Files
llama.cpp/ggml/src/ggml-backend-reg.cpp
Martin Chang 082b326fc7 ggml-et: Initial ET backend (#24179)
* 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>
2026-07-10 12:38:34 +08:00

594 lines
18 KiB
C++

#include "ggml-backend-impl.h"
#include "ggml-backend.h"
#include "ggml-backend-dl.h"
#include "ggml-impl.h"
#include <algorithm>
#include <cstring>
#include <filesystem>
#include <memory>
#include <string>
#include <type_traits>
#include <vector>
#include <cctype>
#ifdef _WIN32
# define WIN32_LEAN_AND_MEAN
# ifndef NOMINMAX
# define NOMINMAX
# endif
# include <windows.h>
#elif defined(__APPLE__)
# include <mach-o/dyld.h>
# include <dlfcn.h>
#else
# include <dlfcn.h>
# include <unistd.h>
#endif
// Backend registry
#ifdef GGML_USE_CPU
#include "ggml-cpu.h"
#endif
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_WEBGPU
#include "ggml-webgpu.h"
#endif
#ifdef GGML_USE_ZDNN
#include "ggml-zdnn.h"
#endif
#ifdef GGML_USE_OPENCL
#include "ggml-opencl.h"
#endif
#ifdef GGML_USE_HEXAGON
#include "ggml-hexagon.h"
#endif
#ifdef GGML_USE_BLAS
#include "ggml-blas.h"
#endif
#ifdef GGML_USE_RPC
#include "ggml-rpc.h"
#endif
#ifdef GGML_USE_VIRTGPU_FRONTEND
#include "ggml-virtgpu.h"
#endif
#ifdef GGML_USE_CANN
#include "ggml-cann.h"
#endif
#ifdef GGML_USE_ZENDNN
#include "ggml-zendnn.h"
#endif
#ifdef GGML_USE_OPENVINO
#include "ggml-openvino.h"
#endif
#ifdef GGML_USE_ET
#include "ggml-et.h"
#endif
namespace fs = std::filesystem;
static std::string path_str(const fs::path & path) {
try {
#if defined(__cpp_lib_char8_t)
// C++20 and later: u8string() returns std::u8string
const std::u8string u8str = path.u8string();
return std::string(reinterpret_cast<const char *>(u8str.data()), u8str.size());
#else
// C++17: u8string() returns std::string
return path.u8string();
#endif
} catch (...) {
return std::string();
}
}
struct ggml_backend_reg_entry {
ggml_backend_reg_t reg;
dl_handle_ptr handle;
};
struct ggml_backend_registry {
std::vector<ggml_backend_reg_entry> backends;
std::vector<ggml_backend_dev_t> devices;
ggml_backend_registry() {
#ifdef GGML_USE_CUDA
register_backend(ggml_backend_cuda_reg());
#endif
#ifdef GGML_USE_METAL
register_backend(ggml_backend_metal_reg());
#endif
#ifdef GGML_USE_SYCL
register_backend(ggml_backend_sycl_reg());
#endif
#ifdef GGML_USE_VULKAN
// Add runtime disable check
if (getenv("GGML_DISABLE_VULKAN") == nullptr) {
register_backend(ggml_backend_vk_reg());
} else {
GGML_LOG_DEBUG("Vulkan backend disabled by GGML_DISABLE_VULKAN environment variable\n");
}
#endif
#ifdef GGML_USE_WEBGPU
register_backend(ggml_backend_webgpu_reg());
#endif
#ifdef GGML_USE_ZDNN
register_backend(ggml_backend_zdnn_reg());
#endif
#ifdef GGML_USE_VIRTGPU_FRONTEND
register_backend(ggml_backend_virtgpu_reg());
#endif
#ifdef GGML_USE_OPENCL
register_backend(ggml_backend_opencl_reg());
#endif
#ifdef GGML_USE_ZENDNN
register_backend(ggml_backend_zendnn_reg());
#endif
#ifdef GGML_USE_HEXAGON
register_backend(ggml_backend_hexagon_reg());
#endif
#ifdef GGML_USE_CANN
register_backend(ggml_backend_cann_reg());
#endif
#ifdef GGML_USE_BLAS
register_backend(ggml_backend_blas_reg());
#endif
#ifdef GGML_USE_RPC
register_backend(ggml_backend_rpc_reg());
#endif
#ifdef GGML_USE_OPENVINO
register_backend(ggml_backend_openvino_reg());
#endif
#ifdef GGML_USE_ET
register_backend(ggml_backend_et_reg());
#endif
#ifdef GGML_USE_CPU
register_backend(ggml_backend_cpu_reg());
#endif
}
~ggml_backend_registry() {
// FIXME: backends cannot be safely unloaded without a function to destroy all the backend resources,
// since backend threads may still be running and accessing resources from the dynamic library
for (auto & entry : backends) {
if (entry.handle) {
entry.handle.release(); // NOLINT
}
}
}
void register_backend(ggml_backend_reg_t reg, dl_handle_ptr handle = nullptr) {
if (!reg) {
return;
}
for (auto & entry : backends) {
if (entry.reg == reg) {
return;
}
}
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
__func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
#endif
backends.push_back({ reg, std::move(handle) });
for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); i++) {
register_device(ggml_backend_reg_dev_get(reg, i));
}
}
void register_device(ggml_backend_dev_t device) {
for (auto & dev : devices) {
if (dev == device) {
return;
}
}
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
#endif
devices.push_back(device);
}
ggml_backend_reg_t load_backend(const fs::path & path, bool silent) {
dl_handle_ptr handle { dl_load_library(path) };
if (!handle) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_str(path).c_str(), dl_error());
}
return nullptr;
}
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
if (score_fn && score_fn() == 0) {
if (!silent) {
GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path_str(path).c_str());
}
return nullptr;
}
auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
if (!backend_init_fn) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path_str(path).c_str());
}
return nullptr;
}
ggml_backend_reg_t reg = backend_init_fn();
if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
if (!silent) {
if (!reg) {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n",
__func__, path_str(path).c_str());
} else {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
__func__, path_str(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
}
}
return nullptr;
}
GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path_str(path).c_str());
register_backend(reg, std::move(handle));
return reg;
}
void unload_backend(ggml_backend_reg_t reg, bool silent) {
auto it = std::find_if(backends.begin(), backends.end(),
[reg](const ggml_backend_reg_entry & entry) { return entry.reg == reg; });
if (it == backends.end()) {
if (!silent) {
GGML_LOG_ERROR("%s: backend not found\n", __func__);
}
return;
}
if (!silent) {
GGML_LOG_DEBUG("%s: unloading %s backend\n", __func__, ggml_backend_reg_name(reg));
}
// remove devices
devices.erase(
std::remove_if(devices.begin(), devices.end(),
[reg](ggml_backend_dev_t dev) { return ggml_backend_dev_backend_reg(dev) == reg; }),
devices.end());
// remove backend
backends.erase(it);
}
};
static ggml_backend_registry & get_reg() {
static ggml_backend_registry reg;
return reg;
}
// Internal API
void ggml_backend_register(ggml_backend_reg_t reg) {
get_reg().register_backend(reg);
}
void ggml_backend_device_register(ggml_backend_dev_t device) {
get_reg().register_device(device);
}
// 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);
}
}