Commit Graph

2309 Commits

Author SHA1 Message Date
Johannes Gäßler 4eac5b4509 CUDA: refactor mma data loading for AMD (#22051)
* CUDA: refactor mma data loading for AMD

* fix CDNA MMQ occupancy

* fix CDNA3 mma

* fix RDNA3 compile
2026-04-19 18:26:59 +02:00
uvos 471540ae8a HIP: Remove unesscary NCCL_CHECK (#21914) 2026-04-19 12:59:44 +02:00
Gaurav Garg bcdcc1044f ggml : reduce CPU overhead in meta backend (#22041)
* cache subgraph splits when cgraph is unchanged

Skip per-call subgraph construction in ggml_backend_meta_graph_compute when the same ggml_cgraph is used consecutively.

Assign uid to every sub-graph so that CUDA's fast uid check path hits too.

* Address review comments

* Keep the scope as is

* Rename last_uid and last_n_subgraphs field. Remove last_max_tmp_size field. Refactor code.

* Address review comments

* Update ggml/src/ggml-backend-meta.cpp

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Update ggml/src/ggml-backend-meta.cpp

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-04-19 12:48:35 +03:00
texasich 09b4efa95f cmake: remove CMP0194 policy to restore MSVC builds (#21934)
#21630 added the CMP0194 NEW policy to silence a CMake warning, but on Windows runners it caused CMake to prefer the MinGW toolchain for ASM and broke MSVC builds.

Reverting only that policy block restores the previous working behavior. The CMake 4.1+ warning comes back, but that is cosmetic and does not break any platform.

Reported-by: oobabooga

Refs: #21630

Co-authored-by: texasich <texasich@users.noreply.github.com>
2026-04-19 10:25:05 +03:00
Radoslav Gerganov 91fef95362 rpc : refactor the RPC transport (#21998)
* rpc : refactor the RPC transport

Move all transport related code into a separate file and use the
socket_t interface to hide all transport implementation details.

* fix win32

* better socket_t construction
2026-04-19 10:21:53 +03:00
SamareshSingh 59accc8863 ggml-backend-meta: add multi-segment read support in get_tensor (#22063) 2026-04-18 10:04:51 +02:00
Reese Levine 45cac7ca70 ggml-webgpu: fix compiler warnings and refactor FlashAttention encoding (#21052)
* Update workflows to remove dependence on llvmpipe

* Try setting Dawn_DIR

* remove c++20 initializers

* Move to proper guid

* Try avoiding segfaults on vulkan backend process exit

* Remove compiler warnings on parameter casting

* Fix soft_max and update reg_tile accumulation to f32 for better precision

* Refactor flash_attn a bit

* remove c++20 initializers and format

* Increase div precision for NVIDIA

* revert div precision and comment out ggml-ci node for now

* Formatting

* Try debugging on a failing CI node

* Revert "Try debugging on a failing CI node"

This reverts commit 1971e33cba919915e12bcfd5828abfbd54ca942e.
2026-04-17 09:17:11 -07:00
Aman Gupta b94050e896 CUDA: use LRU based eviction for cuda graphs (#21611)
* CUDA: use a ring-buffer for cuda graphs

* bump limit to 128

* use LRU eviction

* better naming

* do periodic clean-up
2026-04-17 23:24:21 +08:00
lhez 5e6c0e18b6 opencl: refactor q8_0 set_tensor and mul_mat host side dispatch for Adreno (#21938)
* opencl: refactor q8_0 gemm/gemv Adreno dispatch

* opencl: refactor q8_0 set_tensor

* opencl: fix whitespace
2026-04-16 22:28:33 -07:00
nullname 85dde8dc4a hexagon: optimize HMX matmul operations (#21071)
* optimize hmx_mat_mul functions by calculating row and column tiles upfront

* refactor core_dot_chunk_fp16 to use size_t for tile counts and improve readability

* wip

* set scale outside of loop

* wip

* refactor core_mma_chunk_fp16 and mat_mul_qk_0_d16a32 to use size_t for tile counts

* wip

* wip

* refactor transfer_output_chunk_fp16_to_fp32 to use size_t for dimensions

* refactor core_dot_chunk_fp16 to use size_t for tile row stride calculation

* wip

* refactor hmx_mat_mul functions to use hvx_vec_splat_f16 for column scales initialization

* refactor hmx_mat_mul_permuted_w16a32_batched to streamline scale setting and locking

* refactor core_dot_chunk_fp16 to improve tile stride calculations for output

* refactor hmx_mat_mul functions to use Q6_V_vsplat_R for column scales initialization

* fix compiling error

* wip

* optimize row and column tile indexing in core_mma_chunk_fp16 function

* wip

* Revert "wip"

This reverts commit cde679eff79c4a28dd2d89d32f710015e09592b6.

* Add size limit check for HAP_mmap in htp_iface_mmap and drop_mmap functions

* wip
2026-04-16 13:48:34 -07:00
shaofeiqi e45dbdece8 opencl: add q5_K gemm and gemv kernels for Adreno (#21595) 2026-04-16 12:08:33 -07:00
Aman Gupta 3f7c29d318 ggml: add graph_reused (#21764)
* ggml: add graph_reused

* use versioning instead of reuse flag

* increment version with atomic

* use top bits for split numbering

* add assert

* move counter to ggml.c

* set uid in split_graph only

* fix windows

* address further review comments

* get next_uid rather than doing bit manipulation

* rename + add comment about uid
2026-04-16 17:21:28 +08:00
Kusha Gharahi ae2d34899e metal: Implement ROLL op (#21946)
* 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
2026-04-16 11:54:37 +03:00
rehan-10xengineer 1e796eb41f ggml-cpu: add 128-bit RVV implementation for Quantization Vector Dot (#20633)
* ggml-cpu: add 128-bit impls for i-quants, ternary quants

* ggml-cpu: add 128-bit impls for iq2_xs, iq3_s, iq3_xxs, tq2_0

Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>

* ggml-cpu: refactor; add rvv checks

---------

Co-authored-by: taimur-10x <taimur.ahmad@10xengineers.ai>
Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>
2026-04-16 11:15:15 +03:00
rehan-10xengineer 5637536517 ggml : implemented simd_gemm kernel for riscv vector extension (#20627)
Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>
2026-04-16 11:14:26 +03:00
Reese Levine 82677a6ede ggml-webgpu: compute pass batching and removing profiling overhead (#21873)
* Update register tiling matmul to use f32 accumulation

* fix profiling code

* Fix register tiling matmul for chrome, i'm blaming dawn

* Update batch tuning value for iOS

* compile fix

* Fix use of new load function

* Move to a single query set for GPU profiling

* Move to batching compute passes when not profiling

* Refactor build_multi

* remove iOS throttling now that we're batching compute passes
2026-04-16 11:12:19 +03:00
Katostrofik b1be68e8ca [SYCL] Fix Q8_0 reorder: garbage on 2nd prompt + crash on full VRAM (#21638)
* [SYCL] Fix Q8_0 reorder: add missing dequantize path for GEMM

The Q8_0 reorder optimization (#21527) was missing a reorder-aware
dequantizer for the GEMM code path used during prompt processing.
After token generation reordered Q8_0 weights (via DMMV/MMVQ), the
next prompt processing pass would read them with the standard
dequantizer, producing garbage output.

Add dequantize_block_q8_0_reorder() and wire it into both
ggml_get_to_fp16_sycl() and ggml_get_to_fp32_sycl(), matching the
pattern already used by Q4_0, Q4_K, and Q6_K.

Fixes #21589

AI (Claude) was used to assist with root cause investigation and
writing the kernel code. All code was human-reviewed and tested
on real hardware.

* SYCL: fix reorder crash when device memory is full

The reorder optimization allocates a temporary buffer the full size of
the weight tensor on the device. When VRAM is nearly full (large models
on a single GPU), this allocation fails and the subsequent memcpy crashes
on a NULL pointer.

Fix: try device allocation first, fall back to host memory if device
memory is full. The reorder kernel still works correctly reading from
host memory over PCIe. This is slower for the one-time reorder (~21 t/s
vs ~38 t/s on Intel Arc Pro B70), but the optimization is preserved for
all subsequent inference. If both device and host allocation fail, skip
the reorder and fall back to the unoptimized kernel path.

Also fixes a bug where opt_for_reorder() marked tensors as reordered
even when the reorder was skipped due to allocation failure. This caused
DMMV/MMVQ kernels to read the original AoS data as if it were SoA,
producing garbage output or NaN results.

Tested on Intel Arc Pro B70 (32GB) with Q8_0, Q4_K_M models. Coding was
AI-assisted (Claude), reviewed and tested on hardware by a human.

Fixes #20478

* SYCL: add RAII temp buffer class + macro guard for host fallback

Replace sycl_ext_malloc_with_fallback/sycl_ext_free_fallback free
functions with sycl_reorder_temp_buffer RAII class. The host_fallback
bool is now a private member, and cleanup happens automatically at
scope exit.

Add GGML_SYCL_HOST_MEM_FALLBACK cmake option (default ON) to guard
the host memory fallback code path. Device access to host memory
requires Linux kernel 6.8+ (Ubuntu 26.04+); users on older kernels
can set -DGGML_SYCL_HOST_MEM_FALLBACK=OFF to disable it.

Addresses arthw's review on PR #21638.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* SYCL: document GGML_SYCL_HOST_MEM_FALLBACK build option in SYCL.md

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* SYCL: add reorder-aware DMMV dequantizers for Q4_K and Q6_K

Q4_K and Q6_K had reorder support for MMVQ and GEMM paths but not
DMMV. When the DMMV path encountered reordered data it would abort.

Add DMMV kernels that read from the SOA reorder layout for both
types. Same math as the non-reorder versions, different memory
access pattern.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-16 08:34:05 +03:00
Ruben Ortlam b3d758750a vulkan: optimize im2col (#21713)
* vulkan: improve im2col memory write layout

* cap workgroups

* minimal device tuning

* use vendor_id instead of subgroup size
2026-04-15 19:04:51 +02:00
Pasha Khosravi 7e72b38bc1 cuda: Q1_0 initial backend (#21629)
* [cuda] initial Q1_0 backend

* remove unused code, fix AMD MMA guard

* attempt to support dp4a

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2026-04-15 18:38:38 +02:00
Reese Levine 20d3bc2cc8 ggml-webgpu: Fix dequantization helpers to not pass in pointers (#21872)
* Fix dequantization helpers to not pass in pointers

* Increase XIELU precision
2026-04-15 09:14:40 -07:00
Johannes Gäßler a6206958d2 CUDA: require explicit opt-in for P2P access (#21910) 2026-04-15 16:01:46 +02:00
Johannes Gäßler 014dca49d6 CUDA: manage NCCL communicators in context (#21891)
* 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
2026-04-15 15:58:40 +02:00
Valeriy Dubov adb541a6ad rpc : add native RDMA transport for RPC backend (RoCEv2) (#20590) 2026-04-15 16:44:02 +03:00
Xuan-Son Nguyen 80d8770804 docs: more extensive RoPE documentation [no ci] (#21953)
* more extensive ggml_rope documentation

* add more docs

* nits
2026-04-15 14:45:16 +02:00
Yiwei Shao 5d14e5d19b hexagon: optimization for HMX mat_mul (#21554)
* hexagon: add async HMX worker

Introduce hmx-worker (dedicated thread for HMX compute) to overlap HMX
matmul with HVX dequant/DMA stages in the pipeline path, replacing the
previous synchronous HMX calls that blocked the main thread.

* hexagon: cost-based VTCM chunk search for out-stationary matmul

* hexagon: fix futex race in hmx_worker_drain
Store the boolean to local variable avoid atomic load twice

* hex-mm: hmx optimize scatter/transpose and use HMX intrinsics

* hex-vmem: drop vmem limit a touch under 3GB on v73

* hexagon: add fwd declaration of htp_context

* hex-hmx: replace hmx-worker with hmx-queue that mimics dma-queue interface

Simplifies the overall implemantion, reduces thread wakeup roundtrips.

* hex-mm: add debug log to hmx work func called from hmx-queue

* Update hmx-queue.h

Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>

---------

Co-authored-by: Kim-Chyan Gan <kgan@qti.qualcomm.com>
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
2026-04-14 14:09:03 -07:00
Xuan-Son Nguyen fae3a28070 ggml : remove ggml-ext.h (#21869)
* ggml: correct placement of ggml-ext.h

* ggml : remove ggml-ext.h

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-04-14 17:32:58 +03:00
Georgi Gerganov c0de6eda72 metal : fix FA support logic (#21898) 2026-04-14 17:32:29 +03:00
Jeff Bolz 1f30ac0cea vulkan: Programmatically add RoundingModeRTE to all shaders when the device supports it (#21572)
* vulkan: Programmatically add RoundingModeRTE to all shaders when the device supports it

* use FetchContent to get SPIRV-Headers

* Fetch spirv-headers unconditionally

* remove fetchcontent, rely on installed headers

* fix ubuntu job

* Update docs/build.md
2026-04-14 15:17:45 +02:00
Georgi Gerganov f4b5bf2f32 ci : re-enable mac workflows (#21894)
* ci : re-enable mac workflows

* vulkan : fix compile warning
2026-04-14 15:58:09 +03:00
Seyoung Jeong aa0f1897b7 metal : add XIELU unary op (#20802) 2026-04-14 15:43:59 +03:00
Richard Davison 2e05f06ffb ggml : fix ARM NEON nvfp4 dot product on non-dotprod targets (#21559) 2026-04-14 14:23:45 +03:00
texasich acc37a42ea cmake: fix CMP0194 warning on Windows with MSVC (#21630)
* cmake: fix CMP0194 warning on Windows with MSVC

Set CMP0194 policy to NEW before project() call in ggml/CMakeLists.txt to suppress the "MSVC is not an assembler for language ASM" warning introduced in CMake 4.1.

The ggml project enables ASM globally for Metal (macOS) and KleidiAI (ARM) backends. On Windows/MSVC, no assembler sources are used, but CMake 4.1+ warns because cl.exe is not a valid ASM compiler.

This follows the same pattern used in ggml-vulkan (CMP0114, CMP0147).

Closes ggml-org/llama.cpp#20311

* cmake: apply cisc's formatting suggestion

---------

Co-authored-by: texasich <texasich@users.noreply.github.com>
2026-04-14 13:47:56 +03:00
Reese Levine 5a23695d5a ggml-webgpu: Update register tiling matmul to use f32 accumulation (#21644)
* Update register tiling matmul to use f32 accumulation

* fix profiling code

* Fix register tiling matmul for chrome, i'm blaming dawn

* Update batch tuning value for iOS

* compile fix

* Fix use of new load function
2026-04-14 13:46:41 +03:00
Jeff Bolz 6a6780a232 vulkan: Support GGML_TYPE_NVFP4 (#21455)
This adds nvfp4 support for get_rows, dequant, and mul_mat(_id). For
mul_mat, it does not add support for the dp4/q8_1 path, it's all via
fp16/fp32.
2026-04-14 11:34:23 +02:00
Ruben Ortlam 75f3bc94e6 vulkan: Flash Attention DP4A shader for quantized KV cache (#20797)
* use integer dot product for quantized KV flash attention

* small improvements

* fix SHMEM_STAGING indexing

* add missing KV type quants

* fixes

* add supported quants to FA tests

* readd fast paths for <8bit quants

* fix mmq gate and shmem checks
2026-04-13 14:21:31 +02:00
Oliver Simons 9f5e1edb10 CUDA: Limit DeviceSegmentedSort to immediate mode (#21718)
* CUDA: Limit DeviceSegmentedSort to immediate mode

DeviceSegmentedSort is currently not capturable in a cuda graph. Hence,
we have to go for the slower DeviceSegmentedRadixSort in that case.

Perf numbers on RTX Pro 6000 Blackwell Max-Q:
DeviceSegmentedRadixSort in graph mode (i.e. CUDA Graphs)

  ARGSORT(type=f32,ne=[2048,512,1,1],order=1):                 12291 runs -   105.94 us/run -     8192 kB/run -   73.75 GB/s
  ARGSORT(type=f32,ne=[4096,512,1,1],order=1):                 10245 runs -   115.08 us/run -    16384 kB/run -  135.77 GB/s
  ARGSORT(type=f32,ne=[8192,512,1,1],order=1):                  5125 runs -   221.22 us/run -    32768 kB/run -  141.26 GB/s
  ARGSORT(type=f32,ne=[16384,512,1,1],order=1):                 2565 runs -   430.98 us/run -    65536 kB/run -  145.02 GB/s
  ARGSORT(type=f32,ne=[32768,512,1,1],order=1):                 1028 runs -  1185.83 us/run -   131072 kB/run -  105.41 GB/s
  ARGSORT(type=f32,ne=[65536,512,1,1],order=1):                  387 runs -  2748.62 us/run -   262144 kB/run -   90.95 GB/s

DeviceSegmentedSort in immediate mode

  ARGSORT(type=f32,ne=[2048,512,1,1],order=1):                 16388 runs -    71.17 us/run -     8192 kB/run -  109.78 GB/s
  ARGSORT(type=f32,ne=[4096,512,1,1],order=1):                 12294 runs -    81.38 us/run -    16384 kB/run -  192.00 GB/s
  ARGSORT(type=f32,ne=[8192,512,1,1],order=1):                  5125 runs -   240.81 us/run -    32768 kB/run -  129.77 GB/s
  ARGSORT(type=f32,ne=[16384,512,1,1],order=1):                 2565 runs -   406.60 us/run -    65536 kB/run -  153.71 GB/s
  ARGSORT(type=f32,ne=[32768,512,1,1],order=1):                 1285 runs -   873.23 us/run -   131072 kB/run -  143.15 GB/s
  ARGSORT(type=f32,ne=[65536,512,1,1],order=1):                  516 runs -  2288.46 us/run -   262144 kB/run -  109.24 GB/s

* Add test case for dispatch to DeviceSegmentedRadixSort

We currently lack a way to force graph mode in CUDA, patch callback to
invoke ggml_backend_compare_graph_backend twice to enforce each test to
run in graph mode
2026-04-13 11:14:06 +02:00
Masashi Yoshimura bafae27654 Remove extra conditional check on debug mode. (#21798) 2026-04-12 20:13:04 -07:00
Akarshan Biswas 873c825611 sycl: disable Q1_0 in backend and cleanup unused variables (#21807) 2026-04-13 09:44:58 +08:00
Stephen Cox 547765a93e mtmd: add Gemma 4 audio conformer encoder support (#21421)
* mtmd: add Gemma 4 audio conformer encoder support

Add audio processing for Gemma 4 E2B/E4B via a USM-style Conformer.

Architecture:
- 12-layer Conformer: FFN → Self-Attention → Causal Conv1D → FFN → Norm
- Subsampling Conv Projection: 2x Conv2D(stride=2) with LayerNorm
- Full self-attention with sinusoidal RPE and sliding window mask (24)
- Logit softcapping at 50.0, ClippableLinear clamping
- Output: 1024 → 1536 → RMSNorm → multimodal embedder

Mel preprocessing (dedicated mtmd_audio_preprocessor_gemma4a):
- HTK mel scale, 128 bins, magnitude STFT, mel_floor=1e-3
- Standard periodic Hann window (320 samples), zero-padded to FFT size
- Semicausal left-padding (frame_length/2 samples)
- Frame count matched to PyTorch (unfold formula)
- No pre-emphasis, no Whisper-style normalization
- Mel cosine similarity vs PyTorch: 0.9998

Key fixes:
- Tensor loading dedup: prevent get_tensor() from creating duplicate
  entries in ctx_data. Fixed with std::set guard.
- ClippableLinear clamp_info loading moved after per-layer tensors.
- Sliding window mask (24 positions) matching PyTorch context_size.
- Skip Whisper normalization for Gemma4 mel output.

Tested on E2B and E4B with CPU and Vulkan backends.
Transcribes: "Glad to see things are going well and business is starting
to pick up" (matching ground truth).

Ref: #21325
2026-04-12 14:15:26 +02:00
Johannes Gäßler ff5ef82786 CUDA: skip compilation of superfluous FA kernels (#21768) 2026-04-11 18:52:11 +02:00
shaofeiqi af1127d3c4 opencl: add basic support for q5_k (#21593)
* opencl: add general q5_k mv

* opencl: add flattened Q5_K mv and general Q5_K mm

* opencl: fix Q5_K unit tests
2026-04-11 01:46:19 -07:00
Sigbjørn Skjæret 2b2cd57de6 ggml : fix a few instances of missing GGML_TYPE_Q1_0 cases (#21716) 2026-04-11 09:45:00 +03:00
Aman Gupta a29e4c0b7b CUDA: also store node->src ne/nb for graph equality (#21736) 2026-04-11 10:30:30 +08:00
Max Krasnyansky 9aa2807769 hexagon: improved Op queuing, buffer and cache management (#21705)
* hexagon: introduce op request batching and rewrite buffer managment

The host now prepares batches of requests and dispatches them via a single dspqueue message.

Buffers are mapped explicitly by NPU while processing batches.

* hex-dma: disable l2 bypass since to work around new issue due to no flushes between Ops

* hex-utils: add explicit l2flush and l2clear helpers

* hex-opreq: use fine-grain per tensor l2 management

* hex-opreq: avoid redundant invalidates for tensors we already flushed

* hex-opreq: update debug messages

* htp-opreq: reuse ops_context

* hex-opreq: do not flush or invalidate cache lines beyond buffer boundry

* hex-opreq: fix errors in log message

* Revert "hex-opreq: do not flush or invalidate cache lines beyond buffer boundry"

This reverts commit 8b7f0a55a750a6430ce4eb1874c7feb3d720056d.

* hexagon: limit l2 flushes to 1MB which covers l2 cache

* hex-opreq: limit cache flush to 4MB

Looks like 4MB cont. vitual space should cover the 1MB cache.

* hexagon: drop cache flush size to 2MB

* hex-opreq: start reworking opreq packing

* hex-opreq: introduce new way of packing opbatch where tensors are stored separately

* hex-opreq: add a simple fastrpc call to force unmap all buffers

* hex-l2flush: somehow 2MB does not seem robust, also cleanup step size to use line-size

* hex-opreq: bump opreq batch size to 256

* hex-mm: place src1 spad at the top of vtcm for easy reuse

* hex-ops: introduce internal types and disable src1 reuse for now

Nothing new just formalizing the repack / qyn.quant types we've been using.

* htp-opreq: use tensor pointers instead of copies

* hex-opreq: introduce more robust way for tracking vtcm/spad reuse

This removes the SKIP_QUANTIZE flag that became fragile with the addition of HMX and other ops.

* hex-cumsum: fix error post opreq merge

* hex-opreq: move request batch handling into the session

Prepping everything for using dspqueue buffers and doing that inside the session is much cleaner.

* hex-mm: yet another fix for src1 reuse when we're mixing hmx/hvx

* hex-bufs: introduce pinned mmapings and use non-pinned ones for model buffers

* hex-buf: add support for allocating shared/pinned buffer for opreqs

* hex-opbatch: make opbatches configurable

* hex-naming: better name for ggml_hexagon_shared_buffer

* hex-naming: add session->c_name() helper

* hex-opbatch: start using shm but still copy for now

* hex-opbatch: use shared buffer for packing opbatch

* hex-opbatch: beter naming for opbatch related classes and code

* hex-opbatch: reuse batched tensors with same data/dims/strides

* hex-opbatch: update logging

* hex-opbatch: add support for vmem limit for op batching

* hex-opbatch: update htp side to properly support dynamic mmap/unmap

* hex-opbatch: add OB and OQ params for run-completion script and fix the asserts in batch processing

* hex-opbatch: fixed src1 handling in act ops

* hex-act: fix empty src1 handling in swiglu and friends

Simplify preamble macro while at it

* hex-mm: minor fix vtcm and dma handling in matmul

cleaning up some left-overs from merges

* hex-opbatch: allocate extra 1KB for dspqueue overhead

* hexagon: fix softmax for non-aligned tensors and cleanup vtcm alloc

* hex-mm: properly handle hmx_disabled flag

* hex-ops: update comments

* hex-ops: add debug output for get/set-rows

* hex-mmap: optimize un/mapping of buffers

* hex-opreq: global cache flush and invalidate beyond 128KB threshold

* hex-ops: add super simple opfilter regex for debugging

If an Op matches the regex hex backend will reject it.

* hex-opbatch: wireup newer ops missed in merge and update main switch to detect this in future

* hexagon: improved vtcm acquision to remove inter-op overhead

Fully compatible with QNN-HTP coex

* hex-mm: fixed hvx fallback path

* hex-mm: lower the vmem threshold a bit further to ~3GB

* hexagon: update debug & error logs

This also fixes an issue with newer llvm merging repack and non-repack
functions. We use those pointer to distinguish between buffer types.

* hexagon: move ops context into main context

Just a cleanup. We don't need separate contexts at this point.

* hex-opbatch: cleanup naming and headers for opbatch and related descriptors

* hex-fa: it's now better to enable FA during TG to reduce graph splits

* hexagon: remove GGML_HEXAGON_EXPERIMENTAL env var

It's no longer useful. Please use more flexible GGML_HEXAGON_OPFILTER to disable Ops
if needed for debugging or validation.

* hexagon: fixed editorconfig check

* Update ggml/src/ggml-hexagon/ggml-hexagon.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Trivikram Reddy <tamarnat@qti.qualcomm.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2026-04-10 15:47:43 -07:00
Rithik Sharma bfd1f453cb ggml-webgpu: support non-square subgroup matrix configs for Intel GPUs (#21669) 2026-04-10 10:52:38 -07:00
Chen Yuan e4fed9d08d ggml-webgpu: address quantization precision and backend lifecycle managment (#21521)
* ggml(webgpu): fix the busy-polls in Emscripten  in the waitAny after #20618, and remove the busy webgpu log

* Merge with upstream

* Fix GET_ROWS packed integer NaN when using f16 as memory buffer in shader quants

* Update Unary wgsl EXP and EXPM1 for f16 stability

* Fix GET_ROWS IQ4_XS strcut for NaN f16 canonicalization

* Fix numerical percision for unary sqrt when working with f16

* Fix NaN canonicalization for packed integers using f16

* Update err threshold for binary div ops when using f16

* backend: Keep one Dawn/WebGPU instance alive for the lifetime of the static backend

* clean: uncomment existing code logs

* clean: clean the unncessary debug info

* Refactor and generalize dequant helpers

* Remove deprecated quant structs

* Refactor shader defines to reduce repetition

* Remove error override for F16 type

* fix: fix the accidential removal of the proper initialization of ctx

* clean: clean legacy and format code

* fix: did not modify tests ops

---------

Co-authored-by: Jeremy J. Hartmann <jeremy@mtion.tv>
2026-04-10 10:52:01 -07:00
Jeff Bolz 7b69125331 vulkan: Support Q1_0 (#21539)
* vulkan: Support Q1_0

* use get_dm
2026-04-10 08:35:27 +02:00
Aman Gupta e34f042154 CUDA: fuse muls (#21665) 2026-04-10 10:24:09 +08:00
andyluo7 d132f22fc9 HIP: add CDNA4 (gfx950) architecture support for MI350X/MI355X (#21570)
Add AMD Instinct MI350X/MI355X (gfx950, CDNA4) support:

- vendors/hip.h: Add CDNA4 preprocessor define for __gfx950__
- common.cuh: Add GGML_CUDA_CC_CDNA4 and GGML_CUDA_CC_IS_CDNA4 macros
- mma.cuh: Route CDNA4 to compatible MFMA instructions:
  * f32 matmul: mfma_f32_16x16x4f32 (xf32 variant unavailable on gfx950)
  * bf16 matmul: mfma_f32_16x16x16bf16_1k (same as CDNA3)
  * int8 matmul: mfma_i32_16x16x32_i8/32x32x16 (same as CDNA3)
- mmq.cuh: Include CDNA4 in stream-k kernel dispatch

CDNA4 is largely compatible with CDNA3 except:
- No xf32 MFMA (mfma_f32_16x16x8_xf32) — routes to f32 path
- Different FP8 format (e4m3fn vs e4m3_fnuz) — not changed here

Tested on AMD Instinct MI355X (gfx950), ROCm 7.0.1:
- Build: compiles cleanly with -DAMDGPU_TARGETS=gfx950
- llama-bench (Qwen2.5-1.5B Q4_K_M, single GPU):
  * f16+FA: 40,013 tok/s prefill, 254 tok/s decode
  * q8_0+FA: functional
- Flash attention: works correctly
- MMQ: works correctly with stream-k dispatch

Co-authored-by: Andy Luo <andyluo7@users.noreply.github.com>
2026-04-09 21:13:32 +02:00
Johannes Gäßler d6f3030047 ggml: backend-agnostic tensor parallelism (experimental) (#19378)
* 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>
2026-04-09 16:42:19 +02:00