RotaryPositionEmbedding on CANN fails when src and dst share the same
non-contiguous buffer (inplace + view), because the operator overwrites
source data before it is fully read.
Add a branch that detects this case and uses contiguous temporary
buffers: copy src to temp, run ROPE into another temp, then copy back
to the non-contiguous dst. Fixes 20 failing ROPE tests (f32, v=1,
inplace=1).
Signed-off-by: noemotiovon <757486878@qq.com>
- Allow FLASH_ATTN_EXT when head dimension D is not a multiple of 16 by
padding Q/K/V to D_padded = GGML_PAD(D, 16), running FusedInferAttentionScoreV2,
then slicing the output back to D (ggml-cann.cpp + aclnn_ops.cpp).
- Fix aclnn_get_slope second-part offset: use ggml_type_size(dtype) instead of
sizeof(float) so ALiBi slopes are correct when dtype is F16 (e.g. GQA with
48 heads); fixes buffer overflow and large numerical errors in those cases.
Add element-wise unary ops needed by Qwen 3.5's DeltaNet linear
attention layers. These ops follow the existing unary-ops pattern
with VTCM DMA double-buffering.
- neg: negate via scale by -1.0
- exp: uses existing hvx_exp_f32 HVX intrinsics
- sigmoid: uses existing hvx_sigmoid_f32_aa HVX intrinsics
- softplus: log(1 + exp(x)) scalar fallback
- CONT reuses the existing CPY infrastructure since making a tensor
contiguous is equivalent to a same-type copy.
- REPEAT implements tiled memory copy with multi-threaded execution via
the worker pool, supporting f32 and f16 types. The kernel parallelizes
across output rows and uses memcpy for each tile.
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* kleidiai : fix MUL_MAT support for batched (3D) inputs
The supports_op() check incorrectly rejected MUL_MAT operations with 3D
inputs (ne[2] > 1), but the actual compute_forward_qx() implementation
handles batched inputs correctly via a loop over ne12.
This caused models with Q4_0/Q8_0 weights to crash during graph scheduling
when n_seq_max > 1, because weights were placed in KLEIDIAI buffers during
loading (tested with 2D inputs) but the runtime used 3D inputs.
Also relax the buffer check to allow supports_op() to be called during
weight loading when src[0]->buffer is NULL.
Fixes#20608
* Kleidiai support_ops should only return true for 3D inputs, not also 4D
* vulkan: avoid graphics queue on non-RADV AMD drivers
* avoid graphics queues on small GPUs
* change to only use graphics queue if overridden with env var GGML_VK_ALLOW_GRAPHICS_QUEUE
* reenable transfer queue if graphics queue is not used
* kleidiai: add data type check to get_tensor_traits
* Added check for F16 data type into get_tensor_traits path with input data
not in ggml_backend_cpu_kleidiai_buffer_type format (unsupported for Q4/8)
Signed-off-by: Martin Klacer <martin.klacer@arm.com>
Change-Id: I9aca4b9b8d669d35db6f1dbcc4e080b1919b1de7
* updated ggml/src/ggml-cpu/kleidiai/kleidiai.cpp
updated kleidiai.cpp file as per suggestion
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Martin Klacer <martin.klacer@arm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml/hip: fix APU compatibility - soft error handling for hipMemAdviseSetCoarseGrain
On AMD APU/iGPU devices (unified memory architecture), hipMemAdviseSetCoarseGrain
returns hipErrorInvalidValue because the hint is not applicable to UMA systems.
The previous CUDA_CHECK() call treated this as a fatal error, causing crashes on
APU systems such as AMD Strix Halo (gfx1151).
Fix: treat hipMemAdviseSetCoarseGrain as an optional performance hint - call it
without error checking and clear any resulting error with hipGetLastError().
Also add pre-allocation debug logging (GGML_LOG_DEBUG) to help diagnose memory
issues on APU systems, and store totalGlobalMem in device info.
Context: AMD APUs on Windows are affected by a ROCm runtime bug that limits
hipMallocManaged to ~64GB regardless of available system RAM. A fix has been
submitted upstream: https://github.com/ROCm/rocm-systems/pull/4077
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* ggml/hip: remove unrelated changes, keep only hipMemAdviseSetCoarseGrain fix
---------
Co-authored-by: moonshadow-25 <moonshadow-25@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* hexagon: fix tail corruption with rows sizes not multiple of 256
* hexagon: use different stride for repacking partial blocks
* hex-mm: update repack and kernels to avoid shuffles for full 256-element blocks
Previous commit changed the repacking to use even:odd (0:1,2:3,..) packing
instead of the original (0:128,1:129,...) packing in order to fix tail corruption.
Since the mm kernels already deal with partial tails we can use even:odd
packing only for the last block.
This avoid performance penalty of having to shuffle to zip the elements
in the common case.
* hex-mm: update rmpy x8 for better optimizations
* hex-mm: tighten supported MUL_MAT checks to avoid spurios failures
* hex-mm: use vzero to init accumulators
* hex-mm: properly call partial rmpy_x8
* 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 : 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>
* 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 <noreply@anthropic.com>
* 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 <noreply@anthropic.com>
* vulkan: address review feedback for GATED_DELTA_NET
Pipeline array refactor [3][2], A_TYPE/D_TYPE/FLOAT_TYPE shader macros,
scale in push constants, supports_op fix, dispatch restructuring.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* vulkan: use FLOAT_TYPE for buffer/shared declarations, align formatting
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* vulkan: add explicit FLOAT_TYPE casts for buffer loads
Wrap data_q, data_k, and data_g buffer reads with FLOAT_TYPE() casts
to ensure correct behavior across all Vulkan configurations.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* vulkan: fix Q/K broadcast for interleaved head layout
Adapt to the interleaved broadcast convention from #20340:
head_id / rq1 → head_id % neq1
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Progeny Alpha <ProgenyAlpha@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* 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 <noreply@anthropic.com>
* vulkan: remove unused shared memory declaration in SSM_CONV
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Progeny Alpha <ProgenyAlpha@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Eve
Charles Xu
Chenguang Li
Masashi Yoshimura
Reese Levine
Shaw Nguyen
Georgi Gerganov
uvos
Krishna Sridhar
Ruben Ortlam
Kevin Hannon
Taimur Ahmad
Justin Bradford
Neo Zhang
Martin Klacer
David366AI
Aman Gupta
Sigbjørn Skjæret
Johannes Gäßler
Pascal
MoonShadow
Bartowski
PikaPikachu
Max Krasnyansky
Adrien Gallouët
Wallentri
Zijun Yu
Rail Chabdarov
lhez
rehan-10xengineer
ProgenyAlpha
Jeff Bolz
Masato Nakasaka