* Optimize Metal Tensor API usage for matmul2d
Separates the Metal Tensor API (matmul2d) path in kernel_mul_mm into its own standalone kernel, gated by GGML_METAL_HAS_TENSOR.
The legacy simdgroup_matrix kernel is preserved under #else.
Previously both paths were interleaved via #ifdef blocks within a single kernel, forcing the tensor path to share the legacy kernel's data layout and threadgroup memory scheme. Splitting the kernel enabled memory and dispatch optimizations that weren't possible when the two paths shared code structure.
* cont : cleanup
* cont : cleanup
* cont : cleanup
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* nix: support unified apple-sdk
* Impl roll op for Metal
* Revert "nix: support unified apple-sdk"
This reverts commit abfa473360471532c547de8b202c780507924d4b.
* update ops.md
* update op docs
* ggml: backend-agnostic tensor parallelism
* support for GPT-OSS, Qwen 3 MoE
* partial Vulkan fix
* add support for 4/8 GPUs
* unconditional peer access
* re-use buffers + ggml contexts
* fix output pattern
* NCCL support
* GGML: HIP: add RCCL support
* Remove shfl and AllReduce from backend interface
* move allocation workaround out of ggml-alloc.c
* 2d tensor set/get support
* Fix the seg fault without NCCL
* Apply suggestion from JohannesGaessler
* support for tensor dims % n_devs != 0
* fix view_offs scaling
* arbitrary num. of GPUs/tensor split
* fix compilation
* better granularity estimate
* Support device-specific host buffer types if all underlying backends expose the same type. This allows using pinned memory instead of pageable memory for CUDA.
Fix compilation errors.
* partial Qwen 3 Next support
* Fix qwen3 30b (#8)
* Fix crash with Qwen-30B-A3B Q4_0
Qwen-30B-A3B Q4_0 has an intermediate dimension of 768. Using a granularity of 256 forces an uneven split between GPUs, which is not supported by the current implementation.
* Decide block size based on tensor quantization type
* Fix crashes due to KV cache serialization (#9)
KV cache serialization requires non-zero offsets on the tensor. Add support in the meta backend to set/get a tensor with a non-zero offset.
* metal : fix build (#7)
* static memory allocations, fix usage count
* fix tensor granularity
* more even memory distribution
* use BF16 for allreduce
* rebase fixup
* better error message for unsupported architectures
* Fix device mismatch during scatter of allReduce. (#11)
There is a mismatch between the dst buffer device and the backend device, causing the use of sync copies
* Enable the previous allreduce implementation. It is better in both perf and stability (#12)
* delay AllReduce for Moe for less I/O
* build : clean-up compile warnings
* backend : move most of the meta backend API to ggml-backend-impl.h
* cont : hide unused public API in the implementation
* llama : use llama_device + remove ggml_backend_dev_is_meta()
* ggml-backend : remove unused alloc include
* minor : remove regex include
* ggml : introduce ggml-ext.h for staging new APIs
* rebase fixup
* fix tests
* llama : more robust logic for determining Meta devices (#16)
* llama : more robust logic for determining Meta devices
* cont : fix devs size check
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cont : fix log type
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* disable roundtrip for meta backend
* fix arch selection
* Qwen 3.5 support
* fix Gemma 4 MoE
* fix OpenVino, SYCL
* fix test-llama-archs for CPU-only builds
* Fix Qwen 3.5 MoE
* disable meta backend tests for WebGPU
* tests : filter CPU-based devices from the Meta backend tests (#17)
* meta : formatting, naming, indentation (#18)
* formatting : llama-model.cpp
* formatting : ggml-ext.h
* formatting : ggml-backend-meta.cpp
* meta : add TODO
* add documentation
* better error messages
* fix GPT-OSS
---------
Co-authored-by: Carl Philipp Klemm <carl@uvos.xyz>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* initial Q1_0 Metal backend
* tuning q1_0 metal kernels
* add Q1_0 to test-backend-ops
* add Q1_0<->F32 copy test
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Updates Metal tensor API test probe to fix the dimension constraint violation in the matmul2d descriptor (at least one value must be a multiple of 16).
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* metal:add conv_3d backend
Rebased with master and resolved conflicts.
* Resolved issues related to changes in variable names
* kernel void kernel_upscale_bilinear_f32 was missing in my branch, added back, should pass all tests now
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : transpose fused GDN state access for coalesced memory reads (#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 <noreply@anthropic.com>
* 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 <noreply@anthropic.com>
* llama : rever fgdn argument changes
* graph : remove GDN state transposes
* vulkan : adapt
* cuda : remove obsolete smem code
---------
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
* llama : enable chunked fused GDN path
* models : avoid Q and K repeats when using fused GDA
* cont : fix comment
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix the fix
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* cont : fix
* metal : add GDN kernel (#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 <noreply@anthropic.com>
* metal : validate contiguity of all input tensors in supports_op
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* metal : add algorithm equivalence comment for GDA decay path
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* cont : unslop + optimize
* cont : clean-up
---------
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* CUDA: AR gated delta net improvements (#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
* Actually remove the TODO from 206890897546bd16602c3b79394fd5ea09ef199f
* Get warp size at runtime
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 <devnull@uvos.xyz>
* llama : refactor llm_build_delta_net_base API
---------
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Paul Flynn <paul@arkavo.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Oliver Simons <osimons@nvidia.com>
Co-authored-by: uvos <devnull@uvos.xyz>
* WIP: add NVFP4 quantization support
* tests
* improve NVFP4 dot product implementation performance and fix bad super call
* typo
* Use nvfp4 kvalues
* vulkan : fix NVFP4 shader compilation by including kvalues_mxfp4 lookup table
* vulcal and perf fixes
* wip
* Fix metal
* fix vulcan
* Rename threshold & fix wrong scale
* Fix MOE
* Shelf backend implementations (CUDA, Metal, Vulkan, arch-specific SIMD)
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.
Reverted files:
- ggml-cuda: common.cuh, convert.cu, mmq.cu/cuh, mmvq.cu, vecdotq.cuh,
quantize.cu/cuh, mma.cuh, ggml-cuda.cu, fattn-tile.cuh
- ggml-metal: ggml-metal.metal, ggml-metal-device.cpp, ggml-metal-impl.h,
ggml-metal-ops.cpp
- ggml-vulkan: ggml-vulkan.cpp, all vulkan-shaders/*
- ggml-cpu arch: arm/quants.c, x86/quants.c, powerpc/quants.c, s390/quants.c
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.
tg128 performance: 4.37 t/s (generic) -> 13.66 t/s (NEON) = 3.1x speedup
* Optimize ARM NEON NVFP4 dot product: LUT + vpaddq + vfmaq
- 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
tg128: 8.1 -> 31.0 t/s (3.8x speedup, 77% of Q4_1 speed)
* 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
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* correct comment
* format
* reduce duplication and cleanup
* Address comments
* move detection to prepare_tensors
* Use math instead of const
* Move
* fix comment
* Shelf quantize tests
* Rebase and move check
* cleanup
* lint
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Use fallback quant config
* Simplify
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* organize
* Refactor
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* add quantize_nvfp4 (required for test_quants.py)
* fix return type
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* llama : add support for Nemotron 3 Super
This commit adds support for the Nemotron 3 Super model (120B.A12B)
enabling this model to be converted to GGUF format and run in llama.cpp.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Matt Clayton <156335168+mattjcly@users.noreply.github.com>
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.
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).
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Georgi Gerganov
Ruben Ortlam
Developer-Ecosystem-Engineering
Kusha Gharahi
Seyoung Jeong
Johannes Gäßler
Pasha Khosravi
ren
nuri
Rashid Ul Islam
Richard Davison
Daniel Bevenius
Julian Pscheid
Paul Flynn
Marcel Petrick
will-lms
Christian Kastner
ccbinn