* Make ggml_gated_delta_net take only the initial recurrent state (D, 1, n_seqs) and passes the snapshot count K as an op parameter instead of inferring it from state->ne[1].
Remove the padding hack and copy all emitted snapshots into the recurrent cache with a single strided ggml_cpy
* Make GDN changes in all backends. Address review comments.
* Fix CI build errors
* cpu: add GGML_OP_COL2IM_1D
Add the overlap-add (scatter-add) step of a 1D transposed convolution.
A ConvTranspose1d factorizes as a GEMM followed by col2im: a weight
pre-permuted to [IC, K*OC] is contracted against the [IC, T_in] input
with mul_mat to produce a column matrix [K*OC, T_in], and col2im_1d
scatters those columns back into the [T_out, OC] signal, with
T_out = (T_in - 1)*s0 + K - 2*p0.
Keeping the contraction as a plain mul_mat leaves the heavy work on the
optimized (and quantizable) matmul kernels, so col2im_1d only does the
cheap overlap-add.
CPU uses a gather formulation parallelized over output channels,
supporting F32, F16 and BF16 with an F32 accumulator.
* tests: add backend coverage for GGML_OP_COL2IM_1D
Add test_col2im_1d next to the conv_transpose_1d cases, covering F32,
F16 and BF16 across eight geometries: the canonical kernel = 2*stride
DAC upsampling shape, overlap, no overlap, cropping (p0 = 1 and
p0 = stride/2), kernel < stride with zeroed gaps, kernel not a
multiple of stride, and a single column unfold.
Perf mode gets three real vocoder stage shapes reporting memory
bandwidth. max_nmse_err relaxes to 5e-4 for F16 and BF16.
* cpu: harden GGML_OP_COL2IM_1D
ggml_col2im_1d validates s0, oc, p0 and input contiguity at graph
build time, before the oc division, protecting every backend at once.
The kernel asserts the contiguity its flat indexing assumes and its
doc states the full output length including the crop term.
The kernel parallelizes over the time axis: the split stays balanced
down to OC = 1, where the previous channel split was single threaded.
Values are bit identical on the three real vocoder chains, two out of
three improve.
* tests: extend the GGML_OP_COL2IM_1D grid
The eval grid grows to eleven geometries: OC = 1 (mono output stage),
K = 1 with stride > 1 (sparse scatter, every gap position zeroed) and
a crop down to T_out = 2 where all the gather bounds act at once.
* tests: add col2im_1d equivalence test
tests/test-col2im-1d.cpp proves mul_mat + col2im_1d matches the
native ggml_conv_transpose_1d on the CPU backend, F32 bit exact, F16
and BF16 through casts of the column matrix. test-backend-ops cannot
cover this for a CPU only op since the CPU backend is its own
reference there.
* rpc: bump protocol patch version for GGML_OP_COL2IM_1D
GGML_OP_COUNT goes from 96 to 97 with the new op, which trips the
static_assert in ggml-rpc.h. Bump RPC_PROTO_PATCH_VERSION since the
op is appended and no existing op code shifts.
* ggml: implement `gguf_init_from_buffer`
* test: `gguf_init_from_buffer`
* fix: memory breakdown for a model loaded with `no_alloc` from a file is consistent with being loaded from a buffer
* fix: use `GGML_UNUSED`
Co-authored-by: Copilot <copilot@github.com>
* fix: remove `total_size` from `gguf_reader`
* fix: file offset calculation, rename `offset` to `data_offset`
Co-authored-by: Copilot <copilot@github.com>
* refactor: extract model loader bug fixes to another PR
* feat: add `gguf_init_from_callback`
* fix: always require a max expected size
* fix: change `gguf_reader_callback_t`'s `output` type to `void *`, change `max_expected_size` and offsets to `uint64_t`
* fix: harden against offset overflow in buffer read
* fix: remove seek behavior from the callback
* feat: `max_chunk_read == 0` means `SIZE_MAX`
* fix: seeking in a gguf file with no tensors
---------
Co-authored-by: Copilot <copilot@github.com>
* spec: support MTP
* fix batch size
* rename files
* cont : simplify (llama/7)
* MTP: clean-up (llama/9)
* MTP: clean-up
* review: use llama_context_type instead of llama_graph_type
* review: remove llama_model_has_mtp
* review: fix convert issues
* convert: fix pycheck
* review: formatting
* use `mtp-` for identifying mtp models
* convert: fix mtp conversion
* mtp -> draft-mtp
* remove unused llama_arch
* add need_embd in speculative
* llama: allow partial seq_rm for GDN models for speculative decoding
Currently speculative checkpoint needs to restart from a checkpoint
after some draft tokens are not accepted, this leads to some wastage in
running the target again. This PR adds the ability to rollback upto
`draft_max` by storing the GDN intermediates.
* fix pending state
* vulkan: add GDN partial rollback
* meta: extend check to axis 1
* metal: add GDN partial rollback
Extend the gated delta net kernel to store intermediate states for
partial rollback support on the Metal backend.
- Add K (snapshot slot count) as a function constant
- Read input state from slot 0 of the 3D state tensor
- Write intermediate states to different slots during token loop
- For K=1, maintain backward-compatible single-slot behavior
Ref: 8c05923630
Assisted-by: llama.cpp:local pi
* delta_net_base: use ggml_pad instead of new_tensor
* review: add need_rs_seq
* review: rename part_bounded to n_rs
* review: deslop comments
* review: rename, add asserts
* server : adjust checkpoint logic (llama/11)
* server : adjust checkpoint logic
* cont : rm asserts
* server-context: fix early exit
* spec : fix compatibility with n-gram and add TODOs (llama/13)
* metal : cleanup
* llama : fix faulty bitwise check in recurrent memory
* server : disable RS-based MTP in combination with other spec types
* spec : add TODOs
* cont : fix comment
* cont : update comment
* common : fix logic for ngram + mtp compat
* llama-memory: enable checkpointing with partial rollback
* cont: add test-case for loading into a dirty ctx
* llama-memory-recurrent: clear rs_idx in clear
* download: fix mtp path
* llama-arch: fix enorm op
* docs: update docs
* conversion: fix type annotations
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* CUDA: manage NCCL communicators in context
* add check that all backends are CUDA
* remove unused vector, limit init to > 1 GPUs
* fix warnings
* fix cuda device, cache allreduce
* 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 (llama/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 (llama/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 (llama/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. (llama/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 (llama/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 (llama/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 (llama/17)
* meta : formatting, naming, indentation (llama/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>
* Update build doc
* Add cgraph tensor output name to OV op name
* Update openvino build instructions
* Add initial NPU support
* draft NPU support version 2: prefill + kvcache
* NPU support version 2: prefill + kvcache
* Change due to ggml cgraph changes, not correct yet
* Change due to ggml cgraph changes, llama-3.2 CPU work
* Add AMD64 to CMakeLists
* Change due to ggml cgraph changes, all device work
* Refactor: clean, fix warning
* Update clang-format
* Statful transformation for CPU GPU
* Add SwiGLU
* Fuse to SDPA
* Replace Concat with Broadcast in MulMat for GQA
* Pull out indices creation for kv cache update
* Refactor: remove past_token_len from extra_inputs
* Fix Phi3 SwiGLU and SoftMax
* Pull out sin cos from rope
* Reduce memory: free ov weights node after graph conversion
* Fix CPY due to cgraph change
* Added OpenVINO CI/CD. Updated docs
* Fix llama-cli
* Fix Phi3 ROPE; Add test-backend-ops
* Fix NPU
* Fix llama-bench; Clang-format
* Fix llama-perplexity
* temp. changes for mark decomp
* matmul in fp32
* mulmat input conversion fix
* mulmat type conversion update
* add mark decomp pass
* Revert changes in fuse_to_sdpa
* Update build.md
* Fix test-backend-ops
* Skip test-thread-safety; Run ctest only in ci/run.sh
* Use CiD for NPU
* Optimize tensor conversion, improve TTFT
* Support op SET_ROWS
* Fix NPU
* Remove CPY
* Fix test-backend-ops
* Minor updates for raising PR
* Perf: RMS fused to OV internal RMS op
* Fix after rebasing
- Layout of cache k and cache v are unified: [seq, n_head, head_size]
- Add CPY and FLASH_ATTN_EXT, flash attn is not used yet
- Skip test-backend-ops due to flash attn test crash
- Add mutex around graph conversion to avoid test-thread-safety fali in the future
- Update NPU config
- Update GPU config to disable SDPA opt to make phi-3 run
* Change openvino device_type to GPU; Enable flash_attn
* Update supports_buft and supports_op for quantized models
* Add quant weight conversion functions from genai gguf reader
* Quant models run with accuracy issue
* Fix accuracy: disable cpu_repack
* Fix CI; Disable test-backend-ops
* Fix Q4_1
* Fix test-backend-ops: Treat quantized tensors as weights
* Add NPU Q4_0 support
* NPU perf: eliminate zp
* Dequantize q4_1 q4_k q6_k for NPU
* Add custom quant type: q8_1_c, q4_0_128
* Set m_is_static=false as default in decoder
* Simpilfy translation of get_rows
* Fix after rebasing
* Improve debug util; Eliminate nop ReshapeReshape
* STYLE: make get_types_to_requant a function
* Support BF16 model
* Fix NPU compile
* WA for npu 1st token acc issue
* Apply EliminateZP only for npu
* Add GeGLU
* Fix Hunyuan
* Support iSWA
* Fix NPU accuracy
* Fix ROPE accuracy when freq_scale != 1
* Minor: not add attention_size_swa for non-swa model
* Minor refactor
* Add Q5_K to support phi-3-q4_k_m
* Requantize Q6_K (gs16) to gs32 on GPU
* Fix after rebasing
* Always apply Eliminate_ZP to fix GPU compile issue on some platforms
* kvcachefusion support
* env variable GGML_OPENVINO_DISABLE_SDPA_OPTIMIZATION added
* Fix for Phi3
* Fix llama-cli (need to run with --no-warmup)
* Fix add_sliced_mask; Revert mulmat, softmax; Remove input attention_size, iSWA model not working
* fix after rebasing
* Fix llama-3-8b and phi3-mini q4_0 NPU
* Update to OV-2025.3 and CMakeLists.txt
* Add OV CI cache
* Apply CISC review and update CI to OV2025.3
* Update CI to run OV dep install before build
* Update OV dockerfile to use OV2025.3 and update build docs
* Style: use switch in supports_ops
* Style: middle ptr and ref align, omit optional struct keyword
* NPU Unify PD (llama/14)
* Stateless. Fix llama-cli llama-server
* Simplify broadcast op in attention
* Replace get_output_tensor+memcpy with set_output_tensor
* NPU unify PD. Unify dynamic and static dims
* Clean placeholders in ggml-openvino.cpp
* NPU unify PD (handled internally)
* change graph to 4d, support multi sequences
* Fix llama-bench
* Fix NPU
* Update ggml-decoder.cpp
Hitting error while compiling on windows:
error C3861: 'unsetenv': identifier not found
Reason: unsetenv() is a POSIX function; it doesn’t exist on Windows. Visual Studio (MSVC) won’t recognize it.
Proposed fix: Use _putenv_s() (Windows equivalent)
This is supported by MSVC and achieves the same effect: it removes the environment variable from the process environment.
This keeps cross-platform compatibility.
* Update ggml-decoder.cpp
* Update ggml-decoder.cpp
* Update ggml-decoder.cpp
* Update ggml-decoder.cpp
* Update ggml-decoder.cpp
* Remove the second decoder for node. Moving the function into the model decoder
* Fix error for naive
* NPU prefill chunking
* NPU fix llama-bench
* fallback naive run with accuracy issue
* NPU support llma-perplexity -b 512 --no-warmup
* Refactor: split ov_graph_compute for dynamic and static
* remove unused API GgmlOvDecoder::get_output_stride(const std::string & name)
* minor update due to ov 2025.4
* remove unused API GgmlOvDecoder::get_output_names()
* remove unused API get_output_shape(const std::string & name)
* Modified API GgmlOvDecoder::get_output_type(const std::string & name)
* Removed API GgmlOvDecoder::get_output_op_params(const std::string & name)
* Removed API get_output_ggml_tensor(const std::string & name)
* Removed API m_outputs
* Removed m_output_names
* Removed API GgmlOvDecoder::get_input_names()
* Removed API GgmlOvDecoder::get_input_stride(const std::string& name)
* Removed API get_input_type
* Removed API get_input_type
* Removed API GgmlOvDecoder::get_input_shape(const std::string & name)
* Removed API GgmlOvDecoder::get_input_op_params(const std::string & name)
* Fix error for decoder cache
* Reuse cached decoder
* GPU remove Q6_K requantization
* NPU fix wrong model output shape
* NPU fix q4 perf regression
* Remove unused variable nodes
* Fix decoder can_reuse for llama-bench
* Update build.md for Windows
* backend buffer: allocate on host
* Use shared_buffer for GPU NPU; Refactor
* Add ov_backend_host_buffer; Use cached remote context
* Put kvcache on GPU
* Use ggml_aligned_malloc
* only use remote tensor for kvcache
* only use remote tensor for kvcache for GPU
* FIX: use remote tensor from singleton
* Update build.md to include OpenCL
* NPU always requant to q4_0_128
* Optimize symmetric quant weight extraction: use single zp
* Use Q8_0_C in token embd, lm_head, and for 5 and 6 bits quant
* Update build.md
* Support -ctk f32
* Initial stateful graph support
* Update ggml/src/ggml-openvino/ggml-decoder.cpp
Co-authored-by: Yamini Nimmagadda <yamini.nimmagadda@intel.com>
* code cleanup
* npu perf fix
* requant to f16 for Q6 embed on NPU
* Update ggml/src/ggml-openvino/ggml-decoder.cpp
* Update ggml/src/ggml-openvino/ggml-openvino-extra.cpp
* Create OPENVINO.md in llama.cpp backend docs
* Update OPENVINO.md
* Update OPENVINO.md
* Update OPENVINO.md
* Update build.md
* Update OPENVINO.md
* Update OPENVINO.md
* Update OPENVINO.md
* kq_mask naming fix
* Syntax correction for workflows build file
* Change ov backend buffer is_host to false
* Fix llama-bench -p -n where p<=256
* Fix --direct-io 0
* Don't put kvcache on GPU in stateful mode
* Remove hardcode names
* Fix stateful shapes
* Simplification for stateful and update output shape processing
* Remove hardcode names
* Avoid re-compilation in llama-bench
* Extract zp directly instead of bias
* Refactor weight tensor processing
* create_weight_node accept non-ov backend buffer
* remove changes in llama-graph.cpp
* stateful masking fix (llama/38)
Fix for stateful accuracy issues and cl_out_of_resources error in stateful GPU with larger context sizes.
* Fix test-backend-ops crash glu, get_rows, scale, rms_norm, add
* hardcoded name handling for rope_freqs.weight
* Suppress logging and add error handling to allow test-backend-ops to complete
* Fix MUL_MAT with broadcast; Add unsupported MUL_MAT FLASH_ATTN cases
* Use bias instead of zp in test-backend-ops
* Update OV in CI, Add OV CI Tests in GH Actions
* Temp fix for multithreading bug
* Update OV CI, fix review suggestions.
* fix editorconfig-checker, update docs
* Fix tabs to spaces for editorconfig-checker
* fix editorconfig-checker
* Update docs
* updated model link to be GGUF model links
* Remove GGML_CPU_REPACK=OFF
* Skip permuted ADD and MUL
* Removed static variables from utils.cpp
* Removed initializing non-existing variable
* Remove unused structs
* Fix test-backend-ops for OV GPU
* unify api calling
* Update utils.cpp
* When the dim is dynamic, throw an error, need to is stastic forst
* Add interface compute_model_outputs(), which get the model output through computing the node use count & status in the cgraph to avoid the flag using
* No need to return
* Fix test-backend-ops for OV GPU LNL
* Fix test-thread-safety
* use the shape from infer request of output tensor create to avoid issue
* fix dynamic output shape issue
* fix issue for the unused node in tests
* Remove unused lock
* Add comment
* Update openvino docs
* update to OV release version 2026.0
* add ci ov-gpu self hosted runner
* fix editorconfig
* Fix perplexity
* Rewrite the model inputs finding mechanism (llama/54)
* Rewrite the model inputs finding logistic
* Put stateful shape handle in get input shape
* Put the iteration logistic in func
* Added ggml-ci-intel-openvino-gpu and doc update
* .hpp files converted to .h
* fix ggml-ci-x64-intel-openvino-gpu
* Fix for stateful execution bug in llama-bench
* Minor updates after stateful llama-bench fix
* Update ggml/src/ggml-openvino/utils.cpp
Co-authored-by: Yamini Nimmagadda <yamini.nimmagadda@intel.com>
* Remove multiple get_shape calls
* Bring back mutex into compute
* Fix VIEW op, which slice the input node
* Added token_len_per_seq existence check before slicing masks and moved node retrieval inside guarded block to prevent missing-key access
* Temp. fix for test requant errors
* Update to OV ggml-ci to low-perf
* ci : temporary disable "test-llama-archs"
* ci : cache v4 -> v5, checkout v4 -> v6, fix runner tag
* docs : update url
* Fix OV link in docker and Update docs
---------
Co-authored-by: Ravi Panchumarthy <ravi.panchumarthy@intel.com>
Co-authored-by: Cavus Mustafa <mustafa.cavus@intel.com>
Co-authored-by: Arshath <arshath.ramzan@intel.com>
Co-authored-by: XuejunZhai <Xuejun.Zhai@intel.com>
Co-authored-by: Yamini Nimmagadda <yamini.nimmagadda@intel.com>
Co-authored-by: Xuejun Zhai <Xuejun.Zhai@intel>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.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 (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 <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 (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
* 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>
* ggml-virtgpu: regenerate_remoting.py: add the ability to deprecate a function
* ggml-virtgpu: deprecate buffer_type is_host remoting
not necessary
* ggml-virtgpu: stop using static vars as cache
The static init isn't thread safe.
* ggml-virtgpu: protect the use of the shared memory to transfer data
* ggml-virtgpu: make the remote calls thread-safe
* ggml-virtgpu: backend: don't continue if couldn't allocate the tensor memory
* ggml-virtgpu: add a cleanup function for consistency
* ggml-virtgpu: backend: don't crash if buft->iface.get_max_size is missing
* fix style and ordering
* Remove the static variable in apir_device_get_count
* ggml-virtgpu: improve the logging
* fix review minor formatting changes
* vulkan: extend topk_moe to handle sigmoid w/exp_probs_b for nemotron
Also handle GGML_OP_SCALE at the end (nemotron, deepseek2).
Fewer pipeline variants and spec constants, just use push constants.
In test_topk_moe, change exp_probs_b to be 1D, matching real networks.
Update test-backend-ops and ggml-backend to allow verifying multiple outputs
in a fusion test (topk_moe has two outputs). Previously only the final node
was verified.
* change test_topk_moe to allow results in arbitrary order
* disable sigmoid fusion for moltenvk
* llama: automatically fit args to free memory
llama-fit-params tool
* fix CI
* hints for bug reports, ensure no reallocation
* fix segfault with Vulkan
* add llama-fit-params to CI
* fix CI
* fix CI
* fix CI
* minor adjustments
* fix assignment of 1 dense layer
* fix logger not being reset on model load failure
* remove --n-gpu-layer hint on model load failure
* fix llama-fit-params verbosity
* fix edge case
* fix typo [no ci]
* ggml-cpu:fix RISC-V Q4_0 repack select and RVV feature reporting
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
* using the name VLEN instead of CNT
* Update ggml/include/ggml-cpu.h
---------
Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Feat: Added vulkan circular tiling support
* Feat: Added cpu circular
* Feat: Added cuda kernels
* Added tests
* Added tests
* Removed non-pad operations
* Removed unneded changes
* removed backend non pad tests
* Update test-backend-ops.cpp
* Fixed comment on pad test
* removed trailing whitespace
* Removed unneded test in test-backend-ops
* Removed removed test from calls
* Update ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
Co-authored-by: Ruben Ortlam <picard12@live.de>
* Fixed alignment
* Formatting
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
* Format pad
* Format
* Clang format
* format
* format
* don't change so much stuff
* clang format and update to bool
* fix duplicates
* don't need to fix the padding
* make circular bool
* duplicate again
* rename vulkan to wrap around
* Don't need indent
* moved to const expr
* removed unneded extra line break
* More readable method calls
* Minor wording changes
* Added final newline
* Update ggml/include/ggml.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update ggml/include/ggml.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Added circular pad ext tests
* Gate non circular pad devices
* Cleaned gating of non-circular pad devices
---------
Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: Ruben Ortlam <picard12@live.de>
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Previously, cmake was forcing `_WIN32_WINNT=0x0A00` for MinGW builds,
This caused "macro redefined" warnings with toolchains that define the version.
This also removes the `GGML_WIN_VER` variable as it is no longer needed.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Adjust to pytorch
* Add antialiasing upscale
* Increase number of patches to 1024
* Handle default marker insertion for LFM2
* Switch to flag
* Reformat
* Cuda implementation of antialias kernel
* Change placement in ops.cpp
* consistent float literals
* Pad only for LFM2
* Address PR feedback
* Rollback default marker placement changes
* Fallback to CPU implementation for antialias implementation of upscale
Store the last computed graph and reuse it when possible.
Also do not return response from GRAPH_COMPUTE and assume it always
completes successfully. If this this is not the case, the server closes
the connection. This saves us a network round trip to the server.
* model: add support for extra bufs for all devices
* hexagon: add experimental ggml-hexagon backend for the Hexagon NPU
This commit introduces a new experimental backend `ggml-hexagon` with support for the Hexagon NPU.
Highlights:
- Supports Hexagon versions: v73, v75, v79, and v81
- Targets Android devices based on Snapdragon SoCs: Gen3, 8-Elite, and 8-Elite Gen5
- Supports Q4_0, Q8_0, MXFP4, and FP32 data types
- Implements core LLM ops: MUL_MAT/MUL_MAT_ID, ADD/SUB/MUL/ADD_ID, RMS_NORM, ROPE, GLU/SWIGLU, SOFTMAX
**Note:** This backend is experimental and may exhibit instability or limited performance across supported devices.
It is intended for early testing and feedback from llama.cpp/ggml developer and user community.
Co-Authored-By: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-Authored-By: Todor Boinovski <todorb@qti.qualcomm.com>
* hexagon: fix format checker errors
* hexagon: update readme and cmake presets
* ci: add android-ndk-build jobs that build plain ARM64 and Snapdragon versions
* hexagon: add simple graph optimizer for stacking MUL_MAT ops with the same input
* hexagon: move ADB helper scripts into scripts/snapdragon/adb
* hexagon: replace all f/printfs with GGML_LOG_...
* readme: add hexagon to the list supported backends
* hexagon: stack malmuts with quantized inputs only
* hexagon: add TODO for fixing issues in hexagon_graph_optimize
* hexagon: update to hex-sdk 6.4.0 and add scripts for running on QDC
* scripts: fix lint errors
* scripts: update qdc pytest script to make linter happy
* hexagon: add reduce sum in fp32
* hexagon: reduce number of vector stores in matmul output
* hexagon: remove the need for vdelta in reduce-multiply-x8
* hexagon: consistent use of reduce_sum_fp32 for row_sums
* hexagon: some more matmul optimizations and comments
Optimize cases where tensor dims are not multiple of 1024 (e.g in Qwen models).
We've handled those cases already but at a higher overhead.
* hexagon: update cmake presets
* hexagon: add OPMASK support for run-bench.sh wrapper
* hexagon: update to use GGML_BACKEND_API
* hexagon: remove unused logic for setting tensor flags for the views
* hexagon: add asserts to set/get_tensor to make sure we handle complete tensors
Same asserts as the CPU backend.
* hexagon: use cpy_tensor slow path for non-host buffers
* hexagon: error checks in the buffer allocator
* cmake: move include(extProj) under ggml-hexagon
* hexagon: don't forget to delete the backend on free
* hexagon: set/get_tensor size assert apply only to quantized tensors
* hexagon: reintroduce HEX_VERBOSE wrapper for GGML_LOG_DEBUG for now
GGML_LOG_DEBUG is always enabled for test-backend-ops and the output gets in the way.
Ideally we need a bit more finer log levels.
* docs: typos in hexagon developer docs (libggm-...)
* hexagon: overhaul error handling in the session/device allocation
this should handle all failure paths in the session allocation.
* hexagon: update cmake presets to enable fp16 vectors
* hexagon: remove unused time_usec function
* hexagon: don't forget to release buffer contexts
* hexagon: fixed indents in hvx-utils (missed clang-format auto-format failure)
* hexagon: remove custom can_repeat function and use ggml_can_repeat
---------
Co-authored-by: Rajdeep Ganguly <rganguly@qti.qualcomm.com>
Co-authored-by: Todor Boinovski <todorb@qti.qualcomm.com>
* rpc : report actual free memory
Start reporting the free memory on every device instead of using
fixed values. Now llama-cli users can get a nice memory breakdown
when using RPC devices.
* drop --mem in rpc-server
* CPU: Add support for FLOOR,CEIL,ROUND and TRUNC unary operators
- Added the operators to unary op enum
- Implemented API functions
- Implemented forward and unary-op logic in CPU backend
- Updated ggml_get_n_tasks
- Updated operators names array and static_assert
- Updated docs and enabled automatic tests
* docs: add documentation for ggml_trunc and ggml_trunc_inplace in ggml.h
* chore: remove trailing whitespace from ggml.h
* Remove unresolved merge markers
* Apply review suggestions: cleanup formatting, enum order and leftover artifacts
* Regenerate ops.md using create_ops_docs.py
Gaurav Garg
Pascal
Johannes Gäßler
Gilad S
Ori Pekelman
Aman Gupta
Ismail
Valeriy Dubov
Xuan-Son Nguyen
Georgi Gerganov
Pasha Khosravi
Zijun Yu
Richard Davison
Marcel Petrick
Judd
Kevin Pouget
Christian Kastner
Masashi Yoshimura
Jeff Bolz
ixgbe
Vishal Singh
Phylliida Dev
Adrien Gallouët
Tarek Dakhran
Radoslav Gerganov
Piotr Wilkin (ilintar)
Acly
JJJYmmm
Max Krasnyansky
safranowith