14 Commits

Author	SHA1	Message	Date
Diego Devesa	7cc2d2c889	ggml : move AMX to the CPU backend (#10570) ... * ggml : move AMX to the CPU backend --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-11-29 21:54:58 +01:00
Diego Devesa	5931c1f233	ggml : add support for dynamic loading of backends (#10469) ... * ggml : add support for dynamic loading of backends --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-11-25 15:13:39 +01:00
Diego Devesa	ae8de6d50a	ggml : build backends as libraries (#10256) ... * ggml : build backends as libraries --------- Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: R0CKSTAR <xiaodong.ye@mthreads.com>	2024-11-14 18:04:35 +01:00
Diego Devesa	9f40989351	ggml : move CPU backend to a separate file (#10144)	2024-11-03 19:34:08 +01:00
Diego Devesa	dca1d4b58a	ggml : fix BLAS with unsupported types (#9775) ... * ggml : do not use BLAS with types without to_float * ggml : return pointer from ggml_internal_get_type_traits to avoid unnecessary copies * ggml : rename ggml_internal_get_type_traits -> ggml_get_type_traits it's not really internal if everybody uses it	2024-10-08 14:21:43 +02:00
Olivier Chafik	1c641e6aac	build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809) ... * `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew * server: update refs -> llama-server gitignore llama-server * server: simplify nix package * main: update refs -> llama fix examples/main ref * main/server: fix targets * update more names * Update build.yml * rm accidentally checked in bins * update straggling refs * Update .gitignore * Update server-llm.sh * main: target name -> llama-cli * Prefix all example bins w/ llama- * fix main refs * rename {main->llama}-cmake-pkg binary * prefix more cmake targets w/ llama- * add/fix gbnf-validator subfolder to cmake * sort cmake example subdirs * rm bin files * fix llama-lookup-* Makefile rules * gitignore /llama-* * rename Dockerfiles * rename llama|main -> llama-cli; consistent RPM bin prefixes * fix some missing -cli suffixes * rename dockerfile w/ llama-cli * rename(make): llama-baby-llama * update dockerfile refs * more llama-cli(.exe) * fix test-eval-callback * rename: llama-cli-cmake-pkg(.exe) * address gbnf-validator unused fread warning (switched to C++ / ifstream) * add two missing llama- prefixes * Updating docs for eval-callback binary to use new `llama-` prefix. * Updating a few lingering doc references for rename of main to llama-cli * Updating `run-with-preset.py` to use new binary names. Updating docs around `perplexity` binary rename. * Updating documentation references for lookup-merge and export-lora * Updating two small `main` references missed earlier in the finetune docs. * Update apps.nix * update grammar/README.md w/ new llama-* names * update llama-rpc-server bin name + doc * Revert "update llama-rpc-server bin name + doc" This reverts commit e474ef1df4. * add hot topic notice to README.md * Update README.md * Update README.md * rename gguf-split & quantize bins refs in **/tests.sh --------- Co-authored-by: HanClinto <hanclinto@gmail.com>	2024-06-13 00:41:52 +01:00
snadampal	a07d0fee1f	ggml : add mmla kernels for quantized GEMM (#4966) ... * ggml: aarch64: implement smmla kernel for q8_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q8_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_0_q8_0 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_0_q8_0 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: aarch64: implement smmla kernel for q4_1_q8_1 quantized gemm armv8.2-a and above supports MMLA instructions that have higher throughput than DOT. this commit adds mmla kernel for q4_1_q8_1 gemm. The feature is enabled if the platform supports "__ARM_FEATURE_MATMUL_INT8" On AWS Graviton3 processors this kernel resulted up to 1.5x improvement for prompt evaluation throughput compared to the default sdot kernel. * ggml: update unit tests for the new vec_dot interface * llama.cpp: add MATMUL_INT8 capability to system_info	2024-02-11 15:22:33 +02:00
Michael Klimenko	35a2ee9143	Remove unused data and add fixes (#5154) ... * Remove unused data and add fixes * Add missing file * Address review comments * Replace the scope of vq allocation	2024-01-27 15:25:55 +01:00
Cebtenzzre	bc39553c90	build : enable more non-default compiler warnings (#3200)	2023-09-28 17:41:44 -04:00
Cebtenzzre	3aefaab9e5	check C++ code with -Wmissing-declarations (#3184)	2023-09-15 15:38:27 -04:00
Stephan Walter	1b107b8550	ggml : generalize quantize_fns for simpler FP16 handling (#1237) ... * Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2023-07-05 19:13:06 +03:00
Borislav Stanimirov	9cbf50c041	build : fix and ignore MSVC warnings (#1889)	2023-06-16 21:23:53 +03:00
Kawrakow	1bfc153e2f	ggml : a faster version for Q4_1 x Q8_0 dot products (#1083) ... * A faster version for Q4_1 x Q8_0 dot products The idea nehind being that Q8_0 quantized values get used many times in the matrix multiplications where they are involved. In the current implementations, when we are evaluating the dot products, we need to compute the sum of the quants in the Q8_0 vector, so the same operation is repeated many times. Here we pre-compute the sum during Q8_0 quantization, store it in the now modified block_q8_0 struct, and then reuse this result in the subsequent dot products. In a synthetic benchmark (just compute a bunch of dot products), this change speeds up the Q4_1 * Q8_0 dot product by 80%, making the performance identical to Q4_0 * Q8_0. In practical application, I see a ~15% gain in speed for token prediction on M2, and ~5% gain on Ryzen 7950X. The speed gain in the prompt evaluation is much bigger (around 50%). I have only done the change for the scalar version, ARM_NEON, and AVX2, so we still need an AVX implementation. * Cleaning up --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>	2023-04-21 18:18:26 +03:00
Kawrakow	5ecff35151	Adding a simple program to measure speed of dot products (#1041) ... On my Mac, the direct Q4_1 product is marginally slower (~69 vs ~55 us for Q4_0). The SIMD-ified ggml version is now almost 2X slower (~121 us). On a Ryzen 7950X CPU, the direct product for Q4_1 quantization is faster than the AVX2 implementation (~60 vs ~62 us). --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>	2023-04-18 19:00:14 +00:00