Georgi Gerganov [Fri, 13 Mar 2026 20:12:54 +0000 (22:12 +0200)]
graph : remove redundant GDN state transposes (llama/20443)
* ggml : transpose fused GDN state access for coalesced memory reads (llama/20436)
The fused Gated Delta Net kernel accessed the [S_v, S_v] state matrix
column-wise on row-major storage, causing strided reads (stride S_v =
128 floats = 512 bytes) that waste GPU cache bandwidth. This produced a
39% regression on Qwen3.5-9B (Metal, M4 Max) compared to the unfused
path.
Transpose the state indexing so threads read contiguously:
- Metal: s_ptr[is*S_v] -> s_ptr[is] (stride 1 vs S_v)
- CUDA: curr_state[i*S_v+col] -> curr_state[col*S_v+i] (coalesced)
- CPU: restructured loops for row-wise transposed access
Also add --fused-gdn [on|off|auto] CLI flag (mirrors --flash-attn) so
users can control fused GDN independently of auto-detection.
All GATED_DELTA_NET backend-ops tests pass.
Co-Authored-By: Claude Opus 4.6 <redacted>
* ggml : use SIMD dot products in CPU GDN kernel, couple AR/chunked fused flags
- Replace scalar inner loops with ggml_vec_dot_f32 for SIMD-optimized
dot products in the CPU fused GDN kernel (delta and attention output)
- Couple fused_gdn_ar and fused_gdn_ch flags in auto-detection: if one
path lacks device support, disable both to prevent state layout mismatch
between transposed (fused) and non-transposed (unfused) formats
Co-Authored-By: Claude Opus 4.6 <redacted>
* llama : rever fgdn argument changes
* graph : remove GDN state transposes
* vulkan : adapt
* cuda : remove obsolete smem code
---------
Co-authored-by: Paul Flynn <redacted> Co-authored-by: Claude Opus 4.6 <redacted> Co-authored-by: Oliver Simons <redacted>
ProgenyAlpha [Thu, 12 Mar 2026 10:32:04 +0000 (06:32 -0400)]
vulkan: add GATED_DELTA_NET op support (llama/20334)
* vulkan: add GATED_DELTA_NET op support
Implements the fused gated delta net recurrence as a Vulkan compute
shader with full support for scalar gate, KDA vector gate, GQA
broadcast, multi-token sequences, and permuted (non-contiguous) q/k
inputs. Specialization constants select head size (32/64/128) and
KDA mode at pipeline creation time.
Passes all 13 test-backend-ops cases on AMD Radeon 890M (RADV GFX1150).
Co-Authored-By: Claude Opus 4.6 <redacted>
* vulkan: optimize GATED_DELTA_NET shader (Phase 1)
- vec4 dot products on all inner loops (dp4 hardware intrinsic)
- Cache exp(g) in shared memory for KDA path, eliminating ~32K
redundant global reads and ~16K redundant exp() calls per token
- vec4 fused decay + rank-1 update (3 vec4 ops vs 12 scalar ops)
- Add perf benchmark cases for GATED_DELTA_NET to test-backend-ops
KDA TG: +5.4% throughput. Non-KDA: no regressions.
13/13 test-backend-ops passing on AMD Radeon 890M (RADV GFX1150).
Co-Authored-By: Claude Opus 4.6 <redacted>
* vulkan: address review feedback for GATED_DELTA_NET
ProgenyAlpha [Thu, 12 Mar 2026 09:03:18 +0000 (05:03 -0400)]
vulkan: fix SSM_CONV PP scaling with large ubatch sizes (llama/20379)
* vulkan: optimize SSM_CONV workgroup dispatch for large ubatch
Tile tokens into 2D workgroups (32x16) to reduce workgroup launch
overhead at large ubatch sizes. Add vec4 fast path for nc=4 (common
d_conv size). Fixes PP performance degradation with ubatch > 512.
Ref: ggml-org/llama.cpp#18725
Co-Authored-By: Claude Opus 4.6 <redacted>
* vulkan: remove unused shared memory declaration in SSM_CONV
Co-Authored-By: Claude Opus 4.6 <redacted>
---------
Co-authored-by: Progeny Alpha <redacted> Co-authored-by: Claude Opus 4.6 <redacted>
* models : avoid Q and K repeats when using fused GDA
* cont : fix comment
Co-authored-by: Aman Gupta <redacted>
* cont : fix the fix
Co-authored-by: Aman Gupta <redacted>
* cont : fix
* metal : add GDN kernel (llama/20361)
* metal : add Metal backend for GGML_OP_GATED_DELTA_NET
Add a fused Metal kernel for the gated delta net recurrence op
(#19504), enabling GPU-accelerated inference for DeltaNet-based
models (Qwen3.5, etc.) on Apple Silicon.
Supports both GDA (scalar gate) and KDA (per-row gate) modes
with head_size 64 and 128. Unsupported configurations (head_size
32, non-contiguous tensors) gracefully fall back to CPU.
Performance: Qwen3.5-0.8B Q4_K_M on M4 Max
tg128: 170 -> 213 t/s (+25%)
Co-Authored-By: Claude Opus 4.6 <redacted>
* metal : validate contiguity of all input tensors in supports_op
Co-Authored-By: Claude Opus 4.6 <redacted>
* metal : add algorithm equivalence comment for GDA decay path
Co-Authored-By: Claude Opus 4.6 <redacted>
* cont : unslop + optimize
* cont : clean-up
---------
Co-authored-by: Paul Flynn <redacted> Co-authored-by: Claude Opus 4.6 <redacted>
* CUDA: AR gated delta net improvements (llama/20391)
* Add FastDiv to gated_delta_net_cuda
* Shard columns across warps
This reduces register pressure (avoids spill for S_v = 128) and gives
the warp-scheduler more CTAs to schedule (thus hiding data-access
latencies).
* Remove unneded include in gated_delta_net.cu
* Improve comments
* Apply code-formating
* Make sharding HIP-compatible
1. Use ggml_cuda_get_physical_warp_size() to determine warp size flexibly
2. Add test with partial warp to test sum reduction on CUDA
* Remove fastdiv_s64, as we can treat neqk1 and rq3 as uint32_t
* Rename variables
* Enable GDN also for prefill, move TODO for chunked_GDN
warp_size is not known at compile time in hip host code.
* Don't expose ggml_cuda_get_physical_warp_size on host
---------
Co-authored-by: uvos <redacted>
* llama : refactor llm_build_delta_net_base API
---------
Co-authored-by: Aman Gupta <redacted> Co-authored-by: Paul Flynn <redacted> Co-authored-by: Claude Opus 4.6 <redacted> Co-authored-by: Oliver Simons <redacted> Co-authored-by: uvos <redacted>
Remove NVFP4 support from GPU backends and architecture-specific
optimized dot products. These should be added in separate PRs so
backend specialists can review them independently.
Core NVFP4 support (type definition, CPU fallback dot product,
quantization, dequantization, conversion) is retained.
* Fix arch-fallback.h: add NVFP4 generic fallback for all platforms
After shelving backend-specific SIMD implementations, the generic
CPU dot product needs to be aliased on ARM, x86, PowerPC, and s390
platforms that previously relied on arch-specific versions.
* quantize: add NVFP4 as a quantization type option
* Fix ggml_fp32_to_ue4m3: handle subnormal values
Previously, values with ue4m3_exp <= 0 were clamped to 0, causing
all small scales to underflow. This made NVFP4 quantization via
llama-quantize produce garbage (PPL = 5.8M) since typical transformer
weights have amax/6.0 in the range 0.001-0.01, which falls in the
UE4M3 subnormal range.
Now subnormals are properly encoded as man * 2^-9 (exp=0, man=1..7),
matching the decode path in ggml_ue4m3_to_fp32.
Result: NVFP4 requantization now produces PPL = 15.25 (vs F16 = 14.33),
comparable to Q4_1 (PPL = 15.81) at slightly lower BPW (4.70 vs 5.15).
* Restore ARM NEON NVFP4 dot product implementation
Restores the optimized ggml_vec_dot_nvfp4_q8_0 for ARM NEON using
vqtbl1q_s8 lookup and ggml_vdotq_s32 dot products.
- Add ue4m3_scale_lut[128] to ggml-common.h replacing branch-heavy
ggml_ue4m3_to_fp32() in the hot loop
- Use vpaddq_s32 for pairwise int32 reduction instead of vaddvq_s32
- Accumulate with vfmaq_f32 into float32x4_t vector accumulators
* ARM NEON NVFP4: rearrange q8 to match nibble layout
Alternative approach: rearrange q8 data to match the NVFP4 lo/hi
nibble layout instead of rearranging the looked-up NVFP4 values.
Eliminates vcombine_s8(vget_low, vget_low) shuffles.
Performance is equivalent (~18.5 t/s) - the bottleneck is the 2x
block overhead from QK=16 vs QK=32, not the shuffle instructions.
* CPU only backend 64 super-block layout
* cleanup
* Remove unused LUT
* int
* exclude NVFP4 from unsupported ops in metal build
* remove quantization for now
* store scales as native UE4M3, preserve original model bits when possible
Julian Pscheid [Tue, 10 Mar 2026 06:32:24 +0000 (23:32 -0700)]
metal: handle command buffer failures gracefully in synchronize (llama/20306)
Replace GGML_ABORT("fatal error") in ggml_metal_synchronize() with
error flag + return. This aligns synchronize error handling with
graph_compute, which already returns GGML_STATUS_FAILED for the same
condition.
When a command buffer fails (e.g., iOS GPU access revocation during
backgrounding, macOS eGPU disconnect, OOM), the backend enters an
error state instead of killing the host process. Subsequent
graph_compute calls return GGML_STATUS_FAILED immediately. Recovery
requires recreating the backend.
Failed extra command buffers are properly released on the error path
to avoid Metal object leaks.
Paul Flynn [Mon, 9 Mar 2026 14:48:12 +0000 (10:48 -0400)]
metal : extend mul_mv_ext to BF16, Q2_K, Q3_K (llama/20250)
Enable mul_mv_ext small-batch kernels (BS 2-8) for BF16, Q2_K,
and Q3_K quantization types. These types previously fell through
to the slower single-row mul_mv path.
BF16 uses the float4 dequantize path (like F16). Q2_K and Q3_K
use the float4x4 K-quant path (like Q4_K/Q5_K/Q6_K).
Jeff Bolz [Sun, 8 Mar 2026 11:33:48 +0000 (06:33 -0500)]
vulkan: Fix data races in coopmat1 mul_mat(_id) (llama/20084)
* vulkan: Fix data races in coopmat1 mul_mat(_id)
Add barriers between coopmat store and regular loads. We sort of got away with
this because it was the same subgroup accessing the values, but it's still a
race and may not work.
shalinib-ibm [Fri, 6 Mar 2026 15:22:39 +0000 (20:52 +0530)]
ggml-cpu: Fix gcc 15 ICE on ppc64le (ggml/20083) (llama/20130)
This patch addresses an Internal Compiler Error (Segmentation fault)
observed with gcc 15 by replacing the intrinsic + cast by doing
a cat on the data first and then calling the intrinsic. This bypasses the
buggy compiler path while maintaining identical instruction selection.
Performance Verification:
Assembly analysis on RHEL 9 (GCC 15.1.1) confirms that both the original
code and this fix generate the identical Power10 prefixed load instruction:
`plxv 40, 2(14)`
This ensures zero performance regression while unblocking builds on
newer toolchains.
Reproduced on:
- Alpine Linux + GCC 15.2.0-r2
- RHEL 9 + GCC 15.1.1 (gcc-toolset-15)
Andreas Kieslinger [Thu, 5 Mar 2026 11:53:21 +0000 (12:53 +0100)]
CUDA: Improve performance via less synchronizations between token (llama/17795)
* Adds CPU-to-CUDA copy capability to
ggml_backend_cuda_cpy_tensor_async()
* Adds function to relax sync requirements between input copies on
supported backends (CUDA for now)
* Exchanges synchronous copy with async copy function.
* Adds macro guards to allow compilation in non-CUDA builds
* Reworked backend detection in ggml-backend.cpp to avoid linking
conflicts
* Relax requirement of checks in async CUDA copies from backend and buffer type to just buffer type, to avoid linking issues
* Minor cleanup
* Makes opt-in to relax use of explicit syncs more general. Backends like
vulkan which require a synchronization between HtoD copies and graph
execution could also adopt this change now.
* Reintroduces stricter check for CPU->CUDA backend async copy via
GGML_DEVICE_TYPE_CPU.
* Corrects initialization of ggml_backend_sync_mode in
ggml_backend_sched_split initialization
* Simplifies synchronizations to adhere to `saaasg` pattern.
* Apply suggestion from @ggerganov (src->buffer to buf_src)
Co-authored-by: Georgi Gerganov <redacted>
* Apply suggestion from @ggerganov (src->buffer to buf_src) v2
Co-authored-by: Georgi Gerganov <redacted>
---------
Marcel Petrick [Thu, 5 Mar 2026 07:50:21 +0000 (08:50 +0100)]
chore : correct typos [no ci] (llama/20041)
* fix(docs): correct typos found during code review
Non-functional changes only:
- Fixed minor spelling mistakes in comments
- Corrected typos in user-facing strings
- No variables, logic, or functional code was modified.
Abhijit Ramesh [Tue, 3 Mar 2026 03:35:11 +0000 (19:35 -0800)]
ggml webgpu: fix workgroup dispatch limit for large batch sizes (llama/19965)
* ggml-webgpu: fix workgroup dispatch limit for large batch sizes
WebGPU limits workgroup sizes to 65535 per dimension. Large MUL_MAT
operations with batch sizes exceedeing this limi would fail.
* add compute_2d_workgroups() helper to split total workgroup ID across
X/Y dimensions
* update mul_mat_reg_tile.wgsl to reconstruct linear workgroup ID from 2D
dispatch
* update mul_mat_subgroup_matrix.wgsl to reconstruct linear workgroup ID
from 2D dispatch
* update mul_mat.wgsl to compute global index from 2D workgroup
coordinates
* refactor all three mul_mat dispatch paths to use the shared helper
* ggml-webgpu: add bounds checking for over-dispatched workgroups
2D workgroup dispatch can over-dispatch when total workgroups don't
divide evenly into the 65535 per-dimension limit. Extra workgroups
would compute invalid batch indices, causing memory corruption.
* add batch_idx bound check to mul_mat_reg_tile.wgsl and
mul_mat_subgroup_matrix.wgsl to prevent over-dispatched workgroups
from accessing invalid memory
* fixes test failures with large batch sizes (eg., bs=[128, 1024])
* ggml-webgpu: add back TODO for spliting large sizes into batches
* address review: replace FATTN_WARP_SIZE with constexpr, improve dispatch
- Replace #define FATTN_WARP_SIZE with constexpr int warp_size =
ggml_cuda_get_physical_warp_size() in each device function
- Use ne[1]*gqa_ratio threshold for MMA vs tile dispatch. Benchmarked
crossover on MI300X @ d32768 with power-of-2 GQA models:
hsk=64 (Llama 1B, gqa=4): MMA wins at eff >= 128 (+11%)
hsk=128 (Llama 3B, gqa=4): MMA wins at eff >= 128 (+4%)
Unified threshold: eff_nq >= 128 for all head sizes.
- Remove VEC fallback; small batches fall through to tile kernel
* Update ggml/src/ggml-cuda/fattn.cu
* use ggml_cuda_info().devices warp_size instead of hardcoded check
Vishal Singh [Fri, 27 Feb 2026 00:43:41 +0000 (06:13 +0530)]
ggml-zendnn: update code for latest ZenDNN API (llama/19923)
- adapt ggml-zendnn.cpp to the new lowoha::matmul interface
- update the ZenDNN git tag in CMake to the latest release (ZenDNN‑2026‑WW08)
- add static lib support in CMake
Kevin Pouget [Thu, 26 Feb 2026 12:00:57 +0000 (13:00 +0100)]
ggml-virtgpu: improve the reliability of the code (llama/19846)
* ggml-virtgpu-backend: validate the consistency of the received objects
This patch adds consistency checks in the
ggml-virtgpu-backend (running on the host side) to ensure that the
data received from the guest is consistent (valid pointers, valid
sizes and offsets).
* ggml-virtgpu-backend: add fallback/skips for optional ggml backend methods
these three methods are optional in the GGML interface. `get_max_size`
was already properly defaulted, but `backend sychronize` and `butf
get_max_size` would have segfaulted the backend if not implemented.
* ggml-virtgpu-backend: fix log format missing argument
* ggml-virtgpu-backend: improve the abort message
* ggml-virtgpu-backend: more safety checks
* ggml-virtgpu-backend: new error code
* ggml-virtgpu-backend: initialize all the error codes
* ggml-virtgpu: add a missing comment generated by the code generator
* ggml-virtgpu: add the '[virtgpu]' prefix to the device/buffer names
* ggml-virtgpu: apir_device_buffer_from_ptr: improve the error message
* ggml-virtgpu: shared: make it match the latest api_remoting.h of Virglrenderer APIR
(still unmerged)
* ggml-virtgpu: update the code generator to have dispatch_command_name in a host/guest shared file
* ggml-virtgpu: REMOTE_CALL: fail if the backend returns an error
* docs/backend/VirtGPU.md: indicate that the RAM+VRAM size is limed to 64 GB with libkrun
* ggml-virtgpu: turn off clang-format header ordering for some of the files
Compilation breaks when ordered alphabetically.
* ggml-virtgpu: clang-format
* ggml-virtgpu/backend/shared/api_remoting: better comments for the APIR return codes
Gaurav Garg [Sat, 21 Feb 2026 09:39:36 +0000 (15:09 +0530)]
Improve CUDA graph capture (llama/19754)
* Improve CUDA graph capture
Currently, CUDA graphs are eagerly enabled on the first call to ggml_backend_cuda_graph_compute. If the graph properties keep changing (4+ consecutive updates), the graph is permanently disabled. This is suboptimal because:
- The first call always incurs CUDA graph capture overhead even if the graph is unstable
- Once permanently disabled, CUDA graphs never re-enable even after the graph stabilizes (e.g., switching from prompt processing to decode)
The new approach delays CUDA graph activation until warmup completes: the same cgraph must be called at least twice with matching properties before CUDA graph capture begins. This avoids wasted capture overhead on volatile graphs and allows graphs to become eligible once they stabilize.
This also fixes issues such as https://github.com/ggml-org/llama.cpp/discussions/19708
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Co-authored-by: Johannes Gäßler <redacted>
* Remove EM dashes
* Update ggml/src/ggml-cuda/ggml-cuda.cu
Co-authored-by: Aman Gupta <redacted>
---------
Co-authored-by: Johannes Gäßler <redacted> Co-authored-by: Aman Gupta <redacted>
shalinib-ibm [Thu, 19 Feb 2026 06:28:53 +0000 (11:58 +0530)]
llamafile: powerpc: add FP16 MMA path for Q4/Q8 matmul (llama/19709)
Avoid xvi8ger4pp signed→unsigned bias correction by dequantizing Q4/Q8
inputs to FP16 and using FP16×FP16→FP32 MMA. This removes
post-processing overhead and improves performance.
Performance Impact:
1.5 ~ 2x improvement in PP_Speed for Q4 and Q8 Models,
measured with llama-bench and llama-batched-bench.
Q8 Model: granite-4.0-h-micro-Q8_0.gguf (from huggingface)
Q4 Model: Meta-Llama3-8b Q4 model (generated with llama-quantize from
f32 model)
llama-bench Q8 Model Results:
model size params backend threads test Base t/s Patch t/s
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp8 64.48 ± 4.72 73.99 ± 0.27
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp16 80.11 ± 0.32 112.53 ± 0.40
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp32 89.10 ± 0.27 152.95 ± 0.68
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp64 93.65 ± 0.25 187.83 ± 0.83
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp128 99.93 ± 0.02 201.32 ± 0.11
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp256 102.32 ± 0.40 208.32 ± 0.41
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 pp512 103.42 ± 0.40 209.98 ± 0.14
granitehybrid 3B Q8_0 3.16 GiB 3.19 B CPU 10 tg128 20.35 ± 0.01 19.57 ± 0.01
llama-bench Q4 Model Results:
model size params backend threads test Base t/s Patch t/s
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp8 34.77 ± 0.10 41.23 ± 0.08
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp16 40.81 ± 0.04 64.55 ± 0.15
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp32 44.65 ± 0.05 90.84 ± 0.22
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp64 47.49 ± 0.03 114.39 ± 0.11
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp128 49.29 ± 0.24 120.13 ± 0.19
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp256 49.77 ± 0.23 121.51 ± 0.11
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 pp512 49.89 ± 0.23 117.52 ± 0.10
llama 8B Q4_0 4.33 GiB 8.03 B CPU 10 tg128 13.40 ± 0.01 13.37 ± 0.00
Llama perplexity Results:
Model Base Final PPL Estimate Patch Final PPL Estimate
granite-4.0-h-micro-Q8_0 1.3862 +/- 0.04424 1.3868 +/- 0.04432
Meta-Llama3-8b Q4 1.3801 +/- 0.04116 1.3803 +/- 0.04116
overview / pkg / ggml / sources / whisper.cpp / log