mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-07-23 11:10:55 -05:00
* ggml-et: Add performance logging * ggml-et: Quants helpers * ggml-et: Add MUL_MAT kernel * ggml-et: Add ROPE kernel * ggml-et: Add RMS_NORM kernel * ggml-et: Add GLU kernel * ggml-et: Add SOFT_MAX kernel * ggml-et: Add GET_ROWS kernel * ggml-et: Add CONT kernel * ggml-et: Add SET_ROWS kernel * ggml-et: Add MUL_MAT_ID kernel * ggml-et: Build et kernels as part of ggml * ggml-et: Embed kernels with fs fallback * ggml-et: Build fixes * ggml-et: Add MUL_MAT F32xF32 op * ggml_et: Add MUL_MAT_ID op * ggml-et: Disable offloading for debug * ggml-et: Refactor out block ops * ggml-et: ggml backend API changes * ggml-et: Add RESHAPE/TRANSPOSE to supported * ggml-et: Add CONT_F16 * ggml-et: Add supported ops doc * gglm-et: Initial doc * ggml-et: Remove runtime import hacks We can now import the runtime by a simple find_package(), so we can cleanup the CMakeLists.txt. * ggml-et: Fix GET_ROWS kernel Fix lost batch dimension. Also clean vibe-comments. * ggml-et: Fix SET_ROWS kernel Remove incorrect broadcasting guard. * ggml-et: Use custom instruction for fp32->fp16 * ggml-et: Vectorize set_rows fp32->fp16 * ggml-et: Fix ROPE kernel (yarn) ggml-et: fix et_logf WIP: Fix ramp WIP: fix ROPE! * ggml-et: Better sinf * ggml-et: Fix SOFT_MAX Add `max_bias` and `sink` support. * ggml-et: Fix CONT Reorder from contiguous write to read with atomic stores. * ggml-et: Fix elmap kernel Remainder handlin * ggml-et: Fix MUL_MAT MUL_MAT_ID remainders * ggml-et: Fix ET-SOC reference * ggml-et: Fix embed kernels scripts for old python This allows GGML-ET to build on pre-3.8 python. * Add sysemu support with compile time flag `-DGGML_ET_SYSEMU=ON` (#6) * Example using ET-Soc-1 emulator configuration Example usage: ```bash cmake -B build -DGGML_CUDA=OFF -DGGML_ET=ON -DLLAMA_CURL=OFF -DGGML_CCACHE=ON cmake --build build --config Release -j $(nproc) time ./build/bin/test-backend-ops ./build/bin/llama-server \ --model Qwen3-0.6B-Q8_0.gguf \ --alias Qwen3-0.6B-Q8_0 \ -fa 0 \ --ctx-size 1024 \ --no-warmup \ --host 127.0.0.1 \ --port 8080 ``` * build: proper dep tracking for kernels * support host using MOLD linker * initial multi core GET_ROW F32 implementation * vectorized q8 dequant * wip: cland warning clenaups and initial logging refactor * wip: message default message cleanup * chore: message cleanups * cmake cleanup * migrate to use platform provided functions * cmake back into subdir * support et_print() in kernels * fix: repair kernel building * perf: operations run async by default * debug: proper kernel dep tracking and error detection on kenrel launch * fix: kernel binary dep tracking and fixing get_rows_f32 erroring * perf: back to doing async kernel runs by default * perf: vectorize and parallel device memset * merge matmul work * misc: align allocation and enable all offload * misc: delete deadcode and respect memory limits * fix: repair tensor debug print * fix: loosen RMS_NORM op percision * feat: Q4_0 GET_ROWS * perf: FP32 MUL_MAT using TensorFMA * update limitations * perf: redue L1 load in compute_block_dot_product_q8_0 * feat: save kernel mapping (name to id) when profiling is enabled * chore: memops cleanup * perf: parallelize softmax by rows * perf: vectorize 2nd phase of softmax * perf: ban GET_ROWS from offloaded * perf: vectorize and non-atomic for eltwise ops and sub support * perf: vectorize normal rope * perf: glu runs in parallel * merge: manually merge saqib's work on kernel fixes * perf: more vectorized RoPE * perf: parallelize mul_mat_id * perf: parallelize set_rows_f32 * perf: vectorize softmax * feat: support kernel fusion and fuse RMS_NORM + MUL * fix: mostly resolve test-backend-ops failure in SOFT_MAX and ROPE * fix: bump max rope dims for gemma * feat: GeGLU and SCALE support to fully offload Gemma * perf: faster device memset * feat: get_rows supporting Q4_K and avoid cont cache coherent issues * better F32 MM * feat: NORM for ET backend * feat: SQR for ET backend * feat: UNARY on ET * feat: el_map support broadcasting for ET * feat: SUM_ROWS in ET backend * feat: more ops in ET backend * feat: WKV* operators in ET backend * perf: parallelize operators across cacheline instead of row * perf: parallelize get_rows on cacheline * wip: baseline FlashAttention for ET backend * wip: enough FA and CPY f32->f16 to run llama 3.1 fully offloaded with FA on * feat: f16 x f16 -> f32 MM using matrix engine * wip: f16 FlashAttention using matrix engine * wip: clean up * feat: barriers * perf: optimize FA_F16 in ET * perf: vectorize pack_k_for_transpose16 * perf: prefetch next loop matrix tile * perf: FlashAttention 2nd MM uses TensorFMA and optimizations * cleanup: flashattention reorg * perf: optimizations and fixes * feat: L2SCP API and make FlashAttention support DV = 256 for gemma * perf: parallelize norms beyond single row * feat: GATED_DELTA_NET support and relaxed L2_NORM requirment * feat: loosen RMS_NORM, NORM, ROPE contingous req too * feat: repeat supports brocasting on dim 0 and loosen cont check * feat: FILL and DIAG operator * feat: loosen UNARY support chcek * feat: TRI support * feat: SOLVE_TRI support * feat: basic SET support * feat: loosen CONT req * perf: fp16_to_fp32 use ASM * feat: IMROPE support * feat: PAD support * feat: global barrier * fix: view must live on the same backend as backing tensor * feat: relax CONCAT in ET backend * feat: dead simple CUMSUM implementation * feat: basic SSM_CONV support * feat: loosen CONCAT req * feat: relax GATED_DELTA_NET and add SET support proper * cleanup: cleanup LCM math * feat: SWIGLU single input * feat: SSM_SCAN support * feat: el_map supports non aligned tensors in best effort * feat: basic GROUP_NORM support * feat: loosen MUL_MAT capablities slightly * feat: loosen MUL_MAT and GET_ROWS and add IM2COL * feat: special case for softmax 1x1x1x1 * feat: loosen SOFT_MAX req in ET backend * fix: el_map unaligned acse fixes * perf: optimize zero_acc_vec in flash_attn_ext_f16_me * perf: use hart 1 for packing in MM and FA for FP16 * feat: kernel semaphore * perf: better instruction sequence in FlashAttention * fix: gated_delta_net with proper masking * perf: better parallelization for GATED_DELTA_NET * perf: parallelize SSM_CONV over nr * perf: vectorize SSM_CONV * perf: optimize MUL_MAT for q8 * feat: support Gemma 4 * fix: support multi-device * feat: broader GLU support * feat: unary ops supports view * fix: repair fp16 MM using matrix engine * perf: handle large N GEMV better * perf: better q8_0 MM * perf: better set_rows * add back deleted files * fix: repair after merge * feat: POC version of uberkernel * feat: RMS_NORM in uberkernel * feat: add more kernels into usage * chore: clean up uberkernel compilation * perf: faster flash attention * perf: opt flash attention for large seq length * feat: loosen op bounds. clamp and mean support * perf: vectorize ssm_scan * perf: slightly faster FA * perf: FlashAttention parallel MM and load * perf: fuse Q8 MM and ADD * feat: basic conv kernel for ET * softMAx_test * set_rows_f32 * get_rows and cont * testing * set_rows_exp * Junk addition * Narrowing the issue * Update flash_attn_ext_f16_me.c Focusing FA_ext_f16_me * test * Eviction updated * Detailed cache eviction debug * mulmat * removeal of `BUILD_FOR_UBERKERNEL` flag * cleaning... * fix: balance FCC0 count * feat: implement mul_mat and mul_mat_id for Q4_0 type * optimize uberkernel plan upload * add mul_mat q4 into uberkernel * enable gating flush to just uberkernel * update docs for ET * update op support for ET * et-backend: optimize Q4_0 and Q8_0 mul_mat_id row accumulations * et-backend: specialize mul_mat_id kernels for Q4_0 and Q8_0 * et-backend: fix RoPE YaRN corr_dim formula and handle degenerate inputs * test-backend-ops: add DeepSeek-V2-Lite RoPE test coverage * et-backend: add Q4_0 mul_mat matrix-engine kernel using TensorFMA32 * et-backend: vectorize Q4_0 matrix-engine dequantization * et-backend: support hybrid matrix/vector engine execution for Q4_0 mul_mat tail * et-backend: run partial-N tiles on matrix engine for Q4_0 mul_mat * et-backend: route Q4_0 mul_mat N < 53 to vecdot for better prefill latency * Update uberkernel.c * Update unary_f32.c * gemma 4 * bisect gemma4: enable scale_f32 only * bisect gemma4: +rms_norm_f32 * bisect gemma4: +rms_norm_mul_f32 * bisect gemma4: disable rms_norm_mul_f32 -- BREAKS OUTPUT * bisect gemma4: +rope_f32 (skip rms_norm_mul) * bisect gemma4: +el_map_f32 * bisect gemma4: +softmax_f32 * bisect gemma4: +get_rows_f32 * bisect gemma4: +glu_f32 * bisect gemma4: +mul_mat_f32 +mul_mat_f32_matrix_engine * bisect gemma4: +mul_mat_f16 +mul_mat_f16_matrix_engine * bisect gemma4: +mul_mat_Q8_0 +mul_mat_Q4_0 * bisect gemma4: +flash_attn_ext_f32 +flash_attn_ext_f16_me * bisect gemma4: +mul_mat_id_f32 * bisect gemma4: +sum_rows_f32 * bisect gemma4: +cont_f16 * bisect gemma4: +fill_f32 * bisect gemma4: +unary_f32 (all ops re-enabled except rms_norm_mul) * Update rms_norm_mul_f32.c * bisect2 gemma4 n64: +scale_f32 only * bisect2 gemma4 n64: +rms_norm_f32 +rope_f32 * bisect2 gemma4 n64: +rms_norm_mul_f32 (with ET_UBERKERNEL eviction fix) * bisect2 gemma4 n64: +el_map +get_rows +glu +softmax (skip rms_norm_mul) * bisect2 gemma4 n64: all ops enabled except rms_norm_mul * bisect2 n64: test unary+cont+fill+sum_rows (no mul_mat/flash_attn) * bisect2 n64: +mul_mat_f32 +mul_mat_f32_matrix_engine * bisect2 n64: +mul_mat_f16 +mul_mat_f16_matrix_engine * bisect2 n64: +mul_mat_Q8_0 +mul_mat_Q4_0 * bisect2 n64: +mul_mat_Q8_0 only (disable Q4_0) * bisect2 n64: +mul_mat_Q4_0 only (Q8_0 breaks) * bisect2 n64: +mul_mat_id +flash_attn_ext (skip Q8_0) * run-3: matmul + rms_norm_mul * run-4 * Revert "run-4" * run5 * changes after cleanup * cleanup before upstream * restrict changes into ET backend * move kernel embedding from Python to CMake * move uberkernel gen into CMake * apply clang format * update CMake style * update to match C and C++ style * use source ggml and quant headers instead of ET's * MROPE support * absorb view ops into same branch as none * fix bad rebase * add marty1885 to codeowners * oops * remove redundant newline * fix CI editor warnings --------- Co-authored-by: Vidas <vidas@nuolat.lt> Co-authored-by: Gianluca Guida <glguida@tlbflush.org> Co-authored-by: Gianluca Guida <gianluca@nekko.ai> Co-authored-by: ubergarm <leimgrub@gmail.com> Co-authored-by: SaqibAkram-10xE <saqib.akram@10xengineers.ai> Co-authored-by: Rehan Qasim <rehan.qasim@10xengineers.ai>
594 lines
18 KiB
C++
594 lines
18 KiB
C++
#include "ggml-backend-impl.h"
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#include "ggml-backend.h"
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#include "ggml-backend-dl.h"
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#include "ggml-impl.h"
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#include <algorithm>
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#include <cstring>
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#include <filesystem>
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#include <memory>
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#include <string>
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#include <type_traits>
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#include <vector>
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#include <cctype>
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#ifdef _WIN32
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# define WIN32_LEAN_AND_MEAN
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# ifndef NOMINMAX
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# define NOMINMAX
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# endif
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# include <windows.h>
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#elif defined(__APPLE__)
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# include <mach-o/dyld.h>
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# include <dlfcn.h>
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#else
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# include <dlfcn.h>
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# include <unistd.h>
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#endif
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// Backend registry
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#ifdef GGML_USE_CPU
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#include "ggml-cpu.h"
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#endif
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#ifdef GGML_USE_CUDA
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#include "ggml-cuda.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#ifdef GGML_USE_SYCL
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#include "ggml-sycl.h"
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#endif
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#ifdef GGML_USE_VULKAN
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#include "ggml-vulkan.h"
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#endif
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#ifdef GGML_USE_WEBGPU
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#include "ggml-webgpu.h"
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#endif
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#ifdef GGML_USE_ZDNN
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#include "ggml-zdnn.h"
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#endif
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#ifdef GGML_USE_OPENCL
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#include "ggml-opencl.h"
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#endif
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#ifdef GGML_USE_HEXAGON
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#include "ggml-hexagon.h"
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#endif
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#ifdef GGML_USE_BLAS
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#include "ggml-blas.h"
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#endif
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#ifdef GGML_USE_RPC
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#include "ggml-rpc.h"
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#endif
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#ifdef GGML_USE_VIRTGPU_FRONTEND
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#include "ggml-virtgpu.h"
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#endif
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#ifdef GGML_USE_CANN
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#include "ggml-cann.h"
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#endif
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#ifdef GGML_USE_ZENDNN
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#include "ggml-zendnn.h"
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#endif
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#ifdef GGML_USE_OPENVINO
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#include "ggml-openvino.h"
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#endif
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#ifdef GGML_USE_ET
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#include "ggml-et.h"
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#endif
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namespace fs = std::filesystem;
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static std::string path_str(const fs::path & path) {
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try {
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#if defined(__cpp_lib_char8_t)
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// C++20 and later: u8string() returns std::u8string
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const std::u8string u8str = path.u8string();
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return std::string(reinterpret_cast<const char *>(u8str.data()), u8str.size());
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#else
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// C++17: u8string() returns std::string
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return path.u8string();
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#endif
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} catch (...) {
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return std::string();
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}
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}
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struct ggml_backend_reg_entry {
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ggml_backend_reg_t reg;
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dl_handle_ptr handle;
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};
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struct ggml_backend_registry {
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std::vector<ggml_backend_reg_entry> backends;
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std::vector<ggml_backend_dev_t> devices;
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ggml_backend_registry() {
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#ifdef GGML_USE_CUDA
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register_backend(ggml_backend_cuda_reg());
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#endif
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#ifdef GGML_USE_METAL
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register_backend(ggml_backend_metal_reg());
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#endif
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#ifdef GGML_USE_SYCL
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register_backend(ggml_backend_sycl_reg());
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#endif
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#ifdef GGML_USE_VULKAN
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// Add runtime disable check
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if (getenv("GGML_DISABLE_VULKAN") == nullptr) {
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register_backend(ggml_backend_vk_reg());
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} else {
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GGML_LOG_DEBUG("Vulkan backend disabled by GGML_DISABLE_VULKAN environment variable\n");
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}
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#endif
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#ifdef GGML_USE_WEBGPU
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register_backend(ggml_backend_webgpu_reg());
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#endif
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#ifdef GGML_USE_ZDNN
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register_backend(ggml_backend_zdnn_reg());
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#endif
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#ifdef GGML_USE_VIRTGPU_FRONTEND
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register_backend(ggml_backend_virtgpu_reg());
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#endif
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#ifdef GGML_USE_OPENCL
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register_backend(ggml_backend_opencl_reg());
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#endif
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#ifdef GGML_USE_ZENDNN
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register_backend(ggml_backend_zendnn_reg());
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#endif
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#ifdef GGML_USE_HEXAGON
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register_backend(ggml_backend_hexagon_reg());
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#endif
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#ifdef GGML_USE_CANN
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register_backend(ggml_backend_cann_reg());
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#endif
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#ifdef GGML_USE_BLAS
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register_backend(ggml_backend_blas_reg());
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#endif
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#ifdef GGML_USE_RPC
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register_backend(ggml_backend_rpc_reg());
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#endif
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#ifdef GGML_USE_OPENVINO
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register_backend(ggml_backend_openvino_reg());
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#endif
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#ifdef GGML_USE_ET
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register_backend(ggml_backend_et_reg());
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#endif
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#ifdef GGML_USE_CPU
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register_backend(ggml_backend_cpu_reg());
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#endif
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}
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~ggml_backend_registry() {
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// FIXME: backends cannot be safely unloaded without a function to destroy all the backend resources,
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// since backend threads may still be running and accessing resources from the dynamic library
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for (auto & entry : backends) {
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if (entry.handle) {
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entry.handle.release(); // NOLINT
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}
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}
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}
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void register_backend(ggml_backend_reg_t reg, dl_handle_ptr handle = nullptr) {
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if (!reg) {
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return;
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}
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for (auto & entry : backends) {
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if (entry.reg == reg) {
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return;
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}
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}
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
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__func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
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#endif
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backends.push_back({ reg, std::move(handle) });
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for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) {
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register_device(ggml_backend_reg_dev_get(reg, i));
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}
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}
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void register_device(ggml_backend_dev_t device) {
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for (auto & dev : devices) {
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if (dev == device) {
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return;
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}
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}
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
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#endif
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devices.push_back(device);
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}
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ggml_backend_reg_t load_backend(const fs::path & path, bool silent) {
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dl_handle_ptr handle { dl_load_library(path) };
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if (!handle) {
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if (!silent) {
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GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_str(path).c_str(), dl_error());
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}
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return nullptr;
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}
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auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
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if (score_fn && score_fn() == 0) {
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if (!silent) {
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GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path_str(path).c_str());
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}
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return nullptr;
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}
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auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
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if (!backend_init_fn) {
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if (!silent) {
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GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path_str(path).c_str());
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}
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return nullptr;
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}
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ggml_backend_reg_t reg = backend_init_fn();
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if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
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if (!silent) {
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if (!reg) {
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GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n",
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__func__, path_str(path).c_str());
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} else {
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GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
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__func__, path_str(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
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}
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}
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return nullptr;
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}
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GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path_str(path).c_str());
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register_backend(reg, std::move(handle));
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return reg;
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}
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void unload_backend(ggml_backend_reg_t reg, bool silent) {
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auto it = std::find_if(backends.begin(), backends.end(),
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[reg](const ggml_backend_reg_entry & entry) { return entry.reg == reg; });
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if (it == backends.end()) {
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if (!silent) {
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GGML_LOG_ERROR("%s: backend not found\n", __func__);
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}
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return;
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}
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if (!silent) {
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GGML_LOG_DEBUG("%s: unloading %s backend\n", __func__, ggml_backend_reg_name(reg));
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}
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// remove devices
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devices.erase(
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std::remove_if(devices.begin(), devices.end(),
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[reg](ggml_backend_dev_t dev) { return ggml_backend_dev_backend_reg(dev) == reg; }),
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devices.end());
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// remove backend
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backends.erase(it);
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}
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};
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static ggml_backend_registry & get_reg() {
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static ggml_backend_registry reg;
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return reg;
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}
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// Internal API
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void ggml_backend_register(ggml_backend_reg_t reg) {
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get_reg().register_backend(reg);
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}
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void ggml_backend_device_register(ggml_backend_dev_t device) {
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get_reg().register_device(device);
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}
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|
// Backend (reg) enumeration
|
|
static bool striequals(const char * a, const char * b) {
|
|
for (; *a && *b; a++, b++) {
|
|
if (std::tolower(*a) != std::tolower(*b)) {
|
|
return false;
|
|
}
|
|
}
|
|
return *a == *b;
|
|
}
|
|
|
|
size_t ggml_backend_reg_count() {
|
|
return get_reg().backends.size();
|
|
}
|
|
|
|
ggml_backend_reg_t ggml_backend_reg_get(size_t index) {
|
|
GGML_ASSERT(index < ggml_backend_reg_count());
|
|
return get_reg().backends[index].reg;
|
|
}
|
|
|
|
ggml_backend_reg_t ggml_backend_reg_by_name(const char * name) {
|
|
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
|
|
ggml_backend_reg_t reg = ggml_backend_reg_get(i);
|
|
if (striequals(ggml_backend_reg_name(reg), name)) {
|
|
return reg;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
// Device enumeration
|
|
size_t ggml_backend_dev_count() {
|
|
return get_reg().devices.size();
|
|
}
|
|
|
|
ggml_backend_dev_t ggml_backend_dev_get(size_t index) {
|
|
GGML_ASSERT(index < ggml_backend_dev_count());
|
|
return get_reg().devices[index];
|
|
}
|
|
|
|
ggml_backend_dev_t ggml_backend_dev_by_name(const char * name) {
|
|
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
|
|
if (striequals(ggml_backend_dev_name(dev), name)) {
|
|
return dev;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
ggml_backend_dev_t ggml_backend_dev_by_type(enum ggml_backend_dev_type type) {
|
|
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
|
|
if (ggml_backend_dev_type(dev) == type) {
|
|
return dev;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
// Convenience functions
|
|
ggml_backend_t ggml_backend_init_by_name(const char * name, const char * params) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_by_name(name);
|
|
if (!dev) {
|
|
return nullptr;
|
|
}
|
|
return ggml_backend_dev_init(dev, params);
|
|
}
|
|
|
|
ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const char * params) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_by_type(type);
|
|
if (!dev) {
|
|
return nullptr;
|
|
}
|
|
return ggml_backend_dev_init(dev, params);
|
|
}
|
|
|
|
ggml_backend_t ggml_backend_init_best(void) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
|
|
dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
|
|
dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
if (!dev) {
|
|
return nullptr;
|
|
}
|
|
return ggml_backend_dev_init(dev, nullptr);
|
|
}
|
|
|
|
// Dynamic loading
|
|
ggml_backend_reg_t ggml_backend_load(const char * path) {
|
|
return get_reg().load_backend(path, false);
|
|
}
|
|
|
|
void ggml_backend_unload(ggml_backend_reg_t reg) {
|
|
get_reg().unload_backend(reg, true);
|
|
}
|
|
|
|
static fs::path get_executable_path() {
|
|
#if defined(__APPLE__)
|
|
// get executable path
|
|
std::vector<char> path;
|
|
uint32_t size;
|
|
while (true) {
|
|
size = path.size();
|
|
if (_NSGetExecutablePath(path.data(), &size) == 0) {
|
|
break;
|
|
}
|
|
path.resize(size);
|
|
}
|
|
std::string base_path(path.data(), size);
|
|
// remove executable name
|
|
auto last_slash = base_path.find_last_of('/');
|
|
if (last_slash != std::string::npos) {
|
|
base_path = base_path.substr(0, last_slash);
|
|
}
|
|
return base_path + "/";
|
|
#elif defined(__linux__) || defined(__FreeBSD__)
|
|
std::string base_path = ".";
|
|
std::vector<char> path(1024);
|
|
while (true) {
|
|
// get executable path
|
|
# if defined(__linux__)
|
|
ssize_t len = readlink("/proc/self/exe", path.data(), path.size());
|
|
# elif defined(__FreeBSD__)
|
|
ssize_t len = readlink("/proc/curproc/file", path.data(), path.size());
|
|
# endif
|
|
if (len == -1) {
|
|
break;
|
|
}
|
|
if (len < (ssize_t) path.size()) {
|
|
base_path = std::string(path.data(), len);
|
|
// remove executable name
|
|
auto last_slash = base_path.find_last_of('/');
|
|
if (last_slash != std::string::npos) {
|
|
base_path = base_path.substr(0, last_slash);
|
|
}
|
|
break;
|
|
}
|
|
path.resize(path.size() * 2);
|
|
}
|
|
|
|
return base_path + "/";
|
|
#elif defined(_WIN32)
|
|
std::vector<wchar_t> path(MAX_PATH);
|
|
DWORD len = GetModuleFileNameW(NULL, path.data(), path.size());
|
|
if (len == 0) {
|
|
return {};
|
|
}
|
|
std::wstring base_path(path.data(), len);
|
|
// remove executable name
|
|
auto last_slash = base_path.find_last_of('\\');
|
|
if (last_slash != std::string::npos) {
|
|
base_path = base_path.substr(0, last_slash);
|
|
}
|
|
return base_path + L"\\";
|
|
#else
|
|
return {};
|
|
#endif
|
|
}
|
|
|
|
static fs::path backend_filename_prefix() {
|
|
#ifdef _WIN32
|
|
return fs::u8path("ggml-");
|
|
#else
|
|
return fs::u8path("libggml-");
|
|
#endif
|
|
}
|
|
|
|
static fs::path backend_filename_extension() {
|
|
#ifdef _WIN32
|
|
return fs::u8path(".dll");
|
|
#else
|
|
return fs::u8path(".so");
|
|
#endif
|
|
}
|
|
|
|
static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
|
|
// enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
|
|
const fs::path name_path = fs::u8path(name);
|
|
const fs::path file_prefix = backend_filename_prefix().native() + name_path.native() + fs::u8path("-").native();
|
|
const fs::path file_extension = backend_filename_extension();
|
|
|
|
std::vector<fs::path> search_paths;
|
|
if (user_search_path == nullptr) {
|
|
#ifdef GGML_BACKEND_DIR
|
|
search_paths.push_back(fs::u8path(GGML_BACKEND_DIR));
|
|
#endif
|
|
// default search paths: executable directory, current directory
|
|
search_paths.push_back(get_executable_path());
|
|
search_paths.push_back(fs::current_path());
|
|
} else {
|
|
search_paths.push_back(fs::u8path(user_search_path));
|
|
}
|
|
|
|
int best_score = 0;
|
|
fs::path best_path;
|
|
std::error_code ec;
|
|
|
|
for (const auto & search_path : search_paths) {
|
|
if (!fs::exists(search_path, ec)) {
|
|
if (ec) {
|
|
GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
|
|
} else {
|
|
GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str());
|
|
}
|
|
continue;
|
|
}
|
|
fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
|
|
for (const auto & entry : dir_it) {
|
|
if (entry.is_regular_file(ec)) {
|
|
auto filename = entry.path().filename();
|
|
auto ext = entry.path().extension();
|
|
if (filename.native().find(file_prefix) == 0 && ext == file_extension) {
|
|
dl_handle_ptr handle { dl_load_library(entry) };
|
|
if (!handle && !silent) {
|
|
GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_str(entry.path()).c_str(), dl_error());
|
|
}
|
|
if (handle) {
|
|
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
|
|
if (score_fn) {
|
|
int s = score_fn();
|
|
#ifndef NDEBUG
|
|
GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_str(entry.path()).c_str(), s);
|
|
#endif
|
|
if (s > best_score) {
|
|
best_score = s;
|
|
best_path = entry.path();
|
|
}
|
|
} else {
|
|
if (!silent) {
|
|
GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, path_str(entry.path()).c_str());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (best_score == 0) {
|
|
// try to load the base backend
|
|
for (const auto & search_path : search_paths) {
|
|
fs::path filename = backend_filename_prefix().native() + name_path.native() + backend_filename_extension().native();
|
|
fs::path path = search_path / filename;
|
|
if (std::error_code ec; fs::exists(path, ec)) {
|
|
return get_reg().load_backend(path, silent);
|
|
} else {
|
|
if (ec) {
|
|
GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(path).c_str(), ec.message().c_str());
|
|
}
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
return get_reg().load_backend(best_path, silent);
|
|
}
|
|
|
|
void ggml_backend_load_all() {
|
|
ggml_backend_load_all_from_path(nullptr);
|
|
}
|
|
|
|
void ggml_backend_load_all_from_path(const char * dir_path) {
|
|
#ifdef NDEBUG
|
|
bool silent = true;
|
|
#else
|
|
bool silent = false;
|
|
#endif
|
|
|
|
ggml_backend_load_best("blas", silent, dir_path);
|
|
ggml_backend_load_best("zendnn", silent, dir_path);
|
|
ggml_backend_load_best("cann", silent, dir_path);
|
|
ggml_backend_load_best("cuda", silent, dir_path);
|
|
ggml_backend_load_best("hip", silent, dir_path);
|
|
ggml_backend_load_best("metal", silent, dir_path);
|
|
ggml_backend_load_best("rpc", silent, dir_path);
|
|
ggml_backend_load_best("sycl", silent, dir_path);
|
|
ggml_backend_load_best("vulkan", silent, dir_path);
|
|
ggml_backend_load_best("virtgpu", silent, dir_path);
|
|
ggml_backend_load_best("opencl", silent, dir_path);
|
|
ggml_backend_load_best("hexagon", silent, dir_path);
|
|
ggml_backend_load_best("musa", silent, dir_path);
|
|
ggml_backend_load_best("openvino", silent, dir_path);
|
|
ggml_backend_load_best("cpu", silent, dir_path);
|
|
// check the environment variable GGML_BACKEND_PATH to load an out-of-tree backend
|
|
const char * backend_path = std::getenv("GGML_BACKEND_PATH");
|
|
if (backend_path) {
|
|
ggml_backend_load(backend_path);
|
|
}
|
|
}
|