* 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 (#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 (#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 (#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>
* ggml-Vulkan: add ELU support
* ggml-Vulkan: remove extra spaces and variables
* ggml-Vulkan: fix format issue
* ggml-Vulkan: fix format issue
* fix whitespace issue
* Update Vulkan.csv and ops.md
- 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
* 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
```
1. bck->iface.synchronize(bck)
2. buft->iface.get_alloc_size(buft, op)
3. buft->iface.get_max_size(buft)
```
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
* ggml-virtgpu: add backend documentation
Assisted-by-AI: Claude Code
* CODEOWNERS: add /docs/backend/GGML-VirtGPU/ -> kpouget
* README: add the link to docs/backend/GGML-VirtGPU/ggml-virt.md
* docs/ggml-virt: add link to testing + configuration
* Revert "CODEOWNERS: add /docs/backend/GGML-VirtGPU/ -> kpouget"
This reverts commit 8ece8e72e2.
* drop the ggml- prefix
* s/ggerganov/ggml-org
* Relocate VirtGPU.md
* reorganize the text
* turn turn the ascii diagram into a mermaid
* README.md: update the link to the main doc
Hangs were reported on Jetson Orin AGX if we set CUDA_SCALE_LAUNCH_QUEUES=4x. Reverting the previous PR (#19042) and updating the document to consider setting CUDA_SCALE_LAUNCH_QUEUES=4x for faster throughput on multi-GPU systems.
* sycl: add softplus unary op implementation
* sycl: add softplus unary op implementation
* docs(ops): mark SYCL SOFTPLUS as supported
* docs: update SYCL status for SOFTPLUS
* [CUDA] Reduce CPU-side stalls due to the CUDA command buffer being full
With pipeline parallelism, during prompt processing, the CPU-side CUDA command buffer gets full, stalling the CPU. Due to this, enough work doesn't get submitted to the GPU, causing bubbles in the GPU timeline.
Fix this by setting the CUDA environment variable CUDA_SCALE_LAUNCH_QUEUES to 4x to increase the command buffer size.
* Set the env variable in the CUDA backend registry allocation
* Add link to PR in code comment
* Remove warning logs and update documentation
* hexagon: disable repack buffers if host buffers are disabled, improved handling of env vars
* hexagon: add support for OP_CPY fp16/fp32 -> fp16/fp32
Factore out all hvx_copy functions into hvx-copy.h header and reduced code duplication.
Update HTP ops infra to support OP_CPY
* hexagon: cleanup and refactor hex/hvx/htp headers and helper libs
hex is basically all scalar/core platform stuff (L2, DMA, basic utils)
hvx is all hvx related utils, helpers, etc
htp is higher level stuff like Ops, etc
hvx-utils library got a nice round of cleanup and refactoring to reduce duplication
use hvx_vec_store_a where possible
* hexagon: refactor HVX sigmoid functions to hvx-sigmoid.h
Moved sigmoid and tanh vector functions from hvx-utils.h to a new header
hvx-sigmoid.h. Implemented aligned and unaligned variants for sigmoid
array processing using a macro pattern similar to hvx-copy.h. Updated
act-ops.c to use the new aligned variant hvx_sigmoid_f32_aa. Removed
unused hvx-sigmoid.c.
* hexagon: factor out hvx-sqrt.h
* hexagon: mintor update to hvx-utils.h
* hexagon: remove spurios log
* hexagon: factor out and optimize hvx_add/sub/mul
* hexagon: remove _opt variants of add/sub/mul as they simply fully aligned versions
* hexagon: refactor reduction functions to hvx-reduce.h
Moved `hvx_self_max_f32` and `hvx_self_sum_f32` from `hvx-utils.h`/`.c` to `hvx-reduce.h`.
Renamed them to `hvx_reduce_max_f32` and `hvx_reduce_sum_f32`.
Added aligned (`_a`) and unaligned (`_u`) variants and used macros to unify logic.
Updated `softmax-ops.c` to use the new functions.
* hexagon: refactor the rest of arithmetic functions to hvx-arith.h
Moved `hvx_sum_of_squares_f32`, `hvx_min_scalar_f32`, and `hvx_clamp_scalar_f32` from `hvx-utils.c/h` to `hvx-arith.h`. Implemented aligned/unaligned variants (`_aa`, `_au`, etc.) and used macros to reduce code duplication. Updated `hvx_min_scalar_f32` and `hvx_clamp_scalar_f32` to use `dst, src, ..., n` argument order. Updated call sites in `act-ops.c`.
Refactor Hexagon HVX arithmetic functions (min, clamp) to hvx-arith.h
Moved `hvx_min_scalar_f32` and `hvx_clamp_scalar_f32` from `hvx-utils.c/h` to `hvx-arith.h`. Implemented aligned/unaligned variants (`_aa`, `_au`, etc.) and used macros to reduce code duplication. Updated these functions to use `dst, src, ..., n` argument order and updated call sites in `act-ops.c`. `hvx_sum_of_squares_f32` remains in `hvx-utils.c` as requested.
* hexagon: refactor hvx_sum_of_squares_f32
- Modify `hvx_sum_of_squares_f32` in `ggml/src/ggml-hexagon/htp/hvx-reduce.h` to use `dst, src` signature.
- Implement `_a` (aligned) and `_u` (unaligned) variants for `hvx_sum_of_squares_f32`.
- Update `hvx_reduce_loop_body` macro to support both returning and storing results via `finalize_op`.
- Update existing reduction functions in `hvx-reduce.h` to use the updated macro.
- Update `rms_norm_htp_f32` in `ggml/src/ggml-hexagon/htp/unary-ops.c` to match the new signature.
* hexagon: use hvx_splat instead of memset
* hexagon: consistent use of f32/f16 in all function names to match the rest of GGML
* hexagon: fix hvx_copy_f16_f32 on v75 and older
* hexagon: update readme to include GGML_HEXAGON_EXPERIMENTAL
* scripts: update snapdragon/adb scripts to enable host param
Neo Zhang
Wallentri
Zijun Yu
Masashi Yoshimura
a3894281
Charles Xu
Bertay Eren
GiantPrince
Piotr Wilkin (ilintar)
Marcel Petrick
Mickael Desgranges
Vishal Singh
Kevin Pouget
Maciej Lisowski
Aaron Teo
TriDefender
Sascha Rogmann
Nechama Krashinski
Gaurav Garg
Tamar
Christian Kastner
Max Krasnyansky
s8322
RachelMantel
DDXDB
Todor Boinovski
Ben Chen
David Lima
Francisco Herrera
Reese Levine
hipudding
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Adrien Gallouët