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* convert : use utf8 encoding * convert : update instructions and warning message
* ggml : add ggml_flash_attn_ext API * ggml : fix GQA support in ggml_flash_attn_ext * ggml : online attention (CPU) * metal : initial implementation * metal : f16 precision * metal : reduce branches * metal : specialize for head size * wip : 8 rows per simd group * wip : 4 rows per simd group * wip : template for rows per warp * metal : parallelize across KV size * metal : parallel reduce across heads * metal : efficient flash_attn_f16 implementation * metal : avoid redundant loads of the attention * metal : scale and mask in matrix form * metal : fix comment * llama : avoid ggml_cast, use F32 query * metal : add parallel reduce version (disabled) * metal : move output into local memory + optimize - the result from each simdgroup now stays in the registers - significantly reduced SRAM usage - more efficient skipping of -INF blocks - avoid simdgroup barrier in hot loop - add comments * metal : add tests, fix scaling, support C > 32 * metal : improve precision * ggml : fix f16 mad * metal : minor * metal : support Q > 8 * tests : add ATTN tests * metal : disable buffer allocation logs * tests : more * metal : faster inner loop for C == 32 * metal : fix array initialization * tests : ifdef * ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext * ggml : fix ggml_soft_max mask requirement * cuda : fix soft_max to use correct mask size * cuda : add flash_attn kernel (wip) * metal : optimize softmax for C > 32 * metal : optimize softmax * tests : minor fix * cuda : avoid zeroing fragments * tests : update dims * cuda : fix __hisinf() result check * cuda : avoid warp_reduce for smax * cuda : use int instead of int64_t Noticeably improves performance (thanks to Johannes) * cuda : make loops use the same loop values Thanks Johannes again for the tip * cuda : unroll some of the loops * cuda : avoid __hisinf branches * cuda : use half2 in softmax * cuda : switch to 1 warp for bs > 16 * cuda : speed-up reduce part of the kernel * cuda : unroll Q*K^T loop * cuda : fix -INF block check * cuda : simplify softmax * cuda : fix matrix names * cuda : minor * llama : adapt to F16 KQ_pos * llama : adapt new models to F16 KQ_mask * ggml : fix F16 store (ARM NEON) * llama : fix type of KQ_mask and KQ_pos * ggml : fix CPU soft_max * tests : add hs=256 * cuda : fix build * metal : improve perf via smaller int registers * cuda : adapt soft_max to F16 mask and pos * CUDA: faster FlashAttention, kernel for bs == 1 * 16 cols for Phi-2 * no vec for hs, no hs==256 ncols==32 for Volta * adjust kernel selection logic * 4 warps, 256 stride for all D * no ncols == 64 * Multiple parallel blocks for batch size 1 * fix compile warnings * fix excessive KQ_b loads * fix cmake build * fix KV cache padding, NaN from INFINITY (ggml-org#6438) * llama : flash_attn cparam + fix defrag * server: support flash_attn param * server: bench: enable flash_attn param * CUDA: refactor host code, dyn. par. blocks * fix flash_attn_vec_f16 race condition * flush softmax exp below threshold to 0 * store temp KQ in registers * Calculate KQ as FP32 if KQV has GGML_PREC_F32 * Add __hgt2_mask implementation for CUDA 11 * fix KQ FP32 precision fpr parallel_blocks > 1 * llama-bench : add -fa,--flash-attn arg * metal : add BS=1 kernel for flash attention (ggml-org#6508) * metal : add BS=1 kernel for flash attention (wip) * metal : support more than 1 warps * metal : opts * metal : opt * metal : switch to parallel reduce * metal : reduce registers * metal : simplify * metal : initial FA vec kernel * metal : use F32 attention accumulators * batched-bench : add fattn arg * llama : simplify llama_build_kv_store ggml-ci * llama : adapt build_olmo to changes * ggml : fix arm fp16 store on windows * metal : clean-up * metal : clean-up kernel code * metal : minor * tests : remove benchmarks ggml-ci * ggml : fix avx512 const correctness ggml-ci * ggml : fix soft_max with bias on CPU ggml-ci * common : print --flash-attn in help * ggml : fix num dimensions in ggml_flash_attn_ext * llama : force disable flash attention for incompatible models * ggml : ggml_soft_max support F16/F32 mask/pos ggml-ci * cuda : uint -> uint32_t * cuda : "constexpr dim3" -> "const dim3" ggml-ci * cuda : try to fix __hgt2_mask ggml-ci * ggml : add TODO's for F16/F32 mask/pos support in other backends * llama : replace bool need_kq_pos with use_alibi * llama : prep ALiBi support for BERT models ggml-ci * llama : fix n_batch requirements ggml-ci * cont * server : add help for --flash-attn arg * llama : disable FA for AMD * tests : remove TMP_ATTN_BENCH ggml-ci * llama : support save/load state with FA enabled ggml-ci * ci : add CUDA save-load-state tests ggml-ci * llama : llama_kv_cache_clear zeroes data + fix save-load seq ggml-ci * llama : fix copy-paste errors, add TODO * llama : disallow incompatible states * llama : update llama_state_get_size after v_trans field * metal : remove tmp log * llama : add static reminder for llama_state_get_size * metal : fix max nsg ggml-ci * ci : fix arg order ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
* perplexity: more statistics, added documentation * add LLaMA 3 8b scoreboard
ROCm clang defines _MSC_VER which results in the wrong implementation of LOG_IMPL and LOG_TEE_IMPL being compiled. This fixes ggml-org#6972
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
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) * 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>
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