Commit Graph
100 Commits
Author SHA1 Message Date
Johannes GäßlerandGeorgi Gerganov 3f05208e5c CUDA: remove -sm row, refactor cuBLAS (llama/24216)
* CUDA: remove -sm row, refactor cuBLAS

* fix CDNA + BF16 logic

* fix bad return

* fix src0 strides, contiguous requirements

* fix GGML_CUDA_FORCE_CUBLAS

* fix casts to BF16
2026-07-10 13:06:42 +03:00
Johannes GäßlerandGeorgi Gerganov 388a1fc962 CUDA: consistent use of __restrict__ + PDL for FA (llama/25185) 2026-07-10 13:06:42 +03:00
Johannes GäßlerandGeorgi Gerganov 2b35abc294 CUDA: fix Gemma E4B MTP FlashAttention (llama/25148)
* CUDA: fix Gemma E4B MTP FlashAttention

* remove unused template declaration
2026-07-10 13:06:42 +03:00
Johannes GäßlerandGeorgi Gerganov e815b264eb TP: quantized KV cache support (llama/23792)
* TP: quantized KV cache support

* fix partial view

* remove overly strict assert
2026-06-08 14:36:36 +03:00
Johannes GäßlerandGeorgi Gerganov 2307712d32 CUDA: missing PDL sync for FWHT, better fallback (llama/23690) 2026-05-29 09:47:30 +03:00
Johannes GäßlerandGeorgi Gerganov bcff515150 TP: fix ggml context size calculation (llama/22616)
* TP: fix ggml context size calculation, memory leak

* move split state cache back into the context

* revert to constant ggml context size for cgraphs

* increase headroom for statically allocated tensors

* remove obsolete include
2026-05-25 12:44:04 +03:00
Johannes GäßlerandGeorgi Gerganov 3306af62b1 TP: fix entirely zero-sized slices per device (llama/23525) 2026-05-25 12:26:07 +03:00
Johannes GäßlerandGeorgi Gerganov ec183556c6 CUDA: fix PDL CC check for JIT compilation (llama/23471) 2026-05-25 12:26:07 +03:00
Johannes GäßlerandGeorgi Gerganov e62d5893f4 HIP: RDNA3 mma FA, faster AMD transpose, tune AMD (llama/22880)
Adds RDNA3 support to the CUDA mma FA kernel. To make the RDNA3 tensor cores work with the FP16 accumulation for VKQ the tiles they need to be 32 logical units long in direction of the attention head; for head sizes 80 and 112 that are not exactly divided by 32 the regular length of 16 with FP32 accumulation is used instead. The longer tiles also enable more efficient transposition for a warp size of 32 which is why it's also used for RDNA4. However, this scrambles the data layout of the accumulators along the attention head dimension. To prevent accidental misuse I added another entry to ggml_cuda_mma::data_layout.

I also tuned the kernel parameters for RDNA3, RDNA4, and CDNA1 in general, during which I discovered that the kernel can be made to work for head sizes up to 256 for CDNA. For RDNA3/4 I was not able to get better performance that the tile kernel for head sizes > 128.
2026-05-25 12:26:07 +03:00
Johannes GäßlerandGeorgi Gerganov ea459fba9d CUDA: lower-case PCI bus id, standardize for ggml (llama/22820) 2026-05-14 21:26:48 +03:00
Johannes GäßlerandGeorgi Gerganov 582d2562a4 CUDA: fix tile FA kernel on Pascal (llama/22541) 2026-05-01 13:07:35 +03:00
Johannes GäßlerandGeorgi Gerganov 5301139374 TP: fix delayed AllReduce + zero-sized slices (llama/22489) 2026-04-30 11:29:23 +03:00
Johannes GäßlerandGeorgi Gerganov da738a74f5 CUDA: reduce MMQ stream-k overhead (llama/22298)
* CUDA: reduce MMQ stream-k overhead

* use 32 bit integers for kbc
2026-04-30 11:29:19 +03:00
Johannes GäßlerandGeorgi Gerganov 6429023e5f TP: fix 0-sized tensor slices, AllReduce fallback (llama/21808)
* TP: fix 0-sized tensor slices, AllReduce fallback

* fix layer structure <-> GPU count aliasing

* add missing std::fill

* fix CUDA device set, max ggml ctx size
2026-04-30 11:29:14 +03:00
Johannes GäßlerandGeorgi Gerganov b8f57c9c50 CUDA: refactor mma data loading for AMD (llama/22051)
* CUDA: refactor mma data loading for AMD

* fix CDNA MMQ occupancy

* fix CDNA3 mma

* fix RDNA3 compile
2026-04-30 11:29:13 +03:00
Johannes GäßlerandGeorgi Gerganov 9638e29657 CUDA: require explicit opt-in for P2P access (llama/21910) 2026-04-30 11:29:10 +03:00
Johannes GäßlerandGeorgi Gerganov 7e57b20d53 CUDA: manage NCCL communicators in context (llama/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
2026-04-30 11:29:09 +03:00
Johannes GäßlerandGeorgi Gerganov c0b46c2f8f CUDA: skip compilation of superfluous FA kernels (llama/21768) 2026-04-30 11:29:06 +03:00
bb895c843d ggml: backend-agnostic tensor parallelism (experimental) (llama/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 (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>
2026-04-30 11:29:05 +03:00
Johannes GäßlerandGeorgi Gerganov 82bb26fba1 CUDA: fix FA kernel selection logic (llama/21271) 2026-04-30 11:28:59 +03:00
3987857d2d llama: fix llama-model-saver (llama/20503)
* llama : add fd-based model loading via llama_model_load_from_fd

* llama : address review feedback for fd-based model loading

* llama : use FILE pointer instead of fd in public API

* llama : use FILE pointer consistently, address review feedback

* fixup

* fix tensor names

* fix llama-model-saver

* roundtrip tests

* fixup

* refactor tests

* fix prints

* fix model saving

* fix CI, disable Chameleon

* print seed

---------

Co-authored-by: Siddhesh2377 <siddheshsonar2377@gmail.com>
2026-03-29 15:04:36 +03:00
Johannes GäßlerandGeorgi Gerganov 607c92430f CUDA: fix BF16 FA compilation (llama/20865) 2026-03-29 15:04:36 +03:00
Johannes GäßlerandGeorgi Gerganov d7926e62d4 CUDA: limit number of FA stream-k CUDA blocks (llama/20586) 2026-03-16 13:10:15 +02:00
Johannes GäßlerandGeorgi Gerganov 596b655dbd ggml-cpu: fix data race for debug asserts (llama/20148) 2026-03-16 13:10:15 +02:00
Johannes GäßlerandGeorgi Gerganov 5d25427e58 ggml: fix ggml_is_contiguous_n for ne == 1 (llama/20092) 2026-03-16 13:10:15 +02:00
Johannes GäßlerandGeorgi Gerganov ade724fced CUDA: fix kernel selection logic for tile FA (llama/19686)
* CUDA: fix kernel selection logic for tile FA

* add comment
2026-02-27 20:57:58 +02:00
Johannes GäßlerandGeorgi Gerganov 625c8d863e ggml-backend: fix async set/get fallback sync (llama/19179) 2026-02-08 09:29:10 +02:00
Johannes GäßlerandGeorgi Gerganov 9d94d0f782 CUDA: tune GLM 4.7 Flash FA kernel selection logic (llama/19097) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov 41d5d7bb0e CUDA: fix padding of GQA to power of 2 in FA (llama/19115) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov f63848eada CUDA: faster FA for GQA > 1 but not power of 2 (llama/19092) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov f53eafd745 CUDA: re-use MLA K data for V in MMA FA (llama/19057) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov f21d0cbb1a CUDA: fix alignment check for FA (llama/19023) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov f2f0ba0384 CUDA: fix allignment on register spill for FA (llama/18815) 2026-01-30 15:56:40 +02:00
Johannes GäßlerandGeorgi Gerganov 3a1ea96373 HIP: adjust RDNA3.5 MMQ kernel selction logic (llama/18666) 2026-01-14 09:11:59 +02:00
Johannes GäßlerandGeorgi Gerganov 9956333361 CUDA: fix FA FP16 accumulator overflow for Granite (llama/18614) 2026-01-14 09:11:59 +02:00
Johannes GäßlerandGeorgi Gerganov 60d178cee9 CUDA: only allocate FA tmp buffer if needed (llama/18564) 2026-01-14 09:11:59 +02:00
Johannes GäßlerandGeorgi Gerganov 2d250f8049 CUDA: fix KQ max calculation (llama/18487) 2025-12-31 17:52:09 +02:00
Johannes GäßlerandGeorgi Gerganov 5765c5b04e CUDA: fix replacment of bad archs in CMake (llama/18457) 2025-12-31 17:52:09 +02:00
Johannes GäßlerandGeorgi Gerganov d6cb2407b7 CUDA: Blackwell features for non-native builds (llama/18436) 2025-12-31 17:52:09 +02:00
Johannes GäßlerandGeorgi Gerganov b68b12f2d5 llama: fix RPC for -fit on (llama/18233) 2025-12-31 17:52:09 +02:00
Johannes GäßlerandGeorgi Gerganov aaf3f39b4a llama: automatically set parameters not set by the user in such a way that maximizes GPU utilization (llama/16653)
* 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]
2025-12-18 08:20:56 +02:00
Johannes GäßlerandGeorgi Gerganov feb856d4a1 CUDA: fix overflow in MMA kernel without stream-k (llama/17939) 2025-12-18 08:20:56 +02:00
Johannes GäßlerandGeorgi Gerganov ea1829134f CUDA: fix unpadded strides in MMA FA kernel (llama/17891) 2025-12-12 17:53:23 +02:00
Johannes GäßlerandGeorgi Gerganov bef1f5a57e CUDA: fix FP16 overflow in tile FA kernel (llama/17875) 2025-12-12 17:53:22 +02:00
Johannes GäßlerandGeorgi Gerganov 94be71911f HIP: fix RDNA3 FP16/BF16 matrix multiplication (llama/17817) 2025-12-12 17:53:20 +02:00
Johannes GäßlerandGeorgi Gerganov 4170159dcd HIP : fix RDNA4 build (llama/17792) 2025-12-12 17:53:18 +02:00
Johannes GäßlerandGeorgi Gerganov 14502d6561 CUDA: fix FA VKQ accumulator overflow (llama/17746) 2025-12-12 17:53:17 +02:00
7adbcafb6c CUDA: generalized (mma) FA, add Volta support (llama/17505)
* CUDA: generalized (mma) FA, add Volta support

* use struct for MMA FA kernel config

---------

Co-authored-by: Aman Gupta <aman>
2025-12-12 17:53:16 +02:00
Johannes GäßlerandGeorgi Gerganov 37e4c2ed3a CUDA: no FP16 arithmetic for vector FA kernel (llama/17558) 2025-12-12 17:53:11 +02:00
Johannes GäßlerandGeorgi Gerganov 566c4c4469 CUDA: static assert to prevent misuse of memcpy_1 (llama/17198) 2025-11-17 21:05:46 +02:00
Johannes GäßlerandGeorgi Gerganov 358f77aca7 CUDA: fix MMQ stream-k fixup ne1 indices (llama/17089) 2025-11-09 23:38:03 +02:00
af8a88792f CUDA: fix should_use_mmvf for ne11 == 1 (llama/17085)
* CUDA: fix should_use_mmvf for ne11 == 1

* Apply suggestion from @am17an

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
2025-11-09 23:38:03 +02:00
Johannes GäßlerandGeorgi Gerganov b5d6fa438f CUDA: fix crash on uneven context without FA (llama/16988) 2025-11-09 23:38:03 +02:00
52e1bbb554 CUDA: Volta tensor core support for MMF (llama/16843)
* CUDA: Volta tensor core support for MMF

* more generic checks for hardware support

* Update ggml/src/ggml-cuda/mmf.cuh

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
2025-11-09 23:38:03 +02:00
Johannes GäßlerandGeorgi Gerganov 1471b1fda7 HIP: fix AMDGPU_TARGETS, update documentation (llama/16803) 2025-11-09 23:38:03 +02:00
Johannes GäßlerandGeorgi Gerganov 99cea274e5 CUDA: better error for FA kernel with 0 occupancy (llama/16643) 2025-11-09 23:38:03 +02:00
Johannes GäßlerandGeorgi Gerganov 72d98011db HIP: fix GPU_TARGETS (llama/16642) 2025-10-22 12:58:11 +03:00
Johannes GäßlerandGeorgi Gerganov 1bdd746bc8 CUDA: enable FA for FP32 KV cache (llama/16546) 2025-10-15 09:29:17 +03:00
Johannes GäßlerandGeorgi Gerganov 77272fe0df CUDA: fix numerical issues in tile FA kernel (llama/16540) 2025-10-15 09:29:17 +03:00
Johannes GäßlerandGeorgi Gerganov b5fb9b9f58 CUDA: faster tile FA, add oob checks, more HSs (llama/16492) 2025-10-15 09:29:17 +03:00
Johannes GäßlerandGeorgi Gerganov e856483cd6 CUDA: refactor and deduplicate vector FA kernels (llama/16208)
* CUDA: refactor and deduplicate vector FA kernels
2025-09-29 15:18:11 +03:00
Johannes GäßlerandGeorgi Gerganov cd431223e0 llama: print memory breakdown on exit (llama/15860)
* llama: print memory breakdown on exit
2025-09-29 15:18:10 +03:00
Johannes GäßlerandGeorgi Gerganov 05bdfd4380 CUDA: fix compilation on CC 6.0 (llama/16091) 2025-09-20 13:46:38 +03:00
Johannes GäßlerandGeorgi Gerganov d452f0cf8c CUDA: fix FA occupancy, optimize tile kernel (llama/15982) 2025-09-20 13:45:30 +03:00
Johannes GäßlerandGeorgi Gerganov f0768eb575 CUDA: larger SRAM reads for tile FA, AMD FP16 dot (llama/15927)
* CUDA: larger SRAM reads for tile FA, AMD FP16 dot

* fix logic for availability of v_dot2_f32_f16
2025-09-20 13:45:28 +03:00
Johannes GäßlerandGeorgi Gerganov e35d1375ee HIP: use v_dot2_f32_f16 instruction for FA (llama/15884) 2025-09-20 13:42:52 +03:00
Johannes GäßlerandGeorgi Gerganov 260982232c CUDA: fix GET_ROWS for large tensors (llama/15882) 2025-09-20 13:42:52 +03:00
Johannes GäßlerandGeorgi Gerganov cd70d89628 CUDA: faster tile FA (Pascal/AMD), headsize 256 (llama/15769) 2025-09-20 13:42:50 +03:00
Johannes GäßlerandGeorgi Gerganov 69400f16f1 ggml-cpu: document use of "free" memory [no ci] (llama/15834) 2025-09-20 13:42:50 +03:00
Johannes GäßlerandGeorgi Gerganov 6ff468cfaa CUDA: fastdiv, launch bounds for mmvq + q8_1 quant (llama/15802)
* CUDA: fastdiv, launch bounds for mmvq + q8_1 quant
2025-09-20 13:42:50 +03:00
Johannes GäßlerandGeorgi Gerganov f20a7b0e99 ggml-backend: raise GGML_MAX_SPLIT_INPUTS (llama/15722) 2025-09-20 13:42:47 +03:00
Johannes GäßlerandGeorgi Gerganov f6ba3949b6 llama: use FA + max. GPU layers by default (llama/15434)
* llama: use max. GPU layers by default, auto -fa

* ggml-backend: abort instead of segfault
2025-09-20 13:42:44 +03:00
Johannes GäßlerandGeorgi Gerganov b7809c401b CUDA: use FP32 arithmetic for conv2d (llama/15683) 2025-09-20 13:42:44 +03:00
Johannes GäßlerandGeorgi Gerganov 53010199a1 CUDA: return -1 for nonexistent compiled arch (llama/15587) 2025-09-20 13:42:42 +03:00
Johannes GäßlerandGeorgi Gerganov 1e856b2919 CUDA: MoE helper in device code, better tile sizes (llama/15525)
* CUDA: MoE helper in device code, better tile sizes

* reduce superfluous CUDA blocks
2025-09-20 13:42:41 +03:00
Johannes GäßlerandGeorgi Gerganov b0d15e1eb6 CUDA: fix half2 -> half conversion for HIP (llama/15529) 2025-09-20 13:42:40 +03:00
Johannes GäßlerandGeorgi Gerganov 8f0579a33d CUDA: refactor FA support/selection code (llama/15454) 2025-09-20 13:42:38 +03:00
Johannes GäßlerandGeorgi Gerganov 316ed78d68 CUDA: replace GGML_CUDA_F16 with CUDA arch checks (llama/15433) 2025-09-20 13:42:38 +03:00
Johannes GäßlerandGeorgi Gerganov ba32f5df0a CUDA: fix negative KV_max values in FA (llama/15321) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 2baea5e4b3 CUDA: attention sinks for mma FlashAttention (llama/15157) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 5caf8a1ea2 CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16 (llama/15131)
* CUDA: GEMM for FP32/FP16/BF16 and ne11 <= 16
2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov d6e7315717 CUDA: use mma FA kernel for gqa > 4 on RTX 4000 (llama/15035) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 7e7557ac50 CUDA: fix MMQ nwarps for AMD with warp_size==32 (llama/15014) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 113d88686b CUDA: skip masked KV slices for all FA kernels (llama/14924) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 24d3524bfd CUDA: fix pointer incrementation in FA (llama/14916) 2025-08-18 20:30:45 +03:00
Johannes GäßlerandGeorgi Gerganov 95efcf011d CUDA: fix overflow in FA, tune performance (llama/14840) 2025-07-28 13:02:32 +03:00
Johannes GäßlerandGeorgi Gerganov 8272aa9f14 CUDA: fix compilation with GGML_CUDA_F16 (llama/14837) 2025-07-28 13:02:32 +03:00
Johannes GäßlerandGeorgi Gerganov a65976fc3c CUDA: fix quantized KV cache + multiple sequences (llama/14822)
* CUDA: fix quantized KV cache + multiple sequences

* Update ggml/src/ggml-cuda/fattn-common.cuh

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2025-07-28 13:02:32 +03:00
Johannes GäßlerandGeorgi Gerganov 4aaf8114e7 ggml: backward pass for split swiglu (llama/14483) 2025-07-12 19:23:56 +03:00
Johannes GäßlerandGeorgi Gerganov 70515ed728 CUDA: broadcasting for FlashAttention mask (llama/14500) 2025-07-12 19:23:56 +03:00
Johannes GäßlerandGeorgi Gerganov 06b01ba87b CUDA: mul_mat_v support for batch sizes > 1 (llama/14262)
* CUDA: mul_mat_v support for batch sizes > 1

* use 64 bit math for initial offset calculation
2025-07-01 17:54:53 +03:00
Johannes GäßlerandGeorgi Gerganov 7f4d110f53 CUDA: fix FTZ in FA for Gemma 3 (llama/13991) 2025-06-10 12:40:33 +03:00
Johannes GäßlerandGeorgi Gerganov 9589645e72 gguf: fix failure on version == 0 (llama/13956) 2025-06-10 12:40:33 +03:00
Johannes GäßlerandGeorgi Gerganov a5aff28198 CUDA: fix typo in FlashAttention code (llama/13926) 2025-06-01 15:14:44 +03:00
Johannes GäßlerandGeorgi Gerganov 9a500394ad CUDA: fix FA tg at long context for CC >= 8.9 (llama/13852) 2025-06-01 15:14:44 +03:00
Johannes GäßlerandGeorgi Gerganov f1576b2659 CUDA: fix race condition in FA vector kernels (llama/13742) 2025-05-27 18:03:00 +03:00
Johannes GäßlerandGeorgi Gerganov 926fe234e9 CUDA: skip fully masked-out KV in FA vec kernel (llama/13584)
* CUDA: skip fully masked-out KV in FA vec kernel
2025-05-27 18:03:00 +03:00
Johannes GäßlerandGeorgi Gerganov 0dda27bc0b CUDA: fix crash on large batch size for quant. MoE (llama/13537) 2025-05-19 14:58:39 +03:00
Johannes GäßlerandGeorgi Gerganov ffa4720f25 CUDA: faster Deepseek FA, add Turing support (llama/13435) 2025-05-19 14:58:39 +03:00
Johannes GäßlerandGeorgi Gerganov 405b9c77ad mnist: fix segmentation fault (ggml/1227) 2025-05-19 14:58:39 +03:00