* hex-bufs: introduce tensor->extra and shadow_bufs for repacking
* hex-l2: flush tiny tensors inline
* hex-sync: use explicit l2flush for sync tokens
* hex-extra: track weight flags via tensor extra
* hex-fence: rename sync to fence
* hex-repack: proper handling of set-tensor-2d in the shadow_buf
* hex-trace: remove obsolete opstage mask that we used for profiling
* hex-env: remove obsolete use_hmx variable
* hexagon: new unified run.py and build.py and updated docs
* snapdragon: update run script to auto-escapt test-backend-op -p argument
* hex-scripts: fix trailing spaces
* hex-scripts: fix flake8 warnings
* snapdragon: cleanup dst lib/bin dirs before copying new build
* hex-ops: add support for allreduce
* hex-ar: improved allreduce with dma pipeline
* hex-ar: align macros
* hex-ar: consistent use of fence_seq
* hex-ar: add AR_SELECT env var to select ALLREDUCE kernel or fallback
* hex-ar: add proper synchronize handling for ALLREDUCE
* hex-opbatch: looks like we now just rely on backend.synchronise to flush the batches, no need to flush them by threshold
* hex-ar: bump block size to improve dma efficiency
* hex-ar: fused ALLREDUCE+ADD
* hex-ar: cleaner fence buffer management
* hex-ar: futher allreduce tweaking to remove race conditions
* hex-ar: add simple solver and remove non-dma kernels
* hex-ar: add row-broadcast to fuse with bias ADD
* hex-fence: pass seq numbers via op_params
* hex-ar: allow for both entry/exit seq for completing entry wait
* hex-ar: align macros
* hex-ar: do not refetch broadcast row
* hex-fusion: move all fusion into opbatch::add_op for consistency with ALLREDUCE and things
* hex-fusion: fix incorrect MUL_MAT reordering
* hex-mm: make fused 2x and 3x matmuls more generic
* hex-fusion: move tensor fusion tagging to graph_compute
* hexagon: make sure to copy tensor->extra by value
* hex-get-rows: fix offset calc with row-chunking
* hex-repack: get_tensor_2d fixes for non-zero offsets
* snapdragon: make profile/trace scripts more robust and donot mix stdout/stderr by default
* hex-devices: use legacy device nameing by default to ease the transition
* hex-devices: hardcode CDSP domain IDs for current devices for now
* hex-optrace: improve multi-NPU timestamp alignment and overall handling of cycle values
* hex-optrace: more robust handling of the fence events
* metal: WIP chunked SSD SSM_SCAN kernels for multi-token prefill
* metal: drop scalar SSD path; MMA + sequential tail
* drop WIP ssm scan test noise
* remove state_from_dst and rename CS and NSG constants
* remove unrelated added whitespace padding
* added clarity to mma_tokens calculation
* added clarity to use_mma bool checks
* added comments to metal ssd op constants for clarity
* reserve K tokens for sequential kernel rollback snapshots
* reset concurrency between mma and seq tail
* remove print args no longer used
* fixed comment to no longer point to specific line
* add FC_SSM_SCAN so seq path skips token offlset unless it's mma tail
* added changes to new ssm.metal for rebase after ggml-metal.metal refactor
* specialize ssm_scan tail with a template instead of a function constant
---------
Co-authored-by: dpantaleoni <dominikpantaleoni@gmail.com>
Co-authored-by: forforever73 <690105611@qq.com>
* metal : per-device tuned (Q, NE) for flash-attn vec (#25750)
* rebase Q-generic FA vec body from 01dc93607 (#23114)
* add 53 f16 (Q,NE) flash-attn vec instantiations (vec 80 -> 133)
* add FA vec (Q,NE) tuning table + dispatch wiring + SMEM cap fallback
* add FA vec (Q,NE) perf sweep
* fill tuning result
* fold family table into a per-family representative SKU
* refactor tuning result format
* extend FA vec tuning to quantized KV caches
* sync fa vec tuner bucketing with runtime, use pointwise tuning regret
* update tuned table
* format and cleanup
* prefix fa_vec tuning procs with ggml_backend_metal_tuning_, drop unused fa_vec_override_active
* add device id -> token lookup for the offline tuning tool
* add ggml-metal-tuning skeleton
* add op-agnostic perf cell + median timing for the tuner
* add FA-vec graph build + tensor init to the tuner
* tools : add FA-vec (Q,NE) sweep, compression and table emit
* cool down and re-measure the dirty window on thermal drift
* test-backend-ops : replace the FA vec tune mode with a bounded (Q,NE) slice
* tools : document the Metal tuner, point the table comment at it
* abort on unknown KV type, single-source fa_vec_legal_ne
* cleanup
* honor -o in the FA vec (Q,NE) slice
* retune FA-vec (Q, NE) under a pointwise no-harm gate
* cont : add fa-vec tunings for M1 Pro, M2 Ultra, M5 Max
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Resolve the TODO in test_flash_attn_ext: the branch that creates V as a
sub-view of K (MLA-based models) was hardcoded for the 576/512 head shapes.
Add a v_is_view_of_k test case parameter (default false) and select the
sub-view branch on it; the existing 576/512 (DeepSeek MLA) cases now pass it
explicitly, so the test coverage is unchanged.
Also add more V-is-sub-view-of-K cases: the 320/256 (Mistral4 MLA) and
192/128 head shapes, and full views with equal head sizes (128/128 F16,
64/64 q8_0).
Assisted-by: pi:llama.cpp/Qwen3.8-27B
The Tensor API mat-mat path of kernel_mul_mm (GGML_METAL_HAS_TENSOR) fed a
static K=32 tile to the matmul2d op on every iteration. On the last, partial
K tile (ne00 % 32 != 0) the src1 slice extends past the K extent of the
tensor, and the op reads those out-of-bounds elements (undefined behavior per
the MSL specification, section 2.22.2). Depending on stale memory contents,
this corrupted the result or produced NaN.
Make the matmul2d op use dynamic_extent for K, and clamp the K extent of both
operand tensor views to the remaining valid K range (min(32, K - loop_k)) per
iteration, so the op reads exactly the valid K range on every iteration
(mirroring the tail handling of the MPP matmul2d examples). On K-aligned
inputs the clamp degenerates to the full 32-wide tile: the only difference
from the static-K op is that the dynamic-K op derives K from the operand
extents and edge-checks the tile against the tensor extents (a handful of
integer ops per iteration).
Add test-backend-ops MUL_MAT cases with K not a multiple of 32 to exercise
the unaligned K path.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal: dequantize q8_0 KV to f16 before flash attention
Add a preprocessing pass for GGML_OP_FLASH_ATTN_EXT on the Metal backend:
when the KV cache is quantized (Q8_0 for now), dequantize K and V into a
contiguous F16 scratch buffer and run the existing F16 flash attention
kernels on it, instead of the in-kernel dequantization path.
- new kernel kernel_flash_attn_ext_dequant_to_f16<block_t, QK, deq_t4x4>:
one thread per quant block (K then V), stride-aware so permuted KV is
supported; instantiated for Q8_0 (extending to Q4_0/Q4_1/Q5_0/Q5_1 is
one instantiation + one gate case)
- the gate is type-only: dequantize whenever the KV is quantized,
regardless of head sizes, GQA ratio or n_kv; the attention kernels
themselves are untouched
- the F16 copies live in the op's own scratch allocation
(ggml_metal_op_flash_attn_ext_extra_dequant_f16); the KV pad kernel
reads the dequantized buffers when the path is active
- the FA pipeline getters gain a use_f16_kv flag selecting the existing
f16 kernels and contiguous strides
- ref: https://github.com/ggml-org/llama.cpp/pull/25556
Verification (M2 Ultra):
- test-backend-ops test -o FLASH_ATTN_EXT: 4798/4798 pass, including the
new q8_0 eval cases (decode/prompt, permuted, sinks+ALiBi+softcap,
kv=113 pad path, kv=16384)
- llama-perplexity on Qwen2.5-0.5B with -ctk q8_0 -ctv q8_0 matches the
f16 KV reference (PPL 1.0008 vs 1.0008)
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : launch the FA KV dequant kernel separately for K and V
Simplify kernel_flash_attn_ext_dequant_to_f16: it now dequantizes a single
tensor (its own ne/nb and dst) with no is_v branching, and the op dispatches
it twice with the same pipeline - once for K and once for V. The kargs
struct shrinks to a single ne/nb set plus nblocks.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : dequantize q4_0, q4_1, q5_0 and q5_1 KV to f16 before flash attention
The dequant pass now covers all quantized KV types supported by the Metal
flash attention kernels. The dequant kernel, kargs, scratch allocation and
dispatch are type-generic, so each type is one kernel instantiation plus one
gate case.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : skip the redundant V dequant when V is a view of K
In MLA-based models, the V of the FA op is a view of K (the first ne20
elements of each K row); the dequantized V is then a view of the dequantized
K, so skip the second dequant dispatch, do not reserve the V scratch region,
and let the pad and attention kernels read V from the K F16 buffer with K's
strides. The detection follows the CUDA backend:
V->view_src && (V->view_src == K || (V->view_src == K->view_src && V->view_offs == K->view_offs))
Also fix the FA pipeline getters: ns10/ns20 are function constants baked into
the kernels and must be the actual K/V row widths as seen by the kernel. The
dispatch now passes them explicitly (nb11_attn/nb10_attn, nb21_attn/nb20_attn)
instead of the getters assuming contiguous F16 KV (ns20 = dv), which was wrong
when V is read from K with K's row pitch (e.g. 576 vs 512).
New test cases: 576/512 q8_0 (MLA shape, V is a view of K) at kv=113 (KV pad),
nb=1 (vec) and nb=64 (non-vec).
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* test : remove backend-specific wording from test-backend-ops comments
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* pi : avoid backend mentions in test-backend-ops comments
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* metal : rename the FA dequant_f16 identifiers to kv_f16
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* cont : clean-up
* cont : remove TODO
* vulkan : dequant q8_0 KV once in coopmat1
Assisted-by: Claude (Opus 4.8)
* vulkan : fall back instead of aborting when FA scratch exceeds maxStorageBufferRange
* vulkan : require KV-cache layout in FA dequant path
Assisted-by: Claude (Opus 4.8)
* vulkan : skip FA dequant path on coopmat2
Assisted-by: Claude (Opus 4.8)
* tests : add contiguously-allocated quant K/V FA tests
Assisted-by: Claude (Opus 4.8)
* vulkan : trim comments
* vulkan : tighten permutation checks for FA path
* vulkan : set prealloc_x_need_sync after the FA dispatch
* vulkan : exclude Intel Xe1 from FA dequant path
* add params
* cpu kernel
* metal kernel
* add test backend ops
* gate other backends
* ggml: (cuda) support ggml_rope_set_offset (#27121)
* rm cuda supports_op guard, fix webgpu clang-format
* ggml: support ggml_rope_set_offset on vulkan (#27344)
* ggml: support ggml_rope_set_offset on vulkan
* remove inplace optimization
* vulkan: tiled transpose for 0<->2 permuted CONT
-ggml_vk_get_cpy_pipeline only routed to the tiled shared-memory transpose
shader when dim1 was the innermost dimension, i.e. ggml_transpose (a 0<->1
swap). A 0<->2 swap -- ggml_cont(ggml_permute(x, 2, 1, 0, 3)) -- fell back to
the generic per-element strided copy, whose source reads stride by ne0*ne1
elements: one cache line per lane.
-DeepSeek-V4's lightning indexer performs exactly that permute on a
[n_kv, n_tokens, n_head] tensor. On Vulkan/RADV gfx1151 it ran at ~1-9 GB/s of
a ~200 GB/s part and accounted for 43% of total prefill time.
-Add copy_transpose_02.comp, mirroring copy_transpose.comp but tiling over dst
dims (0, 2) with dims 1 and 3 as the batch, so reads walk src dim2 and writes
walk dst dim0 -- both contiguous. The selection condition additionally requires
a non-contiguous source and a contiguous destination so it cannot take cases
the contiguous-copy shader already handles.
-test-backend-ops only exercised ggml_transpose for CONT, so the strided path
was untested. Add test_cont_permute covering (2,1,0,3), (1,2,0,3) and (0,2,1,3)
over f32/f16 at tile-aligned, tile-unaligned and large shapes. The large shapes
are in the eval set rather than only in perf because perf mode does not verify
results.
-Measured on gfx1151, ne=[n_kv,64,64,1], perm=(2,1,0,3), f32:
n_kv=1024: 9.08 -> 579.85 GB/s
n_kv=1280: 20.03 -> 153.71 GB/s
n_kv=2048: 7.11 -> 91.68 GB/s
n_kv=2304: 16.24 -> 86.49 GB/s
-The ~2.2x penalty previously seen at power-of-two n_kv (destination-stride
aliasing) is gone. End to end, DeepSeek-V4-Flash IQ3_XXS prefill on a 9k-token
prompt goes from 56.33 t/s to 103.74 t/s (+84%).
-Note: at n_tokens=512 a single slow-path dispatch takes ~273 ms and looping it
in perf mode can trip the GPU watchdog, so the perf cases use n_tokens=64.
* tests: fold test_cont_permute into test_cont, add L2-exceeding perf shapes
Review feedback: test_cont gains a permute parameter ({0,0,0,0} = none),
matching test_mul_mat's pattern, and the separate struct is gone. Perf
adds [n_kv, 512, 64, 1] variants (~0.5 GB per run) that exceed GPU L2,
since the 64-token shapes fit in cache on large parts and read above
memory bandwidth.
* tests: trim perf-case comment to the two-line summary
* vulkan: trim comments on the 0<->2 transpose path
Drop the shader file header, the read/write block comments and the
rationale prose in the CONT test cases. Keep the tile-shape and
bank-conflict notes and the permute parameter documentation.
---------
Co-authored-by: Kevin Hopper <no-reply@maestro.press>
* Initial changes for Recurrent state rollback for nemotron for cpu and cuda
* Removing CPU RS rollback. Will enable it in subsequent PRs
* addition of test case
* Removing assert and calling runtime API to check if op is supported
* removing extra API and updating the call sites for K
* replace static cuda detection to runtime fused_op api
* address review comments and fallback when SSM rollback not supprted
* Adding changes for supporting RS-rollback in CPU. Also added test-backend-ops for cpu and cuda
* removing memory manipulation as rs rollback is now supported in CPU
* removing the static probe which is not needed now
* correcting the format
* address review comments
* enabling test for all the backends, unsupported backends will fallback to CPU
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* choose different graph based on the result of fused_ssm_op is supported or not and also handled memory->n_rs_seq >1 case incase of op is not supported
* Support K > 1 in ssm_scan for all backends
* Fix CI Issues
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
* metal: add TQ2_0 support
Add support for the GGML_TYPE_TQ2_0 (ternary, 2 bits per element) type in
the Metal backend.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* cont : optimize mul_mv kernel
- float ops over integer ops
- precalculate sums
- hoist coef out of the inner loop
- contiguous y loads
llama.cpp:DeepSeek-v4-Flash-0731
* vulkan: TQ2_0 (ternary) support — dequant + dedicated mul_mat_vec + matmul via dequant_funcs
First Vulkan ternary type in ggml. Correctness: OM-125m TQ2_0 vs F16 top-12
logprobs identical to 4 decimals fully offloaded (float dequant path, no Q8_K
activation quant). Speed at 125m ~= F16 (overhead-bound at this scale); the
bandwidth win targets larger BitNet SKUs. MMQ/int-dot path intentionally not
wired yet.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* tests: enable TQ2_0 in backend-ops type lists
Vulkan now implements TQ2_0 (dequant, mul_mat_vec, mul_mm, get_rows); backends
without support skip via not-supported as usual. TQ1_0 stays disabled.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Michael Trabalka <michael.trabalka@sqv.ai>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
ggml_roll only asserts nb[0] == ggml_type_size, so a permuted src is a
valid input, but the CUDA and Metal roll kernels index by ne alone and
never read the nb strides. A non-contiguous src therefore produced
silently wrong results. Neither backend declared a contiguity
requirement in supports_op, so the scheduler did not fall back to the
CPU implementation, which does handle strides correctly.
Add the requirement to both backends, matching the existing
GGML_OP_ROPE guard, and add a permuted test_roll case.
ggml_metal_op_norm sized the threadgroup with
`nth = std::min(nth, args.ne00_t)`, which can leave nth not a multiple of
the simdgroup size. The kernels finish their row reduction with a
cross-simdgroup step where each lane of the last simdgroup reads one
per-simdgroup partial sum out of shmem_f32:
if (tiisg == 0) { shmem_f32[sgitg] = sumf; }
threadgroup_barrier(mem_flags::mem_threadgroup);
sumf = shmem_f32[tiisg];
sumf = simd_sum(sumf);
When the last simdgroup is partial it has fewer lanes than the
threadgroup has simdgroups, so the tail of the partial sums is never
read and the row sum is too small. For ne00_t = 33 nth becomes 33: two
simdgroups, but only one lane in the second, so one of the two partial
sums is dropped. The mean and variance are then wrong for the whole row.
Round ne00_t up to a whole number of simdgroups instead. Rounding up
rather than dropping the clamp keeps the threadgroup as small as
possible: deleting the line would raise nth to the next power of two
(ne00_t = 544 -> 1024 instead of 544), which costs idle lanes on 26 row
lengths below 8192 that were already correct, including 1536 and 3584.
GGML_OP_NORM is affected as well as GGML_OP_RMS_NORM - both dispatch
through ggml_metal_op_norm.
No mainstream LLM hidden size hits this: ne00_t is ne00/4 on the
vectorized path, so 4096, 8192, 2048 and friends all give a multiple of
32. It is reachable from other norm shapes, e.g. 320-channel norms.
Add NORM and RMS_NORM cases for ne0 = 33, 132 and 260 across the
existing eps values. 33 exercises the scalar path and 132/260 the
vectorized one, since only those divide by 4.
Before, on M3 Pro:
test-backend-ops test -b MTL0 -o NORM 25/50
test-backend-ops test -b MTL0 -o RMS_NORM 26/51
After:
test-backend-ops test -b MTL0 -o NORM 50/50
test-backend-ops test -b MTL0 -o RMS_NORM 51/51
test-backend-ops test -b MTL0 13943/13943
* tests: add SWIGLU perf cases
perf mode had no GLU coverage. Adds SWIGLU at 17408 columns, 512 and
2048 tokens, f16 and f32, with the operands both fused and split.
* sycl: consolidate fused-GLU kernels
They differed only in which op_* they called, so take the op as an argument and share a common launcher.
Their block sizes were all 256, so launch geometry is unchanged;
SYCL_GELU_BLOCK_SIZE and SYCL_SILU_BLOCK_SIZE lose their last users so are dropped.
* sycl: contiguous fast path for the fused GLU ops
o0 == n and o1 == n collapse the de-interleave index math to the
identity, so dispatch a flat kernel in that case. It fires for
ggml_glu_split with packed operands; a fused [gate|up] tensor keeps the
strided path. test-backend-ops perf -o SWIGLU on an Arc Pro B70: split
+14% f16 and +4% f32, fused unchanged.
Ankit Khandelwal